Capturing 3D microstructures in real time


Author Name: Desrina R

Category Name:  Science and Technology


Researchers at Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry.

"What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions”. For example, with data analyzed by our 3D tool," said researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials."

Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains. The size and distribution of those grains and the voids within a sample are critical microstructural features that affect important physical, mechanical, optical, chemical and thermal properties. Such knowledge is important, for example, to the discovery of new materials with desired properties, such as stronger and harder machine components that last longer.

In the past, scientists have visualized 3D microstructural features within a material by taking snapshots at the microscale of many 2D slices, processing the individual slices, and then pasting them together to form a 3D picture. Such is the case, for example, with the computerized tomography scanning routine done in hospitals.

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Media Contact:

Desrina R
Journal Manager
American journal of computer science and information technology