基于NSGA-Ⅱ与RBF神经网络的车身薄板定位布局研究
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摘要:为了解决车身薄板定位布局设计效率低,装夹易变形的问题,提出了一种基于NSGA-Ⅱ与RBF神经网络的车身薄板定位布局设计方法,以薄板定位时的偏差传递路径最小和稳定性最高为约束条件,应用NSGA-Ⅱ算法优化前3个定位点,在有限元样本的支持下分别构建BP和RBF神经网络预测模型并进行对比,选择预测精度较高的RBF神经网络结果作为个体适应度值。分别应用遗传算法和粒子群算法在RBF神经网络中寻优并对比,选择收敛速度较快和求解精度较高的粒子群算法的求解值作为第4个定位点的最优解。以座椅安装横梁作为模型验证研究内容。结果表明,零件在优化后定位布局下的最大装夹变形仅为优化前最大装夹变形的27%。因此,RBF神经网络可以对薄板装夹变形进行有效预测,研究结果对进一步开展车身焊装夹具设计和机身薄壁件定位布局研究具有参考价值。
关键词:车辆工程;车身薄板;定位布局;NSGA-Ⅱ算法;RBF神经网络
中图分类号:TP3016文献标志码:A
WANG Peng, XU Jiachuan, CAO Fan,et al.Research on location and layout of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network[J].Journal of Hebei University of Science and Technology,2019,40(3):189-198.Research on location and layout of auto-body sheet metal
based on NSGA-Ⅱ and RBF neural network
WANG Peng, XU Jiachuan, CAO Fan, LI Di
(School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo,Shandong 255000, China)
Abstract:In order to solve the problem of low efficiency and easy clamping deformation in the location layout design of auto-body sheet metal,alocation layout design method of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network is proposed.With the minimum deviation transfer path and the highest stability as constraints, the first three locating points are optimized by using...
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