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Article Dans Une Revue Tribology International Année : 2023

Comparison between physical and machine learning modeling to predict fretting wear volume

Résumé

The objective of this study is to compare the performance of machine-learning strategy versus a physical friction-energy wear approach to predict the fretting wear volume of a low-alloyed steel contact by varying several loading parameters. Then, an artificial neural network (ANN) is used to predict the wear volume at each loading condition. These predictions were compared versus a physics-based friction energy wear modeling considering the third-body theory and the contact-oxygenation concept. A parametric study is performed to compare the prediction errors as a function of the proportion of the experiments involved in the modeling process. The results suggest that the physical modeling is more performant than ANN when a restricted number of experimental data is available for the calibration process.
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Dates et versions

hal-03874992 , version 1 (28-11-2022)

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Soha Baydoun, Mohammed Fartas, Siegfried Fouvry. Comparison between physical and machine learning modeling to predict fretting wear volume. Tribology International, 2023, 177, pp.107936. ⟨10.1016/j.triboint.2022.107936⟩. ⟨hal-03874992⟩
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