# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
Feature extraction, or boundary collapse, is performed on the model to simplify the classification of part meshes. Feature extraction identifies critical features in the model, such as boundaries and surfaces. Retain these critical features and remove secondary features and redundancies. Boundary collapse reduces the mesh complexity by merging mesh boundaries to reduce the number of grids. Unnecessary mesh points can be removed according to the error metrics of the algorithm, such as vertex mesh simplification, triangle decimation [42], and Quadric Error Metrics (QEM) simplification [43]. The background and theoretical framework for existing mechatronics product development [44] categorize the components of mechatronic systems into sensors and instrumentation, processing and control software, actuators and drivers, engineering projects, and communication systems. [PERSON] and [PERSON] [45] classified Mechatronic Machine Elements (MMEs) based on three key attributes: \"sensor output\", \"change mechanical properties\", and \"add mechanical energy\". These studies provide the theoretical foundation for constructing the classification system in Figure 5.0 Based on the existing classification system and the mechanical functions and geometric features of the models, the mechanical models are classified into five categories, as shown in Figure 5.1

* Category 0: These components typically do not directly participate in mechanical motion or energy conversion and have a relatively minor impact on the performance of digital twin systems. However, they are commonly present in virtual spaces to ensure the integrity of the virtual environment. * Category 1 components involve mechanical processing technology and precise control, making them vital mechanical components in mechatronic systems. These components achieve precise control by integrating actuators or control mechanisms that alter mechanical properties. Simultaneously, they feature sensor output functions that can monitor critical parameters such as the position and speed of end effectors, thereby improving the system's dynamic response capability and operational accuracy. * Category 2: These components involve complex mechanical processing and transmission technologies that impact the system's dynamic performance and efficiency. In the transmission system, the increase in mechanical energy is achieved through energy conversion and distribution. Figure 5.2 Adaptation function workflow diagram. * Category 3: These components are typically manufactured using a single method, with relatively low material and process selection requirements. They can change mechanical properties through material selection and manufacturing processes. * Category 4: These components are typically standardized and can be quickly installed and replaced, with their characteristics having a relatively minor impact on the system. The classification standards for mechatronic systems outlined above lay the foundation for the precise lightweight of virtual spaces. Adjusting the classification for other domains can enhance the applicability and transferability of DTL-GNN. More general classification methods typically assess the impact of components on system functionality, fundamental stability, and safety, among other aspects. Taking building models as an example, in the application of DTL-GNN, they can be categorized into the following types: structural framework, energy and environment, functional pipelines, decorative experience, and emergency response. ## 4 Application Cases of Mechatronics

In practical applications, digital twin systems for mechatronic models often face issues of model redundancy and a lack of component classification, particularly when dealing with large models that include various mechanical and electrical components, where these problems are especially pronounced. Moreover, the same components may have different levels of importance in different systems, which increases the complexity of model predictions.