# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
Therefore, accurately predicting the performance and characteristics of these models is a challenge we face. DTL-GNN addresses these challenges by dividing the models in the digital twin into different layers, achieving hierarchical model simplification. This approach focuses on retaining the features of key components to enable more efficient simulation and optimization with limited computational resources. This chapter validates the effectiveness and superiority of the DTL-GNN method through case studies of a factory model and a robotic arm model, demonstrating the practicality of the method. First, the actual usage process of DTL-GNN is introduced, followed by the method for hierarchical simplification of components. Finally, the construction of the DTL database is discussed. ### Practical Workflow of DTL-GNN

The workflow of DTL-GNN is shown in Figure 7, which consists of three processes: data extraction, data analysis, and lightweight method assignment. The data extraction stage is the preparation stage of DTL-GNN, and the shape, volume, and mass of the parts in the model are first defined as geometric attributes. A complex association network of the model assembly is established, where each part is regarded as a node, and connections are established based on the assembly relationships between parts. Degree centrality and eigenvector centrality are calculated for each node in the complex association network, which measures the number of parts associated with the part in the model and the importance of its neighboring parts, respectively. In addition, the meson centrality of each node is computed for neighbor node confidence determination. After that, all the parts in the model are traversed to establish the attribute adjacency graph of each part, and the maximum degree and maximum degree centrality in the graph are calculated as a measure of the part's structure. Based on the complex association network, an undirected graph network is generated, and the above geometric attributes and structural indicators are added to the graph network as the attribute information of the nodes. The network structure is preserved during the subsequent data preprocessing phase, and both the adjacency matrix and node attribute matrix are created. ### Simplification Criteria and Effect

According to the results of the classification model, the factory model is divided into five categories, as shown in Figure 5.3 For five types of parts, QEM simplification is performed with a decreasing feature preservation effect. The simplified parameters of models from Class 1 to Class 4 decrease, while Class 0 is the lowest. For more important parts, use finer meshes to retain more features. At the same time, reduce unnecessary faces, eliminate redundant features of the model, and reduce the size of the model file. The comparison before and after simplifying various parts is shown in Figure 5.4

### DTL Knowledge Graph Database

Knowledge Graph (KG) is a graphical knowledge representation for describing models, concepts, and relationships between them [46; 47; 48]. Knowledge graphs built based on specific domain information [49] can often be applied to digital twins to improve performance [50] while acting as a database. The data extracted during the DTL-GNN process, the operation results, and the network structure are stored in the DTL database in the form of a knowledge graph, which provides support for the subsequent model construction. When performing DTL-GNN calculations on a new model, the target model is first searched for graph matching with the DTL database, and if the target graph structure exists in the DTL database or if there is a similar subgraph in the big picture that matches the target graph, the parameters are directly extracted for simplification. Instead, the data analysis phase is continued, and the results are written to the DTL database. Figure 5.5 The mesh model and feature preservation degree before and after simplification of each category of parts. Figure 5.6 DTL-GNN factory application example flow.