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Prediction of Mechanical Properties of Bionic Gradient Circular Multi-Cell Thin-Walled Tubes Based on Machine Learning
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Qi Chong1, Chao Gong1, 2, Changwen Yan1, Zixuan Yang1, Xucheng Bai1, Yinlong Jia1
Automotive Engineer | 2025, (6) : 1 - 8
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Automotive Engineer | 2025, (6): 1-8
Special Topic on 2024 International Conference of Vehicle Safety and Intelligent Transportation
Prediction of Mechanical Properties of Bionic Gradient Circular Multi-Cell Thin-Walled Tubes Based on Machine Learning
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Qi Chong1, Chao Gong1, 2, Changwen Yan1, Zixuan Yang1, Xucheng Bai1, Yinlong Jia1
Affiliations
  • 1 Hefei University of Technology, Hefei 230009
  • 2 Luzhou Rongda Intelligent Transmission Limited Company, Luzhou 644000
Published: 2025-06-15 doi: 10.20104/j.cnki.1674-6546.20250010
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To rapidly and accurately predict the crashworthiness indicators of thin-walled tubes, a bionic gradient circular multi-cell thin-walled tube axial compression model for predicting the average collision force is established based on the Simplified Super Folding Element (SSFE) theory, and a Long Short-Term Memory (LSTM) network model is built to predict the crashworthiness indicators of the bionic gradient circular multi-cell thin-walled tube under different geometric parameters. The results show that the theoretical prediction error is less than 6% compared with the simulation results, indicating the reliability of the theoretical model. The LSTM network model exhibits an error of less than 2% for Energy Absorption (EA) and Initial Peak Force (IPF) on the validation set, and an error of less than 5% on the test set, demonstrating excellent prediction accuracy and generalization capability.

Crashworthiness  /  Thin-walled tubes  /  Theoretical prediction  /  Machine learning
Qi Chong, Chao Gong, Changwen Yan, Zixuan Yang, Xucheng Bai, Yinlong Jia. Prediction of Mechanical Properties of Bionic Gradient Circular Multi-Cell Thin-Walled Tubes Based on Machine Learning[J]. Automotive Engineer, 2025 , (6) : 1 -8 . DOI: 10.20104/j.cnki.1674-6546.20250010
Year 2025 volume Issue 6
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doi: 10.20104/j.cnki.1674-6546.20250010
  • Online Date:2025-11-10
  • Published:2025-06-15
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  • Revised:2025-01-31
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    1 Hefei University of Technology, Hefei 230009
    2 Luzhou Rongda Intelligent Transmission Limited Company, Luzhou 644000
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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