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
- 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
- Edition: 1
- Published: January 22, 2024
- No. of pages (Paperback): 340
- No. of pages (eBook): 430
- 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