Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling

Nowadays, spindle power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate spindle power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle power data were collected from the power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the spindle power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8-18.4% with power data as compared to that between 0.4-17.9% with force data. Findings suggest that spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.