3D-SWiM: 3D vision based seam width measurement for industrial composite fiber layup in-situ inspection. (August 2023) - Zhang, Yinlong; Yuan, Libiao; Liang, Wei; Xia, Xiaofang; Pang, Zhibo
Abstract: Industrial composite fiber has been widely used in the high-end manufacturing because of its superior stiffness and strength. To ensure the quality of composite fiber placement in making high-end and lightweight aerospace products, it needs reliable composite fiber layup in-situ inspection. An accurate seam width measurement is an indispensable procedure during the layup inspection. However, the traditional seam measurements may fail in presence of surface curvature variance, curved edges between sheets, to name a few. This paper intentionally designs a novel 3D vision based Seam Width Measurement for Industrial Composite Fiber Layup In-situ Inspection (called 3D-SWiM). It aims to address three major challenges in measuring the seam width between the composite fiber sheets, namely, seam region over-segmentation, failures on region of interests (ROI) seam detection, seam width measurement inconsistency. Firstly, the region growing with semantic refinement is designed for 3D point cloud clustering, to overcome over-segmentation on seam regions. Afterwards, the gridding model with edge discrimination is applied to extract the 3D potential candidates in the edge areas. Eventually, we use the cubic B-spline fitting model to describe the seam region curves and the improved particle swarm optimization model to estimate the minimum and maximum distance between the seam edges. 3D-SWiM has been evaluated extensively on the developed composite fiber layup platform. The comparisons with the state-of-the-art methods (such as HT and GFM) on the region-of-interest segmentation, seam extraction and seam width measurement have been performed and the experimental results prove the competitive performance in composite fiber layup in-situ inspections. Highlights: 3D vision based seam width measurement. Industrial composite fiber layup in-situ inspection. 3D point cloud clustering using region growing with semantic refinement. 3D potential candidates extraction in the edge areas using the gridding model with edge discrimination. Cubic B-spline fitting model to describe the seam region curves and measure the seam width.