Artificial Intelligence in Manufacturing

Concepts and Methods

Masoud Soroush, Richard D Braatz, Masoud Soroush, Richard D Braatz

• Publisher: Academic Press
• Year: 2024
• Print ISBN:
  9780323991346
• eBook ISBN:
  9780323996723
• Topic: Technology & Engineering
• Subtopic: Industrial Engineering
• Index: Technology & Engineering

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. - 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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Front Matter
• Copyright
• Contents
• Contributors
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : Machine learning methods
• Chapter 2 : Learning first-principles knowledge from data
• Chapter 3 : Convolutional neural networks: Basic concepts and applications in manufacturing
• Chapter 4 : Sparse mathematical programming for fundamental learning of governing equations
• Chapter 5 : Data-driven optimization algorithms
• Chapter 6 : Machine learning for control of (bio)chemical manufacturing systems
• Chapter 7 : Learning first principles systems knowledge from data: Stability and safety with applications to learning from demonstration
• Chapter 8 : Artificial intelligence for materials damage diagnostics and prognostics
• Chapter 9 : Artificial intelligence for machining process monitoring
• Index
• A