We propose a masked self-supervised learning framework, called BRepMAE, for automatically extracting a valuable representation of the input computer-aided design (CAD) model to recognize its machining features. Representation learning is conducted on a large-scale, unlabeled CAD model dataset using the geometric Attributed Adjacency Graph (gAAG) representation, derived from the boundary representation (BRep). The self-supervised network is a masked graph autoencoder (MAE) that focuses on reconstructing geometries and attributes of BRep facets, rather than graph structures. After pre-training, we fine-tune a network that contains both the encoder and a task-specific classification network for machining feature recognition (MFR). In the experiments, our fine-tuned network achieves high recognition rates with only a small amount of data (e.g., 0.1% of the training data), significantly enhancing its practicality in real-world (or private) scenarios where only limited data is available. Compared with other MFR methods, our fine-tuned network achieves a significant improvement in recognition rate with the same amount of training data, especially when the number of training samples is limited.

翻译：我们提出了一种名为BRepMAE的掩码自监督学习框架，用于自动提取输入计算机辅助设计（CAD）模型的有价值表示，以识别其加工特征。表示学习是在大规模、未标注的CAD模型数据集上进行的，使用源自边界表示（BRep）的几何属性邻接图（gAAG）表示。自监督网络是一个掩码图自动编码器（MAE），专注于重建BRep面的几何形状和属性，而非图结构。预训练后，我们对一个包含编码器和任务特定分类网络的网络进行微调，用于加工特征识别（MFR）。在实验中，我们的微调网络仅用少量数据（例如，训练数据的0.1%）即可实现高识别率，显著提升了其在现实世界（或私有）场景中仅有限数据可用时的实用性。与其他MFR方法相比，在相同训练数据量下，我们的微调网络在识别率上取得了显著提升，尤其是在训练样本数量有限的情况下。