Tool Wear Monitoring of Acoustic Emission Signals from Milling Processes

In modern day production, tool condition monitoring systems are needed to get better quality of jobs and to ensure reduction in the downtime of machine tools due to catastrophic tool failures. Tool condition monitors alter the operator about excessive tool wear and stop the machine in case of an impending breakage or collision of tool. Acoustic emission (AE)data from single point turning machining are analyzed in this paper in order to gain a greater insight of the signal statistical properties for tool condition monitoring applications. A statistical analysis of the time series data amplitude and root mean square value at various tool wear levels are performed, finding that aging features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behavior above across over value.