| 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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Nagy, Péter
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024Mapping the microstructure and the mechanical performance of a combinatorial Co–Cr–Cu–Fe–Ni–Zn high-entropy alloy thin film processed by magnetron sputtering techniquecitations
- 2024Combinatorial Design of an Electroplated Multi-Principal Element Alloy: A Case Study in the Co-Fe-Ni-Zn Alloy Systemcitations
- 2022Machine Learning-Based Characterization of the Nanostructure in a Combinatorial Co-Cr-Fe-Ni Compositionally Complex Alloy Filmcitations
- 2022Machine learning-based characterization of the nanostructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy filmcitations
- 2022Combinatorial study of phase composition, microstructure and mechanical behavior of Co-Cr-Fe-Ni nanocrystalline film processed by multiple-beam-sputtering physical vapor depositioncitations
- 2022Combinatorial Study of Phase Composition, Microstructure and Mechanical Behavior of Co-Cr-Fe-Ni Nanocrystalline Film Processed by Multiple-Beam-Sputtering Physical Vapor Depositioncitations
- 2021Microstructure, Hardness, and Elastic Modulus of a Multibeam-Sputtered Nanocrystalline Co-Cr-Fe-Ni Compositional Complex Alloy Filmcitations
- 2021Microstructure, hardness, and elastic modulus of a multibeam-sputtered nanocrystalline Co-Cr-Fe-Ni compositional complex alloy filmcitations
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article
Machine Learning-Based Characterization of the Nanostructure in a Combinatorial Co-Cr-Fe-Ni Compositionally Complex Alloy Film
Abstract
<jats:p>A novel artificial intelligence-assisted evaluation of the X-ray diffraction (XRD) peak profiles was elaborated for the characterization of the nanocrystallite microstructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy (CCA) film. The layer was produced by a multiple beam sputtering physical vapor deposition (PVD) technique on a Si single crystal substrate with the diameter of about 10 cm. This new processing technique is able to produce combinatorial CCA films where the elemental concentrations vary in a wide range on the disk surface. The most important benefit of the combinatorial sample is that it can be used for the study of the correlation between the chemical composition and the microstructure on a single specimen. The microstructure can be characterized quickly in many points on the disk surface using synchrotron XRD. However, the evaluation of the diffraction patterns for the crystallite size and the density of lattice defects (e.g., dislocations and twin faults) using X-ray line profile analysis (XLPA) is not possible in a reasonable amount of time due to the large number (hundreds) of XRD patterns. In the present study, a machine learning-based X-ray line profile analysis (ML-XLPA) was developed and tested on the combinatorial Co-Cr-Fe-Ni film. The new method is able to produce maps of the characteristic parameters of the nanostructure (crystallite size, defect densities) on the disk surface very quickly. Since the novel technique was developed and tested only for face-centered cubic (FCC) structures, additional work is required for the extension of its applicability to other materials. Nevertheless, to the knowledge of the authors, this is the first ML-XLPA evaluation method in the literature, which can pave the way for further development of this methodology.</jats:p>