Domain-informed Machine Learning for Smart Manufacturing

Qiang Huang

ISBN 10: 3031916336 / ISBN 13: 9783031916335

Published by Springer, 2026

About this Item

Domain-informed Machine Learning for Smart Manufacturing | Qiang Huang | Taschenbuch | xvii | Englisch | 2026 | Springer | EAN 9783031916335 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 136132620

Bibliographic Details

• Title: Domain-informed Machine Learning for Smart Manufacturing
• Publisher: Springer
• Publication Date: 2026
• Language: English
• ISBN 10: 3031916336
• ISBN 13: 9783031916335
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 22D millimeters 235 millimeters width by 155 millimeters height by 22 millimeters depth
• Weight: 730 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book introduces the state-of-the-art understanding on domain-informed machine learning (DIML) for advanced manufacturing. Methods and case studies presented in this volume show how complicated engineering phenomena and mechanisms are integrated into machine learning problem formulation and methodology development. Ultimately, these methodologies contribute to quality control for smart personalized manufacturing. The topics include domain-informed feature representation, dimension reduction for personalized manufacturing, fabrication-aware modeling of additive manufacturing processes, small-sample machine learning for 3D printing quality, optimal compensation of 3D shape deviation in 3D printing, engineering-informed transfer learning for smart manufacturing, and domain-informed predictive modeling for nanomanufacturing quality. Demonstrating systematically how the various aspects of domain-informed machine learning methods are developed for advanced manufacturing such as additive manufacturing and nanomanufacturing, the book is ideal for researchers, professionals, and students in manufacturing and related engineering fields.

About the Author

Dr. Qiang S. Huang is a Professor in the Epstein Department of Industrial and Systems Engineering at the University of Southern California, Los Angeles, CA.