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| Automatic Detection of Non-Welding Defects Based on Metallographic Photographs of Diffusion Bonding Samples |
| HU Jinghui1 , XIE Pengzhi 1,2, YANG Wei 1,2, YAO Gang1 |
| (1. AVIC Manufacturing Technology Institute, Beijing 100024, China; 2. Aeronautical Key Laboratory for Digital Manufacturing Technology, Beijing 100024, China) |
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Abstract This paper introduces the application of machine learning methods in industrial informationization, and proposes the construction method of machine learning platform to satisfy the automatic detection requirements in typical equipment production of aviation manufacturing. The method includes: construction of an overall scheme of the machine learning platform, design and optimization of the automatic detection model, training of the detection model and empirical experimental evaluation. This paper takes the automatic detection of diffusion bonding defects of stator vanes as an example, and trains the model with defect detection accuracy which is more than 96%, verifies the feasibility and effectiveness of the machine learning platform.
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2020, 
