薄壁结构(TWS)广泛应用于工程设备中,在应用过程中可能会受到冲击载荷而产生不同程度的结构损伤。然而,确定这些结构损伤的冲击载荷条件是一个困难的问题。在这项研究中,我们开发了一种识别薄壁结构损伤的冲击载荷条件的新方法,基于粒子群优化-反向传播(PSO-BP)神经网络。首先,将已知的冲击位置和速度应用于TWS的有限元模型(FEM)以产生永久塑性变形,并且需要通过调用多元多项式函数来拟合变形的特征形状。然后,该方法用于构建基本数据集。以冲击位置和速度为输入,函数系数为输出,建立了扩展PSO-BP神经网络模型。此外,扩展了基本样本集,解决了样本不足的问题。最终,利用扩展的总样本集作为训练数据,函数系数,将输出冲击位置和速度。根据已知的变形表面的功能系数,建立了预测PSO-BP神经网络模型并进行了预测。此外,我们以相同的方式使用传统的BP神经网络预测碰撞位置和速度。最后,将预测的撞击位置和速度与有限元分析结果进行比较,验证了PSO-BP神经网络算法具有较高的精度。
Thin-walled structures (TWS) were widely used in engineering equipment, and may be subjected to impact loads to produce different degrees of structural damage during application. However, it is a difficult problem to determine the impact load conditions for these structural damages. In this study, we developed a novel method of
identifying the impact load condition of the thin-walled structure damage, which is based on particle swarm optimization-backpropagation (PSO-BP) neural network. First, the known impact position and velocity are applied to the finite element model (FEM) of the TWS to produce permanent plastic deformation, and to fit the characteristic shape of the deformation is needed by invoking the multivariate polynomial function. Then, the method is devoted to build a basic data set. With impact position and velocity as input and function coefficients as output, a model of extended PSO-BP neural network is established. Besides, the basic sample set is expanded to solve the lack of samples. Ultimately, utilizing the expanded total sample set as training data, function coefficients, impact position and velocity will be outputted. On the basis of the known functional coefficients of deformed surfaces, a model of predictive PSO-BP neural network is established and predicted. Furthermore, we predicted the collision position and velocity using a conventional BP neural network in the same way. Finally, the predicted impact position and velocity is compared with the analysis results of the FEM, which verifies that the PSO-BP neural network algorithm has high accuracy.