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Searching for a novel optimization strategy in tensile and fatigue properties of alumina particulates reinforced aluminum matrix composite

Engineering with Computers volume 30, pages 559–568 (2014)Cite this article

Abstract

Development and application of metal matrix composite materials and increased application of calculations, simulations and modeling in the area of semi-solid solidification ask for the knowledge of compocasting for these materials. In this study, a self-organizing hierarchical particle swarm optimizer is implemented for computational modeling and optimization of the compocast high strength and highly uniform Al matrix composites. The matrix of the composite was a 6061 Al alloy and the reinforcement was alumina particle (Al2O3p). Experimental results were obtained for hardness, tensile and fatigue properties of the Al alloys with different vol.% of micro-particles. The tensile strength of the composites increased considerably by increasing the reduction ratio in the cold rolling process. It is observed that the presence of reinforcement in the Al alloy degrades the low-cycle fatigue property when the Al matrix composites are subject to strain-controlled cyclic loading. The method combines position update rules, the standard velocity and the strengths of particle swarm optimization with the ideas of selection, crossover and mutation from GA.

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References

  1. Omkar SN, Rahul K, Ananth TVS, Naik GN, Gopalakrishnan S (2009) Quantum behaved particle swarm optimization (QPSO) for multi-objective design optimization of composite structures. Expert Sys Appl 36:11312–11322

    Article  Google Scholar 

  2. Shabani MO, Mazahery A (2011) The ANN application in FEM modeling of mechanical properties of Al–Si alloy. Appl Math Model 35:5707–5713

    Article  Google Scholar 

  3. Beasley D, Bull DR, Martin RR (1993) A sequential niching technique for multimodal function optimization. Evolut Comput 1(2):101–125

    Article  Google Scholar 

  4. Coelho L d S, Alotto P (2008) Global optimization of electromagnetic devices using an exponential quantum-behaved particle swarm optimizer. IEEE Trans Magnet 44:1074–1077

    Article  Google Scholar 

  5. Mazahery A, Shabani MO (2012) Process conditions optimization in Al–Cu alloy matrix composites. Powder Technol 225:101–106

    Article  Google Scholar 

  6. Krohling RA, Mendel E (2009) Bare bones particle swarm optimization with Gaussian or cauchy jumps. IEEE Cong Evol Comput CEC’09:3285–3291

    Google Scholar 

  7. Mazahery A, Shabani MO (2011) The accuracy of various training algorithms in tribological behavior modeling of A356–B4C composites. Russ Metall (Metally) 7:699–707

    Article  Google Scholar 

  8. Shabani MO, Mazahery A (2012) The performance of various artificial neurons interconnections in the modelling and experimental manufacturing of the composits. Materiali in tehnologije 46(2):109–113

    Google Scholar 

  9. Brits R (2002) Niching strategies for particle swarm optimization. In: Master’s thesis. Department of Computer Science, University of Pretoria, Pretoria, South Africa

  10. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, Perth, Australia, 1942–1948

  11. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the IEEE Congress on Evolutionary Computation 1931–1938

  12. Engelbrecht AP, Masiye BS, Pampara G (2005) Niching ability of basic particle swarm optimization algorithms. In: Proceedings of the IEEE Swarm Intelligence Symposium

  13. Mazahery A, Shabani MO (2012) Nano-sized silicon carbide reinforced commercial casting aluminum alloy matrix: experimental and novel modeling evaluation. Powder Technol 217:558–565

    Article  Google Scholar 

  14. Kennedy J, Mendes R (2002) Topological structure and particle swarm performance. In: Fogel DB, Yao X, Greenwood G, Iba H, Marrow P, Shackleton M (eds) Proceeding of 4th Congr. Evolutionary Computation (CEC- 2002), Honolulu, HI, pp 1671–1676

  15. van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inform Sci 176(8):937–971

    Article  MathSciNet  MATH  Google Scholar 

  16. Kennedy J, Eberhart RC (2001) Swarm intelligence, Morgan Kaufman, Burlington

  17. Shabani MO, Mazahery A (2012) Application of finite element model and artificial neural network in characterization of Al matrix nanocomposites using various training algorithms. Metall Mater Trans A 43:2158–2165

    Article  Google Scholar 

  18. Quaak CJ, Kool WH (1994) Properties of semisolid aluminium matrix composites. Mater Sci Eng A 188:277–282

    Article  Google Scholar 

  19. Hashim J, Looney L, Hashmi MSJ (2002) Particle distribution in cast metal matrix composites. Part I. J Mater Process Technol 123:251–257

    Article  Google Scholar 

  20. Mazahery A, Shabani MO (2012) Tribological behaviour of semisolid–semisolid compocast Al–Si matrix composites reinforced with TiB2 coated B4C particulates. Ceram Int 38:1887–1895

    Article  Google Scholar 

  21. Lim SC, Gupta M, Ren L, Kwok JKM (1999) The tribological properties of Al–Cu/SiCp metal–matrix composites fabricated using the rheocasting technique. J Mater Process Technol 89(90):591–596

    Article  Google Scholar 

  22. Shabani MO, Mazahery A (2012) Artificial intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites. Appl Math Model 36:5455–5465

    Article  Google Scholar 

  23. Mazahery A, Shabani MO (2011) Investigation on mechanical properties of nano-Al2O3-reinforced aluminum matrix composites. J Compos Mater 45:2579–2586

    Article  Google Scholar 

  24. Vugt LV, Froyen L (2000) Gravity and temperature effects on particle distribution in Al–Si/SiCp composites. J Mater Process Technol 104:133–144

    Article  Google Scholar 

  25. Irons GA, Owusu-Boahen K (1995) Settling and clustering of silicon carbide particles in aluminium metal matrix composites. Metall Mater Trans B 26:980–981

    Article  Google Scholar 

  26. Mazahery A, Shabani MO (2012) A comparative study on abrasive wear behavior of semisolid–liquid processed Al–Si matrix reinforced with coated B4C reinforcement. Trans Indian Inst Met 65(2):145–154

    Article  Google Scholar 

  27. Shabani MO, Mazahery A (2012) Optimization of process conditions in casting aluminum matrix composites via interconnection of artificial neurons and progressive solutions. Ceram Int 38:4541–4547

    Article  Google Scholar 

  28. Karnezis PA, Durrant G, Cantor B (1998) Characterization of reinforcement distribution in cast Al-alloy/SiC composites. Mater Charact 40:97–109

    Article  Google Scholar 

  29. Shabani MO, Mazahery A (2011) Microstructural prediction of cast A356 alloy as a function of cooling rate. JOM 63:132–316

    Article  Google Scholar 

  30. Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–457

    Article  Google Scholar 

  31. Mazahery A, Shabani MO (2012) Influence of the hard coated B4C particulates on wear resistance of Al–Cu alloys. Compos Part B 43:1302–1308

    Article  Google Scholar 

  32. Sathiya P, Aravindan S, Haq AN, Paneerselvam K (2009) Optimization of friction welding parameters using evolutionary computational techniques, J Mater Process Technol (209):2576–2584

  33. Shabani MO, Mazahery A (2011) Prediction of mechanical properties of cast A356 alloy as a function of microstructure and cooling rate. Arch Metall Mater 56:671–675

    Google Scholar 

  34. Mazahery A, Shabani MO (2012) A356 reinforced with nano particles: numerical analysis of mechanical properties. JOM 64(2):323–329

    Article  Google Scholar 

  35. Gowri S, Samuel FH (1992) Effect of cooling rate on the solidification behavior of Al-7 Pct Si–SiC p metal-matrix composites. Metall Trans A 23:3369–3376

    Google Scholar 

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Authors and Affiliations

  1. Karaj Branch, Islamic Azad University, Karaj, Iran

    Mohsen Ostad Shabani & Ali Mazahery

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  1. Mohsen Ostad Shabani
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  2. Ali Mazahery
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Correspondence to Ali Mazahery.

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Shabani, M.O., Mazahery, A. Searching for a novel optimization strategy in tensile and fatigue properties of alumina particulates reinforced aluminum matrix composite. Engineering with Computers 30, 559–568 (2014). https://doi.org/10.1007/s00366-012-0299-1

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  • DOI: https://doi.org/10.1007/s00366-012-0299-1

Keywords

  • Optimization
  • Metal matrix
  • Microstructure