Automated Volumetric Feature Extraction from the Machining Perspective

Abstract

This thesis presents a new feature extraction method aiming at recognizing volumetric features from the delta volume (DV), which is the material difference between the part and the stock. The volumetric feature can then be used for feature-based tool path generation directly. To this end, the DV is firstly decomposed into accessible delta volumes (ADVs) along all possible tool approach directions (TADs). The ADVs along each TAD are then decomposed into individual volumetric features (drilling, 2.5D milling, and 3D milling) in which feature interaction problems are resolved and a feasible removal sequence is also established. The proposed algorithm allows multiple feature interpretations with valid manufacturability. Finally, the proposed method has been implemented and case studies show that it is able to handle complicate realistic parts that can be produced using a 3-axis machining centre and there is no limitation in the shape of final part and stock.