Digital Twins of Advanced Materials Processing
- 1st Edition - March 17, 2026
- Latest edition
- Authors: Tarasankar DebRoy, Tuhin Mukherjee
- Language: English
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
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Description
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.
Key features
Key features
- 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
Readership
Readership
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
Table of contents
Table of contents
1. Introduction
1.1 An inspiring event
1.2 What is a digital twin?
1.3 Evolution and history of digital twins
1.4 Capabilities and uses 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 Machine learning and deep learning
2.4 Surrogate and reduced order models
2.5 Statistical models
2.6 Control models
2.7 Big data and data analytics
2.8 Interconnection among the building blocks
3. Mechanistic models
3.1 Introduction
3.2 Mechanistic models of manufacturing processes
3.2.1 Heat transfer and fluid flow modeling
3.2.2 Process modeling
3.3 Mechanistic modeling of microstructure
3.3.1 Solidification morphology
3.3.2 Grain structure
3.3.3 Microstructure evolution
3.4 Mechanistic modeling of mechanical properties
3.5 Mechanistic modeling of performance
3.5.1 Defects
3.5.2 Mitigating composition change - selective vaporization of alloying elements
3.5.3 Modeling residual stress and distortion
3.6 Software for mechanistic modeling of manufacturing
4. Surrogate and reduced order models
4.1 Introduction
4.2 Analytical models
4.3 Dimensionless number-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
5. Machine learning and deep learning
5.1 Introduction
5.2 Machine learning algorithms and applications
5.2.1 Regression algorithms
5.2.2 Classification algorithms
5.3 Deep learning algorithms and applications
5.3.1 Discriminative deep learning
5.3.2 Generative deep learning
5.3.3 Reinforcement learning
5.4 Image processing and feature extraction
5.5 Open-source packages
5.6 Data needs
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
7. Sensing and control
7.1 Introduction
7.2 Sensors
7.2.1 Temperature sensing
7.2.2 Pressure measurements
7.2.3 Flow sensors
7.2.4 Vibration sensors
7.2.5 Sensor data for control models
7.3 Operations Research-based control models
7.3.1 Linear programming
7.3.2 Integer programming
7.3.3 Dynamic programming
7.3.4 Queuing theory
7.3.5 Network flow models
7.3.6 Markov decision processes
7.3.7 Model predictive control
7.3.8 Feedback and feed-forward control models
7.4 Fuzzy Logic-based control models
7.5 Data-driven control models
7.6 Proportional-Integral-Derivative control models
7.6.1 Advantages and disadvantages
7.6.2 Types of proportional-integral derivative controllers
7.7 Processing and storage of data for process control in manufacturing
8. Digital twin implementation and case studies
8.1 Introduction
8.2 Implementation of digital twin
8.2.1 Hardware and software integration
8.2.2 Internet of things for connectivity
8.2.3 Cyber physical systems in digital twins
8.2.4 Validation and testing of the building blocks
8.2.5 Uncertainty quantification for digital twins
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 weld quality control
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
9. Current status, research needs, and outlook
9.1 Introduction
9.2 Current status
9.3 Research needs
9.3.1 Data storage
9.3.2 Blockchain
9.3.3 Accessibility
9.3.4 Cybersecurity
9.3.5 Need for quantum computing
9.3.6 High technology readiness level of building blocks
9.3.7 Standardization
9.4 Outlook
9.4.1 Emerging trends
9.4.2 Challenges and barriers to adoption
9.4.3 Path forward
1.1 An inspiring event
1.2 What is a digital twin?
1.3 Evolution and history of digital twins
1.4 Capabilities and uses 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 Machine learning and deep learning
2.4 Surrogate and reduced order models
2.5 Statistical models
2.6 Control models
2.7 Big data and data analytics
2.8 Interconnection among the building blocks
3. Mechanistic models
3.1 Introduction
3.2 Mechanistic models of manufacturing processes
3.2.1 Heat transfer and fluid flow modeling
3.2.2 Process modeling
3.3 Mechanistic modeling of microstructure
3.3.1 Solidification morphology
3.3.2 Grain structure
3.3.3 Microstructure evolution
3.4 Mechanistic modeling of mechanical properties
3.5 Mechanistic modeling of performance
3.5.1 Defects
3.5.2 Mitigating composition change - selective vaporization of alloying elements
3.5.3 Modeling residual stress and distortion
3.6 Software for mechanistic modeling of manufacturing
4. Surrogate and reduced order models
4.1 Introduction
4.2 Analytical models
4.3 Dimensionless number-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
5. Machine learning and deep learning
5.1 Introduction
5.2 Machine learning algorithms and applications
5.2.1 Regression algorithms
5.2.2 Classification algorithms
5.3 Deep learning algorithms and applications
5.3.1 Discriminative deep learning
5.3.2 Generative deep learning
5.3.3 Reinforcement learning
5.4 Image processing and feature extraction
5.5 Open-source packages
5.6 Data needs
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
7. Sensing and control
7.1 Introduction
7.2 Sensors
7.2.1 Temperature sensing
7.2.2 Pressure measurements
7.2.3 Flow sensors
7.2.4 Vibration sensors
7.2.5 Sensor data for control models
7.3 Operations Research-based control models
7.3.1 Linear programming
7.3.2 Integer programming
7.3.3 Dynamic programming
7.3.4 Queuing theory
7.3.5 Network flow models
7.3.6 Markov decision processes
7.3.7 Model predictive control
7.3.8 Feedback and feed-forward control models
7.4 Fuzzy Logic-based control models
7.5 Data-driven control models
7.6 Proportional-Integral-Derivative control models
7.6.1 Advantages and disadvantages
7.6.2 Types of proportional-integral derivative controllers
7.7 Processing and storage of data for process control in manufacturing
8. Digital twin implementation and case studies
8.1 Introduction
8.2 Implementation of digital twin
8.2.1 Hardware and software integration
8.2.2 Internet of things for connectivity
8.2.3 Cyber physical systems in digital twins
8.2.4 Validation and testing of the building blocks
8.2.5 Uncertainty quantification for digital twins
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 weld quality control
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
9. Current status, research needs, and outlook
9.1 Introduction
9.2 Current status
9.3 Research needs
9.3.1 Data storage
9.3.2 Blockchain
9.3.3 Accessibility
9.3.4 Cybersecurity
9.3.5 Need for quantum computing
9.3.6 High technology readiness level of building blocks
9.3.7 Standardization
9.4 Outlook
9.4.1 Emerging trends
9.4.2 Challenges and barriers to adoption
9.4.3 Path forward
Product details
Product details
- Edition: 1
- Latest edition
- Published: March 17, 2026
- Language: English
About the authors
About the authors
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 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”.
Affiliations and expertise
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.View book on ScienceDirect
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