Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model

Abstract

Abstract Tool wear conditions impact the surface quality of the work-piece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator to streamline the processes of tool wear detection. Our evaluation covered three Point-of-Interest generation methods and further investigated the effects of variations in training dataset sizes and U-Net training intensities on resultant wear segmentation outcomes. The results consistently highlight our approach’s advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets. This feature underscores its potential applicability in industrial scenarios where datasets may be limited.

Authors 3

1. Zongshuo Li Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen, Germany

2. Markus Meurer Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen, Germany

3. Thomas Bergs Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen, Germany