Feature extraction and neural network-based fatigue damage detection and classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07609-3.pdf
Reference37 articles.
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2. Campbell FC (2008) Elements of metallurgy and engineering alloys. ASM International, The Netherlands
3. Worden K, Farrar CR, Manson G, Park G (2007) The fundamental axioms of structural health monitoring. Proc Royal Soc A: Math Phys Eng Sci 463(2082):1639–1664
4. Kumar P, Prashant K (2009) Elements of fracture mechanics. Tata McGraw-Hill Education, US
5. See JE, Drury CG, Speed A, Williams A, Khalandi N (2017) The role of visual inspection in the 21st century, in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 61, SAGE Publications Sage CA: Los Angeles, CA, , pp. 262–266
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1. Convolutional neural network for risk assessment in polycrystalline alloy structures via ultrasonic testing;Fatigue & Fracture of Engineering Materials & Structures;2023-10-20
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