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Modeling of Wear Behavior of Al–Si/Al2O3 Metal Matrix Composites

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Abstract

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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REFERENCES

  1. D. J. Lloyd, “Particle reinforced aluminum and magnesium matrix composites,” Int. Mater. Rev. 39, 1–23 (1994).

    Article  CAS  Google Scholar 

  2. A. E. Nitsham, “New applications for aluminum-based metal matrix composites,” Light Met. Age. 54, 25–31 (1997).

    Google Scholar 

  3. S. Das, “Development of aluminum alloy composites for engineering applications,” Trans. Indian Inst. Met. 57, 325–334 (2004).

    CAS  Google Scholar 

  4. R. S. Rawal, “Metal matrix composites for space applications,” JOM 53, 14–17 (2001).

    Article  CAS  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. 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).

    Article  Google Scholar 

  7. 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).

    Article  CAS  Google Scholar 

  8. F. S. Rashed and T. S. Mahmoud, “Prediction of wear behavior of A356/SiCp MMCs using neural networks,” Tribol. Int. 42, 642–648 (2009).

    Article  CAS  Google Scholar 

  9. A. Baradeswaran and A. Perumal, “Study on mechanical and wear properties of Al 7075/Al2O3/graphite hybrid composites,” Composites, Part B 56, 464–471 (2014).

    Article  CAS  Google Scholar 

  10. J. Hashim, L. Looney, and M. S. J. Hashmi, “Particle distribution in cast metal matrix composites,” J. Mater. Process. Technol. 123, 251–257 (2002).

    Article  CAS  Google Scholar 

  11. J. Hashim, “The production of cast metal matrix composite by a modified stir casting method,” J. Technol. 35, 9–20 (2007).

    Google Scholar 

  12. A. Vencl, I. Bobić, M. Jovanović, M. Babić, and S. Mitrovic, “Microstructural and tribological properties of A356 Al–Si alloy reinforced with Al2O3 particles,” Tribol. Lett. 32, 159–170 (2008).

    Article  CAS  Google Scholar 

  13. S. Suresh, N. Shenbaga, V. Moorthi, S. C. Vettivel, and N. Selvakumar, “Mechanical behavior and wear prediction of stir cast Al–TiB2 composites using response surface methodology,” Mater. Des. 59, 383–396 (2014).

    Article  CAS  Google Scholar 

  14. S. Koksal, F. Ficici, R. Kayikci, and O. Savas, “Experimental optimization of dry sliding wear behavior of in situ AlB2/Al composite based on Taguchi′s method,” Mater. Des. 42, 124–130 (2012).

    Article  CAS  Google Scholar 

  15. A. Fathy and A. Megahed, “Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks,” Int. J. Adv. Manuf. Technol. 62, 953–963 (2012).

    Article  Google Scholar 

  16. O. Yılmaz and S. Buytoz, “Abrasive wear of Al2O3-reinforced aluminium-based MMCs,” Compos. Sci. Technol. 61, 2381–2392 (2001).

    Article  Google Scholar 

  17. K. Swingler, Applying Neural Networks: A Practical Guide (Academic, London, 1996).

    Google Scholar 

  18. M. Logan, Biostatistical Design and Analysis Using R: A Practical Guide (Wiley, Chichester, UK, 2010).

    Book  Google Scholar 

  19. R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis (Pearson Prentice Hall, USA, 2007).

    Google Scholar 

  20. D. Jun, L. Y. Hui, Y. Si-rong, and L. Wen-fang, “Dry sliding friction and wear properties of Al2O3 and carbon short fibres reinforced Al–12Si alloy hybrid composites,” Wear 257, 930–940 (2004).

    Article  Google Scholar 

  21. 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).

    Article  CAS  Google Scholar 

  22. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, USA, 1991).

    Google Scholar 

  23. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice-Hall, USA, 1999).

    Google Scholar 

  24. 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).

    Article  Google Scholar 

  25. Neurosolutions Software, Version 5. http:// www.nd.com/, 2006.

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Authors and Affiliations

  1. Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, 44519, Zagazig, Egypt

    M. Megahed & M. A. Agwa

  2. Materials Engineering Department, Faculty of Engineering, Zagazig University, 44519, Zagazig, Egypt

    D. Saber

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  1. M. Megahed
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  2. D. Saber
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  3. M. A. Agwa
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Correspondence to D. Saber.

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Megahed, M., Saber, D. & Agwa, M.A. Modeling of Wear Behavior of Al–Si/Al2O3 Metal Matrix Composites. Phys. Metals Metallogr. 120, 981–988 (2019). https://doi.org/10.1134/S0031918X19100089

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  • DOI: https://doi.org/10.1134/S0031918X19100089

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