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FCGR-Net: A Novel Approach to Predict Fatigue Crack Growth Rate Behavior in Metals Using Machine Learning

dc.contributor.authorMahesh, S.
dc.contributor.authorAnil Chandra, A. R.
dc.contributor.authorRavikumar, L.
dc.contributor.authorManjunatha, C. M.
dc.date.accessioned2026-02-17T21:39:23Z
dc.date.available2026-02-17T21:39:23Z
dc.date.copyright5/13/2025 12:00:00 AM
dc.date.issued2025
dc.identifier.issn0094-4289
dc.identifier.othermats-24-1240.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4310434
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleFCGR-Net: A Novel Approach to Predict Fatigue Crack Growth Rate Behavior in Metals Using Machine Learning
dc.typeJournal Paper
dc.journal.volume147
dc.journal.issue4
dc.journal.titleJournal of Engineering Materials and Technology
dc.identifier.doi10.1115/1.4068534
dc.journal.fristpage41004-1
dc.journal.lastpage41004-12
dc.page12
dc.treeJournal of Engineering Materials and Technology:;2025:;volume( 147 ):;issue: 004
dc.contenttypeFulltext


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