基于价值评估的废旧产品拆卸序列与拆卸深度决策  

Decision of Disassembly Sequence and Disassembly Depth of Waste Products Based on Value Evaluation

作　　者：张华[1,2] 殷俊鸿 鄢威[1,3] 马峰 江志刚[1,2] 朱硕 ZHANG Hua;YIN Junhong;YAN Wei;MA Feng;JIANG Zhigang;ZHU Shuo(Academy of Green Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan,430081,China;Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan,430081,China;School of Automatic and Traffic Engineering,Wuhan University of Science and Technology,Wuhan,430081,China)

机构地区：[1]武汉科技大学绿色制造工程研究院,武汉430081 [2]武汉科技大学机械传动与制造工程湖北省重点实验室,武汉430081 [3]武汉科技大学汽车与交通工程学院,武汉430081

出　　处：《组合机床与自动化加工技术》2025年第1期143-149,共7页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(51975432,52375508);武汉科技大学“十四五”湖北省优势特色学科(群)项目(2023B0405);湖北省科技重大项目资助项目(2023BCA006)。

摘　　要：为解决拆卸序列规划(disassembly sequence planning, DSP)中存在的复杂模型构建难题、组合爆炸以及拆解深度决策不合理等问题,提出了一种基于知识图谱属性图模块化的改进方法实现废旧产品拆卸信息的获取,并提出了一种基于剩余回收效益评估的拆解序列与拆解深度综合决策方法。首先,通过分析废旧产品的拆卸特征以及产品内部零部件的信息和拆卸联接关系,构建支持拆解的模块化属性图模型;其次,采用组合赋权及改进的TOPSIS灰色关联分析法构建了零件回收综合评价模型的多属性决策模型,对产品综合内部零部件剩余回收效益进行排序;再次,提出了基于改进遗传-粒子群算法的完全拆解序列生成方法,并结合剩余回收效益值进行废旧产品零件的拆解深度决策。以废旧汽车动力电池包为例对上述模型和方法进行了验证,证明了该方法的可行性和高效性。The problems of complex model construction,combination explosion and unreasonable decision of disassembly depth in disassembly sequence planning were studied,and an improved method based on knowledge graph attribute graph modularization was proposed to optimize disassembly sequence and to evaluate the residual value of product parts for decision of disassembly depth.By analyzing the disassembly characteristics of waste products and the information and disassembly connection relationship of the internal parts of the products,it is clearly expressed in the model,and the modular concept is introduced to form a modular attribute diagram model supporting disassembly.At the same time,the multi-attribute decision of the comprehensive evaluation model of parts recycling was constructed by using the combined weighting and improved TOPSIS grey correlation analysis method,and the residual recovery benefits of the integrated internal parts of the product were sorted.An improved genetic particle swarm optimization algorithm was used to optimize the disassembly sequence,and the disassembly depth decision was optimized.The high efficiency and feasibility of the proposed method are proved by the example of used automobile power battery pack.

关 键 词：废旧产品 知识图属性图模型 零件回收综合评价 拆卸序列优化 拆卸深度决策 

分 类 号：TH165[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]