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Procedia Manufacturing




Monitoring tool wear in machining processes is one of the critical factors in reducing downtime and maximizing profitability and productivity. A worn out tool can deteriorate the surface finish or dimensional accuracy of the part. Due to the uncertainties that originate from machining, workpiece material composition, and measurement, predicting tool wear is a challenging task in modern manufacturing processes. Low cost sensing technology for measuring spindle current is commonly deployed in the CNC machine to measure spindle power consumption for predicting tool wear. In this study, spindle power information was integrated into a Kalman filter methodology to predict tool flank wear in cutting hard-to-machine gamma-prime strengthened alloys. Results show a maximum of 18% error in estimation, which indicates a good potential of using Kalman filter in predicting tool flank wear.