Assessment of artificial neural network for thermohydrodynamic lubrication analysis

Purpose: In this study, artificial neural networks (ANNs) are constructed and validated by using the bearing data generated numerically from a thermohydrodynamic (THD) lubrication model. In many tribological simulations, a surrogate model (meta-model) for obtaining a fast solution with sufficient accuracy is highly desired. Design/methodology/approach: The THD model is represented by two coupled partial differential equations, a simplified generalized Reynolds equation, considering the viscosity variation across the film thickness direction and a transient energy equation for the 3-D film temperature distribution. The ANNs tested are having a single- or dual-hidden-layer with two inputs and one output. The root-mean-square error and maximum/minimum absolute errors of validation points, when comparing with the THD solutions, were used to evaluate the prediction accuracy of the ANNs. Findings: It is demonstrated that a properly constructed ANN surrogate model can predict the THD lubrication performance almost instantly with accuracy adequately retained. Originality/value: This study extends the use of ANNs to the applications other than the analyses dealing with experimental data. A similar procedure can be used to build a surrogate model for computationally intensive tribological models to have fast results. One of such applications is conducting extensive optimum design of tribological components or systems. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0109/.