Evaluation of the Chemical Composition and Microcleanliness of the Steel Samples from the Heavy Forging Ingot

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Abstract

The paper presents new results obtained from the evaluation of the chemical composition, microcleanliness and structure of the 90-ton heavy ingot cast in two successive heats, in which the content of Cu and Ni was intentionally modified in order to assess the degree of mutual mixing of the two heats in the ingot volume during the steel casting and solidification. For determination of chemical composition, spectral analysis and LECO were used. Microcleanliness evaluation was carried out on a Hitachi microanalytical complex equipped with the energy-dispersive spectrometer Vantage. To assess the composition of oxide non-metallic inclusions ternary diagrams were used. Structure of the basic steel matrix was induced by etching. The evaluation showed that in the casting of two successive heats, a certain degree of inhomogeneity of chemical composition, especially in the lower part of the ingot can be assumed in case of different composition. A greater segregation of sulphur in the central top part of the ingot was also detected. However, microcleanliness of the entire ingot is in general very good with low proportions of non-metallic inclusions.

[1] L. Zhang, B. Thomas, Evaluation and control of steel cleanliness - review. In 85th Steelmaking Conference Proceedings, ISS-AIME, Warrendale, PA, 431-452 (2002).

[2] P. Machovcá k, A. Ople r, M. Tkadlecková, K. Michalek, K. Gryc, V. Krutis, M. Kováč, The utilization of Numerical Modelling To optimize the Production of Heavy Forging Ingots in V´ıtkovice Heavy Machinery, a.s., In 1st International Conference on Ingot Casting, Rolling and Forging ICRF, Aachen, Germany, 3-7 (2012).

[3] M. Tkadlečková, K. Gryc, P. Machovčá k, P. Klus, K. Michalek, L. Soch a, M. Kováč, Setting a numerical simulation of filling and solidification of heavy steel ingots based on real casting conditions, Materiali in Technologije 46 (4), 399-402 (2012).

[4] M. Tkadlečková, P. Machovcá k, K. Gryc, K. Michalek, L. Soch a, P. Klus, Numerical modelling of macrosegregation in heavy steel ingot, Archives of metallurgy and materials 58 (1), 171-177 (2013).

[5] B. Smetan a, M. ˇZaludová, M. Tkadlečková, J. Dobrovská, S. Zlá, K. Gryc, P. Klus, K. Michalek, P. Machovčá k, L. Reháčková, Experimental verification of hematite ingot mould heat capacity and its direct utilisation in simulation of casting process, Journal of Thermal Analysis and Calorimetry, 112 (1), 473-480 (2013).

[6] K. Tashir o, S. Watanabe, I. Kitagawa, I. Tamu - r a, Influence of Mould Design on the Solidification and Soundness of Heavy Forging Ingots. ISIJ Inter., 312-321 (1983).

[7] A. Kermanpu s, M. Eskandar i, H. Purmo- hamad, M.A. Soltan i, B. Shater i, Influence of mould design on the solidification of heavy forging ingots of low alloy steels by numerical simulation, Materials and Design, 31 (3), 1096-1104 (2010).

[8] O. Bogdan, Numerical Analysis of Casting Technology and A-segregation Prediction in AISI 4340 Forgings Products. Industrial Soft, Montreal, Canada, http://castingsnet.com/AISI4340-casting-report.pdf, (2010).

[9] M. Kearne y, M. Crabbe, J. Talamantes - Silv a, Development and manufacture of large plate mill rolls, Ironmaking and Steelmaking, 34 (5), 380-383 (2007).

[10] K. Janiszewsk i, Influence of slenderness ratios of a multi-hole ceramic filters at the effectiveness of process of filtration of non-metallic inclusions from liquid steel, Archives of metallurgy and Materials 57 (1), 135-143 (2012).

[11] L. Bulkowsk i, U. Galisz, H. Kani a, Z. Kudlins- k i, J. Pieprzyc a, J. Baranski, Industrial tests of steel filtering process, Archives of metallurgy and Materials 57 (1), 363-369 (2012).

[12] P.G. Joensson, A. Tilliander, S. Yokoya, Z.A. Zhan g, Anumerical study of swirl blade effects in uphill teeming casting, ISIJ INT. 50 (12), 1756-1762 (2010).

Archives of Metallurgy and Materials

The Journal of Institute of Metallurgy and Materials Science and Commitee on Metallurgy of Polish Academy of Sciences

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