Digital Twins in Manufacturing: Concepts and Methods

Nyhet

Häftad, Engelska, 2026

2 719 kr

Kommande

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.

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

Produktinformation

Utgivningsdatum2026-10-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor450
FörlagElsevier Science
ISBN9780443444982

Tillhör följande kategorier

Tillverkningsteknik inom Naturvetenskap och teknik
Affärsapplikationer inom Data och IT
Företagsekonomi inom Ekonomi och Ledarskap

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. 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.

1. Systems Components of Digital Twins2. Foundations of Digital Twins: Definitions and Core Concepts3. Modeling and Simulation for Digital Twins4. Data Integration and Management for Digital Twins5. Communication Technologies and Connection Architectures for Digital Twins6. AI and Machine Learning in Digital Twin Systems7. Cyber-Physical Systems and Digital Twin Architectures8. Interoperability in Digital Twin Platforms9. Standards and Protocols for Digital Twins10. Digital Twins in IoT and Edge Computing11. Security and Privacy Considerations in Digital Twins12. Scalability Challenges in Digital Twin Implementations13. Real-Time Data Processing for Digital Twins14. Human-Machine Interaction in Digital Twin Environments15. Digital Twin Methodologies for Process Optimization16. Digital Twin Development Life Cycle: From Design to Deployment17. Future Trends in Digital Twin Concepts and Methods

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