Application of multi-sensor signals for monitoring tool/workpiece condition in broaching

Abstract

Process monitoring can be used for improving machining reliability and failure prediction. As manufacturing anomalies are a potential cause of gas turbine disc failure in aero-engines, engine life and performance can be improved by developments of manufacturing anomaly detection. The current paper identifies appropriate techniques for monitoring tool condition and surface anomalies in broaching, and presents results of the output signals obtained from multiple sensors such as acoustic emission, cutting forces, vibration, hydraulic pressure and table displacement. The results show that the signals obtained proved to be efficient in detecting surface deviations and anomalies. Tool wear was identified by the cutting force, pressure and table displacement signals. While the acoustic emission signals did not prove to be sensitive in detecting tool wear, they were efficient in detecting surface anomalies such as smearing, scoring and overheating.