带有人为因素负载的人机协作锂电池拆卸线平衡研究

Jie Jiao, Guangsheng Feng, Gang Yuan
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引用次数: 0

摘要

废旧锂电池的拆解是实现产品高效回收的前提,是再制造的第一个环节,其操作形式已从传统的人工拆解逐渐转变为机器人辅助的人机协同拆解。机器人具有强大的承载能力,能稳定地完成重复性任务,而人类则拥有主观经验和隐性知识。这使得拆卸活动更具适应性和人性化。然而,现有的人机协作拆卸研究忽略了随时间变化的人类条件,如安全、认知行为、工作量和人类姿势变化。首先,为了克服现有研究的局限性,我们提出了一种平衡人机协作拆卸线的模型,该模型考虑了与人的参与相关的负载因素。这就需要开发一个多目标数学模型,旨在最大限度地减少拆卸线的周期时间及其相关成本,同时降低综合平滑指数。其次,我们提出了一种改进的多目标果蝇优化算法。该算法结合了混沌理论和全局合作机制,以提高算法的性能。我们添加了高斯突变和拥挤距离,以高效解决离散优化问题。最后,我们通过求解和分析梅赛德斯电池组拆卸的实例,证明了改进的多目标果蝇优化算法的有效性和灵敏度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Human–Robot Collaborative Disassembly Line Balancing of Spent Lithium Batteries with a Human Factor Load
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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