ML algorithm captures 3D microstructures in real time
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 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.
“At first,” says Mathew Cherukara, an assistant scientist at CNM, “we thought of designing an intercept-based algorithm to search for all the boundaries among the numerous grains in the sample until mapping the entire microstructure in all three dimensions, but as you can imagine, with millions of grains, that is extraordinarily time-consuming and inefficient.”
“The beauty of our machine learning algorithm is that it uses an unsupervised algorithm to handle the boundary problem and produce highly accurate results with high efficiency,” says Chan. “Coupled with down-sampling techniques, it only takes seconds to process large 3D samples and obtain precise microstructural information that is robust and resilient to noise.”
The researchers successfully tested the algorithm by comparison with previous research data obtained from analyses of several different metals (aluminum, iron, silicon and titanium) and soft materials (polymers and micelles).
“For researchers using our tool, the main advantage is not just the impressive 3D image generated but, more importantly, the detailed characterization data,” says Sankaranarayanan. “They can even quantitatively and visually track the evolution of a microstructure as it changes in real time.”
The machine-learning algorithm is not restricted to solids. The researchers have extended it to include characterization of the distribution of molecular clusters in fluids with important energy, chemical, and biological applications.
The machine-learning tool, say the researchers, should prove especially impactful for future real-time analysis of data obtained from large materials characterization facilities, such as the Advanced Photon Source, another DOE Office of Science User Facility at Argonne, and other synchrotrons around the world. For more, see “Machine learning enabled autonomous microstructural characterization in 3D samples.”
Center for Nanoscale Materials
Related articles:
Algorithm mines materials science literature for new discoveries
Atomic ‘Swiss Army knife’ precisely measures quantum materials
AI material prediction platform launched
New measuring technique can characterize micron-scaled materials
If you enjoyed this article, you will like the following ones: don't miss them by subscribing to :