An Automated Feature-Based Image Registration Strategy for Tool Condition Monitoring in CNC Machine Applications

The implementation of Machine Vision (MV) systems for Tool Condition Monitoring (TCM) plays a critical role in reducing the total cost of operation in manufacturing while expediting tool wear testing in research settings. However, conventional MV-TCM edge detection strategies process each image independently to infer edge positions, rendering them susceptible to inaccuracies when tool edges are compromised by material adhesion or chipping, resulting in imprecise wear measurements. In this study, an MV system is developed alongside an automated, feature-based image registration strategy to spatially align tool wear images, enabling a more consistent and accurate detection of tool edge position. The MV system was shown to be robust to the machining environment, versatile across both turning and milling machining centers and capable of reducing tool wear image capturing time up to 85% in reference to standard approaches. A comparison of feature detector-descriptor algorithms found SIFT, KAZE, and ORB to be the most suitable for MV-TCM registration, with KAZE presenting the highest accuracy and ORB being the most computationally efficient. The automated registration algorithm was shown to be efficient, performing registrations in 1.3 s on average and effective across a wide range of tool geometries and coating variations. The proposed tool reference line detection strategy, based on spatially aligned tool wear images, outperformed standard methods, resulting in average tool wear measurement errors of 2.5% and 4.5% in the turning and milling tests, respectively. Such a system allows machine tool operators to more efficiently capture cutting tool images while ensuring more reliable tool wear measurements.