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首页> 外文期刊>日本機械学会論文集. A編 >Joint Stiffness Estimation of Body Structure Using Neural Network (Estimating The Joint Stiffness for The Influence Factors)
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Joint Stiffness Estimation of Body Structure Using Neural Network (Estimating The Joint Stiffness for The Influence Factors)

机译：利用神经网络估算人体结构的联合刚度（估算影响因素的联合刚度）

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摘要

In this paper, an application of hierarchical neural networks to joint stiffness estimation of automobile body structure is described. We deal with two simple joint structures, T-or-L-shape models, which are composed of thin walled box beams combined like capital letters T or L, as typical models of actual body structure. Plate thickness, sectional dimension, length of partition, and position of flange, are considered as influence factors on joint stiffness. The joint stiffness is expressed with a joint stiffness matrix. The influence factors and joint stiffness are used as input and output data for a neural network, respectively. The sample data of some factors vs. joint stiffness are calculated by the finite element method as the training data sets for a neural network. The error- back-propagation neural network is trained using the sample data. Finally, it is found that the neural network after sufficiently trained is useful for estimating the value of joint stiffness.
机译：本文介绍了层次神经网络在汽车车身结构关节刚度估计中的应用。我们处理两个简单的关节结构，即T形或L形模型，它们是由薄壁箱形梁组成的，例如大写字母T或L，是实际人体结构的典型模型。板的厚度，截面尺寸，隔板的长度和法兰的位置被认为是影响接头刚度的因素。关节刚度用关节刚度矩阵表示。影响因子和关节刚度分别用作神经网络的输入和输出数据。通过神经网络的训练数据集，通过有限元方法计算出一些因素与关节刚度之间的关系的样本数据。使用样本数据来训练错误反向传播神经网络。最后，发现经过充分训练的神经网络对于估计关节僵硬度的值很有用。

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