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Machine learning approach for predicting and understanding fatigue crack growth rate of austenitic stainless steels in high-temperature water environments

Dayu Fajrul Falaakh, Jongweon Cho, Chi Bum Bahn

2024Theoretical and Applied Fracture Mechanics12 citationsDOI

Topics & Concepts

Categorical variableParis' lawAusteniteGrowth rateEmpirical modellingMachine learningArtificial intelligenceFeature (linguistics)Empirical researchMaterials scienceCorrosion fatigueComputer scienceCorrosionFracture mechanicsMetallurgyMathematicsCrack closureStatisticsSimulationComposite materialLinguisticsPhilosophyMicrostructureGeometryNon-Destructive Testing TechniquesHydrogen embrittlement and corrosion behaviors in metalsFatigue and fracture mechanics
Machine learning approach for predicting and understanding fatigue crack growth rate of austenitic stainless steels in high-temperature water environments | Litcius