Health Assessment for Tools in Milling Machine Using the Mahalanobis–Taguchi System Based on WPT-AR

A real-time tool health assessment has a profound significance on reliable machining operations. This paper proposes a health assessment method for tools in milling machine sing the Mahalanobis–Taguchi system (MTS) based on Wavelet Packet Transformation and Autoregression (WPT-AR). In this method, the nonlinear and non-stationary vibration signal from milling process is first decomposed by wavelet packet transforms. Second, an AR model is constructed for each coefficient of the reconstructed signal, and the parameters and the variance of the remnant of each AR model are employed to form the initial feature matrix. Singular values of this feature matrix are obtained by Singular Value Decomposition, at which point MTS is employed. In this study, MTS provides: (1) a computational scheme based on the Mahalanobis distance (MD) for obtaining the health index of a tool; and (2) Taguchi methods to extract the key features and reduce the redundant ones. Finally, the performance and effectiveness of the proposed method was validated by vibration signals acquired from milling machining process.