HUMAN-ROBOT COLLABORATION ASSEMBLY LINE MODEL DEVELOPMENT WITH MAXIMUM NUMBER OF ROBOT TOOLS AND SETUP TIME CONSIDERATION
utomation technology needs to be applied to increase the flexibility of assembly lines by using robots that can cooperate with humans in the assembly process, otherwise known as human-robot collaboration. This collaboration aims to increase the efficiency of the assembly process, by combining the...
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Main Author: | |
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Format: | Theses |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/66047 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | utomation technology needs to be applied to increase the flexibility of assembly
lines by using robots that can cooperate with humans in the assembly process,
otherwise known as human-robot collaboration. This collaboration aims to
increase the efficiency of the assembly process, by combining the strengths of two
sides: human manual skills and the load capacity of the robot. Efficiency is also
achieved by increasing the cycle time of the assembly process. Therefore, this
research was conducted using human, robot, and human-robot collaboration
resources that can minimize the production cycle time.
This research develops a mathematical model that aims to obtain a minimum cycle
time on an assembly line that considers human, robot, and human-robot
collaboration resources. The alteration of several types of tools for different
operations on the robot, and the setup time of robots are considered in this
research. The proposed solution was validated by a numerical test with a specific
set of jobs in several work stations to see the characteristics of the results.
The results of the numerical test of the proposed model indicate that the maximum
number of tools installed on robot’s end-effector will affect the job allocation for
the resources of robot and human-robot collaboration. If there is an idle time
between jobs, then the robot settings can take advantage of idle time so that there
is no idle time between jobs. Larger setup time affects the increased cycle time
solution and affects the tasks allocation for each resource. The additional
investment of robots can reduce the cycle time and influence each resource’s task
allocation.
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