Open Access

REVIEW

A Decade of Digital Twins in Materials Science and Engineering

Diego Vergara^*, Antonio del Bosque, Pablo Fernández-Arias

Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Ávila, C/Canteros s/n, Ávila, 05005, Spain

* Corresponding Author: Diego Vergara. Email:

Computers, Materials & Continua 2025, 85(1), 41-64. https://doi.org/10.32604/cmc.2025.067881

Received 15 May 2025; Accepted 21 July 2025; Issue published 29 August 2025

Abstract

Digital twins (DTs) are rapidly emerging as transformative tools in materials science and engineering, enabling real-time data integration, predictive modeling, and virtual testing. This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science. Using a five-stage methodology, the review examines publication trends, thematic areas, citation metrics, and keyword patterns. The results reveal exponential growth in scientific output, with Materials Theory, Computation, and Data Science as the most represented area. A thematic analysis of the most cited documents identifies four major research streams: foundational frameworks, DTs in additive manufacturing, sector-specific applications, and intelligent production systems. Keyword co-occurrence and strategic mapping show a strong foundation in modeling, simulation, and optimization, with growing links to machine learning and sustainability. The review highlights current challenges and proposes future research directions for advancing DTs in materials science.

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Keywords

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Supplementary Material

Supplementary Material File

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