Multi-scale statistical signal processing of cutting force in cutting tool condition monitoring

In a machining system, accurate tool wear condition monitoring is paramount for guaranteeing the quality of the workpiece and tool life. The cutting force signal has been proved to be the most sensitive signal to depict the tool wear variation during the machining process. This paper introduces a data-driven modeling framework for tool wear monitoring in a machining process, which is based on statistical processing of cutting force wavelet transform by a hidden Markov tree. As a kind of data-driven prognostic approach, this method exploits the tool wear states feature from a deeply data mining perspective while the Markov dependence of wavelet transformation at different frequencies or scales is captured. With lathe turning as the research object, a detailed study on the statistical analysis of cutting force in different tool conditions is presented. A two phases monitoring process that assesses the tool wear conditions from generated model by using the statistical features of cutting force wavelet transform is built. Compared to the traditional classifiers, which usually have a difficult condition in distinguishing tool wear states when given a limited amount of samples, the proposed approach make more efficient use of the training data with high sensitivity to the tool wear conditions. Experimental studies of Inconel 718 cutting show that this approach is robust and makes the cutting force information exploited effectively in data mining. Based on the experimental results, the proposed method for tool condition monitoring outperforms the traditional used Hidden Markov model and Gaussian mixture model approach.