Statistical Control of Defects in a Continuously Cast Billet Based on Machine Learning and Data Analysis Methods

 
PIIS000523100001250-9-1
DOI10.31857/S000523100001250-9
Publication type Article
Status Published
Authors
Affiliation: Cherepovets State University
Address: Russian Federation, Cherepovets
Affiliation: Cherepovets State University
Address: Russian Federation, Cherepovets
Affiliation: Cherepovets State University
Address: Russian Federation, Cherepovets
Journal nameAvtomatika i Telemekhanika
EditionIssue 8
Pages101-110
Abstract

We consider the problems of defects arising in the production of continuously cast billets at continuous casting plants. We propose a model for predicting slab cracks based on the random forest machine learning algorithm. We determine the main technological parameters that influence the appearance of cracks and present the results of the model.

 
KeywordsStatistical control, continuous casting defect, continuous steel casting, cracks on the slab, hot charging, random forests, influencing parameters
Received30.09.2018
Publication date30.09.2018
Number of characters355
Cite   Download pdf To download PDF you should sign in
Размещенный ниже текст является ознакомительной версией и может не соответствовать печатной

views: 1534

Readers community rating: votes 0

1. Yakushin B. Teoriya svarivaemostistalej i splavov. M: MGTU im. N.Eh. Baumana. 2014.

2. Kolpakov S.V., Starov R.V., Smoktij V.V. i dr. Tekhnologiya proizvodstva stali v sovremennykh konverternykh tsekhakh. M.: Mashinostroenie, 1991.

3. Mischenko I.O. Upravlenie urovnem defektov poverkhnosti i makrostruktury nepreryvnolitykh slyabov MNLZ radial'no-krivolinejnogo tipa. Diss. kand. tekhn. nauk. M.: 2006.

4. Nazarenko N.V., Chichkarev E.A. Povyshenie kachestva nepreryvnolitykh zagotovok iz peritekticheskikh marok stali za schet vybora ratsional'nykh parametrov razlivki // Nauch. tr. DonNTU. Metallurgiya. 2009. № 11. S. 124–130.

5. Chambers D., Uiler D. Statisticheskoe upravlenie protsessami. Optimizatsiya biznesa s ispol'zovaniem kontrol'nykh kart Shukharta. M.: Al'pina Pablisher, 2016.

6. Varfolomeev I.A., Ershov E.V., Bogachev D.V. Electro-Optical Monitoring of the Chromatic Error of the Polymeric Coating of a Galvanized Strip // Russ. J. Nondestruct. 2016. V. 52. No. 5. P. 287–293.

7. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2 ed. San-Francisco: Morgan Kaufmann, 2005.

8. Hosmer D.W. Applied Logistic Regression. 2 ed. N.Y.: Wiley, 2000.

9. V'yugin V.V. Matematicheskie osnovy mashinnogo obucheniya i prognozirovaniya. M.: MTsMNO, 2014.

10. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Chapter 15. Random Forests. 2nd ed. Germany: Springer, 2009.

11. Kartiev S.B., Kurejchik V.M. Algoritm klassifikatsii, osnovannyj na printsipakh sluchajnogo lesa, dlya resheniya zadachi prognozirovaniya // Programmnye produkty i sistemy. 2016. № 2. S. 11–15.

12. Chistyakov S.P. Sluchajnye lesa: obzor // Tr. Karel'skogo nauch. tsentra RAN. Petrozavodsk: 2013. № 2. S. 117–136.

13. Kabakov R.I. R v dejstvii. Analiz i vizualizatsiya dannykh na yazyke R. M.: DMK Press, 2014. C. 33–36.

14. Ferna´ndez-Delgado M., Cernadas E., Barro S., Amorim D. Do We Need Hundreds of Classifiers to Solve Real World Classification Problems? // J. Machine Learning Res. 2014. No. 15. S. 3133–3181.

Система Orphus

Loading...
Up