Efficient 3-D Model Machining Strategy Prediction with Topology-Spanning Aggregation and GMU Data Augmentation

During the machining process of parts, choosing appropriate machining strategies optimizes production costs effectively. However, when the amount of data is limited, existing neural networks often struggle to fit the data accurately. Meanwhile, existing neural networks suffer from information dilution and lack effective mechanisms for direct information transfer between nonadjacent surfaces. This article proposes a method to extract General Machining Unit data. This data improves few-shot training performance. We investigate the distribution and information flow within General Machining Units and design a new way of data augmentation. In addition, to address the information dilution, a novel wormhole mechanism is proposed to aggregate information that spans the topological connections. In the backbone, we propose the Brep-WH layer that integrates wormhole mechanisms and attention pool layers. Both the Brep-WH network and the General Machining Unit data successfully improve the accuracy of the milling strategy dataset and the Fusion 360 Gallery segmentation dataset.