The use of artificial neural networks to the analysis of lubricating performance of vegetable oils for plastic working applications

• Autorzy: Szewczyk Marek , Mezher Marwan T. , Jaber Tanya Abdulsattar

Identyfikatory

DOI

10.7862/rm.2025.1

Warianty tytułu

PL

Zastosowanie sztucznych sieci neuronowych do analizy wydajności smarowania olejów roślinnych w przeróbce plastycznej metali

• Języki publikacji: EN

Abstrakty

EN

Sheet metal forming is the basic method of processing of deep-drawing quality steel sheets used in the automotive industry. A properly planned technological process of forming should include guidelines for friction conditions, or rather the coefficient of friction. Determination of the coefficient of friction is carried out using various methods. In this article, the strip drawing test was used to analyse the friction of low-carbon DC04 steel sheets. The tests were carried out at different contact pressures and with the use of different vegetable-oil based biolubricants. The most common edible and non-edible oils were selected for the tests: sunflower, rape-seed, moringa and karanja. The analysis of the experimental results was carried out using multilayer artificial neural networks (ANNs). Different learning algorithms and different transfer functions were considered in ANNs. Based on the analysis of experimental data, it was noticed that the coefficient of friction decreased with increasing contact pressure. The lowest values of the coefficient of friction, in the entire range of analysed pressures, were observed during lubrication with karanja oil. It was also found that Levenberg-Marquardt training algorithm with log-sigmoid transfer function provided the lowest values of performance errors and at the same time the highest value of the coefficient of determination R2 = 0.94719.

PL

Kształtowanie blach stalowych głębokotłocznych jest podstawową metodą obróbki blach stosowanych w przemyśle motoryzacyjnym. Prawidłowo zaplanowany proces technologiczny formowania powinien uwzględniać wytyczne dotyczące warunków tarcia, a właściwie współczynnika tarcia. Wyznaczanie współczynnika tarcia przeprowadza się różnymi metodami. W tym artykule do analizy tarcia blach ze stali nisko-węglowej DC04 wykorzystano test przeciągania pasa blachy. Testy przeprowadzono przy różnych naciskach kontaktowych i z wykorzystaniem różnych biosmarów na bazie olejów roślinnych. Do testów wybrano najpopularniejsze oleje jadalne i niejadalne: słonecznikowy, rzepakowy, moringa i karanja. Analizę wyników eksperymentów przeprowadzono z wykorzystaniem wielowarstwowych sztucznych sieci neuronowych. W sieciach neuronowych rozważano różne algorytmy uczenia sieci i różne funkcje przejścia. Na podstawie analizy danych eksperymentalnych zauważono, że współczynnik tarcia zmniejszał się wraz ze wzrostem nacisku kontaktowego. Najmniejsze wartości współczynnika tarcia, w całym zakresie analizowanych nacisków, zaobserwowano podczas smarowania olejem karanja. Stwierdzono również, że algorytm uczenia Levenberga-Marquardta z logarytmiczno-sigmoidalną funkcją przejścia zapewnił najmniejsze wartości błędów sieci neuronowej i jednocześnie największą wartość współczynnika determinacji R2= 0,94719.

Słowa kluczowe

EN

artificial neural networks; friction; lubrication; plastic working; steel sheets

PL

sztuczne sieci neuronowe; tarcie; smarowanie; obróbka plastyczna; blachy stalowe

• Wydawca: Oficyna Wydawnicza Politechniki Rzeszowskiej
• Czasopismo: Advances in Mechanical and Materials Engineering
• Rocznik: 2025
• Tom: Vol. 42, nr 1
• Strony: 5--15
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Szewczyk Marek

• Department of Integrated Design and Tribology Systems, Faculty of Mechanics and Technology, Rzeszów University of Technology

autor

Mezher Marwan T.

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

autor

Jaber Tanya Abdulsattar

• Institute of Applied Arts, Middle Technical University, Iraq

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).