A novel in-situ tool wear monitoring approach using multivariate signal processing and intrinsic multiscale entropy analysis

Abstract

Tool wear significantly affects the interface condition between the machining tool and the workpiece, causing nonlinear vibrations that negatively impact machining quality. The vibration on the axes of X, Y and Z are both generated during machining process, and multivariate vibration signals collected by triaxial accelerometers contain dynamical information of tool wear accurately and comprehensively. This paper proposes a novel in-situ tool wear monitoring approach using multivariate signal processing and intrinsic multiscale entropy analysis. Multivariate variational mode decomposition (MVMD) is firstly used to process multivariate vibration signals. The multivariate band-limited intrinsic mode functions (BLIMFs) contain nonlinear and nonstationary wear characteristics of multivariate vibration signals. Afterwards, the refined composite multiscale dispersion entropy (RCMDE) is employed to measure the complexity and regularity of multivariate BLIMFs quantitatively. Finally, the feature matrices composed of entropy values on multiple scale of multivariate BLIMFs are adopted as the input of CNN to achieve accurate tool wear monitoring. The results show the proposed approach is promising for tool wear monitoring.