High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
This work addresses the need for automated, objective analysis in materials science, specifically for metallography, but it is incremental as it applies existing deep learning methods to a new dataset.
The researchers tackled the problem of automating microstructure segmentation for complex microstructures in ultrahigh carbon steel, which is typically done manually and subjectively, by applying a deep convolutional neural network model to segment cementite particles and other microconstituents, enabling empirical distributions of particle size and denuded zone width.
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly-available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov (https://materialsdata.nist.gov/handle/11256/964).