Laser grating ultrasonic testing of porosity in AM components with rough surface combining intelligent analysis of Gramian angular field images

Additive manufacturing (AM) allows the fabrication of complex, high-performance components, but these components are prone to porosity defects, which reduce their mechanical integrity and fatigue resistance. To enable real-time detection and enhance applicability to rough surfaces, this study proposes an intelligent porosity evaluation method for AM components based on grating laser ultrasonics. Specifically, the surface acoustic waves (SAWs) generated by a grating laser source interact with internal porosities, and the resulting time- and frequency-domain ultrasonic signals are converted into two-dimensional images by the Gramian angular field (GAF) algorithm. These images are subsequently processed using a Residual neural network (ResNet) model for porosity prediction. Firstly, through numerical simulations, the effects of porosity and surface roughness on both time-domain and frequency-domain signals are systematically analysed. Based on these results, various input feature representations (one-dimensional signals, continuous wavelet transform (CWT) images, and GAF images) are integrated with the ResNet architecture to perform porosity prediction on simulated and experimental datasets. After analysis, the GAF representation consistently achieves superior prediction performance under both smooth and rough surface conditions, demonstrating its strong generalisation ability and robustness. This validates the effectiveness and feasibility of the proposed method, providing a novel and reliable approach for accurate porosity assessment of AM components.