摘要
采用了人工神经网络技术对继电器制造中铜线在磷铜片上的点焊进行了质量监测。利用BP(back propagation)神经网络模型及其算法,建立以焊接电流和电极间电压作为输入参量、焊点拉剪强度作为输出参量的神经网络质量监测模型。在Matlab中对不同隐层节点和转移函数的模型进行仿真,选择合适的隐层节点数和转移函数。在逆变电阻点焊机上进行了试验验证,表明所建立的人工神经网络质量监测模型的精度能满足工程应用的要求。
The spot welding quality between copper wire and phosphor-copper sheet in relay manufacturing was monitored by using artificial neural networks(ANN) technology.Based on BP(back-propagation) ANN model and algorithm,the model took welding voltage and current as input parameters,shear load of welded joint as output parameter.Models with different hidden layer node number and transfer function were simulated in Matlab so as to select appropriate hidden layer node number and transfer function.Finally the model was tested on inverted resistance spot welder.The results show that the quality monitor model's precision by ANN estimation method is able to satisfy engineering application demand.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2006年第9期47-50,54,共5页
Transactions of The China Welding Institution
基金
广东省科技攻关项目(2005B10201008)
教育部留学回国人员基金资助项目
关键词
人工神经网络
电阻点焊
智能监测
焊点质量
artificial neural networks
resistance spot welding
quality monitor
welding spot quality
