Minimising The Machining Energy Consumption For A Part By Sequencing The Features

Increasing energy price and emission reduction requirements are new challenges faced by modern manufacturers.A considerable amount of their energy consumption is attributed to the machining energy consumption of the machine tool(MTE),including the cutting and non-cutting energy consumption(CE and NCE).Thus,reducing the MTE becomes significant for alleviating the energy crisis and environmental pollution.The value of the MTE is affected by the processing sequence of features of a specific part(PFS)because the plans of cutting and non-cutting operations,including the tool path,tool change,and change of spindle rotation speed,will vary based on the different PFS.Based on this discovery,a novel management ap-proach is investigated to reduce the MTE by merely adjusting the PFS,without purchasing addi-tional energy-saving and energy-recycling devices.Thus,this article aims to search for the op-timal PFS that results in the minimisation of the MTE.Then,an energy-aware feature sequenc-ing problem is introduced that minimises the MTE.The energy-aware feature sequencing problem considers two types of parts,including the part without the feature intersection and the part with the feature intersections.By analysing and characterising the MTE while machining the part without the feature intersection,only its NCE is affected by the PFS.Thus,an NCE model is developed for its enery-aware feature sequenc-ing problem,including three sub-models for the energy consumption of tool path,tool change,and change of spindle rotation speed.By analysing the MTE while machining the part with the feature intersections,both the CE and the NCE are affected by the PFS.Thus,a CE model is developed,including two sub-models for the specific energy consumption and the actual cutting volume,and integrated with the existing NCE model to obtain the completed MTE model.By analysing the machining time of the machine tool(MTT)while machining the part,the MTT is also affected by the PFS,and it validates the possibility that the reduction of the MTE can result in the increase of the MTT.Thus,the bi-objective model that minimises both the MTT and the MTE is developed.To solve the single objective model that minimises the MTE,two optimisation approaches,including Depth-First Search(DFS)and Genetic Algorithm(GA),are employed.They aim to obtain the optimal PFS that results in the minimisation of the MTE within a tolerant computa-tion time.To solve the bi-objective model that minimises both the MTT and the MTE,two op?timisation approaches,including Non-dominated Inserting Enumeration Algorithm(NIEA)and Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),are proposed and employed.They aim to obtain the non-dominated set of Pareto-optimal solutions,and an optimal solution represents a PFS that results in the optimal trade-off between the MTE and the MTT.Finally,the optimal PFS that results in the minimisation of the MTE under the MTT constraint can be selected.A case study is conducted to demonstrate the models and optimisation approaches.Four-teen parts that have 8 to 15 actual features are asummed to be processed by a machining centre(XHF-714F).The experiment results show that the optimal or near-optimal PFSs can be ob-tained.By comparing DFS with GA for the single objective model,GA is recommended because it requires much shorter computation time than that of DFS with little sacrifice in the solution quality.By comparing NIEA with NSGA-Ⅱ for the bi-objective model,NIEA is recommended for the part with fewer than 12 actual features because it can always obtain the global optimal solution set with consuming shorter computation time than that of NSGA-Ⅱ.For the part with 12 or more features,the computation time of NIEA becomes longer than that of NSGA-Ⅱ,and a trade-off between the computation time and the solution quality should be made when selecting an algorithm.By comparing the results with a traditional technique,about 14.13%MTE and 20.69%MTT can be reduced,which validates the effectiveness of the developed approaches.