Gas metal arc welding process monitoring and quality evaluation using neural networks

被引：37

作者：

Wu, CS ^{[1
]}

Polte, T

Rehfeldt, D

机构：

[1] Shandong Univ Technol, Inst Joining Technol, Jinan 250061, Peoples R China

[2] Univ Hannover, Inst Joining Mat, D-30159 Hannover, Germany

来源：

SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | 2000年 / 5卷 / 05期

关键词：

D O I：

10.1179/136217100101538380

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To ensure product quality, it is essential to ensure process quality. Thus, early monitoring and detection of process disturbances in welding production lines are of great significance. The present paper introduces a neural network system for process monitoring and quality evaluation in gas metal arc welding. The system is based only on the measured and statistically processed data for welding voltage and short circuiting time. It is a self-organising feature map Kohonen network which can automatically recognise and classify process disturbances occurring during welding.

引用

页码：324 / 328

页数：5