Research On Energy Consumption Optimization In Manufacturing Processes

Manufacturing industry consumes a significant amount of energy while transforming resources into products or service.Machining is the major process in manufacturing industry,and machine tools are the core energy-consuming resources when performing manufacturing processes.To cope with global warming and improve the sustainability of manufacturing industry,study on the energy consumption optimization in manufacturing processes for mechanical parts is beneficial to saving energy sources and realizing low carbon manufacturing.Hence,an energy-saving method by the integration of planning and scheduling(IPPS)was proposed in this thesis,and several key issues,including the modeling and quantitative evaluation of machine tool state,the quantitative calculation of the energy consumption in manufacturing processes,and the IPPS for low carbon manufacturing were studied respectively.To solve the problem of the modeling and quantitative evaluation of machine tool state,the difference between the capacity and the state of manufacturing resources was firstly discussed,and the machine tool state is described in a formal model.Then,the index system for the quantitative analysis of machine tool state was established.According to the established machine tool state model and the evaluation index system,two-level fuzzy synthesis judgments method based on analytical hierarchical process(AHP)was applied to evaluate the machine tool state and the state score of each machine tool was obtained to support resource decision in process planning.To solve the problem of calculating the energy consumption in manufacturing processes,some reasonable hypotheses were given in the defined research scope,and the Therblig-based energy consumption model was used to establish a theoretical energy calculation model when mechanical parts were manufactured.To solve the problem of IPPS for low carbon manufacturing,this thesis carried out the research based on nonlinear process planning(NLPP)model,a typical IPPS model.The characteristics of NLPP were firstly analyzed,and the building process of the AOS tree expressing NLPP as well as the generation process of the alternative process plans based on NLPP AOS tree was described with the AOS tree theory.Then,based on current flexible job shop scheduling problem(FJSP)research,a mathematical model of IPPS was established through reasonable hypotheses,aiming at the selected machines with the best average state,the shortest makespan,and the minimum energy consumption when manufacturing mechanical parts.The intelligent algorithm-based implementation approach for IPPS was used,and the established integration model was solved by an approach based on the genetic algorithm with layered coding genes in chromosomes.Consequently,given the flexible process schemes of each part generated by NLPP,the established integration model can help choose the suitable process plan and machines for each part and generate the scheduling scheme simultaneously by making trade-offs among different optimization objectives.In order to verify the energy-saving effect of the proposed method,a case study was carried out in an enterprise.Through the comparison of the energy consumption in manufacturing processes in IPPS mode and that in the traditional sequential mode when manufacturing a batch of mechanical parts,the energy-saving effect of the presented method was clearly verified.Moreover,the feasibility of the implementation approach of IPPS based on the genetic algorithm with layered coding genes in chromosomes was validated by making comparative experiments on a set of benchmark problem instances.In the end,the research contents and the innovation points of this thesis were summarized,and the future research directions were discussed.