Abstract
The wear characteristics of wrought magnesium alloy AZ91D is assessed by varying the wear test parameters namely sliding velocity, sliding distance and normal load in the pin-on-disc tribometer. The experimental results are used to develop a statistical model, and soft computing models based on artificial neural network and Sugeno–Fuzzy logic to predict the wear rate of AZ91D alloy. Sugeno–Fuzzy model had the highest accuracy in prediction and hence used to study the effect of wear test parameters on the wear rate of AZ91D alloy. It is observed that the wear rate increases with decrease in load, increase in sliding velocity, and increase in sliding distance.
Similar content being viewed by others
References
Stalmann, A., Sebastian, W., Friedrich, H., Schumann, S., and Dröder, K., Properties and processing of magnesium wrought products for automotive applications, Adv. Eng. Mater., 2001, vol. 3, no. 12, pp. 969–974.
Kawalla, R. and Bazhin, V.Y., Properties of isotropy of magnesium alloy strip workpieces, J. Mining Inst., 2016, vol. 222, pp. 828–832.
Shanthi, M., Lim, C.Y.H., and Lu, L., Effects of grain size on the wear of recycled AZ91 Mg, Tribol. Int., 2007, vol. 40, no. 2, pp. 335–338.
Zafari, A., Ghasemi, H.M., and Mahmudi, R., Tribological behavior of AZ91D magnesium alloy at elevated temperatures, Wear, 2012, vols. 292–293, pp. 33–40.
Cižek, L., Greger, M., Pawlica, L., Dobrzanski, L.A., and Tanski, T., Study of selected properties of magnesium alloy AZ91 after heat treatment and forming, J. Mater. Process. Technol., 2004, vols. 157–158, pp. 466–471.
Regulaa, T., Czekaja, E., Fajkiela, A., Saja, K., Lech-Gregab, M., and Bronickic, M., Application of heat treatment and hot extrusion processes to improve mechanical properties of the AZ91 alloy, Arch. Foundry Eng., 2010, vol. 10, no. 2, pp. 141–146.
Strzelecka, M., Iwaszko, J., Malik, M., and Tomczynski, S., Surface modification of the AZ91 magnesium alloy, Arch. Civil Mech. Eng., 2015, vol. 15, no. 4, pp. 854–861.
Huang, Y., Lan., Y., Thomson, S.J., Fang, A., Hoffmann, W.C., and Lacey, R.E., Development of soft computing and applications in agricultural and biological engineering, Comput. Electron. Agric., 2010, vol. 71, no. 2, pp. 107–127.
Kuo, R. and Cohen, P., Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling, Artif. Intel. Eng., 1998, vol. 12, no. 3, pp. 229–242.
Vignesh, R.V., Padmanaban, R., Arivarasu, M., and Karthick, K., Sundar, A.A., and Gokulachandran, J., IOP Conference Series: Materials Science and Engineering, IOP, 2016.
Padmanaban, R., Balusamy, V., and Nouranga, K., Effect of process parameters on the tensile strength of friction stir welded dissimilar aluminum joints, J. Eng. Sci. Technol., 2016, vol. 11, no. 1, p.12.
Novák, V., I., Perfilieva, and J. Mockor, Mathematical Principles of Fuzzy Logic, Springer Science & Business Media, 2012, vol.517.
Yager, R.R. and Zadeh, L.A., An Introduction to Fuzzy Logic Applications in Intelligent Systems, Springer Science & Business Media, 2012, vol.165.
Matía, F., Marichal, G.N., and Jimênez, E., Fuzzy Modeling and Control: Theory and Applications, Atlantis, 2014.
Nguyen, H.T. and Prasad, N.D., Fuzzy Modeling and Control: Selected Works of Sugeno, Taylor & Fransis, 1999.
Sahoo, P.K., Satpathy, P.K., and Mohanty, M.N., Elman neural network backpropagation based evaluation of critical busbars in power systems with renewable sources, Int. J. Renew. Energy Res., 2015, vol. 5, no. 2, pp. 532–541.
Ramanlingam, V.V. and Ramasamy, P., Modelling corrosion behavior of friction stir processed aluminium alloy 5083 using polynomial: Radial basis function, Trans. Indian Inst. Met., 2017, vol. 70, no. 10, pp. 2575–2589.
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
About this article
Cite this article
Vignesh, R.V., Padmanaban, R. Forecasting Tribological Properties of Wrought AZ91D Magnesium Alloy Using Soft Computing Model. Russ. J. Non-ferrous Metals 59, 135–141 (2018). https://doi.org/10.3103/S1067821218020116
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1067821218020116