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

Merayo, David, Rodríguez Prieto, Álvaro y Camacho, Ana María . (2020) Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys. Materials

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Título Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
Autor(es) Merayo, David
Rodríguez Prieto, Álvaro
Camacho, Ana María
Materia(s) Ingeniería Mecánica
Abstract 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%.
Palabras clave aluminum
artificial neural network
chemical composition
heat treatment
Brinell hardness
material properties’ prognosis
yield strength
UTS
Editor(es) MDPI
Fecha 2020-10-02
Formato application/pdf
Identificador bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0010
http://e-spacio.uned.es/fez/view/bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0010
DOI - identifier 10.3390/ma13225227
ISSN - identifier 1996-1944
Nombre de la revista Materials
Número de Volumen 13
Número de Issue 22
Publicado en la Revista Materials
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in Materials, is available online at the publisher's website: MDPI https://doi.org/10.3390/ma13225227
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Materials, está disponible en línea en el sitio web del editor: MDPI https://doi.org/10.3390/ma13225227

 
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Creado: Tue, 06 Feb 2024, 01:36:23 CET