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Deep learning method for wear assessment and regeneration support in gear hobbing tools
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DOI：10.1007/s10845-026-02944-x.png)
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Abstract
En 中文
Efficient monitoring and regeneration of cutting tools are essential for maintaining product quality and process continuity in modern manufacturing environments. Consistent and robust assessment of tool wear remains a practical challenge, particularly when evaluations rely heavily on expert interpretation of visual data. This study presents a vision-based inspection framework for the evaluation of gear hob cutters that integrates defect detection, wear segmentation, and dimensional wear quantification within a unified deep learning pipeline. The system combines a lightweight Ghost Slim U-Net architecture with a compound loss function and is designed for operation within a controlled industrial inspection setup. When evaluated on an expert-annotated dataset acquired from an industrial inspection station, the framework achieved an average $$F_{1}$$ -score of $$0.886\pm 0.004$$ for wear segmentation. Concurrently, dimensional wear estimation yielded a mean absolute error of $$4.05\pm 0.23\mu m$$ . Additional experiments conducted under controlled perturbations of illumination, focus blur, and surface contamination provided a comparative assessment of robustness under non-ideal imaging conditions. The obtained results suggest that the proposed framework may serve as an operator-assistance tool for semi-automated inspection, supporting more consistent wear assessment while retaining expert oversight in regeneration decisions.
Keywords:
Machine vision
Deep learning
Cutting tools
Vision inspection
Wear assessment
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