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Rapid simplification of 3D geometry model of mechanisms in the digital twins-driven manufacturing system design.
- Source :
- Journal of Intelligent Manufacturing; Aug2024, Vol. 35 Issue 6, p2765-2786, 22p
- Publication Year :
- 2024
-
Abstract
- With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09565515
- Volume :
- 35
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Journal of Intelligent Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 178293597
- Full Text :
- https://doi.org/10.1007/s10845-023-02178-1