Data analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloys

Fernández , Daniel, Rodríguez-Prieto, Álvaro y Camacho, Ana María . (2024) Data analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloys. Mathematics

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Título Data analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloys
Autor(es) Fernández , Daniel
Rodríguez-Prieto, Álvaro
Camacho, Ana María
Materia(s) Ingeniería Mecánica
Abstract Abstract: Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) meth- odologies, specifically Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the prediction model and the time for data preprocessing is less than the time needed in SVR. This new methodology is proven to be effective as alternative to the traditional approaches and aligned with the new trends in the industry based on simulation and data analytics.
Palabras clave Data analytics
Methodologies
Multi-material
Co-extrusion
FEM
Machine Learning
SVR
MCDM
Editor(es) MDPI
Fecha 2024-03-10
Formato application/pdf
Identificador bibliuned:DptoICyF-ETSI-Articulos-Amcamacho-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoICyF-ETSI-Articulos-Amcamacho-0001
DOI - identifier https://doi.org/10.3390/math12060813
ISSN - identifier 2227-7390
Nombre de la revista Mathematics
Número de Volumen 12
Número de Issue 6
Publicado en la Revista Mathematics
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 La versión registrada de este artículo, publicado por primera vez en Mathematics (2024) 12-6, está disponible en línea en el sitio web de la editorial: MDPI https://doi.org/10.3390/math12060813
Notas adicionales The registered version of this article, first published in Mathematics (2024) 12-6, is available online at the publisher's website: MDPI https://doi.org/10.3390/math12060813

 
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Creado: Mon, 18 Mar 2024, 22:32:01 CET