Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network

•A constant life diagram modelling based on artificial neural network is proposed.•An artificial neural network based on a MLP network trained with the BPM is trained.•A hybrid ANN-Stüssi model to determine the values is proposed.•The probabilistic Stüssi fatigue model based on Weibull distribution...

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Veröffentlicht in:	International journal of fatigue Jg. 135; S. 105527 - 11
Hauptverfasser:	Barbosa, Joelton Fonseca, Correia, José A.F.O., Júnior, R.C.S. Freire, Jesus, Abílio M.P. De
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.06.2020 Elsevier BV
Schlagworte:	Algorithms Artificial neural network Artificial neural networks Back propagation networks Back-propagation algorithm Constant life diagram Fatigue Fatigue life Fatigue limit Fatigue strength High cycle fatigue Life prediction Machine learning Materials fatigue Mechanical components Multilayer perceptrons Neural networks Pressure vessels Resistance factors S N diagrams Safety Stüssi model Stüssi model Constant life diagram Fatigue Artificial neural network Back-propagation algorithm
ISSN:	0142-1123, 1879-3452
Online-Zugang:	Volltext
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