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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Michelic, Susanne
Montanuniversität Leoben
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (27/27 displayed)
- 2024Investigating the Origin of Non-Metallic Inclusions in Ti-Stabilized ULC Steels Using Different Tracing Techniquescitations
- 2024The simple microsegregation model for steel considering MnS formation in the liquid and solid phasescitations
- 2024Influence of Tramp Elements on Surface Properties of Liquid Medium-Carbon Steelscitations
- 2023Different Approaches to Trace the Source of Non-Metallic Inclusions in Steel
- 2023Application of tracing techniques to determine the source of alumina inclusions in the clogging layer of Ti-stabilized ULC steels
- 2023The impact of tramp elements on the wetting behavior of non-metallic inclusions in a medium-carbon steel
- 2023Optimization of the Two- and Three-DimensionalCharacterization of Rare Earth-Traced Deoxidation Productscitations
- 2023Comparison between image based and tabular data-based inclusion class categorization
- 2023Comparison of tracing deoxidation products with rare earth elements in the industry and on a laboratory scale
- 2022Different Approaches to Trace the Source of Non-Metallic Inclusions in Steelcitations
- 2022Dissolution of Al2O3, MgO●Al2O3, and SiO2 in alkali oxide containing secondary metallurgical slags
- 2022Classification of non-metallic inclusions in steel by data-driven machine learning methodscitations
- 2022Evaluation of different alloying concepts to trace non-metallic inclusions by adding rare earths on a laboratory scalecitations
- 2022Application of ICP-MS to study the evolution of non-metallic inclusions in steelmaking
- 2022How to increase scrap recycling
- 2021Mathematical Modeling of the Early Stage of Clogging of the SEN During Continuous Casting of Ti-ULC Steelcitations
- 2021Influence of Slag Viscosity and Composition on the Inclusion Content in Steelcitations
- 2020Study on the Possible Error Due to Matrix Interaction in Automated SEM/EDS Analysis of Nonmetallic Inclusions in Steel by Thermodynamics, Kinetics and Electrolytic Extractioncitations
- 2020HT-LSCM as a Tool for Indirect Determination of Precipitates by Real-Time Grain Growth Observationscitations
- 2020Study on inclusion evolution through Si/Mn deoxidation in medium-carbon steelscitations
- 2019Study on the Influence of FeTi‐Addition on the Inclusion Population in Ti‐Stabilized ULC Steels and Its Consequences for SEN‐Cloggingcitations
- 2019The Role of FeTi Addition to Micro-inclusions in the Production of ULC Steel Grades via the RH Process Routecitations
- 2017Charakterisierung von azikularferritischen Phasenanteilen in HSLA- Stählen und deren Auswirkung auf die mechanischen Kennwerte bei Kleinstproben
- 2017Modeling Inclusion Formation during Solidification of Steelcitations
- 2016Study on Oxide Inclusion Dissolution in Secondary Steelmaking Slags using High Temperature Confocal Scanning Laser Microscopycitations
- 2016On the modelling of microsegregation in steels involving thermodynamic databases
- 2016Acicular Ferrite Formation and Its Influencing Factors-A Reviewcitations
Places of action
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article
The simple microsegregation model for steel considering MnS formation in the liquid and solid phases
Abstract
<p>A simple microsegregation model for steel considering MnS formation in the liquid and solid phases is proposed. The concentration of the solutes during the solidification is calculated using the discretized Scheil-Gulliver model for steel (SGS). In the calculation, the planar dendrite is divided into a finite number (n) of elements to record the local solid concentrations and calculate the mass fraction of MnS precipitation during further cooling. The solidification part of the model is validated by measured solidification temperatures and the MnS formation amount predicted by the FactSage thermochemical software. The model was applied to evaluate the high-temperature ductility of the selected steel. The optimum Mn content of the assumed steel was obtained based on the simulation.</p>