Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts

Project Details

• Student(s): Joe Issa and Alain El Hajj
• Advisor(s): Dr. Wassim Habchi
• Department: Industrial & Mechanical
• Academic Year(s): 2022-2023

Abstract

This study extends the use of Machine Learning (ML) approaches for lubricant film thickness predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by considering wide and narrow contacts over a wide range of ellipticity and operating conditions. Finite element (FEM) simulations are used to generate substantial training and testing datasets that are used within the proposed ML framework. The complete dataset entails 915 samples; split into an 823-sample training dataset and a 92-sample testing dataset, corresponding to 90% and 10% of the combined dataset samples, respectively. The proposed ML model consists of a pre-processing stage in which conventional EHD dimensionless groups are used to minimize the number of inputs to the model, reducing them to only three. The core of the model is based on Gaussian Process Regression (GPR); a powerful ML regression tool, well-suited for small-sized datasets, producing output central and minimum film thicknesses also in dimensionless form. The last stage is a post-processing one, in which the output film thicknesses are retrieved in dimensional from. The results reveal the capabilities and potential of the proposed ML framework; producing quasi-instantaneous predictions, that are far more accurate than conventional film thickness analytical formulae. In fact, produced central and minimum film thickness predictions are on average within 0.3% and 1.0% of the FEM results, respectively.

Publications

• Issa J., El Hajj A., Vergne P. and Habchi W. Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts, Lubricants, Vol. 11, p. 497, 2023.