On-line evaluating on quality of mild steel joints in resistance spot welding  

作　　者：张鹏贤 陈剑虹 

机构地区：[1]Key Laboratory of Non-ferrous Metal Alloys and Processing of Ministry of Education,Lanzhou University of Technology [2]State Key Laboratory of Gansu Advanced Non-ferrous Metal Materials,Lanzhou University of Technology

出　　处：《China Welding》2008年第4期33-38,共6页中国焊接（英文版）

基　　金：supported by National Natural Science Foundation of China (No.50275028)

摘　　要：A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.

关 键 词：resistance spot welding weld quality characteristic parameter quality evaluating Radial Basic Function neural network 

分 类 号：TG441[金属学及工艺—焊接]