Application of support vector machines to quality monitoring in robotized Arc Welding

被引：2

作者：

Feng, Y ^{[1
]}

Lun, SY ^{[1
]}

Di, L ^{[1
]}

Zong, LY ^{[1
]}

机构：

[1] S China Univ Technol, Dept Mech Engn, Guangzhou 510640, Peoples R China

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

D O I：

10.1109/IJCNN.2002.1007504

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a quality monitoring method by means of Support Vector Machines (SVM) for robotized Gas Metal Arc Welding (GMAW) is introduced. Through the feature extraction of the welding process, a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. The results show that the method can be feasible to identify defects online in welding production.

引用

页码：2321 / 2326

页数：4

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