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

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 algori...

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Bibliographic Details
Published in:Advances in manufacturing 2024-03, Vol.12 (1), p.76-93
Main Authors: Xu, Long-Hua, Huang, Chuan-Zhen, Wang, Zhen, Liu, Han-Lian, Huang, Shui-Quan, Wang, Jun
Format: Article
Language:English
Subjects:
Algorithms
Control
Engineering
Fuzzy logic
Intelligent manufacturing systems
Machine learning
Machines
Manufacturing
Mechatronics
Milling (machining)
Nanotechnology and Microengineering
Optimization models
Particle swarm optimization
Performance prediction
Prediction models
Process parameters
Processes
Reasoning
Robotics
Tool wear
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Summary: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.
ISSN:2095-3127
2195-3597
DOI:10.1007/s40436-023-00451-3