HARDNESS OPTIMIZATION FOR AL6061-MWCNT NANOCOMPOSITE PREPARED BY MECHANICAL ALLOYING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

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MAHDAVI JAFARI MEHRDAD; SOROUSHIAN SOHEIL; KHAYATI GHOLAM REZA

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Paper Information

Journal: JOURNAL OF ULTRAFINE GRAINED AND NANOSTRUCTURED MATERIALS Year:2017 | Volume:50 | Issue:1 Page(s): 23-32

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Title

HARDNESS OPTIMIZATION FOR AL6061-MWCNT NANOCOMPOSITE PREPARED BY MECHANICAL ALLOYING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

Author(s)

MAHDAVI JAFARI MEHRDAD | SOROUSHIAN SOHEIL | KHAYATI GHOLAM REZA | Issue Writer Certificate

Keywords

CARBON NANOTUBES

METAL–MATRIX COMPOSITES

GENETIC ALGORITHM

ARTIFICIAL NEURAL NETWORK

Abstract

Among artificial intelligence approaches, ARTIFICIAL NEURAL NETWORKs (ANNs) and GENETIC ALGORITHM (GA) are widely applied for modification of materials property in engineering science in large scale modeling. In this work ARTIFICIAL NEURAL NETWORK (ANN) and GENETIC ALGORITHM (GA) were applied to find the optimal conditions for achieving the maximum hardness of Al6061 reinforced by multiwall CARBON NANOTUBES (MWCNTs) through modeling of nanocomposite characteristics. After examination the different ANN architectures an optimal structure of the model, i.e.6-18-1, is obtained with 1.52% mean absolute error and R2=0.987. The proposed structure was used as fitting function for GENETIC ALGORITHM. The results of GA simulation predicted that the combination sintering temperature 346 °C, sintering time 0.33 h, compact pressure 284.82 MPa, milling time 19.66 h and vial speed 310.5 rpm give the optimum hardness, (i.e., 87.5 micro Vickers) in the composite with 0.53 wt% CNT. Also, sensitivity analysis shows that the sintering time, milling time, compact pressure, vial speed and amount of MWCNT are the significant parameter and sintering time is the most important parameter. Comparison of the predicted values with the experimental data revealed that the GA–ANN model is a powerful method to find the optimal conditions for preparing of Al6061-MWCNT.

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APA: Copy

MAHDAVI JAFARI, MEHRDAD, SOROUSHIAN, SOHEIL, & KHAYATI, GHOLAM REZA. (2017). HARDNESS OPTIMIZATION FOR AL6061-MWCNT NANOCOMPOSITE PREPARED BY MECHANICAL ALLOYING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM. JOURNAL OF ULTRAFINE GRAINED AND NANOSTRUCTURED MATERIALS, 50(1), 23-32. SID. https://sid.ir/paper/347369/en

Vancouver: Copy

MAHDAVI JAFARI MEHRDAD, SOROUSHIAN SOHEIL, KHAYATI GHOLAM REZA. HARDNESS OPTIMIZATION FOR AL6061-MWCNT NANOCOMPOSITE PREPARED BY MECHANICAL ALLOYING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM. JOURNAL OF ULTRAFINE GRAINED AND NANOSTRUCTURED MATERIALS[Internet]. 2017;50(1):23-32. Available from: https://sid.ir/paper/347369/en

IEEE: Copy

MEHRDAD MAHDAVI JAFARI, SOHEIL SOROUSHIAN, and GHOLAM REZA KHAYATI, “HARDNESS OPTIMIZATION FOR AL6061-MWCNT NANOCOMPOSITE PREPARED BY MECHANICAL ALLOYING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM,” JOURNAL OF ULTRAFINE GRAINED AND NANOSTRUCTURED MATERIALS, vol. 50, no. 1, pp. 23–32, 2017, [Online]. Available: https://sid.ir/paper/347369/en

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