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| Citation: |
XU Donghui, MENG Fanpeng, SUN Peng, ZHENG Xuchen, CHENG Yongchao, MA Zhi, CHEN Shujun. Online monitoring of GMAW welding defect based on deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(3): 114-119. DOI: 10.12073/j.hjxb.20230117002
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Utilizing the aluminum alloy exterior plate of the driver's cab of high-speed railway in rail transit as the substrate, the research is conducted on key intelligent welding technologies, focusing on the issue of online monitoring of welding defects. With the help of process test platform and welding procedure specification, welding defect experiment design, batch data collection, expert experience calibration and database construction are implemented. The convolutional neural network algorithm is used to construct multi-dimensional information fusion models for different types of data, and parameters of the fusion models are optimized. Finally, training, verification and testing of fusion models are completed. The results show that the fusion model after training has better recognition results for welding defects than the single information model. The monitoring accuracy of welding defects in the training set and the testing set is 99.0% and 88.3%, respectively. The data acquisition and model response total time for this monitoring system is less than 100 ms, which meets the requirements for engineering applications, enhances the level of intelligence in robotic welding, and drives the digital transformation and upgrading of enterprises.
| [1] |
Zhang Zhifen, Chen Xizhang, Chen Huabin, et al. Online welding quality monitoring based on feature extraction of arc voltage signal[J]. The International Journal of Advanced Manufacturing Technology, 2014, 70(9-12): 1661 − 1671. doi: 10.1007/s00170-013-5402-2
|
| [2] |
程永超, 肖珺, 陈树君, 等. 基于弧压反馈的薄板GTAW智能熔透控制[J]. 焊接学报, 2018, 39(12): 1 − 4. doi: 10.12073/j.hjxb.2018390287
Cheng Yongchao, Xiao Jun, Chen Shujun, et al. Intelligent penetration welding of thin-plate GTAW process based on arc voltage feedback[J]. Transactions of the China Welding Institution, 2018, 39(12): 1 − 4. doi: 10.12073/j.hjxb.2018390287
|
| [3] |
Cheng Yongchao, Chen Shujun, Xiao Jun, et al. Dynamic estimation of joint penetration by deep learning from weld pool image[J]. Science and Technology of Welding and Joining, 2021, 26(4): 279 − 285. doi: 10.1080/13621718.2021.1896141
|
| [4] |
Lü Na, Zhong Jiyong, Chen Huabin, et al. Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length[J]. The International Journal of Advanced Manufacturing Technology, 2014, 74(1-4): 235 − 249. doi: 10.1007/s00170-014-5875-7
|
| [5] |
Graham G M, Ume I C. Automated system for laser ultrasonic sensing of weld penetration[J]. Mechatronics, 1997, 7(8): 711 − 721. doi: 10.1016/S0957-4158(97)00031-7
|
| [6] |
Ghanty P, Vasudevan M, Mukherjee D P, et al. Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool[J]. Science and Technology of Welding and Joining, 2008, 13(4): 395 − 401. doi: 10.1179/174329308X300118
|
| [7] |
Huang Yiming, Wu Di, Lyu Na, et al. Investigation of porosity in pulsed GTAW of aluminum alloys based on spectral and X-ray image analyses[J]. Journal of Materials Processing Technology, 2017, 243: 365 − 373. doi: 10.1016/j.jmatprotec.2016.12.026
|
| [8] |
胡文刚, 陆云鹏, 郭世雄, 等. 基于DR数字射线成像技术的铝合金焊缝缺陷检测[J]. 焊接, 2021(2): 46 − 51.
Hu Wengang, Lu Yunpeng, Guo Shixiong, et al. Weld defect detection of aluminum alloy based on digital radiography[J]. Welding & Joining, 2021(2): 46 − 51.
|
| [9] |
Moon H S, Kim Y B, Beattie R J. Multi sensor data fusion for improving performance and reliability of fully automatic welding system[J]. The International Journal of Advanced Manufacturing Technology, 2006, 28: 286 − 293. doi: 10.1007/s00170-004-2359-1
|
| [10] |
Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521: 436 − 444. doi: 10.1038/nature14539
|
| [11] |
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278 − 2323. doi: 10.1109/5.726791
|
| [12] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84 − 90.
|
| [13] |
Szegedy C, Liu Wei, Jia Yangqing, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, 2015.
|
| [14] |
He Kaiming, Zhang Xianyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016.
|
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