Artificial Intelligence in Manufacturing
Applications and Case Studies
- 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 5 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 6 7 1 - 6
Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case stud… Read more
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Request a sales quoteArtificial 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
- Edition: 1
- Published: January 22, 2024
- No. of pages (Paperback): 340
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323991353
- eBook ISBN: 9780323996716
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