列车车体柔性装配精度预测的力学-数据混合建模方法
Hybrid Mechanistic and Data-driven Modeling Method of Compliant Assembly Variation Prediction for Train Body
王俊玖 1刘金玉 2侯秀娟 1齐振国 1李志敏 3刘涛3
作者信息
- 1. 河北京车轨道交通车辆装备有限公司 保定 072150
- 2. 上海交通大学机械与动力工程学院 上海 200240
- 3. 上海交通大学船舶海洋与建筑学院 上海 200240
- 折叠
摘要
列车车体作为典型的焊接薄壁装配结构,具有刚度低、易变形、受焊接工艺影响显著等特点,使得车体制造精度控制难度大,影响装配质量与生产节拍.车体焊接装配精度预测需要同时考虑随机几何偏差、零部件整体柔性变形和局部焊接变形的综合影响.结合柔性偏差分析和基于数据的焊接变形预测方法,提出一种力学-数据混合柔性偏差建模方法以实现焊接薄壁结构高效统计偏差仿真分析.提取各零件刚度矩阵,采用子结构方法缩减得到结构变形的力学模型;利用有限元获取焊接接头在不同匹配间隙下的局部收缩和角变形数据,构建几何误差与局部变形的BP神经网络(Back propagation neutral network)数据模型;最后,考虑重力、间隙与焊接变形作用,结合上述力学与数据模型建立焊接结构的柔性装配偏差分析模型.基于提出的偏差建模方法开展车体侧墙高度与轮廓度分析,对比实测数据验证方法的有效性.仿真结果表明,力学-数据混合建模方法避免了随机输入偏差下的焊接仿真过程,结合子结构方法能够极大提高统计偏差仿真的计算效率.
Abstract
As a typical welded thin-wall assembly structure,the train body has characteristics of low stiffness,easy deformation,and significant influence by welding process,which makes it difficult to control manufacturing accuracy,and affects the assembly quality and takt time.Assembly variation prediction for train body requires a comprehensive consideration of random geometric deviation,component's global deformation and local welding deformation.Combined with compliant variation analysis theory and data-based welding deformation prediction method,a physical-data hybrid modeling method is proposed to predict the welding assembly dimensions of train body.First,the stiffness matrixes of components are extracted and reduced by using substructure method to construct the physical relation between loads and deformation.Then,a dataset of local welding shrinkage and angular distortion is obtained for typical welding form by using finite element analysis,which is used to construct the mapping relation between match gaps and local deformations with a data model by using BP neural network.Finally,a physical-data hybrid compliant variation analysis model is established for the welding structures with consideration of the effects of global gravity,local gap and welding distortion.The proposed method is validated by comparing predicted the height and profile variation with the actual measurement data.Simulation results show that the physical-data hybrid modeling method avoids the welding simulation process with random input deviations,and can greatly improve the computational efficiency of statistical deviation simulation by combining substructure method.
关键词
列车车体/柔性装配偏差分析/子结构方法/BP神经网络/力学-数据混合模型Key words
train body/assembly variation prediction/substructure method/BP neural network/physical-data hybrid model引用本文复制引用
基金项目
国家自然科学基金(52005334)
河北京车轨道交通车辆装备有限公司项目(2020-FW0055-Cg)
出版年
2024
NETL
NSTL
万方数据