基于反演再设计的机床装备再制造可靠性增长问题研究

Based on the mechanical structure, failure mode and reliability growth principles of remanufactured machine tools, reliability growth issues for machine tool remanufacturing are analyzed from the aspect of inverse redesign in this project. Firstly, reliability growth principles of remanufactured equipment is investigated based on experiment analysis, including reliability simulation model of remanufactured function components based on parameter model and Monte Carlo method, FMECA of remanufactured machine tools and reliability growth evaluation model of remanufactured machine tools based on AMSAA model. Secondly, inverse redesign of remanufactured machine tools is defined, which can look for model design parameters from behavior parameters. In addition, a solution model of inverse redesign for remanufactured machine tools based on Artificial Neural Network (ANN) or other intelligent algorithms is proposed. Then, based on the theory of inverse redesign, inverse optimization method of remanufacturing process parameters for reliability growth is proposed and inverse optimization method of structure redesign parameters for reliability growth is proposed, respectively from the aspects of remanufacturing process and equipment structure. Finally, the proposed method of reliability growth is validated with the remanufacturing practices of machine tools. The theory and method of reliability growth for machine tool remanufacturing based on inverse redesign will be proposed which is conducive to improve the quality and reliability of remanufactured machine tools.

在分析再制造机床装备结构特征、故障模式及可靠性增长规律的基础上，本项目从反演再设计的角度对机床装备再制造可靠性增长问题展开研究：探索再制造机床装备可靠性增长规律，提出一种基于参数化及Monte Carlo方法的再制造功能部件可靠性仿真分析模型，并在开展再制造机床装备FMECA分析的基础上，提出一种基于AMSAA模型的再制造机床装备可靠性增长评估方法；提出“由行为参数求模型参数”的再制造机床装备反演再设计的概念及理论模型，并引入人工神经网络等智能求解算法；基于反演再设计理论，分别从再制造工艺、再制造机床装备结构的角度出发，形成面向可靠性增长的再制造工艺参数反演优化方法、面向可靠性增长的再制造机床装备结构再设计参数反演优化方法；调研机床装备再制造产业，开展实践验证与应用研究。本项目将形成一套基于反演再设计的机床装备再制造可靠性增长理论与方法，有利于提升再制造机床质量及可靠性。

可靠性是再制造机床安全服役的关键指标。本项目在分析再制造机床装备结构特征、故障模式及可靠性增长规律的基础上，从反演再设计的角度对机床装备再制造可靠性增长问题展开研究：1）搭建了再制造功能部件及机床装备可靠性增长试验平台，分析了再制造机床装备服役过程故障模式及可靠性增长规律，构建了基于参数化及蒙特卡洛方法的再制造功能部件可靠性分析模型，并提出了基于AMSAA模型的再制造机床装备可靠性增长评估的基本步骤及方法。2）提出了“由行为参数求模型参数”的再制造机床装备反演再设计的概念，构建了基于人工神经网络的再制造机床装备反演再设计求解方法。3）提出了基于灰色关联分析与自适应混沌差分进化算法的激光熔覆再制造工艺参数反演优化方法、响应面法与粒子群算法集成的激光熔覆再制造工艺参数优化方法，形成了一种基于GO法与等比例分配法的零部件再制造工艺可靠性分配与反演优化方法，可实现机床再制造工艺可靠性提升。4）提出了基于模糊评价重要度与故障影响的再制造机床可靠性分配与优化方法、基于神经网络和再制造因子的再制造机床可靠性分配与优化方法，可实现面向可靠性的再制造机床结构再设计参数的反演优化。5）所形成的理论与方法，已在机床再制造过程中展开了应用，并逐步扩展到泵类设备再制造。围绕上述研究内容，共发表或录用学术论文20篇（均为第一标注），其中SCI/EI论文13篇；申请发明专利3项、授权发明专利2项，申请软件著作权1项；项目研究成果获2018年度中国产学研合作创新成果一等奖；培养绿色制造与再制造等方向硕士研究生3名。本项目属于应用基础研究，形成了一套基于反演再设计的机床装备再制造可靠性增长理论与方法，有助于提升再制造机床装备的质量及可靠性，为推动我国装备再制造产业高质量发展提供理论支撑。