An effective freeform surface retrieval approach for potential machining process reuse

With the increasing of the machining process data, which are the direct and effective carrier of knowledge, intelligence and experience of skilled engineers, machining process data-driven intelligent machining process planning is becoming more and more important in manufacturing industries. One of the key technologies is to retrieve the similar geometry with machining process reuse value in a fine manner. However, existing 3D CAD model retrieval methods for manufacturing reuse mainly focus on the parts composed of non-freeform surface features machined with 2 1/2-axis CNC milling, while the parts including complex freeform surfaces accounted for a large proportion are little involved. In this paper, a novel freeform surface retrieval approach for potential machining process reuse is presented. First, similar tensor field pattern freeform surface feature is introduced to represent the complex freeform surface into structured freeform surface model. Then, freeform surface content code for accelerating freeform surface retrieval is given to filter out unmatched freeform surfaces efficiently. Moreover, the principal pathline indicating the overall evolution trend of feature pathlines is extracted and represented using D2 shape descriptor to establish the feature similarity assessment model. Finally, sub-graph isomorphism-based matched feature pairs extraction algorithm is presented to calculate the similarity between matched freeform surfaces. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.