A novel curved surface profile monitoring approach based on geometrical-spatial joint feature

Abstract

With the development of high-end manufacturing, a variety of sophisticated parts with complex curved surfaces have emerged, and curved surface profile monitoring is of great importance for achieving the higher performance of a part. Benefiting from the recent advancements in non-contact measurement systems, millions of high-density point clouds are rapidly collected to represent the entire curved surface, which can reflect the geometrical and spatial features. The traditional discrete key quality characteristics-based monitoring approaches are not capable of handling complex curved surfaces. A novel curved surface profile monitoring approach based on geometrical-spatial joint features is proposed, which consists of point cloud data preprocessing, Laplace–Beltrami spectrum calculation, spatial geodesic clustering degree definition, and multivariate control chart construction. It takes full advantage of the entire wealth information on complex curved surfaces and can detect the small shifts of geometrical shape and spatial distribution information of non-Euclidean surfaces. Two real-world engineering surfaces case studies illustrate the proposed approach is effective and feasible.