Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion

Abstract

Chatter is a kind of self-excited vibration that often occurs in the milling process of thin-walled parts, which has become the main factor restricting production efficiency and quality. Due to the occurrence of chatter, the signal becomes more complex and unstable. In order to realize milling chatter detection of thin-walled parts, the method of multi-sensor signal fusion is used. A chatter detection method based on variational mode decomposition (VMD) and nonlinear dimensionless index is proposed by analyzing the characteristics of signals in time–frequency domain. Firstly, a series of intrinsic mode function (IMF) components are obtained by decomposing force and acceleration signals with VMD. When chatter occurs, the energy is transferred to the chatter frequency band. Each IMF signal’s nonlinear energy entropy (EE) is extracted to construct the feature vector. A support vector machine chatter identification model based on multi-sensor signal fusion is established. To solve the problem of model incremental updating, supervised learning and unsupervised learning are combined to provide a method for chatter detection.