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Dear Colleagues,

Data-driven decision making is becoming increasingly important in various fields of engineering and management and more widely recognized with the rapid development of data science, decision science and interpretable artificial intelligence. In real-world decision-making problems, data usually come from different sources in different formats and are often associated with various types of uncertainty, including randomness, incompleteness, inaccuracy, and inconsistency. In addition, subjective judgment and knowledge also play important roles in making informed decisions. In recent years, decision analysis in social network environments has also attracted wide interest.

This Special Issue aims to provide a forum for the exchange of new findings and advances in the areas of data-driven decision making and decision analytics. The topics of interest include, but are not limited to:

Data-driven modelling and inference;
Decision making under uncertainty;
Decision analysis in social networks;
Group decision making;
Multiple criteria decision analysis;
Knowledge-based decision support;
Decision analytics and interpretable artificial intelligence;
Applications of data-driven decision making in engineering and management.

Prof. Dr. Yu-Wang Chen
Dr. Mi Zhou
Guest Editors

Tao Wen
Guest Editor Assistant

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Keywords

decision making
decision support
data-driven modelling
group decision making
preference relations
knowledge representation
evidential reasoning
rule-based system
interpretable artificial intelligence
social network

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22 pages, 2485 KiB

Open AccessArticle

Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys

by Daniel Fernández, Álvaro Rodríguez-Prieto and Ana María Camacho

Mathematics 2024, 12(6), 813; https://doi.org/10.3390/math12060813 - 10 Mar 2024

Viewed by 457

Abstract

The selection of the most suitable material is one of the key decisions to be made during the design stage of a manufacturing process. Traditional approaches, such as Ashby maps based on material properties, are widely used in industry. However, in the production of multi-material components, the criteria for the selection can include antagonistic approaches. The aim of this work is to implement a methodology based on the results of process simulations for several materials and to classify them by applying an advanced data analytics method based on machine learning (ML)—in this case, the support vector regression (SVR) or multi-criteria decision-making (MCDM) methodology. Specifically, the multi-criteria optimization and compromise solution (VIKOR) was combined with entropy weighting methods. To achieve this, a finite element model (FEM) was built to evaluate the extrusion force and the die wear during the multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR in combination with the entropy weighting methodology, a comparison was established based on material selection and the complexity of the methodology used. The results show that the material chosen in both methodologies is very similar, but the MCDM method is easier to implement because there is no need for evaluating the error of the prediction model, and the time required for data preprocessing is less than the time needed when applying SVR. This new methodology is proven to be effective as an alternative to traditional approaches and is aligned with the new trends in industry based on simulation and data analytics. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)

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