Intelligent chatter detection for CNC machine based on RFE multi-feature selection strategy
Baoqiang Wang, Yuan Wei, Shulin Liu, DeFeng Gu, Dongfang Zhao
Abstract
Abstract Chatter is an unstable self-excited vibration generated during processing. It not only reduces the machining efficiency, machining accuracy, the service life of machine tools and cutting tools, but also results in sound pollution and material waste. To improve the machining stability and product quality of thin-walled workpieces, effective chatter detection of machine tools is essential. This paper presented a signal feature evaluation model and multi-feature recognition system for chatter detection. First, the original signals obtained from the acceleration sensor were processed through local mean decomposition to reduce the noise in the signal. Thereafter, the correlation between the system state and the different features of amplitude domain, frequency domain and nonlinear domain was analyzed. Further, through the feature evaluation model based on recursive feature elimination, the main feature parameters related to machine tool state are obtained, and different recognition algorithms were used to verify the rationality of the fusion features. Finally, an end-to-end chatter detection method and the corresponding software system have been established. Experimental results show that the proposed method can effectively improve the accuracy of vibration detection.