Modelling of the draw bead coefficient of friction in sheet metal forming
Vol 113 No 3 (2021), Articles
Vol 113 No 3 (2021)

Modelling of the draw bead coefficient of friction in sheet metal forming

Articles
Published September 30, 2021
Łukasz Chodoła
Rzeszow University of Tech¬nology, Faculty of Mechanics and Technology, Depart¬ment of Integrated Design and Tribology Systems, Stalowa Wola, Poland
https://orcid.org/0000-0002-9637-4669
Daniel Ficek
Rzeszow University of Techno¬logy, Faculty of Mechanical Engineering and Aeronau¬tics, Department of Aerospace Engineering, Rzeszów, Poland
https://orcid.org/0000-0002-9978-8939
Ireneusz Szczęsny
Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Department of Aerospace Engineering, Rzeszów, Poland
https://orcid.org/0000-0002-3712-8548
Tomasz Trzepieciński
Rzeszow University of Technology, Faculty of Mechanical Engineering and Ae¬ronautics, Department of Manufacturing and Production Engineering, Rzeszów, Poland
https://orcid.org/0000-0002-4366-0135
Łukasz Wałek
Rzeszow University of Techno¬logy, Faculty of Mechanical Engineering and Aeronau¬tics, Avionics and Control Department, Rzeszów, Poland
https://orcid.org/0000-0002-2446-4403
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Keywords

artificial neural networks, coefficient of friction, drawbead, friction, sheet metal forming

How to Cite

Chodoła, Łukasz, Ficek, D., Szczęsny, I., Trzepieciński, T., & Wałek, Łukasz. (2021). Modelling of the draw bead coefficient of friction in sheet metal forming. Technologia I Automatyzacja Montażu (Assembly Techniques and Technologies), 113(3), 3-9. Retrieved from https://journals.prz.edu.pl/tiam/article/view/913
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Abstract

This paper presents the results of determining the value of the coefficient of friction on the drawbead in sheet metal forming. As the research material, steel, brass and aluminium alloy sheets cut at different directions according to the sheet rolling direction were used. Sheet strip specimens were tested under dry friction and lubrication of sheet surfaces using machine oil. Results of experiments were used to study the effect of process parameters on the coefficient of friction using artificial neural networks. Input data was optimized using genetic algorithm, forward stepwise selection and backward stepwise selection. The aim of the research was to determine the effect of the value of the unit penalty on the significance of individual input parameters of the neural network and the value of the error generated by the multilayer perceptron. It was found that in the case of all materials the value of coefficient of friction for specimen orientation 90° was greater than for the specimen orientation 0°. Friction tests also reveal that sheet lubrication reduced the frictional resistance by 12-39%, depending on the grade of sheet material. Among all input parameters that significantly affect the value of the coefficient of friction the most important are the lubrication conditions and the orientation of the sample.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

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