Probing the microstructure of advanced and innovative materials is indispensable to next-generation materials characterization. Dragonfly's tools for quantitatively measuring and describing key microstructure features help advance research and production goals.

Image source: Automated segmentation of ceramic matrix composite with deep learning. Data courtesy of Aly Badran, University of Colorado.


Fiber breaks, fiber orientation, fiber length, connected porosity, volume fractions, and thickness analysis of coatings can help predict high-value performance metrics in modern composites. Dragonfly's AI segmentation combines with these it's advanced microstructure quantitative findings to assess composites at micro-, meso-, and macro-scale. 

Ceramics, foams and porous materials

Total porosity, surface-connected porosity, and matrix wall thickness distributions are frequently the defining attributes for commercially relevant ceramics and other porous media. Dragonfly can report directly on these factors as well as simulate thermal and fluid flow physics directly on imaged samples.


Before After

Automated segmentation of ceramic matrix composite with deep learning. Data courtesy of Aly Badran, University of Colorado.

Phase separation in a solid oxide fuel cell. Data courtesy of Aaron Stebner, Colorado School of Mines.


Use Dragonfly to tackle important questions at the relevant length scales. Measure and report porosity, grain size distributions and grain cracking in nano-scale studies. Characterize electrode thicknesses, delaminations, and current collector anomalies in whole-cell studies.

Battery anode cathode segmentation. Data courtesy of ZEISS.


Characterizing powder feedstocks for metals manufacturing and other applications can be essential to manufacturing quality. Analyze grain size distribution, grain porosity, grain aspect ratio and other morphological performance indicators with Dragonfly's microstructure measurement toolset.


Processing SEM to identify grain boundaries and compute distributions of grain sizes and morphologies is invaluable to metallography. Dragonfly's AI and conventional image processing toolchains equip metallographers to transform images directly to these critical performance indicators.

Sphericity analysis of AM powder particles. Data courtesy of William Harris, ZEISS USA.
Scan of a metal cube

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