Tool Condition Monitoring Methods Applicable in the Metalworking Process

Abstract This article reviews and analyzes the approaches utilized for monitoring cutting tool conditions. The Research focuses on publications from 2012 to 2022 (10 years), in which Machine Learning other statistical processes are used determine quality, condition, wear, remaining useful life (RUL) of shearing tools. paper quantifies typical signals by researchers scientists (vibration workpiece, force, tool’s temperature, example). These sensitive changes workpiece quality condition; therefore, they as a proxy degradation product. selection analyze wear level is still development; however, shows main over years their correlation with condition. can be taken directly or choice varies, both have shown promising results. In parallel, presents, analyzes, some most techniques that serve filters cleanse collected data before prediction classification phase. methods also extract relevant wear-sensitive information signals, easing classifiers’ work numerically changing alongside product quality.