The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) plays a crucial role in modern manufacturing, facilitating seamless transitions from digital designs to physical products. However, a significant challenge within this integration is the Automatic Feature Recognition (AFR) of CAD models, especially in the context of hybrid manufacturing that combines subtractive and additive manufacturing processes. Traditional AFR methods, focused mainly on the identification of subtractive (machined) features including holes, fillets, chamfers, pockets, and slots, fail to recognize features pertinent to additive manufacturing. Furthermore, the traditional methods fall short in accurately extracting geometric dimensions and orientations, which are also key factors for effective manufacturing process planning. This paper presents a novel approach for creating a synthetic CAD dataset that encompasses features relevant to both additive and subtractive machining through Python Open Cascade. The Hierarchical Graph Convolutional Neural Network (HGCNN) model is implemented to accurately identify the composite additive-subtractive features within the synthetic CAD dataset. The key novelty and contribution of the proposed methodology lie in its ability to recognize a wide range of manufacturing features, and precisely extracting their dimensions, orientations, and stock sizes. The proposed model demonstrates remarkable feature recognition accuracy exceeding 97% and a dimension extraction accuracy of 100% for identified features. Therefore, the proposed methodology enhances the integration of CAD, CAPP, and CAM within hybrid manufacturing by providing precise feature recognition and dimension extraction. It facilitates improved manufacturing process planning, by enabling more informed decision-making.

翻译：计算机辅助设计（CAD）、计算机辅助工艺规划（CAPP）与计算机辅助制造（CAM）的集成在现代制造中扮演着关键角色，它促进了从数字设计到物理产品的无缝衔接。然而，这种集成中的一个重大挑战是CAD模型的自动特征识别（AFR），尤其是在结合了减材与增材制造工艺的混合制造背景下。传统的AFR方法主要专注于识别减材（机加工）特征，如孔、圆角、倒角、型腔和槽，无法识别与增材制造相关的特征。此外，传统方法在精确提取几何尺寸与方位方面也存在不足，而这些同样是有效制造工艺规划的关键因素。本文提出了一种新颖的方法，通过Python Open Cascade创建了一个包含增材与减材加工相关特征的合成CAD数据集。研究实现了分层图卷积神经网络（HGCNN）模型，以准确识别该合成CAD数据集中的复合增材-减材特征。所提方法的关键创新与贡献在于其能够识别广泛的制造特征，并精确提取其尺寸、方位及毛坯尺寸。所提模型对已识别特征的特征识别准确率超过97%，尺寸提取准确率达到100%。因此，所提方法通过提供精确的特征识别与尺寸提取，增强了混合制造中CAD、CAPP与CAM的集成。它通过支持更明智的决策，促进了制造工艺规划的改进。