<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The article presents the results of modelling the friction phenomenon using artificial neural networks and analysis of variance. The test material was composed of strip specimens made of 0.5-mm-thick alpha-beta Grade 5 (Ti-6Al-4V) titanium alloy sheet. A special tribotester was used in the tests to simulate the friction conditions between the punch and the sheet metal in the sheet metal forming process. A test called the strip drawing test has been conducted in conditions in which the sheet surface is lubricated with six environmentally friendly oils (palm, coconut, olive, sunflower, soybean and linseed). Based on the results of the strip drawing test, a regression model and an artificial neural network model were built to determine the complex interactions between the process parameters and the friction coefficient. A multilayer perceptron with one hidden layer and eight neurons in this layer showed the best fit to the training data. The network training was conducted using three algorithms, i.e. Levenberg-Marquardt, back propagation and quasi-Newton. Taking into consideration both the coefficient of determination R2 (0.962) and S.D. ratio (0.272), the best regression characteristics were presented by the network trained using the Levenberg-Marquardt algorithm. From the response surfaces of the quadratic regression model it was found that an increase in the density of lubricant at a specific pressure causes a reduction in the coefficient of friction. Low density and high kinematic viscosity of the oil leads to a high coefficient of friction.</p>
</abstract>

The article presents the results of modelling the friction phenomenon using artificial neural networks and analysis of variance. The test material was composed of strip specimens made of 0.5-mm-thick alpha-beta Grade 5 (Ti-6Al-4V) titanium alloy sheet. A special tribotester was used in the tests to simulate the friction conditions between the punch and the sheet metal in the sheet metal forming process. A test called the strip drawing test has been conducted in conditions in which the sheet surface is lubricated with six environmentally friendly oils (palm, coconut, olive, sunflower, soybean and linseed). Based on the results of the strip drawing test, a regression model and an artificial neural network model were built to determine the complex interactions between the process parameters and the friction coefficient. A multilayer perceptron with one hidden layer and eight neurons in this layer showed the best fit to the training data. The network training was conducted using three algorithms, i.e. Levenberg-Marquardt, back propagation and quasi-Newton. Taking into consideration both the coefficient of determination R2 (0.962) and S.D. ratio (0.272), the best regression characteristics were presented by the network trained using the Levenberg-Marquardt algorithm. From the response surfaces of the quadratic regression model it was found that an increase in the density of lubricant at a specific pressure causes a reduction in the coefficient of friction. Low density and high kinematic viscosity of the oil leads to a high coefficient of friction.

Multivariate Modelling of Effectiveness of Lubrication of Ti-6al-4v Titanium Alloy Sheet using Vegetable Oil-Based Lubricants

Department of Materials Forming and Processing

Doctoral School of Engineering and Technical Sciences

Advances in Materials Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The article presents the results of modelling the friction phenomenon using artificial neural networks and analysis of variance. The test material was composed...

Multivariate Modelling of Effectiveness of Lubrication of Ti-6al-4v Titanium Alloy Sheet using Vegetable Oil-Based Lubricants

Published Online: Jul 01, 2021

Page range: 26 - 39

DOI: https://doi.org/10.2478/adms-2021-0009

Keywords
coefficient of friction, sheet metal, sheet metal forming, Ti-6Al-4V, titanium alloy

© 2021 Tomasz Trzepieciński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Multivariate Modelling of Effectiveness of Lubrication of Ti-6al-4v Titanium Alloy Sheet using Vegetable Oil-Based Lubricants

Published Online: Jul 01, 2021

Page range: 26 - 39

DOI: https://doi.org/10.2478/adms-2021-0009

Keywordscoefficient of friction, sheet metal, sheet metal forming, Ti-6Al-4V, titanium alloy

© 2021 Tomasz Trzepieciński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
coefficient of friction, sheet metal, sheet metal forming, Ti-6Al-4V, titanium alloy