Acoustic Emission-Based Characterization of Wear Progression in Coated Cutting Tools When Turning

Understanding the wear behavior of coated cutting tools is essential for reliable tool life prediction and the implementation of intelligent tool condition monitoring. Tool wear influences not only process stability but also surface integrity and dimensional accuracy. In this study, AlTiN-coated indexable inserts were tested during longitudinal turning, with acoustic emission signals continuously recorded throughout the process. The wear progression was documented at discrete intervals using optical and microscopic analyses to capture characteristic wear forms and their development in dependence of tool life. The AE signals were segmented and analyzed using wavelet transformation and statistical feature extraction methods to describe both global signal evolution and locally occurring transient events. Correlation analyses were performed to identify dependencies between AE features and measured wear states. The results reveal distinct AE features that exhibit strong correlations with tool wear progression, indicating their potential as sensitive indicators of wear-induced changes in coated tools. The findings establish a data-driven basis for predictive tool condition monitoring using wear-sensitive acoustic emission characteristics.