Artificial Intelligence in Manufacturing
Concepts and Methods
- 1st Edition - January 22, 2024
- Imprint: Academic Press
- Editors: Masoud Soroush, Richard D Braatz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 1 3 4 - 6
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 6 7 2 - 3
Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intellige… Read more
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Request a sales quoteArtificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully developed methods that can apply to a range of engineering applications.
The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems.
- Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates
- Provides worked examples throughout to help readers fully engage with the methods described
- Includes concepts that are supported by definitions for key terms and chapter summaries
Researchers in industry and academia with an interest in advanced manufacturing or industrial applications of AI
- Cover image
- Title page
- Copyright
- Contents
- Contributors
- Preface
- CHAPTER 1 Machine learning methods
- 1.1 Introduction
- 1.2 Holistic view of learning models
- 1.3 Classification of learning techniques
- 1.4 Machine learning methods
- 1.5 Conclusion
- Acknowledgment
- References
- CHAPTER 2 Learning first-principles knowledge from data
- 2.1 Background
- 2.2 Approaches to analyze manufacturing data
- 2.3 Automation of model selection and hyperparameter search
- 2.4 Conclusion
- References
- CHAPTER 3 Convolutional neural networks: Basic concepts and applications in manufacturing
- 3.1 Introduction
- 3.2 Data objects and mathematical representations
- 3.3 Convolutional neural network architectures
- 3.4 Case studies
- 3.5 Conclusion
- Acknowledgment
- References
- CHAPTER 4 Sparse mathematical programming for fundamental learning of governing equations
- 4.1 Introduction
- 4.2 Problem definitions
- 4.3 Physics-informed machine learning
- 4.4 Regression-based approaches
- 4.5 Techniques based on mathematical programming
- 4.6 Demonstration of moving horizon discovery for a batch chemical process
- 4.7 Conclusion
- References
- CHAPTER 5 Data-driven optimization algorithms
- 5.1 Introduction
- 5.2 Algorithmic approaches for data-driven optimization
- 5.3 Applications to large-scale manufacturing systems
- 5.4 Extensions to other classes of problems
- 5.5 Remarks
- 5.6 Conclusion
- References
- CHAPTER 6 Machine learning for control of (bio)chemical manufacturing systems
- 6.1 Introduction
- 6.2 (Bio)chemical processes
- 6.3 The machine learning Oracle and machine learning approaches in a nutshell
- 6.4 Machine learning-supported modeling for monitoring and control
- 6.5 Control via machine learning
- 6.6 Conclusion
- References
- CHAPTER 7 Learning first principles systems knowledge from data: Stability and safety with applications to learning from demonstration
- 7.1 Introduction
- 7.2 Learning robot motions using dynamical systems primitive
- 7.3 Conclusion
- Acknowledgment
- References
- CHAPTER 8 Artificial intelligence for materials damage diagnostics and prognostics
- 8.1 Introduction
- 8.2 AI methods for materials diagnostics and prognostics
- 8.3 Challenges and opportunities for AI methods for damage diagnostics and prognostics
- 8.4 Conclusion
- References
- CHAPTER 9 Artificial intelligence for machining process monitoring
- 9.1 Introduction
- 9.2 Data acquisition systems
- 9.3 Feature engineering and machine learning
- 9.4 Signal decomposition methods
- 9.5 Deep learning
- 9.6 Transfer learning
- 9.7 Conclusion
- Acknowledgment
- References
- Index
- Edition: 1
- Published: January 22, 2024
- Imprint: Academic Press
- No. of pages: 430
- Language: English
- Paperback ISBN: 9780323991346
- eBook ISBN: 9780323996723
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, USARead Artificial Intelligence in Manufacturing on ScienceDirect