• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2024, Vol. 60 ›› Issue (24): 365-376.doi: 10.3901/JME.2024.24.365

• 交叉与前沿 • 上一篇    

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工序随机失效及其修复条件下的退役机电产品拆解工艺路线决策研究

任亚平1,2,3, 任莹4, 郭洪飞1,2,3,5,6, 张超勇7   

  1. 1. 暨南大学广东省大湾区智慧物流国际科技合作基地 珠海 519070;
    2. 暨南大学智能科学与工程学院 珠海 519070;
    3. 暨南大学物联网与物流工程研究院 珠海 519070;
    4. 暨南大学管理学院 广州 510632;
    5. 内蒙古科学技术研究院先进制造技术研究所 呼和浩特 010020;
    6. 内蒙古工业大学数据科学与应用学院 呼和浩特 010051;
    7. 华中科技大学数字制造装备与技术国家重点实验室 武汉 430074
  • 收稿日期:2023-12-28 修回日期:2024-05-08 出版日期:2024-12-20 发布日期:2025-02-01
  • 作者简介:任亚平,男,1995年出生,博士,副教授,硕士研究生导师。主要研究方向为可持续设计与制造、产品拆解决策理论与方法、优化算法设计及应用。E-mail:renyp1@163.com;郭洪飞(通信作者),男,1980年出生,博士,教授,博士研究生导师。主要研究方向为智能制造、工业物联网、数字孪生等。E-mail:ghf-2005@163.com
  • 基金资助:
    国家重点研发计划资助项目(2023YFB3406900)。

Research on Decision-making of Disassembly Process Route for End-of-life Mechanical and Electrical Products under Random Failure and Repair Conditions

REN Yaping1,2,3, REN Ying4, GUO Hongfei1,2,3,5,6, ZHANG Chaoyong7   

  1. 1. International Science and Technology Cooperation Base for Intelligent Logistics in the Greater Bay Area of Guangdong Province, Jinan University, Zhuhai 519070;
    2. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070;
    3. Institute of Physical Internet, Jinan University, Zhuhai 519070;
    4. School of Management, Jinan University, Guangzhou 510632;
    5. Inner Mongolia Academy of Science and Technology, Institute of Advanced Manufacturing Technology, Hohhot 010020;
    6. Inner Mongolia University of Technology, College of Data Science and Application, Hohhot 010051;
    7. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074
  • Received:2023-12-28 Revised:2024-05-08 Online:2024-12-20 Published:2025-02-01

摘要: 对退役机电产品进行合理拆解并回收利用不仅可以促进资源的回收利用,还有利于减少环境污染与安全隐患等不良后果。针对退役机电产品拆解过程中时常出现的工序失效行为,从其内部零件失效角度及拆解工艺流程执行层面,系统性研究拆解工序的随机失效行为机理,包括多工序间失效因果关联机理的准确解析和单个工序随机失效(条件)概率的精准估计,建立工序随机失效及其修复条件下的拆解工艺路线决策模型,并提出贝叶斯网络-遗传混合算法,通过贝叶斯网络分别获取拆解工序间的失效因果关联和各拆解工序的随机失效条件概率,在此基础上运用遗传算法高效求解全局近似最优拆解工艺路线。最后,选择退役动力电池作为实际案例,对提出的模型和算法进行验证,试验结果表明通过提出的模型和方法求出的失效修复条件下的拆解工艺路线,其回收效益明显优于一般随机失效条件下的拆解工艺路线。

关键词: 退役机电产品, 拆解工序随机失效, 贝叶斯网络, 失效修复, 拆解工艺路线决策

Abstract: The rational disassembly and recycling of retired mechanical and electrical products not only promotes resource recycling but also helps to reduce environmental pollution and associated risks. Responding to the frequently occurring failure behaviors during the disassembly process of retired mechanical and electrical products, the stochastic failure mechanisms are systematically investigated from the perspective of internal component failures and the execution level of disassembly procedures. This mechanisms are divided into an accurate analysis of the causal relationships between failures among multiple operations and a precise estimation of the conditions probabilities of stochastic failures in individual operation. The decision model of disassembly process route under stochastic failures and repair conditions is established and a hybrid Bayesian network-genetic algorithm is proposed with the aim of obtaining the causal relationships among disassembly operations and the conditional probabilities of stochastic failures in each disassembly operation, efficiently solving the global approximate optimal disassembly process route. Finally, a case study is conducted using end-of-life power batteries to validate the proposed model and algorithm. Through the experimental results, it can be seen that the recycling benefits of the disassembly process routes under failure repair conditions derived from the proposed model and method are significantly better than those of the disassembly process routes under general failure conditions.

Key words: retired mechanical and electrical products, stochastic failure of disassembly operation, bayesian network, failure repair, decision-making of disassembly process route

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