Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography
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Springer Science and Business Media LLC
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https://link.springer.com/content/pdf/10.1007/s10845-024-02416-0.pdf
Reference98 articles.
1. Abdelrahman, M., Reutzel, E. W., Nassar, A. R., & Starr, T. L. (2017). Flaw detection in powder bed fusion using optical imaging. Additive Manufacturing, 15, 1–11. https://doi.org/10.1016/j.addma.2017.02.001
2. Acharya, P., Chu, T. P., Ahmed, K. R., & Kharel, S. (2022). A deep learning approach for defect detection and segmentation in x-ray computed tomography slices of additively manufactured components. International Journal of Artificial Intelligence and Applications. https://doi.org/10.5121/ijaia.2022.13401
3. Achenbach, J. D. (2000). Quantitative nondestructive evaluation. International Journal of Solids and Structures, 37(1–2), 13–27. https://doi.org/10.1016/S0020-7683(99)00074-8
4. Ajmi, C., Zapata, J., Martínez-Álvarez, J. J., Doménech, G., & Ruiz, R. (2020). Using deep learning for defect classification on a small weld X-ray image dataset. Journal of Nondestructive Evaluation, 39, 1–13. https://doi.org/10.1007/s10921-020-00719-9
5. Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795. https://doi.org/10.1016/j.compbiomed.2020.103795
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1. Correction: Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography;Journal of Intelligent Manufacturing;2024-07-17
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