Monitoring method of milling tool wear status based on IPSO optimized LS-SVM

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (22) : 137-143.

NIE Peng1，MA Yao1，GUO Yongyi2，LI Zhengqiang1，SHAN Chunfu2

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Abstract

Tool wear is an important part of machining. Tool wear can affect machining accuracy and efficiency, so it is very important to accurately grasp the tool wear status during machining. An improved particle swarm optimization (IPSO) algorithm optimized the least square support vector machine (LS-SVM) tool wear status monitoring method was proposed.The cutting force signals during milling were collected, and the feature extraction and dimension reduction were carried out by empirical mode decomposition (EMD) and principal component analysis（PCA） respectively. The IPSO algorithm was proposed by improving the updating strategy of particle velocity and position and weight search method. The IPSO algorithm iteratively optimized the penalty factor and kernel parameters of the LS-SVM recognition model by calculating the fitness function.The results show that the features with dimensionality reduction are highly separable, and the optimization ability of IPSO algorithm is stronger than that of traditional PSO and LdPSO algorithms. When the features with dimensionality reduction are taken as the input of IPSO-LS-SVM model, the recognition accuracy and efficiency of the model are better than that of LS-SVM model optimized by PSO and LdPSO.
Key words: tool wear status; empirical mode decomposition; feature dimensionality reduction; improved particle swarm;least squares support vector machine

Key words

tool wear status / empirical mode decomposition / feature dimensionality reduction / improved particle swarm;least squares support vector machine

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NIE Peng1，MA Yao1，GUO Yongyi2，LI Zhengqiang1，SHAN Chunfu2. Monitoring method of milling tool wear status based on IPSO optimized LS-SVM[J]. Journal of Vibration and Shock, 2022, 41(22): 137-143

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