Integrated vibration and acoustic data fusion for chatter and tool condition classification in milling

Improved data quality and availability, along with lower computation costs, have generated interest in sensor-based tool condition monitoring technologies. In this study, an integrated vibration and acoustic sensor is used for tool condition monitoring, particularly for chatter detection and tool condition classification. Based on feature extraction in the time and frequency domains, chatter and tool condition classification studies are conducted using linear Support Vector Machines. The combination of acoustic and vibration data is found to have a higher classification accuracy as compared to the individual data sources.