数字孪生环境下考虑可靠性的多目标智能排序策略

Multi-objective intelligent sorting strategy considering reliability in digital twin environment

面对智能制造模式下,在多品种、变批量的高复杂度制造场景中,存在各工装/夹具、工具/量具和系统用例切换频繁而造成的设备的动态可靠性问题,基于可靠性数字孪生的车间监控系统,通过对车间设备多部件的可靠性问题进行感知、分析和处理,对可能发生的可靠性问题进行预维护来保障设备性能可靠性,提出了可靠性维护时长(Reliability Maintenance Duration,RMD)和可靠性加工系数(Reliability Processing Coefficient,RPC),研究考虑RMD的工序排布,除了保证设备可靠性,同时建立了相应的作业排序策略,构建了引入RPC关键变量的多目标车间调度数学模型,并基于非支配排序遗传算法Ⅱ(Nondominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)实现了某军工装备制造车间调度问题的高效求解,验证了该方法的可行性和有效性。

In the intelligent manufacturing mode,the dynamic reliability problem of equipment is caused by frequent switching of tooling/fixture,tool/measuring tool and system use case in the high-complexity manufacturing scene with multiple varieties and varying batches.The workshop monitoring system based on reliability digital twinning is based on the perception,analysis and treatment of the reliability problems of multiple components of the workshop equipment,and the possible reliability problems are pre-maintained to ensure the performance reliability of the equipment.The Reliability Maintenance Duration(RMD)and Reliability Processing Coefficient(RPC)were proposed,and the process arrangement of RMD was studied.In addition to ensuring the reliability of equipment,the corresponding job ranking strategy was established,and a multi-objective mathematical model of shop scheduling was constructed by introducing RPC key variables.Based on Nondominated Sorting Genetic AlgorithmⅡ(NSGA-Ⅱ),the efficient solution of a military equipment manufacturing shop scheduling problem was realized,which verified the feasibility and effectiveness of the method.