EXPLORING TORQUE AND DEFLECTION RESPONSE
CHARACTERISTICS TO EVALUATE THE ERGONOMICS OF DC
TORQUE TOOLS VIA A TOOL TEST RIG
A Thesis
Presented in Partial Fulfillment of the Requirements for
the Degree Master of Science in the
Graduate School of The Ohio State University
By
Shritama Mukherji, B.E.
*****
The Ohio State University
2008
Master’s Examination Committee:
Dr. Anthony Luscher, Adviser
Approved by
Dr. Carolyn Sommerich
__________________________________
Advisor
Graduate Program in Mechanical Engineering
ABSTRACT
Torque tools used in assembly applications generate impulsive reaction forces
during torque build-up that often displace the operator hand and arm, and are associated
with an increased risk of muscle damage and injury. Tools are available in a number of
handle shapes, sizes, and output capacities and are operated in various working positions
and orientations. These factors affect the dynamic interaction between the tool and the
operator and the operator’s ability to react against impulsive forces.
DC torque tools are controlled fastening tools that are instrumented with sensors for
direct measurement of the applied torque and rotation of the threaded fastener during the
assembly process. They have several advantages in terms of torque accuracy, error
detection, and torque verification over other torquing systems. DC torque tools interface
with a tool controller that can be used to set tightening parameters and program different
tightening algorithms, making it highly flexible.
The objective of this thesis is to quantify the ergonomic impact of various DC
torque tool controller settings. This impact was determined by the use of an ergonomic test
rig which will capture the interaction between the physical tool, control software, and a
model of the human arm. The output of the rig is the reaction force and displacement of
ii
the tool handle and therefore simulated arm as a function of time. The response curves
from the rig were analyzed and a set of metrics were formulated for ergonomic
assessment. The work of this thesis will lead to an improved understanding of the
interactions between stiffness of the joint to be fastened (joint stiffness), the simulated
human arm system, and the tightening algorithms controlling the tool.
iii
DEDICATION
To my parents for their unconditional love and support
iv
ACKNOWLEDGMENTS
With the deepest gratitude, I wish to thank all the people who made this thesis
possible.
Firstly, I would like to thank my advisor, Dr. Anthony Luscher for his guidance and
support throughout this thesis. I am very grateful to him for all that I have learnt during
the course of my graduate studies. I would also like to thank Dr. Carolyn Sommerich for
co-advising me on this project and for being a part of my thesis defense committee. Her
feedback and encouragement have been invaluable for the successful completion of this
thesis.
I would like to sincerely thank Duane Bookshar, Doug Versele and Jim Steverding
from Stanley Assembly Technologies. The technical information provided by them has
helped me understand DC torque tools. I would also like to thank various individuals in
the Department of Mechanical Engineering, namely, Gary Gardner and Neil Gardner for
their help in building the ergonomic assessment rig and Joe West for his assistance with
the measurement system. Thanks also belong to all the members of the Fastening lab for
their ideas and suggestions during this research.
v
I have been fortunate to have many friends who have supported me and kept me
motivated for the last two years. For this, I am very thankful.
Finally I would like to thank my family who made all of this possible. I am grateful
to my parents, for having unwavering faith in me and for being a constant source of
support and strength. I am also thankful to my sister and brother-in-law, for encouraging
me at all times and keeping me in good spirits.
vi
VITA
December 10, 1983……………………. Born – Hyderabad, India
June, 2005……………………………... BE, Visveswariah Technological University
Bangalore, India
January, 2006 - present…………………Graduate Research Associate,
The Ohio State University
FIELDS OF STUDY
Major Field: Mechanical Engineering
Design and Manufacturing
vii
TABLE OF CONTENTS
Page
Abstract............................................................................................................................... ii
Acknowledgement.............................................................................................................. v
Vita.................................................................................................................................... vii
List of Tables...................................................................................................................... xi
List of Figures.................................................................................................................... xii
Nomenclature.................................................................................................................... xv
Chapters
1. Introduction ................................................................................................................... 1
1.1 Introduction to bolted joints................................................................................ 2
1.2 Torque tool options............................................................................................ 6
1.3 Motivation for current work.............................................................................. 10
1.4 Thesis objectives............................................................................................... 12
2. Literature Review......................................................................................................... 14
2.1 Dynamic models of tool-human operator system.............................................. 14
2.2 Ergonomic injury risk assessment..................................................................... 22
2.3 Effect of work station design, operator posture and position............................ 26
2.4 Design and application of an instrumented tool handle .................................... 29
2.5 Literature summary............................................................................................ 32
3. Design of ergonomic assessment rig...........................................................................
3.1 Description of original ergonomic test rig........................................................
3.1.1 Tool and bolted joint assembly..............................................................
3.1.2 Human arm model with measurement system.......................................
3.2 Rig improvements.............................................................................................
viii
33
34
35
37
39
3.2.1 Improved spring design to represent arm stiffness ................................. 39
3.2.2 Design of pneumatic system to drive arm stiffness cylinder................... 43
3.2.3 Improved design of arm mass system .................................................... 45
3.2.4 Load cell modifications.......................................................................... 48
3.2.5 Additional modifications........................................................................ 50
3.3 Final ergonomic test rig .................................................................................... 52
3.4 Repeatability tests ............................................................................................. 54
4. Experimental method ................................................................................................... 58
4.1 Description of the factors .................................................................................. 59
4.1.1 Tightening algorithm .............................................................................. 59
4.1.2 Soft stop feature...................................................................................... 64
4.1.3 Arm mass and stiffness........................................................................... 65
4.1.4 Joint stiffness .......................................................................................... 67
4.1.5 Summary of factors with their levels...................................................... 68
4.2 Measured response and other dependent variables........................................... 69
4.3 Design of experiments (DOE).......................................................................... 69
4.4 Formulation of ergonomic metrics ................................................................... 73
4.4.1 Torque impulse at different percentages of the target torque ................ 73
4.4.2 Deflection - peaks in positive and negative direction, maximum range.. 76
4.4.3 Reaction torques - peaks in positive and negative direction,
maximum range ...................................................................................... 77
4.4.4 Latency impulse - torque impulse with muscle latency included........... 78
5. Ergonomic assessment of response curves - results .................................................... 83
5.1 Raw data from screening experiments............................................................... 84
5.2 Assessment of response curves - statistically significant results ................
90
5.2.1 Torque impulse at different percentages of the target torque ................ 92
5.2.2 Peak deflection negative, peak deflection positive, maximum
deflection range ...................................................................................... 97
5.2.3 Peak torque negative, peak torque positive, maximum torque range.... 101
5.2.4 Latency impulse.................................................................................... 104
5.3 Comparison of rig curves with curves from human testing ............................ 106
5.4 Chapter summary............................................................................................ 111
6. Discussions ................................................................................................................ 112
6.1 Results of torque impulse................................................................................. 112
6.2 Results of deflection peaks and range ............................................................. 115
6.3 Results of torque peaks and range ................................................................... 116
6.4 Results of latency impulse .............................................................................. 118
6.5 Chapter summary............................................................................................ 120
ix
7. Conclusions and future work ..................................................................................... 121
7.1 Summary of major findings ............................................................................ 121
7.1.1 Tightening algorithm........................................................................
122
7.1.2 Joint stiffness ....................................................................................... 122
7.1.3 Arm mass and stiffness......................................................................... 123
7.1.4 Soft stop feature................................................................................... 123
7.2 Contributions................................................................................................... 124
7.3 Recommendations for future work.................................................................. 125
List of references............................................................................................................. 128
Appendix A Spring rate analysis of air cylinder ............................................................ 132
Appendix B Design drawings......................................................................................... 137
Appendix C Data sheets for sensors................................................................................ 151
Appendix D Controller program parameter sets.............................................................. 166
Appendix E Matlab codes for torque impulse and latency.............................................. 168
x
LIST OF TABLES
Table
3.1
4.1
4.2
4.3
5.1
D.1
D.2
Page
Mean and standard deviation for peak reaction force and deflection
from the repeatability tests .................................................................................... 57
Levels of mass and stiffness .................................................................................. 67
Factors with their corresponding levels.................................................................. 68
Orthogonal array .................................................................................................... 72
Statistically significant sources for the responses ...................................................91
Explanation of parameter set names..................................................................... 166
Stanley controller parameter values ..................................................................... 167
xi
LIST OF FIGURES
Table
1.1
Page
Bolted joints classified by external load (a) Tensile joint
(b) Shear joint [28]................................................................................................... 2
1.2
Three phases of bolt tightening operation [35] ........................................................3
1.3
Torque distribution of a typical fastener [30]..... ......................................................4
1.4
(a) Electric screwdriver [38] (b) Air impact wrench [39] (c) Air angle
Nutrunner [40]... ...................................................................................................... 7
1.5 Cordless tool [41] .................................................................................................... 8
1.6
(a) Pistol grip (b) Right angle (c) Inline [43]........................................................... 9
1.7
A DC electric nutrunner with a tool controller [44] .............................................. 10
2.1 Forces acting on a right angle power hand tool [19].............................................. 15
2.2
Pistol grip tool-operator system represented as a torsional system [12].................17
2.3
Average values of human arm parameters for the pistol grip model (a)
Torsional stiffness (b) Mass moment of inertia (c) Torsional damping [12]..........19
2.4
(a) Pistol grip on a vertical surface (b) Inline on a horizontal surface (b) Pistol
grip on a horizontal surface (d) Right angle on a horizontal surface [13].... ..........20
2.5 “Near” location is 30 cm in front of the ankles, 140 cm above the floor, “Far”
location is 60 cm in front of the ankles and 80 cm above the floor [11]................ 22
2.6
Three shut off mechanisms studied by Kihlberg et al [5]...................................... 24
2.7 Modified Borg’s scales used by Kihlberg et al. [6]................................................ 25
2.8
“Time-torque” value as defined by Kihlberg et al. [6] .......................................... 26
2.9
Two postures used in Lindquist’s study (a) Horizontal lower arm (b) Vertical
lower arm [15]........................................................................................................ 27
2.10 EMG latency as demonstrated by Oh and Radwin [17]........................................ 28
2.11 Location of strain gauges on the grip force sensing device [16]........................... 30
2.12 Instrumented handles used in Lin’s study (a) Pistol grip tool
(b) Right angle tool [9] ..........................................................................................31
3.1
Inputs and outputs of the ergonomic test rig ..........................................................34
3.2
Initial ergonomic test rig design [27] ..................................................................... 35
3.3 Tool and bolted joint assembly [27]....................................................................... 36
3.4 Bolted joint assembly [27] .................................................................................... 36
xii
3.5
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
Top view of the human arm model with the measurement system [27] ............... 37
Double acting cylinder (a) Single rod (b) Double rod ........................................... 41
Analysis of double acting double rod cylinder ...................................................... 42
Schematic of pneumatic system............................................................................. 44
Pneumatic control box ........................................................................................... 45
Arm mass box to carry arm mass plates ................................................................ 46
Two sizes of arm mass plates..................................................................................47
Arm mass box supported on rollers ........................................................................48
Device to protect Sensotec model 31 load cell ......................................................49
Arm mass system and load cell assembly...............................................................50
LVDT, air cylinder and mass assembly..................................................................51
Final ergonomic assessment rig for right angle DC torque tools ...........................53
Repeatability test plots with Manual Downshift algorithm (a) Deflection
(b) Reaction force ...................................................................................................55
3.18 Repeatability test plots with ATC algorithm (a) Deflection (b) Reaction force.....56
4.1
Speed and torque control using Manual Downshift [46] .......................................60
4.2
Speed and torque control using Two Stage algorithm [46] ....................................61
4.3
Speed and torque control using ATC [46].... ..........................................................62
4.4 Parameters associated two modes of ATC algorithm (a) ATC Automatic mode
(b) ATC Custom mode [45]....................................................................................63
4.5
Parameters for the soft stop feature on Stanley controllers [45] ............................65
4.6 Reaction torque versus time (a) Actual curve (b) Torque impulse 20%
(c) Torque impulse 50%..........................................................................................75
4.7 Peak deflections positive and negative, maximum deflection range.... ..................76
4.8 Peak reactions positive and negative, maximum torque range ...............................77
4.9 Effect of torque build-up on muscle EMG latency [18].... .....................................79
4.10 Linear regression between EMG latency and torque build-up time.... ...................80
4.11 Method used to calculate latency impulse (a) Deflection-time plot
(b) Torque-time plot .............................................................................................. 82
5.1 Response curves with ATC, hard joint, low MK: with soft stop default (a)
Reaction torque and (b) Deflection, with no soft stop (c) Reaction torque
(d) Deflection .........................................................................................................86
5.2 Comparing the three algorithms at medium MK, soft stop default, and hard joint:
Reaction torque (a) ATC (b) Manual Downshift (c) Two Stage Control,
Deflection (d) ATC (e) Manual Downshift (f) Two Stage Control ........................88
5.3
Reaction torque-time curves with Manual Downshift, medium MK level,
soft stop default (a) Hard joint (b) Medium joint (c) Soft joint .............................89
5.4
Interaction plots for torque impulse at (a) 0 % (b) 20 % (c) 45 % (d) 50 %.... ......93
5.5
Interaction plots for torque impulse at (a) 60 % (b) 70 % (c) 75 %..... ..................94
5.6
Main effect plots at the three controller algorithms (a) Torque impulse 0 %
(b) Torque impulse 75 %.... ....................................................................................95
5.7 Main effect plots at the three MK levels (a) Peak deflection negative (b) Peak
deflection positive (c) Deflection range .................................................................98
5.8 Interaction plots for (a) Peak deflection negative (b) Peak deflection positive
xiii
5.9
5.10
5.11
5.12
5.13
5.14
A.1
B.1
B.2
B.3
B.4
B.5
B.6
B.7
B.8
B.9
B.10
B.11
B.12
B.13
(c) Deflection range.... ..........................................................................................100
Interaction plots for (a) Peak torque negative (b) Peak torque positive
(c) Torque range ...................................................................................................103
Interaction plot for latency impulse..... .................................................................104
Main effect plot for latency impulse at the three controller algorithms................105
Deflection versus time with ATC, hard joint and soft stop default (a) Human
operator 1 (b) Rig with medium mass and stiffness............................................. 108
Deflection versus time with ATC, hard joint, no soft stop (a) Human
operator 1 (b) Rig with medium mass and stiffness ............................................ 108
Deflection versus time with Two Stage, soft joint, soft stop default (a) Human
operator 1 (b) Rig with medium mass and stiffness ............................................ 109
Schematic of double rod cylinder with volume plenums .................................... 132
Device to protect 50 lb load cell from bending loads .......................................... 138
Blue tube of load cell protection device ............................................................... 139
Pink rod of load cell protection device that slides into the blue tube .................. 140
Orange rod of load cell protection device ............................................................ 141
Pivot plate of the arm mass box ........................................................................... 142
Arm mass plate that connects to air cylinder ....................................................... 143
Bottom plate of the arm mass box........................................................................ 144
First slotted plate of arm mass box ...................................................................... 145
Second slotted plate of arm mass box.................................................................. 146
Top plate of the arm mass box............................................................................. 147
Larger size arm mass plate .................................................................................. 148
Smaller size arm mass plate.................................................................................. 149
Angle plate at clevis end of the rig ...................................................................... 150
xiv
NOMENCLATURE
N
Newton
mm
Millimeters
ms
Milliseconds
lbs
Pounds
Nm
Newton meter
kg
Kilogram
MK
Mass and stiffness
xv
CHAPTER 1
INTRODUCTION
Fasteners represent complex and critical design elements which are necessary for
the reliability and long service life of machinery and structures. Different types of
fasteners have been developed for specific requirements, such as higher strength, easier
maintenance, greater reliability at different temperatures, lower material and
manufacturing costs. The selection and use of a particular fastener is dictated by the
design requirements and the conditions under which the fastener will be used. The
majority of fasteners used in industry include mechanical fasteners both threaded and
non-threaded. Threaded fasteners, mainly bolts, nuts and screws, are used in applications
that require components to be disassembled. Other advantages of threaded fasteners
include ease of assembly, which generally requires no special equipment and usability on
most materials.
1
1.1 Introduction to bolted joints
Bolted joints can be categorized based on the direction of external loads acting on
the joint [28]. The two types of joints are shown in figure 1.1 (a) and (b). If the forces on
the joint are parallel to the axis of the bolt, the joint is loaded in tension and is called a
tensile joint. If the forces on the joint are perpendicular to the axis of the bolt the joint is
loaded in shear and is called a shear joint.
(a)
(b)
Figure 1.1: Bolted joints classified by external load
(a) Tensile joint (b) Shear joint [28]
A typical bolt tightening operation occurs in three distinct phases [30] [35]. Figure
1.2 is a plot of torque against fastener rotation showing the three phases of bolt
tightening.
2
Figure 1.2: Three phases of bolt tightening operation [35]
The three phases can be described as follows:
i. Rundown: In this phase, prevailing torque which is primarily due to thread friction is
overcome, before the fastener comes into contact with the bearing surface.
ii. Alignment: This is the “snugging” zone that occurs when the fastener comes into
contact with the parts being joined. This is a non-linear zone where non-parallel
bearing surfaces may cause the bolt to bend, and coatings, surface roughness and
deforming threads will add to the torque load in an unpredictable manner.
iii. Elastic clamping: The actual tightening is done in this phase. A tangent plotted to this
linear portion of the curve and extended back to zero torque locates the “elastic
origin”. The tension in the fastener is directly proportional to the angle of turn in the
region from the elastic origin to the point where torquing was stopped in this zone.
Bolted joints, in effect, behave like two sets of springs [30]. The bolt behaves like
a spring in tension as it is tightened and the joint material acts like a spring in
3
compression while it resists the bolt’s tension. Preload is the tension built-up in the
fastener and is created when torque is applied. During the assembly process the challenge
is to establish the right amount of clamping force between the bolt and the joint members.
There has been no practical and reliable way to determine bolt tension either during the
assembly process or afterward. It can be done using special strain gauged bolts or with
ultrasonic microphone procedures, but these methods are not practical in a production
environment or with very small fasteners. The common methods used to control bolt
preload during assembly are [33]:
i. Torque control tightening: The most conventional method of controlling the bolt
preload has been to measure the torque applied during the assembly operation [30].
However, most of the tightening torque goes into overcoming friction under the
fastener head and in the threads, as shown in Figure 1.3. In many bolted joints only
10% of the applied torque actually produces the clamp load in the joint.
Figure 1.3: Torque distribution of a typical fastener [30]
4
In most cases the relationship between torque and preload can be described by the
following equation [36]:
Torque = K X d X F
(1.1)
Where K= Nut factor
d = Nominal bolt diameter (in, mm, etc)
F = Bolt preload (lbs., N)
The nut factor, K is a combination of three factors - K1, a geometric factor, K2, a
thread friction factor, K3, an underhead friction related factor. There are published
tables of nut factor values for various combinations of materials, surface finishes,
plating, coatings and lubricants. However for most critical applications it is often
necessary to determine this value experimentally.
ii. Angle control tightening: In this method, the bolt is tightened to a predetermined
angle beyond elastic range. The main disadvantages of angle control tightening lie in
the need for a precise determination of the angle.
iii. Yield control tightening: This method uses wrenches with a control system
instrumented with sensors to measure torque and angle during the tightening process.
The control system is sensitive to the torque gradient of the bolt being tightening and
stops the tightening process when it detects a change in the slope of this gradient.
iv. Bolt stretch method: This method involves the measurement of the elastic
deformation that the fastener undergoes as it clamps down. By measuring this stretch
and knowing the physical properties of the bolt and its material composition, the
preload can be calculated.
5
v. Heat tightening: Thermal expansion characteristics of the bolt are utilized in this
method. When bolt is heated and undergoes expansion, the nut is indexed and the
system is allowed to cool. As the bolt attempts to contract it is constrained
longitudinally by the clamped material and a preload results.
1.2 Torque tool options
Torque tools used for tightening threaded fasteners include hand tools and power
tools. Hand tools such as wrenches and screwdrivers rely on the human operator for
generating force and torque while power tools depend on an external energy source. The
increased use of power tools in numerous assembly applications is due to their ability to
tighten threaded fasteners rapidly, their capacity to generate high torque and their
reliability in achieving target torque levels.
Power torque tools commonly used for securing threaded fasteners are powered
screwdrivers, nutrunners and impact wrenches, shown in figure 1.4. Nutrunners provide
tight torque control and are used in precision fastening and assembly applications. They
come in different handle shapes and can provide access in tight quarters. An impact
wrench is a socket wrench power tool designed to deliver high torque output with
minimal exertion by the user. In operation, a rotating mass (the hammer) is accelerated by
the motor, storing energy, and is suddenly connected to the output shaft (the anvil)
creating a high-torque impact. The hammer mechanism is designed such that after
delivering the impact, the hammer is again allowed to spin freely, and does not stay
6
locked [37]. With this design, the operator feels very little torque, even though a very
high peak torque is delivered to the socket.
(a)
(b)
(c)
Figure 1.4: (a) Electric screwdriver [38] (b) Air impact wrench [39]
(c) Air angle nutrunner [40]
7
Power tools can be categorized in the following ways:
i. Based on external energy source:
•
Pneumatic - Tools driven by compressed air.
•
Hydraulic - Tools driven by hydraulic pressure, these are used in the
construction industry for very high torque applications.
•
Electric - Tools driven by electricity running through a cable.
•
Cordless - Tools that run on rechargeable batteries and can be used in
situations where compressed air or electricity is unavailable or impractical. A
cordless tool is shown in figure 1.5.
Figure 1.5: Cordless tool [41]
ii. Based on type of drive [42]
•
Discontinuous drive - Pulse tools are discontinuous drive tools that generate
torque in brief pulses under load. The pulsing allows tool users to benefit from
the advantages of reduced transmission of torque reaction. These tools have
traditionally been hydraulic but there are a few DC torque tools available.
8
•
Continuous drive - these tools are gear-driven and continuously supply power
during the entire cycle.
iii. Based on handle shape (figure 1.6)
• Pistol grip
• Right angle
• Inline
(b)
(a)
(c)
Figure 1.6: (a) Pistol grip (b) Right angle (c) Inline [43]
Both pneumatic and electric tools can be of different levels of sophistication
depending upon the type of application. Less critical applications use torque tools which
can be adjusted to different torque levels, but do not have feedback. Critical assemblies
use controlled fastening tools, shown in figure 1.7. These tools offer better performance,
precision and versatility. Controlled pneumatic and electric tools are instrumented with
sensors that allow the direct measurement of both the dynamic applied torque and the
angle of rotation of the threaded fastener during the assembly process. These tools
interface with a controller which can be programmed to store different fastening
9
parameters and tightening strategies. The controllers display digital torque values
allowing torque verification and also allow data to be collected and recorded. The
controllers can also be connected to a personal computer and be programmed using
proprietary fastening software. Electric tools, of the type described above are called DC
torque tools.
Figure 1.7: A DC electric nutrunner with a tool controller [44]
1.3 Motivation for current work
Power tools have been associated with repetitive and forceful exertions during their
use, which often displace the operator hand and arm. Impulsive reaction forces and
prolonged exposure to vibration transmitted to the operators of power tools have been
related to symptoms of carpal tunnel syndrome (CTS), vibration white finger (VWF)
disease, loss of muscle strength, and disorders of the nervous symptom. These disorders
belong to a category knows as work related musculoskeletal disorders (WMSD), develop
gradually as a result of repeated trauma and are also called cumulative trauma disorders
10
(CTD). Thus, power tool use, due to its repetitive nature and forceful exertions, has been
considered a risk factor for work related musculoskeletal disorders [32].
Various factors affect the dynamic interaction between the tool and the operator
and the operator’s ability to react against impulsive forces. Factors that have been studied
include tool shape, target torque levels, joint hardness, work location and orientation and
operator characteristics [4] [17] [18]. Appropriate selection of the process factors (torque,
joint hardness, etc.) and workstation design factors (orientation and distance from
operator) is important to maximize performance and quality, while minimizing physical
stress.
An important factor in the consideration of DC torque tools is that the tool
controller allows enormous flexibility in setting tightening parameters and profiles. For
example, the tool speed, torque and angle limits for an acceptable joint, and different
tightening strategies, can all be set at the controller. These factors can have a great impact
on the dynamic interaction between the tool and the operator and on the operator’s
perceptions of exertion and acceptability of the tool. Although there have been several
studies on the ergonomics of power tools, there is a limited understanding of the
ergonomic impact of tool controller programs and strategies and their interaction with
human and joint variables. This project aims to begin to address the above voids.
11
1.4 Thesis objectives
The purpose of this thesis is to quantify the ergonomic impact of various DC torque
tool controller settings. This impact will be determined by the use of an ergonomic test
rig which will capture the interaction between the physical tool, control software, and a
simulation of a human arm. The rig contains a simulator of human arm mass and
stiffness, and incorporates other input factors such as torque and rotation requirements,
joint hardness, and tightening program parameters. The output of the rig is the reaction
force and displacement of the tool handle and therefore simulated arm as a function of
time. This study involved the following:
i. Modification of an existing ergonomic assessment rig which was designed and
constructed as a part of a student design project [27]. The current study involved an
improved model of the human arm stiffness and mass, the use of a more portable
measurement system, and several mechanical improvements.
ii. Investigation of how three controller algorithms interact with human arm parameters
and joint variables. The DC torque tool and tool controller used in for this thesis was
manufactured by Stanley Assembly Technologies. The three speed management
algorithms that were studied were Manual Downshift, Two Stage Control and
Adaptive Tightening control (ATC).
iii. Analyses of the response curves obtained from the rig to estimate the ergonomic
impact on the body for a specific set of arm parameters. A set of metrics were
developed for ergonomic assessment including peak reaction torque values, peak
deflection values, range of handle movement, area under the torque-time curves, etc.
12
The ergonomic assessment rig used in this thesis incorporates several variables and
produces a single curve that combines the effects of all of these. This reduces the number
of factors needed for ergonomic assessment and leads to an abstraction of the results.
This thesis does not intend to study the effect of different types of tools, arm positions, or
associate population variations. Associates may also be fatigued over an extended period
of time, and this factor is not incorporated in the force and deflection curves from the
assessment rig. The work of this thesis will lead to an improved understanding of the
interactions between stiffness of the joint to be fastened (joint stiffness), the simulated
human arm system, and the program algorithms controlling the tool, which could
eventually thus help minimize injury associated with the use of these tools.
This thesis is organized in the following manner. Chapter 2 discusses the existing
literature on the ergonomics of power tools. Chapter 3 describes the design of the
ergonomic assessment rig. The experimental methods used in this study are explained in
Chapter 4, and the ergonomic metrics used for assessment are introduced. Chapter 5
presents the results of the experiments conducted using the ergonomic rig, which are
discussed in detail in chapter 6. The conclusions and contributions of this thesis are stated
in Chapter 7, with recommendations for future research.
13
CHAPTER 2
LITERATURE REVIEW
This thesis has benefited from several studies conducted on the ergonomics of
power tools. This chapter gives a description of the literature pertinent to the current
work, and has been divided into four sections. The first section focuses on dynamic
models of the tool-operator system that were developed to quantify human arm
parameters. The second section describes studies on ergonomic injury risk assessment.
The third section talks about the research on the ergonomic effects of work station design
and operator posture. The fourth section describes the design and application of an
instrumented tool handle that allows direct measurement of grip forces and moments
acting on the handle.
2.1 Dynamic models of tool-human operator system
Reaction force acting against the hand was estimated under the conditions of static
equilibrium by Radwin et al [23]. Static hand reaction force was given by the spindle
torque divided by the handle length as (figure 2.1):
14
FHz
=
Tnut
LHy
(2.1)
Where FHz = hand reaction force in z direction
Tnut = torque
LHy = length of the tool
If excessive hand movement occurs during tool operation, the hand force estimated by the
static model may be less accurate because of inertial effects. The authors Oh, Radwin and
Fronczak proposed a dynamic model to provide a more accurate estimation of the
reaction force [19]. This model, shown in figure 2.1, was based on physical tool
parameters and can be used to calculate the hand reaction force.
Figure 2.1 Forces acting on a right angle power hand tool [19]
15
The dynamic hand forces and moments were described by the following equations:
∑M
z:
WTLTy + WHLHy − FHxLHy =0
∑M −T
x:
nut
+ FHzLHy =( Itool + mHLHy2 ) αtool
(2.2)
(2.3)
Hand reaction force FHz and tool support force FHx can be solved from equation 2.2 and
2.3:
FHx =
FHz =
1
(WTLTy + WHLHy )
LHy
1
⎡⎣( Itool + mHLHy 2 ) αtool + Tnut ⎤⎦
LHy
(2.4)
(2.5)
Where M = moment, W = weight, I = moment of inertia, m = mass, α = angular
acceleration and subscript T = tool.
The model assumes the hand and lower arm to be a point mass applied at the
handle. The forces calculated using the above equations were compared with the hand
reaction force measured directly using a strain gauge attached to the tool handle. Subjects
used the tool with three target torque levels (25, 40 and 55 Nm) and five different torque
build up times between 35 and 900 ms, so that the effects of target torque and joint
hardness on reaction force could be estimated. Direct force measurements showed that
the dynamic model overestimated the peak hand force by 9 %. The model proposed did
not account for different postures and positions of the operator which could affect the
force components, and this was stated as a plausible reason for the overestimation. Peak
hand force was the least for the hard joint and greatest for the medium hardness joint (150
ms build up time). Peak force increased by 76 % as target torque increased from 25 to 55
16
Nm. Comparing the results from the static and dynamic equations showed that the static
model overestimated the hand force; the error ranged from 10 % for a soft joint to 40 %
for a hard joint. This was because the static model did not include the inertia of the tool
which played a major role in reducing hand reaction force.
Lindqvist hypothesized that mass-spring-damper mechanical system could be used
to describe the handle response to impulsive reaction forces encountered in nutrunner
operation, but did not identify specific parameters for these elements [15]. Lin et al.
developed a similar biomechanical model in which a pistol grip tool operator is
represented as a torsional system [12]. The hand and arm elements are represented as
equivalent mass moment of inertia, rotational stiffness and damper, as shown in figure
2.2.
Figure 2.2: Pistol grip tool - operator system represented as a torsional system [12]
17
The equation of angular motion describing this system with a torque input of T (t) is:
( Jsubject + Jtool ) +
d 2θ
dθ
+ csubject
+ ksubject = T (t )
2
dt
dt
(2.6)
Where: Jsubject = effective mass moment of inertia of the hand and arm.
Jtool = mass moment of inertia of the tool.
csubject = rotational damping for the hand and arm
ksubject = torsional stiffness for the hand and arm
The values of these elements were determined by measuring the free vibration
frequency and amplitude decay of a known mechanical system when externally loaded by
the human arm. The effect of gender, horizontal distance, and vertical distance from the
ankles to the handle was tested. The model was able to predict the hand force and handle
displacement as a function of the human arm parameters and the input torque. The model
predictions were validated using actual tool operation.
The results shown in figure 2.3 are the average human arm parameters for 25
subjects. The bars represent one standard deviation. The plots demonstrate that the values
of human arm parameters vary between operators and are also affected by work place
conditions.
18
(a)
(b)
(c)
Figure 2.3 Average values of human arm parameters for the pistol grip model
(a) Torsional stiffness (b) Mass moment of inertia (c) Torsional damping [12]
The model predictions for handle displacement had a correlation of 0.88 with the
actual measurements. The model under predicted the handle displacement by 27%. The
experiment to determine stiffness, mass and damping parameters used maximal exertions
by the operators, but it was unlikely that the tool operators used their maximum
19
capability during actual tool operation and this could be a possible reason for the under
prediction.
Lin et al [13] used a mechanical vibrating apparatus similar to that used previously
by the authors [12] to quantify mechanical model parameters for operation with various
tool shapes such as in-line, pistol grip and right angle. The different handle
configurations are shown in figure 2.4.
(a)
(b)
(c)
(d)
Figure 2.4: (a) Pistol grip on a vertical surface (b) In-line on a horizontal surface
(c) Pistol grip on a horizontal surface (d) Right angle on a horizontal surface [13]
The tests involved both male and female subjects. Test results showed a decrease
in stiffness as the horizontal distance between the tool and the operator increased. Males
had greater arm parameter values than females. Thus the parameters obtained in [13] and
20
[12] can be used to model the operator arm response to power tool operation for different
hand tool and work place designs.
Previous studies by Lin et al [12] [13] used maximum operator exertion levels to
estimate mechanical parameters and thus the model under-estimated the actual handle
displacement. A subsequent study by the same authors used normalized forearm flexor
muscle group electromyography (EMG) to adjust the mechanical stiffness parameter
[14]. Subjects were asked to exert the maximal force before each experimental condition
to determine their maximum voluntary contraction (MVC) using EMG. The muscle was
assumed to maintain constant stiffness 50 ms prior to torque build up, and the EMG from
this phase was normalized to MVC to assess actual exertion during tool operation. This
resulted in an improved correlation (r = 0.98) between the measured and predicted handle
displacement and reduced the model prediction error from 27% [12] to 3%.
Further applications for the mechanical model developed were explored by Lin,
Radwin and Nembhard [11]. A right angle and pistol grip tool were operated by 25
subjects at two discrete work locations described as “near” and far”, shown in figure 2.5.
This model could be used to predict group means and variations of handle displacement
and force for a given tool configuration. For instance, the pistol grip nutrunner used on
horizontal surface at 30 cm in front of the ankles and 140 cm above the floor resulted in a
predicted mean handle displacement of 39 mm for males. The study also describes an
interpolation method which can be used to calculate subject parameters at any work
location expressed as a linear combination of the work locations used in the study.
21
Figure 2.5: “Near” location - 30 cm in front of the ankles, 140 cm above the floor,
“Far” location is 60 cm in front of the ankles and 80 cm above the floor [11].
2.2 Ergonomic injury risk assessment
Power tool use has been associated with repetitive and forceful exertions associated
with increased risk for musculoskeletal disorders. It is important to control the forces
acting against the operator’s hands to reduce the risk of injuries, disorders and muscle
fatigue. Several authors have investigated the factors that can influence the reaction force
due to power tool operation.
Johnson and Childress [4] investigated the effect of target torque, tool grip
diameter, type of tool, tool shape and tool weight on the operator. Electromyography and
subjective evaluations of the operators were used to assess operator response. Torque was
the most significant factor in the amount of effort and stress associated with a tool and
subjects indicated a preference for the tools set at lower torque level. Tool grip diameter
22
was significant only at higher torque levels, with small diameters resulting in higher
EMG levels and lower subject preferences. The effect of tool weight was not significant
in the analysis of EMG levels (p > 0.05) though subjects preferred lighter tools.
Another study that used EMG and subjective ratings to determine operator
exertion was by Frievalds and Eklund [2]. The factors that were studied included
different work orientations, joint stiffnesses, type of tool, tool speed, air pressure levels
and handle configurations. Peak reaction torque was measured using a torque transducer
and torque impulse was calculated as the area under torque-time curve. The subjective
ratings were significantly correlated (p < 0.05) to the peak reaction torques, torque
impulse and the EMG levels. The results showed that running electric tools at lower
speeds, pneumatic tools at lower pressures and using a soft joint resulted in larger
impulses and more stressful operator ratings.
Radwin et al. [23] used EMG to study the reaction force acting against the hand,
forearm muscle activity and grip force for operators of right angle air shut off nutrunners.
The independent variables for this test were four tools with increasing torque output
capacity ranging between 30 Nm to 100Nm, which were operated at two torque build up
times (0.5 and 2 seconds). Peak hand force increased with increasing tool output capacity
which is consistent with the results of Johnson and Childress [4]. Average grip force was
50 N greater for the shorter torque build up time.
Kihlberg, Lindbeck and Kjellberg discuss methods to assess torque reaction
associated with pneumatic nutrunners in three separate studies. In the first study [5], three
nutrunners with the same pre-set spindle torque of 75 Nm, but different shut-off
23
mechanisms - fast, slow and delayed (figure 2.6), were studied. The test setup included a
force platform that measured ground reaction forces between subject and the floor. Other
performance measures included muscle activity, hand arm motion and discomfort ratings.
Fast shut off nutrunners gave the smallest handle displacement and force, while delayed
shut-off gave the largest displacement and force. EMG measurements showed no
significant relationships among the three tools. The tool torque impulse according to ISO
6544 [25] is defined as the area under the torque-time curves above a threshold torque
equal to 10% of the target torque. Discomfort ratings were weakly correlated with torque
impulse values (r = 0.74).
Figure 2.6: Three shut off mechanisms studied by Kihlberg et al. [5]
The second study by Kihlberg et al. [6] tested nine angle nutrunners with three
different preset torques (25, 50 and 75 Nm) and different shut off mechanisms tested in
[5]. Reaction force, hand arm displacements were measured and subjective ratings were
obtained using a modified Borg’s strength and discomfort scales shown in figure 2.7.
24
Strength
Discomfort
10
9
8
7
6
5
4
3
2
1
0.5
0
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Extremely strong
Very strong
Strong
Moderate
Weak
Very weak
Extremely weak
Nothing at all
Almost unbearable
Extremely discomforting
Very discomforting
Rather discomforting
Somewhat discomforting
Hardly discomforting at all
No discomfort at all
Figure 2.7: Modified Borg’s scales used by Kihlberg et al. [6]
The authors also proposed a method to calculate a parameter called “time - torque
value” which was the time period during which the torque exceeded 75 % of the present
torque, multiplied by the torque. This is the shaded area shown in figure 2.8. Strong
correlations between subjective ratings and displacements (r = 0.977) and ratings and
vertical ground reaction force (r = 0.987) were found. Subjective ratings had strong
correlations with the torque impulse values (r = 0.945) calculated according to ISO 6544
and the time-torque value (r = 0.962).
25
Figure 2.8: “Time - torque” value as defined by Kihlberg et al [6]
A third study by Kihlberg et al. [7] aimed to test the results of their previous work
in an industry setting with experienced workers and to establish acceptability limits for
ratings, tool handle displacements and reaction forces. The results indicated that no
subject would accept to work a whole workday at a discomfort over 9 on their 20 point
scale (figure 2.7). It was also concluded that for a tool to be accepted by 90% of the
operators, it should produce handle displacement of less than 30 mm.
2.3 Effect of work station design, operator posture and position
The human arm parameters quantified by the single degree of freedom mechanical
model show that the arm mass, stiffness and damping values are affected by the work
location. The operator’s ability to resist the forces from power tool use will depend on his
posture, position and work station design as investigated by the studies below.
Lindqvist [15] tested two different postures using a right angle nutrunner, one with
a horizontal lower arm and other with a vertical lower arm, as shown in figure 2.9. The
tests were done on a hard joint and a medium soft joint. The results show a higher
26
displacement for the tests with a vertical lower arm, thus showing the influence of arm
posture on the handle displacement during the tightening sequence.
(a)
(b)
Figure 2.9: Two postures used in Lindquist’s study
(a) Horizontal lower arm (b) Vertical lower arm [15]
Oh and Radwin’s [17] study on right angle nutrunner operation included two
categories of independent variables. The first was the process factors determined by two
target torque levels, 25 Nm and 50 Nm and two joint hardnesses characterized by torque
build up times of 35 ms (hard joint) and 900 ms (soft joint). The second category was the
work station design factors which included orientation (vertical and horizontal) and
operator distance (10 cm and 35 cm) from the tool. Handle displacement and velocity,
work done on the tool - arm system, and muscle EMG activity were used as dependent
variables. One objective of this study was to determine conditions that minimize tool
handle instability. Peak handle velocity and peak handle displacement were used as
indicators of work done by the operator (positive work) or work done against the operator
(negative work). The handle was most stable when torque was 25 Nm, when vertical
27
workstations were closest and horizontal workstations were farthest. The work done
against the operator was lower for the hard joint. Subject EMG measurements show a
burst of muscle activity after the onset of torque build up, as seen in figure 2.10. Oh and
Radwin describe a method to determine the start of this event and also define EMG
latency as the time difference from the start of torque build up to the onset of the muscle
activity burst.
Figure 2.10: EMG latency as demonstrated by Oh and Radwin [17]
Another study by Oh and Radwin [18] uses three levels of target torque (25, 40
and 55 Nm), five torque build up times between 35 ms and 900 ms, horizontal and
vertical orientation. The study showed higher handle stability for the horizontal
orientation demonstrating that a horizontal work station is preferable for right angle tool
28
use. Higher target torque resulted in greater muscular exertion, similar to the conclusions
by Johnson and Childress [4] and Radwin et al [23].
2.4 Design and application of an instrumented tool handle
Direct measurement of the grip forces and moments applied during hand tool use is
an important aspect of ergonomic evaluation. Different approaches to the measurement of
force at the hand-handle interface have included the use of pressure sensitive materials
[3] and EMG [22]. These methods have been ineffective in capturing real time grip force
data and are limited in their use with different handle shapes.
McGorry’s work [16] describes a device that is capable of directly measuring grip
forces and moments exerted while using hand tools. The hand tool analysis system
consists of a grip force sensing core, which is a symmetrical arrangement of three beams
with strain gauges fixed to each end of the beams, as illustrated in figure 2.11. Handles
of various shapes and sizes can be mounted to the grip core.
29
Figure 2.11: Location of strain gauges on the grip force sensing device [16]
The device was configured as a knife and evaluated in a laboratory simulation of a
meat cutting task. The tests indicated that the device had a working range greater than
700 N for grip force and 28 Nm and 16 Nm for the two applied moment axes. The system
had good linearity (r2 = 0.999) with negligible hysteresis and creep and possessed a
flexible design.
The work of Lin et al [9] was the first study to used the grip force sensors
described above to investigate how work location and joint hardness effect power tool
operator response. The tools used were equipped with a simulated handle that contained a
30
grip force sensing core instrumented with strain gauges. The simulated handle positions
for the pistol grip and right angle tool are shown in figure 2.12. The results of this study
demonstrate that a tool used on a specific joint results in different handle displacements
depending on the working postures. The study provides quantitative measures of handle
displacement and grip force at different work locations which can be used to design work
stations and select appropriate tool-task combinations. Another study by Lin, McGorry
and Chang [8] extends the application of the grip force sensor to an inline tool and further
tests the impact of all three handle shapes on different working postures and joint
hardness.
(a)
(b)
Figure 2.12: Instrumented handles used in Lin’s study
(a) Pistol grip tool (b) Right angle tool [9]
31
2.5 Literature summary
Previous studies on the ergonomics of power tools can be summarized in the
following manner. A single degree of freedom model was developed for common power
tool shapes and the human operator was represented as a mass, spring, and damper
system. Model parameters were proposed for specific work conditions and the response
predicted by the model was validated using actual tool operation. Other studies used
several criteria to identify operator discomfort and exertion that results from power tool
use such as subjective ratings, ground reaction force, handle displacement, muscle
activity from EMG. In addition, different postures and work locations have been explored
providing quantitative information that can be used to design work stations and select the
appropriate tool for the task.
This thesis is motivated by the existing work on power tool ergonomics and the
need to better understand the interaction between the tool and the operator. The
ergonomic test rig that will be used in this project is built on the single degree of freedom
mechanical model developed by Lin, Radwin et al [11] [12] [13]. The factors that will be
studied include target torque, joint hardness and human arm values with a right angle
nutrunner. The operator mass and stiffness will be based on the work of Lin and his
colleagues for a right angle nutrunner used in a horizontal location [11] [13]. Tool
programming strategies will be included as a new independent variable and its effect on
the human arm model will be investigated.
32
CHAPTER 3
DESIGN OF ERGONOMIC ASSESSMENT RIG
This project uses a stationary DC torque tool ergonomic test rig which is based on
the dynamic model of the tool-operator system formulated by Lin et al. [11] [12] [13].
The rig contains a model of the human arm in which the hand and arm elements are
represented by an equivalent mass and stiffness. It also incorporates other inputs, shown
in figure 3.1, which include the target torque, joint hardness and controller program
parameters. The output of the rig is the reaction force and displacement of the arm as a
function of time and these response curves will be analyzed to provide an ergonomic
assessment. One key advantage of this rig will be its repeatability which will eliminate
the variability associated with human subject testing as seen in some of the papers [12]
[13].
This chapter begins with a description of the initial ergonomic test rig that was
built as an undergraduate student project, the second section talks about the changes
made to the human arm model, measurement system and other components of rig, and the
33
third section displays the final ergonomic test rig used for this project. The results of the
repeatability tests conducted are presented in the final section.
Figure 3.1: Inputs and outputs of the ergonomic test rig
3.1 Description of the original ergonomic test rig
An ergonomic assessment rig was designed and built during a student design
project conducted with Honda to investigate the impact of DC tools on safety and
ergonomics by considering factors such as joint hardness and controller program
algorithms by different tool manufacturers [27]. This test device, shown in figure 3.2,
was modular in construction so as to accommodate different tool lengths, but was
designed specifically for assessing right angle tools.
34
Figure 3.2: Initial ergonomic test rig design [27]
This ergonomic test rig consisted of two major systems - the tool and bolted joint
assembly, and the human arm model with the measurement system. The various
components of these systems are described below.
3.1.1 Tool and bolted joint assembly
The tool and bolted joint assembly is shown in figures 3.3 and 3.4. The tool used
was a Stanley DC right angle torque tool (model number E44LA19-70) rated at 70 Nm.
The right angle head of the tool was supported by a shoulder bolt so that only a rotational
degree of freedom remained. The tool socket rested on a drive plate and the fastener head
was placed between the socket and the drive plate and was free to rotate. The bolted joint
also consisted of a torque plate that moved vertically as the fastener was tightened by the
35
tool (figure 3.4). The joint hardness could be varied by placing Belleville washers
between the two plates either in series or parallel. The other end of the tool handle was
connected to the human arm model.
Right angle tool
Shoulder bolt
Drive plate
Figure 3.3: Tool and bolted joint assembly [27]
Belleville washers to
adjust joint stiffness
Drive plate
Torque plate
Fastener
Figure 3.4: Bolted joint assembly [27]
36
3.1.2 Human arm model with measurement system
The human arm model, along with the measurement system, rested on an
aluminum table, as shown in figure 3.5. The tool handle was held by U-bolts to the mass
system. The plate attached to the U-bolts was pivoted to allow the handle to rotate during
tool operation. The human arm mass consisted of an aluminum box to which steel plates
were held by C clamps to model different arm masses. The human arm stiffness was
modeled by an air spring whose stiffness could be varied by changing air pressure and
volume.
The air spring threaded into a load cell which measured tension and
compression force. A linear variable differential transformer (LVDT) was connected to
the arm model to measure handle displacement. The sensors were connected to an Instron
measurement system to obtain force and displacement measurements in the time domain.
Mass system
Air spring
LVDT
U-bolts
Load cell
Tool handle
Figure 3.5: Top view of human arm model with the measurement system [27]
37
The entire set-up, consisting of the tool and bolted joint assembly and human arm
model rested on a steel optical table with a grid of threaded holes on its surface which
allowed the components of the rig to be bolted down.
Several improvements to the existing test rig marked the beginning of this project.
These modifications aimed to address some of the drawbacks of the rig which are
explained below.
i.
The model of human arm stiffness had to simulate arm motion in forward and
reverse directions, but the air spring did not function well in tension and would
bottom out if stretched too much [27]. Also, based on previous studies by Lin and
Radwin [11] [13], arm stiffness is assumed to be constant during tool operation. But
the air spring resulted in a non-linear stiffness curve over its displacement range.
Thus, there was a need for an improved arm stiffness system.
ii.
The present method of varying the arm mass using steel plates and C- clamps was
cumbersome and also limited in the range of arm masses that could be obtained. It
was necessary to design a more convenient method of incrementing arm mass and
also one that would satisfy a wider range of values.
iii. The purpose of the load cell was to measure the reaction force on the operator hand
during tool operation. The load cell in the original test rig was located behind the
arm mass and at a considerable distance from the tool handle. As the rig was
originally configured, the load cell was also measuring the inertia of the arm which
38
is not what is required. Thus, it was necessary to position the load cell on the other
side of the mass and closer to the tool handle.
iv. The rig used the Instron system for calibration and data acquisition, which limited
its portability. A more compact and convenient data acquisition system was needed.
3.2 Rig improvements
The following modifications were made to the rig throughout the course of this
research and resulted in the final design of the ergonomic test rig.
3.2.1 Improved spring design to represent arm stiffness
Some of the requirements of the new spring design were a larger displacement
range and an ability to simulate eccentric and concentric motion of the human arm. A
spring whose spring rate could be modified by controlling a limited number of variables
was needed to model different human arm capabilities. Due to the assumption of constant
arm stiffness, the spring also had to meet conditions of a constant spring rate over its
entire range of displacement.
There were a few alternatives for the new arm stiffness system. Using different
combinations of mechanical springs in series and parallel to vary the spring rates was one
alternative. Another option was to use a pneumatic cylinder as a linear actuator, which
can be driven by pressure differential in the cylinder chamber. Since one of the
requirements of the arm stiffness system was to produce a large number of spring rates
with minimum design changes, a pneumatic cylinder was chosen as a replacement for the
39
air spring. Combining mechanical springs in different series and parallel configurations
would be more complicated, while an air cylinder of specific bore and stroke dimensions
could be used with varying internal pressures and external volume capacities to produce
different stiffness curves. Appropriate selection of these factors can ensure the linearity
of the spring rate within the desired range. A double acting cylinder was selected as both
sides of the piston can be pressurized to extend and retract the piston rod, thereby
modeling the movement of the human arm in forward and reverse directions.
An initial analysis of the piston movement and the resulting force - displacement
curves was done using a single rod double acting cylinder. The piston was positioned at
the midpoint of its stroke initially so that the maximum displacement of the rod in both
directions is equal. Both sides of the piston are pressurized equally to ensure equal spring
rates in forward and reverse strokes. However in a single rod cylinder, the effective
working area of the rod side of the piston is lesser than that of the other side (figure 3.6
(a)). Thus, the forces on the two sides are unequal and the resultant force on the piston is
not zero in its initial position. The only way to have the piston balanced at the midpoint is
to either have different pressures on the two sides or use a double rod double acting
cylinder, as shown in figure 3.6 (b). A double rod cylinder was chosen as this would
eliminate additional pressure calculations to keep the piston balanced.
40
Area 1
Area 2
(a)
Area 1 ≠ Area 2
Area 1
Area 2
(b)
Area 1 = Area 2
Figure 3.6: Double acting cylinder (a) Single rod (b) Double rod
The dimensions of the air cylinder were decided by calculating the spring rate from
the force-displacement curves for different bore and stroke specifications. This analysis
was based on the ideal gas law according to which pressure, volume and temperature are
related by the equation:
PV = n R T
Where P = absolute pressure (Pa)
V = volume (m3)
n = number of moles of the gas
R = Universal gas constant (8.3143 m3 Pa/mol -K)
41
(3.1)
T = absolute temperature (K)
The basis of calculations for the spring rate analysis is explained below with help of
figure 3.7.
Piston rod
Piston at mid-point
of stroke
Pin
Pin
F1
Side 1
F2
Side 2
x
Figure 3.7: Analysis of double acting double rod cylinder
Initially the pressure on both sides of the piston is equal to the inlet pressure Pin
and the piston is at the mid-point of its stroke. Since the pressures and volumes are the
same on both sides, the piston is balanced. The number of moles of air on each side of the
piston can be calculated by the ideal gas equation as:
n = Pin Vin/RT
(3.2)
where T is equal to room temperature (298 K). It is assumed that the piston is perfectly
sealing and gas does not leak from one side of the piston to the other so that the number
of moles remains constant at all times. Now suppose the piston moves a certain distance x
towards side 2. Then the volumes and pressures on both sides are no longer equal. The
volume on side 2 is lesser than the volume on side 1, and so pressure on side 2 is greater
42
than the pressure on side 1 .This produces an unbalanced force on the piston (F2 − F1)
which is the difference in pressure multiplied by the effective area. This resultant force is
plotted against the displacement of the piston and the spring rate is calculated from the
force-displacement curve.
The complete analysis of the air cylinder and algorithm developed to relate inlet
line pressure to spring rate is described in Appendix A. The final specifications for the
arm stiffness system consisting of the air cylinder and volume plenums are as follows:
• Air cylinder bore equals one and one - half inches, stroke equals four inches.
• Two volume plenums of eight cubic inches capacity connected to either side of
the piston.
The pneumatic cylinder and volume chambers were manufactured by Clippard
Minimatic (models SDD-24-4 and AVT-24-8 respectively). This arm stiffness system is
capable of producing spring rates from 1500 N/m to 7900 N/m which would cover Lin’s
stiffness values ranging from 1200 N/m to 3200 N/m [11].
3.2.2 Design of pneumatic system to drive arm stiffness cylinder
The completion of the arm stiffness design was followed by a lay-out of the
pneumatic system that would drive the cylinder. The air cylinder connected to two
volume chambers, one on either side of the piston, constitutes the arm stiffness system.
At the start of each run, the piston is at the mid-point of its stroke to allow equal
displacement in forward and reverse directions. Each run begins with equal pressure on
43
both sides of the piston, so that the spring rates in the forward and reverse strokes are the
same.
Figure 3.8: Schematic of pneumatic system
The schematic of the pneumatic system and the plumbing connections is shown in
figure 3.8. There are three pressure gauges in total; two gauges are connected to the two
sides of the air cylinder through the volume plenums, and the third gauge is connected to
a pressure regulator through the shut-off valve denoted as valvecenter. The pressure
regulator is adjusted manually to a pressure corresponding to a specific spring rate, and
this pressure is read off the center gauge. Compressed air from the inlet line flows
through the regulator into the system when valvecenter is open. The air then flows through
valve1 and valve2 each connected to one side of the piston. To pressurize the cylinder all
44
three valves are open initially, and the three gauges show the same read out. Before each
run the arm stiffness system (cylinder and volume chambers) is isolated from the inlet
lines by closing valve1 and valve2.
A pneumatic control box was built to enclose all the fittings and tubing. As seen in
figure 3.9 the pressure gauges were mounted on the top plate and a sheet metal front
cover helped in concealing all the plumbing connections.
To air cylinder
To air cylinder
Figure 3.9: Pneumatic control box
3.2.3 Improved design of arm mass system
The original test rig used steel plates and C clamps to add and vary the mass of the
human arm model. Although this method worked satisfactorily, it restricted the range of
masses that could be obtained and also was limited in its usability. A new mass system
was required which would allow mass values to be varied conveniently during
45
experimentation. One alternative for the mass system was using a threaded rod and
adding hollow discs of different dimensions to it. Another option was to use a slotted box
and inserting steel plates of different dimensions. The design calculations showed that
using the hollow discs to add mass limited the range of values that could be attained.
Thus, the slotted aluminum box design was chosen. The new mass system used an
aluminum box with a partially open top as shown in figure 3.10. This box contained slots
into which steel plates could be inserted and bolted. A combination of steel plates of
different dimensions would add up to the desired mass. The final design of the arm mass
system had seven slots and the steel plates used could vary the mass from 3.3 kilograms
to 7.5 kilograms. Two sizes of arm mass plates were used which are shown figure 3.11.
The drawings for the components of the arm mass system are included in Appendix B.
Figure 3.10: Arm mass box to carry mass plates
46
4” X 3.75” X 3/8”
2.5” X 3.75”
X 3/8”
Figure 3.11: Two sizes of arm mass plates
The arm mass box was located next to the load cell and was supported on three
rollers so as to avoid imposing bending moment on the sensor (figure 3.12). The plate on
the side of the load cell was pivoted at the top and bottom to allow the rotary motion of
the tool handle. The mass system rested on a plate whose height could be adjusted by
jacking screws threaded into the main aluminum table that supported the human arm
model.
47
Pivot
Roller
Adjustment plate
Figure 3.12: Arm mass box supported on rollers
3.2.4 Load cell modifications
A Sensotec model 45A fatigue rated pancake load cell with a range of +/- 500lbs
was used for force measurement in the initial rig. The location of this load cell was a
cause of concern since it was positioned behind the arm mass system at a considerable
distance from the tool handle. The response that needed to be measured was the reaction
force between the tool handle and the human arm. The load cell in its existing location
was also measuring the inertia of the arm which was not required, and so it had to be
moved closer to the tool handle on the other side of the mass system. The 500lb load cell
was replaced by a model 31 miniature load cell also from Sensotec; the range was
48
lowered to +/- 50 lb (+/- 222 N) to increase the resolution as a percentage of full scale
output.
Moving the load cell closer to the tool handle increased its proximity to the arm
mass system and made it susceptible to bending loads. The model 31 load cell can
withstand a maximum bending moment of 8 in-lbs (0.9 Nm) without permanently
damaging it, and the bending moment from the arm mass system was several times this
limit. Due to this concern a device was designed to isolate the load cell so that it
measures only axial loads and is protected from the bending moment. Its major
components were an inner round rod and an outer hollow tube which react out bending
moment as a couple. The load cell is threaded to these parts as shown in figure 3.13. The
drawings for the various parts of this device are shown in Appendix B.
To mass box
To tool handle
Load cell
Figure 3.13: Device to protect Sensotec model 31 load cell
The load cell protection device was located between the tool handle and the arm
mass box and is shown in figure 3.14. It is threaded to the plate that holds the tool handle
on one end. The other end is bolted to the pivoted plate of the arm mass box.
49
Figure 3.14: Arm mass system and load cell assembly
3.2.5 Additional modifications
i.
The LVDT was mounted on top of the air cylinder since this would measure the
movement of the piston rod more accurately. The original location of the LVDT
also caused the core rod to bend due to its movement, while locating it above the
cylinder would eliminate this problem. The LVDT and the air cylinder threaded
into one of the plates of the arm mass box as shown in figure 3.15.
ii.
The other end of the air cylinder is threaded into a hollow connecting piece which is
connected to a clevis (figure 3.15). This allows the cylinder to move about the
clevis axis when the tool is run. The clevis is bolted to an angle plate which is
fastened to the aluminum table supporting the entire human arm system.
50
Clevis
LVDT
Air cylinder
Figure 3.15: LVDT, air cylinder and mass assembly
iii.
The Instron controller was replaced by a portable National Instruments data
acquisition and signal-conditioning system. This system has a NI SCXI-1520
module consisting of eight channels capable of providing 0-10V excitation for each
channel independently. The SCXI-1600 data acquisition module is used with the
SCXI-1520 to provide data acquisition and control capabilities. The SCXI 1314
terminal block mounts to the front of the module and provides connections to
sensors at the screw terminals located within a fully shielded enclosure. The
National Instruments system communicates with a computer via a USB connection
and is interfaced with LabView software which displays the voltage reading from
the sensors and also records the data as a text file.
51
3.3 Final ergonomic test rig
The fully assembled ergonomic assessment rig is shown in figure 3.16.
52
LVDT
Arm stiffness
cylinder
Arm mass
system
Load cell
53
Test joint
Stanley DC torque tool
NI Data acquisition
system
Pneumatic control box
Figure 3.16: Final ergonomic assessment rig for right angle DC torque tools
53
3.4 Repeatability tests
Following the completing of the ergonomic assessment rig, it was important to test
its repeatability. The protocol for the repeatability tests is given below, the speed control
algorithms and soft stop feature are described in detail in chapter 4.
i.
A medium-hard joint, requiring around 120° of rotation from snug to tight was
used, where snug torque was defined as 10 % of the target torque.
ii.
The target torque was set to 60 Nm for all runs. The entire set of controller
parameters can be found in Appendix C.
iii.
The tests were conducted at two tightening algorithms, Manual Downshift and
Adaptive Tightening Control (ATC). The tightening algorithms used in this thesis
are described in section 4.1.1.
iv.
The soft stop feature was used with the following timer values, which are Stanley’s
recommended values:
-
Current off = 0.001 seconds.
-
Current hold = 0.025 seconds.
-
Current ramp = 0.075 seconds.
A detailed description of the soft stop feature is provided in section 4.1.2.
v.
For the human arm model, the arm mass was set at 4.46 kilograms, and the
stiffness at 4150 N/m, which were chosen to represent a medium level of arm mass
and stiffness.
vi.
Ten replicates were done at each control algorithm and the runs were randomized.
54
The force-time and deflection-time plots from the repeatability tests are shown
below. Figure 3.17 shows the plots with Manual Downshift and figure 3.18 are for the
runs with ATC.
(a)
(b)
Figure 3.17: Repeatability test plots with Manual Downshift algorithm
(a) Deflection (b) Reaction force
55
(a)
(b)
Figure 3.18: Repeatability test plots with ATC algorithm
(a) Deflection (b) Reaction force
56
It can be seen from these figures that the reaction force and deflection plots are
highly repeatable in their shape and the magnitudes of the peaks. For both Manual
Downshift and ATC algorithms, the reaction force plots contain one large peak at around
-100 N and a second peak around -70 N. The reaction force rebounds to about 10 N. The
deflection plots in figure 3.17 (a) have peak values between -14 and -16 millimeters and
the rebound deflections are around four millimeters. The deflection peak values with
ATC are also repeatable and range from -14 to -16 mm. The means and standard
deviations for the peak force and deflections for the runs with the two algorithms are
displayed in table 3.1. The low coefficients of variability values validate the repeatability
of the ergonomic assessment rig.
Reaction force (N)
Deflection (mm)
Algorithm
Mean
Standard
deviation
Coeff. Of
variability
Mean
Standard
deviation
Coeff. Of
variability
Manual Downshift
−102.6
2.8
2.7%
−15.487
0.842
5.4%
ATC
−102.57
1.93
1.8%
−15.65
0.747
4.7%
Table 3.1: Mean and standard deviation for peak reaction force and deflection from
the repeatability tests
57
CHAPTER 4
EXPERIMENTAL METHOD
This chapter describes the experimental efforts undertaken to study the interaction
between the tool controller settings, joint stiffness and the human arm model using the
ergonomic rig described in the previous chapter. The purpose of these experiments is to
investigate the response of the model of the operator arm under combinations of factors
that act as inputs during torque tool operation. The first section in this chapter describes
the various input factors that were investigated, and the second section talks about the
responses that were measured. The third section describes the design of experiments
approach used to develop the orthogonal array and the final section discusses the various
ergonomic metrics that were developed and used to compare the response curves.
58
4.1 Description of the factors
A factor is a controlled independent variable whose levels are set by the
experimenter. This project investigated the effect of four factors which are stated below:
1. Tightening algorithm (tool controller setting)
2. Soft stop feature (tool controller setting)
3. Arm mass and stiffness
4. Joint stiffness
Each of the factors is explained in detail in the following subsections.
4.1.1 Tightening algorithm
The speed management algorithms are various approaches used to manage the speed
and torque of the motor during a threaded fastening cycle using a DC nutrunner. Three
different speed management approaches can be programmed at the Stanley tool controller
and these are explained below [46].
Manual Downshift
Running the tool at a fixed speed from start until the final target can result in
significant overshoot of the target torque if the speed is high while running at very low
speed would increase the cycle time and result in overheating of the tool. Therefore, the
traditional approach for DC nutrunners has been to use downshifting. In the Manual
Downshift technique the controller runs the tool at a higher initial speed for increased
productivity and reduced heat. Once it senses a preliminary torque target, the speed is
59
reduced and the tool runs at a lower speed until achieving the final target torque, for
improved accuracy (figure 4.1). The initial speed, the downshift torque level, and the
final speed are the important parameters that are required to program this algorithm. It is
important to set an appropriate downshift torque, since downshifting too late may result
in torque overshoot with a hard joint, as seen in figure 4.1.
Torque overshoot
Figure 4.1: Speed and torque control using Manual Downshift [46]
Two Stage Control
The Two Stage Control algorithm, as shown in figure 4.2, runs the tool at
maximum speed to a preliminary torque target, then shuts off the tool for 50 milliseconds,
and then accelerates continuously to the final torque target. In the two stage approach it is
important to find a balance between the initial speed, first shut off target and the final
speed making this algorithm more complicated to use on different joint stiffnesses.
60
Figure 4.2: Speed and torque control using Two Stage algorithm [46]
Adaptive Tightening Control (ATC)
Stanley Tools has a patented algorithm called Adaptive Tightening Control (ATC).
This algorithm senses the joint rate and dynamically changes the speed every millisecond
to achieve the best possible capability at a given starting speed. The speed and torque
control for ATC is shown in figure 4.3. This algorithm has a number of advantages,
including no requirement to test or learn a joint, and has been proven to decrease cycle
time on applications with a mix of hard and soft joints. This algorithm also increases tool
life by gradually loading the gears.
61
Figure 4.3: Speed and torque control using ATC [46]
The ATC algorithm can be used in two modes, the ATC automatic mode and the
ATC custom mode. These two modes are shown in figures 4.4 (a) and (b) with the
parameters associated with them and the codes required to program them on the
controller. The custom mode allows the adjustment of four parameters that help shape the
ATC deceleration ramp. These are start torque, end torque, free speed and end speed. In
the automatic mode, the speed and torque parameters take on their default values as
shown in figure 4.4 (a). The ATC custom mode is especially used for extremely hard
joints and high prevailing torque applications. This project used the ATC automatic mode
with its default speed and torque settings.
62
End Torque
Torque
(a)
75% of target
P212
Target Torque
Start Torque
25% of target
P211
Time
End Speed 10% of rated
Speed
P213
Free Speed 100% of rated
P214
End Torque
Torque
50 – 100% (>P211)
(b)
P212
Target Torque
Start Torque
10 – 75% (<P212)
P211
Time
End Speed 10 – 30% (<P214)
Speed
P213
P214
Free Speed 40 – 100% (>P213)
Figure 4.4: Parameters associated two modes of ATC algorithm
(a) ATC Automatic mode (b) ATC Custom mode [45]
63
4.1.2 Soft stop feature
While the tightening algorithms discussed in the previous subsection are related to
the process of building the load to the final target, the process of releasing the load is also
important from an ergonomic perspective [46]. The primary purpose of the “soft stop”
feature is to improve the ergonomics of torque tools by reducing the jerk on the operator
arm caused by releasing the load too quickly at the end of the run. The various controller
parameters associated with the soft stop are displayed in figure 4.5. The way soft stop
works is as follows: upon sensing the final target torque the controller shuts off the tool
and pauses for a time described by a parameter called the “current off time”. After the
current off time has elapsed the current flow is restored to a level slightly below that at
the shut off point and held at that level for the “current hold time.”After the hold time has
elapsed the current is ramped down linearly to zero over the course of the “current ramp
time.” For a rundown without the soft-stop feature the current off, hold and ramp times
will be equal to zero. Figure 4.5 shows the default values for the current off, hold and
ramp times in milliseconds used on the Stanley controller and the maximum
recommended value for the ramp time which is 250 milliseconds. The figure also shows
codes for the parameters which are required to program the soft stop feature on the tool
controller.
64
Figure 4.5: Parameters for the soft stop feature on Stanley controllers [45]
The soft stop factor was set at two levels for this project. For the first level, the
current off, hold and ramp times were set at zero which meant that the soft stop feature
was not active. This is denoted as the none condition. The second level used the default
soft stop time values which were 0.001, 0.025 and 0.075 seconds for current off, hold and
ramp times respectively (as shown in figure 4.5). This level is referred to as “default”.
4.1.3 Arm mass and stiffness
The arm mass and stiffness were combined as a single factor since both these are
related to the muscle’s capacity to resist reaction forces. The effective mass is thought to
reflect the quantity of muscle that is involved in the net muscle contraction and the
stiffness is the inherent spring like property of the muscle that is important in the control
of movement and maintenance of posture.
65
The ergonomic assessment rig described in the previous chapter contains a model
of the arm mass and stiffness represented by a mass box and an air cylinder respectively.
The values for the arm mass and stiffness levels were decided based on the papers by Lin
et al. [11] and also the capabilities of the test rig used in this project. Lin and Radwin’s
papers describe arm properties for specific test conditions (horizontal and vertical
distances from the tool) using a right angle tool of 9 Nm torque output. The arm mass and
stiffness factor was set at three levels so that a wide portion of human population could
be represented.
The paper by Lin et al [11] lists the human arm parameters for an operator using a
right angle tool on a horizontal surface. The arm mass values range from a minimum of
0.51 kg at horizontal and vertical distances of 60 cm and 110 cm respectively to a
maximum of 7.67 kg at a horizontal distance of 30 cm and vertical distance of 80 cm.
Although the rig in this thesis was built to be an accurate representation of Lin’s model,
the minimum arm mass that could be obtained on the rig was 3.371 kilograms due to the
combined mass of all the moving parts. These included the mass box (without any mass
plates in it), the piston rod, the load cell with the protection device and the tool support
plate. The plates and the mass box were designed to attain a maximum mass of 7.5 kg to
represent the maximum value observed by Lin et al [11]. The second level of mass was
set half way between at about 5.43 kilograms. The stiffness values in Lin’s paper ranged
from 1289 N/m (at a horizontal distance 90 cm, vertical distance 80 cm) to a maximum of
3117 N/m (at a horizontal distance 90 cm, vertical distance 110 cm) [11]. Pilot tests
conducted at these stiffness values resulted in very high deflections. A possible reason for
66
this could have been the higher torque rating of the Stanley tool (maximum torque output
of 70N/m) as compared to the tool used by Lin which was rated at 9 N/m. Thus, it was
concluded that higher values have to be used for this rig. The value of the highest
stiffness level was set at 7000 N/m, more than twice the maximum stiffness observed by
Lin. The remaining stiffness levels were calculated by using the ratios of the different
mass levels. Table 4.1 shows the three levels of the mass and stiffness factor. The three
levels of the arm mass and stiffness factor are denoted by the factor called MK (kg N/m)
obtained by multiplying the corresponding levels of mass and stiffness.
Levels
Mass (kg)
Stiffness (N/m)
MK (kg N/m)
Level 1
3.371
3153
10629
Level 2
5.43
5076
27562
Level 3
7.5
7000
52500
Table 4.1: Levels of mass and stiffness
4.1.4 Joint stiffness
Joint stiffness is characterized by the fastener rotation required to go from the snug
torque until the target torque. The snug torque is the torque required to initiate contact
between the joint members and is expressed as 10% of the target torque. According to the
ISO 5393, a hard joint is one in which the degrees of fastener rotation required to go from
the 10 % to 100 % of target torque is 27° or less, and a soft joint is one requiring greater
than 720° of rotation [26].
67
Three levels of joint stiffness were studied in this project. A joint requiring about
120° of rotation was set as the first level, since the ATC program did not work very
effectively with very hard joints. The second level required 240° of rotation and the third
level required 360° of rotation. According to the ISO 5393 definition [26], all the joints in
this thesis belong to the “medium” joint category. But the levels will be referred to as the
“hard”, “medium” and “soft” for comparison purposes. The joint hardness was
manipulated using different configurations of Belleville spring washers.
4.1.5 Summary of the factors with their levels
The table 4.2 lists all the independent variables (factors) used for this project with
their corresponding levels. The target torque was set at 60 Nm for all conditions. The
complete set of controller parameters is included in Appendix D.
Factors
Levels
Speed management
algorithm
Manual Downshift
Two Stage Control
Adaptive tightening
control (automatic)
Arm mass and
stiffness (MK)
10629 kg N/m
27562.7 kg N/m
52500 kg N/m
Joint hardness
Hard
Medium
Soft
Soft stop feature
None
Default
Table 4.2: Factors with their corresponding levels
68
4.2 Measured response and other dependent variables
The dependent variables in an experiment refer to those that are observed to change
in response to the factors. The experiments conducted for this thesis assessed two
categories of dependent variables. The first was the response directly measured by the
load cell and LVDT which are part of the ergonomic test rig. The outputs of these sensors
are the reaction force at the arm model and deflection of the tool handle as a function of
time. The second kind of dependent variables were obtained by processing the reaction
force and deflection data using Matlab. Different ergonomic metrics were created based
on signal analysis of the curve. These included calculating area under the curve above a
certain threshold to calculate the impulse, finding peak values, ranges and incorporating
the effect of muscle latency on the curves. A complete description of these ergonomic
metrics is given in section 4.4.
4.3 Design of Experiments (DOE) and statistical analysis
Design of experiments (DOE) is a statistical method used to determine the
relationship between different factors affecting a process and the output of the process
[29]. This method involves designing a set of experiments in which the factors are varied
systematically. The first step in using DOE is to define the input variables of the
experiment and their corresponding levels. The next step is to identify the response that
will be measured to describe the outcome of the experiment. Following this an
experimental design needs to be selected from several standard designs depending on the
objective of the experiment, the number of factors and the number of experimental runs
69
that can be conducted. An experimental array is then produced which specifies the levels
of the factors for each run. The response is measured for each run and is analyzed to find
differences between the responses for different groups of the input changes. The change
in the response that resulted only from the change in an input factor is termed as a main
effect. An interaction effect is said to occur when a change in the response due to a
variation in one input factor depends on the level of the another input factor.
Two choices of designs were available for the experiments in this project, which
were a full factorial design and a fractional factorial design. A full factorial design is one
in which an experimental run is performed at every combination of the factor levels. The
sample size is the product of the numbers of levels of the factors. A full factorial is the
most conservative design approach and also the most time consuming because of the
large number of runs. However a full factorial is capable of providing information about
every main effect and every interaction effect. A fractional factorial design includes
selected combinations of factors and levels which are a representative subset of a full
factorial design. The disadvantage of using a fractional factorial approach is that,
depending on the resolution of the design, some higher-order interactions are confounded
with main effects or lower-order interactions.
A full factorial design was chosen since the interactions between the different
factors were important in order to determine the most optimum factor conditions. The
sample size was 3 X 2 X 3 X 3 equaling 54 runs. It was also decided to perform three
replicates. In statistics, replication is the repetition of an experiment under the same
conditions so that the variability associated with the system can be estimated. Thus the
70
total number of runs was 162. The experimental array specifying the levels of the factors
for each run was generated and one complete set of 54 runs is shown in table 4.3. The
runs were not randomized since the effect of external conditions or the environment was
assumed to be insignificant on the response.
The model tested in the statistical analysis is: Dependent variable = Mass and
stiffness (MK) Joint hardness (J) Algorithm (A) Soft stop (S), MK X J, MK X A, MK X
S, J X A, J X S, A X S. The dependent variables are described in section 4.4. A repeated
measure ANOVA was used to test the statistical significance of each factor on the
response and each factor was treated as a fixed effect. The Tukey multiple comparison
test was performed for selected significant interactions. A 95% confidence interval was
used to denote statistical significance.
71
72
Run
number
MK
(kgN/m)
Joint hardness
Speed algorithm
Soft stop
Run
number
MK
(kgN/m)
Joint hardness
Speed algorithm
Soft stop
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
10629
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
240 degrees
240 degrees
240 degrees
240 degrees
240 degrees
240 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
240 degrees
240 degrees
240 degrees
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
27562.7
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
52500
240 degrees
240 degrees
240 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
120 degrees
240 degrees
240 degrees
240 degrees
240 degrees
240 degrees
240 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
360 degrees
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
ATC
ATC
Manual Downshift
Manual Downshift
Two Stage Control
Two Stage Control
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
None
Default
72
Table 4.3: Orthogonal array
4.4 Formulation of ergonomic metrics
A set of ergonomic metrics were developed for the purpose of this thesis by
synthesizing published research results with some new ideas. The reaction force and
deflection data that were obtained from the sensors were processed using different Matlab
scripts developed to isolate desired portions of the curve or identify peak values, ranges
etc. Four types of ergonomic metrics were formulated and they are explained in detail in
the following subsections.
4.4.1 Torque impulse at different percentages of the target torque
The reaction torque impulse was defined in ISO 6544 [25] as the area under the
reaction torque-time curve above a certain percentage of the target torque. It serves as a
suitable measure of the ergonomic impact since it incorporates the magnitudes of the
torque and also the duration of the reaction. The threshold level in ISO 6544 was set at
10% of the target torque. Kihlberg et al. [5] created a criterion called t90% which was
defined as the time for which the tool torque exceeded 90% of the target torque. This
value was found to be highly correlated with the discomfort ratings (r = 0.94). Another
study by Kihlberg et al. [6] defined a metric called “time torque value” which was
defined as the product of the target torque and the time for which tool torque exceeded
75% of the target. The authors found the discomfort ratings to be highly correlated to the
time-torque value (r = 0.962).
Based on the above studies, a few new ways of calculating the reaction torque
impulse were put forward in this thesis. The reaction force curve from the load cell was
73
converted to reaction torque, expressed in Newton-meters, by multiplying the reaction
force by the tool’s handle length (equal to 18.5 inches or 0.47 m). The reaction torque
impulse was calculated at seven different percentages of the target torque of 60 Nm.
These were 0, 20, 45, 50, 60, 70 and 75 percent. It was expected that this method would
be useful in identifying factors that increase the duration of the rundown, which would be
perceived by a tool operator as conditions requiring more effort. For example, the torque
build up is slow for a soft joint and the impulse should therefore be higher for a soft joint
than for a hard joint. The different thresholds can help in identifying whether the effect of
a particular factor, say joint hardness, is significant at the higher torques or the lower
torques.
A Matlab script, included in Appendix E, was used to isolate the portions of the
curve at the seven thresholds and the area under the curve was calculated using the
trapezoidal method. Figure 4.6 (a) shows a reaction torque time curve with the Two Stage
Control algorithm, hard joint, at low MK and with soft stop default. The reaction occurs
in the negative direction but the curve has been inverted (torque values multiplied by
negative one). Figures 4.6 (b) and (c) display the areas of the curve that were used to
calculate the torque impulse at 20% and 50% respectively.
74
75
Threshold torque
30 Nm (50% of
target)
Threshold torque
12 Nm (20% of
target)
Figure 4.6: Reaction torque versus time (a) Actual curve (b) Torque impulse 20% (c) Torque impulse 50%
75
4.4.2 Deflection - peaks in positive and negative direction, maximum range
Tool handle deflection has been used as a direct measurement of the effect of
“jerks” produced during power tool operation. Several studies have found strong
correlations between handle deflections and subjective discomfort ratings [5] [6] [7].
During the repeatability tests conducted on the rig, the deflection time curves from the
LVDT showed peaks in the negative direction and also rebound deflections on the
positive side. Thus three deflection metrics, peak deflection negative, peak deflection
positive and maximum deflection range were developed, as shown in figure 4.7. These
metrics can be used to identify factor conditions that cause maximum forward and
reverse movement of the handle, and the range of movement while operating the tool.
Peak deflection positive
Max. deflection
range
Peak deflection negative
Figure 4.7: Peak deflections positive and negative, maximum deflection range
76
4.4.3 Reaction torques - peaks in positive and negative direction, maximum range
The reaction force transmitted to the operator during tool operation has been stated
as one of the risk factors for cumulative trauma disorders [6]. In this thesis, the reaction
torque obtained from the load cell data has been used to evaluate the ergonomic impact of
the input factors. These quantities are an indication of the maximum reaction force on the
operator arm during eccentric and concentric exertions. The peak reaction torque in the
negative and positive directions and maximum torque range were compared to determine
factor conditions which result in lower values. These metrics are explained with the help
of figure 4.8.
Peak reaction
torque positive
Max. torque
range
Peak reaction
torque negative
Figure 4.8: Peak reactions torque positive and negative, maximum torque range
77
4.4.4 Latency impulse - torque impulse with muscle latency included
Latency impulse was a metric introduced in this thesis and has its basis on two
studies by Oh and Radwin. A brief description of their findings is important to fully
understand how the latency impulse was calculated for the response curves.
Oh and Radwin [17] studied the effect of two types of joint hardnesses (35
millisecond and 900 millisecond build-up times) on muscle activity. From the EMG
data, the authors observed a burst of muscle activity after the onset of torque build-up,
shown in figure 2.10 in chapter 2. This time difference between the onset of torque buildup and the onset of the muscle activity burst was called EMG latency. The authors found
the EMG latencies increased significantly (p < 0.05) as the build up time increased.
In another study, the authors Oh and Radwin tested the effect of five joint
hardnesses on the EMG activity of finger flexors, biceps and triceps [18]. The torque
build-up times corresponding to the five joints were 35, 150, 300, 500 and 900
milliseconds respectively. From all the trials analyzed in this study, the EMG burst was
observed 87% of the time for finger flexors, 88% of the time for biceps and 94% of the
time from triceps. The results showed that EMG latency was significantly influenced by
the type of joint, and increased with increasing build-up time. The plot of average EMG
latencies for the five joints from this study is shown in figure 4.9. In this paper, the
subject perceived exertion was less and task acceptance rates were higher for the 35
millisecond build-up time than for longer build-up times. The EMG latency for this joint
was on an average 40 milliseconds after the onset of torque build up. This implied that
the muscles were not activated until after target was achieved. Thus, it was concluded
78
that reaction forces that occur too quickly do not result in muscular contractions and are
not felt by the operator, resulting in lower exertion.
The latency times for the joints used in this thesis were derived by fitting a linear
regression model to the average values from Oh and Radwin’s study, as shown in figure
4.10. The regression coefficient (r2) is 0.99, and p value is 0.00 showing a highly linear
relationship between EMG latency and build-up time. The torque build up times for the
joints in this research, according to the ISO 6544 definition of joint hardness used in Oh
and Radwin’s study (50 % to 100 % of target torque) were 27 milliseconds, 33
milliseconds, and 60 milliseconds.
The latencies calculated using the regression equation were 44 milliseconds for the
hard joint, 46 milliseconds for the medium and 55 milliseconds for the soft joint.
Figure 4.9: Effect of torque build-up times on muscle EMG latency [18]
79
Fitted Line Plot
latency (ms) = 35.10 + 0.3286 Build up time (ms)
350
S
R-Sq
R-Sq(adj)
EMG latency (ms)
300
12.9930
99.0%
98.7%
R2= 0.99, p=0.000
250
200
150
100
50
0
0
100
200
300 400 500 600 700
Torque build-up time (ms)
800
900
Figure 4.10: Linear regression between EMG latency and torque build-up time
The “latency impulse” was defined as the area under the reaction torque-time curve
after excluding a part of the curve whose duration was equal to the EMG latency for that
joint. The Matlab script used to calculate the latency impulse in included in Appendix E.
The method used to calculate latency impulse is described with the help of figure 4.11:
i. The time corresponding to the start of deflection (below zero) was determined.
This is the point when the muscle tendon begins to extend and this is when a
person using the tool would start feeling the pull on his arm. The corresponding
time on the reaction torque curve was found out and the EMG latency period was
counted starting from this point denoted as “latency start point”.
ii. The curve portion lasting for the EMG latency time is excluded since it is
assumed that the response of the arm during this time results from stretch reflex,
80
and not from voluntary muscle contraction, and is thus perceived as requiring less
exertion by the operator.
iii. The area under the torque time curve, above 0 Nm is calculated. This area
(indicated by the red hatched lines) is called the latency impulse.
81
Latency start point
(a)
Excluded portion
of the curve
Curve portion used to
calculate latency
impulse
Latency time for the joint
(b)
Figure 4.11: Method used to calculate latency impulse
(a) Deflection-time plot (b) Torque-time plot
82
CHAPTER 5
ERGONOMIC ASSESSMENT OF RESPONSE CURVES RESULTS
This chapter presents the results of the experiments conducted for this thesis and
demonstrates the ergonomic impact of the four factors that were studied. In the first
section of this chapter, the raw data from a few screening experiments will be presented.
In the second section the response curves are evaluated based on the ergonomic metrics
developed in chapter 4 and the statistically significant effects of the factors are discussed.
In the third section, the curves from the rig are compared with those obtained from a few
pilot tests with human subjects and possible reasons for differences between them are
stated. The chapter is summarized in the final section.
83
5.1 Raw data from screening experiments
A one-factor-at-a-time screening experiment was conducted before executing the
162 runs of the DOE array, to get qualitative information about the effect of the factors
on the response curves. The output of the ergonomic assessment rig is the reaction force
and deflection of the human arm model as a function of time. The reaction force curve
was converted to reaction torque, expressed in Newton-meters, by multiplying the
reaction force with the handle length of the tool (equal to 18.5 inches or 0.47 m), since
these units were used in the papers by Kihlberg et al. [5] [6]. In this section, the shapes
of the torque and deflection plots will be compared to each other to understand their
similarities and differences due to the different factor levels. The results of the statistical
analysis from the DOE array will be stated in section 5.2.
A reaction torque-time curve using the ATC algorithm with a hard joint, at the
lowest mass and stiffness level, and with soft stop is shown in figure 5.1 (a). The
oscillations at the beginning of the curve are due to the free running of the nut after the
trigger is pressed. The largest peak in the plot occurs when the tool reaches the target
torque. The effect of soft stop can be seen clearly in this plot. During the tightening
process, the entire drive train, including the gears between the motor itself and the
fastener, is torsionally deflected. During the “off time” of the soft stop there is no current
available to the stator and the gear train begins to relax to its neutral state. During the
“hold time” the current is restored in the stator which then attempts to hold the rotor in
some interim position between fully deflected and fully relaxed. Finally, during the
“ramp time” the motor current is gradually ramped down to zero thereby allowing the
84
rotor to gradually relax to its neutral state.
The reaction torque - time plot using ATC with a hard joint, at the lowest MK level
and without the soft stop is shown in figure 5.1 (c). When this curve is compared to
figure 5.1 (a), the absence of the second peak at around -30 N can be seen. However there
is a small bump towards the end of the run down which can be explained as follows.
After reaching the final target, the load on the tool is released to bring the tool to a stop.
Before the gears and other elements of the drive train come to a complete stop, they
rotate in the reverse direction for a brief period due to the inertia of the elements. As the
gear train is reversed at some point the backlash is overcome, and this is seen as a change
in the sign of the torque from the tool. This effect causes the small bump in the reaction
torque plots from the rig.
The purpose of the soft stop parameters in reducing oscillations at the end of the
run is illustrated through two deflection plots in figure 5.1 (b) and (d). Both these runs
use ATC algorithm, low mass and stiffness and a hard joint. The run in figure 5.1 (b) uses
the soft stop default settings and it can be seen that the oscillations are damped out at the
end of the run. The run in figure 5.1 (d) is without the soft stop feature, and shows that
setting the soft stop timers to zero causes more oscillations at the end of the run.
85
This area denotes
the use of soft
stop
Soft stop feature damps out
the oscillations
(a)
(b)
86
Caused when gears overcome
the backlash
(c)
(d)
86 ATC, hard joint, low MK: with soft stop default (a) Reaction
Figure 5.1 Response curves with
torque and (b) Deflection, with no soft stop (c) Reaction torque (d) Deflection
The plots in figure 5.2 compare the reaction torque and deflection curves as a
function of time for the three algorithms at the hard joint, with medium mass and stiffness
and soft stop default. Comparing traces 5.2 (a) and 5.2 (b) shows that the shapes of the
reaction torque curves with ATC and Manual downshift algorithms are quite similar if
other input factors are kept the same. The same thing can be said for the deflection plots
in 5.2 (d) and (e). For both these algorithms, the tool runs continuously before reaching
the target torque, unlike the Two Stage algorithm. The difference is that for Manual
Downshift, the speed is reduced abruptly at a specified downshift torque while in ATC
the speed is ramped down between specified torque levels. Although this is expected to
increases the duration for ATC, the difference is not very noticeable in the above plots. If
at all thee exists a difference, it can be confirmed from the quantitative torque impulse
results in section 4.1.1. Figures 5.2 (c) and (f) show the reaction torque and deflection
curves for the Two Stage Control algorithm. It is evident that the curves for the Two
Stage Control algorithm are more complex in their shapes than for ATC and Manual
Downshift. The first peak at -30 Nm shows the first stage of the algorithm after which the
tool stops for 50 milliseconds and then continues to the final target. The peak after the
target is due to the soft stop settings. The deflection plot also has two peaks in the
negative direction corresponding to the two stages.
87
(a)
(d)
(b)
(e)
Soft stop
First stage
Second stage
(c)
(f)
Figure 5.2: Comparing the three algorithms at medium MK, soft stop default, and
hard joint: Reaction torque (a) ATC (b) Manual Downshift (c) Two Stage Control,
Deflection (d) ATC (e) Manual Downshift (f) Two Stage Control
88
(a)
Effect of joint
stiffness
(b)
(c)
Figure 5.3: Reaction torque-time curves with Manual Downshift, medium MK level,
soft stop default (a) Hard joint (b) Medium joint (c) Soft joint
The effect of joint stiffness on the torque-time traces is shown in figures 5.3 (a), (b)
and (c). All the curves are with Manual Downshift, medium mass and stiffness, and soft
stop default. During the torque build up, it is evident from figures that the slope of the
torque curves for a medium and soft joint (figures 5.3 (b) and (c)) is lesser than for the
hard joint (figures 5.3 (a)). Since a softer joint takes more fastener rotations to go from
snug torque to target torque, the torque build up time is longer. In contrast a hard joint
89
takes less rotation and this makes the curve steeper. The effect of joint stiffness was not
very noticeable in the deflection-time curves.
5.2 Assessment of response curves - statistically significant results
The response curves obtained from the test rig were evaluated based on the metrics
defined and explained in section 4.4. The results of the statistical analysis are presented in
this section and effects of the factors and their interactions are described.
Table 5.1 shows the p values for all the factors and two way interactions, and the
statistically significant sources are highlighted. The significant factors and interactions
are discussed for each of the evaluation metrics in the following subsections.
For all plots in this section the three controller algorithms are abbreviated as ATC
for Adaptive Tightening Control, MDS for Manual Downshift and TSC for Two Stage
Control. The three levels of joint hardness are denoted by 120, 240 and 360, referring to
the degrees of rotation from snug torque (10% of target torque) to the target torque. The
three levels of mass and stiffness are referred to as 1-Low, 2-Medium and 3-High. The
two soft stop levels are indicated as default and none.
90
Torque Impulse
Source
Deflection
Torque
Range
Peak
positive
Range
Latency
Impulse
91
0%
20%
45%
50%
60%
70%
75%
Joint
hardness
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Mass
and
stiffness
0.000
0.000
0.003
0.000
0.000
0.001
0.004
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Algorithm
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Soft Stop
0.000
0.000
0.000
0.000
0.016
0.783
0.673
0.000
0.000
0.000
0.421
0.000
0.000
0.000
0.304
0.012
0.068
0.042
0.182
0.055
0.003
0.000
0.000
0.000
0.000
0.000
0.000
0.009
0.000
0.000
0.000
0.000
0.000
0.000
0.024
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.896
0.658
0.006
0.044
0.019
0.060
0.361
0.000
0.000
0.000
0.172
0.000
0.000
0.607
MK X
Algorithm
0.015
0.014
0.067
0.007
0.000
0.001
0.000
0.000
0.002
0.000
0.000
0.000
0.057
0.001
MK X
Soft Stop
0.178
0.116
0.002
0.000
0.001
0.544
0.820
0.000
0.000
0.000
0.001
0.000
0.000
0.316
Algorithm
X Soft
Stop
0.009
0.002
0.000
0.000
0.003
0.037
0.358
0.000
0.000
0.000
0.001
0.000
0.000
0.001
Joint
hardness
X MK
Joint
hardness
X
Algorithm
Joint
hardness
X Soft
stop
Peak
positive
Peak
negative
Peak
negative
Note: Highlighted areas are significant.
Table91
5.1: Statistically significant sources for the responses
5.2.1 Torque impulse at different percentages of the target torque
The torque impulse was calculated at seven percentages of the target torque, as
explained in section 4.4.1. From table 5.1, it can be seen the joint hardness, mass and
stiffness levels and algorithm were statistically significant at all torque impulses, while
the soft stop factor was significant for all impulses except at 70 % and 75 %. Many of the
two-way interactions were found to be statistically significant for the seven torque
impulses. Hence, in this subsection, the significant sources of interactions will be stated,
and following that, the main effects that are consistently observed, will be indicated. A
few interactions were further tested using a Tukey pairwise comparison test, and the
major findings from these will be stated.
The interaction plots are shown in figures 5.4 and 5.5. Statistically significant
interactions from table 5.1 are:
• Interaction between MK and joint hardness at torque impulse 20 %, 50% and 75 %.
• The interaction between the tightening algorithm and joint hardness at all the
torque impulses.
• Interaction between joint hardness and soft stop at torque impulse 45 %, 50 % and
60 %.
• Interaction between mass and stiffness and algorithm at all torque impulses except
at 50 %.
• Interaction between mass and stiffness and soft stop at torque impulse 45 %, 50 %
and 60 %.
• The interaction between algorithm and soft stop at all torque impulses except at
75 %.
92
(a)
(b)
Interaction Plot for Torque Impulse 0% (Nm-s)
Data Means
A TC
M DS
TS C
120
240
360
default
Interaction Plot for Torque Impulse 20% (Nm-s)
Data Means
none
A TC
12
8
Mass and Stiffness
4
12
8
Controller algorithm
4
12
8
Joint hardness
4
Mass and
Stiffness
1-Low
2-Medium
3-High
MDS
TSC
120
240
360
8
Mass and Stiffness
4
12
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
TSC
3-High
8
Controller algorithm
4
12
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low 120
ATC
2-Medium
MDS
240
3-High360
TSC
8
Joint hardness
93
(d)
TSC
120
240
360
default
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
TSC
3-High
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
Interaction Plot for Torque Impulse 50% (Nm-s)
Data Means
Data Means
MDS
4
Mass and
Stiffness
1-Low
2-Medium
3-High
Soft stop
Interaction Plot for Torque Impulse 45% (Nm-s)
A TC
none
12
Soft stop
(c)
default
ATC
none
12
8
Mass and Stiffness
4
12
8
Controller algorithm
4
12
8
Joint hardness
4
Soft stop
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
TSC
3-High
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
MDS
TSC
120
240
360
default
none
12
8
Mass and Stiffness
4
12
8
Controller algorithm
4
12
8
Joint hardness
4
Soft stop
Figure 5.4: Interaction plots for torque impulse at (a) 0 % (b) 20 % (c) 45 % (d) 50 %
93
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
3-High
TSC
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
(a)
(b)
Interaction Plot for Torque Impulse 60% (Nm-s)
Interaction Plot for Torque Impulse 70% (Nm-s)
Data Means
Data Means
A TC
A TC
M DS
TS C
120
240
360
default
MDS
TSC
120
240
360
default
none
none
12
12
Mass and
Stiffness
1-Low
2-Medium
3-High
8
Mass and Stiffness
4
12
Controller algorithm
4
12
4
12
4
8
Controller algorithm
4
12
C ontroller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low
A
TC 120
2-Medium
MDS
240
3-High
TSC 360
8
Joint hardness
Mass and Stiffness
C
ontroller
Mass
and
algorithm
Stiffness
A TC
1-Low
MDS
2-Medium
3-High
TSC
8
8
4
Soft stop
Soft stop
94
(c)
8
Joint hardness
Interaction Plot for Torque Impulse 75% (Nm-s)
Data Means
A TC
MDS
TSC
120
240
360
default
none
12
8
Mass and Stiffness
4
12
8
Controller algorithm
4
12
8
Joint hardness
4
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
1-Low
ATC
2-Medium
MDS
3-High
TSC
Controller
MassJoint
and
algorithm
Stiffness
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
Soft stop
Figure 5.5: Interaction plots for torque impulse at (a) 60 % (b) 70 % (c) 75 %
94
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
3-High
TSC
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
One main effect that was observed consistently in all the interaction plots was the
effect of algorithm on the torque impulse. Two Stage Control resulted in higher torque
impulses at all mass and stiffness levels, joint hardnesses, and soft stop conditions. The
main effects plots for torque impulse at 0 % and 75 % are shown in figure 5.6 (a) and (b)
respectively, for the three controller algorithms.
Main Effects Plot for Torque Impulse 75% (Nm-s)
Main Effects Plot for Torque Impulse 0% (Nm-s)
Data Means
Data Means
12
12
8
8
4
4
ATC
MDS
Controller algorithm
ATC
TSC
MDS
TSC
Controller algorithm
(b)
(a)
Figure 5.6: Main effect plots at the three controller algorithms
(a) Torque impulse 0 % (b) Torque impulse 75 %
The Tukey tests for the controller algorithm and joint hardness interaction showed
that the response at a particular joint hardness depends upon the algorithm. The important
findings were:
• For torque impulse 0 %, the response with ATC and Manual Downshift are the
same at all three joints. Hard and medium joints result in equal impulses, and soft
joints result in the highest. With the Two Stage Control algorithm, the impulse is
the least with a hard joint, and equal at medium and soft joints.
95
• At torque impulse 20 %, 45 % and 50 %, the response with ATC and Manual
Downshift are the same as that at 0 %. But with the Two Stage Control, the
impulses at hard and soft joints are equal and lesser than at the medium joint.
• At torque impulse 60 %, there is no difference between the impulses with Manual
Downshift and ATC at all three joint hardnesses. The response with Two Stage
Control is the same as at torque impulse 20 %, 45 % and 50 %.
• At torque impulse 70 % and 75 %, the response with Manual Downshift and ATC
are equal at all three joints hardnesses, and there is no difference between the
values at the three joints. There is also no difference between the impulse at the
three joints with Two Stage Control algorithm, but the response with Two Stage
Control was higher than the responses at ATC and Manual Downshift, at all joints.
From the Tukey pairwise comparison tests on the controller algorithm and soft stop
interaction, it was found that:
• At torque impulse 0 % and 20 %, soft stop default resulted in higher values than
none for all three algorithms. However, there was no difference between the values
of ATC and Manual Downshift at the corresponding soft stop levels. Two stage
control values were higher than for ATC and Manual Downshift.
• At torque impulse 45 % and 50 %, soft stop default resulted in higher values with
ATC and Two Stage Control. The impulses with Manual Downshift were equal at
both levels. Also, the difference between ATC and Manual Downshift was
significant only at the soft stop default level, with ATC resulting in higher impulse.
96
• At torque impulse 60 %, there was no difference in the impulse at the two soft stop
levels, for all three algorithms. However, the values with Two Stage Control were
higher than with ATC and Manual Downshift.
5.2.2 Peak deflection negative, peak deflection positive and maximum range
The three deflection metrics were explained in section 4.4.2. From table 5.1, it can
be seen that all factors and two way interactions were found to be statistically significant
for the deflection metrics. The main effects plots for the peak deflections positive and
negative and the range for the MK factor are shown in figure 5.7 (a), (b) and (c). It can be
seen from the interaction plots, in figure 5.8, that the effect of mass and stiffness is
consistent for all the three deflection metrics, with the highest values resulting at the
lowest MK level.
97
Main Effects Plot for Peak deflection positive (mm)
Main Effects Plot for Peak deflection negative (mm)
Data Means
Data Means
9
24
22
8
20
7
18
6
16
14
5
12
4
10
1-Low
2-Medium
3-High
1-Low
2-Medium
Mass and Stiffness
Mass and Stiffness
(a)
(b)
3-High
Main Effects Plot for Deflection Range (mm)
D ata M eans
34
30
26
22
18
14
1-Low
2-Medium
3-High
Mass and Stiffness
(c)
Figure 5.7: Main effect plots at the three MK levels (a) Peak deflection negative
(b) Peak deflection positive (c) Deflection range
The interactions between controller algorithm and joint hardness were further
evaluated using the Tukey pairwise comparison test. The important findings from the
Tukey tests were:
• For the peak deflection negative, there was no difference between ATC and Manual
Downshift algorithms responses. The responses at hard and medium joints were
equal and lesser than the response at the soft joint. The Two Stage Control response
was the same at all three joints, and equal to the soft joint response with ATC and
Manual Downshift.
98
• For the peak deflection positive, ATC and Manual Downshift response trends were
the same as that for peak deflection negative. However with Two Stage Control, the
deflection at the medium joint was the least. With Two Stage Control, hard and soft
joints resulted in equal positive deflections, which were found to be equal to the
deflection with ATC and Manual downshift with a soft joint.
• For the deflection range, ATC and Manual Downshift responses had the same trend
as that for the other two deflection metrics. The deflection range with Two Stage
Control was the same across all the three joints, and equal to the ATC and Manual
Downshift range at the soft joint.
The Tukey pairwise comparison test was also used to assess the controller
algorithm and soft stop interactions: The major findings were:
• For the peak deflection negative, there was no effect of the two soft stop levels on
Manual Downshift and Two Stage Control. The value at ATC and default soft stop
was higher than at none. The values at Two Stage Control were equal to that at
ATC and default soft stop.
• For the peak deflection positive, the effect of soft stop was seen only with the Two
Stage Control algorithm. The default soft stop resulted in lower positive deflection.
The values at ATC and Manual Downshift were equal at both soft stop levels.
• The observations for the deflection range were similar to that at peak deflection
positive.
99
(a)
(b)
Interaction Plot for Peak deflection negative (mm)
Data Means
A TC
MDS
TS C
120
240
360
Interaction Plot for Peak deflection positive (mm)
Data Means
default
none
A TC
30
Mass and
Stiffness
1-Low
2-Medium
3-High
20
Mass and Stiffness
10
30
Controller algorithm
10
30
Joint hardness
10
120
240
360
default
none
7.5
Mass and Stiffness
5.0
10.0
7.5
Controller algorithm
5.0
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ontroller
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hardness
1-Low
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ontroller
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7.5
Joint hardness
5.0
Mass and
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1-Low
2-Medium
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Controller
Mass and
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ATC
1-Low
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2-Medium
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and
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Stiffness
algorithm
hardness
1-Low120
ATC
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3-High360
TSC
Soft stop
Soft stop
100
(c)
Interaction Plot for Deflection Range (mm)
Data Means
A TC
M DS
TS C
120
240
360
default
none
30
20
Mass and Stiffness
10
30
20
Controller algorithm
10
30
20
Joint hardness
10
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
TSC
3-High
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low120
ATC
2-Medium
MDS
240
3-High360
TSC
Soft stop
Figure 5.8: Interaction plots for (a) Peak deflection negative (b) Peak deflection positive (c) Deflection range
100
5.2.3 Peak torque negative, peak torque positive and torque range
The three torque metrics, peak deflection negative, positive and the torque range
were described in section 4.4.3. From table 5.1, it can be seen that all factors and
interactions were found to be statistically significant for the torque metrics, except for the
soft stop factor and the interaction between soft stop and joint hardness for peak torque
negative.
The interaction plots for the torque metrics are shown in figure 5.9. None of the
main effects were consistent for all factor conditions. Hence, the Tukey pairwise
comparison test was used to assess selected interactions.
The major findings from the Tukey test performed on the controller algorithm and
joint hardness interactions are:
• For peak torque negative and torque range, there is no difference between the
values with ATC and Manual Downshift. The peak torque values with the hard and
medium joints are equal and lesser than that at the soft joint. For the Two Stage
Control algorithm, the peak torque negative and range is equal at the hard and soft
joint, and this is greater than the response at the medium joint.
• For the peak torque positive, there was no difference between the values of ATC
and Manual Downshift at all three joints. The trend with the Two Stage Control
algorithm was the same as that for peak torque negative.
The major findings from the Tukey test performed on the controller algorithm and
soft stop interactions are:
• For the peak torque negative and torque range, the difference in the response at the
two soft stop levels for seen only with Manual Downshift, with default values
101
reducing the response. The highest negative peak and range values at all soft stop
conditions was seen with the Two Stage Control algorithm. The only difference
between ATC and Manual Downshift was seen at the default soft stop, for the peak
torque negative. ATC resulted in higher negative peak than Manual Downshift.
• The soft stop default settings were found to reduce the peak torque positive at all
the algorithms. The peak torque positive with ATC and Manual Downshift were
equal at the corresponding soft stop levels (ATC with default = Manual Downshift
with default, ATC with none = Manual Downshift with none). The highest peak
torque positive occurred with Two Stage Control at the no soft stop condition.
For the peak torque negative, the interaction between mass and stiffness and
controller algorithm show some trends that are not consistent with the other torque
metrics. So the Tukey pairwise comparison test was performed to evaluate the differences
at the different algorithms and MK levels. The major findings were:
• The peak torque negative with ATC and Manual Downshift algorithms were the
same at the corresponding MK levels. The peaks at the low and medium MK levels
were equal and lesser than the peak at the high MK level.
• With Two Stage Control, the peak torque negative was the same at all three mass
and stiffness levels.
102
(a)
Interaction Plot for Peak torque negative (Nm)
(b)
Data Means
A TC
M DS
TS C
120
240
360
default
Interaction Plot for Peak torque positive (Nm)
Data Means
none
A TC
56
Mass and
Stiffness
1-Low
2-Medium
3-High
52
M ass and Stiffness
48
56
C ontr oller algor ithm
48
56
48
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4
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Soft stop
(c)
120
Mass and Stiffness
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ontroller
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Stiffness
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Joint har dness
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ontroller
Mass
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A TC
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MDS
Interaction Plot for Torque Range (Nm)
Data Means
103
A TC
M DS
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default
none
65
Mass and Stiffness
60
55
65
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60
55
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60
55
Mass and
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1-Low
2-Medium
3-High
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Mass and
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Stiffness
ATC
1-Low
2-Medium
MDS
3-High
TSC
Controller
MassJoint
and
algorithm
Stiffness
hardness
ATC
1-Low120
2-Medium
MDS
240
3-High360
TSC
Soft stop
Figure 5.9: Interaction plots for (a) Peak torque negative (b) Peak torque positive (c) Torque range
103
Mass and
Stiffness
1-Low
2-Medium
3-High
Controller
Mass and
algorithm
Stiffness
ATC
1-Low
MDS
2-Medium
TSC
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Controller
MassJoint
and
algorithm
Stiffness
hardness
ATC
1-Low120
MDS
2-Medium
240
TSC
3-High360
5.2.4 Latency impulse
The latency impulse was a metric introduced in this thesis and was explained in
section 4.4.4. From table 5.1, it can be seen that all factors and two-way interactions,
excluding the interaction between MK and soft stop, and joint hardness and soft stop,
were found to be statistically significant for the latency impulse. The interaction plot for
latency impulse is shown in figure 5.10.
Interaction Plot for Latency Impulse (Nm-s)
Data Means
A TC
M DS
TS C
120
240
360
default
none
12
8
Mass and Stiffness
4
12
8
Controller algor ithm
4
12
8
Joint hardness
4
Mass and
Stiffness
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2-Medium
3-High
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Mass and
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Stiffness
A TC
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MDS
2-Medium
TSC
3-High
Controller
Mass
and
Joint
Stiffness
algorithm
hardness
1-Low
A
TC 120
2-Medium
MDS
240
3-High360
TSC
Soft stop
Figure 5.10: Interaction plot for latency impulse
The Two Stage Control algorithm resulted in the maximum latency impulse at all
the MK levels, joint hardnesses and soft stop conditions. This main effect was consistent
at all factor conditions and is shown in figure 5.11.
104
Main Effects Plot for Latency Impulse (Nm-s)
Data Means
11
10
9
8
7
6
5
4
3
ATC
MDS
TSC
Controller algorithm
Figure 5.11: Main effect plot for latency impulse at the three controller algorithms
The Tukey pairwise comparison test was performed on the controller algorithm and
joint hardness interactions. The major findings were:
•
The latency impulse at the corresponding joint hardnesses was equal for ATC and
Manual Downshift algorithms. The latency impulses with the hard and medium
joints were equal, and lesser than the impulse with the soft joint.
•
The latency impulses with the Two Stage Control were different at all three joint
hardnesses. The least impulse resulted with the hard joint, and the maximum was
in case of the medium joint.
The major findings from the Tukey tests on the controller algorithm and soft stop
interactions were:
•
The soft stop default resulted in higher latency impulse than the none condition at
105
all three algorithms.
•
The latency impulse with ATC and Manual Downshift were equal at the
corresponding soft stop conditions (ATC with default = Manual Downshift with
default, ATC with none = Manual Downshift with none).
•
The latency impulse with Two Stage Control was greater than with ATC and Manual
Downshift. The maximum impulse resulted when the Two Stage Control algorithm
was used with default soft stop.
5.3 Comparison of rig curves with curves from human testing
A few pilot tests were conducted to compare the human response with that of the
ergonomic rig. The pilot test method was as follows:
•
Two colleagues were tested with the same three joint stiffnesses as used for the rig.
•
The tool was run with the same three controller programs that were used for the
experiments with the rig. All program parameters including the target torque, snug
torque, torque and angle limits were also the same. The three algorithms were used
with and without the soft stop feature resulting in six controller settings totally.
•
The pilot test operators were allowed to conduct few trial runs to get used to the tool
and the actual runs were conducted once they were ready, in random order.
•
The deflection of the handle was measured using a rate gyro from Analog Devices
with a range of +/- 300°/sec mounted on the tool handle. The data sheet for the rate
gyro is included in Appendix C. The angular velocity was integrated to find the
angular displacement, which was then converted to linear displacement.
106
The main purpose of these pilot tests was to compare the shapes of the deflection
curves obtained with human operators to the rig deflection curves. The pilot tests would
also demonstrate whether the deflection values measured by the rig were comparable to
those experienced by people. However, there are several other factors that affect the
human response. For instance, operators who have a greater experience in the use of
power tools would find it easier to counter the reaction force. There is also a learning
component involved with the use of these tools. It may be easier to respond to the torque
reaction if you are familiar with the run down and know what to expect, especially in the
case of the Two stage algorithm.
A few samples plots compare the rig and human response curves. Note that the yaxis scale on the human and rig plots are different. All plots from the human pilot tests
were conducted by operator 1. Figures 5.12 (a) and (b) show the deflection-time plots
with ATC algorithm used on a hard joint, with the soft stop feature on. The plots in figure
5.13 are for runs with ATC on a hard joint, but with the soft stop feature turned off. The
figures 5.14 (a) and (b) correspond to curves with Two stage control on a soft joint with
the soft stop feature. A few key observations from the above plots are:
•
The peak deflection magnitudes are much higher for the human responses curves
than for the rig. The actual human arm deflections with the Two stage algorithm
are significantly greater than that seen on the rig.
•
The shapes of the curves are similar till the peak deflection occurs. But the
oscillations that were observed towards the end of the run with the rig are absent in
the human curves.
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(a)
(b)
Figure 5.12: Deflection versus time with ATC, hard joint, soft stop default
(a) Human operator 1 (b) Rig with medium mass and stiffness
(b)
(a)
Figure 5.13: Deflection versus time with ATC, hard joint, no soft stop
(a) Human operator 1 (b) Rig with medium mass and stiffness
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(a)
(b)
Figure 5.14: Deflection versus time with Two Stage, soft joint, soft stop default
a) Human operator 1 b) Rig with medium mass and stiffness
There could be several reasons for the observed differences, but they are only
suppositions without further testing. Some possible reasons are given below.
• The ergonomic test rig does not contain a damping element and this could be a
reason for the oscillations seen in the rig curves.
•
One important observation during the pilot tests with human subjects was the
whole body movement of a person to overcome the torque reaction from the tool.
It was seen that not only the arm, but the head and shoulders were also used
occasionally to resist the kickback. Since the rig contains only a model of the
human arm, it does not take into account the effect of other body parts that support
the arm.
•
Another reason for the differences could be the assumption of a constant arm mass
and stiffness during a run. The effective stiffness of the muscles will change during
a run, if different muscle groups are activated. The effective mass depends on the
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different parts of the body that assist in resisting the reaction and this will not be
constant during a rundown. The constant mass and stiffness assumption does not
account for these changes.
•
Also, the mass and stiffness values used in the rig were higher than the values
determined by Lin et al. [11]. This was done to account for higher torque rating of
the Stanley tool.
•
For the experiments conducted with the rig, the arm mass and stiffness were
combined as a single factor. How well a person counters the torque reaction
depends on how fit he/she is. The fitness of a person is a function of both his mass
and stiffness. For instance a person with low arm mass but better toned muscles
(more stiffness) is fitter than someone with high mass but less toned muscles (low
stiffness). Thus it more appropriate to separate the mass and stiffness levels as two
different factors and investigate different levels of these in combinations (low
mass, high stiffness versus high mass low, stiffness).
The rig designed and built in this project was based on the dynamic model of the
human arm developed by Lin and Radwin [11] [12] [13] which assumes the mass and
stiffness elements to be constant during a run. This rig is a highly repeatable physical
representation of Radwin’s model. It is not a perfect replication of the human arm and
developing such a system requires further testing of human subjects and comparing the
response with that of the rig. For this it is also important to determine how the mass,
stiffness and damping elements vary during a run and develop a model that will simulate
this variation. The results presented in section 5.2 are pertinent to the response curves
110
from the rig, but the ergonomic metrics that were developed as a part of this research can
be used with data from human subjects, provided the reaction torque at the subject’s hand
can be measured.
5.4 Chapter summary
This chapter displayed the raw data obtained from screening experiments on the rig
and the shapes of the curves for different input conditions were compared. The results of
the statistical analysis conducted on the rig responses were presented and statistically
significant sources were discussed. The curves from pilot tests with human operators
were compared with the rig response curves and possible reasons for the differences were
stated. The results of the statistical analysis conducted on all the evaluation metrics will
be discussed in chapter 6 and possible reasons for the observed effects will be stated.
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CHAPTER 6
DISCUSSIONS
The analytical results and statistically significant sources for all the evaluation
metrics were presented in chapter 5. In this chapter, the effects of the factors and
interactions on the four assessment criteria i.e. torque impulse, deflection, reaction torque
and latency impulse, will be discussed and the results will be related to previous
ergonomic studies.
6.1 Results of torque impulse
The torque impulse for this thesis was calculated at seven different percentages of
the target torque. The reaction torque data was obtained from the reaction force measured
at the tool handle by the load cell. Studies by Kihlberg et al. [5] [6] had correlated the
torque impulse, as defined in ISO 6544 [25], with discomfort ratings. Frievalds and
Eklund had studied the effect of joint stiffness on torque impulse [2]. However in these
studies the torque from the tool was used to calculate the impulse. As stated in ISO 6544,
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the installation torque (torque transmitted to the fastener) may be higher or lower than the
reaction torque (torque transmitted to the operator) depending upon the inertia of the tool,
operator characteristics and work orientation [25]. The torque impulse calculations for
this thesis used the reaction force measurements at the tool handle, which were
considered to be a more accurate measurement of the reaction transmitted to the model of
the human arm.
Among the four input factors investigated in this thesis, the effect of three of these
i.e., mass and stiffness, controller algorithm and soft stop feature, have not been studied
in relation to torque impulse. Frievalds and Eklund [2] included joint stiffness as a factor
and studied its effect on torque impulse.
The three levels of mass and stiffness were statistically significant at all the torque
impulses (table 5.1). However, there was not much of a difference between the values
obtained at the three MK levels, when used at the different algorithms, joint hardnesses or
soft stop conditions. A possible reason for this could be that the range of mass and
stiffness levels that were investigated was not wide enough, and there may not have been
enough separation between the three levels. Also the values chosen might have been too
high, and this may have masked the effects that might have occurred, had the MK values
been lower.
The effect of the three algorithms was consistent across all the MK levels, joint
hardnesses and soft stop conditions. A much higher torque impulse was seen with the
Two Stage Control algorithm at all the cut off percentages. This was not surprising since
torque impulse calculations take into account the duration of reaction. This program takes
113
longer than ATC and Manual Downshift, because of the run occurring in two stages and
also the tool being stopped for 50 milliseconds at the end of the first stage, and thus,
would result in a higher area under the curve. The results of the Tukey pairwise
comparison tests showed that there was no significant difference between the torque
impulse with ATC and Manual Downshift at all joint hardnesses and soft stop levels.
The response at a particular joint hardness depended upon the algorithm that was
used. The impulse with ATC and Manual Downshift algorithms was the highest with the
soft joint at 0 %, 20 %, 45 % and 50 %. This was consistent with the finding of Frievalds
and Eklund who found that a softer joint resulted in a higher impulse [2]. With the Two
Stage Control algorithm, the highest torque impulse occurred with the medium joint at all
cut off percentages except at 70 % and 75 %. In the study by Oh, Radwin and Fronczak
[19], the effect of five joint hardnesses (35, 150, 300, 500 and 900 milliseconds build up
times) on the hand force was investigated. The greatest hand force occurred at the 150
millisecond joint and the least occurred at the 35 millisecond joint. This was similar to
the observation with the Two Stage Control algorithm. However, only three levels of
joint hardnesses were studied in this thesis, so it would be necessary to test the effect of
the algorithms at other levels of joints (such as very hard and very soft). This would help
generate more information, and the effect of joint stiffness can be understood better.
The two soft stop conditions had a significant effect on the torque impulse at lower
thresholds (0 %, 20 %, 45 % and 50 %). The effect of soft stop was not seen at the higher
cut off percentages (70 % and 75 %), since the “bump” caused by the default soft stop is
usually at 20 Nm to 35 Nm, while the threshold torque at 70 % and 75 % were 42 Nm (70
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% of target 60 Nm) and 45 Nm (75 % of target 60 Nm) respectively. The higher impulse
values with soft stop can be explained by the fact that enabling the soft stop timers causes
the tool to stop after reaching the target torque, the speed is then held at a certain torque
level and finally ramped down to zero. This increases the total duration of the run and
therefore results in a higher impulse.
6.2 Results of deflection peaks and range
Three deflection metrics, explained in section 4.4.2, peak deflection negative, peak
deflection positive and deflection range. Studies by Kihlberg [5] [6] correlated the hand
arm displacement to discomfort ratings and also suggested a displacement limit for
acceptability [7]. Oh and Radwin also studied the effect two levels of joint hardness (35
and 900 milliseconds) on the peak handle displacement [17].
Four input factors i.e., mass and stiffness, controller algorithm, joint hardness and
the soft stop feature, were incorporated in this thesis and their effects on the deflection
response is discussed below.
For the mass and stiffness factor, it was observed that the highest peak deflection
negative, positive and range occurred at the lowest MK level, at all algorithms, joint
hardnesses and soft stop levels. This is in agreement with what is expected, that is a
weaker arm would deflect more than a stronger arm. There was not much of a difference
between the medium and high MK levels.
The effect of tightening algorithms was also significant for the deflection metrics.
The Two Stage Control algorithm resulted in higher deflection positive, negative and
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range at the lowest MK level and at the hard joint, as compared to ATC and Manual
Downshift. The negative peak and range with the Two Stage Control algorithm was not
affected by joint hardness. With the ATC and Manual Downshift algorithms the response
was the highest with the soft joint at all three deflection metrics. This was consistent with
the results from Oh and Radwin’s study [17] where the peak handle displacement was the
greatest with the soft joint (900 milliseconds build up time).
The effect of the soft stop was seen at the peak deflection positive and deflection
range values when Two Stage Control was used. The rebound deflection was
significantly lower with the default soft stop. This result is consistent with the purpose of
the soft stop feature which is to gradually release the load on the tool which also
decreases the rebound (positive) deflection. There was no effect of soft stop on Manual
Downshift and ATC.
An important observation from the experiments conducted was that the maximum
deflection value obtained on the rig was about three centimeters which was much lower
than the peak handle deflection seen with the pilot human tests. Peak handle
displacements seen in the Oh and Radwin paper were about 5.5 centimeters for a tool
rated at 50 Nm [17]. A possible reason for lower deflections with the rig could have been
the levels of mass and stiffness that were chosen, which may have restricted a greater
movement of the handle, which might have been possible with lower MK values.
6.3 Results of reaction torque peaks and ranges.
The reaction torque was obtained from the reaction force measurements at the tool
116
handle. The peak reaction torque in the negative and positive direction, and the maximum
range of torque values were used as ergonomic assessment criteria. Previous studies have
used EMG to estimate the grip force exerted during tool operation. Studies by Kihlberg et
al., measured the ground reaction forces between the subject and the floor using a force
plate, and correlated the reaction force to discomfort ratings [5] [6]. Lin et al [8] used
right angle, pistol grip and inline tools with instrumented handles to measure the handhandle interface force and moment generated by the operator hand, for different tool
shapes, torques and joint hardnesses. Four input factors i.e., mass and stiffness, controller
algorithm, joint hardness and the soft stop feature, were incorporated in this thesis and
their effects on the reaction torque response is discussed below.
For the mass and stiffness factor, the lowest level resulted in higher peak torque
positive, while there was not much of a difference between the three MK levels for the
torque range. Some unusual trends were observed for the peak torque negative, especially
for when the mass and stiffness interacted with the controller algorithm and joint
hardness. The Tukey test performed on the MK and controller algorithm showed that the
highest peak torque negative with ATC and Manual Downshift occurred at the highest
MK level, which is contrary to what would be expected. With Two Stage Control, there
the peak torque negative values at the three mass and stiffness levels were not
significantly different.
The effect of tightening algorithms significant on the torque metrics with the Two
Stage Control resulting in the highest peak torque negative and torque range at all MK
levels, soft stop and hard and soft joint. The peak torque positive was the highest for Two
117
Stage Control at all other factors combinations. There was no significant difference
between ATC and Manual Downshift at the different mass and stiffness levels and joint
hardness for all the torque metrics.
There was a considerable amount of interaction between joint hardness and the
controller algorithm. With the Two Stage Control algorithm, the maximum value for all
the torque metrics occurred were equal at the hard and soft joints and greater than at the
medium joint. With ATC and Manual Downshift the greatest peak torque negative and
torque range resulted from the soft joint. This was in agreement with the results of Lin et
al., who found the hand-handle interface force and the torque at the handle to be the
greatest with the soft joint, for all tool shapes. There was no effect of joint hardness on
the peak torque positive values with ATC and Manual Downshift.
The soft stop default settings were found to reduce the peak torque positive at all
the algorithms. The only significant difference between the response with ATC and
Manual Downshift was observed at the default soft stop for peak torque negative, where
the peak value was higher with ATC.
All the torque values used for ergonomic assessment in this thesis were based on
the measurements from the load cell. This thesis did not use the torque from the tool
controller for any assessment. The torque data from the tool would help determine how
different the trends of the torque curves are, whether the peak tool torque coincided with
the peak torque at the handle or if there is a delay between these. Although the peak
reaction torque (negative) values were lower than the target torque of 60 Nm for all the
runs, it would be important to incorporate other levels of mass and stiffness and joint
118
hardness and compare the tool and reaction torque.
6.4 Results of latency impulse
The latency impulse is an ergonomic evaluation criterion that was defined and
explained in detail in chapter 4, and was based on the studies on Oh and Radwin [17]
[18]. For this metric, the area under the reaction torque curve is calculated, after
excluding a portion of the curve, whose duration equaled the muscle latency time for a
particular joint hardness. It was assumed that there is no voluntary muscular activation in
response to the reaction torque during this period and thus no exertion is perceived by the
operator. The latency times for this thesis were derived by fitting a regression model to
the data from Oh and Radwin’s study [18].
For the mass and stiffness factor, there was no major difference in the magnitude of
latency impulse at the three levels. This was similar to the results of torque impulse.
The effect of tightening algorithms was significant for the latency impulse and the
highest latency impulse was seen with Two Stage Control at all joint hardnesses, MK
levels and soft stop settings. There was no difference between the latency impulse
obtained with ATC and Two Stage Control algorithms.
The response at a particular joint hardness was dependent upon the controller
algorithm that was used. The latency impulses with ATC and Manual Downshift were the
greatest at the soft joint. This was in agreement with the finding by Frievalds and Eklund
[2] as mentioned in section 6.1. Similar to the torque impulse results, the maximum
latency impulse with the Two Stage Control algorithm occurred at the medium joint.
119
The soft stop default resulted in higher latency impulse than the none condition at
all three algorithms. This was due to an increased duration of the run as a result of the
soft stop settings, which ramp down the tool speed gradually, instead of bringing the tool
to an abrupt stop.
The latency times for the joints used in this thesis were obtained from the
regression model fitted to Oh and Radwin’s data [18]. An extension of this work can
involve human testing with the Stanley tool at different joint hardnesses. Operator EMG
measurements can be used to determine the muscle latency under the given test
conditions, and these values can be compared to those obtained from Oh and Radwin’s
study.
6.5 Chapter summary
The observed effects of the input factors on the responses were discussed in this chapter
and the results were compared to those from previous ergonomic studies. Some attempts
were made at explaining the observed differences. The findings of this thesis will be
summarized in chapter 7 along with the most important contributions of this thesis.
120
CHAPTER 7
CONCLUSIONS AND FUTURE WORK
This thesis studied the interaction between tool controller algorithms, arm and joint
variables with the help of an ergonomic assessment rig. The ergonomic impact of these
factors was quantified by developing a set of metrics. The first section of this chapter
summarizes the conclusions that were drawn from the statistical analyses of the results.
The contributions made by this thesis are listed in the second section. The final section of
this chapter contains recommendations for future research based on the learning from this
work.
7.1 Summary of major findings
This thesis studied the impact of four factors, which were the tightening algorithm,
the joint stiffness, the arm mass and stiffness and the soft stop feature on the response of
the human arm model. The different levels of these factors will be compared in this
section based on the discussions in the previous chapter.
121
7.1.1 Tightening algorithm
The three controller algorithms had a significant effect on the evaluation metrics.
The Two stage algorithm was seen to have the worst ergonomic effect based on most of
the metrics that were developed. A higher torque impulse and latency impulse for the
Two stage algorithm was not surprising since these results take into account the duration
of reaction as well as magnitudes. Thus, clearly among the three tightening approaches,
the Two Stage Control was the worst as seen with this rig.
There was no statistically significant difference between the effect of ATC and
Manual Downshift for almost all the metrics. It is possible that this is due to the narrow
range of joint hardnesses that were studied. Also, all three joints belonged to the medium
joint category according to ISO 5393 [26]. It would be important to test these algorithms
at very hard and very soft joints, and see whether one of these is ergonomically preferable
with such joints. This thesis used the ATC automatic mode which uses the default values
of ATC start torque, end torque, free speed and end speed. The ATC envelope can be
shaped in the custom mode and this may result in better ergonomic results than Manual
Downshift.
7.1.2 Joint stiffness
The effect of the joint stiffness was found to be significant for all the torque
impulse metrics, deflection and torque metrics. The response at the particular joint was
found to depend on the algorithm that was used. One limitation of this thesis is the range
of joint stiffnesses that were investigated. Since all three joints belong to the medium
122
joint category according to the ISO 5393 definition [26], the effect of the input factors
need to be determined at the extremes, i.e. very hard versus very soft joints. Investigating
a wider range of joint hardnesses would lead to a better understanding of the factor
effects.
7.1.3 Arm mass and stiffness
This levels of this factor had significant effect only for the deflection metrics
(positive, negative and range) and the peak torque positive. The torque impulses and the
latency impulse values were not different at the three mass and stiffness levels, at all
other factor combinations. The values of the mass and stiffness used in this rig may have
been set too high. Also, the mass and stiffness was combined as a single factor in this
research. The “fitness” of a person is what helps in countering the reaction force, and the
fitness is a function of both the mass and stiffness as independent factors. For example, a
person with low arm mass but better toned muscles resulting in higher stiffness falls into
a high fitness category and would have a different response from someone in the low
fitness category that has high arm mass and low muscle stiffness.
7.1.4 Soft stop feature
The two soft stop conditions had a significant effect on all the torque impulses
except at 70 % and 75 % and for the latency impulse. For these measures the default soft
stop parameters resulted in a higher impulse. The higher impulse values with soft stop
were attributed to a longer duration of the run due to a gradual release of the load on the
123
tool. The soft stop was seen to be effective in reducing the rebound (positive) reaction
torque and deflection for most conditions.
The rig results show that using soft stop causes opposing effects on two types of
metrics. Enabling the soft stop feature increases the area under the curve thus increasing
effort, but it also decreases the rebound reaction and deflections which are related to arm
oscillations. Testing human subjects and comparing these quantitative results (area under
the curve and rebound reaction, deflection) to their subjective ratings of perceived
exertion will demonstrate which of these effects is ergonomically better. This will help
conclude whether or not using the soft stop is beneficial.
7.2 Contributions
The work described in this thesis made the following contributions:
i.
Creation of a repeatable ergonomic assessment rig which contained a model of
the human arm mass and stiffness and incorporated other process variables (joint
stiffness, tool program parameters etc).
ii. A better understanding of DC torque tools, speed control algorithms, and
different parameters such as torque limits, angle limits, speed, acceleration etc
that control the tool. An important part of this thesis was learning to program the
Stanley tool controller and set values for the parameters in order to have a
successful rundown.
iii. Quantitative information provided by the experimental results that describes how
joint stiffness, arm characteristics, and tool controller settings interact. This
124
information can be used to develop optimum factor combinations for better
ergonomics.
iv. A set of ergonomic metrics were created using published research results for
evaluating the curves from the rig. Some new ways of assessing the response
were put forward, such as torque impulses at several different thresholds, and
using the muscle latency time in calculating the area under the reaction curve.
These metrics can be used in future for response curves from human subject
tests.
7.3 Recommendations for future work
The contributions of this thesis provide a foundation for future studies on the
ergonomic impact of torque tools, program algorithms, joints and other associated
factors. Based on the learning from this work, several recommendations for future
research are made in this section.
The ergonomic assessment rig used in this research was based on the dynamic
model developed by Radwin et al [11] [13]. The arm mass and stiffness elements were
combined as a single factor and their values were chosen so that a wide range of
population can be represented. But the effect of this factor was not significant on many of
the metrics. The mass and stiffness should be separated into two independent variables
for future experiments with the rig and a wider range of values need to be explored. Also
“fitness” of a person is a more appropriate factor since it is a function of both the mass
and stiffness and its effect should be investigated. A person with “high fitness” (low
125
mass-high stiffness) may have a better control over the tool than someone with “low
fitness” (high arm mass-low muscle stiffness). A damping element also needs to be
included in the rig for a better simulation of the human arm.
Another limitation of this thesis was the levels of joint hardnesses that were
selected, and all three levels being a form of medium joint. Future experiments need to
investigate a wider range of joints, and incorporate levels from the hard and soft category.
This may also help bring out the differences between ATC and Manual Downshift, and
help understand which algorithm is ergonomically better for a certain range of joint
hardnesses.
The pilot study with the human subjects showed the differences between the
deflections in the rig and humans. The mechanical elements of the human arm do not
remain constant during a run as was indicated by the whole body movement of a person.
Future research should include the creation of an ergonomic arm tester for assessing
mechanical properties of the human arm which include the effective stiffness, mass, and
damping coefficients for different work conditions. These will act as inputs for the
ergonomic assessment rig. The arm tester will also demonstrate how the arm properties
vary during run down and the rig model of the human arm should be modified to account
for these variations by using, for instance, a viscous liquid or other systems that can
simulate these variations. Following this, the response of the rig and human subjects
should be compared under identical conditions to verify how closely the rig replicates the
human arm. It is also necessary to correlate the output of the rig with the biomechanical
and physiological response of the tool operator, and thus develop measures that are more
126
accurate predictors of musculoskeletal discomfort or injury. An eventual goal would be to
move away from human testing when a more accurate representation of the human arm is
possible and strong correlation between the rig and human response has been established.
127
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10. Lin, J., Radwin, R.G., Fronczak, F.J., Richard, T.G., Forces associated with
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(2001).
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moments during hand tool use, Applied Ergonomics, Vol. 32, 271-279 (2001).
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18. Oh, S.A., Radwin, R.G., The influence of target torque and torque build-up time
on physical stress in right angle nutrunner operation, Ergonomics, Vol. 41 No. 2,
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force in right angle nutrunner operation, Human Factors, Vol. 39 No.4, 497-506
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22. Radwin, R.G., Armstrong, T.J., Chaffin, D.B., Power hand tool vibration effects
on grip exertions, Ergonomics, Vol. 30 No.5, 833-855 (1987).
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hand tool torque reaction forces, Ergonomics, Vol. 32 No.6, 655-673 (1989).
24. Sesto, M.E, Radwin, R.G., Block, W.F., Best, T.M., Upper limb dynamic
responses to impulsive forces for selected assembly workers, Journal of
Occupational and Environmental Hygiene , Vol. 3, 72-79 (2006).
25. ISO 6544: Hand held pneumatic assembly tools for installing threaded fasteners Reaction torque and torque impulse measurements (1981).
26. ISO 5393: Rotary tools for threaded fasteners - Performance test method (1994).
27. Amini, A., Bambaeur, N., Candra, T., Kerlek, A., DC Torque tools ergonomic
improvement, ME 565 Report, The Ohio State University, Unpublished (2005).
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Third edition, Revised and expanded, Marcel Dekker, Inc. (1995).
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Technology International, June/July 2005.
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International, June/July 2005.
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fasteners”, accessed 4 September 2007.
34. http://www.boltscience.com/pages/basics1.htm, “A Tutorial on the Basics of
Bolted Joints”, accessed 4 September 2007.
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analysis”, accessed 4 September 2007.
“Torque
signature
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130
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131
APPENDIX A
SPRING RATE ANALYSIS OF AIR CYLINDER
This appendix contains a detailed description of the analysis of the double rod
double acting cylinder to arrive at the force-displacement curves. The spring rate for
different cylinder specifications was calculated from these curves. The Matlab script that
relates the inlet pressure to spring rate is also included.
L
Xc
Pin
Pin
d
dr
F1
Side 1
F2
Side 2
X
Vex
Vex
Figure A.1: Schematic of double rod cylinder and volume plenums
132
A.1 Calculations to obtain force-displacement curve
Consider a double acting double rod cylinder connected to two volume plenums
one on each side of the piston shown in figure A.1. The piston is initially positioned at
the mid-point of its stroke. The variables used are defined below and are followed by the
calculations.
L = Cylinder stroke
d = Cylinder bore
Xc = Half of cylinder stroke =
L
2
dr = Diameter of piston rod
Vex = Volume of each plenum
Pin = Initial pressure on both sides of the piston
Area of the piston, Ap =
Area of piston rod, Ar =
πd2
4
π dr 2
4
Effective area on each side of the piston, A = Ap− Ar =
π
{d
4
2
− dr 2 }
Initial volume on each side of the piston, Vin = Vex + ( AXc )
According to the ideal gas law, the pressure, volume and temperature are related as
PV = nRT
where P = absolute pressure (Pa)
V = volume (m3)
133
(A.1)
n = number of moles of the gas
R = Universal gas constant (8.3143 m3 Pa/mol -K)
T = absolute temperature (K)
Assuming that air behaves as an ideal gas, the number of moles of air on each side of the
piston can be calculated as
n=
PinVin
RT
(A.2)
At the initial position, the pressures, volumes and number of moles of air are equal on
side 1 and side 2. Thus the piston is balanced and the resultant force on it equals zero. It
is assumed that the piston is perfectly sealing and air does not leak from one side of the
piston to the other, or through any of the air ports, so that the number of moles remains
constant at all times. Now, when the piston moves a distance X towards side 2, the
volumes on side 1 and side 2 are no longer the same and can be calculated as shown
below.
V 1 = Vex + A ( Xc + X )
V 2 = Vex + A ( Xc − X )
(A.3)
(A.4)
P1 =
nRT
Vex + A ( Xc + X )
(A.5)
P2 =
nRT
Vex + A ( Xc − X )
(A.6)
It is evident that the pressure on side 2 is greater than the pressure on side 1. Since force
is pressure multiplied by area, the force F2 is greater than F1 and the net force (F) acting
on the piston is calculated as:
134
F = F 2 − F 1 = P 2 A − P1 A
⎡
⎤
nRT
nRT
F = A⎢
−
⎥
⎣Vex + A ( Xc − X ) Vex + A ( Xc + X ) ⎦
(A.7)
(A.8)
The resultant force (F) was plotted against incremental displacements (X) of the piston
over the entire stroke to arrive at the force-displacement curve. This was done for
different values of bores, strokes, external volumes and initial pressures. The dimensions
of the cylinder were selected based on cylinder and external volume specifications that
gave a force-displacement curve which had the most linear behavior over the entire
stroke. The stiffness was calculated by fitting an equation of a line using Matlab. The
cylinder specifications were also decided based on dimensions that could produce a range
of spring rates that covered the arm stiffness values observed by Lin et al. [4] [6]. The
Matlab script for this analysis is included.
135
A.2 Matlab code to find spring rate of cylinder
clc;
clear;
l=4;
%stroke length in inches
bore=1.5;
% bore inches
dr=7/16;
%enter piston rod dia in inches
vextern=8;
%enter external volume in in^3'
Vex=vextern*1.6387*(10^-5);
% m^3
z=input('\nenterinitial pressure Pin\n');
%Absolute pressure = atm P + gauge P, 1atm =14.8psi, 1psi =
6894.8 pascals
Pin=(14.8+z)*6894.8;
T=298; %Kelvin
R=8.3143; %gas constant (m^3 pa/mole K)
rc=bore*.0254/2
%cylinder radius in m
Ap=rc^2*pi;
%area of piston in m^2
r=dr*.0254/2;
%piston rod radius (m)
Ar=r^2*pi;
%Area of piston rod in m^2
cylinderlength=l*.0254;
%Stroke in m
xc=cylinderlength/2;
%half of stroke in m
x1=xc-.001;
Vin=Vex+(Ap-Ar).*xc;
n=(Pin.*Vin)/(R.*T);
x=-x1:.0001:x1;
V1=Vex+(Ap-Ar).*(xc-x);
V2=Vex+(Ap-Ar).*(xc+x);
P1=(n.*R.*T)./V1;
P2=(n.*R.*T)./V2;
f=(P1.*(Ap-Ar))-(P2.*(Ap-Ar));
plot(x,f);
grid on;
axis ([-xc xc -200 200]);
title(['Pin= ',num2str(z),'psi ',' stroke length
=',num2str(l),'in bore= ',num2str(bore),'(in) external
volume=',num2str(vextern),'(in^3)'])
x1=min(x);
x2=max(x);
y1=min(f);
y2=max(f);
%equation of stiffness curve y=mx+c, m is spring rate
p=polyfit(x,f,1);
136
APPENDIX B
DESIGN DRAWINGS
The drawings used in the design of different components of the ergonomic
assessment rig are given in this appendix. These may be used for future design
improvements and for duplicating various parts of the rig.
137
138
Figure B.1: Device to protect 50 lb load cell from bending loads
138
139
Figure B.2: Blue tube of load cell protection device
139
140
Figure B.3: Pink rod of load cell protection device that slides into the blue tube
140
141
Figure B.4: Orange rod of load cell protection device
141
142
Figure B.5: Pivot plate of the arm mass box
142
143
Figure B.6: Arm mass plate that connects to air cylinder
143
144
Figure B.7: Bottom plate of the arm mass box
144
145
Figure B.8: First slotted plate of arm mass box
145
146
Figure B.9: Second slotted plate of arm mass box
146
147
Figure B.10: Top plate of the arm mass box
147
148
Figure B.11: Larger size arm mass plate
148
149
Figure B.12: Smaller size arm mass plate
149
Figure B.13: Angle plate at clevis end of the rig
150
APPENDIX C
DATA SHEETS FOR SENSORS
The specification sheets for the sensors used in this research are documented in this
appendix for easy reference. The load cell used was a Model 31 Mid range precision
Miniature load cell with a range of +/− 50 pounds. The LVDT used was a Schaevitz DCEC 2000, with a range of +/− 2 inches. A 300 degree/sec rate gyro (model number
ADXRS300) from Analog Devices was used for the pilot tests with humans.
151
C.1 Specification sheet for Model 31 Sensotec load cell
152
153
154
C.2 Calibration certificate for Model 31 Sensotec load cell
155
C.3 Installation instruction for Model 31 Sensotec load cell
156
157
C.4 Specification sheet for Schaevitz 2000 DC-EC LVDT
158
159
160
161
C.5 Calibration sheet for Schaevitz 2000 DC-EC LVDT
162
C.6 Installation and wiring instruction for Schaevitz 2000 DC-EC LVDT
163
C.7: Specification sheet for Analog Devices ADXRS300 rate gyro
164
165
APPENDIX D
STANLEY CONTROLLER PARAMETER SETS
The controller parameter sets that were used for this thesis are documented in this
section. Three program algorithms were used, and each was used at the two soft stop
conditions, resulting in six parameter sets. The parameter set names as stored in the
controller refer to the program algorithm and soft stop setting used. The notation is
explained in table A.1. The entire set of parameter values is tabulated in table A.2.
Notation
Parameter set name
MDS 0.000
Manual Downshift with no soft stop
MDS 0.075
Manual Downshift with default soft stop
TSC 0.000
Two Stage Control with no soft stop
TSC 0.075
Two Stage Control with default soft stop
ATC 0.000
ATC 0.075
ATC with no soft stop
ATC with default soft stop
Table D.1: Explanation of parameter set names
166
Parameter set name
Parameters
MDS SS 0.000
MDS SS 0.075
Step Name
Secure
Secure
Strategy
TC/AM
TC/AM
Fastener replace
66
66
TSC SS 0.000
Stage 1
TSC SS 0.075
ATC SS 0.000
ATC SS 0.075
Stage 2
Stage 1
Stage 2
Secure
Secure
TC/AM
TC/AM
TC/AM
TC/AM
TC/AM
TC/AM
66
66
66
66
66
66
167
High torque
66
66
66
66
66
66
66
66
Target torque
60
60
45
60
45
60
60
60
Low torque
60
60
45
60
45
60
60
60
Snug torque
6
6
6
6
6
6
6
6
High angle
999999
999999
999999
999999
999999
999999
999999
999999
Low angle
0
0
0
0
0
0
0
0
Accumulate angle
No
No
No
No
No
No
No
No
Motor power
100
100
100
100
100
100
100
100
Acceleration
5000
5000
5000
500
5000
500
5000
5000
Tool speed
636
636
636
159
636
159
ATC
ATC
Downshift speed
159
159
0
0
0
0
ATC
ATC
Downshift torque
30
30
0
0
0
0
ATC
ATC
Cycle abort
Delay between
steps
Current off
5
5
5
5
5
5
5
5
0
0
0.05
0
0.05
0
0
0
0.000
0.001
0.000
0.000
0.000
0.001
0.000
0.001
Current hold
0.000
0.025
0.000
0.000
0.000
0.025
0.000
0.025
Current ramp
0.000
0.075
0.000
0.000
0.000
0.075
0.000
0.075
167
Table D.2: Stanley controller parameter values
APPENDIX E
MATLAB CODES - TORQUE IMPULSE AND LATENCY IMPULSE
The Matlab codes that used to calculate torque impulse and latency impulse are
documented in this appendix.
168
E.1 Matlab code for torque impulse
clear all;
clc;
x=input('\nenter percentage of target torque such as x%\n');;
% threshold value of the torque
threshold=(x/100)*60;
file=xlsread('run_1.xls');
time=file(:,1);
force=file(:,2);
distance=file(:,3);
torque=(-1.*force).*0.470;
% 18.5 inches length of handle
n1=length(time);
%finding the first point in the main array after which the torque
crosses threshold
n=0;
for i=1:n1;
if (torque(i)<=threshold)
n=n+1;
else break;
end;
end;
%finding curve portion above threshold torque
j=1;
for i=1:n1
if(torque(i)>= threshold)
newtime(j)=time(i);
newtorque(j)=torque(i);
j=j+1;
end
end
if (j==1)
newtime=[];
newtorque=[];
end
%split first time
t1=length(newtime)-1;
for i=1:t1
if((newtime(i+1)-newtime(i))>0.00025)
j=i+1;
break;
end
end
169
if(i==t1)
newtime2=[];
newtorque2=[];
else
newtime2=newtime(j:t1+1);
newtorque2=newtorque(j:t1+1);
end
newtime1=newtime(1:j-1);
newtorque1=newtorque(1:j-1);
%interpolate newtime1,newtorque1
l1=length(newtime1);
Xo_beg1=time(n);
%Beginning of newtime1,newtorque1
Yo_beg1=torque(n);
X1_beg1=time(n+1);
Y1_beg1=torque(n+1);
X_beg1=Xo_beg1 + ((threshold-Yo_beg1)/(Y1_beg1Yo_beg1))*(X1_beg1-Xo_beg1);
Impulse_beg1=abs(0.5*(X1_beg1-X_beg1)*(Y1_beg1-threshold));
Xo_end1=time(n+l1+1);
%End of newtime1,newtorque1
Yo_end1=torque(n+l1+1);
X1_end1=time(n+l1);
Y1_end1=torque(n+l1);
X_end1=Xo_end1 + ((threshold-Yo_end1)/(Y1_end1Yo_end1))*(X1_end1-Xo_end1);
Impulse_end1=abs(0.5*(X1_end1-X_end1)*(Y1_end1-threshold));
%split second time
t2=length(newtime2)-1;
j=1;
for i=1:t2
if((newtime2(i+1)-newtime2(i))>0.00025)
j=i+1;
break;
end
end
if (i==t2)
newtime2b=[];
newtorque2b=[];
newtime2a=newtime2;
newtorque2a=newtorque2;
else
newtime2b=newtime2(j:t2+1);
170
newtorque2b=newtorque2(j:t2+1);
newtime2a=newtime2(1:j-1);
newtorque2a=newtorque2(1:j-1);
end
%interpolate newtime2a,newtime2b
l2a=length(newtime2a);
if (l2a>0)
%Beginning of newtime2a,newtorque2a
firstindex2a=((newtime2a(1)-time(1))/0.0002)+1;
beg_2a=int16(firstindex2a - 1);
index1_2a=int16(firstindex2a);
Xo_beg2a=time(beg_2a);
Yo_beg2a=torque(beg_2a);
X1_beg2a=time(index1_2a);
Y1_beg2a=torque(index1_2a);
X_beg2a=Xo_beg2a + ((threshold-Yo_beg2a)/(Y1_beg2aYo_beg2a))*(X1_beg2a-Xo_beg2a);
Impulse_beg2a=abs(0.5*(X1_beg2a-X_beg2a)*(Y1_beg2a-threshold));
else Impulse_beg2a=0;
end;
if (l2a>0)
%End of newtime2a,newtorque2a
indexlast2a=int16(index1_2a+l2a-1);
end_2a=int16(indexlast2a+1);
Xo_end2a=time(indexlast2a);
Yo_end2a=torque(indexlast2a);
X1_end2a=time(end_2a);
Y1_end2a=torque(end_2a);
X_end2a=Xo_end2a + ((threshold-Yo_end2a)/(Y1_end2aYo_end2a))*(X1_end2a-Xo_end2a);
Impulse_end2a=abs(0.5*(X1_end2a-X_end2a)*(Y1_end2a-threshold));
else Impulse_end2a=0;
end;
%interpolate newtime2b,newtime2b
l2b=length(newtime2b);
if (l2b>0)
%Beginning of newtime2b,newtorque2b
firstindex2b=((newtime2b(1)-time(1))/0.0002)+1;
beg_2b=int16(firstindex2b - 1);
index1_2b=int16(firstindex2b);
Xo_beg2b=time(beg_2b);
Yo_beg2b=torque(beg_2b);
X1_beg2b=time(index1_2b);
Y1_beg2b=torque(index1_2b);
X_beg2b=Xo_beg2b + ((threshold-Yo_beg2b)/(Y1_beg2bYo_beg2b))*(X1_beg2b-Xo_beg2b);
Impulse_beg2b=abs(0.5*(X1_beg2b-X_beg2b)*(Y1_beg2b-threshold));
else Impulse_beg2b=0;
end;
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if (l2b>0)
%End of newtime2b,newtorque2b
indexlast2b=int16(index1_2b+l2b-1);
end_2b=int16(indexlast2b+1);
Xo_end2b=time(indexlast2b);
Yo_end2b=torque(indexlast2b);
X1_end2b=time(end_2b);
Y1_end2b=torque(end_2b);
X_end2b=Xo_end2b + ((threshold-Yo_end2b)/(Y1_end2bYo_end2b))*(X1_end2b-Xo_end2b);
Impulse_end2b=abs(0.5*(X1_end2b-X_end2b)*(Y1_end2b-threshold));
else Impulse_end2b=0;
end;
%Torque-time curve for impulse
finaltime1=[X_beg1;newtime1';X_end1];
finaltorque1=[threshold;newtorque1';threshold];
if(l2a>0)
finaltime2a=[X_beg2a;newtime2a';X_end2a];
finaltorque2a=[threshold;newtorque2a';threshold];
else
finaltime2a=[];
finaltorque2a=[];
end;
if(l2b>0)
finaltime2b=[X_beg2b;newtime2b';X_end2b];
finaltorque2b=[threshold;newtorque2b';threshold];
else
finaltime2b=[];
finaltorque2b=[];
end;
%add impulses
impulse1=trapz(newtorque1)*0.0002;
impulse2a=trapz(newtorque2a)*0.0002;
impulse2b=trapz(newtorque2b)*0.0002;
impulse_A=impulse1+impulse2a+impulse2b;
impulse_B=Impulse_beg1+Impulse_end1+Impulse_beg2a+Impulse_end2a+I
mpulse_beg2b+Impulse_end2b;
impulse_total=impulse_A+impulse_B; %total torque impulse
%Plots
figure(1);
plot(time,torque);
grid on;
title(‘Entire torque-time curve’);
xlabel(‘Time (s)’);
ylabel(‘Torque (Nm)’);
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figure(2);
plot(finaltime1,finaltorque1,'r');
grid on;
hold on;
plot(finaltime2a,finaltorque2a,'b');
hold on;
plot(finaltime2b,finaltorque2b,'g');
title('Impulse curve');
xlabel('Time (s)');
ylabel('Torque (Nm);')
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E.2 Matlab code for latency impulse
clear all;
clc;
file=xlsread('run_1.xls');
time=file(:,1);
force=file(:,2);
distance=file(:,3);
torque=(-1.*force).*0.470;
n1=length(time);
% 18.5 inches length of handle
peakdistance=max(abs(distance));
%finding index of peak deflection point
for i=1:n1
if(abs(distance(i))== peakdistance)
j=i;
break;
end;
end;
%emg latency clock starts when deflection is equal to 0
for i=j:-1:1
if(distance(i)>= 0)
emg_startpt=i;
break;
end;
end;
if(i==1)
emg_startpt=[];
break;
end;
% impulse area calculation stops when torque=0
peaktorque=max(torque);
%finding index for peak torque point
for i=1:n1
if(torque(i)== peaktorque)
k=i;
break;
end;
end;
for i=k:1:n1
if(torque(i)<=0)
emg_endpt=i;
break;
end;
end;
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latency = 0.0614;
time_after_latency=time(emg_startpt)+ latency;
for i = 1:n1
if(time(i)= time_after_latency)
n = i;
break;
end;
end;
%Curve with latency excluded
torque1_new=torque(n:emg_endpt,:);
time1_new=time(n:emg_endpt,:);
impulse1=trapz(torque1_new)*0.0002
%Interpolation at Beginning of emg clock
indexstart=int16(emg_startpt);
Xo_beg1=time(indexstart+1);
Yo_beg1=distance(indexstart+1);
X1_beg1=time(indexstart);
Y1_beg1=distance(indexstart);
Y_beg1=0;
X_beg1=Xo_beg1 + ((Y_beg1-Yo_beg1)/(Y1_beg1-Yo_beg1))*(X1_beg1Xo_beg1);
%Interpolation at end of emg clock
timex=X_beg1+latency;
x=((timex-time(1))/0.0002)+1;
indexlatency=int16(x);
Xo_latency1=time(indexlatency-1);
Yo_latency1=torque(indexlatency-1);
X1_latency1=time(indexlatency);
Y1_latency1=torque(indexlatency);
X_latency1=timex;
Y_latency1=Yo_latency1 + ((X_latency1-Xo_latency1)/(X1_latency1Xo_latency1))*(Y1_latency1-Yo_latency1);
Impulse_latency=abs(0.5*(X_latency1-Xo_latency1)*(Y_latency1Yo_latency1));
%Interpolation at end of torque curve
indexend=int16(emg_endpt);
Xo_end1=time(indexend);
Yo_end1=torque(indexend);
X1_end1=time(indexend-1);
Y1_end1=torque(indexend-1);
Y_end1=0;
X_end1=Xo_end1 + ((Y_end1-Yo_end1)/(Y1_end1-Yo_end1))*(X1_end1Xo_end1);
Impulse_end=abs(0.5*(X_end1-Xo_end1)*(Y_end1-Yo_end1));
%Calculate total latency impulse
latency_impulse=impulse1-Impulse_end-Impulse_latency;
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