Domain-Informed Machine Learning for Smart Manufacturing
暫譯: 智慧製造的領域知識機器學習

Huang, Qiang

  • 出版商: Springer
  • 出版日期: 2025-07-04
  • 售價: $3,300
  • 貴賓價: 9.5$3,135
  • 語言: 英文
  • 頁數: 411
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031916301
  • ISBN-13: 9783031916304
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

本書介紹了針對先進製造的領域知識驅動機器學習(Domain-Informed Machine Learning, DIML)的最新理解。本書中所呈現的方法和案例研究展示了如何將複雜的工程現象和機制整合到機器學習問題的定義和方法論的開發中。最終,這些方法論有助於智能個性化製造的質量控制。主題包括領域知識驅動的特徵表示、個性化製造的降維、對增材製造過程的製造感知建模、小樣本機器學習在3D列印質量中的應用、3D列印中3D形狀偏差的最佳補償、針對智能製造的工程知識驅動轉移學習,以及針對納米製造質量的領域知識驅動預測建模。本書系統性地展示了領域知識驅動機器學習方法在增材製造和納米製造等先進製造領域的各個方面的發展,適合製造及相關工程領域的研究人員、專業人士和學生。

作者簡介

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.

作者簡介(中文翻譯)

黃強博士是南加州大學洛杉磯分校工業與系統工程系的教授。