Structure-informed neural network for predicting fracture-prone regions in laser-deposited aluminum alloys with pores

Hydrogen pores are critical microscale defects in laser-deposited aluminum alloys, causing local strain concentration and reducing ductility. To spatially predict this structure–property relationship, this study proposes a neural network model based on structural feature extraction and fusion. Al-Mg-Sc specimens were fabricated by coaxial laser wire directed energy deposition, and micro-computed tomography (μ-CT) and Digital Image Correlation (DIC) experiments were conducted sequentially. Pore structure point clouds and normalized strain maps obtained from the experiments were uniformly segmented into spatially aligned blocks and used as training data. The model integrates PointNet and Convolutional Neural Network (CNN) modules to extract structural features and learn spatial correlations. A composite loss was introduced to capture both the continuous strain distribution and discrete high-strain regions. With limited data, the model achieved a pixel-level Area Under the Curve (AUC) of 0.69 and a custom distance-weighted AUC (dw-AUC) of 0.74, which is weighted by spatial proximity. On a full-scale specimen, the model accurately predicted the high-strain regions, one of which coincided with the actual fracture site. Sensitivity analysis shows that using a segmentation block size of around 300 μm and applying random point cloud dropout helps maintain spatial resolution and improves training performance. This work provides a structure-informed modeling approach for predicting damage-prone regions in defect-containing alloys.