An Evaluation and Comparison of Machine Learning Methods for Prediction of Lubricant Film Thickness

Lubricant film thickness is a tribological parameter that significantly impacts the performance of a tribo-system, primarily by affecting the resulting friction and wear. Measuring or predicting film thickness is therefore crucial for any hydrodynamically lubricated machine element. Numerical techniques are often employed to solve the film thickness under specified operating conditions. However, those approaches are computationally expensive and time-consuming. Machine learning methods have been proven to be a highly accurate and faster alternative to predicting lubricant film thickness. Still, these methods have their own benefits and drawbacks with regards to accuracy, speed, and model interpretability. In this paper, a speed/accuracy comparison is provided between the numerical solution, artificial neural network, and other machine learning approaches for determination of lubricant film thickness. It is found that non-ANN methods can be ideal in cases where prediction time is prioritized while high accuracy is still desired; the tested non-ANN models are orders of magnitude faster than both the numerical solution and the ANN approach, while accuracy is still high with a maximum R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.997. This finding, combined with the possible benefit of easy and accurate model interpretability, shows that non-ANN methods can be very useful for this application.