Joint optimization of feature sequences and toolpath strategies in multi-feature workpiece machining for minimizing energy consumption and processing time

The machining feature energy consumption (MFEC) optimization of workpieces is of vital importance to improve the overall energy efficiency of manufacturing systems. The MFEC is significantly affected by the feature processing sequence (FPS) and toolpath strategy (TPS). However, existing studies optimized the FPS and TPS independently and failed to consider the combined impact of flexible FPS and TPS on MFEC. To this end, a joint optimization approach for minimizing the machining energy consumption and processing time (PT) of multiple features considering flexible FPS and TPS is proposed in this paper. Firstly, the dynamic energy characteristics of multiple features are analyzed and then a dual-objective optimization model is developed by taking the total MFEC and PT as the objectives under the constraint of workpiece quality. Finally, a Non-dominated sorting genetic algorithm with tabu search strategy (TS-NSGA-II) is designed to solve the joint optimization model of the FPS and TPS. The case study results show that the proposed approach could lead to a 14.91% and 11.08% decrease in MFEC when compared with the empirical method and separated optimization method. This approach can provide effective guidance for process planners to minimize energy consumption for machining multiple-feature workpieces by jointly adjusting FPS and TPS.