Deep Learning for X-ray-Based Material Analysis and CNN Approaches.

X-ray based materials characterization remains a cornerstone technique for probing electronic and atomic structures, with increasing relevance in data-driven and automated analysis. We present a deep learning framework for the spectral classification of materials using laboratory-scale X-ray transmission measurements. Two aluminum-based systems commercially pure aluminum (Al 1050) and an aluminum–copper alloy (Al 2017), were examined at 50 keV and 300 μA across sample thicknesses of 1 mm, 2 mm, and 5 mm. The experimental geometry was extended to a 7 cm wedge configuration to improve absorption contrast and spatial resolution. Convolutional neural networks (CNNs) were trained on polychromatic transmission spectra to learn the mapping between spectral features, material composition, and thickness. To address class imbalance and enhance model generalization, focal loss and class-balanced loss functions were incorporated. The optimized CNNs achieved accurate and robust discrimination of materials exhibiting subtle variations in energy-dependent attenuation behavior. These findings demonstrate that physics-guided spectral learning offers a scalable pathway toward automated X-ray–based material identification, advancing high-throughput and reproducible characterization workflows in materials science.