Digital Twins of Advanced Materials Processing

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

Dr. DebRoy is a Professor of Materials Science and Engineering at Penn State. He is the author of a 2023 Wiley textbook (in press) on "Theory and Practice of Additive Manufacturing, a book for everyone on "Innovations in Everyday Engineering Materials, five edited books, and over 380 well-cited technical articles. His work has been recognized by over 20 scholastic awards including a Fulbright Distinguished Chair in Brazil from the US State Department, the UK Royal Academy of Engineering's Distinguished Visiting Fellowship at Cambridge University, and Penn State's highest scholastic award, the Faculty Scholar medal. He has served as a Distinguished Visiting Professor at IIT Bombay, Aditya Birla Chair at IISc, Bangalore, Visiting Professor at the African University of Science and Technology at Abuja, Nigeria, and Visiting Professor at KTH, Stockholm. He is a Founding Editor of the journal "Science and Technology of Welding and Joining.Dr. Mukherjee is an Assistant Professor of Mechanical Engineering at Iowa State University, USA. Previously, he was a Postdoctoral Researcher in the Department of Materials Science and Engineering at Pennsylvania State University, USA, from where he also got his Ph.D. He is the author of many papers in leading journals, including Nature, Nature Reviews Materials, Nature Materials, and Progress in Materials Science. He authored a textbook on "Theory and Practice of Additive Manufacturing (Wiley, 2023) as well as two edited books entitled "The Science and Technology of 3D Printing (MDPI, 2021) and "Applications of Modeling and Machine Learning in Additive Manufacturing (MDPI, 2025). He served as a Guest Editor for the journals "NPJ Advanced Manufacturing, "Computational Materials Science, "Materials, and "Science and Technology of Welding and Joining. He is an Editorial Board Member of the journals "Engineering Science in Additive Manufacturing, "International Journal of AI for Materials and Design, "Science and Technology of Welding and Joining, and "Welding Journal.

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