Tensor Data Analytics in Advanced Manufacturing Processes

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The emergence of edge computing, coupled with the growth of the Industrial Internet of Things (IIoT), along with sensors and intelligent/smart technologies, has opened up significant possibilities for the progression of advanced manufacturing. Together with data science and artificial intelligence, manufacturing data analytics are transforming manufacturing from limited factory floor automation to fully autonomous and interconnected systems. These data analytics methods are mainly based on vectors; however, real-world manufacturing data are presented in the format of high-order tensors. Accordingly, tensor data analytics has become a fast-growing area for advanced manufacturing. In this chapter, two robust tensor decomposition methods, motivated by specific engineering problems, are introduced for process monitoring in metal additive manufacturing.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer
Pages107-121
Number of pages15
DOIs
StatePublished - 2024

Publication series

NameSpringer Optimization and Its Applications
Volume211
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

All Science Journal Classification (ASJC) codes

  • Control and Optimization

Keywords

  • Advanced manufacturing
  • Robust tensor decomposition
  • Smooth sparse decomposition

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