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

作　　者：Li Di Song Yonglun Ye Feng Mechatronics Engineering Department, South China University of Technology 

出　　处：《Chinese Journal of Mechanical Engineering》2001年第2期106-110,共5页中国机械工程学报（英文版）

基　　金：ProvincialNaturalScience FoundationofGuangdong (No .990 5 5 0 )

摘　　要：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.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.

关 键 词：WELD Quality Defect SOM Neural networks CO 2 arc welding 

分 类 号：TG444[金属学及工艺—焊接]