Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

doi:10.1007/s40436-023-00451-3

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1): 76-93.doi: 10.1007/s40436-023-00451-3

• • 上一篇

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Long-Hua Xu¹, Chuan-Zhen Huang¹, Zhen Wang¹, Han-Lian Liu², Shui-Quan Huang¹, Jun Wang³

1. School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China;
2. Center for Advanced Jet Engineering Technologies (CaJET), 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, 250061, People's Republic of China;
3. Institute of Manufacturing Technology, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China

收稿日期:2022-09-10 修回日期:2022-11-10 发布日期:2024-03-14
通讯作者: Chuan-Zhen Huang,E-mail:huangchuanzhen@ysu.edu.cn E-mail:huangchuanzhen@ysu.edu.cn
作者简介:Long-Hua Xu is the lecturer of the school of mechanical engineering, Yanshan University. His research interests include intelligent manufacturing system, ceramic cutting tools, clean and high speed machining process;
Chuan-Zhen Huang is the professor and doctoral supervisor of the school of mechanical engineering, Yanshan University. His research interests are ultraprecision machining process, ceramic cutting tools and structural ceramics, intelligent manufacturing and 3D-bioprinting;
Zhen Wang is the lecturer of the school of mechanical engineering, Yanshan University. His research interest is biological 3D printing;
Han-Lian Liu is the professor and doctoral supervisor of the school of mechanical engineering, Shandong University. Her research interests are research and development of high performance ceramic cutting tools, and high speed machining process;
Shui-Quan Huang is the lecturer of the school of mechanical engineering, Yanshan University. His research interests are study on the mechanism and technology of high efciency ultra precision machining of brittle materials and nano lubrication mechanism and technology;
Jun Wang is the professor and doctoral supervisor of the institute of manufacturing technology, Guangdong University of Technology. His research interests include machining process simulation, laser water jet and ultra-precision grinding.
基金资助:
This work was financially supported by the National Natural Science Foundation of China (Grant No. 52275464), the Natural Science Foundation for Young Scientists of Hebei Province (Grant No. E2022203125), the Scientific Research Project for National High-level Innovative Talents of Hebei Province Full-time Introduction (Grant No. 2021HBQZYCXY004), and the National Natural Science Foundation of China (Grant No. 52075300).

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Long-Hua Xu¹, Chuan-Zhen Huang¹, Zhen Wang¹, Han-Lian Liu², Shui-Quan Huang¹, Jun Wang³

1. School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China;
2. Center for Advanced Jet Engineering Technologies (CaJET), 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, 250061, People's Republic of China;
3. Institute of Manufacturing Technology, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China

Received:2022-09-10 Revised:2022-11-10 Published:2024-03-14
Contact: Chuan-Zhen Huang,E-mail:huangchuanzhen@ysu.edu.cn E-mail:huangchuanzhen@ysu.edu.cn
Supported by:
This work was financially supported by the National Natural Science Foundation of China (Grant No. 52275464), the Natural Science Foundation for Young Scientists of Hebei Province (Grant No. E2022203125), the Scientific Research Project for National High-level Innovative Talents of Hebei Province Full-time Introduction (Grant No. 2021HBQZYCXY004), and the National Natural Science Foundation of China (Grant No. 52075300).

摘要/Abstract

摘要： Accurate intelligent reasoning systems are vital for intelligent manufacturing. In this study, a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters. The developed system consists of a self-learning algorithm with an improved particle swarm optimization (IPSO) learning algorithm, prediction model determined by an improved case-based reasoning (ICBR) method, and optimization model containing an improved adaptive neural fuzzy inference system (IANFIS) and IPSO. Experimental results showed that the IPSO algorithm exhibited the best global convergence performance. The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods. The IANFIS model, in combination with IPSO, enabled the optimization of multiple objectives, thus generating optimal milling parameters. This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00451-3

关键词: Improved particle swarm optimization (IPSO) algorithm, Improved case-based reasoning (ICBR) method, Adaptive neural fuzzy inference system (ANFIS) model, Tool wear prediction, Intelligent manufacturing

Abstract: Accurate intelligent reasoning systems are vital for intelligent manufacturing. In this study, a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters. The developed system consists of a self-learning algorithm with an improved particle swarm optimization (IPSO) learning algorithm, prediction model determined by an improved case-based reasoning (ICBR) method, and optimization model containing an improved adaptive neural fuzzy inference system (IANFIS) and IPSO. Experimental results showed that the IPSO algorithm exhibited the best global convergence performance. The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods. The IANFIS model, in combination with IPSO, enabled the optimization of multiple objectives, thus generating optimal milling parameters. This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00451-3

Key words: Improved particle swarm optimization (IPSO) algorithm, Improved case-based reasoning (ICBR) method, Adaptive neural fuzzy inference system (ANFIS) model, Tool wear prediction, Intelligent manufacturing

Long-Hua Xu, Chuan-Zhen Huang, Zhen Wang, Han-Lian Liu, Shui-Quan Huang, Jun Wang. Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling[J]. Advances in Manufacturing, 2024, 12(1): 76-93.

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Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

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