[3C1] Integrating mmWave radar into digital manufacturing for tool health assessment
K Khalil, A Abotoor, M Hassan, Z Murat Kilic, M D O’Toole and A Peyton
University of Manchester, UK
This research investigates using millimetre-wave (mmWave) radar technology to track and detect cutting tools in real time, identifying tool defects through micro-Doppler measurements. mmWave radar was chosen for its robustness in challenging industrial environments, such as poor lighting and dust. Unlike acoustic sensors, which struggle in noisy environments typical of workshops, and lasers, which cannot withstand dust and unclean conditions, mmWave radar provides a reliable solution. The system accurately quantifies variations in the cutting tools’ distance, position and vibrations by analysing reflected microwave signals over time. These dynamic quantities are then assessed to detect changes in tool behaviour, such as wobbling, deformation or partial damage. By establishing a digital baseline for the undamaged tool, the radar system can identify any deviation in behaviour that indicates damage. Monitoring the cutting tools with radar enables early-stage fault detection, supports predictive maintenance, enhances product quality and improves surface roughness. The system also lays the foundation for applying machine learning and artificial intelligence (AI) techniques to classify tool faults accurately. Integrating radar systems into manufacturing creates a valuable digital feedback loop, enhancing process reliability and reducing unplanned downtime. This research presents mmWave radar as a non-destructive, presymptomatic solution for assessing cutting tool conditions, offering a scalable method for defect detection in existing manufacturing processes and allowing it to be integrated into current systems with minimal disruption.
