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Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding
تشخیص نقص تصویر جوش تشخیص عمیق مبتنی بر یادگیری عمیق برخط با استفاده از شبکه های عصبی همگرا برای آلیاژ آل در جوش قوس رباتیک-2019 Accurate on-line weld defects detection is still challenging for robotic welding manufacturing due to the complexity
of weld defects. This paper studied deep learning–based on-line defects detection for aluminum alloy in robotic arc
welding using Convolutional Neural Networks (CNN) and weld images. Firstly, an image acquisition system was
developed to simultaneously collect weld images, which can provide more information of the real-time weld images
from different angles including top front, top back and back seam. Then, a new CNN classification model with 11
layers based on weld image was designed to identify weld penetration defects. In order to improve the robustness and
generalization ability of the CNN model, weld images from different welding current and feeding speed were captured
for the CNN model. Based on the actual industry challenges such as the instability of welding arc, the complexity
of the welding environment and the random changing of plate gap condition, two kinds of data augmentation
including noise adding and image rotation were used to boost the CNN dataset while parameters optimization was
carried out. Finally, non-zero pixel method was proposed to quantitatively evaluate and visualize the deep learning
features. Furthermore, their physical meaning were clearly explained. Instead of decreasing the interference from arc
light as in traditional way, the CNN model has taken full use of those arc lights by combining them in a various way
to form the complementary features. Test results shows that the CNN model has better performance than our previous
work with the mean classification accuracy of 99.38%. This paper can provide some guidance for on-line
detection of manufacturing quality in metal additive manufacturing (AM) and laser welding. Keywords: Deep learning | Defects detection | Al alloy | Robotic arc welding | Convolutional neural networks | Weld images | Feature visualization |
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