Li, ZongshuoZongshuoLiHuo, DingDingHuoMeurer, MarkusMarkusMeurerPanesso Perez, Miguel AntonioMiguel AntonioPanesso PerezDrossel, Welf-GuntramWelf-GuntramDrosselBergs, ThomasThomasBergs2025-06-172025-06-172025https://publica.fraunhofer.de/handle/publica/48875410.1016/j.procir.2025.02.184Automated optical measurement of cutting tools offers a rapid and direct approach to monitoring tool wear. While the Segment Anything Model (SAM) exhibits strong zero-shot generalization capabilities in typical scenarios due to its powerful image encoder and prompt engineering, it underperforms in tool wear segmentation because of its lack of domain-specific knowledge. In this study, an image dataset containing various tools with different prompts was initially established, and SAM was fine-tuned using Low-Rank Adaptation (LoRA) and Mixture-of-Expert (MoE) LoRA to incorporate domain-specific knowledge of tool wear. The results consistently show that both LoRA-SAM and MoE-LoRA-SAM significantly outperform the original SAM in tool wear segmentation with over 55% improvement. Additionally, using a combination of three prompt points is sufficient for accurate tool wear segmentation. This finding highlights the potential applicability of SAM in industrial scenarios.enTool wearDeep learningImage segmentationSAM600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaftenjournal article