Health Monitoring of Milling Cutters with Nonlinear Entropy and Self-organizing Mapping

The cutter is one critical component in a milling tool, and its operational condition directly affects the part machining quality and production efficiency. In this paper, a new method for milling cutters health monitoring is proposed. The proposed method extracts nonlinear entropy features with adaptive decomposition of the original multi-sensor monitoring signals. Then the extracted features are selected and adaptively fused into a virtual health indicator (HI) by self-organizing mapping (SOM) network to characterize the operational health condition of the milling cutter. High speed milling data from 2010 prognostics and health management (PHM) challenge is studied to demonstrate performance of the presented method. Experimental results show that the approach can effectively integrate the online multi-sensor signals to reliably describe health degradation of the milling cutter.