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Influence of High-Temperature Oxidizing Conditions on AlCoCrCuNi High Entropy Alloys with and without Silicon Addition

R. Gawel Ł. Rogal K. Przybylski Kenji Matsuda
Archives of Metallurgy and Materials | 2022 | vol. 67 | No 2 | 471-478 | DOI: 10.24425/amm.2022.137779
Keywords high entropy alloys high temperature oxidation phase analysis alloying element
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Abstract

Initial investigations on oxidation behaviour and phase transformations of equimolar AlCoCrCuNi high entropy alloy with and without 1 at.% silicon addition during 24-hr exposure to air atmosphere at 1273 K was carried out in this work. After determining the oxidation kinetics of the samples by means of thermogravimetric analysis, the morphology, chemical and phase compositions of the oxidized alloys were determined by means of scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffraction analysis. Additional cross-section studies were performed using transmission electron microscopy combined with energy dispersive X-ray spectroscopy and selected area electron diffraction. From all these investigations, it can be concluded that minor silicon addition improves the oxidation kinetics and hinders the formation of an additional FCC structure near the surface of the material.
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Authors and Affiliations

R. Gawel
1
Ł. Rogal
2
ORCID: ORCID
K. Przybylski
1
Kenji Matsuda
3
  1. AGH University of Science and Technology, Faculty of Materials Science and Ceramics, Department of Physical Chemistry and Modelling, Al. Mickiewicza 30, 30 -059 Kraków, Poland
  2. Polish Academy of Sciences, Institute of Metallurgy and Materials, 25 Reymonta Str., 30-059 Kraków, Poland
  3. University of Toyama, Faculty of Sustainable Design, Department of Materials Design and Engineering, 3190 Gofuku, Toyama 930-8555, Japan

Application of Artificial Neural Network to Predict the Tensile Properties of Dual-Phase Steels

Seung-Hyeok Shin Sang-Gyu Kim Byoungchul Hwang
Archives of Metallurgy and Materials | 2021 | vol. 66 | No 3 | 719-723 | DOI: 10.24425/amm.2021.136368
Keywords Artificial Neural Network (ANN) dual-phase steels alloying element microstructural factor tensile properties
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Abstract

An artificial neural network (ANN) model was developed to predict the tensile properties of dual-phase steels in terms of alloying elements and microstructural factors. The developed ANN model was confirmed to be more reasonable than the multiple linear regression model to predict the tensile properties. In addition, the 3D contour maps and an average index of the relative importance calculated by the developed ANN model, demonstrated the importance of controlling microstructural factors to achieve the required tensile properties of the dual-phase steels. The ANN model is expected to be useful in understanding the complex relationship between alloying elements, microstructural factors, and tensile properties in dual-phase steels.
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Authors and Affiliations

Seung-Hyeok Shin
1
ORCID: ORCID
Sang-Gyu Kim
1
ORCID: ORCID
Byoungchul Hwang
1
ORCID: ORCID
  1. Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

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