Abstract
Aluminium matrix composites are widely used in many applications due to its numerous advantages. Experimental investigation of wear characteristics and prediction of wear is the order of the day. The present study examines the aluminium alloy AA2219-graphite (Gr) composites in terms of its wear characteristics in dry sliding condition. The conventional stir casting method was adopted to fabricate the composites with reinforcements in proportion of 1.5, 3 and 4.5 wt% to determine its capability as self-lubricating material in dry sliding conditions. Taguchi Method was utilized to study each parametric influence on the responses of wear test. A generalized regression neural network (GRNN) is used to predict the wear characteristics of AA2219-Gr composites at different factors and levels based on Taguchi L27 orthogonal array optimized outcomes. The GRNN extrapolations were confirmed by the responses of the study and the predictions were on par with the obtained results.
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Prasad, S.; Asthana, R.: Aluminum metal-matrix composites for automotive applications: tribological considerations. Tribol. Lett. 17, 445–453 (2004)
Rohatgi, P.K.; Ray, S.; Liu, Y.: Tribological properties of metal matrix-graphite particle composites. Int. Mater. Rev. 37, 129–152 (1992)
Hayajneh, M.T.; Hassan, A.M.; Al-Omari, M.A.-H.: The effect of graphite particles addition on the surface finish of machined Al-4 wt% Mg alloys. J. Mater. Eng. Perform. 10, 521–525 (2001)
Ahn, J.; Ochiai, S.: Wear behaviour and friction property of SiCp/Al composites at elevated wear environment temperature. Adv. Compos. Lett. 11, 096369350201100 (2002)
Rajesh Kumar, L.; Saravanakumar, A.; Bhuvaneswari, V.; Gokul, G.; Dinesh Kumar, D.; Jithin Karunan, M.P.: Optimization of wear behaviour for AA2219-MoS2 metal matrix composites in dry and lubricated condition. Mater. Today: Proc. 27(3), 2645–2649 (2020)
Saravanakumar, A.; Sasikumar, P.; Sivasankaran, S.: Effect of graphite particles in drilling of hybrid aluminum matrix composite. Proc. Eng. 97, 495–504 (2014)
Ames, W.; Alpas, A.T.: Wear mechanisms in hybrid composites of graphite-20 Pct SiC in A356 aluminum alloy (Al-7 Pct Si-0.3 Pct Mg). Metall. Mater. Trans. A 26, 85–98 (1995)
Kumar, A.S.; Sasikumar, P.; Nilavusri, N.: Study on drilling of Al/Al2O3/Gr hybrid particulate composites. Appl. Mech. Mater. 766–767, 852–857 (2015)
Shanmughasundaram, P.; Subramanian, R.: Study of parametric optimization of burr formation in step drilling of eutectic Al–Si alloy–Gr composites. J. Mater. Res. Technol. 3, 150–157 (2014)
Altunpak, Y.; Ay, M.; Aslan, S.: Drilling of a hybrid Al/SiC/Gr metal matrix composites. Int. J. Adv. Manuf. Technol. 60, 513–517 (2011)
Arunachalam, S.; Perumal, S.: Investigation of effect of graphite particles on drillability of metal matrix composite. Mater. Sci. 22, 390–396 (2016)
Mohan, S.; Pathak, J.P.; Gupta, R.C.; Srivastava, S.: Wear behaviour of graphitic aluminium composite sliding under dry conditions. Zeitschrift für Metallkunde 93, 1245–1251 (2002)
Daniel, A.A.; Murugesan, S.; Manojkumar, S.; Sukkasamy, S.: Dry sliding wear behaviour of aluminium 5059/SiC/MoS2 hybrid metal matrix composites. Mater. Res. 20, 1697–1706 (2017)
Palanisamy, S.; Ramanathan, S.; Rangaraj, R.: Analysis of dry sliding wear behaviour of aluminium-fly ash composites: the Taguchi approach. Adv. Mech. Eng. 5, 658085 (2013)
Kumar, G.V.; Pramod, R.; Rao, C.; Gouda, P.S.: Artificial neural network prediction on wear of Al6061 alloy metal matrix composites reinforced with-Al2o3. Mater. Today: Proc. 5, 11268–11276 (2018)
Seela, C.R.; Ravisankar, B.; Raju, B.: A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions. Egypt. J. Pet. 27, 641–647 (2018)
Janakiraman, V.M.; Nguyen, X.; Assanis, D.: Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis. Appl. Soft Comput. 13, 2375–2389 (2013)
Nawi, N.M.; Atomi, W.H.; Rehman, M.: The effect of data pre-processing on optimized training of artificial neural networks. Proc. Technol. 11, 32–39 (2013)
Ramsami, P.; Oree, V.: A hybrid method for forecasting the energy output of photovoltaic systems. Energy Convers. Manag. 95, 406–413 (2015)
Ibrić, S.; Djuriš, J.; Parojčić, J.; Djurić, Z.: Artificial neural networks in evaluation and optimization of modified release solid dosage forms. Pharmaceutics 4, 531–550 (2012)
Bendu, H.; Deepak, B.; Murugan, S.: Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol. Energy Convers. Manag. 122, 165–173 (2016)
Rajesh Kumar, L.; Amirthagadeswaran, K.S.: Corrosion and wear behaviour of nano Al2O3 reinforced copper metal matrix composites synthesized by high energy ball milling. Part. Sci. Technol. 38(2), 228–235 (2020)
Specht, D.F.: The general regression neural network—rediscovered. Neural Netw. 6, 1033–1034 (1993)
Panda, B.N.; Bahubalendruni, M.V.A.R.; Biswal, B.B.: A general regression neural network approach for the evaluation of compressive strength of FDM prototypes. Neural Comput. Appl. 26, 1129–1136 (2014)
Rajesh Kumar, L.; Amirthagadeswaran, K.S.: Variations in the properties of copper-alumina nanocomposites synthesized by mechanical alloying. Materiali in Technologije 53(1), 57–63 (2019)
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Saravanakumar, A., Rajeshkumar, L., Balaji, D. et al. Prediction of Wear Characteristics of AA2219-Gr Matrix Composites Using GRNN and Taguchi-Based Approach. Arab J Sci Eng 45, 9549–9557 (2020). https://doi.org/10.1007/s13369-020-04817-8
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DOI: https://doi.org/10.1007/s13369-020-04817-8