Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

doi:10.1007/s40436-020-00339-6

Advances in Manufacturing ›› 2021, Vol. 9 ›› Issue (3): 388-402.doi: 10.1007/s40436-020-00339-6

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

Long-Hua Xu¹, Chuan-Zhen Huang¹, Jia-Hui Niu¹, Jun Wang², Han-Lian Liu¹, Xiao-Dan Wang³

1 Center for Advanced Jet Engineering Technologies, Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education), National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University), School of Mechanical Engineering, Shandong University, Jinan, People's Republic of China;
2 School of Mechanical and Manufacturing Engineering, The University of New South Wales(UNSW), Sydney, NSW 2052, Australia;
3 Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, People's Republic of China

收稿日期:2020-09-03 修回日期:2020-11-01 出版日期:2021-09-25 发布日期:2021-09-13
通讯作者: Chuan-Zhen Huang E-mail:chuanzhenh@sdu.edu.cn
基金资助:
This study was financially supported by the National Natural Science Foundation of China (Grant No. 51675312).

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

Long-Hua Xu¹, Chuan-Zhen Huang¹, Jia-Hui Niu¹, Jun Wang², Han-Lian Liu¹, Xiao-Dan Wang³

1 Center for Advanced Jet Engineering Technologies, Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education), National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University), School of Mechanical Engineering, Shandong University, Jinan, People's Republic of China;
2 School of Mechanical and Manufacturing Engineering, The University of New South Wales(UNSW), Sydney, NSW 2052, Australia;
3 Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, People's Republic of China

Received:2020-09-03 Revised:2020-11-01 Online:2021-09-25 Published:2021-09-13
Supported by:
This study was financially supported by the National Natural Science Foundation of China (Grant No. 51675312).

摘要/Abstract

摘要： During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neurofuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into:membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual highspeed milling processes, thereby enabling sustainable and intelligent manufacturing.

The full text can be downloaded at https://link.springer.com/article/10.1007%2Fs40436-020-00339-6

关键词: Novel adaptive neuro-fuzzy inference system (NANFIS) model, Improved particle swarm optimization (IPSO) algorithm, Energy consumption, Surface roughness, Multiobjective optimization

Abstract: During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neurofuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into:membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual highspeed milling processes, thereby enabling sustainable and intelligent manufacturing.

The full text can be downloaded at https://link.springer.com/article/10.1007%2Fs40436-020-00339-6

Key words: Novel adaptive neuro-fuzzy inference system (NANFIS) model, Improved particle swarm optimization (IPSO) algorithm, Energy consumption, Surface roughness, Multiobjective optimization

Long-Hua Xu, Chuan-Zhen Huang, Jia-Hui Niu, Jun Wang, Han-Lian Liu, Xiao-Dan Wang. Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process[J]. Advances in Manufacturing, 2021, 9(3): 388-402.

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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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