The limited annealing treatment of the Zr55Cu30Al10Ni5 metallic glass below its glass transition temperature

Chai, K.H.; Lin, Tiesong; He, Peng; Sun, Jianfei

doi:10.1016/j.jallcom.2014.09.122

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…A value of 1 for n indicates diffusion controlled growth of the transformation products of interest. Here, values of n close to 1 were calculated based on segmentation results, suggesting the discontinuous precipitation reaction is diffusion-controlled, and nucleation grain boundaries [62,63]. The calculated n-values here are consistent with previously reported results in Reference [15].…”

Section: Kinetic Modelingsupporting

confidence: 90%

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Kautz¹,

Ma²,

Jana³

et al. 2019

Preprint

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Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification

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Section: Kinetic Modelingsupporting

confidence: 90%

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Kautz¹,

Ma²,

Jana³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…It must ensure that the Zr 55 Cu 30 Al 10 Ni 5 BMG side remained in non-crystalline during the experimental processes, the choice of temperature and isothermal aging time must be included in the range of limited thermal treatment time of Zr 55 Cu 30 Al 10 Ni 5 BMG below the glass transition temperature (683 K), which could corresponding to our previous work [26]. The constants of the growth rate for interfacial layers were calculated as a function of time and temperature by fitting the Arrhenius plot.…”

Section: Introductionmentioning

confidence: 93%

Time exponent suppression of growth of the Zr–Sn diffusion layers between amorphous Zr55Cu30Al10Ni5 and Sn

Chai

Lin

et al. 2015

Journal of Alloys and Compounds

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Crystallization mechanism of Zr55Cu30Al10Ni5 metallic glass in an extended range of heating rates

et al. 2021

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The limited annealing treatment of the Zr55Cu30Al10Ni5 metallic glass below its glass transition temperature

Cited by 8 publications

References 24 publications

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Time exponent suppression of growth of the Zr–Sn diffusion layers between amorphous Zr55Cu30Al10Ni5 and Sn

Crystallization mechanism of Zr55Cu30Al10Ni5 metallic glass in an extended range of heating rates

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