A data-driven approach for volume feature recognition based on cell graph

Machining feature recognition is one of the key technologies for realizing automated process planning. The machining feature represented by volume contains more complete information due to its property of forming a closed region which facilitates subsequent process planning. The existing volume feature recognition approaches mainly rely on expert-defined rules to combine volumes into the desired feature. When dealing with some complex parts, volume feature recognition faces combinatorial explosion, and it is difficult to formulate complete combination rules manually. This research proposes a novel data-driven approach for volume machining feature recognition. The proposed approach does not require manually defined rules and can learn various complex volume combination rules from the data, which can be implicitly represented by the structure and parameters of the data-driven model. The volume machining feature recognition is first converted to machining feature subgraph (MFS) detection by creating the zone graph. MFS detection can then be achieved by graph edge cut. Finally, the edge cut is modeled as a binary classification deep learning problem, which is solved by graph neural network. Two datasets are created, and case studies are carried out on them. The results show that the proposed approach is effective for volume machining feature recognition.