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To address the issues of low efficiency and low qualification rates in mesh generation for plate and shell structures, this paper proposes an intelligent finite element meshing technology based on deep learning. First, typical features of plate and shell structures are classified, and meshing strategies are developed for each feature type. Second, a feature recognition model is trained using convolutional neural networks to automatically invoke the corresponding strategies for meshing in feature regions. Finally, geometry cleanup and mesh optimization are performed for non-feature regions. Verified by the white body of a passenger car, this method increases the automatic meshing qualification rate from 82.1% to 92.6% and reduces total working hours by 66.7% compared with the mainstream batchmesh approach, significantly improving both mesh quality and efficiency. By combining AI models with predefined strategies, this technology minimizes manual intervention and provides an intelligent solution for meshing in plate and shell structures.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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