Digital Twins of Advanced Materials Processing
- 1st Edition - January 1, 2026
- Authors: Tarasankar DebRoy, Tuhin Mukherjee
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 2 9 1 8 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 2 9 1 9 - 7
Digital twins represent an emerging technology of immense potential across various industries. Their significance is particularly pronounced within Industry 4.0 and smart… Read more
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
Professionals, researchers, and students in Materials Science and Engineering, Mechanical Engineering, Industrial Engineering, Manufacturing Engineering, Computer Science, Data Science, and related fields would be the target audience
1: Introduction
1.1 What is a digital twin?
1.2 Evolution and history of digital twins
1.3 Functions of a digital twin
1.4 Use of digital twins in materials processing industries
1.5 Solution of major challenges in materials processing
1.6 Content of this book
2: Building blocks of a digital twin
2.1 Introduction
2.2 Mechanistic models
2.3 Surrogate models
2.4 Machine learning and deep learning
2.5 Statistical models
2.6 Control models
2.7 Big data and data analytics
2.8 Interconnection among the building blocks (using the internet of things)
2.9 Summary
3: Mechanistic models
3.1 Introduction
3.2 Process modeling (heat transfer, material flow, and removal, deformation, layout)
3.3 Prediction of microstructure (solidification, grain growth, phases)
3.4 Prediction of properties (mechanical and corrosion)
3.5 Models to improve performance (thermomechanical, defects)
3.6 Available software
3.7 Summary
4: Surrogate and reduced order models
4.1 Introduction
4.2 Analytical models
4.3 Dimensionless numbers based calculations
4.4 Reverse models
4.5 Back of the envelop calculations
4.6 Data-driven surrogate models
4.6.1 Linear regression
4.6.2 Support vector regression
4.6.3 Radial basis functions
4.6.4 Kriging
4.6.5. Mixture of surrogates
4.6.6 Available software
4.7 Summary
5: Machine learning and deep learning
5.1 Introduction
5.2 Regression algorithms
5.3 Classification algorithms
5.4 Neural nets and deep neural nets
5.5 Image processing and feature extraction
5.6 Open-source packages
5.7 Data needs
5.8 Summary
6: Statistical models
6.1 Introduction
6.2 Different statistical models for digital twins
6.2.1 Regression Models
6.2.2 Time Series Analysis
6.2.3 Monte Carlo Simulations
6.2.4 Hidden Markov Models
6.2.5 Principal Component Analysis
6.2.6 Optimization algorithms
6.3 Roles of statistical models in digital twins of materials processing
6.4 Synergy between mechanistic and statistical models
6.5 Summary
7: Control models
7.1 Introduction
7.2 Sensor Data for Control Models
7.3 Operations Research-Based Control Models
7.4 Fuzzy Logic-Based Control Models
7.5 Data-Driven Control Models
7.6 Proportional-Integral-Derivative Control Models
7.7 Processing and Storage of Data
7.8 Summary
8: Testing and case studies
8.1 Introduction
8.2 Validation and testing of the building blocks
8.3 Examples of important applications
8.3.1 A digital twin of additive manufacturing for part qualification
8.3.2 A digital twin of fusion welding for reduction of defects
8.3.3 A digital twin of continuous die casting for quality control
8.3.4 A digital twin for production control and planning
8.3.5 A digital twin for controlling the microstructure of metallic parts
8.4 Summary
9: Current status, research needs, and outlook
9.1 Introduction
9.2 Current status
9.3 Research needs
9.3.1 Need for quantum computing
9.3.2 Data storage
9.3.3 Blockchain
9.3.4 Accessibility
9.3.5 High technology readiness level of building blocks
9.3.6 Standardization
9.4 Outlook
1.1 What is a digital twin?
1.2 Evolution and history of digital twins
1.3 Functions of a digital twin
1.4 Use of digital twins in materials processing industries
1.5 Solution of major challenges in materials processing
1.6 Content of this book
2: Building blocks of a digital twin
2.1 Introduction
2.2 Mechanistic models
2.3 Surrogate models
2.4 Machine learning and deep learning
2.5 Statistical models
2.6 Control models
2.7 Big data and data analytics
2.8 Interconnection among the building blocks (using the internet of things)
2.9 Summary
3: Mechanistic models
3.1 Introduction
3.2 Process modeling (heat transfer, material flow, and removal, deformation, layout)
3.3 Prediction of microstructure (solidification, grain growth, phases)
3.4 Prediction of properties (mechanical and corrosion)
3.5 Models to improve performance (thermomechanical, defects)
3.6 Available software
3.7 Summary
4: Surrogate and reduced order models
4.1 Introduction
4.2 Analytical models
4.3 Dimensionless numbers based calculations
4.4 Reverse models
4.5 Back of the envelop calculations
4.6 Data-driven surrogate models
4.6.1 Linear regression
4.6.2 Support vector regression
4.6.3 Radial basis functions
4.6.4 Kriging
4.6.5. Mixture of surrogates
4.6.6 Available software
4.7 Summary
5: Machine learning and deep learning
5.1 Introduction
5.2 Regression algorithms
5.3 Classification algorithms
5.4 Neural nets and deep neural nets
5.5 Image processing and feature extraction
5.6 Open-source packages
5.7 Data needs
5.8 Summary
6: Statistical models
6.1 Introduction
6.2 Different statistical models for digital twins
6.2.1 Regression Models
6.2.2 Time Series Analysis
6.2.3 Monte Carlo Simulations
6.2.4 Hidden Markov Models
6.2.5 Principal Component Analysis
6.2.6 Optimization algorithms
6.3 Roles of statistical models in digital twins of materials processing
6.4 Synergy between mechanistic and statistical models
6.5 Summary
7: Control models
7.1 Introduction
7.2 Sensor Data for Control Models
7.3 Operations Research-Based Control Models
7.4 Fuzzy Logic-Based Control Models
7.5 Data-Driven Control Models
7.6 Proportional-Integral-Derivative Control Models
7.7 Processing and Storage of Data
7.8 Summary
8: Testing and case studies
8.1 Introduction
8.2 Validation and testing of the building blocks
8.3 Examples of important applications
8.3.1 A digital twin of additive manufacturing for part qualification
8.3.2 A digital twin of fusion welding for reduction of defects
8.3.3 A digital twin of continuous die casting for quality control
8.3.4 A digital twin for production control and planning
8.3.5 A digital twin for controlling the microstructure of metallic parts
8.4 Summary
9: Current status, research needs, and outlook
9.1 Introduction
9.2 Current status
9.3 Research needs
9.3.1 Need for quantum computing
9.3.2 Data storage
9.3.3 Blockchain
9.3.4 Accessibility
9.3.5 High technology readiness level of building blocks
9.3.6 Standardization
9.4 Outlook
- Edition: 1
- Published: January 1, 2026
- Language: English
TD
Tarasankar DebRoy
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”.
Affiliations and expertise
Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, USATM
Tuhin Mukherjee
Dr. Mukherjee is a Postdoctoral Scholar at the Pennsylvania State University and will serve as Assistant Professor at Iowa State starting August 2023. He is the author of many papers in leading journals including Nature Reviews Materials, Nature Materials, and Progress in Materials Science. He edited a book entitled “The Science and Technology of 3D Printing” (MDPI, 2021), and his textbook on “Theory and Practice of Additive Manufacturing” will be published by Wiley in October 2023. He served as a Guest Editor for the journals “Computational Materials Science”, “Materials”, and “Science and Technology of Welding and Joining”. He is an Editorial Board Member of the journal “Science and Technology of Welding and Joining” and “Welding Journal”.
Affiliations and expertise
Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, USA