International Journal of Engineering & Tech Development

E-ISSN: 3067-672X

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

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Volume 3 Issue 10
October 2026

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Digital Twin Models for Intelligent Engineering Process Optimization

Author(s) Wen-Ren Jong
Country Taiwan
Abstract Digital twin technology has emerged as an important approach for improving the monitoring, simulation, prediction, and optimization of complex engineering processes. A digital twin establishes a dynamic digital representation of a physical asset, process, or system and connects it with operational data to support continuous analysis and decision-making. The integration of digital twins with artificial intelligence, machine learning, Internet of Things technologies, simulation, and optimization algorithms provides opportunities for intelligent engineering process optimization. This paper examines digital twin models for intelligent engineering process optimization, focusing on their architecture, data integration mechanisms, predictive capabilities, optimization functions, and applications across modern engineering domains. A structured review-based methodology is employed to analyze digital twin approaches used in manufacturing, energy, mechanical systems, process engineering, infrastructure, and industrial operations.
The analysis indicates that digital twins can improve engineering optimization by enabling virtual experimentation, real-time performance assessment, predictive analysis, condition monitoring, and adaptive decision-making. AI-enabled digital twins can further enhance optimization by learning from operational data and identifying improved process configurations. However, practical implementation is affected by challenges involving data interoperability, model synchronization, cybersecurity, computational complexity, scalability, model fidelity, and integration with existing engineering infrastructure. The study highlights the importance of hybrid digital twin architectures that combine physics-based simulation, data-driven models, real-time sensing, and optimization algorithms. Future development should emphasize autonomous adaptation, explainable AI, uncertainty management, interoperable architectures, and human-centered decision support. Digital twin models therefore represent a significant foundation for developing more intelligent, adaptive, efficient, and sustainable engineering processes.
Keywords Digital Twin, Engineering Optimization, Intelligent Manufacturing, Process Optimization, Artificial Intelligence, Machine Learning, Simulation, Smart Engineering.
Field Engineering
Published In Volume 2, Issue 5, May 2025
Published On 2025-05-06
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E-ISSN: 3067-672X
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