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

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.

Keywords

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

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Funding

Independent Training and Innovation Team Project of Jinan Science and Technology Bureau(2019GXRC009); National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52275464); Natural Science Foundation for Young Scientists of Hebei Province(E2022203125)