Weld Surface Imperfection Detection by 3D Reconstruction of Laser Displacement Sensing

It is of great significance to detect the weld surface to ensure welding quality. Aiming at the problem of weld surface defect detection and 3D reconstruction algorithm, a method of defect detection and three - dimensional reconstruction based on laser scanning 3D point cloud has been proposed. The method of BP neural network is used to filter the point cloud data obtained by laser displacement sensor scanning, and the result is qualitatively and quantitatively analyzed. The three-dimensional reconstruction model of pit defects on weld surface is established. The reconstructed error is within the allowable range. The experimental results show that applying BP neural network to weld defect detection can effectively reduce the complexity of 3D reconstruction and improve the accuracy of 3D reconstruction of weld surface.