| Title: | OS26-9 Deep Learning in Manufacturing: A Focus on Welding Defect Classification with CNNs |
|---|---|
| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 877-882 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.OS26-9 |
| Author(s): | Tin Chang Ting, Hameedur Rahman, Tiong Hoo Lim, Chin Hong Wong, Chun Kit Ang, M.K.A Ahamed Khan, Sew Sun Tiang, Wei Hong Lim |
| Publication Date: | February 22, 2024 |
| Keywords: | Convolutional neural network, Classification, Deep learning, Welding defects |
| Abstract: | Welding is integral to modern manufacturing, yet the complex process often leads to defects, impacting the quality of the final product. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have shown remarkable results in applications like defect recognition. This study evaluated AlexNet, ResNet-18, ResNet50, ResNet-101, MobileNet-v2, ShuffleNet, and SqueezeNet for their effectiveness in identifying welding defects, using accuracy, precision, sensitivity, specificity, and F-score as metrics. The dataset covered defects like cracks, lack of penetration, porosity, and a no-defect class. Our analysis shows that most of these architectures deliver promising results in accuracy, sensitivity, specificity, precision, and F1-score, highlighting their potential in defect recognition. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/OS/OS26-9.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
(c)2008 Copyright The Regents of ALife Robotics Corporation Ltd. All Rights Reserved.