Application of spindle power signals in tool condition monitoring based on HHT algorithm

Abstract

In the present study, we present the application of spindle power signals in tool condition monitoring (TCM) under different cutting conditions based on the Hilbert-Huang transform (HHT) algorithm. We extracted two features from the original collected data using the HHT algorithm to detect the tool wear and conducted six sets of cutting experiments to verify the feasibility of this tool condition monitoring method. The results show that these features are highly correlated with the wear state of cutting tools, regardless of the cutting parameters, workpiece materials, and machining methods. The calculated correlation coefficients between the extracted features and the actual tool wear reach 0.79–0.98. This demonstrates that the HHT algorithm is suitable for extracting features from the spindle power signals to construct the online tool condition monitoring system.