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25 April 2023 Crack detection on sheet metals using deep learning and a novel data augmentation method (Conference Presentation)
Penghua Zhang, Seungpyo Jo, Yinan Miao, Jun Young Jeon, Gyuhae Park
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Proceedings Volume 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE; 124890A (2023) https://doi.org/10.1117/12.2657080
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States
Abstract
Cracks on sheet metals can significantly affect the overall strength. Crack detection during manufacturing is, thus, an important process for the quality assessment on a press line. Deep learning, a data-driven structure, has been extensively used to detect cracks on various surfaces. In this study, a crack detection technique for a press line using Retina Net and a novel data augmentation method is proposed, which mainly focuses on three steps, shape acquisition, style transfer, and edge fusion. First, the shapes of crack on different materials are extracted. Then, images are created by providing metal crack textures to those shapes using a fusion network with a relatively small number of real crack images. Real crack images are captured from a sheet metal forming line. Training data can be enriched using the proposed data augmentation method. Validation experiments are conducted to demonstrate the effectiveness of the proposed crack detection and data augmentation techniques.
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Penghua Zhang, Seungpyo Jo, Yinan Miao, Jun Young Jeon, and Gyuhae Park "Crack detection on sheet metals using deep learning and a novel data augmentation method (Conference Presentation)", Proc. SPIE 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, 124890A (25 April 2023); https://doi.org/10.1117/12.2657080
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KEYWORDS
Metals
Image fusion
Image processing
Data acquisition
Data fusion
Manufacturing
Retina
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