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Artificial Intelligence in Manufacturing
Applications and Case Studies
1st Edition - January 19, 2024
Editors: Masoud Soroush, Richard D Braatz
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 1 3 5 - 3
Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies… Read more
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Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation. Artificial intelligence is increasingly being applied to all engineering disciplines, producing 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 used it in a range of applications. Processes including additive manufacturing, pharmaceutical manufacturing, painting, chemical engineering and machinery maintenance are all addressed.
Case studies, worked examples, basic introductory material and step-by-step instructions on methods make the work accessible to a large group of interested professionals.
- Explains innovative computational tools and methods in a practical and systematic way
- Addresses a wide range of manufacturing types, including additive, chemical and pharmaceutical
- Includes case studies from industry that describe how to overcome the challenges of implementing these methods in practice
Researchers in industry, academia with an interest in advanced manufacturing or industrial applications of AI
1. Machine Learning in Paints and Coatings
2. Machine Learning in Lithium-ion Batteries
3. Machine Learning for Emerging Two-phase Cooling Technologies
4. Algorithm-driven Design of Composite Materials Realized through Additive Manufacturing
5. Machine‐learning‐based Monitoring of Laser Powder Bed Fusion
6. Data Analytics and Cyber-physical Systems for Maintenance and Service Innovation
7. Machine Learning in Catalysis
8. Artificial Intelligence in Petrochemical Industry
9. Machine Learning-assisted Plasma Medicine
10. Dynamic Data Feature Engineering for Process Operation Troubleshooting
11. Geometric Structure-Property Relationships Captured by Theory-Guided, Interpretable Machine Learning
12. Molecular Design Blueprints from Machine Learning for Catalysts and Materials
13. Physics-driven Machine Learning for Characterizing Surface Microstructure of Complex Materials
14. Process Performance Assessment Using Machine Learning
15. Artificial Intelligence in Chemical Engineering
16. Production of Polymer Films with Optimal Properties Using Machine Learning
2. Machine Learning in Lithium-ion Batteries
3. Machine Learning for Emerging Two-phase Cooling Technologies
4. Algorithm-driven Design of Composite Materials Realized through Additive Manufacturing
5. Machine‐learning‐based Monitoring of Laser Powder Bed Fusion
6. Data Analytics and Cyber-physical Systems for Maintenance and Service Innovation
7. Machine Learning in Catalysis
8. Artificial Intelligence in Petrochemical Industry
9. Machine Learning-assisted Plasma Medicine
10. Dynamic Data Feature Engineering for Process Operation Troubleshooting
11. Geometric Structure-Property Relationships Captured by Theory-Guided, Interpretable Machine Learning
12. Molecular Design Blueprints from Machine Learning for Catalysts and Materials
13. Physics-driven Machine Learning for Characterizing Surface Microstructure of Complex Materials
14. Process Performance Assessment Using Machine Learning
15. Artificial Intelligence in Chemical Engineering
16. Production of Polymer Films with Optimal Properties Using Machine Learning
- No. of pages: 354
- Language: English
- Published: January 19, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323991353
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