Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

Merayo Fernández, David, Rodríguez Prieto, Álvaro y Camacho, Ana María . (2020) Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data.

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Título Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data
Autor(es) Merayo Fernández, David
Rodríguez Prieto, Álvaro
Camacho, Ana María
Materia(s) Ingeniería Mecánica
Abstract Aluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5𝑘 entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95% . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.
Palabras clave aluminum alloy
big data
artificial intelligence
multi-layer artificial neural network
python
stress-strain curve
material selection
decision support system
material characterization
Editor(es) MDPI
Fecha 2020-07-06
Formato application/pdf
Identificador bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0001
DOI - identifier 10.3390/met10070904
ISSN - identifier 2075-4701
Nombre de la revista Metals
Número de Volumen 10
Número de Issue 7
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 metals, is available online at the publisher's website: MDPI, https://doi.org/10.3390/met10070904

 
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Creado: Mon, 29 Jan 2024, 23:30:21 CET