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Elsevier

Artificial Intelligence in Manufacturing

Concepts and Methods

  • 1 Edición - 22 de enero de 2024
  • Última edición
  • Editores: Masoud Soroush, Richard D Braatz
  • Idioma: Inglés

Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intellige… Leer más

Descripción

Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more 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 developed methods that can apply to a range of engineering applications.

The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems.

Puntos claves

  • Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates
  • Provides worked examples throughout to help readers fully engage with the methods described
  • Includes concepts that are supported by definitions for key terms and chapter summaries

De interès para

Researchers in industry and academia with an interest in advanced manufacturing or industrial applications of AI

Índice

1. Data‐driven Physics‐based Digital Twins

2. Hybrid Modeling Approach Integrating PLS Models with First-principles Knowledge

3. Dynamical Systems-Guided Learning of PDEs from Data

4. Learning First-principles Knowledge from Data

5. Actual Learning through Machine Learning

6. Iterative Cross Learning

7. Learning an Algebraic Model from Data

8. Data‐driven Optimization Algorithms

9. Interpretable Machine Learning

10. Learning Science and Algorithms

11. Reinforcement Learning

12. Machine Learning: Trends, Perspectives, and Prospects

13. Artificial Intelligence: Trends, Perspectives, and Prospects

14. Artificial Intelligence Education for Chemical Engineers

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 25 de enero de 2024
  • Idioma: Inglés

Sobre los editores

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.
Afiliaciones y experiencia
Professor of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, USA

RD

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.
Afiliaciones y experiencia
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, USA

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