Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model
Journal:
arXiv
Published Date:
Apr 21, 2025
Abstract
The traditional techniques for extracting polycrystalline grain structures
from microscopy images, such as transmission electron microscopy (TEM) and
scanning electron microscopy (SEM), are labour-intensive, subjective, and
time-consuming, limiting their scalability for high-throughput analysis. In
this study, we present an automated methodology integrating edge detection with
generative diffusion models to effectively identify grains, eliminate noise,
and connect broken segments in alignment with predicted grain boundaries. Due
to the limited availability of adequate images preventing the training of deep
machine learning models, a new seven-stage methodology is employed to generate
synthetic TEM images for training. This concept-oriented synthetic data
approach can be extended to any field of interest where the scarcity of data is
a challenge. The presented model was applied to various metals with average
grain sizes down to the nanoscale, producing grain morphologies from
low-resolution TEM images that are comparable to those obtained from advanced
and demanding experimental techniques with an average accuracy of 97.23%.
