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

<p>This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from the pin-on-disk tribometer. As test material, deep-drawing quality DC01, DC03 and DC05 steel sheets were used. The experimental results show that the coefficient of friction depends on the measured angle from the rolling direction and corresponds to the surface topography. The number of input variables of the artificial neural network was optimized using genetic algorithms. In this process, surface parameters of the sheet, sheet material parameters, friction conditions and pressure force were used as input parameters to train the artificial neural network. Some of the obtained results have pointed out that genetic algorithm can successfully be applied to optimize the training set. The trained multilayer perceptron predicted the value of the friction coefficient for the DC04 sheet. It was found that the tested steel sheet exhibits anisotropic tribological properties. The highest values of the coefficient of friction under dry friction conditions were registered for sheet DC05, which had the lowest value of the yield stress. Prediction results of coefficient of friction by multilayer perceptron were in qualitative and quantitative agreement with the experimental ones.</p>
</abstract>

This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from the pin-on-disk tribometer. As test material, deep-drawing quality DC01, DC03 and DC05 steel sheets were used. The experimental results show that the coefficient of friction depends on the measured angle from the rolling direction and corresponds to the surface topography. The number of input variables of the artificial neural network was optimized using genetic algorithms. In this process, surface parameters of the sheet, sheet material parameters, friction conditions and pressure force were used as input parameters to train the artificial neural network. Some of the obtained results have pointed out that genetic algorithm can successfully be applied to optimize the training set. The trained multilayer perceptron predicted the value of the friction coefficient for the DC04 sheet. It was found that the tested steel sheet exhibits anisotropic tribological properties. The highest values of the coefficient of friction under dry friction conditions were registered for sheet DC05, which had the lowest value of the yield stress. Prediction results of coefficient of friction by multilayer perceptron were in qualitative and quantitative agreement with the experimental ones.

Modelling Anisotropic Phenomena of Friction of Deep-Drawing Quality Steel Sheets Using Artificial Neural Networks

Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics

Department of Materials Forming and Processing

Department of Mechanical and Structural Engineering

Rzeszow University of Technology, Faculty of Mechanics and Technology

Department of Integrated Design and Tribology Systems

Advances in Materials Science

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

This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from...

Modelling Anisotropic Phenomena of Friction of Deep-Drawing Quality Steel Sheets Using Artificial Neural Networks

Publicado en línea: 05 oct 2021

Páginas: 31 - 42

DOI: https://doi.org/10.2478/adms-2021-0016

Palabras clave
artificial neural networks, coefficient of friction, genetic algorithm, friction, sheet metal forming

© 2021 Tomasz Trzepieciński et al., published by Sciendo

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

Modelling Anisotropic Phenomena of Friction of Deep-Drawing Quality Steel Sheets Using Artificial Neural Networks

Publicado en línea: 05 oct 2021

Páginas: 31 - 42

DOI: https://doi.org/10.2478/adms-2021-0016

Palabras claveartificial neural networks, coefficient of friction, genetic algorithm, friction, sheet metal forming

© 2021 Tomasz Trzepieciński et al., published by Sciendo

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

Palabras clave
artificial neural networks, coefficient of friction, genetic algorithm, friction, sheet metal forming