基于遗传算法优化BP神经网络的TIG焊缝尺寸预测模型

建立了4-12-4结构的误差反向传播(BP)神经网络.以训练样本预测误差作为适应度函数，采用具有全局寻优功能的遗传算法得到最优化的BP神经网络的权值和阀值.以TIG焊接工艺参数电弧长度、保护气流量、焊接电流和焊接速度作为网络输入，焊缝的上余高、下余高、上焊宽和下焊宽作为网络的输出，优化后的BP网络模型具有良好的泛化能力和预测能力.

Prediction Model of TIG Welding Seam Size Based on BP Neural Network Optimized by Genetic Algorithm

A BP neural network of 4-12-4 was established in this paper. Taking the prediction error of training data as fitness function, the genetic algorithm with a global optimization ability was used to search for the optimal initial weights and thresholds of the BP neural network. The input parameters of the BP model consist of arc gap, flow rate, welding current and welding speed of TIG welding, while the outputs of the model include welding seam sizes, that is, the front height, front width, back height and back width. The optimized BP network model shows good generalization and prediction ability, and the prediction precision is improved significantly compared with the BP model without optimization.