Lay‐up defects inspection for automated fiber placement with structural light scanning and deep learning

Abstract Automated fiber placement (AFP) is an advanced manufacturing technology for large‐scale carbon fiber reinforced plastics (CFRP) composite parts. During AFP processes, unidirectional CFRP prepreg tapes are laid up on the mold to become laminates by a robotic arm under heating and pressure conditions. However, the efficient monitoring of lay‐up defects during AFP, especially the out‐of‐plane defects including wrinkles, bridging, gaps, and overlaps, needs more advanced solutions, as manual inspection of the lay‐up defects still accounts for a large proportion of real manufacturing processes at present. To tackle this issue, an innovative method to realize efficient and accurate detection of AFP lay‐up defects was developed in this work. First, a bespoke AFP platform containing a 5‐degree‐of‐freedom moving platform, air cylinder, compaction roller, heating gun, and structural‐light camera was constructed for AFP experiments. A defect detection algorithm based on PointNet++, a convolutional neural network (CNN) for 3D point cloud segmentation, was then created. PointNet++ was trained by the dataset containing 3D point clouds of intentionally laid‐up defects, which were created on the AFP platform and captured by the structural‐light camera. Compared to the existing AFP defect inspection methods, such as laser profilometer and thermography, this method has the advantages of low cost and convenience to operate. The completed method was experimentally validated to be capable of precisely tracking the locations of out‐of‐plane AFP defects in less than 1 second. The detection accuracy of the method can achieve above 72% in Intersection over Union (IoU) for the out‐of‐plane defects. Highlights A lay‐up defect detection method for AFP by structural light scanning. 3D point cloud segmentation by PointNet++ was used for defect segmentation. A bespoke AFP platform was constructed for AFP experiments. The method can track the locations of AFP defects in less than 1 second. The method has the advantages of low cost and convenience to operate.