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

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Long-Hua Xu, Chuan-Zhen Huang, Zhen Wang, Han-Lian Liu, Shui-Quan Huang, Jun Wang
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引用次数: 0

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.

Abstract Image

Abstract Image

智能铣削刀具磨损预测与参数优化的智能推理系统
精确的智能推理系统对智能制造至关重要。本研究针对铣削加工过程开发了一种新的智能推理系统,用于准确预测刀具磨损并动态优化加工参数。所开发的系统包括改进粒子群优化(IPSO)学习算法的自学习算法、基于改进案例推理(ICBR)方法确定的预测模型,以及包含改进自适应神经模糊推理系统(IANFIS)和 IPSO 的优化模型。实验结果表明,IPSO 算法的全局收敛性能最好。与标准 CBR 方法相比,ICBR 方法在预测刀具磨损方面表现更佳。IANFIS 模型与 IPSO 的结合实现了多目标优化,从而产生了最佳铣削参数。本文为可持续智能制造提供了一种开发精确智能推理系统的实用方法。
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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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