Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

In this study, we innovatively proposed a deep learning model architecture to address the industry challenges in the detection of porosity in magnesium alloys. Magnesium alloys, known for their lightweight and high-strength characteristics, are extensively utilized in aerospace, automotive, and biomedical fields. However, the absorption of hydrogen during the production process leads to the formation of pores, which not only reduce the material's strength and durability but may also cause premature failure of the material. The formation of pores typically occurs during the solidification stage of magnesium alloys, where hydrogen dissolved in the molten metal is released upon cooling, forming tiny gas pores. The presence of these gas pores significantly affects the mechanical properties of the material, potentially leading to crack initiation and propagation under high stress. Therefore, accurate detection and quantification of pores are crucial for enhancing the quality control of magnesium alloys. Our developed model integrates Window-Shaped Perception Blocks (WSPB) with Convolutional Neural Networks (CNN), enhanced by Aggregated Sensing Layers (ASL) on long-range connections. Extensive training on real samples demonstrated that our model outperforms mainstream algorithms such as U-Net and TransUNet across various evaluation metrics, particularly in fine target detection tasks under complex scenarios. Specifically, our model achieved a Dice coefficient of 74.77% and an Intersection over Union (IoU) index of 71.00%, significantly surpassing other models. Moreover, the method also demonstrated superior accuracy in pore edge prediction, effectively mitigating issues of oversegmentation and undersegmentation, especially for small and irregular pores. An ablation study further confirmed the effectiveness of each component, with the ASL module showing particular strength in feature extraction and reducing upsampling loss. In summary, this research highlights the significant potential of deep learning technology in material defect detection and provides an efficient, automated solution for practical production, contributing to advancements in materials science and industrial quality control.