Improving Scattered Defect Grading in Castings Digital Radiographs via Smoothing the One-Hot Encoding
Author:
Funder
Natural Science Foundation of Liaoning Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40962-024-01335-3.pdf
Reference39 articles.
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3. F. Nikolić, I. Štajduhar, M. Čanađija, Casting defects detection in aluminum alloys using deep learning: a classification approach. Int. Metalcast. 17, 386–398 (2023). https://doi.org/10.1007/s40962-022-00777-x
4. D. Blondheim, Improving manufacturing applications of machine learning by understanding defect classification and the critical error threshold. Int. Metalcast. 16, 502–520 (2022). https://doi.org/10.1007/s40962-021-00637-0
5. X. Zhao, Z. He, S. Zhang, D. Liang, A sparse-representation-based robust inspection system for hidden defects classification in casting components. Neurocomputing 153, 1–10 (2015). https://doi.org/10.1016/j.neucom.2014.11.057
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