Application of artificial neural networks for the prediction of the service conditions of an elastohydrodynamic EHL contact in the presence of solid pollutant
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Laboratory of Mechanical Engineering and Materials, University of August 20, 1955- Skikda, Algeria
Department of Mechanical Engineering, University of August 20, 1955-Skikda, Algeria
Department of Petrochemical, University of August 20, 1955-Skikda, Algeria
Submission date: 2023-05-22
Final revision date: 2023-08-16
Acceptance date: 2024-01-06
Online publication date: 2024-01-09
Publication date: 2024-01-16
Corresponding author
Sabrina Mattallah   

Department of Mechanical Engineering Laboratory of Mechanical Engineering and Materials, University of 20 August 1955- Skikda, Algeria.
Diagnostyka 2024;25(1):2024107
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, We also found that the quality of the predicted model is very good, with overall determination coefficients of R^2 learning = 0.9985 and R^2 validation = 0.9996.
This research received no external funding.
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