摘要
针对钢轨闪光对焊的特点,根据GAAS80/580焊机记录的压力、电流和动端位移随时间而变化的曲线,从中提取了10个主要影响接头灰斑面积的特征参数作为BP神经网络预测模型的输入量,建立了钢轨闪光对焊接头的灰斑面积预测模型.采用粒子群算法优化了BP神经网络的权值和阈值,并利用优化后的BP网络模型对接头灰斑面积进行了预测.结果表明,提取的特征参数能较好地反映焊接接头灰斑情况,粒子群算法优化的BP神经网络预测模型能较准确地预测出焊接接头灰斑面积.
According to the characteristic of the rail flash butt welding and the time-varying curve of pressure,current and displacement recorded by GAAS80/580 welding machine,ten weld quality characters which had influence on the grey-spot flaw area in the rail flash butt welded joint,were used as input data of BP network model. The particle swarm algorithm was used to optimize the weight and threshold of BP neural network model. The results showed that the characteristic parameters can well reflect the grey-spot flaw of the welding joint. In addition,the optimized BP neural network model can predict the grey-spot flaw area of welding joint accurately.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2016年第11期11-14,共4页
Transactions of The China Welding Institution
基金
国家自然科学基金资助项目(50775105)
关键词
钢轨闪光对焊
灰斑面积
BP神经网络
预测模型
粒子群算法
rail flash butt welding
grey-spot area
BP neural network
prediction model
particle swarm optimization
