Performance Analysis of
Robotic Cells
David P. Harnois
MEAE 6960
Modeling and Analysis of Manufacturing Systems
Term Project
April 12, 2001
Table of Contents
Abstract ........................................................................... 3
Introduction ..................................................................... 4
Models ............................................................................. 7
Results ............................................................................ 11
Conclusions ..................................................................... 13
Appendixes ..................................................................... 14
ProModel Models .................................................... 15
Robots ............................................................. 15
People ............................................................. 18
People/Robots Mix .......................................... 21
Simulation Results .................................................. 24
Robots ............................................................. 24
People ............................................................. 28
People/Robots Mix .......................................... 32
References ...................................................................... 36
2
Abstract
It is a common practice in today's high tech, fast paced world to implement
robotic cells in the manufacturing process. Many companies are able to successfully
implement productive robotic cells that decrease process time and reduce product cost.
Yet, other companies are not successful in their implementation.
negative impact on the manufacturing process.
This results in a
The goal is to use discrete event
simulation techniques to simulate a manufacturing process with both robotic cells and
manual labor cells to determine the performance of the robotic cell compared with a
manual cell performing the same operation. The belief is there is a better solution than a
purely robotic or purely manual cell.
Therefore, a third model is introduced that
combines elements of a robotic and manual cell together. The process used in this
modeling and analysis is the assembly of electric circular saws.
Process time was
determined randomly, but operations the required more dexterity were shorter when
performed by an operator. Assembly steps and times are provided for both robotic and
operator assembly. After each type of cell was modeled a new process was modeled that
took the fastest process time for each operation, whether it was a robot or operator. This
model produced a shorter cycle time than either of the original cells.
3
Introduction
This paper addresses the fact that an entirely robotic cell may not be the optimal
solution, but looks at a manual and robotic combination solution to achieve the optimum
results. Many companies are able to successfully implement productive robotic cells that
decrease process time and reduce product cost. Yet there those who are not successful in
their implementation. This results in a negative impact on the manufacturing process.
Robots work great for simple repetitive process such as installing fasteners, spot welding,
and other tasks that only require simple linear movements. They are also appropriate for
some complicated tasks such as precision welding and delicate assemblies. However the
robot may not be the best choice when an operation has a high failure rate and an
operator could easily identify parts the require rework. Robots also may be a poor choice
in assembly tasks that require complex movements to manipulate parts for assembly.
Robotic cells can also be set up where robots perform all of the actual assembly work, but
an operator is responsible for loading the parts and materials for each assembly. This
eliminates the need for complex robotics to feed and load the assembly cell. The operator
is able to load and monitor the robots, while the robots perform an accurate and
repeatable process.
Modern simulation tools allow manufacturing engineers to develop manufacturing
cells that are built to the optimum layout. If properly used, and if the engineer is willing
to consider a multitude of different types of cell layouts the optimum solution can be
reached. This optimum solution most likely will not be an entirely automated cell, nor
will it be manual. New three Dimensional simulation software is available for modeling
robotic cells, in addition to the two dimensional program used in this project. Besides
4
being used to optimize robotic cells, 3-D software allows for offline programming by
allowing manufacturing engineers to visualize work cells, identify potential problems,
perform feasibility studies, and quickly modify the environment.
The models in this paper only reflect changing between an operator and robot to
do the actual work. This has greatly simplified the actual considerations that must be
made when determining how the work should be divided in and actual cell. In a real life
modeling situation, it would be necessary to factor the type of handling, the actual layout,
required part set-up, and the operation. The handling aspect relates to the transport of the
product from one operation to the next, is it better with a robot or an operator? Factors to
be considered when making this choice is distance, queues, and special part
characteristics such as temperature or chemical exposure. Cell layout is also important,
because the layout may prevent or require the use of certain options. Part set-up can also
affect choices. Complicated set-ups may not even be possible with a robot. The type of
operation must also be considered, operations that require a lot of dexterity may be
difficult for a robot, where simple, repetitive tasks are well suited to a robot. Certain
operations may be better suited to a robot for safety issues.
When considering all the variable that exist when developing the most efficient
cell one can easily see how a company can make wrong decisions. Often times they just
assume that robots are the way to go. In some cases cost is not an issue, and robots are
the best choice at any cost. The semiconductor industry is a heavy user of robots for both
process and handling. The processes used in the manufacture of semiconductors require
many toxic chemicals, so robots reduce the risk to operators by keeping them away from
chemicals. A second reason that robots are so widely used is the cost of the products. An
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unprocessed lot of silicon wafers cost can cost over $5000, by the time they reach the end
of the process their value exceeds $1 million. Robots reduce handling damage and
operator error. In most cases the choice of whether or not to use robots is not a clear cut
as in the semiconductor industry and it is important that careful, detailed analysis be
made.
This paper includes some simple models that illustrate a robotic cell, a manual
cell, and a cell that is a combination of robotic and manual operations. By using some
simple models and basic assumptions the attempt is to illustrate to the reader that a purely
robotic cell may not be the optimum solution. This information may help to prevent
expensive robotic lines and cells that are not profitable.
6
Models
The system to be evaluated will be a seven-station assembly line for the
manufacture of electric circular saws. The system will be modeled in ProModel Student
Edition with both a Robotic Assembly Cell and a manual assembly cell. A new system
will then be modeled that uses the fastest processing time for each operation to create a
cell that integrates the use of robots and operators.
Assumptions:
It is assumed that there is a dedicated worker or robot at each station. All supplies
are delivered to the assembly cell another cell that kits the parts to be assembled. The
parts move by automatic transfer station from one operation to the next in both the
robotic and manual cells and the transfer time is included in the operation time. Kited
parts arrive at the cell every 1.5 minutes via an automated conveyor. The workweek
consists of three 8-hour shifts, five days a week, for 120 working hours per a week.
Robots fail once every 250 cycles and take 15 minutes to repair. The operators take one
15-minute break every four hours. All products move into the system, through the
system, and out of the system by automatic transfer stations. Each Station has a queue of
size 25 in front of it.
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Process:
The table below shows the seven different stations and the respective robotic and
manual processing times.
Process
Robot Time
Manual Time
Attach Electric cord to
electric motor and mount
motor to bottom half of
exterior case.
Mount motor to bottom half
of exterior case.
Attach top half of exterior
case.
Attach Safety Shield
Attach Guide Plate
Attach Saw Blade
Attach labels and test saw
Total Time
1.7 minutes
1.5 minutes
1.1 minutes
1.4 minutes
1.3 minutes
1.5 minutes
1.5 minutes
1.2 minutes
1.7 minutes
1.8 minutes
10.3 minutes
1.3 minutes
1.5 minutes
1.5 minutes
1.2 minutes
9.9 minutes
The approach to solving this problem is to model using discrete event simulation
and the ProModel software to model a robotic cell, a manual cell, and then modeling a
cell that is a combination of robotic and manual. As can be seen from the table some
tasks are better performed manually than by robot. In developing this process it was
assumed that tasks requiring more dexterity would be performed faster by a human and
simple tasks would be performed faster by a robot. By finding the best combination of
tasks, the optimal solution can be found and assumptions made as to why robotic cells are
not always profitable. The table below shows the process that was modeled using the
fastest processing time for each operation.
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Process
Process Time
Processor
Attach Electric cord to
electric motor and mount
motor to bottom half of
exterior case.
Mount motor to bottom half
of exterior case.
Attach top half of exterior
case.
Attach Safety Shield
Attach Guide Plate
Attach Saw Blade
Attach labels and test saw
Total Time
1.5minutes
Operator
1.1 minutes
Robot
1.3 minutes
Robot
1.3 minutes
1.2 minutes
1.5 minutes
1.2 minutes
9.1 minutes
Operator
Robot
Operator
Operator
The print outs of the models used in this paper are included in the appendix. The
file project-robot.mod is the model of the robotic cell, project-people.mod is the model of
the manual cell, and project-mix.mod is the combination cell. These printouts include
details on all of the locations, downtimes, entities, processing, and arrivals. There is also
a graphical printout for each model. Summaries of all three models are included in the
table below.
Arrival Frequency
Downtime
Frequency
Downtime Logic
Processes:
Attach Electric cord
to electric motor and
mount motor to
bottom half of
exterior case.
Mount motor to
bottom half of
exterior case.
Attach top half of
exterior case.
Robot
1.5 minutes
250
Operator
1.5 minutes
240
Wait 15
Wait 15
Robot/Operator
1.5 minutes
250 for robots
240 for operators
Wait 15
Wait 1.7
Wait 1.5
Wait 1.5
Wait 1.1
Wait 1.4
Wait 1.1
Wait 1.3
Wait 1.5
Wait 1.3
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Attach Safety Shield
Attach Guide Plate
Attach Saw Blade
Attach labels and
test saw
Wait 1.5
Wait 1.2
Wait 1.7
Wait 1.8
Wait 1.3
Wait 1.5
Wait 1.5
Wait 1.2
Wait 1.3
Wait 1.2
Wait 1.5
Wait 1.2
10
Results
These models showed that there are some advantages to using a hybrid assembly
cell that uses both robots and operators. The cell modeled here used faster assembly
times for the robot on simple tasks and assigned longer times for tasks requiring more
manipulation. The results of this experiment are included in the appendix. The model of
the manual cell shows higher throughput than the robotic cell since most operations are
require manipulation, hence longer process times. The hybrid cell was essentially the
same as the manual cell, therefore there were no improvement in efficiency. In fact, the
hybrid cell actually produced slightly fewer saws. The primary reason for this was that
the longest processing time was the same in both models. In the hybrid cell, most of the
utilizations were lower than in the manual cell. The results are summarized in the
following table.
Throughput
Utilization:
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Robot
Operator
3868
4502
Robot/Operator
Hybrid
4492
96.85
86.03
91.53
94.72
93,12
96.73
96.69
99.98
91.96
99.92
87.91
99.87
99.84
79.84
99.98
67.50
82.50
87.8
75.00
99.63
79.68
Although these models do not show the hybrid cell to be the most optimal, they
do show that the robotic cell is not always optimal. The results show that in the 120 hour
work week modeled that the hybrid and operator cell both have the same throughput. No
noticeable improvement in throughput exists in this case because of the model. In the
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case of the hybrid cell the longest processing time is 1.5 minutes, which is equal to the
longest operation in the operator cell. Therefore the limiting operations in each model
are equal, so one would expect equal throughputs. However, upon carefully evaluating
the results one will notice that there is more availability in the hybrid cell. A new model
was developed that uses a reduced process time for some of the operator steps. This
model creates process times that are all shorter than the longest times in either the robot
or operator models. The results for these models are shown below.
Throughput
Utilization:
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Robot
Operator
1285
1502
Robot/Operator
Hybrid
1500
96.81
64.34
80.83
90.42
85.61
96.43
96.33
89.09
98.29
92.35
97.57
99.63
79.63
79.57
99.93
67.50
75.00
82.50
75.00
93.75
75.00
The results of this model still shows the Operator model to have a higher
throughput, however when looking at the models it appears that the main reason the
hybrid model lags behind is because its throughput is much lower at the first operation
between the two models. This fact is difficult to understand since the first process is
identical in both operations. It is believe that a bug exists in the software or the model
which is causing strange results, since one would expect the throughput at identical
operations to be the same when the operation is the very first process.
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Conclusions
This paper illustrates that an entirely robotic cell is not always the optimal
solution. The results show that by using a mix of operators and robots better results are
obtained than by the use of only robots. The basic models and results presented in this
paper show that if a cell is properly modeled, and the use of both robots and operators are
taken into account, an optimal solution may exist in which both robots and operators are
utilized. It is a rare case where a totally robotic cell is the optimal solution. Besides the
straight throughput issue, other factors that must be considered when making these type
of decisions is capital costs, repair costs, and salaries. The cost of robots is usually very
high, besides capital costs there are spare part and maintenance costs. Many companies
have been able to successfully implement productive robotic cells that decrease process
time and reduce product cost. These companies most likely took advantage of the tools
available today to carefully simulate their processes and determine that robots were the
optimal solution. The many other companies who have not been successful in their
implementation should take a careful look at their modeling and simulation tools and
processes
This paper shows how modern simulation tools allow manufacturing engineers to
develop manufacturing cells that are built to the optimum layout. The simple simulations
used in this paper show that there are a variety of performances based on the makeup of
the cell.
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Appendixes
14