Smart tool wear state and chatter onset identification system for legacy manual drilling machine operators

The recent industrial transformations aim to augment Information and Communication Technologies (ICT) with manufacturing equipment for prognosis, predictive maintenance, and enhanced human–machine interactions. Integrating ICT with legacy manual machines is challenging as human operators are solely responsible for operation monitoring and control. This paper presents a smart system that complements manual drilling machine operators in identifying tool wear states and chatter onset. The system also assists in twist drill replacement or regrinding decisions and achieving stable operating conditions. The in-process twist drill wear state and chatter onset are identified by integrating accelerometer and acoustic emission sensors with decision-making algorithms and presenting the status using Human Machine Interface (HMI) devices. The Root Mean Square (RMS) and Support Vector Machine (SVM) algorithms extract features and in-process drilling operation status from the sensor data. The SVM is trained by performing a set of drilling experiments and utilizing the expertise of skilled operators. The decision-making model and sensors are integrated with a legacy manual drilling machine using an HMI device to display tool wear state and chatter onset, thereby improving operator perceptions while performing operations. The system performance is corroborated by conducting experiments for various twist drill-work material combinations. It is concluded that the developed system can effectively capture in-process tool wear and stability states. The proposed system can be implemented as a potential solution for the guided operation and monitoring of legacy manual drilling machines.