Abstract

Compacted graphite iron (CGI) plays an important role in contemporary manufacturing of automobile engine, and coated tool is the best choice for milling of CGI. But studies about the estimation of the wear of coated tool are still rare and incomplete. As tool wear is the main factor that affects the quality of machined surface, in this study, we proposed an intelligent model-adaptive neuro fuzzy inference system (ANFIS) to estimate the tool wear, and ANFIS was learned by the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm is easy to fall into the local minimum, the vibration and communication particle swarm optimization (VCPSO) algorithm was proposed by introducing the self-random vibration and inter-particle communication mechanisms. Besides that, to obtain the optimal combination of milling parameters, the multi-objective optimization based on minimum cutting power, surface roughness and maximum material removal rate (MRR) was studied using VCPSO algorithm. The experimental results showed that the ANFIS learned by VCPSO algorithm (ANFIS-VCPSO) has better performance for the estimation of tool wear compared with other intelligent models. The VCPSO algorithm was tested using Benchmark functions, and the results showed VCPSO algorithm has the global optimization ability. Meantime, the best combinations of milling parameters under different tool wear status were obtained through VCPSO algorithm. The proposed ANFIS-VCPSO model as a new intelligent model can be applied for real-time tool wear monitoring, which can improve the machining efficiency and prolong tool life. In order to meet the requirements of green and intelligent manufacturing, the best combination of milling parameters was also obtained in this work.

中文翻译：

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基于面向智能加工的新型ANFIS-PSO方法的刀具磨损估计和切削参数优化

摘要

压实石墨铁（CGI）在现代汽车发动机制造中起着重要作用，而涂层工具是CGI铣削的最佳选择。但是，关于涂层刀具磨损估算的研究仍然很少，而且还不完整。由于刀具磨损是影响加工表面质量的主要因素，因此在本研究中，我们提出了一种智能模型自适应神经模糊推理系统（ANFIS）来估算刀具磨损，并且通过改进的粒子群优化算法学习了ANFIS（ PSO）算法。由于粒子群优化算法很容易陷入局部最小值，通过引入自随机振动和粒子间通信机制，提出了振动与通信粒子群优化（VCPSO）算法。除此之外，为了获得铣削参数的最佳组合，利用VCPSO算法研究了基于最小切削力，表面粗糙度和最大材料去除率（MRR）的多目标优化方法。实验结果表明，与其他智能模型相比，VCPSO算法（ANFIS-VCPSO）学习的ANFIS在估计刀具磨损方面具有更好的性能。利用Benchmark函数对VCPSO算法进行了测试，结果表明VCPSO算法具有全局优化能力。同时，通过VCPSO算法获得了不同刀具磨损状态下的铣削参数的最佳组合。提出的ANFIS-VCPSO模型作为一种新的智能模型，可用于刀具磨损的实时监测，提高了加工效率，延长了刀具寿命。为了满足绿色智能制造的要求，