关键词: 斜轧穿孔顶头, 缺陷在线检测, 点云深度学习, 缺陷分类

Abstract: A non-contact measurement method was proposed based on laser scanning， spatial point cloud data processing and depth learning to meet the practical requirements of on-line inspection for the surface defects of cross-rolling piercing plugs for seamless steel tubes. According to the characteristics of seamless steel tube production lines， the detection positions， system structures and data acquisition schemes of plug contours were determined， and the iterative closest point(ICP) registration method was introduced to achieve the registration of the measurement point cloud with the standard CAD model. The corresponding classification number set and gradual shape were designed for the head defects， and the point cloud depth learning method was used to realize the defect accurate classification and quantitative early warning. Aiming at the surface wear defects， the upper threshold of wear depth was set to monitor the wear degree accurately. In order to verify the reliability of the system， a physical simulation platform was built for plug inspection， and a plug model with different defects was customized by using 3D printing technology. The testing results show that the contour inspection errors are less than 0.06 mm， the classification accuracy of head defect may reach 97.7%， and the accuracy degree may reach 98.1%， which meets the requirements of on-line inspection.

Key words: cross-rolling piercing plug, on-line inspection for defect, deep learning of point cloud, defect classification