Research On The Optimization Of The Process For Dynamic Disturbance In The Workshop

In the fierce market competition environment,the optimization of processing is helpful for enterprises to respond quickly and flexibly to the needs of market diversification and customization with the lowest cost,the highest efficiency,so as to improve the competitiveness of enterprises.In the process of production,a large number of machining equipment are used,among which numerical control equipment is the most representative.In the process of CNC equipment machining,it is necessary to consider not only the reasonable machining parameters for a single device,but also the scheduling problem of multiple devices in the workshop.Therefore,the optimization of machining parameters and workshop job scheduling should be taken into account at the same time,which plays an important role in improving the performance of CNC machining.In addition,in the actual production process,there are usually order urgent,order insertion,machine failure and other dynamic events.Therefore,optimizing the machining parameters and job shop scheduling scheme is very important to quickly respond to these dynamic events.With support of the National Natural Science Foundation of China project "Research on Energy Consumption Optimization of NC Machining Process Based on Co-evolutionary Algorithm"(61803169).It takes manufacturing workshop CNC equipment as the research object,and considers the dynamic disturbance of the manufacturing process.This paper studies the process optimization problem of dynamic disturbance in the workshop.Firstly,the application status of cloud manufacturing model is discussed.On this basis,considering the reality that there are a lot of heterogeneous computing resources in the workshop,combining cloud computing and edge computing technology,the cloud-edge collaborative architecture is proposed.In addition,the edge layer computing task unloading and edge computing resource scheduling strategy is developed.Secondly,the dynamic flexible job shop scheduling problem based on fault perception is described.Based on this,the fault probability calculation method of job scheduling scheme is proposed.Taking the spindle speed,feed rate,cutting depth and cutting width of CNC machine tool as the optimization variables,and taking the minimum fault probability,the minimum completion time and the minimum cutting energy consumption as the optimization multi-objective model,NSGA-? was used to solve the multi-objective model.A benchmark test case and an agricultural machinery component processing example are used to verify that the proposed method can obtain a robust initial job scheduling scheme.Then,considering the relationship between machining parameters and makespan,cutting energy consumption and tool wear,a multi-objective optimization model with minimum makespan,minimum cutting energy consumption and minimum tool wear was established.In addition,considering the dynamic nature of manufacturing process,an improved dynamic double-archiving algorithm was proposed to solve the dynamic optimization problem of machining parameters by discussing the classification of dynamic events.By adding or subtracting optimization objectives in the solution process to respond to the corresponding dynamic events(for example,order expediting,tool fracture,etc.),the stability of the workshop manufacturing process can be increased.The effectiveness and versatility of the proposed method are verified based on test cases.Finally,considering the interaction between machining parameters and shop scheduling on energy efficiency,production cost and production efficiency,the integrated optimization problem of machining parameters and shop scheduling was described in detail.Based on this,an integrated optimization architecture of machining parameter-shop scheduling with minimum makespan as the optimization objective was proposed to respond to dynamic events,and the proposed strategy was calculated by island-pool hybrid population distributed evolutionary algorithm.The stability and effectiveness of the framework are proved by experiments.