On adequacy of parameters of strip cross-section profile. Part 1. Predictive interval
https://doi.org/10.17073/0368-0797-2021-1-7-13
Abstract
Increasing the level of automation of metallurgical units and the development of industrial information systems increases the number of p ters of production and technological processes available for analysis. The consequence is an increase in the complexity and duration of preliminary data preparation for subsequent mathematical and statistical analysis. It is therefore important to develop new and improve existing techniques for the automated process of primary data production. When developing methods of primary data preparation, it should be taken into account that accuracy and adequacy of results of subsequent mathematical analysis are determined by accuracy and adequacy of used initial data. The cross-sectional profile parameters of hot-rolled strips, such as wedge, convexity, thickness variation, displacement, wedge in near-rim zones, local thickenings and thinning of the strip are calculated parameters, i.e. secondary to actual strip thickness measurements over the length and width of hot-rolled strips. As technology is improved in cold rolling shops, the number of grade groups is increasing, for which technological modes of units and processing routes are selected. They are based on actual values of parameters of cross-section profile in order to further reduce the probability of formation of inappropriate products and increased metal consumption. The presented article provides an overview of conventional calculation methods for parameters of cross-section profile of hot-rolled strip and gives an assessment of accuracy and adequacy of application of the parameters averaged along strip length to the whole strip.
Supporting agencies: The work was financially supported by the Russian Foundation for Basic Research within the framework of scientific project No. 19-38-90257.
About the Authors
S. M. Bel’skii
Lipetsk State Technical University
Russian Federation
Russian Federation
Sergei M. Bel’skii, Dr. Sci. (Eng.), Prof. of the Chair “Metal Forming”
30, Moskovskaya str., Lipetsk 398600
I. I. Shopin
Lipetsk State Technical University
Russian Federation
Russian Federation
Ivan I. Shopin, Cand. Sci. (Eng.), Assist. Prof. of the Chair “Metal Forming”
30, Moskovskaya str., Lipetsk 398600
A. N. Shkarin
Lipetsk State Technical University
Russian Federation
Russian Federation
Aleksandr N. Shkarin, Postgraduate of the Chair “Metal Forming”
30, Moskovskaya str., Lipetsk 398600
References
1. Ginzburg V.B. Flat-Rolled Steel Processes: Advanced Technologies. CRC Press, 2009, 384 p.
2. Ginzburg V.B. Metallurgical Design of Flat Rolled Steels. Marcel Dekker, 2005, 710 p.
3. Roberts W.L. Cold Rolling of Steel. Marcel Dekker, 1978, 799 p.
4. Bel’skii S.M., Mukhin Yu.A. Classification of regulation principles for strip flatness. Steel in Translation. 2009, vol. 39, no. 11, pp. 1012–1015.
5. Bel’skii S.M., Mukhin Yu.A. Hot strip rolling with local thickening. Steel in Translation. 2009, vol. 39, no. 5, pp. 420–424.
6. Sazhin Yu.V., Ivanova I.A. Econometrics. Saransk: Mordovian St. Univ., 2014, 316 p. (In Russ.).
7. Ishkhanyan M.V. Econometrics. Moscow: Russ. Transport Univ. (MIIT), 2017, 65 p. (In Russ.).
8. Shinkin V.N. Mathematical model of technological parameters’ calculation of flanging press and the formation criterion of corrugation defect of steel sheet’s edge. CIS Iron and Steel Review. 2017, vol. 2017, no. 1, pp. 44–47. https://doi.org/10.17580/cisisr.2017.01.10
9. Shinkin V.N. Arithmetical method of calculation of power parameters of 2N-roller straightening machine under flattening of steel sheet. CIS Iron and Steel Review. 2017, vol. 14, pp. 22–27. https://doi.org/10.17580/cisisr.2017.02.05
10. Predeleanu M., Gilormini P. Advanced Methods in Materials Processing Defects. Vol. 45. Elsevier Science, 1997, 422 p.
11. Emmens W.C. Formability: A Review of Parameters and Processes that Control, Limit or Enhance the Formability of Sheet Metal. Springer, 2011, 112 p.
12. Muhin U., Belskij S., Koynov T. Study of the influence between the strength of antibending of working rolls on the widening during hot rolling of thin sheet metal. Frattura ed Integrita Strutturale. 2016, vol. 10, no. 37, pp. 318–324. https://doi.org/10.3221/IGF-ESIS.37.42
13. Bel’skii S.M., Shopin I.I. Parametrical model of stress-strain state of the roll on a coiler. Izvestiya. Ferrous Metallurgy. 2017, vol. 60, no. 11, pp. 925–931. (In Russ.). https://doi.org/10.17073/0368-0797-2017-11-925-931
14. Ikonnikova I.A., Vikhor N.A. Econometrics. Tomsk: Tomsk St. Univ. Architect. and Civil Eng., 2012, 87 p. (In Russ.).
15. Kennedy P. A Guide to Econometrics. MIT Press, 2003, 623 p.
16. Shinkin V.N. Calculation of steel sheet’s curvature for its flattening in the eight-roller straightening machine. Chernye Metally. 2017, no. 2, pp. 46–50. (In Russ.).
17. Shinkin V.N. Calculation of bending moments of steel sheet and support reactions under flattening on the eight-roller straightening machine. Chernye Metally. 2017, no. 4, pp. 49–53. (In Russ.).
18. Banabic D. Multiscale Modeling in Sheet Metal Forming. Springer, 2016, 405 p.
19. Hu P., Ma N., Liu L.-Z., Zhu Y.-G. Theories, Methods and Numerical Technology of Sheet Metal Cold and Hot Forming: Analysis, Simulation and Engineering Applications. Springer, 2013, 210 p.
20. Bel’skiy S.M., Shopin I.I. Application of the saddle coefficient for estimating the quality of a hot-rolled steel strip. Chernye Metally. 2019, no. 9, pp. 9–13. (In Russ.).
21. Bel’skii S.M., Shchedrin I.N., Shopin I.I. Problem solution of stability loss of coils made of steel strip with polymer coating. Proizvodstvo prokata. 2018, no. 7, pp. 5–8. (In Russ.).
22. Draper N., Smith H. Applied Regression Analysis. John Wiley & Sons, Inc., 1981, 709 p. (Russ. ed.: Draper N., Smith H. Prikladnoi regressionnyi analiz. Kniga 1. Moscow: Finansy i statistika, 1986, 366 p.)
23. Spokoiny V., Dickhaus T. Basics of Modern Mathematical Statistics. Springer, 2015, 296 p.
24. Freedman D.A. Statistical Models: Theory and Practice. Cambridge University Press, 2009, 442 p.
25. Shinkin V.N. Destruction of large-diameter steel pipes at rolled burnt-on defect. Izvestiya. Ferrous Metallurgy. 2017, vol. 60, no. 6, pp. 436–442. (In Russ.). https://doi.org/10.17073/0368-0797-2017-6436-442
26. Shinkin V.N. Simplified method for calculation of bending moments of steel sheet and reactions of working rollers in multiroll straightening machine. Izvestiya. Ferrous Metallurgy. 2017, vol. 60, no. 10, pp. 777–784. (In Russ.). https://doi.org/10.17073/0368-0797-2017-10-777-784
27. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2009, 745 p.
28. Venables W.N., Ripley B.D. Modern Applied Statistic with S. Springer, 2002, 498 p.
29. Maindonald J., Braun W.J. Data Analysis and Graphics using R: An Example based Approach. Cambridge University Press, 2010, 552 p.
30. Graybill F.A., Iyer H.K. Regression Analysis: Concepts and Applications. Duxbury Press, 1994, 701 p.
Review
For citations:
Bel’skii S.M., Shopin I.I., Shkarin A.N. On adequacy of parameters of strip cross-section profile. Part 1. Predictive interval. Izvestiya. Ferrous Metallurgy. 2021;64(1):7-13. (In Russ.) https://doi.org/10.17073/0368-0797-2021-1-7-13
Views:
528
Email this article 