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.

准确的智能推理系统对于智能制造至关重要。在这项研究中，为铣削工艺开发了一种新的智能推理系统，以准确预测刀具磨损并动态优化加工参数。所开发的系统由具有改进的粒子群优化（IPSO）学习算法的自学习算法、由改进的基于案例的推理（ICBR）方法确定的预测模型以及包含改进的自适应神经模糊推理系统（IANFIS）的优化模型组成。 ）和IPSO。实验结果表明IPSO算法表现出最好的全局收敛性能。据观察，ICBR 方法在预测刀具磨损方面比标准 CBR 方法具有更好的性能。IANFIS 模型与 IPSO 相结合，实现了多个目标的优化，从而生成最佳的铣削参数。本文提供了一种为可持续和智能制造开发精确智能推理系统的实用方法。