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Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation and Damage Analysis

<p>Many properties of commonly used materials are driven by their microstructure, which can be influenced<br>by the composition and manufacturing processes. To optimise future materials, understanding the<br>microstructure is critically important. Here, we present two novel approaches based on artificial intelligence<br>that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such<br>as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative<br>adversarial networks (GAN).<br>Using scanning electron microscopy images collected from large areas (~1 mm&sup2;) of dual-phase steels as a<br>case study, we demonstrate how both methods effectively segment intricate microstructural details,<br>including martensite, ferrite, and damage sites, for subsequent analysis.<br>Either method shows substantial generalizability across a range of image sizes and conditions, including<br>heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large<br>image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater<br>step-by-step control and flexibility over the segmentation process.<br>This study highlights the benefits of segmented microstructural data for various purposes, such as<br>calculating phase fractions, modelling material behaviour through finite element simulation, and<br>conducting geometrical analyses of damage sites and the local properties of their surrounding<br>microstructure.</p> <p>https://doi.org/10.1016/j.matdes.2024.113031</p>