Abstract
Online penetration monitoring of complex grooves remains challenging due to steel plates’ groove instability and welding heat distortion. Penetration is an accumulation process of material deposition. Temporal signals, such as video, can provide a more comprehensive characterization of the melt pool state. A deep learning method based on continuous video is designed to monitor groove welding penetration in-process. The proposed Fast Video-feature Extraction Net (FVENet) consists of a video extraction module and a multi-feature screening module. The efficient network can quickly extract high-dimensional data features in complex arc environments and achieve accurate results for backside melt width prediction. The feature extraction process of the network is explored by visualizing the results of different network layers. Experimental results indicate that the mean squared error (MSE) of FVENet reaches 0.0634 mm, outperforming other mainstream deep learning frameworks. The inference time under video input reaches 100 FPS. The network structure designed in this paper has the potential to become a universal template for processing melt pool images.
Similar content being viewed by others
References
Nomura K, Fukushima K, Matsumura T, Asai S (2021) Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation. J Manuf Process 61:. https://doi.org/10.1016/j.jmapro.2020.10.019
Jeon I, Liu P, Sohn H (2023) Real-time melt pool depth estimation and control during metal-directed energy deposition for porosity reduction. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-023-11689-3
Sweeney NE, Parke S, Lines D, et al (2023) In-process phased array ultrasonic weld pool monitoring. NDT and E International 137:. https://doi.org/10.1016/j.ndteint.2023.102850
Lin R, Wang H ping, Lu F, et al (2017) Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys. Int J Heat Mass Transf 108:. https://doi.org/10.1016/j.ijheatmasstransfer.2016.12.019
Wu J, Huang C, Li Z, et al (2023) An in situ surface defect detection method based on improved you only look once algorithm for wire and arc additive manufacturing. Rapid Prototyp J 29:. https://doi.org/10.1108/RPJ-06-2022-0211
Dai P, Wang Y, Li S, et al (2020) FEM analysis of residual stress induced by repair welding in SUS304 stainless steel pipe butt-welded joint. J Manuf Process 58:. https://doi.org/10.1016/j.jmapro.2020.09.006
Zhang K, Yan M, Huang T, et al (2019) 3D reconstruction of complex spatial weld seam for autonomous welding by laser structured light scanning. J Manuf Process 39:. https://doi.org/10.1016/j.jmapro.2019.02.010
Yu R, Han J, Bai L, Zhao Z (2021) Identification of butt welded joint penetration based on infrared thermal imaging. Journal of Materials Research and Technology 12:. https://doi.org/10.1016/j.jmrt.2021.03.075
Chen C, Xiao R, Chen H, et al (2020) Arc sound model for pulsed GTAW and recognition of different penetration states. International Journal of Advanced Manufacturing Technology 108:. https://doi.org/10.1007/s00170-020-05462-z
Zhang T, Xu C, Cheng J, et al (2023) Research of surface oxidation defects in copper alloy wire arc additive manufacturing based on time-frequency analysis and deep learning method. Journal of Materials Research and Technology 25:. https://doi.org/10.1016/j.jmrt.2023.05.227
Li S, Jiang P, Gao Y, et al (2023) A penetration depth monitoring method for Al-Cu laser lap welding based on spectral signals. J Mater Process Technol 317:. https://doi.org/10.1016/j.jmatprotec.2023.117972
Liu T, Zheng P, Bao J (2023) Deep learning-based welding image recognition: a comprehensive review. J Manuf Syst 68. https://doi.org/10.1016/j.jmsy.2023.05.026
Liang R, Yu R, Luo Y, Zhang YM (2019) Machine learning of weld joint penetration from weld pool surface using support vector regression. J Manuf Process 41:. https://doi.org/10.1016/j.jmapro.2019.01.039
Lei Z, Shen J, Wang Q, Chen Y (2019) Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding. J Manuf Process 43:. https://doi.org/10.1016/j.jmapro.2019.05.013
Wu D, Hu M, Huang Y, et al (2021) In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion. J Manuf Process 66:. https://doi.org/10.1016/j.jmapro.2021.04.007
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.90
Jia Q, He J, Li F, Hua X (2023) Penetration depth predicition of thin plate plasma arc lap welding based on machine learning. Cailiao Kexue yu Gongyi/Material Science and Technology 31:. https://doi.org/10.11951/j.issn.1005-0299.20220247
Wang Z, Chen H, Zhong Q, et al (2022) Recognition of penetration state in GTAW based on vision transformer using weld pool image. International Journal of Advanced Manufacturing Technology 119:. https://doi.org/10.1007/s00170-021-08538-6
Liao S, Huang C, Liang Y, et al (2022) Solder joint defect inspection method based on ConvNeXt-YOLOX. IEEE Trans Compon Packaging Manuf Technol 12:. https://doi.org/10.1109/TCPMT.2022.3224997
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.1706.03762
Liu Z, Mao H, Wu CY, et al (2022) A ConvNet for the 2020s. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR52688.2022.01167
Yu R, Kershaw J, Wang P, Zhang YM (2022) How to accurately monitor the weld penetration from dynamic weld pool serial images using CNN-LSTM deep learning model? IEEE Robot Autom Lett 7:. https://doi.org/10.1109/LRA.2022.3173659
Jiao W, Wang Q, Cheng Y, et al (2020) Prediction of weld penetration using dynamic weld pool arc images. researchgate.netW Jiao, Q Wang, Y Cheng, R Yu, Y ZhangWeld J, 2020•researchgate.net. https://doi.org/10.29391/2020.99.027
Liu T, Wang J, Huang X, et al (2022) 3DSMDA-Net: an improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition. J Manuf Syst 62:. https://doi.org/10.1016/j.jmsy.2021.01.017
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. https://doi.org/10.1109/CVPR.2017.243
Liu Z, Lin Y, Cao Y, et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE International Conference on Computer Vision. https://doi.org/10.48550/arXiv.2103.14030
Dosovitskiy A, Beyer L, Kolesnikov A, et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR 2021 - 9th International Conference on Learning Representations. https://doi.org/10.48550/arXiv.2010.11929
Gao P, Wu Z, Wang Y, et al (2023) Method for monitoring and controlling penetration of complex groove welding based on online multi-modal data. J Intell Manuf. https://doi.org/10.1007/s10845-023-02107-2
Funding
This work was supported by the National Natural Science Foundation of China (grant nos. 62101265, U23A20283, 62271263), the China Postdoctoral Science Foundation (2021M691592), and the Fundamental Research Funds for the Central Universities (No. 30922010705).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Recommended for publication by Commission I - Additive Manufacturing, Surfacing, and Thermal Cutting
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhao, Z., Gao, P., Lu, J. et al. Dynamic penetration prediction based on continuous video learning. Weld World 68, 867–877 (2024). https://doi.org/10.1007/s40194-024-01745-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40194-024-01745-1