An effective retrieval approach of 3D CAD models for macro process reuse

With the increasing of the process data, process data-driven intelligent machining process planning is becoming more and more important in manufacturing industries. In the process data, the macro process contains abundant process design intent, which has important reuse value. However, existing 3D CAD model retrieval methods for macro process reuse are mainly on the premise of geometric similarity, which make them difficult to guarantee the reusability of the macro process associated with the similar results. In this paper, an effective retrieval approach of 3D CAD models for macro process reuse is presented. First, a process skeleton model is introduced to guide the structuralization of process data based on the macro process of existing parts. Then, a probability statistics approach is presented to map machining features of query part onto the macro process of existing part. Finally, process design intent-driven accessible machining region extraction is proposed, and then the part similarity assessment model based on the machining region is established to calculate the macro process similarity between query part and existing part. A prototype system has been developed to verify the effectiveness of the proposed approach.