# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
Figure 12: The comparison of the micro-average F1 score, micro-average recall, micro-average accuracy, macro-average F1 score, macro-average recall, and macro-average accuracy of the related algorithm and DTL-GNN under the model dataset. preserved by 66.7%, and the file size was reduced by 17.9%. DTL-GNN paid more attention to preserving details by simplifying critical parts and ensuring the accuracy of essential features in the model. For other parts manufactured by integrated processes, 33.3% of the details were retained, with a 31.6% reduction in file size; single process parts retained 24.4% of the features, with a 44.4% reduction in file size; and standard parts retained only the outer contour features, with a 14.3% retention and a 71.3% reduction in file size. Non-critical parts were four times as compressed as critical parts, and critical parts retained 2 to 6 times as many features as non-critical parts. The overall model file size was reduced by 57.3%, while the representation of critical features increased by 20.4%. The results indicate that the DTL-GNN method effectively reduces model volume while retaining critical features. In addition, the effectiveness of importance sampling, as discussed in Section 3.2, is validated. Figure 13 illustrates the probability improvement achieved by importance sampling compared to random sampling on the target sample. This improvement is attributed to the sampling method, which reorganizes the main nodes when the number of neighboring nodes exceeds a specified threshold. Each xz-plane represents the probability distribution of a set of neighboring nodes, where both the degree and master node IDs are discrete variables. To better visualize these variations, the graph has been smoothed. In summary, DTL-GNN effectively performs in part classification, simplification, and feature retention. The lightweight model is realized based on accurate classification and preserves the crucial details of the model. The lightweight model can improve the operation efficiency of the digital twin, and the retention of crucial details can ensure the accuracy of the system's decision-making to a certain extent. This

Figure 13: The purple surface below the image represents the probability distribution of neighboring nodes during random sampling, while the other surface represents the probability distribution after importance sampling. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **File Size** & **Feature** \\  & **Reduction** & **Preservation** \\ \hline
0: Other parts (Infrastructure, electrical components) & 64.3\% & 11.0\% \\
1: Important parts (Complex surfaces, critical datums) & 17.9\% & 66.7\% \\
2: Ordinary parts (Variety of processes and features) & 31.6\% & 33.3\% \\
3: General parts (simple process) & 44.4\% & 24.4\% \\
4: Standard parts & 71.3\% & 14.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of simplification effects. the computational cost and improve the system efficiency but also ensures the feature retention of critical parts, thus realizing the lightweight of the digital twin while ensuring the accuracy of the system output. The combined advantages of the DTL-GNN make it a feasible approach that meets the need for model simplification and ensures the effective retention of critical information. ## 6 Conclusions and Future Work

DTL-GNN uses graph neural network techniques and mainly focuses on mechatronic models. DTL-GNN implements the entire virtual entity operation process from data extraction to model hierarchical simplification. Classifying and simplifying each virtual component specifically for its category reduces the computational burden while accurately reflecting physical space details. A mechatronic component knowledge graph database has been constructed based on the DTL-GNN approach to unleash computational power further and enhance efficiency. This article takes the comprehensive application of mechatronic systems as an example for experimentation.