重载大跨距横梁弯曲变形分析与补偿

针对重载大跨距横梁的弯曲变形问题，将有限元数值计算和BP神经网络相结合，提出横梁弯曲变形预测方法，通过预制补偿曲线辅助进行横梁弯曲补偿，提高横梁几何精度。首先，利用ANSYS分析软件获得溜板位于横梁一系列工作位置的变形量，作为神经网络的训练样本；其次，通过在Matlab中编程调整网络参数，建立了满足误差要求的BP神经网络模型，并进行训练，利用训练后的神经网络预测横梁变形曲形；最后，对预制补偿曲线的横梁进行弯曲变形测量，实验表明神经网络预测值与实验数据较吻合，相对误差<15%，并且运行时间只需0.27 s。研究结果表明，该方法能够较为准确地预测横梁弯曲变形并进行补偿，为重载大跨距横梁结构设计与预制补偿曲线提供了新的思路和技术支持。

Bending Deformation Analysis and Compensation of Heavy Load and Long Span Crossbeam

In order to deal with the bending deformation of long span and heavy load crossbeams, a method is proposed to improve the geometric precision of the crossbeam, which combines FEA numerical computation and BP neural networks to predict the bending deformation curve of the crossbeam and prefabricate the compensation curve. The slide carriage is located at a series of working states, and the deformation is calculated using ANSYS software in order to obtain the training samples. By adjusting the appropriate parameters in Matlab software, the BP neural network is established to satisfy the error requirement. The deformation and compensation curve of the crossbeam are predicted through the trained neural network and the crossbeam is thus manufactured according to the compensation curve. The results of the bending deformation measurement demonstrate that the predicted values of the neural network match well with the experimental results, in which the relative error is less than 15% and the computing time is just 0.27 seconds. This method can accurately predict the bending deformation of the crossbeam and carry on compensation. Moreover, it provides new ideas for research as well as technical guidance for improving the crossbeam structure design and obtaining a perfect arch in advance.