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Artificial neural network (ANN) approach, analysis of variance (ANOVA), and multiple regression model were developed to predict the wear rate for the aluminum (Al)-silicon (Si) alloy. These methods were based on weight fractions of alumina (Al2O3), load, and sliding distance as inputs. Metal matrix composites (MMCs) were prepared using stir casting method. The Al–Si alloy was reinforced with the addition of 0, 10, 15, 20, and 25 wt % of Al2O3 particles. The ANN model was utilized to predict the wear rates of the composites. Experimental results indicated that the increase of both load and sliding distance increases the wear rate. However, the increase of weight fractions of alumina (Al2O3) decreases the wear rate. Both ANN and ANOVA revealed that the sliding distance has the major influence on the wear rate in comparison with the factor of alumina weight fraction. However, the applied load has a relatively low influence on the wear rate of Al–Si/Al2O3 composite. A multiple regression approach suggested in this study reveals the correlation between the weight fractions of Al2O3, load, and sliding distance and the wear rate.
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D. J. Lloyd, “Particle reinforced aluminum and magnesium matrix composites,” Int. Mater. Rev. 39, 1–23 (1994). CrossRef
A. E. Nitsham, “New applications for aluminum-based metal matrix composites,” Light Met. Age. 54, 25–31 (1997).
S. Das, “Development of aluminum alloy composites for engineering applications,” Trans. Indian Inst. Met. 57, 325–334 (2004).
R. S. Rawal, “Metal matrix composites for space applications,” JOM 53, 14–17 (2001). CrossRef
R. N. Rao, S. Das, D. P. Mondal, and G. Dixit, “Dry sliding wear behaviour of cast high strength aluminum alloy (Al–Zn–Mg) and hard particle composites,” Wear 267, 1688–1695 (2009). CrossRef
L. F. Xavier and P. Suresh, “Wear behavior of aluminum metal matrix composite prepared from industrial waste,” Sci. World J. 2016, article ID 6538345 (2016). CrossRef
R. N. Rao and S. Das, “Effect of sliding distance on the wear and friction behavior of as cast and heat-treated Al–SiCp composites,” Mater. Des. 32, 3051–3058 (2011). CrossRef
F. S. Rashed and T. S. Mahmoud, “Prediction of wear behavior of A356/SiC p MMCs using neural networks,” Tribol. Int. 42, 642–648 (2009). CrossRef
A. Baradeswaran and A. Perumal, “Study on mechanical and wear properties of Al 7075/Al 2O 3/graphite hybrid composites,” Composites, Part B 56, 464–471 (2014). CrossRef
J. Hashim, L. Looney, and M. S. J. Hashmi, “Particle distribution in cast metal matrix composites,” J. Mater. Process. Technol. 123, 251–257 (2002). CrossRef
J. Hashim, “The production of cast metal matrix composite by a modified stir casting method,” J. Technol. 35, 9–20 (2007).
A. Vencl, I. Bobić, M. Jovanović, M. Babić, and S. Mitrovic, “Microstructural and tribological properties of A356 Al–Si alloy reinforced with Al 2O 3 particles,” Tribol. Lett. 32, 159–170 (2008). CrossRef
S. Suresh, N. Shenbaga, V. Moorthi, S. C. Vettivel, and N. Selvakumar, “Mechanical behavior and wear prediction of stir cast Al–TiB 2 composites using response surface methodology,” Mater. Des. 59, 383–396 (2014). CrossRef
S. Koksal, F. Ficici, R. Kayikci, and O. Savas, “Experimental optimization of dry sliding wear behavior of in situ AlB 2/Al composite based on Taguchi′s method,” Mater. Des. 42, 124–130 (2012). CrossRef
A. Fathy and A. Megahed, “Prediction of abrasive wear rate of in situ Cu–Al 2O 3 nanocomposite using artificial neural networks,” Int. J. Adv. Manuf. Technol. 62, 953–963 (2012). CrossRef
O. Yılmaz and S. Buytoz, “Abrasive wear of Al 2O 3-reinforced aluminium-based MMCs,” Compos. Sci. Technol. 61, 2381–2392 (2001). CrossRef
K. Swingler, Applying Neural Networks: A Practical Guide (Academic, London, 1996).
M. Logan, Biostatistical Design and Analysis Using R: A Practical Guide (Wiley, Chichester, UK, 2010). CrossRef
R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis (Pearson Prentice Hall, USA, 2007).
D. Jun, L. Y. Hui, Y. Si-rong, and L. Wen-fang, “Dry sliding friction and wear properties of Al 2O 3 and carbon short fibres reinforced Al–12Si alloy hybrid composites,” Wear 257, 930–940 (2004). CrossRef
M. Ramachandra and K. Radhakrishna, “Effect of reinforcement of flyash on sliding wear, slurry erosive wear and corrosive behavior of aluminum matrix composite,” Wear 262, 1450–1462 (2007). CrossRef
J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, USA, 1991).
S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice-Hall, USA, 1999).
M. S. Chun, J. Biglou, J. G. Lenard, and J. G. Kim, “Using neural networks to predict parameters in the hot working of aluminum alloys,” J. Mater. Process. Technol. 86, 245–251 (1998). CrossRef
Neurosolutions Software, Version 5. http:// www.nd.com/, 2006.
- Modeling of Wear Behavior of Al–Si/Al2O3 Metal Matrix Composites
M. A. Agwa
- Pleiades Publishing