Artificial Intelligence in Manufacturing: Applications and Case Studies
Editat de Masoud Soroush, Richard D Braatzen Limba Engleză Paperback – 24 ian 2024
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
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Specificații
ISBN-13: 9780323991353
ISBN-10: 0323991351
Pagini: 340
Dimensiuni: 152 x 229 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323991351
Pagini: 340
Dimensiuni: 152 x 229 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
Public țintă
Researchers in industry and academia with an interest in advanced manufacturing or industrial applications of AI.Cuprins
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