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Temperature dependencies of the elastic moduli and thermal expansion coefficient of an equiatomic, single-phase CoCrFeMnNi high-entropy alloy, Journal of Alloys and Compounds (2014), doi: http://dx. AbstractThe equiatomic CoCrFeMnNi alloy is now regarded as a model face-centered cubic singlephase high-entropy alloy. Therefore, determination of its intrinsic properties such as the temperature dependencies of elastic moduli and thermal expansion coefficient are important to improve understanding of this new class of material. These temperature dependencies were measured over a large temperature range (200 -1270 K) in this study.

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The present work shows that thermal expansion experiments can be used to measure the γʼ-solvus temperatures of four Ni-base single-crystal superalloys (SX), one with Re and three Re-free variants. In the case of CMSX-4, experimental results are in good agreement with numerical thermodynamic results obtained using ThermoCalc. For three experimental Re-free alloys, the experimental and calculated results are close. Transmission electron microscopy shows that the chemical compositions of the γ- and the γʼ-phases can be reasonably well predicted. We also use resonant ultrasound spectroscopy (RUS) to show how elastic coefficients depend on chemical composition and temperature. The results are discussed in the light of previous results reported in the literature. Areas in need of further work are highlighted. Graphical abstract

In this work, an automated image analysis procedure for the quantification of microstructure evolution during creep is proposed for evaluating scanning electron microscopy micrographs of a single crystal Ni-based superalloy before and after creep at 950 °C and 350 MPa. scanning electron microscopy-micrographs of γ/γ′ microstructures are transformed into binary images. Image analysis, which involves pixel by pixel classification and feature extraction, is then combined with a supervised machine learning algorithm to improve the binarization and the quality of the results. The binarization of the gray scale images is not always straight forward, especially when the difference in gray levels between the γ-channels and the γ′-phase is small. To optimize feature extraction, we utilized a series of bilateral filters as well as a machine learning algorithm, known as the gradient boosting method, that was used for training and classifying the micrograph pixels. After testing the two methods, the gradient boosting method was identified as the most effective. Subsequently, a Python routine was written and implemented for the automated quantification of the γ′ area fraction and the γ channel width. Our machine learning method is documented and the results of the automatic procedure are discussed based on results which we previously reported in the literature.