A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ′ precipitates in Ni-based superalloys

Smith, Timothy M.; Bonacuse, Peter J.; Sosa, J.M.; Kulis, Michael J.; Evans, Laura J.

doi:10.1016/j.matchar.2018.03.051

Cited by 41 publications

(9 citation statements)

References 16 publications

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“…Machine learning methods have emerged as next-generation tools for segmentation of large data sets. Convolutional neural networks (CNNs) have been used for identification of dendritic patterns, 136 classification of steel microstructures, 137 segmentation of precipitates and nanoparticles, 138,139 phase mapping in multicomponent alloys, 140 classification of ambiguous microstructures, 141 denoizing of synchrotron X-ray computed tomography (XCT) experiments, 142 and calibrating the rotation axis in XCT. 143 While, in principle, more data are a positive development, our ability to process and extract physical and chemical meaning from ballooning data sets has not kept pace.…”

Section: Integration Of Structure−property and Processing−structure R...mentioning

confidence: 99%

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

et al. 2021

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Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

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Section: Integration Of Structure−property and Processing−structure R...mentioning

confidence: 99%

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

et al. 2021

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“…Subsurface precipitates can be identified by their gradual transition into the matrix, compared with sharp interfaces for particles closer to the surface [26]. The etching of the matrix to reveal the precipitate phase also contributes to error when approximating the SEM backscatter image as a clean cross-section through the matrix [27]. The etching process can remove small particles and further reveal larger particles, impacting the observed 2D distribution [18].…”

Section: Discussionmentioning

confidence: 99%

Automated Stereology and Uncertainty Quantification Considering Spherical Non-Penetrating Dispersions

Anderson

Basoalto

2023

Crystals

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Automated stereological methods are presented for approximating the 3D size distribution of unimodal or bimodal precipitate dispersions considering 2D and 1D measurements taken from polydisperse spherical non-penetrating particle dispersions. A method to quantify the uncertainty of the approximation as a function of the number of sampled particles is presented and demonstrated to experimental data. The derivation and verification of the analytical stereological expressions used are included. Two procedures are presented for estimating the 3D size distribution of bimodal particle populations depending upon the relative size of the two particle populations. If the particles can be characterised using micrographs of the same magnification, it is possible to estimate the volume fraction of each particle population. For cases where micrographs have been taken at different magnification, an estimate of the area fractions of the particle populations is needed to combine the datasets and allow for the approximation of the 3D size distribution. These methods are useful for use in determining the initial particle size distribution for use in modelling and determining the appropriate number of micrographs and particles to measure when characterising a precipitate dispersion.

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“…To obtain quantitative variation laws about the volume fractions of eutectic phase and primary γ phase affected by isothermal treatment temperature, the quantitative analysis results of these solid particles are shown in Figure 5 for the solid fraction. The volume fraction is calculated by the method that the quenching microstructure are binarized, and the eutectic phase is distinguished from the primary γ phase [22].…”

Section: Effects Of Isothermal Treatment Temperature On the Microstrumentioning

confidence: 99%

Characterization of the Microstructure Evolution of Ni-Based Superalloy at Liquidus Temperature by Electromagnetic Field

Gao

Tan

Guo

et al. 2018

Metals

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The effect of isothermal treatment temperatures and isothermal treatment time on the microstructure was studied. The results showed that the globular and equiaxed grains with the average grain size of 60 μm and the shape factor of circle of 0.95 can be obtained when the melt of Ni-Cr-W superalloy was subjected to the heat treatment of 10 min at 1400 °C. The quenching results showed the volume fraction of the eutectic phase was the largest and the volume fraction of primary γ phase was the smallest after the isothermal treatment of 1400 °C. The optimal melt treatment temperature and time were 1400 °C and 10 min, respectively. Moreover, the effect of electromagnetic field on the solidification was also investigated. It was demonstrated that applying electromagnetic field was beneficial to the uniform temperature, solute field and the high density of the secondary nuclei, which contributed to grain refinement.

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A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ′ precipitates in Ni-based superalloys

Cited by 41 publications

References 16 publications

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Automated Stereology and Uncertainty Quantification Considering Spherical Non-Penetrating Dispersions

Characterization of the Microstructure Evolution of Ni-Based Superalloy at Liquidus Temperature by Electromagnetic Field

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