34th INTERNATIONAL CONFERENCE ON
PRODUCTION ENGINEERING
29. - 30. September 2011, Niš, Serbia
University of Niš, Faculty of Mechanical Engineering
TOWARDS A CONCEPTUAL DESIGN OF AN INTELLIGENT MATERIAL
TRANSPORT BASED ON MACHINE LEARNING AND AXIOMATIC DESIGN THEORY
1
Milica PETROVIĆ1, Zoran MILJKOVIĆ1, Bojan BABIĆ1, Najdan VUKOVIĆ2, Nebojša ČOVIĆ3
University of Belgrade – Faculty of Mechanical Engineering, Production Engineering Department, Kraljice Marije 16
11120 Belgrade 35, Republic of Serbia:
mmpetrovic@mas.bg.ac.rs, zmiljkovic@bg.ac.rs, bbabic@mas.bg.ac.rs
2
University of Belgrade-Faculty of Mechanical Engineering, Innovation Center, Kraljice Marije 16 11120
Belgrade 35, Republic of Serbia:
nvukovic@mas.bg.ac.rs
3
Company FMP d.o.o. - Belgrade, Lazarevački drum 6, 11030 Belgrade, Republic of Serbia:
nebojsa.covic@fmp.co.rs
Abstract: Reliable and efficient material transport is one of the basic requirements that affect
productivity in sheet metal industry. This paper presents a methodology for conceptual design of
intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and
artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot
system Khepera II within the laboratory model of manufacturing environment. Matlab © software package
was used for manufacturing process simulation, implementation of search algorithms and neural network
training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal
material transport flows thanks to the use of artificial neural networks. Achieved positioning error of
mobile robot indicates that conceptual design approach can be used for material transport and handling
tasks in intelligent manufacturing systems.
Keywords: intelligent manufacturing systems, conceptual design, axiomatic design theory, neural
networks, mobile robot
1. INTRODUCTION
For the last thirty years manufacture concepts have had
several redefinitions. In the eighties and nineties, the
concept of flexible manufacturing systems (FMS) was
introduced to develop a new family of products with
similar dimensions and constraints [1]. The
manufacturing enterprises of the 21st century are in an
environment where markets are frequently shifting, new
technologies are continuously emerging, and competition
is globally increasing. Rapid changes in product demand,
product design, introduction of new products and
increasing global competition require manufacturing
systems to be highly flexible, adaptable and responsive
[1].
A methodology that includes the technological migration
[1] from established flexible manufacturing systems
(FMS) to intelligent manufacturing system (IMS) is
presented in this paper. For needs to be addressed at the
design stage of new manufacturing system with all
intelligent characteristics, this paper would like to present
a methodology for conceptual design of manufacturing
systems using axiomatic design approach.
Beside axiomatic design methodology, the mentioned
requirements cannot be fulfilled without artificial
intelligence. According to the literature published by
CIRP and other manufacturing periodicals during the past
decade, nearly 34 modern manufacturing systems and
production modes have been proposed and 35
mathematical methods have been used for building
intelligent systems [2]. The wide application of these
intelligent mathematical methods or their combinations in
manufacturing will definitely enhance the development of
manufacturing system modelling and provide the new
solutions. Some of the methods are: machine learning,
artificial neural networks, heuristic search, and graph
theory, etc. Evolutionary computation (i.e. genetic
algorithms,
genetic
programming,
evolutionary
programming, and evolutionary strategies) and artificial
neural network are the most widespread [3]. Intelligent
material transport implies solving path generation
problem and control movement of an intelligent agent - a
mobile robot. The graph algorithms are used to generate
path and artificial neural networks for prediction of
duration of manufacturing operations. In [4] different
graph search algorithms are presented.
2. AXIOMATIC DESIGN THEORY
Axiomatic design theory is an attempt at synthesis of the
basic principles of design in various engineering fields
and in all phases of design. This design methodology is
based on identifying customer needs and their
transformation into correspondent functional requirements
in the physical domain. According to [5], going from one
domain to another is called mapping and it happens in the
each design phase: conceptual, product and process
design phase, respectively. Furthermore, the design
Customer Attributes {CAs}
process is done through the iterative mapping between the
functional requirements (FRs) in the functional domain,
and the design parameters (DPs) in the physical domain,
for each hierarchical level (Fig.1).
Design Parameters {DPs}
Functional Requirements {FRs}
FR1
FR11
FR12
DP1
FR13
DP11
FR14
DP12
DP14
DP13
Needs
specification
FR111 FR112 FR121 FR122 FR141
Customer Domain
DP111 DP112 DP121 DP122 DP141
Physical Domain
Functional Domain
Fig.1. Concept of domain, mapping and axiomatic decomposition
In mathematical terms, the relationship between the FRs
and DPs is expressed as [5]:
{FR} = |A| ⋅ {DP}
(1)
where {FR} denotes the functional requirement vector,
{DP} denotes the design parameter vector, and |A|
denotes the design matrix that characterizes the design
process. The structure of the matrix |A| defines the type of
design being considered and for the three hierarchical
levels particular design matrices |A| are presented in the
Table 1. It can be concluded that |A| matrix in the second
hierarchical level is triangular and for that reason we can
change some DPs to set some other FRs without affecting
the rest of FRs [5]. Such a design is called a decoupled
design. In the third hierarchical level |A| matrix is
diagonal and each of the FRs can be satisfied
independently by means of one DP. Such a design is
called an uncoupled design.
DP122: Control algorithms
X
0
0
0
0
0
X
0
0
0
0
0
0
0
0
0
X
0
0
0
X
0
0
0
X
DP141: Parameters for neural
networks training
DP121: Path planning algorithms
DP14: Neural networks
DP13: Manufacturing process
simulation
DP111: Sensory information from
encoders
DP112: Sensory information from the
camera
FR1: Intelligent material transport
FR11: Determining mobile robot position and orientation
FR12: Path planning
FR13: Prediction of manufacturing process parameters
FR14: Machine learning of material transport flows
FR111: Determining parameters in motion model
FR112: Determining position and orientation of the
characteristic objects in the environment
FR121: Generating path nodes
FR122: Path following
FR141: Getting expected performance of IMS
DP12: Path planning module
Impact
No impact
DP1: Mobile robot
X
0
DP11: Odometry motion model
Table 1. List of the functional requirements, correspondent design parameters and correspondent |A| matrices
X
X
0
0
0
X
X
0
0
X
X
X
0
X
X
X
X
3. MOBILE ROBOT IN A MANUFACTURING
ENVIROMENT
is written using matrix M (matrix of machines) and D
(matrix of parts) [7].
To explain mobile robot motion and actions in
manufacturing environment, five modules are developed.
 p11


M DM =  pi1



 p ND1
3.1. Motion model
The position of the mobile robot is determined by the
system state vector xt = (x, y, θ), where x and y are the
components that define the position vector, and θ is the
angle orientation. Mathematical formulation for mobile
robot odometry is given by (2):
 x '   x   s cos(   / 2) 
 y '   y    s sin(   / 2) 
    

  '     


(2)
3.2. Material flow analysis
Material transport analysis in manufacturing environment
was recognized as the first task in a path planning
module. First of all, flow line layout design is adopted.
After that, the data about machines, parts and time
duration of operations should be gathered and analyzed.
Table 2 presents a list of machines, and Table 3 presents a
list of parts.
Table 2. List of machines in manufacturing plant
Machine
Machine type
M#1
Shearing machine
CNC punch press for punching
and blanking
Hydraulic punch press
Punch press for punching and
blanking
Pillar drill (bench drill)
Circular saw
M#3
M#4
M#5 and M#6
M#7
M#8
M#9
pij
pNDj
(3)
If we need time dependence between machines and parts,
we put the time duration of machine operation to a
correspondent machine instead of parameter pij. At the
end, using graph theory, we define matrix of distances
between machines (R) [6].
3.3. Path planning algorithms
where x', y' and θ'are the components of the state vector at
time t', x, y and θ components at time t; Δs the
incremental path lengths [6].
M#2
p1NM 


piNM 



pNDNM 
p1 j
Whetting machine
Line for machining parts made
of cooper
Table 3. List of representative parts in manufacturing
plant
Three algorithms are developed and implemented for the
mobile robot path planning task. The first one is A*
search algorithm, that is used for finding the shortest path
between start and goal points. It combines Dijkstra
algorithm and bread-first search algorithms. Using MDM
matrix, the second algorithm determinates sequence of
machines for each representative part and chooses
machine the robot should visit, according to a minimal
distance criteria. Finally, the third algorithm is used for
determining the order of machines in accordance to
manufacturing process. This algorithm generates
characteristic time parameters of the manufacturing
process (the duration of the operation on the machine) and
time parameters related to part transport to the machine
(time needed for mobile robot to travel between
machines).
3.4. Prediction
parameters
of
manufacturing
process
It is known that engineering processes generally do not
have deterministic nature. The processes that are
important for the material transport task in terms of
duration are the machining process and the process of
robot movement between the defined nodes (machines).
Considering the fact that these processes have stochastic
nature, we can conclude that nominal time duration of
operations, as well as time of transport from one node to
another, are different for each part. For that reason,
uniform distribution is chosen to model stochasticity of
the nominal time duration.
3.5. Neural Networks for prediction of duration
of manufacturing operations
Part
Description
P#1
P#2
P#3
P#4
Transport fuse
Mainbusbar support
Support d800
Busbar 2 L1
After defining number of parts and machines, we need to
define quantitative relations between them. In general,
this dependence can be presented with matrix MDM, which
Implementation of the neural networks (NN) to model
various problems in production engineering goes back to
the 20th century. According to [8], there are three basic
categories of their use: classification, prediction and
functional approximation. Prediction of the next node
(machine) in the path, where robot needs to go and deliver
the part, is based on past values of the system state (in this
case the time parameters of the process and the time of
robot movement between the machines) and the current
values of the system state (the node where the robot is
currently located). For NN training the Matlab Neural
Network Toolbox is used, with supervised learning
algorithm (Levenberg-Marquardt) [8] and the sigmoid
activation function.
4. EXPERIMENTAL RESULTS
The experimental model of manufacturing environment is
static and positions of machines are a priori known.
Experimental model and the Khepera II mobile robot are
shown in Fig. 2. The first goal is test accuracy of path
following. During tracking the trajectory, the robot has to
deliver part to machines, according to manufacturing
process, defined by matrix M DM. Coordinates of start and
goal point is known. While executing the transport task,
the robot optimizes the path between the machines using
A* algorithm [6]. The mean position errors during the
first experiment in x and y directions are Δx=0.5598 [cm]
and Δy=1.4624 [cm].
priority servicing of machine tools. Mobile robot learns
the optimal transport routes and sequence of manipulation
by using neural network [6]. Neural network was
developed to predict the parameters of manufacturing
process and to learn characteristic time parameters of the
process. For the purposes of the simulation we used the
nominal time parameters (estimated using empirical data)
of the manufacturing process, and its stochastic nature is
modeled according to uniform distribution [6]. Search
algorithms and neural network models are developed in
Matlab environment and implemented on a Khepera II
mobile robot. Achieved positioning error of mobile robot
indicates that conceptual design approach based on
axiomatic design theory and neural networks can be used
for material transport and handling tasks in intelligent
manufacturing systems.
ACKNOWLEDGMENTS
This paper is part of the project: An innovative,
ecologically based approach to implementation of
intelligent manufacturing systems for production of sheet
metal parts, financed by the Ministry of Education and
Science of the Serbian Government, Grant TR-35004.
Goal
y
x
Start
Fig.2. Mobile robot motion in laboratory model of
manufacturing environment
The next experiment is conducted in same conditions, but
the coordinates of the goal point are not known at the
beginning. This parameter depends on the time robot
needes to travel from one machine to another. When the
robot finishes transport of the last representative part to
machine for the first operation, its current pose is passed
to NN. Based on this information, NN predict the nearest
machine where manufacturing operations are completed
and generate information about the future robot actions.
5. CONCLUSION
This paper presents a method for conceptual design of
mobile robot material transport in intelligent
manufacturing system. Intelligent mobile robot, with a
priori known static obstacles in the environment, has the
ability to generate an optimal motion path in accordance
with the requirements of the manufacturing process and
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