# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
Current simplification methods fall short in the lightweight of digital twin virtual entities, as they cannot selectively preserve key model details and struggle to adapt to the increasing scale of digital twin virtual models. There is an urgent need for an optimization approach that supports accurate decision-making in digital twin systems and enhances computational efficiency. This article proposes a fine-grained simplification method for virtual models, realizing the development of lightweight digital twin systems. Prior to performing lightweight operations, it is essential to categorize the parts within the model and allocate the most suitable simplification methods. This process primarily focuses on retaining the model's critical components, ensuring that the lightweight version accurately represents key information in the real-world space. Employing techniques such as graph neural networks, we accurately classify and simplify digital twin virtual models, thereby achieving a sustainable and dynamic balance between simulation effectiveness and computational efficiency. Table 1 describes the main symbols used in the paper. In the experimental section, without loss of generality, we validate the feasibility of DTL-GNN using three-dimensional (3D) models of a factory and robotic arms as case studies. The factory model, consisting of an architectural structure, encompasses two electromechanical non-standard production lines and an array of robotic arms. Mechanical and electrical equipment constitute the factory model's backbone, encompassing a range of components such as drive mechanisms, transmission systems, fixtures, workbenches, and storage shelves. The custom dataset extracted from the model consists of two components: (1) topological and attribute information derived from the parts and (2) connections in the graph data mapped from assembly relationships. Upon establishing the dataset, it is calibrated according to the classification of parts presented in Table 2. In summary, we make the following contributions. \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbols** & **Descriptions** \\ \hline \(fc_{i},v_{i},m\) & The number of features, volume, and material \\  & characteristics of the part \\ \(\mathbf{h}_{att},\mathbf{h}_{struc}\) & Attribute embedding and structural \\  & embedding vector. \\  & The set of neighboring nodes that satisfy the \\ \(N_{i,\max}\) & confidence requirement and the degree condition of \\ \(A_{d}\) & the node, where the subscript \(i\) denotes the node \\ \(Adap\) & \
\begin{tabular}{l} Adaptation function \\ Degree centrality and Eigenvector centrality of node. \\ The constant of proportionality, the importance \\ measure of the node, the number of nodes. \\ \(step_{i},step_{\max}\) \\ \end{tabular} \\ \(d_{i}\) &
\begin{tabular}{l} the distance of node from the center node. \\ the average value of elements in \(\mathbf{h}\) \\ \(k\) is the order of neighbor sampling, \(S_{n}\) is the number \\ of samples from n-hop neighbors \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main symbols and descriptions. \begin{table} \begin{tabular}{c c} \hline \hline
**Category** & **Classification Criteria** \\ \hline
0 & Decorative parts, infrastructure, electrical components \\
1 & Machined complex surfaces, articulated bearings, end-effector \\
2 & Machined curved surface, Complex process, Transmission components \\
3 & Sheet metal parts, castings, Single processes \\
4 & Fasteners, Standardized parts \\ \hline \hline \end{tabular}   
* This paper performs precise simplification of virtual entities to achieve lightweight digital twin systems, addressing model redundancy in the virtual space of digital twins and achieving a dynamic balance between computational cost and accurate decision-making. * DTL-GNN employs attribute adjacency graphs and complex network techniques to model the graph data of virtual entities and extract multidimensional information about the components. * An enhanced graph neural network is employed to classify mechatronic 3D models precisely.