Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

Merayo, David, Rodríguez Prieto, Álvaro y Camacho, Ana María . (2020) Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks. IEEE Access ( Volume: 8)


Título Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks
Autor(es) Merayo, David
Rodríguez Prieto, Álvaro
Camacho, Ana María
Materia(s) Ingeniería Mecánica
Abstract In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively.
Editor(es) IEEE
Fecha 2020-01-10
Formato application/pdf
Identificador bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0008
bibliuned:DptoICyF-ETSI-Articulos-Arodriguez-0008
DOI - identifier 10.1109/ACCESS.2020.2965769
ISSN - identifier 2169-3536
Nombre de la revista IEEE Access
Número de Volumen 8
Publicado en la Revista IEEE Access ( Volume: 8)
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 IEEE Access, is available online at the publisher's website: IEEE, https://doi.org/10.1109/ACCESS.2020.2965769

 
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Creado: Tue, 30 Jan 2024, 02:29:02 CET