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Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Merayo, David ; Rodríguez-Prieto, Alvaro ; Camacho, Ana María

Materials, 2020-11, Vol.13 (22), p.5227 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
Autor: Merayo, David ; Rodríguez-Prieto, Alvaro ; Camacho, Ana María
Assuntos: Aerospace industry ; aluminum ; Aluminum alloys ; Aluminum base alloys ; Artificial intelligence ; artificial neural network ; Artificial neural networks ; Brinell hardness ; Chemical composition ; Data collection ; Empirical equations ; Engineering ; Finite element method ; Food packaging ; Food packaging industry ; heat treatment ; Machine learning ; Material properties ; material properties’ prognosis ; Mechanical properties ; Metal forming ; Neural networks ; Precipitation hardening ; Strain hardening ; Tensile strength ; Ultimate tensile strength ; Weight reduction ; Yield stress
É parte de: Materials, 2020-11, Vol.13 (22), p.5227
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.
Editor: Basel: MDPI AG
Idioma: Inglês

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Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Merayo, David ; Rodríguez-Prieto, Alvaro ; Camacho, Ana María

Basel: MDPI AG

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