Digital Twins in Manufacturing: Concepts and Methods
- 1st Edition - October 1, 2026
- Latest edition
- Editors: Masoud Soroush, Richard D Braatz
- Language: English
Digital Twins in Manufacturing: Concepts and Methods explores digital twin technologies, providing innovative perspectives and practical insights that transcend traditional covera… Read more
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Description
Description
Digital Twins in Manufacturing: Concepts and Methods explores digital twin technologies, providing innovative perspectives and practical insights that transcend traditional coverage. The book addresses the complexities of understanding and applying digital twin concepts in manufacturing environments where professionals often struggle to bridge the gap between physical and virtual systems. Sections cover critical areas such as the systems components of digital twins, foundational definitions, modeling and simulation techniques, data integration, and the role of AI and machine learning. Readers will also gain insights into communication technologies, interoperability, security considerations, and the development lifecycle of digital twins.
Each chapter is designed to equip professionals with the knowledge needed to effectively replicate and monitor real-world assets, ultimately enhancing operational efficiency and driving innovation. This is an invaluable asset for engineers, data scientists, IT professionals, and researchers in manufacturing and industrial engineering.
Each chapter is designed to equip professionals with the knowledge needed to effectively replicate and monitor real-world assets, ultimately enhancing operational efficiency and driving innovation. This is an invaluable asset for engineers, data scientists, IT professionals, and researchers in manufacturing and industrial engineering.
Key features
Key features
- Explains fundamental concepts of digital twin modeling
- Demonstrates advanced simulation and data integration techniques
- Outlines lifecycle management methods that guide readers through the stages of digital twin implementation
- Evaluates predictive analytics and maintenance models
- Explores scalability and integration strategies
Readership
Readership
Graduate students; researchers and instructors in academia, national labs, and manufacturing industries; mechanical, systems and manufacturing engineers; and professionals in industries that use or apply digital twins
Table of contents
Table of contents
1. Systems Components of Digital Twins
2. Foundations of Digital Twins: Definitions and Core Concepts
3. Modeling and Simulation for Digital Twins
4. Data Integration and Management for Digital Twins
5. Communication Technologies and Connection Architectures for Digital Twins
6. AI and Machine Learning in Digital Twin Systems
7. Cyber-Physical Systems and Digital Twin Architectures
8. Interoperability in Digital Twin Platforms
9. Standards and Protocols for Digital Twins
10. Digital Twins in IoT and Edge Computing
11. Security and Privacy Considerations in Digital Twins
12. Scalability Challenges in Digital Twin Implementations
13. Real-Time Data Processing for Digital Twins
14. Human-Machine Interaction in Digital Twin Environments
15. Digital Twin Methodologies for Process Optimization
16. Digital Twin Development Life Cycle: From Design to Deployment
17. Future Trends in Digital Twin Concepts and Methods
2. Foundations of Digital Twins: Definitions and Core Concepts
3. Modeling and Simulation for Digital Twins
4. Data Integration and Management for Digital Twins
5. Communication Technologies and Connection Architectures for Digital Twins
6. AI and Machine Learning in Digital Twin Systems
7. Cyber-Physical Systems and Digital Twin Architectures
8. Interoperability in Digital Twin Platforms
9. Standards and Protocols for Digital Twins
10. Digital Twins in IoT and Edge Computing
11. Security and Privacy Considerations in Digital Twins
12. Scalability Challenges in Digital Twin Implementations
13. Real-Time Data Processing for Digital Twins
14. Human-Machine Interaction in Digital Twin Environments
15. Digital Twin Methodologies for Process Optimization
16. Digital Twin Development Life Cycle: From Design to Deployment
17. Future Trends in Digital Twin Concepts and Methods
Product details
Product details
- Edition: 1
- Latest edition
- Published: October 1, 2026
- Language: English
About the editors
About the editors
MS
Masoud Soroush
Masoud Soroush is the George B. Francis Chair Professor of Engineering at Drexel University and directs the Future Layered nAnomaterials Knowledge and Engineering (FLAKE) Consortium, collaborating with over 30 researchers from Drexel, the University of Pennsylvania, and Purdue. He has held positions as a Visiting Scientist at DuPont and a Visiting Professor at Princeton. An Elected Fellow of AIChE and Senior Member of IEEE, Soroush has received numerous awards, including the AIChE 2023 Excellence in Process Development Research Award. He holds a BS from Abadan Institute of Technology and MS/PhD degrees from the University of Michigan, with research focusing on advanced manufacturing and nanomaterials.
Affiliations and expertise
Professor of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, USARD
Richard D Braatz
Dr. Richard D. Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at MIT, specializing in advanced manufacturing systems. His research focuses on process data analytics, mechanistic modeling, and robust control systems, particularly in monoclonal antibody, vaccine, and gene therapy production. He holds an M.S. and Ph.D. from Caltech and previously served as a professor at the University of Illinois and a visiting scholar at Harvard. Dr. Braatz has received several prestigious awards, including the Donald P. Eckman Award and the Curtis W. McGraw Research Award, and is a Fellow of multiple professional organizations and a member of the U.S. National Academy of Engineering.
Affiliations and expertise
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, USA