@article{Qin2026, 
author = {Xu Qin and Qinghang Wang and Xinqian Zhao and Shouxin Xia and Li Wang and Jiabao Long and Yuhui Zhang and Yanfu Chai and Daolun Chen},
title = {Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys},
year = {2026},
journal = {Journal of Magnesium and Alloys},
volume = {14},
number = {C},
keywords = {Magnesium alloys, Composition prediction, Scanning electron microscope images, Multi-scale simplified residual convolutional neural network},
url = {https://www.sciopen.com/article/10.1016/j.jma.2025.06.005},
doi = {10.1016/j.jma.2025.06.005},
abstract = {This study proposes a multi-scale simplified residual convolutional neural network (MS-SRCNN) for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope (SEM) images. A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications. The MS-SRCNN significantly reduces computational runtime by over 90 % compared to traditional architectures like ResNet50, VGG16, and VGG19, without compromising prediction accuracy. The model demonstrates more excellent predictive performance, achieving a >5 % increase in R2 compared to single-scale models. Furthermore, the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys, including Mg-La, Mg-Sn, Mg-Ce, Mg-Sm, Mg-Ag, and Mg-Y, thereby emphasizing its generalization and extrapolation potential. This research establishes a non-destructive, microstructure-informed composition analysis framework, reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.}
}