Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner.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.

Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze spindle power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the power meter (also called load meter) of the machine spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the spindle power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.