Automated segmentation of point data in a feature-based reverse engineering system

Abstract

This paper proposes a novel methodology for robust segmentation of scanned point data that has been implemented in the feature-based reverse engineering system (FBRES). In the proposed method, firstly triangle meshes are generated from the input point data. The normal and the area of generated meshes are checked to find boundary meshes using the angle deviation criterion and the area criterion based upon a region-growing technique. Boundary meshes of each segmented region are connected into loops. Then, the meshes closed by each boundary loop are segmented into a single distinctive region. Finally, all segmented regions are mapped into a single feature using an artificial neural network (ANN) based feature recognizer. The FBRES is currently dedicated for reconstructing prismatic features such as a block, pocket, step, slot, hole and boss, which are very common and crucial in mechanical engineering products. The effectiveness of the proposed segmentation method is validated with experimental results.