Towards a Zero-Defect in Welding: An Exploration of Factors to Improve the Training Data for Image Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66329-1_30
Reference21 articles.
1. Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. J. Big Data 8, 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8
2. Bacioiu, D., Melton, G., Papaelias, M., Shaw, R.: Automated defect classification of aluminium 5083 tig welding using hdr camera and neural networks. J. Manuf. Process. 45, 603–613 (2019). https://doi.org/10.1016/j.jmapro.2019.07.020
3. Chen, C., Xiao, R., Chen, H., Lv, N., Chen, S.: Prediction of welding quality characteristics during pulsed gtaw process of aluminum alloy by multisensory fusion and hybrid network model. J. Manuf. Process. 68, 209–224 (2021). https://doi.org/10.1016/j.jmapro.2020.08.028
4. Fan, X., Gao, X., Liu, G., Ma, N., Zhang, Y.: Research and prospect of welding monitoring technology based on machine vision. Int J Adv Manuf Tech 115, 3365–3391 (2021). https://doi.org/10.1007/s00170-021-07398-4
5. Feng, Y., Chen, Z., Wang, D., Chen, J., Feng, Z.: Deepwelding: a deep learning enhanced approach to gtaw using multisource sensing images. IEEE Trans. Industr. Inf. 16(1), 465–474 (2019). https://doi.org/10.1109/TII.2019.2937563
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