ABSTRACT

Existing CAD model classification methods are usually performed by extracting the geometric and topological information of the models. This is not suitable for the classification of Model-based Definition (MBD) models because of the lack of non-geometric semantic information. They cannot solve the problem, as the geometry and topology are similar but the product manufacturing information (PMI) is completely different. Firstly, this paper proposed a multi-granularity classification model based on MBD attribute adjacency graph (MAAG). And then an improved Long Short-Term Memory (LSTM) neural network with a new loss function and adaptive training times is put forward. This neural network model is trained to achieve fast and accurate classification of the MBD models. Finally, the experimental results show that the proposed method not only improves the accuracy of classification, but also effectively reduces the training time cost of the model.

抽象的

现有的CAD模型分类方法通常是通过提取模型的几何和拓扑信息来执行的。由于缺少非几何语义信息，因此这不适用于基于模型的定义（MBD）模型的分类。由于几何形状和拓扑相似，但产品制造信息（PMI）完全不同，因此它们无法解决问题。首先，提出了一种基于MBD属性邻接图（MAAG）的多粒度分类模型。然后，提出了一种具有新的损失函数和自适应训练时间的改进的LSTM短期神经网络。训练该神经网络模型以实现MBD模型的快速准确分类。最后，