Enhancing precision of defect 3D reconstruction in metal ultrasonic testing through point cloud completion

During the ultrasound multi-layer focused scanning inspection process, the sequential images characterizing the defect morphology suffer from inter-layer contour information loss, which causes a reduction in the accuracy of defect 3D reconstruction, subsequently leading to errors in the characterization of the defect size and morphology. In order to address the above issues, a new method based on the Attention-based Residual Completion Network (ARC) is proposed for ultrasonic point cloud completion to characterize metal defects. Firstly, the ARC network extracts global contour morphological features and local edge detail features from the ultrasonic point cloud through consecutive residual convolutions. Subsequently, the two sets of features are concatenated and finally fed into a decoder based on self-attention, realizing the reconstruction of lost contour information and enhancing the 3D reconstruction accuracy of defects. In the experiment, an ultrasonic microscope was used to inspect actual steel plates. The internal defects were then completed using the ARC network, and the completion results were compared with the metallographic images of the defects. The experiment results indicated that, after completion, the characterization accuracy of defect morphology and sizes is enhanced by an average of 10.31 %. The ARC network provides a novel method for high-precision 3D characterization of internal defects in metal materials.