| Abstract Scope |
A digital twin is a dynamic virtual representation of a physical manufacturing system that is continuously updated through sensors, communication networks, and simulation tools. In welding and broader materials processing, digital twins are emerging as a unifying research framework that tightly couples physics-based mechanistic models, data-driven methods, sensing, and control within a two-way cyber-physical architecture. The foundational elements of a digital twin include heat transfer and fluid flow models, microstructure and property evolution models, surrogate and reduced-order formulations for rapid computation, and machine learning tools for pattern recognition and data interpretation. These components are linked through real-time sensor feedback and control strategies, enabling the twin to both reflect and guide the behavior of the physical process. By integrating these elements, digital twins can predict temperature fields, metal flow, solidification morphology, residual stress, distortion, and defect formation, while supporting process control, materials characterization, and part qualification. Case studies across welding and other manufacturing processes show that this approach reduces reliance on trial-and-error experimentation, enables predictive maintenance, improves quality through continuous comparison of simulated and sensed data, and contributes to more sustainable manufacturing practices. Current research needs include improved data management, rigorous uncertainty quantification, standardization, cybersecurity, and stronger integration between mechanistic understanding and data analytics. Digital twins connect modeling, data, sensing, and control to address long-standing challenges in welding and materials processing. |