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Artificial Intelligence in Manufacturing
Applications and Case Studies
- 1st Edition - January 22, 2024
- 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
- Cover image
- Title page
- Copyright
- Contents
- Contributors
- Preface
- CHAPTER 1 Artificial intelligence for paints/coatings manufacturing
- 1.1 Introduction
- 1.2 The machine learning problem
- 1.3 Overview of applied methods
- 1.4 Results and discussion
- 1.5 Qualitative analysis of modeling approaches
- 1.6 Experimental setup
- 1.7 Conclusion
- Acknowledgment
- References
- CHAPTER 2 Data-driven discovery and design of additives for controlled polymer morphology and performance
- 2.1 Introduction
- 2.2 Polymer crystal-growth predictions using molecular dynamics
- 2.3 Data-driven modeling and search through a materials genome
- 2.4 Experimental validation of nucleating agents in polymers
- 2.5 Conclusion
- Acknowledgment
- References
- CHAPTER 3 Data, machine learning, first-principles, and hybrid models in the petrochemical industry
- 3.1 Introduction
- 3.2 Data type and processing in petrochemical industry
- 3.3 Machine learning and other data-driven models in the petrochemical industry
- 3.4 First-principles models versus hybrid and data-driven models: a sliding scale
- 3.5 Outlook
- 3.6 Conclusion
- References
- CHAPTER 4 Perspectives on artificial intelligence for plasma-assisted manufacturing in semiconductor industry
- 4.1 Introduction
- 4.2 Plasma-assisted processes in semiconductor manufacturing
- 4.3 AI for process design and production
- 4.4 AI for process optimization and efficiency enhancement
- 4.5 AI for process operation and adaptive control
- 4.6 Conclusion
- Acknowledgment
- References
- CHAPTER 5 Machine learning in reaction engineering
- 5.1 Introduction
- 5.2 Machine learning in catalyst design
- 5.3 Machine learning for predicting reaction mechanism and kinetics
- 5.4 Data-driven reaction predictions
- 5.5 Conclusion
- References
- CHAPTER 6 Artificial intelligence in catalysis
- 6.1 Introduction
- 6.2 Applications of AI in improving catalytic models
- 6.3 Applications of AI in computational design of catalysts
- 6.4 AI to elucidate and design catalytic systems directly using experiments
- 6.5 Allied and cross-cutting topics of AI in catalysis
- 6.6 Conclusion
- References
- CHAPTER 7 Advanced manufacturing of touch-sensitive textiles
- 7.1 Introduction
- 7.2 Sensor interaction and machine learning
- 7.3 Gesture recognition system
- 7.4 Experiments
- 7.5 Methods and results
- 7.6 Building interactive applications
- 7.7 Conclusion
- References
- CHAPTER 8 Cyber–physical systems framework for AI in smart manufacturing and maintenance
- 8.1 Introduction
- 8.2 Cyber–physical systems framework for maintenance and service innovation
- 8.3 Development of digital twins: methodology and analytics
- 8.4 Case studies
- 8.5 Conclusion
- References
- CHAPTER 9 Dynamic data feature engineering for process operation troubleshooting
- 9.1 Introduction
- 9.2 Latent dynamic time-series analytics
- 9.3 Dynamic latent variable feature extraction
- 9.4 The DELFA troubleshooting procedure
- 9.5 Troubleshooting plant-wide oscillations
- 9.6 Comparing DiCCA with slow feature analysis
- 9.7 Conclusion
- Acknowledgment
- References
- CHAPTER 10 Advanced manufacturing of biopharmaceuticals
- 10.1 Introduction
- 10.2 Mammalian cell bioreactor simulator
- 10.3 Data-driven bioreactor process modeling
- 10.4 Model predictive control
- 10.5 Data-driven modeling and closed-loop control results from mammalian cell bioreactor simulator
- 10.6 Integration of artificial intelligence in biopharmaceutical production processes
- 10.7 Conclusion
- Acknowledgment
- References
- Index
- No. of pages: 430
- Language: English
- Edition: 1
- Published: January 22, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323991353
- eBook ISBN: 9780323996716
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, USARead Artificial Intelligence in Manufacturing on ScienceDirect