Application of artificial neural networks for the prediction of the service conditions of an elastohydrodynamic EHL contact in the presence of solid pollutant

Abstract

Lubricated mechanical mechanisms operate under service conditions influenced by several environmental parameters, and their lifetimes may be threatened due to inappropriate use or by the presence of solid contaminants. The objective of this work is to study the effect of three operating parameters, namely: rotational speed 𝑉, load 𝑄 and kinematic viscosity 𝜈 in the presence of three sizes of solid contaminants 𝑇, on the degradation of an EHL contact, to predict the ranges of effects that may lead to the damage of the contacting surfaces. In our investigation, an experimental design of nine trials is used to combine four factors with three levels each to accomplish the experimental investigation. Artificial neural network regression and the desirability function were used for the interpretation and modelling of the responses, which are: wear 𝑊, arithmetic mean height 𝑅𝑎, total profile height 𝑅𝑡 and maximum profile height 𝑅𝑧. From these methods we observed that the sand grain sizes have a significant impact on the wear 𝑊 and the roughness 𝑅𝑎, but that viscosity has the primary influence on the variation of the roughnesses 𝑅𝑡 and 𝑅𝑧. We also found that the quality of the predicted models is very good, with overall determination coefficients of 𝑅 2 learning = 0.9985 and 𝑅 2 validation = 0.9996. Several levels of degradation depending on the operating conditions are predicted using the desirability function.