Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

Quality Analysis Method for Hot Strip Rolling Based on Data Mining Technology

Authors
Zhen Hou, Hai Gao, Chen Chen
Corresponding Author
Zhen Hou
Available Online July 2016.
DOI
https://doi.org/10.2991/iccia-17.2017.91How to use a DOI?
Keywords
hot rolling of strip steel, data mining technology, correlation analysis, quality analysis, control parameters.
Abstract
The hot rolling of strip steel is a continuous, multi-staged, complex production process, and there are about one hundred of control parameters which are directly related to the quality of strip steel products in this complicated process. Researches have shown that, according to the industrial characteristics of hot strip rolling, using data mining technology to extract the useful, potential and ultimately understandable process knowledge, and get the corresponding relationship between the strip steel quality defects and control parameters. It can quickly locate the causes of strip steel quality problems, and find out the key control parameters to make adjustments, improve production efficiency and production quality, and reduce economic losses. It provides a scientific and accurate way to analyze the quality problems of hot rolled strip steel products.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Part of series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
978-94-6252-361-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-17.2017.91How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhen Hou
AU  - Hai Gao
AU  - Chen Chen
PY  - 2016/07
DA  - 2016/07
TI  - Quality Analysis Method for Hot Strip Rolling Based on Data Mining Technology
BT  - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.91
DO  - https://doi.org/10.2991/iccia-17.2017.91
ID  - Hou2016/07
ER  -