Unsupervised Machine Learning for Blind Rivets Quality Inspection
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57496-2_8
Reference11 articles.
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2. Tavares, S.M.O., de Castro, P.M.S.T.: An overview of fatigue in aircraft structures. Fatigue Fract. Eng. Mater. Struct. 40, 1510–1529 (2017). https://doi.org/10.1111/ffe.12631
3. Amancio-Filho, S.T., dos Santos, J.F.: Joining of polymers and polymer–metal hybrid structures: recent developments and trends. Polym. Eng. Sci. 49, 1461–1476 (2009). https://doi.org/10.1002/pen.21424
4. Zhu, X.: Aviation rivet classification and anomaly detection based on deep learning. Int. J. Aerosp. Eng. 2023, 3546838 (2023). https://doi.org/10.1155/2023/3546838
5. Papacharalampopoulos, A., Petrides, D., Stavropoulos, P.: A defect tracking tool framework for multi-process products. Procedia CIRP 79, 523–527 (2019). https://doi.org/10.1016/j.procir.2019.02.100
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