Design, Manufacturing and Properties of Refractory Materials
Author:
Affiliation:
1. Faculty of Materials Science and Ceramics, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Krakow, Poland
Abstract
Publisher
MDPI AG
Link
https://www.mdpi.com/1996-1944/17/7/1673/pdf
Reference18 articles.
1. Sado, S., Jastrzębska, I., Zelik, W., and Szczerba, J. (2023). Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review. Materials, 16.
2. Zelik, W., Sado, S., and Lech, R. (2022). The Wear Rate Forecast of MgO-C Materials Type MC95/10 in the Slag Spout Zone of an Oxygen Converter in Terms of the Bayesian Estimation. Materials, 15.
3. Jančar, D., Machů, M., Velička, M., Tvardek, P., Kocián, L., and Vlček, J. (2022). Use of Neural Networks for Lifetime Analysis of Teeming Ladles. Materials, 15.
4. Stec, J., Tarasiuk, J., Wroński, S., Kubica, P., Tomala, J., and Filipek, R. (2021). Investigation of Molten Metal Infiltration into Micropore Carbon Refractory Materials Using X-ray Computed Tomography. Materials, 14.
5. Jastrzębska, I., and Piwowarczyk, A. (2023). Traditional vs. Automated Computer Image Analysis—A Comparative Assessment of Use for Analysis of Digital SEM Images of High-Temperature Ceramic Material. Materials, 16.
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1. Deep learning revealed statistics of the MgO particles dissolution rate in a CaO–Al2O3–SiO2–MgO slag;Scientific Reports;2024-09-11
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