| People | Locations | Statistics |
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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
Comparison between image based and tabular data-based inclusion class categorization
Abstract
Non-metallic inclusions (NMI) have a significant impact on the final properties of steel products. As of today, the scanning electron microscope equipped with energy-dispersive spectroscopy (SEM-EDS) serves as the state of art characterization tool to study NMIs in steel. The automated 2D analysis method with the SEM-EDS allows for a comprehensive analysis of all the inclusions observed within a selected area of the sample. The drawback of this method is the time taken to complete the analysis. Therefore, machine learning methods have been introduced which can potentially replace the usage of EDS for obtaining chemical information of the inclusion by making quick categorizations of the inclusion classes and types. The machine learning methods can be developed by either training it directly with labeled backscattered electron (BSE) images or by tabular data consisting of image features input such as morphology and mean gray value obtained from the BSE images. The current paper compares both these methods using two steel grades. The advantages and the disadvantages have been documented. The paper will also compare the usage of shallow and deep learning methods to classify the steels and discuss the outlook of the existing machine learning methods to efficiently categorize the NMIs in steel.<br/><br/>