Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Data-Analytics-Driven Selection of Die Material in Multi-material Co-Extrusion of Ti-Mg Alloys. Mathematics2024, 12, 813.
Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Data-Analytics-Driven Selection of Die Material in Multi-material Co-Extrusion of Ti-Mg Alloys. Mathematics 2024, 12, 813.
Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Data-Analytics-Driven Selection of Die Material in Multi-material Co-Extrusion of Ti-Mg Alloys. Mathematics2024, 12, 813.
Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Data-Analytics-Driven Selection of Die Material in Multi-material Co-Extrusion of Ti-Mg Alloys. Mathematics 2024, 12, 813.
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) methodolo-gies, in this case 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 pre-diction model and the time for data preprocessing is less than the time needed in SVR.
Keywords
Data analytics, Methodologies, Multi-material; Co-extrusion; FEM; Machine Learning; SVR; MCDM.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
