Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes-Reference-Cited by-同舟云学术

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Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes

Published:2024-09-09 Issue: Volume: Page:1-18
ISSN:2472-5854
Container-title:IISE Transactions
language:en
Short-container-title:IISE Transactions

Author:

Jeong Cheoljoon¹^ORCID,Byon Eunshin¹^ORCID,He Fei²^ORCID,Fang Xiaolei³^ORCID

Affiliation:

1. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA

2. The Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, China

3. Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725854.2024.2385670

Reference37 articles.

1. A new look at the statistical model identification

2. Balmashnova, E., Bruurmijn, M., Dissanayake, R., Duits, R., Kampmeijer, L. and van Noorden, T. (2013) Image recognition of shape defects in hot steel rolling, in Proceedings of the 84th European Study Group Mathematics with Industry (SWI 2012), pp. 22–38. European Consortium for Mathematics in Industry (ECMI), Delft, Netherlands.

3. A practical randomized CP tensor decomposition;Battaglino C.;SIAM Journal on Matrix Analysis and Applications,2018

4. Convex Optimization

5. Model Selection and Multimodel Inference

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