A data-driven time-sequence feature-based composite network of time-distributed CNN-LSTM for detecting pore defects in laser penetration welding
Author:
Funder
Innovative Research Group Project of the National Natural Science Foundation of China
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02391-6.pdf
Reference34 articles.
1. Ai, Y., Jiang, P., Wang, C., Mi, G., & Geng, S. (2018). Experimental and numerical analysis of molten pool and keyhole profile during high-power deep-penetration laser welding. International Journal of Heat and Mass Transfer, 126, 779–789. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.031
2. Brock, C., Hohenstein, R., & Schmidt, M. (2014). Mechanisms of vapour plume formation in laser deep penetration welding. Optics and Lasers in Engineering, 58, 93–101. https://doi.org/10.1016/j.optlaseng.2014.02.001
3. Feng, Y., Chen, Z., Wang, D., Chen, J., & Feng, Z. (2020). DeepWelding: A deep learning enhanced approach to GTAW using multisource sensing images. IEEE Transactions on Industrial Informatics, 16, 465–474.
4. Gong, J., Peng, G., Li, L., Xia, H., Meng, S., & Wang, J. (2021). Effect of plasma plume produced by vacuum laser welding on energy transmission. Optics & Laser Technology, 136, 106744. https://doi.org/10.1016/j.optlastec.2020.106744
5. Guo, L., Wang, H., Liu, H., Huang, Y., Wei, Q., Leung, C. L. A., Wu, Y., & Wang, H. (2023). Understanding keyhole induced-porosities in laser powder bed fusion of aluminium and elimination strategy. International Journal of Machine Tools and Manufacture, 184, 103977. https://doi.org/10.1016/j.ijmachtools.2022.103977
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