Domain-informed Machine Learning for Smart Manufacturing - Rilegato

Huang, Qiang Sean

Rilegato

ISBN 10: 3031916301 ISBN 13: 9783031916304

Casa editrice: Springer Nature, 2025

Riassunto

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.

Su questo libro

Informazioni sull'autore

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.

Dalla quarta di copertina

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.

• Introduces domain-informed learning problem formulation, contextualized data representation, and dimension reduction 
• Introduces small-sample machine learning, transfer learning, and quality control methods for 3D printing and more 
• Reinforces concepts, methods, and tools described with real world manufacturing case studies, examples, and data

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031916301
• ISBN 13: 9783031916304
• Rilegatura: Copertina rigida
• Numero di pagine: 450
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania