ON LINE MONITORING OF BURNING THROUGH FOR SHORT CIRCUIT CO_2 ARC WELDING BASED ON THE SELF-ORGANIZE FEATURE MAP NEURAL NETWORKS

A method for automatic detection of burning through of short circuit CO 2 arc welding is presented. It is based on the extraction of arc signal features as well as classification of the obtained features using self organize feature map(SOM) neural networks in order to get the weld quality information, for example, to determine if there is defect in the product. This is important for the on line monitoring of weld quality especially in robotic welding and lay the foundation for the further real time control of weld quality.

基于电弧声波特征的CO_2焊接飞溅预测

在对短路过渡CO2 焊电弧声波信号的时频特征及其与过渡过程、焊接飞溅的相关性分析的基础上 ,利用小波变换的分析方法提取不同频段上的声波能量作为表征飞溅大小的特征向量 ,通过神经网络模型建立特征向量到飞溅量的映射模型 ,从而对CO2 焊接飞溅量的预测。结果表明 ,利用电弧声波信号能够正确地预测焊接飞溅 。