Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining

Abstract

Built-up edge (BUE) has a significant influence on the process outputs of machining. Unstable BUE can damage the cutting tool edge and adversely affect the machined workpiece surface. However, stable BUE formation can protect the cutting tool surface from further wear, improving the productivity of AISI 304 stainless steel machining. This research proposes a new approach for monitoring and classification of BUE formation with 178 sets of turning experimental tests at different cutting speeds and wear states. The Daubechies wavelet transform is used to differentiate the BUE formation signals from the tool wear signals at different cutting velocities. The acquired acoustic emission (AE) and cutting force signals were filtered using both discrete wavelet, and wavelet packet transforms. Then, wavelet coefficients were processed in both the time and frequency domains with various features being extracted. An adaptive-network fuzzy inference system (ANFIS) model was implemented to detect BUE height at different cutting speeds and different wear states. Finally, two distinct criteria are processed to categorize the BUE state as well as the machined surface roughness throughout the cutting test. The results confirmed the ability of the monitoring system to predict BUE height using AE and force signals.