MetaScope: A Model for Microstructural Analysis of Carbon Steels Using Machine Learning Techniques - Bellini, Lucian Fernando; Santos, Éric Cassiano Drey dos; Kang, Yeun Haur; Haupt, William; Hölbig, Carlos Amaral (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16633, 2025)
Microstructural characterization in metallography often relies on manual analysis, which introduces bias and reduces reproducibility. To overcome this, we propose MetaScope, a deep learning model for pixel-level segmentation of ferrite and pearlite in SAE 1020 and 1045 steels. Using 2,200 images with masks generated by a Gaussian Mixture Model, the model combines an U-Net architecture with an EfficientNetB0 encoder. This approach demonstrates the potential of using machine learning techniques to support metallographic workflows and future expansion to other materials.