Computer aided design (CAD) models are widely employed in the current computer aided engineering or finite element analysis (FEA) systems that necessitate an optimal meshing as a function of their geometry. To this effect, the sub-mapping method is advantageous, as it segments the CAD model into different sub-parts, with the aim mesh them independently. Many of the existing 3D shape segmentation methods in literature are not suited to CAD models. Therefore, we propose a novel approach for the segmentation of CAD models by harnessing deep learning technologies. First, we refined the model and extracted local geometric features from its shape. Subsequently, we devised a convolutional neural network (CNN)-inspired neural network trained with a custom dataset. Experimental results demonstrate the robustness of our approach and its potential to adapt to augmented datasets in future.

中文翻译：

---

通过深度学习进行 CAD 模型分割

计算机辅助设计 (CAD) 模型广泛用于当前的计算机辅助工程或有限元分析 (FEA) 系统，这些系统需要根据其几何形状进行最佳网格划分。为此，子映射方法是有利的，因为它将 CAD 模型分割成不同的子部分，目标是独立地对它们进行网格划分。文献中许多现有的 3D 形状分割方法不适合 CAD 模型。因此，我们提出了一种利用深度学习技术分割 CAD 模型的新方法。首先，我们改进了模型并从其形状中提取了局部几何特征。随后，我们设计了一个受卷积神经网络 (CNN) 启发的神经网络，使用自定义数据集进行训练。