基于神经网络代理模型的车身多学科轻量化优化设计

doi:10.3969/j.issn.1674-8484.2025.01.003

摘要/Abstract

摘要：

通过车身零件厚度优化，在保证正碰、侧碰、模态和刚度多个学科关键性能基本不变的前提下，实现车身轻量化设计。采用代理模型法代替仿真与协同优化方法结合开展优化；考虑到碰撞工况的高度非线性特性，选择基于机器学习算法的全连接神经网络（FCNN）来建立代理模型；基于代理模型法获得轻量化方案最终通过仿真验证。结果表明：与传统响应面模型和Kriging模型相比，FCNN模型具有更强的非线性回归和泛化能力；碰撞工况FCNN的预测精度相较于其他2种模型提升约12.5%，R²达到0.9左右；优化前后车身整体性能变化不大，实现减重7.5 kg。

关键词: 汽车轻量化, 多学科优化, 神经网络, 协同优化, 代理模型

Abstract:

The thicknesses of a vehicle body were optimized to achieve lightweight design on the basis of little effect on the key performances in frontal collision, side collision, modal and stiffness conditions. Surrogate model method was used to replace simulation to conduct optimization process combined with collaboration optimization method. Considering high nonlinearity in collision conditions, a fully connected neural network (FCNN) based on machine learning algorithm was established as surrogate model. The lightweight solution obtained from surrogate model was finally validated through simulation. The results show that FCNN model exhibits more powerful nonlinear regression and generalization abilities compared with traditional response surface model and Kriging model. The prediction accuracy of FCNN is higher by about 12.5% than the other two models in collision conditions, and the R² increases to about 0.9. The difference between the overall performances of the vehicle body before and after optimization is insignificant, meanwhile, a weight reduction of 7.5 kg is ultimately achieved.

Key words: automobile lightweight, multidisciplinary design optimization, neural network, collaboration optimization, surrogate model

中图分类号:

荣海, 蒋建中, 姚再起, 马凯, 杜柯南. 基于神经网络代理模型的车身多学科轻量化优化设计[J]. 汽车安全与节能学报, 2025, 16(1): 32-42.

RONG Hai, JIANG Jianzhong, YAO Zaiqi, MA Kai, DU Kenan. Multidisciplinary design optimization of a vehicle body for lightweight based on a neural network surrogate model[J]. Journal of Automotive Safety and Energy, 2025, 16(1): 32-42.

图/表 20

参考文献 26

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模型编号	性能指标	工况	设计变量
#1	I_FRB	FRB	s₁~s₂₉
#2	a_FRB	FRB	s₁~s₂₉
#3	I_MDB1~I_MDB4	MDB	s₃₀~s₆₁
#4	I_RP	RP	s₃₀~s₆₁
#5	M_t and M_b	模态	s₁~s₈₂
#6	S_t and S_b	刚度	s₁~s₈₂

模型编号	性能指标	工况	设计变量
#1	I_FRB	FRB	s₁~s₂₉
#2	a_FRB	FRB	s₁~s₂₉
#3	I_MDB1~I_MDB4	MDB	s₃₀~s₆₁
#4	I_RP	RP	s₃₀~s₆₁
#5	M_t and M_b	模态	s₁~s₈₂
#6	S_t and S_b	刚度	s₁~s₈₂

模型编号	#1	#2	#3	#4	#5	#6
输入层神经元个数	29	29	32	32	82	82
输出层神经元个数	1	1	4	1	2	2
输出层激活函数	线性	线性	线性	线性	线性	线性
隐藏层层数	3	3	4	3	2	2
隐藏层神经元个数	18	21	20	20	15	12
隐藏层激活函数	ReLU	ReLU	ReLU	ReLU	ReLU	ReLU

模型编号	#1	#2	#3	#4	#5	#6
输入层神经元个数	29	29	32	32	82	82
输出层神经元个数	1	1	4	1	2	2
输出层激活函数	线性	线性	线性	线性	线性	线性
隐藏层层数	3	3	4	3	2	2
隐藏层神经元个数	18	21	20	20	15	12
隐藏层激活函数	ReLU	ReLU	ReLU	ReLU	ReLU	ReLU

工况	性能指标	FCNN	RSM	Kriging
FRB	I_FRB	0.92	0.81	0.79
FRB	a_FRB	0.88	0.78	0.79
MDB	I_MDB1~I_MDB4	0.91	0.80	0.82
RP	I_RP	0.91	0.81	0.83
模态	M_t / M_b	0.99/0.94	0.96/0.91	0.95/0.92
刚度	S_t / S_b	0.99/0.98	0.98/0.98	0.97/0.98