國立虎尾科技大學 |

Domain-informed machine learning for smart manufacturing

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Domain-informed machine learning for smart manufacturing/ by Qiang Huang.
作者:	Huang, Qiang.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xvii, 411 p. :ill. (chiefly col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Industrial and Production Engineering. -
電子資源:	https://doi.org/10.1007/978-3-031-91631-1
ISBN:	9783031916311

Domain-informed machine learning for smart manufacturing
Huang, Qiang.

Domain-informed machine learning for smart manufacturing[electronic resource] /by Qiang Huang. - Cham :Springer Nature Switzerland :2025. - xvii, 411 p. :ill. (chiefly col.), digital ;24 cm.

Introduction -- Domain-informed Feature Engineering for Smart Manufacturing -- Domain-informed -- Dimension Reduction for Smart Manufacturing -- Fabrication-Aware Machine -- Learning Models for Additive Manufacturing -- Domain-Informed Machine Learning -- Models for Nanomanufacturing -- Engineering-Informed Transfer Learning -- Engineering-Informed -- Process Compensation and Adjustment -- Domain-informed Data Pre-Processing in Additive Manufacturing -- Future Perspective for Domain-informed Machine -- Learning for Smart Manufacturing.

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.

ISBN: 9783031916311

Standard No.: 10.1007/978-3-031-91631-1doiSubjects--Topical Terms:

593943
Industrial and Production Engineering.

LC Class. No.: TS183

Dewey Class. No.: 670.285

Domain-informed machine learning for smart manufacturing
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520 $a 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.
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