Based on molecular dynamics simulation results, a model was developed for determining elastic properties of aluminum nanocomposites reinforced with silicon carbide particles. Also, two models for prediction of density and price of nanocomposites were suggested. Then, optimal volume fraction of reinforcement was obtained by genetic algorithm method for the least density and price, and the highest elastic properties. Based on optimization results, the optimum volume fraction of reinforcement was obtained equal to 0.44. For this optimum volume fraction, optimum Young’s modulus, shear modulus, the price and the density of the nanocomposite were obtained 165.89 GPa, 111.37 GPa, 8.75 $/lb and 2.92 gr/cm3, respectively.
Dashtbayazi, M., & Esmaeili, R. (2022). Optimizing Properties of Aluminum-Based Nanocomposites by Genetic Algorithm Method. Journal of Advanced Materials in Engineering (Esteghlal), 34(2), 19-30. doi: 10.18869/acadpub.jame.34.2.19
MLA
M.R. Dashtbayazi; R. Esmaeili. "Optimizing Properties of Aluminum-Based Nanocomposites by Genetic Algorithm Method", Journal of Advanced Materials in Engineering (Esteghlal), 34, 2, 2022, 19-30. doi: 10.18869/acadpub.jame.34.2.19
HARVARD
Dashtbayazi, M., Esmaeili, R. (2022). 'Optimizing Properties of Aluminum-Based Nanocomposites by Genetic Algorithm Method', Journal of Advanced Materials in Engineering (Esteghlal), 34(2), pp. 19-30. doi: 10.18869/acadpub.jame.34.2.19
VANCOUVER
Dashtbayazi, M., Esmaeili, R. Optimizing Properties of Aluminum-Based Nanocomposites by Genetic Algorithm Method. Journal of Advanced Materials in Engineering (Esteghlal), 2022; 34(2): 19-30. doi: 10.18869/acadpub.jame.34.2.19
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