Microstructural evolution and coarsening behavior of the precipitates in 2205 duplex stainless steel aged at 850 °C

Han, Yuexing; Chi, Ruting; Chen, Qiaochuan; Wang, Bing; Liu, Wei; He, Yanlin

doi:10.1016/j.jmrt.2023.08.034

Journal of Materials Research and Technology

2023

DOI: 10.1016/j.jmrt.2023.08.034

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Microstructural evolution and coarsening behavior of the precipitates in 2205 duplex stainless steel aged at 850 °C

Yuexing Han

Ruting Chi

Qiaochuan Chen

et al.

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2024

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Cited by 5 publications

References 40 publications

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Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Zhu,

Luo,

Chen

et al. 2024

Materials Genome Engineering Advances

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Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys.

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Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Zhu,

Luo,

Chen

et al. 2024

Materials Genome Engineering Advances

Self Cite

View full text Add to dashboard Cite

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A Comparison of Sigma Phase Formation in Solubilized Hyper Duplex Stainless Steel and Super Duplex Stainless Steel Filler Metals

Acuna,

Riffel,

Ramirez

2024

Metall Mater Trans A

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This study focuses on the kinetic analysis of sigma phase formation in filler metal wires on Super Duplex Stainless Steel (SDSS) and Hyper Duplex Stainless Steel (HDSS). Precipitation data reveal that in the solubilized microstructure, sigma phase kinetics are more prominent in SDSS. This increased susceptibility is attributed to the greater number of nucleation sites, which is facilitated by the larger interface area/volume and the higher chromium content in the ferrite. The difference in interface area/volume is significantly more influential in determining kinetics than the composition difference, with nucleation sites playing a central role. The sigma phase transformation in both materials was modeled using the JMAK kinetic law. The JMAK plots exhibit a transition in kinetic mechanisms, evolving from discontinuous precipitation to diffusion-controlled growth. In SDSS, the JMAK values indicate “grain boundary nucleation after saturation,” followed by “thickening of large plates.” In contrast, HDSS values point to “grain edge nucleation after saturation,” followed by “thickening of large needles.” The higher kinetics in SDSS are characterized by a smaller nucleation activation energy of 56.4 kJ/mol, in contrast to HDSS's 490.0 kJ/mol. CALPHAD-based data support the JMAK results, aligning with the maximum kinetics temperature of SDSS (875 °C to 925 °C) and HDSS (900 °C to 925 °C). Therefore, the JMAK sigma phase kinetics effectively describe the experimental data and its dual kinetics behavior, even though CALPHAD-based TTT calculations often overestimate sigma formation.

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Assessment of Degree of Sensitization of Cryo-rolled Standard Duplex Stainless Steel 2205

Vinothkumar,

Srinivasan

2024

Metallogr. Microstruct. Anal.

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Microstructural evolution and coarsening behavior of the precipitates in 2205 duplex stainless steel aged at 850 °C

Cited by 5 publications

References 40 publications

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

A Comparison of Sigma Phase Formation in Solubilized Hyper Duplex Stainless Steel and Super Duplex Stainless Steel Filler Metals

Assessment of Degree of Sensitization of Cryo-rolled Standard Duplex Stainless Steel 2205

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