Application of feed-forward neural networks for modeling friction phenomena with presence of vegetable oil-based bio-lubricants

• Autorzy: Trzepieciński Tomasz , Mezher Marwan T. , Luiz Valmir Dias , Laouini Salah Eddine , Lemu Hirpa G.

Identyfikatory

DOI

10.2478/adms-2025-0003

• Języki publikacji: EN

Abstrakty

EN

Reducing the use of petroleum-based oil lubricants in metal forming processes is a major task of modern sustainable industry. In order to meet the expectations of industry, this article presents the friction results of DC04 sheets in a friction pair with 145Cr6 tool steel tested with corn, sunflower, rapeseed, cottonseed and soybean vegetable oils. The friction process variables were also contact pressure and surface roughness of countersamples. The assessment of the lubricant efficiency was based on the friction coefficient value. Complex phenomena in the contact zone make the interpretation of results difficult. Therefore, feed-forward neural networks were used to analyse the relationship between input parameters and coefficient of friction. Different training algorithms and transfer functions were tested to find the optimal architecture of the neural network. The friction coefficient value, depended on the friction conditions and considering all tested oils, ranged between 0.155 and 0.181. The 3-10-1 neural network trained using the Levenberg-Marquardt algorithm and consisted of neurons with a radial basis transfer function provided the lowest mean squared error (MSE = 2.2233×10−6) and the root mean squared error (RMSE = 0.001491). The prediction quality of this network defined by the coefficient of determination was R2 = 0.9697.

Słowa kluczowe

EN

feedforward neural network; coefficient of friction; friction; steel; training algorithm; transfer function; tribology

PL

jednokierunkowa sieć neuronowa; współczynnik tarcia; tarcie; stal; algorytm uczenia; funkcja przenoszenia; tribologia

• Wydawca: Politechnika Gdańska
• Czasopismo: Advances in Materials Science
• Rocznik: 2025
• Tom: Vol. 25, nr 1(83)
• Strony: 51--65
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Trzepieciński Tomasz

• Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland

autor

Mezher Marwan T.

• Departamento de Deseño na Enxeñaría, Universidade de Vigo, 36310 Vigo, Spain
• Institute of Applied Arts, Middle Technical University, Baghdad 10074, Iraq

autor

Luiz Valmir Dias

• Department of Metallurgy and Chemistry, Centro Federal de Educação Tecnológica de Minas Gerais, Timóteo 35180-008, Brazil

autor

Laouini Salah Eddine

• Department of Process Engineering and Petrochemical, Faculty of Technology, University of El Oued, El-Oued 39000, Algeria
• Laboratory of Biotechnology Biomaterial and Condensed Matter, Faculty of Technology, University of El Oued, El-Oued 39000, Algeria

autor

Lemu Hirpa G.

• Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, N-4036 Stavanger, Norway

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