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Full Description
Digital twins represent an emerging technology of immense potential across various industries. Their significance is particularly pronounced within Industry 4.0 and smart manufacturing paradigms, which strive to elevate efficiency and quality through seamless digital integration. By amassing and scrutinizing extensive data streams, digital twins empower data-centric decision-making—a pivotal asset in contemporary industry. Digital Twins of Advanced Materials Processing bridges the gap in comprehensive resources concerning advanced materials processing, a domain characterized by rapid evolution. It provides pragmatic remedies and real-world case studies, catering to tangible implementation needs. Moreover, digital twins hold the capacity to amplify efficiency and innovation within materials processing—a perspective deeply explored within this book, rendering it invaluable for professionals, researchers, and students alike. The prospects of employing digital twins in materials processing span diverse horizons: refining materials innovation, streamlining processes, enabling data-driven maintenance, enhancing product quality, and unearthing insights rooted in data. The book also undertakes the challenge of addressing key issues encompassing data amalgamation and integrity, model validation and calibration, software and data safeguarding, scalability, and cost considerations.
Contents
1. Introduction
2. Building blocks of a digital twin
3. Mechanistic models
4. Surrogate and reduced order models
5. Machine learning and deep learning
6. Statistical models
7. Sensing and control
8. Digital twin implementation and case studies
9. Current status, research needs, and outlook
