Milling chatter detection with WPD and power entropy for Ti-6Al-4V thin-walled parts based on multi-source signals fusion

Unstable cutting has a relatively large negative impact on the machining quality and efficiency of thin-walled parts. It can easily cause severe vibration of milling systems, resulting in poor surface roughness of workpieces. Although some promising signal processing methods, such as wavelet packet decomposition (WPD), have been applied to process nonlinear signals, few studies have paid attention to determining a wavelet basis and the number of decomposition layers in WPD. Parameters of WPD play a crucial role and they are often empirically determined. In this paper, an adaptive denoising model based on WPD and recursive least squares with a variable forgetting factor (RLSVFF) is firstly established, and the influence of the forgetting factor in the model on signal denoising are investigated. Then, a novel chatter detection method based on multi-source signals fusion using WPD and power entropy is presented. On this basis, an automated selection method based on a margin indicator and power is proposed for applications to WPD. And the influence of different parameters of WPD on a margin indicator and power are investigated. In order to improve the efficiency of different levels of signal acquisition, a method based on a stability lobe diagram (SLD) is used to design experimental parameters. Compared with tradi-tional denoising models and chatter detection methods based on a single signal, simulation and experimental results show that the adaptive denoising model and chatter detection method based on multi-source signals fusion proposed in this paper can more reliably detect the occurrence of early chatter and different levels of chatter.