Multi-objective optimisation method of cutting parameters of CNC machine tools based on improved genetic algorithm

被引：0

作者：

Wang, Zhenzhuo ^{[1
]}

Zhu, Yijie ^{[2
]}

机构：

[1] Department of Automation Engineering, Henan Polytechnic Institute, NanYang

[2] Department of Electronic Information Engineering, Henan Polytechnic Institute, NanYang

来源：

International Journal of Manufacturing Technology and Management | 2025年 / 39卷 / 3-5期

关键词：

CNC machine tools; constraints; cutting parameters; goal optimisation; improved genetic algorithm; objective function;

D O I：

10.1504/IJMTM.2025.145933

中图分类号：

学科分类号：

摘要：

To improve the energy efficiency level of CNC machine tool processing, reduce production costs and energy consumption, a multi-objective optimisation method for cutting parameters of CNC machine tools based on improved genetic algorithm is proposed. Firstly, a multi-objective optimisation model for cutting parameters of CNC machine tools was constructed with the minimum energy consumption, maximum benefit, and maximum timeliness as objective functions, combined with three constraints of cutting speed, cutting power, and feed rate. Then, the objective function values of the cutting parameters of the CNC machine tool are sorted by non-dominated sorting based on the dominated effect. Finally, an improved Pareto genetic multi-objective optimisation method is designed using the improved niche technique and the vector modulus fitness function as the criterion for outliers, in order to obtain the optimal multi-objective optimisation of the cutting parameters of the CNC machine tool. The experimental results show that the method improves the feed speed, cutting speed and cutting depth, reduces the machining energy consumption, machining cost and machining time, and has good application effect. Copyright © 2025 Inderscience Enterprises Ltd.

引用

页码：195 / 215

页数：20

共 20 条

[1]

Chen L., Wang Z., Mo Y., Improved genetic algorithms for adaptive replication crossover and mutation, Computer Simulation, 39, 8, pp. 323-326, (2022)

[2]

Fang Y., Zhao L., Lou P., Yan J., Cutting parameter optimization method in multipass milling based on improved adaptive PSO and SA, Journal of Physics: Conference Series, 1848, 1, (2021)

[3]

Hao Y., Zhu L., Yan B., Ren T., Zhao J., Ning X., Et al., Stiffness design and multi-objective optimization of machine tool structure based on biological inspiration, Journal of Vibration and Control, 29, 11–12, pp. 2774-2788, (2023)

[4]

Hongyi W., Kun Z., Xinjun Z., Limei S., Feng D., Multi-objective optimization design of cement grate cooler control system based on improved long short-term memory network, Transactions of the Institute of Measurement and Control, 43, 15, pp. 3399-3412, (2021)

[5]

Ivanov V.M., Simulation model of the cutting speed stabilization system for CNC metal-cutting machine tools, Mekhatronika Avtomatizatsiya Upravlenie, 21, 2, pp. 110-116, (2020)

[6]

Kong Y., Mei Y., Wang X., Ben Y., Solution selection from a pareto optimal set of multi-objective reservoir operation via clustering operation processes and objective values, Water, 13, 8, (2021)

[7]

Kumar H.D., Ilangovan S., Radhika N., Optimization of cutting parameters for MRR, tool wear and surface roughness characteristics in machining ADC12 piston alloy using DOE, Tribology in Industry, 42, 1, pp. 32-40, (2020)

[8]

Kumar M.V.S., Kumar M.P., Krishna S.V., Kumar K.V., Optimization of CNC turning parameters in machining en19 using face centered central composite design based RSM, International Journal of Recent Technology and Engineering, 9, 2, pp. 889-896, (2020)

[9]

Liu H., Yan F., Tian H., Towards low-carbon cities: patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-ii, Ecological Indicators, 134, 4, (2022)

[10]

Madi M., Gadallah M.H., Petkovi D., Analysis of process efficiency in laser fusion cutting and some single- and multi-objective optimization aspects, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 236, 2, pp. 589-599, (2022)

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