Classification of peritectic steels by experimental methods, computational thermodynamics and plant data: An Overview
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Proc. 6th ESTAD. 2023.
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TY - GEN
T1 - Classification of peritectic steels by experimental methods, computational thermodynamics and plant data: An Overview
AU - Presoly, Peter
AU - Kavić, Daniel
AU - Bernhard, Christian
AU - Hahn, Susanne
AU - Ilie, Sergiu
PY - 2023/6/12
Y1 - 2023/6/12
N2 - Modern steel grades are subjected to constant development to perform weight reduction, energy-saving, and automobile safety performance. In the last decades, high strength and ductile steels were developed with increasing quantities of silicon and manganese. Apart from the research on these new steels' material and product properties, the knowledge about the production process, particularly the continuous casting (CC) and the initial solidification in a water-cooled copper mould is of significant importance. In this regard, the high-temperature phase transformations and the thermodynamic properties play a particular role. An efficient pre-identification of hypo-peritectic steel grades by experiments or thermodynamics is relevant to ensure surface quality, productivity, and operational safety in the casting process. The potential of different laboratory experimental methods and thermodynamic approaches is critical evaluated in comparison with operational experience from voestalpine Stahl Linz. Since process data in the continuous casting process often overlap with different operating influences (e.g. casting speed changes, width adjustments…), a new approach is presented to identify the process behaviour of peritectic steels without additional effects. For this purpose, operating data from the mould monitoring were processed statistically, and only data areas with a steady-state casting length of more than 100 m were used for further consideration. Using this data preparation method, the peritectic area in the continuous casting process can be clearly described. Statistically prepared process data and experimentally verified thermodynamic data are the basis for the development and validation of demanding process models.
AB - Modern steel grades are subjected to constant development to perform weight reduction, energy-saving, and automobile safety performance. In the last decades, high strength and ductile steels were developed with increasing quantities of silicon and manganese. Apart from the research on these new steels' material and product properties, the knowledge about the production process, particularly the continuous casting (CC) and the initial solidification in a water-cooled copper mould is of significant importance. In this regard, the high-temperature phase transformations and the thermodynamic properties play a particular role. An efficient pre-identification of hypo-peritectic steel grades by experiments or thermodynamics is relevant to ensure surface quality, productivity, and operational safety in the casting process. The potential of different laboratory experimental methods and thermodynamic approaches is critical evaluated in comparison with operational experience from voestalpine Stahl Linz. Since process data in the continuous casting process often overlap with different operating influences (e.g. casting speed changes, width adjustments…), a new approach is presented to identify the process behaviour of peritectic steels without additional effects. For this purpose, operating data from the mould monitoring were processed statistically, and only data areas with a steady-state casting length of more than 100 m were used for further consideration. Using this data preparation method, the peritectic area in the continuous casting process can be clearly described. Statistically prepared process data and experimentally verified thermodynamic data are the basis for the development and validation of demanding process models.
KW - peritectic steel grades
KW - continuous casting
KW - mould monitoring
KW - quality prediction
M3 - Conference contribution
BT - Proc. 6th ESTAD
T2 - 6th ESTAD (European Steel Technology and Application Days) 2023
Y2 - 12 June 2023 through 16 June 2023
ER -