This article focuses on the development of a comprehensive method for optimization of casting parameters including stirring time and speed of stirrer. Particulate AA6061 aluminum alloy matrix composites were produced by compocasting. First, the effects of extrusion and particles coating on the microstructures and mechanical properties of the composites were investigated. Then, a hybrid algorithm based on PSO and GA was implemented in order to solve the global problems and optimize the coefficients of equations with much better performance, resulting in higher possibility for industry application. PSO evolved the population over a certain number of generations, retained the best M particles and excluded the remaining pop size-M particles. Selection, crossover and mutation GA operators generated pop size M new individuals and combined them with the best M particles to form a new population for the next generation. The model based on combination of GA and PSO is capable to predict with greater reliability than single optimization methods including ACO and multiple linear regression.
Tripathi PK, Bandyopadhyay S and Pal SK. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Information Sciences 2007; 177(22): 5033–5049.
2.
Mazahery A, Shabani MO, Alizadeh M, et al. Concurrent fitness evaluations in searching for the optimal process conditions of Al matrix nanocomposites by linearly decreasing weight. J Compos Mater 2012; 47(14): 1765–1772.
3.
Kennedy J and Eberhart RC. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol. IV, Perth, Australia, 1995, pp. 1942–1948.
4.
Shabani MO and Mazahery A. Optimization of Al Matrix Reinforced with B4C Particles. JOM 2013; 65: 272–277.
5.
MazaheryAShabaniMO. Process conditions optimization in Al-Cu alloys matrix composites. Powder Technol2012; 225: 101–106.
6.
LiangJJQinAKSuganthanPN. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput2006; 10: 281–295.
7.
ShabaniMOMazaheryA. Artificial intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites. Appl Math Model2012; 36: 5455–5465.
8.
BeasleyDBullDRMartinRR. A sequential niching technique for multimodal function optimization. Evol Comput1993; 1(2): 101–125.
9.
Brits R. Niching strategies for particle swarm optimization. Master’s thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, November 2002.
10.
ShabaniMOMazaheryA. Application of FEM and ANN in characterization of Al matrix nano composites using various training algorithms. Metall Mater Trans A2012; 43: 2158–2165.
11.
Fieldsend JE. Optimizing decision trees using multi-objective particle swarm optimization. Studies in Computational Intelligence 2009; 242: 93–114.
12.
MazaheryAShabaniMO. A356 reinforced with nano particles: Numerical analysis of mechanical properties. JOM2012; 64(2): 323–329.
13.
Shabani MO and Mazahery A. Application of a linearly decreasing weight particle swarm to optimize the process conditions of AL matrix nanocomposites. Metallurgist 2012; 56(5–6): 414–422.
14.
Kennedy J and Mendes R. Population structure and particle swarm performance, In: Proceedings of the IEEE world congress on evolutionary computation, Honolulu, Hawaii, May 2002, 1671–1676.
15.
van den BerghFEngelbrechtAP. A study of particle swarm optimization particle trajectories. Inform Sci2006; 176(8): 937–971.
16.
ShabaniMOMazaheryA. The synthesis of the particulates Al matrix composites by the compocasting method. Ceram Int2013; 39: 1351–1358.
17.
BauriRSurappaM.K. Processing and compressive strength of Al–Li–SiCp composites fabricated by compound billet technique. J Mater Process Technol2009; 209: 2077–2084.
18.
MazaheryAShabaniMORahimipourMR. The numerical modeling of abrasion resistance in casting aluminum–silicon alloy matrix composites. J Compos Mater2012; 46(21): 2647–2658.
19.
HashimJLooneyLHashmiMSJ. Particle distribution in cast metal matrix composites. Part I. J Mater Process Technol2002; 123: 251–257.
20.
MazaheryAShabaniMO. Investigation on mechanical properties of nano-Al2O3-reinforced aluminum matrix composites. J Compos Mater2011; 45(24): 2579–2586.
21.
BlumenthalWRGrayGTClaytorTN. Response of aluminium-infiltrated boron carbide cermets to shock wave loading. J Mater Sci1994; 29: 4567–4567.
22.
ShabaniMOMazaheryA. The performance of various artificial neurons interconnections in the modelling and experimental manufacturing of the composites. Materiali in Tehnologije2012; 46(2): 109–113.
23.
Pyzik AJ and Aksay IA. Processing of ceramic and metal matrix composites, In: Proceedings of the international symposium on advances in processing of ceramic and metal matrix composites, New York, NY, 1989, p. 269.
24.
MazaheryAShabaniMO. Influence of the hard coated B4C particulates on wear resistance of Al–Cu alloys. Composites: Part B2012; 43: 1302–1308.
25.
RheeSK. Wetting of AlN and TiC by liquid Ag and liquid Cu. J Am Ceram Soc1970; 53: 386–386.
26.
ShabaniMOMazaheryA. Prediction performance of various numerical model training algorithms in solidification process of A356 matrix composites. Indian J Eng Mater Sci2012; 19: 129–134.
27.
RazaviMRahimipourMRKaboliR. Synthesis of TiC nanocomposite powder from impure TiO2 and carbon black by mechanically activated sintering. J Alloys Compounds2008; 460: 694–698.
28.
IronsGAOwusu-BoahenK. Settling and clustering of silicon carbide particles in aluminium metal matrix composites. Metall Mater Trans B1995; 26: 980–981.
29.
ShabaniMOMazaheryA. The GA optimization performance in the microstructure and mechanical properties of MMNCs. Trans Indian Inst Met2012; 65(1): 77–83.
30.
GuptaMLuLAngSE. Effect of microstructural features on the aging behaviour of Al–Cu/SiC metal matrix composites processed using casting and rheocasting routes. J Mater Sci1997; 32: 1261–1267.
31.
KarnezisPADurrantGCantorB. Characterization of reinforcement distribution in cast Al-alloy/SiC composites. Mater Charact1998; 40: 97–109.
32.
MazaheryAShabaniMO. Study on microstructure and abrasive wear behavior of sintered Al matrix composites. Ceram Int2012; 38(5): 4263–4269.
33.
QuaakCJKoolWH. Properties of semisolid aluminium matrix composites. Mater Sci Eng A1994; 188: 277–282.
34.
ShabaniMOMazaheryA. Prediction of mechanical properties of cast A356 alloy as a function of microstructure and cooling rate. Arch Metall Mater2011; 56(3): 671–675.
35.
AsthanaAJ. Reinforced cast metals, part II evolution of the interface. J Mater Sci Eng1998; 35: 1959–1980.
36.
LeonCADrewRL. Preparation of nickel-coated powders as precursors to reinforced metal matrix composites. J Mater Sci2000; 35: 4763–4768.
37.
MazaheryAShabaniMO. Mechanical properties of squeeze-cast A356 composites reinforced with B 4C particulates. J Mater Eng Perform2012; 21(2): 247–252.
38.
ThamLMGuptaMChengL. Effect of limited matrix–reinforcement interfacial reaction on enhancing the mechanical properties of aluminium–silicon carbide composites. Acta Mater2001; 49: 3243–3253.
39.
ShabaniMOMazaheryA. Application of GA to optimize the process conditions of Al Matrix nano-composites. Composites: Part B2013; 45: 185–191.
40.
ThakurSKDhinanBK. The influence of interfacial characteristics between SiCp and Mg/Al metal matrix on wear, coefficient of friction and microhardness. Wear2001; 247: 201–201.
41.
MoustafaSFBadrySALSanadAM. Friction and wear behavior of graphite–copper composites. Wear2002; 253: 699–710.
42.
MazaheryAShabaniMO. The accuracy of various training algorithms in tribological behavior modeling of A356-B 4C composites. Russ Metall (Metally)2011; 7: 699–707.
McKimpsonMScottT. Processing and properties of metal matrix composites containing discontinuous reinforcement. Mater Sci Eng1989; 107: 93–106.
45.
MazaheryAShabaniMO. A comparative study on abrasive wear behavior of semisolid–liquid processed Al–Si matrix reinforced with coated B4C reinforcement. Trans Indian Inst Met2012; 65(2): 145–154.
46.
VarmaVKKamathSVKutumabaraoVV. Tensile behaviour of powder metallurgy processed (Al–Cu–Mg)/SiCp composites. Mater Sci Technol2000; 17: 921–936.
47.
ZhongL. Deformation behavior and microstructure effect in 2124Al/SiCp. Compos J Compos Mater2000; 34: 101–115.
48.
ShabaniMOMazaheryA. Microstructural prediction of cast A356 alloy as a function of cooling rate. JOM2011; 63(8): 132–136.
49.
PetalasYGAntonopoulosCGBountisTC. Detecting resonances in conservative maps using evolutionary algorithms. Phys Lett A2009; 373: 334–341.
50.
MazaheryAShabaniMO. Assistance of novel artificial intelligence in optimization of aluminum matrix nanocomposite by genetic algorithm. Metall Mater Trans A2012; 43: 5279–5285.
51.
WuCHDongNIpWH. Chaotic hybrid algorithm and its application in circle detection. Lecture Notes Comput Sci2010; 6024: 302–311.
52.
ShabaniMOAlizadehMMazaheryA. Modelling of mechanical properties of cast A356 alloy. Fatigue Fract Eng Mater Struct2011; 34: 1035–1040.
53.
ZhaoSZSuganthanPN. Two-lbests based multi-objective particle swarm optimizer. Eng Optim2011; 43(1): 1–17.
54.
SathiyaPAravindanSHaqAN. Optimization of friction welding parameters using evolutionary computational techniques. J Mater Process Technol2009; 209: 2576–2584.
