Institutional Repository of Coll Elect & Informat Engn
| Quality classification of ultra-narrow gap welding based on GAF-ResNet | |
Ma, Peijie; Zhang, Aihua ; He, Weilong; Wang, Ping; Ma, Jing
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| 2023 | |
| Conference Name | 5th International Conference on Industrial Artificial Intelligence, IAI 2023 |
| Source Publication | 2023 5th International Conference on Industrial Artificial Intelligence, IAI 2023
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| Conference Date | August 21, 2023 - August 24, 2023 |
| Conference Place | Shenyang, China |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Abstract | Ultra-narrow gap welding with flux band-constrained arc is a welding method with high efficiency and low heat input, but its welding process is complex, and it is difficult to realize online prediction of welding quality. The traditional signal feature extraction method can not make full use of time series information. In addition, the use of convolutional neural network training will have the problem of gradient disappearance as the number of network layers increases. Because of the above problems, this paper proposes a welding quality classification prediction method based on GAF-ResNet, carries out ultra-narrow gap welding experiments, and performs model accuracy verification and performance analysis. The one-dimensional time series is encoded into a two-dimensional image by GAF image, which retains the time dependence of the time series. The ResNet network is used to deeply mine the time series information in the image array to ensure the depth of feature extraction. The analysis results show that the experimental results of GASF are better than those of GADF. The accuracy and F1 value of the GAF-ResNet model reached 88.163 % and 88.109 %, respectively. Compared with other modes, the overall performance of the model is better than that of the control group. © 2023 IEEE. |
| Keyword | Computerized tomography - Convolutional neural networks - Extraction - Feature extraction - Quality control - Time series GAF - High-low - Higher efficiency - Narrow gap welding - Quality classification - Resnet - Time series informations - Ultra-narrow gap welding - Welding method - Welding quality |
| DOI | 10.1109/IAI59504.2023.10327577 |
| Indexed By | EI |
| Language | 英语 |
| EI Accession Number | 20235115240121 |
| EI Keywords | Network layers |
| EI Classification Number | 723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 802.3 Chemical Operations - 913.3 Quality Assurance and Control - 922.2 Mathematical Statistics |
| Original literature type | Conference article (CA) |
| Citation statistics | none
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| Document Type | 会议论文 |
| Identifier | https://ir.lut.edu.cn/handle/2XXMBERH/169326 |
| Collection | 电气工程与信息工程学院 |
| Corresponding Author | Ma, Peijie |
| Affiliation | Lanzhou University of Technology, College of Electrical and Information Engineering, Lanzhou, China |
| First Author Affilication | Lanzhou University of Technology |
| Corresponding Author Affilication | Lanzhou University of Technology |
| Recommended Citation GB/T 7714 | Ma, Peijie,Zhang, Aihua,He, Weilong,et al. Quality classification of ultra-narrow gap welding based on GAF-ResNet[C]:Institute of Electrical and Electronics Engineers Inc.,2023. |
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