ML algorithm captures 3D microstructures in real time

August 31, 2020 //By Rich Pell
ML algorithm captures 3D microstructures in real time
Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, say they have invented a machine-learning (ML) based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers.

Characterization of microstructural and nanoscale features in full 3D samples of materials, say the researchers, is emerging to be a key challenge across a range of different technological applications. It is well known that there is a strong correlation between such microstructural/nanoscale features - which can range from grain size distribution in metals; voids and porosity in soft materials such as polymers; to hierarchical structures and their distributions during self- and directed-assembly processes - in materials and their observed properties.

However, say the researchers, grain size characterization is performed on 2D samples and the information from 2D slices is collated to derive the 3D microstructural information, which is inefficient and leads to potential loss of information. As such, a direct 3D classification approach for arbitrary polycrystalline microstructure is crucial and highly desirable - especially given the advancement in 3D characterization techniques such as tomography, high energy diffraction microscopy (HEDM), and coherent diffraction X-ray imaging.

Their algorithm, say the researchers, addresses this and can be applied to the analysis of most structural materials of interest to industry.

"What makes our algorithm unique," says Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago, "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," says Henry Chan, CNM postdoctoral researcher and lead author of the study on the research, "with data analyzed by our 3D tool 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

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