Tensor Data Analytics in Advanced Manufacturing Processes
Document Type
Syllabus
Publication Date
1-1-2024
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.
Identifier
85194578269 (Scopus)
Publication Title
Springer Optimization and Its Applications
External Full Text Location
https://doi.org/10.1007/978-3-031-53092-0_6
e-ISSN
19316836
ISSN
19316828
First Page
107
Last Page
121
Volume
211
Recommended Citation
Shen, Bo, "Tensor Data Analytics in Advanced Manufacturing Processes" (2024). Faculty Publications. 987.
https://digitalcommons.njit.edu/fac_pubs/987