A multiweight mesh simplification algorithm for feature preservation in digital twin models

Bin Zhou; Hongda Luo; Jie Lu

doi:10.1117/12.3046550

27 September 2024 A multiweight mesh simplification algorithm for feature preservation in digital twin models

Bin Zhou, Hongda Luo, Jie Lu

Author Affiliations +

Proceedings Volume 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024); 132614P (2024) https://doi.org/10.1117/12.3046550
Event: 10th International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 2024, Wuhan, China

Abstract

The digital twin production line recreates the real production line scene, involving vast amounts of data and resulting in high storage and computation costs, while simplifying the line models reduces these burdens, making storage, computation, and rendering more efficient. However, most simplification algorithms apply a uniform reduction across the model. As a result, feature information may be lost when the production line model, containing regions with multiple features and local features, is simplified. In this study, a multi-weight mesh simplification algorithm is proposed for feature preservation in digital twin models. Initially, spectral clustering is utilized to segment the model into feature regions and assign curvature weights, where the spectral cluster graph is constructed by an adjacency matrix and importing feature decomposition into GPU to reduce the time cost. Subsequently, weights are mapped based on the area and regularity of the facets constituting local features. Finally, these multiple weights formulate an improved cost function for mesh simplification. Experimental results demonstrate that the algorithm achieves a more than tenfold increase in segmentation efficiency and reduces simplification errors by 20.74% and 16.68% compared to the quadric error metric (QEM) and regional hierarchical QEM (RH-QEM) algorithms, respectively, while effectively and uniformly maintaining model details.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Zhou, Hongda Luo, and Jie Lu "A multiweight mesh simplification algorithm for feature preservation in digital twin models", Proc. SPIE 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 132614P (27 September 2024); https://doi.org/10.1117/12.3046550

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KEYWORDS

Image segmentation

3D modeling

Data modeling

Mathematical optimization

Algorithm development

Data processing

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