Skip to main content
Log in

Strategic developments to improve the optimization performance with efficient optimum solution and produce high wear resistance aluminum–copper alloy matrix composites

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The aim of the present study was to find better developments to improve the performance of population optimization, especially from the angle of keeping the population diversity, to enhance the global search in the early part of the optimization and to encourage the particles to converge toward the global optima at the end of the search. The results were used to optimize the fabrication process conditions of the high wear resistance boron carbide-reinforced Al matrix composites. An experimental investigation was then carried out on the abrasive wear behavior of Al alloy matrix composites in terms of abrasive particle size, weight fraction and applied load in pin-on-disk type of wear machine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Mandala A, Murtyb BS, Chakraborty M (2009) Sliding wear behaviour of T6 treated A356–TiB2 in situ composites. Wear 266:865–872

    Article  Google Scholar 

  2. Liu D, Zhang SQ, Li A, Wang HM (2010) Creep rupture behaviors of a laser melting deposited TiC/TA15 in situ titanium matrix composite. Mater Des 31:3127–3133

    Article  Google Scholar 

  3. Mazahery A, Shabani MO (2012) Study on microstructure and abrasive wear behavior of sintered Al matrix composites. Ceram Int 38:4263–4269

    Article  Google Scholar 

  4. Anandkumar R, Almeida A, Vilar R (2012) Microstructure and sliding wear resistance of an Al–12 wt% Si/TiC laser clad coating. Wear 282–283:31–39

    Article  Google Scholar 

  5. Gopalakrishnan S, Murugan N (2011) Prediction of tensile strength of friction stir welded aluminium matrix TiCp particulate reinforced composite. Mater Des 32:462–467

    Article  Google Scholar 

  6. 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 

  7. Blumenthal WR, Gray GT, Claytor TN (1994) Response of aluminium-infiltrated boron carbide cermets to shock wave loading. J Mater Sci 29:4567

    Article  Google Scholar 

  8. Pyzik AJ, Aksay IA, Sarikaya M (1986) Processing and microstructural characterization of B4C-AI cermets. Mater Sci Res 21:45

    Google Scholar 

  9. 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(3):671–675

    Google Scholar 

  10. Pyzik AJ, Beaman DR (1995) Al-B-C phase development and effects on mechanical properties of B4C/Al-derived composites. J Am Ceram Soc 78:305

    Article  Google Scholar 

  11. Rhee SK (1970) Wetting of AlN and TiC by liquid Ag and liquid Cu. J Am Ceram Soc 53:386

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. 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 

  14. 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 

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

    Google Scholar 

  16. Shabani MO, Mazahery A (2013) Application of GA to optimize the process conditions of Al matrix nano-composites. Compos Part B 45:185–191

    Article  Google Scholar 

  17. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  20. 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 

  21. 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, July 1999, pp 1931–1938

  22. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol. IV. Perth, Australia, pp 1942–1948

  23. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufman, San Francisco

    Google Scholar 

  24. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the IEEE world congress on evolutionary computation. Honolulu, Hawaii, May 2002, pp 1671–1676

  25. Mazahery A, Shabani MO (2012) Assistance of novel artificial intelligence in optimization of aluminum matrix nanocomposite by genetic algorithm. Metall Mater Trans A 43:5279–5285

    Article  Google Scholar 

  26. Petalas YG, Antonopoulos CG, Bountis TC, Vrahatis MN (2009) Detecting resonances in conservative maps using evolutionary algorithms. Phys Lett A 373:334–341

    Article  MATH  Google Scholar 

  27. 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 

  28. Wu CH, Dong N, Ip WH, Chen CY, Yung KL, Chen ZQ (2010) Chaotic hybrid algorithm and its application in circle detection. Lect Notes Comput Sci 6024:302–311

    Article  Google Scholar 

  29. Tripathi PK, Bandyopadhyay S, Pal SK (2007) Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf Sci 177:5033–5049

    Article  MathSciNet  MATH  Google Scholar 

  30. 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 

  31. 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

    Article  Google Scholar 

  32. Bauri R, Surappa MK (2009) Processing and compressive strength of Al–Li–SiCp composites fabricated by compound billet technique. J Mater Process Technol 209:2077–2084

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohsen Ostad Shabani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rahimipour, M.R., Tofigh, A.A., Mazahery, A. et al. Strategic developments to improve the optimization performance with efficient optimum solution and produce high wear resistance aluminum–copper alloy matrix composites. Neural Comput & Applic 24, 1531–1538 (2014). https://doi.org/10.1007/s00521-013-1375-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1375-1

Keywords

Navigation