Deep-Learning Approach to the Self-Piercing Riveting of Various Combinations of Steel and Aluminum Sheets
نویسندگان
چکیده
Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets predict cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) three alloys (Al5052, Al5754, Al5083) considered as materials for top bottom sheets, respectively. The key objective was consider material properties these metal (Young's modulus, Poisson's ratio, ultimate tensile strength) in deep-learning framework. Two models considered: In first model, adopted scalar inputs, second graphically assigned channels input image. Both generated segmentation image cross-section. To assess accuracy predictions, images compared ground truth images, geometrical factors (interlock, thickness, effective length) measured. achieved prediction accuracies 91.95% 92.22%,
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3084296