1. A U-Net-Based Approach for Tool Wear Area Detection and Identification.
- Author
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Miao, Huihui, Zhao, Zhibin, Sun, Chuang, Li, Bing, and Yan, Ruqiang
- Subjects
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AUTOMATION , *HUMAN facial recognition software , *CUTTING tools , *STATISTICAL correlation , *PRODUCT quality , *FEATURE extraction - Abstract
The tool wear condition monitoring is key to ensuring product quality. This article develops a direct technique dealing with cutting tool images to automate the tool wear detection and identification. The constructed U-Net-based network can realize an effective and reliable extraction of the tool wear area. The introduction of deep supervision with a Matthews correlation coefficient (MCC)-based surrogate loss function helps to address the few-shot and data imbalance issues. Experiments on the images with wear on the flank face of cutting tools from a computer numerical control (CNC) turning machine show the effectiveness, competitiveness, and reliability of the proposed method under different types of loss functions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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