Concurrent fitness evaluations in searching for the optimal process conditions of Al matrix nanocomposites by linearly decreasing weight

Abstract

In this article, the effect of nanoceramic reinforcements on the mechanical properties of aluminum matrix composites was critically reviewed. Reinforcement of an Al alloy with nanoceramic particulates is expected to improve its tensile strength. This article also reports the role of an integrated optimization approach using an artificial neural network and a modified particle swarm to solve a process parameter design problem in casting of this class of metal matrix nanocomposites. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the particle swarm is used to perform the optimization with multiple objectives.

Keywords

Ceramic particulates nanoscale optimization

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References

Mazahery

Shabani

. Process conditions optimization in Al–Cu alloy matrix composites. Powder Technol 2012; 217: 558–565.

Shabani

Mazahery

. The ANN application in FEM modeling of mechanical properties of Al–Si alloy. Appl Math Model 2011; 35: 5707–5713.

Razavi

Rahimipour

Rajabi-Zamani

. Effect of nanocrystalline TiC powder addition on the hardness and wear resistance of cast iron. Mater Sci Eng A 2007; 454–455: 144–147.

Mazahery

Shabani

. Influence of the hard coated B4C particulates on wear resistance of Al–Cu alloys. Composites Part B 2012; 43: 1302–1308.

Vencl

Bobic

Arostegui

. Structural, mechanical and tribological properties of A356 aluminium alloy reinforced with Al₂O₃, SiC and SiC + graphite particles. J Alloys Compd 2010; 506: 631–639.

Shabani

Mazahery

. Application of finite element model and artificial neural network in characterization of al matrix nanocomposites using various training algorithms. Metall Mater Trans A 2012; 43: 2158–2165.

Xiong

Yan

. Effects of SiC volume fraction and aluminum particulate size on interfacial reactions in SiC nanoparticulate reinforced aluminum matrix composites. J Alloys Compd 2011; 509: 1187–1191.

Sajjadi

Ezatpour

Torabi Parizi

. Comparison of microstructure and mechanical properties of A356 aluminum alloy/Al₂O₃ composites fabricated by stir and compo-casting processes. J Mater Des 2012; 34: 106–111.

Nguyen

Gupta

. Enhancing compressive response of AZ31B using nano-Al₂O₃ and copper additions. J Alloys Compd 2010; 490: 382–387.

10.

Hassan

Gupta

. Enhancing physical and mechanical properties of Mg using nanosized Al₂O₃ particulates as reinforcement. Metall Mater Trans A 2005; 36: 2253–2258.

11.

Shabani

Mazahery

. Prediction of wear properties in A356 matrix composite reinforced with B₄C particulates. Synth Met 2011; 161: 1226–1231.

12.

Ferkel

Mordike

. Magnesium strengthened by SiC nanoparticles. Mater Sci Eng A 2001; 298: 193–199.

13.

Groza

. Sintering of nanocrystalline powders. Int J Powder Metall 1999; 35: 59–66.

14.

Akio

Atsushi

Toshiro

. Fabrication, process of metal matrix composite with nano-size SiC particle produced by vortex method. J Jpn Inst Light Met 1999; 49: 149–154.

15.

Mazahery

Shabani

. Investigation on mechanical properties of nano-Al₂O₃-reinforced aluminum matrix composites. J Compos Mater 2011; 45: 2579–2586.

16.

Habibnejad-Korayema

Mahmudi

Pooleb

. Enhanced properties of Mg-based nano-composites reinforced with Al₂O₃ nano-particles. Mater Sci Eng A 2009; 519: 198–203.

17.

Amirkhanlou

Niroumand

. Development of Al356/SiC_p cast composites by injection of SiC_p containing composite powders. J Mater Des 2011; 32: 1895–1902.

18.

Taha

El-Mahallawy

. Metal–matrix composites fabricated by pressure-assisted infiltration of loose ceramic powder. J Mater Process Technol 1998; 73: 139–146.

19.

Kok

. Production and mechanical properties of Al₂O₃ particle-reinforced 2024 aluminium alloy composites. J Mater Process Technol 2005; 161: 381–387.

20.

Asthana

Rohatgi

. Melt infiltration of SiC compacts-I. Study of infiltration dynamics. Z Metallkd 1992; 83(12): 887–892.

21.

Ibrahim

Mohamed

Lavernia

. Particulate reinforced metal matrix composites - a review. J Mater Sci 1991; 26: 1137–1156.

22.

Zhao

Zhang

Chen

. In situ (Al2O3 + Al3Zr)np/Al nanocomposites synthesized by magneto-chemical melt reaction. Compos Sci Technol 2008; 68: 1463–1470.

23.

Gómez-Carracedo

Gestal

Dorado

. Linking chemical knowledge and genetic algorithms using two populations and focused multimodal search. Chemom Intell Lab Syst 2007; 87: 173–184.

24.

Behnamian

Zandieh

. A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Syst Appl 2011; 38: 14490–14498.

25.

Sadigh

Mozafari

Karimi

. Manufacturer–retailer supply chain coordination: a bi-level programming approach. Adv Eng Software 2012; 45: 144–152.

26.

Kaveh

Talatahari

. Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct 2010; 88: 1220–1229.

27.

Talatahari

Farahmand Azar

Sheikholeslami

. Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simulat 2012; 17: 1312–1319.

28.

Shabani

Mazahery

Bahmani

. Solidification of A356 Al alloy: experimental study and modeling. Kovove Mater 2011; 49: 253–258.

29.

Tsai

C-Y

Kao

I-W

. Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 2011; 38: 6565–6576.

30.

Zhao

Qian

Yang

. Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization. Appl Soft Comput 2010; 10: 938–944.

31.

Shabani

Mazahery

Davami

. Silicon morphology modelling during solidification process of A356 Al alloy. Int J Cast Met Res 2012; 25: 53–58.

32.

Pendharkar

. A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Comput Oper Res 2005; 32: 2561–2582.

33.

Celli

Ghiani

Mocci

. A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. IEEE Trans Power Syst 2005; 20(2): 750–757.

34.

Safari

Jahani

Shayanfar

. Optimal DG allocation in distribution network. Int J Electr Eng 2010; 4(8): 550–553.

35.

Yoshida

Kawata

Fukuyama

. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 2000; 15: 1232–1239.

36.

Shabani

Mazahery

. The GA optimization performance in the microstructure and mechanical properties of MMNCs. Trans Indian Inst Met 2012; 65: 77–83.

37.

Kennedy J and Mendes R. Population structure and particle swarm performance. In: Proceedings of the IEEE congress on evolutionary computation, Honolulu, HI, 12–17 May 2002, vol. 2, pp.1671–1676. Piscataway, NJ: IEEE.

38.

Mazahery

Shabani

. A356 reinforced with nanoparticles: numerical analysis of mechanical properties. JOM J Miner Met Mater Soc 2012; 64: 323–329.

39.

Liang JJ, Qin AK, Suganthan PN, et al. Particle swarm optimization algorithms with novel learning strategies. In: Proceedings of the IEEE conference on systems, man and cybernetics, the Hague, Netherlands, 10–13 October 2004, vol. 4, pp.3659–3664. Piscataway, NJ: IEEE.