Stability assessment of 30ХНМЛ steel melting process in electric arc furnaces on the basis of technological audit of serial meltings
DOI:
https://doi.org/10.15587/2312-8372.2018.149263Keywords:
electric arc furnace, steel melting, melting stability coefficient, membership function, fuzzy number, fuzziness boundaryAbstract
The object of research is the process of 30ХНМЛ steel melting in two electric arc furnaces with a capacity of 6 tons. Technological audit of the process is carried out on existing furnaces in the steel foundry of a machine-building enterprise specializing in the manufacture of large shaped castings for products of transport engineering. The audit is aimed at analyzing the compliance of the performed main technological melting operations with the required regulated technological instructions.
On the basis of carrying out serial meltings, a sample of experimental and industrial data is obtained to determine the tensile strength of steel samples 30KhNML. It has been established that according to the actual production data of serial heats it is impossible to postulate the distribution law, in particular, to speak of a normal distribution. Therefore, the use of statistical sampling functions to assess the melting stability is not advisable. It is proposed to use the stability coefficient (η), based on the calculation of entropy (H), as a criterion for assessing the melting stability. It is proposed to use a fuzzy description of these values for practical use in assessing the melting stability. In this case, it can be assumed that the calculated values of entropy and melting stability coefficient for each sample separately and the total sample form the left (αjp) and right (βjp) fuzziness boundary. It is proposed in the fuzzy description to use the membership function of (L–R) type. In a specific case, it can be assumed that αjp=2.63, βjp=2.71 (for a fuzzy number H) and αjp=0.22, βjp=0.24 (for a fuzzy number η).
Thanks to the proposed method for assessing the melting stability, it is possible to obtain objective data without relying on the assumption of a normal distribution law. The proposed method is invariant to the type of technological process in the blank production. These can be metal forming, heat treatment and other metallurgical processes. The importance of the proposed method is related to the fact that the quality of further technological operations for the production of finished parts depends on the inheritance of the quality of blank production as the previous technological stages of production.
References
- Domin, D. A. (2013). Artificial orthogonalization in searching of optimal control of technological processes under uncertainty conditions. Eastern-European Journal of Enterprise Technologies, 5 (9 (65)), 45–53. Available at: http://journals.uran.ua/eejet/article/view/18452/16199
- Domin, D. A. (2013). Mathematical modeling in the problem of selecting opti-mal control of obtaining alloys for machine parts in un-certainty conditions. Journal of Mechanical Engineering, 16 (6), 15–23. Available at: http://journals.uran.ua/jme/article/view/21309
- Bhonsle, D. C., Kelkar, R. B. (2016). Analyzing power quality issues in electric arc furnace by modeling. Energy, 115, 830–839. doi: http://doi.org/10.1016/j.energy.2016.09.043
- Trufanov, I. D., Chumakov, K. I., Bondarenko, A. A. (2005). Obshheteoreticheskie aspekty razrabotki stokhasticheskoy sistemy avtomatizirovannoy ekspertnoy otsenki dinamicheskogo kachestva proizvodstvennykh situatsiy elektrostaleplavleniya. Eastern-European Journal of Enterprise Technologies, 6 (2 (18)), 52–58.
- Khodabandeh, E., Ghaderi, M., Afzalabadi, A., Rouboa, A., Salarifard, A. (2017). Parametric study of heat transfer in an electric arc furnace and cooling system. Applied Thermal Engineering, 123, 1190–1200. doi: http://doi.org/10.1016/j.applthermaleng.2017.05.193
- Grachev, V. A., Kuznetsov, B. L., Bochkarev, V. E., Venger, V. V. (1988). Metallurgiya plavki chuguna v dugovoy pechi. Liteynoe proizvodstvo, 2, 19–21.
- Shumikhin, V. S., Grachev, V. A. (1988). Tekhniko-ekonomicheskoe sravnenie protsessov plavki chuguna. Liteynoe proizvodstvo, 2, 15–17.
- Khrapko, S. A. (2003). Optimizatsiya rezhima vedeniya plavki stali v dugovoy staleplavil'noy pechi po pribyli predpriyatiya. Sovremennaya elektrometallurgiya, 2, 37–40.
- Domin, D. A., Pelikh, V. F., Ponomarenko, O. I. (1998). Complex alloying of grey cast iron. Liteinoe proizvodstvo, 10, 18–19.
- Razzhivin, A. V., Sagayda, I. M. (2000). Informatsionnoe obespechenie sistemy avtomaticheskogo upravleniya dugovoy staleplavil'noy pech'yu po temperature metalla. Vіsnіk SUDU, 3 (25), 215–220.
- Domin, D. A. (2012). Synthesis process control elektrodugovoy smelting iron. Eastern-European Journal of Enterprise Technologies, 2 (10 (56)), 4–9. Available at: http://journals.uran.ua/eejet/article/view/3881
- Domina, O. B. (2011). Optimal strategy with technical re-manufacture of basic metals. Technology Audit and Production Reserves, 2 (2 (2)), 40–52. doi: http://doi.org/10.15587/2312-8372.2011.4866
- Galkin, M. F., Krol', Yu. S. (1971). Kiberneticheskie metody analiza elektroplavki stali. Voprosy tekhnologii. Moscow: Metallurgiya, 304.
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Copyright (c) 2018 Vadim Seliverstov, Viktoria Boichuk, Vadym Dotsenko, Victor Kuzmenko
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