Prediction of the tensile properties of ultrafine grained Al–SiC nanocomposites using machine learning

We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated by accumulative roll bonding (ARB). The effect of the number of cycles and SiC content on the microstructure, phase analys...

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Vydané v:Journal of materials research and technology Ročník 24; s. 7666 - 7682
Hlavní autori: Najjar, I.M.R., Sadoun, A.M., Elaziz, Mohamed Abd, Ahmadian, H., Fathy, A., Kabeel, A.M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.05.2023
Elsevier
Predmet:
Accumulative roll bonding
Al– SiC nanocomposite
Growth optimizer algorithm
Hardness
Machine learning
Tensile strength
Hardness
Al– SiC nanocomposite
Accumulative roll bonding
Tensile strength
Machine learning
Growth optimizer algorithm
ISSN:2238-7854
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Popis
Shrnutí:We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated by accumulative roll bonding (ARB). The effect of the number of cycles and SiC content on the microstructure, phase analysis, tensile, and hardness properties have been investigated for the ARBed sheets and their composites. The experimental results showed the distribution of SiC particles improved by increasing ARB passes. The ARB approach greatly enhanced the ultimate tensile strength (UTS), yield strength (YS), and hardness. The UTS achieved was 254 MPa for 4% SiC after 9 ARB cycles. The hardness values of the ARBed AA1050, and AA1050-4 wt% SiC are 60, and 76.5, respectively, after 9 ARB cycles. The modified version of random vector functional link based on Growth Optimizer Algorithm is developed as a machine-learning model to predict the tensile properties of the produced composites. The efficiency of the developed ML model is evaluated with other methods according to the performance criteria.
ISSN:2238-7854
DOI:10.1016/j.jmrt.2023.05.035