基于图网络的汽车零部件应力场快速预测方法研究

doi:10.19562/j.chinasae.qcgc.2024.01.018

Abstract

Abstract:

Finite Element Analysis （FEA）， as an important Computer-aided Engineering （CAE） technology， plays a significant role in the area of automotive part development. However， it costs too much time when solving complicated problems， which affects the development cycle. In this paper， a neural network method is proposed， in which sample data is provided by finite element simulation and the mapping relationship between finite element input and output is established by graph network technology. The graph network method is used to predict the stress field of the seat frame assembly. The prediction method simulates the connection relationship between nodes in the finite element model using graph nodes and graph edges， which can effectively express the topological relationship between elements in the finite element model. The prediction results are compared with the results of the finite element simulation. The results show that the method can precisely predict the maximum stress and its corresponding location of the seat frame assembly， with strong predictive capabilities for stress distribution consistency. Additionally， the model has a significant computational advantage， with a calculation speed three orders of magnitude faster than that of the corresponding finite element solver.

Key words: finite element analysis, deep learning, graph network, automotive seat

Ze Gao, Zunkang Chu, Jiasheng Shi, Fu Lin, Weixiong Rao, Haiyan Yu. Research on Fast Prediction Method of Stress Field of Automotive Parts Based on Graph Network[J].Automotive Engineering, 2024, 46(1): 170-178.

Figures/Tables 22

References 20

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编号	1	2	3	4	5	6	7	8	9	10	11	12
初始厚度/mm	1.4	1.2	2.0	1.8	3.4	1.8	1.4	3.4	4.5	7	2.0	2.0
采样下界/mm	1.0	0.84	1.4	1.26	2.4	1.26	1.0	2.4	3.15	4.9	1.4	1.4
采样上界/mm	1.8	1.56	2.6	2.34	4.4	2.34	1.8	4.4	5.85	9.1	2.6	2.6

硬件/系统/IDE	说明/版本
CPU	Inter（R） 13900K
GPU	Nvidia GeForce RTX 4090
显存	24 GB
CUDA	12.0
Windows	Windows11

传递步数	MSE	RMSE
15	1.100 842	0.277 184
20	0.755 519	0.190 884
25	0.558 019	0.141 125
30	0.543 721	0.140 798

编号	FEM仿真应力/MPa	预测应力/MPa	绝对误差/MPa	相对误差/%
1	437.799 7	431.977 45	5.822 3	1.329 9
2	330.791 7	331.800 72	1.009 0	0.305 0
3	383.638 9	385.096 37	1.457 5	0.379 9
4	476.107 9	477.871 03	1.763 1	0.370 3
5	464.164 3	470.417 97	6.253 7	1.347 3
6	411.926 5	413.097 35	1.170 9	0.284 2
7	355.065 1	354.502 30	0.562 8	0.158 5
8	369.533 5	369.539 15	0.005 7	0.001 5
9	393.871 8	394.924 13	1.052 3	0.267 2
10	412.051 8	413.097 44	1.045 6	0.253 8
平均	403.495 1	404.232 39	2.014 3	0.469 8

验证集编号	FEM预测的最大应力节点id	GNN预测的最大应力节点id	最大应力位置的距离/mm
1	44180	44183	4.079
2	42928	42928	0
3	42928	42928	0
4	44183	44180	4.079
5	44180	44180	0
6	42928	42928	0
7	42928	42928	0
8	42928	42928	0
9	44180	44180	0
10	42928	42928	0

Research on Fast Prediction Method of Stress Field of Automotive Parts Based on Graph Network

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误差/MPa	<1	1~2	2~5	5~10	>10
数量	61 363	1 986	899	55	36
百分比/%	95.37	3.09	1.40	0.08	0.06