It’s well known that microstructure plays a key role in determining material properties. One common way of assessing material microstructure is via Scanning Electron Microscopy (SEM) images. On Citrination, we have the capability to use these microstructural images as inputs to our data-driven models.
We have developed customized deep learning techniques to automatically detect which textures are present in the images. Those textures can then be used as inputs to machine learning models to label the microstructure and predict material properties. This framework is shown schematically in the figure below.
This schematic illustrates the deep learning framework for featurizing SEM images. The SEM image on the left shows steel with pearlite microstructure. That image is transformed through deep learning into a vector of textures. A machine learning model is then able to correctly label the microstructure of this image with high confidence.
A tutorial video of how SEM images can be ingested onto the platform and used to build models is available here. This tutorial used data from the Ultra High Carbon Steel Database 1, which is accessible here on the public Citrination platform, complete with deep learning texture vectors.
This capability is an example of how Citrine’s platform provides cutting edge artificial intelligence solutions specialized for materials science use cases.
–Dr. Julia Ling, Principal Scientist at Citrine
1 DeCost, Brian L., et al. “UHCSDB: UltraHigh Carbon Steel Micrograph DataBase.” Integrating Materials and Manufacturing Innovation (2017): 1-9.