Assessing the effects of steel composition on surface cracks in continuous casting with solidification simulations and phenomenological quality criteria for quality prediction applications
Norrena, Julius; Louhenkilpi, Seppo; Visuri, Ville-Valtteri; Alatarvas, Tuomas; Bogdanoff, Agne; Fabritius, Timo (2022-12-17)
Norrena, J., Louhenkilpi, S., Visuri, V., Alatarvas, T., Bogdanoff, A. and Fabritius, T. (2023), Assessing the Effects of Steel Composition on Surface Cracks in Continuous Casting with Solidification Simulations and Phenomenological Quality Criteria for Quality Prediction Applications. steel research int., 94: 2200746. https://doi.org/10.1002/srin.202200746
© 2022 Wiley-VCH GmbH. This is the peer reviewed version of the following article: Norrena, J., Louhenkilpi, S., Visuri, V., Alatarvas, T., Bogdanoff, A. and Fabritius, T. (2023), Assessing the Effects of Steel Composition on Surface Cracks in Continuous Casting with Solidification Simulations and Phenomenological Quality Criteria for Quality Prediction Applications. steel research int., 94: 2200746, which has been published in final form at https://doi.org/10.1002/srin.202200746. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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https://urn.fi/URN:NBN:fi-fe2023050541409
Tiivistelmä
Abstract
Crack formation is an issue that significantly undermines the quality and productivity of steel production. In previous studies, a solidification and microstructure model known as InterDendritic Solidification (IDS) has been developed and implemented in various slab casters in Finland. Numerous quality criteria have been derived from the model outputs to identify the general phenomena which increase the risks of defect formation in different steel grades. The aim of this study is to study the feasibility of these criteria in providing input data for predicting quality in a group of defect-prone steel grades with rule-based decision-making and machine learning algorithms. To this end, three steel grades are studied by utilizing measured compositions and comparing the quality criteria with plant data regarding reported defects. The computations are carried out by coupling IDS with a fundamental model for simulating heat transfer (Tempsimu) in continuous casting. The results indicate that for the studied steel grades, phenomenological quality criteria can be applied to predict the formation of cracks and other defects. Trends contributing to increased risks of defect formation are identified for all the studied steel grades, and possibilities for avoiding defects by changes in the compositions of these steel grades are also proposed.
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