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Published in: Metallurgist 9-10/2023

28-02-2023

Digitalization as the Most Important Tool for the Improvement of Metallurgical Technologies

Authors: A. V. Muntin, M. N. Shamshin, A. G. Ziniagin, O. S. Hlybov, A. S. Zonov, L. M. Kavitsian, S. D. Skachkov

Published in: Metallurgist | Issue 9-10/2023

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Abstract

This article presents examples of digitalization projects implemented most recently at Vyksa Steel Works (Russia). The results are obtained using the methods of machine learning (i.e., predicting the mechanical properties of rolled steel), computer vision (i.e., controlling leaks during hydrotesting of pipes and analyzing thermal scans of the surface of hot-rolled plates at Mill 5000), and solving non-steady equations of heat conduction (i.e., predicting the temperatures of stacks of hot-rolled plates at Mill 5000), as well as problems of linear programming (i.e., calculating the optimal cutting of billets for the production of weldless pipes).

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Footnotes
1
The process engineer of EWPW-3 S.S. Zaitsev took part in the work.
 
Literature
1.
go back to reference “Russia forges digital metal,” Connect, No. 1-2, 28–39 (2022). “Russia forges digital metal,” Connect, No. 1-2, 28–39 (2022).
2.
go back to reference O. A. Romanova and D. V. Sirotin, “Digitalization of production processes in metallurgy: trends and measurement methods,” Izv. Ural’sk. Gos. Gorn. Univ., No. 3 (63), 136–148 (2021). O. A. Romanova and D. V. Sirotin, “Digitalization of production processes in metallurgy: trends and measurement methods,” Izv. Ural’sk. Gos. Gorn. Univ., No. 3 (63), 136–148 (2021).
3.
go back to reference OST 14-1-34–90, Statistical Acceptance Control of the Quality of Metal Products by Correlation between Parameters. OST 14-1-34–90, Statistical Acceptance Control of the Quality of Metal Products by Correlation between Parameters.
4.
go back to reference GOST R ISO 3534-1, Statistical Methods. Probability and Bases of Statistics. Terms and Definitions. GOST R ISO 3534-1, Statistical Methods. Probability and Bases of Statistics. Terms and Definitions.
5.
go back to reference GOST R 50779.53–98, Statistical Methods. Acceptance Quality Control on a Quantitative Basis for the Normal Distribution Law. Part 1. Known Standard Deviation. GOST R 50779.53–98, Statistical Methods. Acceptance Quality Control on a Quantitative Basis for the Normal Distribution Law. Part 1. Known Standard Deviation.
6.
go back to reference GOST R 50779.77, Statistical Methods. Plans and Procedures for Statistical Acceptance Control of Non-Piece Products. GOST R 50779.77, Statistical Methods. Plans and Procedures for Statistical Acceptance Control of Non-Piece Products.
7.
go back to reference GOST 27.202, Equipment Reliability. Technological Systems. Methods for Assessing Reliability in Terms of Quality Parameters of Manufactured Products. GOST 27.202, Equipment Reliability. Technological Systems. Methods for Assessing Reliability in Terms of Quality Parameters of Manufactured Products.
9.
go back to reference V. V. Kitov, “Investigation of the accuracy of the gradient boosting method with random rotations,” Statistika Ekon., No. 4, 22–26 (2016). V. V. Kitov, “Investigation of the accuracy of the gradient boosting method with random rotations,” Statistika Ekon., No. 4, 22–26 (2016).
10.
go back to reference O. S. Khlybov, D. V. Khrameshin, and Z. K. Kabakov, “Development and implementation of an automatic system for certification of metal by mechanical properties at the Vyksa production site,” Metallurg, No. 8, 14–20 (2017). O. S. Khlybov, D. V. Khrameshin, and Z. K. Kabakov, “Development and implementation of an automatic system for certification of metal by mechanical properties at the Vyksa production site,” Metallurg, No. 8, 14–20 (2017).
11.
go back to reference V. F. Zotov, Rolled Products Manufacture [in Russian], Intermet Engineering, Moscow (2000). V. F. Zotov, Rolled Products Manufacture [in Russian], Intermet Engineering, Moscow (2000).
12.
go back to reference G. Ya. Gun, Theoretical Foundations of Metal Pressure Treatment [in Russian], Metallurgiya, Moscow (1980). G. Ya. Gun, Theoretical Foundations of Metal Pressure Treatment [in Russian], Metallurgiya, Moscow (1980).
13.
go back to reference V. A. Arutyunov, V. V. Bukhmirov, and S. A. Krupennikov, Mathematical Modeling of Thermal Performance of Industrial Furnaces, Textbook for Higher Education [in Russian], Metallurgiya, Moscow (1990). V. A. Arutyunov, V. V. Bukhmirov, and S. A. Krupennikov, Mathematical Modeling of Thermal Performance of Industrial Furnaces, Textbook for Higher Education [in Russian], Metallurgiya, Moscow (1990).
15.
go back to reference P. Kaewtrakulpong and R. Bowden, “An improved adaptive background mixture model for real-time tracking with shadow detection,” Video Based Surv. Sys., Springer, 135–144 (2002). P. Kaewtrakulpong and R. Bowden, “An improved adaptive background mixture model for real-time tracking with shadow detection,” Video Based Surv. Sys., Springer, 135–144 (2002).
16.
go back to reference S. Gonzalez Rafael and E. Woods Richard, Digital Image Processing [transl. from English by L. I. Rubanov, P. A. Chochia], 3rd edition, revised and corrected, Tekhnosfera, Moscow (Moscow, Tipografiya Nauka RAN) (2012). S. Gonzalez Rafael and E. Woods Richard, Digital Image Processing [transl. from English by L. I. Rubanov, P. A. Chochia], 3rd edition, revised and corrected, Tekhnosfera, Moscow (Moscow, Tipografiya Nauka RAN) (2012).
17.
go back to reference S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,” Comput. Vis. Graph. Image Process., 32–46 (1985). S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,” Comput. Vis. Graph. Image Process., 32–46 (1985).
18.
go back to reference K. F. Riley, M. P. Hobson, and S. J. Bence, Mathematical Methods for Physics and Engineering, Cambridge University Press (1997). K. F. Riley, M. P. Hobson, and S. J. Bence, Mathematical Methods for Physics and Engineering, Cambridge University Press (1997).
Metadata
Title
Digitalization as the Most Important Tool for the Improvement of Metallurgical Technologies
Authors
A. V. Muntin
M. N. Shamshin
A. G. Ziniagin
O. S. Hlybov
A. S. Zonov
L. M. Kavitsian
S. D. Skachkov
Publication date
28-02-2023
Publisher
Springer US
Published in
Metallurgist / Issue 9-10/2023
Print ISSN: 0026-0894
Electronic ISSN: 1573-8892
DOI
https://doi.org/10.1007/s11015-023-01418-2

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