Application of Tribological Artificial Neural Networks in Machine Elements
| العنوان: | Application of Tribological Artificial Neural Networks in Machine Elements |
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| المؤلفون: | J. Walker, H. Questa, A. Raman, M. Ahmed, M. Mohammadpour, S. R. Bewsher, G. Offner |
| المصدر: | Tribology Letters. 71 |
| بيانات النشر: | Springer Science and Business Media LLC, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Mechanics of Materials, Mechanical Engineering, Surfaces and Interfaces, Surfaces, Coatings and Films |
| الوصف: | Traditionally, analytical equations used in tribo-dynamic modelling, such as those used for predicting central film thickness within elastohydrodynamic lubricated contacts, have led to timely computations, but tend to lack the accuracy of numerical solvers. However, it can be shown that data-driven solutions, such as machine learning, can significantly improve computational efficiency of tribo-dynamic simulations of machine elements without comprising accuracy relative to the numerical solution. During this study, artificial neural networks (ANNs) are trained using data produced via numerical solutions, which are constrained by the regimes of lubrication to ensure the quality of the training data set. Multiple ANNs are then implemented to predict EHL central film thickness, as well as viscous and boundary friction, in multiple commonly used machine elements, such as a rolling element bearing and a spur gear. The viscous and boundary friction ANN prediction are compared directly against ball-on-disc experimental measurements to validate its accuracy. |
| تدمد: | 1573-2711 1023-8883 |
| DOI: | 10.1007/s11249-022-01673-5 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::1d5c0ebf53ff8a959f1444cc722ecf93 https://doi.org/10.1007/s11249-022-01673-5 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........1d5c0ebf53ff8a959f1444cc722ecf93 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15732711 10238883 |
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| DOI: | 10.1007/s11249-022-01673-5 |