Novel method for measuring a wear scar using deep learning

This research presents a new method for estimating the wear scar in ball-on-disk experiments using deep learning. The wear scar on the ball in this context has traditionally been estimated using microscopy, which is time-consuming. To address this issue, we propose a deep learning-based approach that can efficiently estimate the area of the wear scar on the ball using segmentation and region fitting methods. Our method offers a faster and more convenient alternative to the conventional method. The proposed approach has the potential to significantly improve measurements of wear scar in ball-on-disk tests, which could have important implications in various wear test applications.