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

• Autorzy: Mattallah Sabrina , Kelaiaia Ridha , Louahem M’Sabah Hanane , Kerboua Adlen

Identyfikatory

DOI

10.29354/diag/178317

• Języki publikacji: EN

Abstrakty

EN

Lubricated mechanical mechanisms operate under service conditions influenced by several environmental parameters, and their life times 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, anexperimental 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, whichare: 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 𝑅² learning = 0.9985 and 𝑅² validation = 0.9996. Several levels of degradation depending on the operating conditions are predicted using the desirability function.

Słowa kluczowe

EN

elastohydrodynamic contact; solid pollution; artificial neural network; wear

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024107
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Mattallah Sabrina

• Laboratory of Mechanical Engineering and Materials, University of August 20, 1955- Skikda, Algeria
• Department of Mechanical Engineering, University of August 20, 1955-Skikda, Algeria

• https://orcid.org/0000-0002-7932-6905

autor

Kelaiaia Ridha

• Laboratory of Mechanical Engineering and Materials, University of August 20, 1955- Skikda, Algeria
• Department of Mechanical Engineering, University of August 20, 1955-Skikda, Algeria

• https://orcid.org/0000-0003-3078-462X

autor

Louahem M’Sabah Hanane

• Department of Mechanical Engineering, University of August 20, 1955-Skikda, Algeria

• https://orcid.org/0009-0007-5919-8997

autor

Kerboua Adlen

• Department of Petrochemical, University of August 20, 1955-Skikda, Algeria

• https://orcid.org/0000-0003-3626-0288

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).