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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References

Antosz K., Chandima R.R.M. 2019. „Spare parts’ criticality assessment and prioritization for enhancing manufacturing systems’ availability and reliability”. Journal of Manufac¬turing Systems 50 : 212-225.

Buj-Corral I., Sivatte-Adroer M., Llanas-Parra X. 2020. „Adaptive indirect neural network model for roughness in honing processes”. Tribology International 141 : 105891.

Jamli M.R., Farid N.M. 2019. „The sustainability of neural network applications within finite element analysis in sheet metal forming: A review”. Measurement 138: 446-460.

Jasiukiewicz-Kaczmarek M., Antosz K., Wyczółkowski R., Mazurkiewicz D., Sun B., Qian C., Ren Y. 2021. „Applica¬tion of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Eva¬luation of the Maintenance Factors Affecting Sustainable Manufacturing”. Energies 14 (5) : 1436.

Ke J., Liu Y., Zhu H., Zhang Z. 2018. „Formability of sheet metal flowing through drawbead–an experimental investi¬gation”. Journal of Materials Processing Technology 254 : 283-293.

Kluz R., Antosz K., Trzepieciński T., Bucior M. 2021. „Mo¬delling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Tre¬atable Steel”. Materials 14 : 1175.

Liu X., Tian S., Tao F., Yu W. 2021. „A review of artificial neural networks in the constitutive modeling of composi¬te materials”. In Press. https://doi.org/10.1016/j.composi¬tesb.2021.109152

Meng F., Gong J., Yang S., Huang L., Zhao H., Tang X. „Study on tribo-dynamic behaviors of rolling bearing-rotor system based on neural network”. Tribology International 156 : 106829.

Ramkumar T., Selvakumar M., Chandrasekar P., Mohanraj M., Krishnasharma R. 2021. „Monitoring the neural network modelling of wear behaviour of Ti-6Al-4 V reinforced with nano B4C particle”. Materials Today: Proceedings 41 : 942- 950.

Schmid H., Hetz P., Merklein M. 2019. „Failure behaviour of different sheet metal after passing a drawbead”. Procedia Manufacturing 34 : 125-132.

Seshacharyulu K., Bandhavi C., Balu Naik B., Rao S.S., Singh S.K. 2018. „Understanding Friction in sheet metal forming-A review”. Materials Today: Proceedings 5 (9) : 18538-18244.

Shisode M., Hazrati J., Mishra T., de Rooij M., van den Bo¬ogaard T. 2021. „Mixed lubrication friction model including surface texture effects for sheet metal forming”. Journal of Materials Processing Technology. 291 : 117035.

Sigvant M., Pilthammar J., Hol J., Wiebenga J.H., Chezan T., Carleer B., van den Boogard T. 2019. „Friction in sheet metal forming: influence of surface roughness and strain rate on sheet metal forming simulation results”. Procedia Manufacturing 29 : 512-519.

Smith L.A., Zhou Y.J., Zhou D.J., Du C., Wanintrudal C. 2009. „A new experimental test apparatus for angle binder draw bead simulations”. Journal of Materials Processing Technology 209 (10) : 4942-4948.

Trzepieciński T., Fejkiel R. 2017. „On the influence of de¬formation of deep drawing quality steel sheet on surface topography and friction”. Tribology International 115 : 78- 88.

Wang W., Zhao Y., Wang Z., Hua M., Wei X. 2016. „A stu¬dy on variable friction model in sheet metal forming with advanced high strength steels”. Tribology International 93 : 17-28.

Wang Z., Zhang Q., Liu Y., Zhang Z. 2017. „A robust and accurate geometric model for automated design of drawbe¬ads in sheet metal forming”. Computer-Aided Design 92 : 42-57.

Xu Z., Huang J., Mao M., Peng L., Lai X. 2020. „An inve¬stigation on the friction in a micro sheet metal roll forming processes considering adhesion and ploughing”. Journal of Materials Processing Technology 285 : 116790.