Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

• Published in: International journal of advanced manufacturing technology Vol. 120; no. 1-2; pp. 1237 - 1251
• Main Authors: Zheng, Qingzhen, Chen, Guangsheng, Jiao, Anling
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2022; Springer Nature B.V
• Subjects: Acceleration; CAE) and Design; Chatter; Computer-Aided Engineering (CAD; End milling; Engineering; Feature recognition; Genetic algorithms; Geometric accuracy; High speed machining; Industrial and Production Engineering; Machine tools; Mathematical models; Mechanical Engineering; Media Management; Optimization; Original Article; Parameters; Particle swarm optimization; Prediction models; Radial basis function; Signal processing; Support vector machines; Surface finish; Vibration; Wavelet transforms; Workpieces; Chatter detection; PSO-SVM; Chatter recognition; Multi-feature vectors; Wavelet packet transform
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-022-08856-3

Chatter is one of the biggest unfavorable factors during the high speed machining process of a machine tool. It severely affects the surface finish and geometric accuracy of the workpiece. To address this obstacle and improve the quality and efficiency of products, it is significantly essential to detect chatter during machining. Therefore, a multi-feature recognition system for chatter detection on the basis of the fusion technology of wavelet packet transform (WPT) and particle swarm optimization support vector machine (PSO-SVM) was proposed in this paper. Firstly, the original vibration signals collected from the acceleration sensor were processed through wavelet packet transform (WPT). The noise and the irrelevant information were remarkably decreased. In addition, the wavelet packets containing chatter-emerging information were chosen and reconstructed. The fourteen time–frequency domain characteristics of the reconstructed vibration signal were calculated and chosen as the multi-feature vectors of chatter detection. Finally, to obtain the optimal radial basis function parameter g and penalty parameter C of the SVM prediction model, the optimization algorithms of k -fold cross-validation ( k -CV), genetic algorithm (GA), and particle swarm optimization (PSO) were employed in optimizing the model parameters of SVM. It was indicated that the PSO-SVM improved obviously the accuracy of chatter recognition than the others. In addition, we applied the optimized SVM prediction model by PSO for detecting chatter state in end milling machining. Chatter recognition results indicated that the model accurately predicted the slight chatter state in advance.