Assessment of the effectiveness of lubrication of Ti-6Al-4V titanium alloy sheets using radial basis function neural networks
Keywords:Artificial neural networks, coefficient of friction, friction, sheet metal forming, Ti-6Al-4V, titanium sheets.
The aim of the research presented in this article was to determine the value of the friction coefficient using a simple tribological test and to build an empirical model of friction with the use of radial basis function artifi-cial neural networks. The friction tests were carried out on a specially designed friction simulator that allows a sheet metal strip to be drawn between two fixed dies. The test materials were sheets of Ti-6Al-4V titanium alloy with a thickness of 0.5 mm. The friction tests were carried out with variable contact forces of counter-samples with rounded surfaces and in various lubrication conditions. Mineral oils and bio-degradable oils with the addition of boric acid (5 wt %) were tested. Based on the results of friction investigations, neural models of friction were built using RBF artificial neural networks. The good properties of the RBF network 2:2-35-1:1 were confirmed by a high value of the determination coefficient R2 = 0.9984 and a low value of the S.D. ratio equal to 0.0557. It was found that the COF value was the highest for the average values of both the nominal pressure and kinematic viscosity. Over the entire range of nominal pressures applied, SAE10W-40 engine oil ensured the most effective reduction of the COF. The COF value was the highest for the average values of both the nominal pressure and kinematic viscosity.
C. V. Nielsen, N. Bay. Review of friction modeling in metal forming processes. Journal of Materials Processing Technology 255:234 – 241, 2018. https://doi.org/10.1016/j.jmatprotec.2017.12.023.
T. Trzepiecinski, R. Fejkiel. On the influence of deformation of deep drawing quality steel sheet on surface topography and friction. Tribology International 115:78 – 88, 2017. https://doi.org/10.1016/j.triboint.2017.05.007.
J. Shen, C. Wu, L. Zhang. Effects of sliding speed and lubrication on the tribological behaviour of stainless steel. The International Journal of Advanced Manufacturing Technology 94:341 – 350, 2018. https://doi.org/10.1007/s00170-017-0907-8.
T. Trzepiecinski, H. G. Lemu. Frictional conditions of AA5251 aluminium alloy sheets using drawbead simulator tests and numerical methods. Strojniški vestnik - Journal of Mechanical Engineering 60(1):51 – 60, 2014. https://doi.org/10.5545/sv-jme.2013.1310.
J. Jaworski, T. Trzepiecinski. Research on durability of turning tools made of low-alloy high-speed steels. Kovove Materialy - Metallic Materials 54:17 – 25, 2016. https://doi.org/10.4149/km_2016_1_17.
A. L. Yerokhin, X. Nie, A. Leyland, A. Matthews. Characterisation of oxide films produced by plasma electrolytic oxidation of a Ti–6Al–4V alloy. Surface and Coatings Technology 130(2):195 – 206, 2000. https://doi.org/10.1016/S0257-8972(00)00719-2.
M. Fellah, M. Labaiz, O. Assala, et al. Tribological behavior of Ti-6Al-4V and Ti-6Al-7Nb alloys for total hip prosthesis. Advances in Tribology 2014:1 – 13, 2014. https://doi.org/10.1155/2014/451387.
S. Kaur, K. H. Ghadirinejad, R. H. Oskouei. An overview on the tribological performance of titanium alloys with surface modifications for biomedical applications. Lubricants 7:65, 2019. https://doi.org/10.3390/lubricants7080065.
J. Cao, R. Zhou, Q. Wang, Z. C. Xia. Strip-oncylinder test apparatus for die wear characterization. CIRP Annals - Manufacturing Technology 58(1):251 – 254, 2009. https://doi.org/10.1016 j.cirp.2009.03.098.
I. Masters, D. K. Williams, R. Roy. Friction behaviour in strip draw test of pre-stretched high strength automotive aluminium alloys. International Journal of Machine Tools and Manufacture 73:17 – 24, 2013. https://doi.org/10.1016/j.ijmachtools.2013.05.002.
V. Prakash, D. R. Kumar. Performance evaluation of bio-lubricants in strip drawing and deep drawing of an aluminium alloy. Advances in Materials and Processing Technologies 6:1 – 14, 2020. https://doi.org/10.1080/2374068X.2020.1838134.
L. Figueiredo, A. Ramalho, M. C. Oliveira, L. Menezes. Experimental study of friction in sheet metal forming. Wear 271(9):1651 – 1657, 2011. https://doi.org/10.1016/j.wear.2011.02.020.
M. R. Lovell, Z. Deng. Characterization of interfacial friction in coated sheet steels: influence of stamping process parameters and wear mechanisms. Tribology International 35(2):85 – 95, 2002. https://doi.org/10.1016/S0301-679X(01)00097-4.
M. Ciavarella, J. Joe, A. Papangelo, J. Barber. The role of adhesion in contact mechanics. Journal of the Royal Society Interface 16:20180738, 2019. https://doi.org/10.1098/rsif.2018.0738.
Y. Fan, Z. K. Li, J. Liu. Simulation of surface contact process and study of friction and wear mechanisms. Advanced Materials Research 562 - 564:540 – 543, 2012. https://doi.org/10.4028/www.scientific.net/AMR.562- 564.540.
J. Hol, J. H. Wiebenga, B. Carleer. Friction and lubrication modelling in sheet metal forming: Influence of lubrication amount, tool roughness and sheet coating on product quality. Journal of Physics: Conference Series 896:012026, 2017. https://doi.org/10.1088/1742-6596/896/1/012026.
F. Sgarabotto, A. Ghiotti. Frictional behaviour of environmentally friendly lubricants for sheet metal forming processes. Key Engineering Materials 504 - 506:537 – 542, 2012. https://doi.org/10.4028 www.scientific.net/KEM.504- 506.537.
S. Weidel, U. Engel, M. Merklein, M. Geiger. Basic investigations on boundary lubrication in metal forming processes by in situ observation of the real contact area. Production Engineering 4:107 – 114, 2009. https://doi.org/10.1007/s11740-009-0198-5.
T. Trzepiecinski. Tribological performance of environmentally friendly bio-degradable lubricants based on a combination of boric acid and bio-based oils. Materials 13:3892, 2020. https://doi.org/10.3390/ma13173892.
M. Lüchinger, I. Velkavrh, K. Kern, et al. Development of a constitutive model for friction in bulk metal forming. Lubricants 6:42, 2018. https://doi.org/10.3390/lubricants6020042.
Y. Huang, S. Gilmour, K. Mylona, P. Goos. Optimal design of experiments for nonlinear response surface models. Journal of the Royal Statistical Society Series C - Applied Statistics 68(3):623 – 640, 2019. https://doi.org/10.1111/rssc.12313.
S. Crino, D. Brown. Global optimization with multivariate adaptive regression splines. IEEE Transactions on Systems, Man, and Cybernetics Part B, Cybernetics 37(2):333 – 340, 2007. https://doi.org/10.1109/TSMCB.2006.883430.
M. Chwalina. Demand modelling in telecommunications comparison of standard statistical methods and approaches based up-on artificial intelligence methods including neural networks. Acta Polytechnica 49(2):48 – 52, 2009. https://doi.org/10.14311/1121.
Y. L. Karnavas, A. Vairis. Modelling of frictional phenomena using neural networks: friction coefficient estimation. In Proc. Of the IASTED Int. Conf. Applied Simulation and Modelling (ASM 2011), pp. 54 – 58. Crete, Greece, 2011.
H. Cetinel. The artificial neural network based prediction of friction properties of Al2O3-TiO2 coatings. Industrial Lubrication and Tribology 64(5):288 – 293, 2012. https://doi.org/10.1108/00368791211249674.
Rustam, A. Y. Gunawan, M. T. A. P. Kresnowati. Artificial neural network approach for the identification of clove buds origin based on metabolites composition. Acta Polytechnica 60(5):440 – 447, 2020. https://doi.org/10.14311/AP.2020.60.0440.
I. Bukovský, M. Kolovratník. A neural network model for predicting NOx at the Melník 1 coal-powder power plant. Acta Polytechnica 52(3):17 – 22, 2012. https://doi.org/10.14311/1538.
A. Bahari, R. Lewis, T. Slatter. Friction and wear phenomena of vegetable oil–based lubricants with additives at severe sliding wear conditions. Tribology Transactions 61(2):207 – 219, 2018. https://doi.org/10.1080/10402004.2017.1290858.
M. Fan, L. Ma, C. Zhang, et al. Biobased green lubricants: Physicochemical, tribological and toxicological properties of fatty acid ionic liquids. Tribology Transactions 61(2):195 – 206, 2018. https://doi.org/10.1080/10402004.2017.1290856.
S. Haykin. Neural Networks: A Comprehensive Foundation. Macmillan Publishing, New York, 1994.
C. Wang, B. Guo, D. Shan. Friction related size-effect in microforming - A review. Manufacturing Review 1:23, 2014. https://doi.org/10.1051/mfreview/2014022.
Copyright (c) 2021 Tomasz Trzepieciński, Marcin Szpunar
This work is licensed under a Creative Commons Attribution 4.0 International License.