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

Qingzhen Zheng,Guangsheng Chen,Anling Jiao

doi:10.1007/s00170-022-08856-3

Abstract

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.

Highlights

Chatter is one of the biggest unfavorable factors in achieving high performance metal-cutting operations, which is a self-excited vibration happened between workpieces and cutting tools[1]
This paper proposed a novel method of chatter recognition based on the combination of wavelet packet transform (WPT) and particle swarm optimization (PSO)-support vector machine (SVM) in milling
This study emphasizes on the in-depth analysis of the chatter-emerging frequency band of vibration signals with WPT

Summary

Introduction

Chatter is one of the biggest unfavorable factors in achieving high performance metal-cutting operations, which is a self-excited vibration happened between workpieces and cutting tools[1]. The cutting process in the milling is non-stationary due to machine tool spindle wear, the change of operating temperature and workpiece stiffness, and other non-linear factors[2]. In order to detect the phenomenon of chatter, some sensors were generally applied to obtain chatter signal, such as acceleration sensor, acoustic emission, current sensor, microphone and so on [3-6]. Tangjitsitcharoen [8] used the power spectrum density of dynamic cutting force signals to detect chatter state in turning process. In order to improve the robustness and reliability of chatter detection under variable cutting conditions, multi-sensor fusion is used for chatter detection. In order to extract more chatter feathers, Wan et al [12] extracted manually selected 8 features in time domain and frequency domain and 8 features automatically extracted by features extracted by stacked-denoising autoencoder, highly improving the accuracy and reliability of milling chatter identification based on Adaboost-SVM

Methods

Results

Conclusion