Comparison and explanation of data-driven modeling for weld quality prediction in resistance spot welding
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02108-1.pdf
Reference28 articles.
1. Ao, S., Li, C., Huang, Y., & Luo, Z. (2020). Determination of residual stress in resistance spot-welded joint by a novel X-ray diffraction. Measurement, 161, 107892. https://doi.org/10.1016/j.measurement.2020.107892
2. Batista, M., Furlanetto, V., & Duarte Brandi, S. (2020). Analysis of the behavior of dynamic resistance, electrical energy and force between the electrodes in resistance spot welding using additive manufacturing. Metals, 10(5), 690. https://doi.org/10.3390/met10050690.
3. Chen, J., Feng, Z., Wang, H. P., Carlson, B. E., Brown, T., & Sigler, D. (2018). Multi-scale mechanical modeling of al-steel resistance spot Welds. Materials Science and Engineering: A, 735, 145–153. https://doi.org/10.1016/j.msea.2018.08.039.
4. El-Sari, B., Biegler, M., & Rethmeier, M. (2021). Investigation of the extrapolation capability of an artificial neural network algorithm in combination with process signals in resistance spot welding of advanced high-strength steels. Metals, 11(11), 1874. https://doi.org/10.3390/met11111874.
5. Hwang, I., Yun, H., Yoon, J., Kang, M., Kim, D., & Kim, Y. M. (2018). Prediction of resistance spot weld quality of 780 MPA grade steel using adaptive resonance theory artificial neural networks. Metals, 8(6), 453. https://doi.org/10.3390/met8060453.
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1. Advanced process characterization and machine learning-based correlations between interdiffusion layer and expulsion in spot welding;Journal of Manufacturing Processes;2024-01
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