Abstract
Deep learning has great potential to reshape manufacturing industries. In this article, we present DeepWelding, a novel framework that applies deep learning techniques to improve gas tungsten arc welding process monitoring and penetration detection using multisource sensing images. The framework is capable of analyzing multiple types of optical sensing images synchronously and consists of three deep learning enhanced consecutive phases: image preprocessing, image selection, and weld penetration classification. Specifically, we adopted generative adversarial networks (pix2pix) for image denoising and classic convolutional neural networks (AlexNet) for image selection. Both pix2pix and AlexNet delivered satisfactory performance. However, five individual neural networks with heterogeneous architectures demonstrated inconsistent generalization capabilities in the classification phase when holding out multisource images generated with specific experimental settings. Therefore, two ensemble methods combining multiple neural networks are designed to improve the model performance on unseen data collected from different experimental settings. We have also found that the quality of model prediction is heavily influenced by the data stream collection environment. We think these findings are beneficial for the broad intelligent welding community.
Original language | English |
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Article number | 8815879 |
Pages (from-to) | 465-474 |
Number of pages | 10 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 16 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2020 |
Funding
Manuscript received June 30, 2019; accepted August 20, 2019. Date of publication August 27, 2019; date of current version January 4, 2020. This article was supported in part by the US Department of Energy, in part by the Office of Nuclear Energy (Advanced Methods for Manufacturing Program), in part by the Office of Science (Advanced Scientific Computing Research Program), and in part by the AI Initiative at Oak Ridge National Laboratory. Paper no. TII-19-2952. (Corresponding author: Dali Wang.) Y. Feng and Z. Chen are with the University of Tennessee, Knoxville, TN 37996 USA (e-mail:,[email protected]; [email protected]).
Funders | Funder number |
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U.S. Department of Energy | |
Office of Science | |
Office of Nuclear Energy | |
Oak Ridge National Laboratory | TII-19-2952 |
Keywords
- Arc welding
- deep neural networks
- gas tungsten arc welding (GTAW)
- monitoring and classification
- multisource
- pix2pix
- sensing images