首页|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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Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelli-gent 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 exhib-ited the best global convergence performance.The ICBR method was observed to have a better performance in pre-dicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimiza-tion of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to devel-oping accurate intelligent reasoning systems for sustainable and intelligent manufacturing.
Long-Hua Xu、Chuan-Zhen Huang、Zhen Wang、Han-Lian Liu、Shui-Quan Huang、Jun Wang
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School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,People's Republic of China
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
Institute of Manufacturing Technology,Guangdong University of Technology,Guangzhou 510006,People's Republic of China
国家自然科学基金河北省自然科学基金青年科学基金Scientific Research Project for National High-level Innovative Talents of Hebei Province Fulltime Introduction国家自然科学基金
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