A novel 3D laser scanning defect detection and measurement approach for automated fibre placement

Abstract

Automated fibre placement (AFP) machine in-process layup inspection is essential for composite structure fabrication efficiency and quantity improvement. The laser profilometer scanning technique is one of the automated in-process inspection techniques. Laser profilometers can capture a large number of intensive high-resolution point clouds during the layup. To date, few studies have been published about processing point clouds at high speed for in-process layup defect detection and layup feature measurement. In this study, an algorithm called the cross-sectional line-processing algorithm was proposed, and a testbed was constructed to validate the algorithm. The algorithm processes each laser line captured by the laser profilometer and clusters the processed results together. Finally, the defect features and layup features can be segmented and recognized from the collected point cloud. Layup defect types can be recognized, and the dimensions of the layup features can be measured. The experimental results show that this approach can process at most 200 laser lines (about 160 000 points) per second, and the overall defect type recognition accuracy rate reaches about 78%, which meets basic AFP in-process inspection requirements.