Detection of tool breakage in milling operations—II. The neural network approach

On-line monitoring of tool cutting conditions and tool breakage is very important for automated factories of the future. In this paper, the time series based tooth period modeling technique (TPMT) is proposed for detecting tool breakage by monitoring a cutting force or torque signal in any direction. TPMT uses the fast a posteriori error sequential technique (FAEST) for on-line modeling of cutting force or torque signals. Tool breakage is detected by evaluating variations of the characteristics of the monitored signal in each tooth period. TPMT was tested on simulated and experimental end milling data. The proposed technique detected tool breakage in all of the test cases without giving any false alarms in the transition cases.