Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning

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dc.contributor.authorAwd, Mustafa
dc.contributor.authorMünstermann, Sebastian
dc.contributor.authorWalther, Frank
dc.date.accessioned2024-01-22T14:29:02Z
dc.date.available2024-01-22T14:29:02Z
dc.date.issued2022-08-25
dc.description.abstractThe posterior statistical distributions of fatigue strength are determined using Bayesian inferential statistics and the Metropolis Monte Carlo method. This study explores how structural heterogeneity affects ultrahigh cycle fatigue strength in additive manufacturing. Monte Carlo methods and procedures may assist estimate fatigue strength posteriors and scatter. The acceptable probability in Metropolis Monte Carlo relies on the Markov chain's random microstructure state. In addition to commonly studied variables, the proportion of chemical composition was demonstrated to substantially impact fatigue strength if fatigue lifetime in crack propagation did not prevail due to high threshold internal notches. The study utilizes an algorithm typically used for quantum mechanics to solve the complicated multifactorial fatigue problem. The inputs and outputs are modified by fitting the microstructural heterogeneities into the Metropolis Monte Carlo algorithm. The main advantage here is applying a general-purpose nonphenomenological model that can be applied to multiple influencing factors without high numerical penalty.en
dc.identifier.urihttp://hdl.handle.net/2003/42290
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24126
dc.language.isoende
dc.relation.ispartofseriesFatigue & fracture of engineering materials & structures;45(11)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subjectAdditive manufacturingen
dc.subjectMachine learningen
dc.subjectMarkov chainen
dc.subjectMonte Carlo simulationen
dc.subjectReinforcement learningen
dc.subjectVery high cycle fatigueen
dc.subject.ddc660
dc.subject.rswkRapid Prototyping <Fertigung>de
dc.subject.rswkMaschinelles Lernende
dc.subject.rswkMarkov-Kettede
dc.subject.rswkMonte-Carlo-Simulationde
dc.subject.rswkBestärkendes Lernen <Künstliche Intelligenz>de
dc.subject.rswkErmüdung bei sehr hohen Lastspielzahlende
dc.titleEffect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learningen
dc.typeTextde
dc.type.publicationtypeArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationAwd M, Münstermann S, Walther F. Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning. Fatigue Fract Eng Mater Struct. 2022; 45(11): 3267-3287. doi:10.1111/ffe.13816de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1111/ffe.13816de

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