Segregation of alloying elements at the bcc-Fe/B2–NiAl interface and the corresponding effects on the interfacial energy

Gao, Xueyun; Wang, Haiyan; Ma, Cainv; Lv, Meng; Ren, Huiping

doi:10.1016/j.intermet.2021.107096

Cited by 28 publications

(4 citation statements)

References 45 publications

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“…The interfacial free energy between the precipitate and matrix was also calculated by TC-Prisma. The energy values correspond to a coherent interface, as expected for this type of precipitation [12,17,51]. TC-Prisma enables us to analyze the precipitation process in a concomitant way of the nucleation, growth, and coarsening of precipitates.…”

Section: Methodsmentioning

confidence: 65%

Analysis of Phase Transformations in Fe-Ni-Al Alloys Using Diffusion Couples of Fe/Fe-33at.%Ni-33at.%Al Alloy/Ni

Perez-Badillo

Dorantes-Rosales

Saucedo‐Muñoz

et al. 2023

Metals

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The present work focused on analyzing the phase transformation in Fe-Ni-Al alloys employing a diffusion couple of Ni/Fe-Ni-Al/Fe, and Calphad-based diffusion and thermodynamic software. Diffusion couples were prepared by annealing at 1100 °C for 200 h and then air-cooled. These couples were also aged at 750 °C for 100 and 275 h. Both numerical and experimental results indicated that the diffusion path between Ni or Fe pure metal and the Fe-33at.%Ni-33at.%Al alloy is not linear. The phases formed during the diffusion anneal at 1100 °C correspond to those shown in the Calphad-calculated Fe-Ni-Al equilibrium diagram. The aging treatment at 750 °C promoted the inverse precipitation β′ → β′ + α, which caused the softening of the alloy. Moreover, the normal precipitation reactions, α→α + β′ and γ → γ + γ′, were also observed to occur during the aging of diffusion couple at 750 °C, originating precipitation hardening.

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Section: Methodsmentioning

confidence: 65%

Analysis of Phase Transformations in Fe-Ni-Al Alloys Using Diffusion Couples of Fe/Fe-33at.%Ni-33at.%Al Alloy/Ni

Perez-Badillo

Dorantes-Rosales

Saucedo‐Muñoz

et al. 2023

Metals

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“…5 and 6, both B2eNiAl and L2 1 -Ni 2 AlTi precipitates are formed from BCC-Fe matrix, therefore, one of the main tasks of simulation is to investigate the mechanical properties of the phase interfaces between precipitates and BCC-Fe matrix. Gao et al [34] studied the B2eNiAl/BCC-Fe interface with different interface orientations and found that the B2eNiAl(001)/BCC-Fe(001) achieved the lowest interface energy. In addition, it was generally believed that <001> is the weakest direction under tension process for BCC metals [51,52].…”

Section: Discussionmentioning

confidence: 99%

Ductility Deterioration Induced by L21 Phase in Ferritic Alloy Through Ti Addition

Peng

Liu

et al. 2023

Preprint

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“…Because the traditional least square method is unlikely to perceive through experimental results the subtle changes in model parameters, [9] GA is adopted in our work to learn their changes with the precipitation kinetic data, with the population number being set to 50. In the optimization, the ranges for ρ, Q pipe , and γ were set to be from 10 9 to 10 16 m À2 , [37,38] from 0.4 to 1.3 times of the body diffusion activation energy for Nb, [39] and from 0.01 to 30 J m À2 , [40,41] respectively. The type of selection operation was chosen as Roulette, and the crossover operation adopted single-point crossover algorithm with the crossover probability of 0.85.…”

Section: The Theoretically Guided ML For P S and P Fmentioning

confidence: 99%

Physical Metallurgy Guided Machine Learning for Strain‐Induced Precipitation of Nb (C, N) Based on the Orthogonalized Small Data

Jiang

Cui

et al. 2023

steel research int.

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Strain‐induced precipitation (SIP) plays an important role in controlling the microstructure and properties of micro‐alloyed steels during hot rolling. However, due to lack of systematic experiments, the existing precipitation data are scarce and insufficient for accurate modeling of SIP behavior with good extrapolation. Herein, the data space of Niobium Carbonitride (Nb(C,N)) SIP kinetics is analyzed based on principle of orthogonal design, and the missing points in the orthogonal space are identified and supplemented in addition to the available data. The parameters in models for precipitation start and finish times are optimized by using the precipitation–time–temperature curves through genetic algorithm, and their relationships with compositions and processing conditions are established by support vector machine. Based on the orthogonally modified dataset, the machine learning (ML) models outperform the classical and ML models with the original data in terms of accuracy and are in good agreement with theoretical expectations. By using the new ML modeling, the evolution behavior of Nb (C, N) during hot deformation is predicted and verified with transmission electron microscope, and the influences of Nb content in steel, strain, strain rate, and the sensitivity of compositions and processing conditions on precipitation kinetics are quantitatively analyzed.

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Segregation of alloying elements at the bcc-Fe/B2–NiAl interface and the corresponding effects on the interfacial energy

Cited by 28 publications

References 45 publications

Analysis of Phase Transformations in Fe-Ni-Al Alloys Using Diffusion Couples of Fe/Fe-33at.%Ni-33at.%Al Alloy/Ni

Analysis of Phase Transformations in Fe-Ni-Al Alloys Using Diffusion Couples of Fe/Fe-33at.%Ni-33at.%Al Alloy/Ni

Ductility Deterioration Induced by L21 Phase in Ferritic Alloy Through Ti Addition

Physical Metallurgy Guided Machine Learning for Strain‐Induced Precipitation of Nb (C, N) Based on the Orthogonalized Small Data

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