Digital Twin Development for Serial Manipulators: Data Driven Optimized Planning and Sequencing of Tasks
background studies, such as developing
frameworks for applications of DT. 10 11
planning and simulation are executed to
teach the robot with expected movements.
Subsequently, the virtual robot setup
enables engineers to review and verify the
new robot motion path. Finally, the
approved path is released to the robot
controller linked with the programmable
logic controller (PLC) of the assembly line to
execute assembly operations. During
training and operation, the cobot keeps
publishing its critical status information to
the virtual environment in real time. By
following these steps, which are also aligned
with the vision of a cyber-physical system
implementation in Industry 4.0, 13 14 the
order is executed with efficient monitoring,
planning, simulation and optimization. Such
an implementation is novel as it is generic
and can be used for any serial manipulators.
This differs from the state of the art which is
targeted towards specific machine or work
cell DT POCs.
Although studies on DT are various, its
prevalent implementation has not been
realized. One of the main reasons is that
companies still encounter challenges to
identify the implementation scope where DT
will create worthwhile business value. 12
Motivated by the above research gap, in this
article, a practical use case of DT developed
and implemented in the Model Factory (MF)
program at Advanced Remanufacturing and
Technology Centre (ARTC), Singapore, is
introduced. In this use case, a DT is
developed for a gearbox assembly line
automated by a collaborative robot (cobot)
for new gearbox sub-assembly configuration
requirements. Once the customer order
containing customized configuration of
gearbox sub-assembly is received, new
assembly and component designs are
trained to build with an object recognition
model using machine vision and deep
learning algorithms. The trained model is
integrated with the desired operation
sequence to pass the information to the
robot motion planning software. Next, robot
In the first section of this article, the
definition and advantages of a DT model will
be recapped. Next, the DT model developed
by ARTC will be presented. In particular, the
novelty and innovation of the development
of the DT model will be discussed in detail.
10
Söderberg, R., Wärmefjord, K., Madrid, J., Lorin, S., Forslund, A., & Lindkvist, L. (2018). An Information and Simulation
Framework for Increased Quality in Welded Components, CIRP Annals, 67:1, 165–168.
11
Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex
product assembly shop-floor. The International Journal of Advanced Manufacturing Technology, 96(1-4), 1149-1163.
12
Parrott, A., & Warshaw, L. (2017), Industry 4.0 and the digital twin. Deloitte University Press.
13
Luo, W., Hu, T., Zhang, C., & Wei, Y. (2019). Digital twin for CNC machine tool: modeling and using strategy. Journal of Ambient
Intelligence and Humanized Computing, 10(3), 1129-1140.
14
Zhao, R., Yan, D., Liu, Q., Leng, J., Wan, J., Chen, X., & Zhang, X. (2019). Digital Twin-Driven Cyber-Physical System for
Autonomously Controlling of Micro Punching System. IEEE Access, 7, 9459-9469.
IIC Journal of Innovation
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