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.- Describes the building components of digital twins, their assembly, testing and validation, and applications in advanced materials processing such as additive manufacturing and fusion welding- Delivers data-driven insights about material qualities and manufacturing processes, as well as insights into enhancing the structure and properties of parts- Spans several interdisciplinary fields, including materials science, manufacturing engineering, data analytics, and computer science
Table of Contents
1. Introduction2. Building blocks of a digital twin3. Mechanistic models4. Surrogate and reduced order models5. Machine learning and deep learning6. Statistical models7. Sensing and control8. Digital twin implementation and case studies9. Current status, research needs, and outlook
