On the generalization capability of artificial neural networks used to estimate fretting fatigue life

This study assesses the generalization capacity of artificial neural networks (ANNs) for predicting fretting fatigue of mechanical contacts using different materials and geometries. These ANN utilize as inputs, material properties and stress quantities that have been physically related to the fatigu...

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Bibliographic Details
Published in:Tribology international 2024-04, Vol.192, p.109222, Article 109222
Main Authors: Oliveira, Giorgio André Brito, Cardoso, Raphael Araújo, Júnior, Raimundo Carlos Silverio Freire, Doca, Thiago, Araújo, José Alexander
Format: Article
Language:English
Subjects:
Aeronautical alloys
Artificial neural networks
Fretting fatigue
Multiaxial fatigue
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Summary:This study assesses the generalization capacity of artificial neural networks (ANNs) for predicting fretting fatigue of mechanical contacts using different materials and geometries. These ANN utilize as inputs, material properties and stress quantities that have been physically related to the fatigue crack initiation mechanism under multiaxial loading. Initially trained and validated on aeronautical aluminum alloys data, one tests their generalization performance by applying them to fretting fatigue data for Ti‐6Al‐4V, ASTM A743 CA6NM steel, and Al 7050-T745, employing both cylindrical and spherical pads under various loading conditions, including out-of-phase loading. The ANNs adeptly predict fatigue lives across this extensive dataset, surpassing classical multiaxial fatigue criteria in accuracy. This underscores the effectiveness of ANN-based methodologies in diverse fretting fatigue scenarios. •ANN models trained on 132 aluminum FF data to predict fatigue life.•Inputs are based on established multiaxial fatigue criteria stress parameters.•They provided accurate life estimates for more than 200 FF tests.•The ANN models outperformed multiaxial fatigue models based on experimental values.•Demonstrated excellent generalization capability for new materials.
ISSN:0301-679X
1879-2464
DOI:10.1016/j.triboint.2023.109222