Thin-Walled Aircraft Panel Edge Extraction From 3-D Measurement Surfaces via Feature-Aware Displacement Learning

To enable the generation of reliable tool paths for precision machining along the edges of thin-walled aircraft assembly panels, we introduce a feature-aware displacement learning framework for accurately extracting the edges of aircraft panels. Specifically, we design a dual-task neural network, named aircraft panel edge extraction network (APEE-Net). This network serves the dual purpose of identifying points located near the edges of aircraft panels and predicting displacement vectors pointing toward local edge features. The detected edge points are subsequently repositioned using these displacement vectors, resulting in the extraction of precise edge points. Our proposed method is fortified with feature-aware displacement optimization loss during the training phase, significantly enhancing its robustness and accuracy when dealing with noisy sharp geometric features. Extensive experiments demonstrate that our approach outperforms existing extraction methods in terms of accuracy. Furthermore, practical machine applications further validate its feasibility and real-world applicability.