An on-machine tool wear area identification method based on image augmentation and advanced segmentation

In industrial manufacturing, tool wear monitoring (TWM) is essential for ensuring high-quality machining, operational efficiency, cost-effectiveness, and safety. However, due to the complexities of on-machine imaging and the constraints of direct measurement techniques, TWM methods face challenges such as unclear boundaries, class imbalance between wear and unworn area, and image diversity. This paper proposes a novel two-step approach for identifying tool wear areas. Firstly, DeepLabV3${}^{+}$ with Focal Loss is utilized to identify the Region of Interest (ROI) of the tool. Secondly, the method employs Intuitionistic Fuzzy C-Means Clustering (IFCM) for detailed segmentation of the wear area. This integration effectively addresses challenges arising from uneven illumination that blur image boundaries and the class imbalance between images with tool wear and those without. To enhance image diversity and quality, we utilize Denoising Diffusion Probabilistic Models (DDPM) for image augmentation, significantly enriching the training dataset. The proposed approach achieves a Mean Pixel Accuracy (MPA) of 95.32% and a Mean Intersection over Union (MIoU) of 93.67%, which marks a substantial improvement over existing TWM models. This progress not only provides a more reliable and efficient tool wear monitoring solution but also sets a new standard for precision in industrial machining processes.