Feature-based optimization method integrating sequencing and cutting parameters for minimizing energy consumption of CNC machine tools

• Published in: International journal of advanced manufacturing technology Vol. 121; no. 1-2; pp. 503 - 515
• Main Authors: Feng, Chunhua, Huang, Yugui, Wu, Yilong, Zhang, Jingyang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2022; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Cutting energy; Cutting parameters; Data analysis; Energy consumption; Engineering; Genetic algorithms; Impact analysis; Industrial and Production Engineering; Machine tools; Machining; Mathematical models; Mechanical Engineering; Media Management; Multiple objective analysis; Numerical controls; Optimization; Original Article; Process parameters; Specific energy; Energy-efficient machining; Tool path optimization; Cutting parameters; Feature sequencing
• DOI: 10.1007/s00170-022-09340-8
• ISSN: 0268-3768

There is a variety of schemes when a part with multiple features is processed in CNC machines, and hence, different feature sequencing and cutting parameter selections affect not only productivity but also energy consumption. This paper concentrates on the energy-saving strategy by optimizing the feature processing sequence and machining parameters in the part processing stage through reducing the energy consumption of the non-cutting and cutting process respectively. Firstly, the energy consumption of the cutting of parts is established using unit volume cutting energy (SEC) on cutting volume, while the normal feed and the rapid feed are established in different moving axes, respectively. Meanwhile, the detailed energy model is established considering rapid feed and general feed path in the X, Y, Z + , Z − directions for analyzing the impact of feature sorting on reducing the energy consumption of non-cutting. The non-cutting energy consumption model is established involving automatic tool change, ordinary feed, and fast feed factors. Based on the developed model, the multi-objective optimization of cutting energy consumption, machining quality, and machining time is carried out by NSGA-II algorithm, and the path optimization of empty cutting energy consumption is carried out by genetic algorithm. Finally, a cutting orthogonal experiment is executed to collect energy consumption data, analyze the data, and fit the data to establish a specific energy consumption model for each processing stage. A case study of a part with eight features is used to optimize sequencing and parameters, which shows the effectiveness and validity of the proposed method.