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Artificial Intelligence in Manufacturing
Concepts and Methods
1st Edition - January 19, 2024
Editors: Masoud Soroush, Richard D Braatz
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 1 3 4 - 6
Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is… Read more
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Artificial 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
1. Data‐driven Physics‐based Digital Twins
2. Hybrid Modeling Approach Integrating PLS Models with First-principles Knowledge
3. Dynamical Systems-Guided Learning of PDEs from Data
4. Learning First-principles Knowledge from Data
5. Actual Learning through Machine Learning
6. Iterative Cross Learning
7. Learning an Algebraic Model from Data
8. Data‐driven Optimization Algorithms
9. Interpretable Machine Learning
10. Learning Science and Algorithms
11. Reinforcement Learning
12. Machine Learning: Trends, Perspectives, and Prospects
13. Artificial Intelligence: Trends, Perspectives, and Prospects
14. Artificial Intelligence Education for Chemical Engineers
2. Hybrid Modeling Approach Integrating PLS Models with First-principles Knowledge
3. Dynamical Systems-Guided Learning of PDEs from Data
4. Learning First-principles Knowledge from Data
5. Actual Learning through Machine Learning
6. Iterative Cross Learning
7. Learning an Algebraic Model from Data
8. Data‐driven Optimization Algorithms
9. Interpretable Machine Learning
10. Learning Science and Algorithms
11. Reinforcement Learning
12. Machine Learning: Trends, Perspectives, and Prospects
13. Artificial Intelligence: Trends, Perspectives, and Prospects
14. Artificial Intelligence Education for Chemical Engineers
- No. of pages: 430
- Language: English
- Published: January 19, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323991346
MS
Masoud Soroush
Masoud Soroush is a professor of chemical and biological engineering at Drexel University. He received his B.S. in chemical engineering from Abadan Institute of Technology, Iran, and M.S.E. degrees in chemical engineering and electrical engineering and Ph.D. in chemical engineering from the University of Michigan, Ann Arbor, United States. He was a visiting scientist at DuPont Marshall Lab, Philadelphia, 2002–2003 and a visiting professor at Princeton University in 2008. He was the AIChE Area 10b Program Coordinator in 2009, and the AIChE Director on the American Automatic Control Council Board of Directors from 2010–2013. His awards include the U.S. National Science Foundation Faculty Early CAREER Award in 1997 and the O. Hugo Schuck Best Paper Award of American Automatic Control Council in 1999. He is an elected fellow of AIChE and a senior member of IEEE. His research interests are in process systems engineering, polymer reaction engineering, electronic-level modeling of reactions, polymer membranes, multiscale modeling, probabilistic modeling and inference, and renewable power generation and storage systems. He has authored or co-authored more than 320 publications, including over 180 refereed papers.
Affiliations and expertise
Professor of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, USARD
Richard D Braatz
Richard D Braatz works in the Department of Chemical Engineering at Massachusetts Institute of Technology, Cambridge, USA.
Affiliations and expertise
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, USA