University of Oulu

Tiensuu, H., Tamminen, S., Haapala, O., & Röning, J. (2020). Intelligent methods for root cause analysis behind the center line deviation of the steel strip, Open Engineering, 10(1), 386-393. doi: https://doi.org/10.1515/eng-2020-0041

Intelligent methods for root cause analysis behind the center line deviation of the steel strip

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Author: Tiensuu, Henna1; Tamminen, Satu1; Haapala, Olli1;
Organizations: 1Biomimetics and Intelligent Systems Group (BISG), P.O.Box 8000, FI-90014 University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020060841046
Language: English
Published: De Gruyter, 2020
Publish Date: 2020-06-08
Description:

Abstract

This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs.

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Series: Open engineering
ISSN: 2391-5439
ISSN-E: 2391-5439
ISSN-L: 2391-5439
Volume: 10
Issue: 1
Pages: 386 - 393
DOI: 10.1515/eng-2020-0041
OADOI: https://oadoi.org/10.1515/eng-2020-0041
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
112 Statistics and probability
Subjects:
GBM
Funding: The authors thank Outokumpu Stainless Oy, Tornio, Finland for providing the data and their expertise for the application. Further acknowledgements are given to Business Finland, Dimecc Oy and Centre for Advanced Steels Research (CASR) for supporting this research.
Copyright information: © 2020 H. Tiensuu et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 License.
  https://creativecommons.org/licenses/by/4.0/