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Title: Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human–Robot Collaboration
Abstract Disassembly is an essential step for remanufacturing end-of-life (EOL) products. Optimization of disassembly sequences and the utilization of robotic technology could alleviate the labor-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human–robot collaboration. The proposed framework combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations between human and robot. A multi-attribute utility function is used to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is used to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot.  more » « less
Award ID(s):
2026276 2026533
NSF-PAR ID:
10465129
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Journal of Mechanical Design
Volume:
145
Issue:
2
ISSN:
1050-0472
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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