ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Digital Manufacturing
Volume 11 - 2025 | doi: 10.3389/fmech.2025.1655565
This article is part of the Research TopicTransformative Impact of AI and ML on Modern Manufacturing ProcessesView all 3 articles
Digital Twin Integration in Metalworking: Enhancing Efficiency and Predictive Maintenance
Provisionally accepted
- University of the Americas, Quito, Ecuador
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In the era of Industry 4.0, the integration of advanced technologies such as digital twins represents a strategic opportunity for process optimization in the metalworking industry. Although their potential has been widely acknowledged, many companies face significant challenges in implementation, particularly in terms of operational efficiency, predictive maintenance, and economic feasibility. This study addresses how a digital twin can be effectively deployed within metalworking operations to solve concrete production issues, enhance decision-making, and optimize resource utilization. The proposed system models critical processes, such as milling, welding, and material flow, and integrates real-time data to enable continuous improvement. Through a longitudinal evaluation, the implementation of the digital twin resulted in a 30% reduction in material waste, a 40% decrease in the rejection rate of milled parts, and a return on investment (ROI) of 233% over five years. These results provide empirical evidence of the digital twin's capacity to drive both operational excellence and economic return. This work contributes to the existing literature by offering a robust quantitative assessment of digital twin deployment in metalworking, emphasizing its practical benefits and strategic relevance.
Keywords: DATA FUSION, deep learning, Industry 4.0, artificial intelligence, Digital Twin
Received: 28 Jun 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Villegas, Gutierrez and Govea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: William Villegas, University of the Americas, Quito, Ecuador
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