Artificial Intelligence in Manufacturing

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. 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.

• 1. Machine Learning in Paints and Coatings2. Machine Learning in Lithium-ion Batteries3. Machine Learning for Emerging Two-phase Cooling Technologies4. Algorithm-driven Design of Composite Materials Realized through Additive Manufacturing5. Machine‐learning‐based Monitoring of Laser Powder Bed Fusion6. Data Analytics and Cyber-physical Systems for Maintenance and Service Innovation7. Machine Learning in Catalysis8. Artificial Intelligence in Petrochemical Industry9. Machine Learning-assisted Plasma Medicine10. Dynamic Data Feature Engineering for Process Operation Troubleshooting11. Geometric Structure-Property Relationships Captured by Theory-Guided, Interpretable Machine Learning12. Molecular Design Blueprints from Machine Learning for Catalysts and Materials13. Physics-driven Machine Learning for Characterizing Surface Microstructure of Complex Materials14. Process Performance Assessment Using Machine Learning15. Artificial Intelligence in Chemical Engineering16. Production of Polymer Films with Optimal Properties Using Machine Learning