Knowledge Graph Construction of End-of-Life Electric Vehicle Batteries for Robotic Disassembly

Wang, Jiangbiao; Huang, Jun; Li, Ruiya

doi:10.3390/app132413153

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…It significantly increased the flexibility of the disassembly operation. Wang et al 2023 proposed a SLIB disassembly planning method based on semantic ontology structure and knowledge graph [29]. Zhang et al 2023 designed a knowledge-driven flexible human-robot hybrid disassembly line [30].…”

Section: Introductionmentioning

confidence: 99%

Research on the Human–Robot Collaborative Disassembly Line Balancing of Spent Lithium Batteries with a Human Factor Load

Jiao,

Feng,

Yuan

2024

Batteries

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The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly.

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Section: Introductionmentioning

confidence: 99%