Research and Implementation of Lightweight Technology for Digital Twin Models of Complex Equipment
Chenhao Zhou, Liang Liang · 2023
As their complexity and size grow, digital twin models need help with performance challenges, including sluggish processing and poor transmission efficiency. In order to do this, this research suggests an efficient edge-folding-based approach for mesh models. By adopting the triangular grid area as the weight and boundary restriction and using the quadratic error metric matrix's prediction and correction model to perform quick calculations and optimize the grid model, this approach considers the local peculiarities of the grid model. According to experimental findings, the algorithm can produce more precise simplified models of complicated equipment and significantly raise the quality of simplified digital twin system models. The study findings presented in this work provide detailed guidance for developing and using the digital twin system and aid in resolving issues related to model data transfer and WebGL based visualization performance.