A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones

The mechanical performance of metallic components is intrinsically linked to their microstructural features. However, the manual quantification of microconstituents in metallographic images remains a time-consuming and subjective task, often requiring over 15 min per image by a trained expert. To address this limitation, this study proposes an automated approach for quantifying the microstructural constituents from low-carbon steel welded zone images using convolutional neural networks (CNNs). A dataset of 210 micrographs was expanded to 720 samples through data augmentation to improve model generalization. Two architectures (AlexNet and VGG16) were trained from scratch, while three pre-trained models (VGG19, InceptionV3, and Xception) were fine-tuned. Among these, VGG19 optimized with stochastic gradient descent (SGD) achieved the best predictive performance, with an R2 of 0.838, MAE of 5.01%, and RMSE of 6.88%. The results confirm the effectiveness of CNNs for reliable and efficient microstructure quantification, offering a significant contribution to computational metallography.