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Ye Feng
College of Mechanical Engineering,
South China University of Technology,
Guangzhou 510640, China
Song Yonglun
College of Mechatronic
Engineering,
Beijing University of
Technology,
Beijing 100022, China
Li Di
Lai Yizong
College of Mechanical Engineering,
South China University of Technology,
Guangzhou 510640, China
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PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON
SUPPORT VECTOR MACHINE*
Abstract:
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 signal, a SVM
classifier is constructed to establish the relationship between
the feature of process parameters and the quality of weld
penetration. Under the samples obtained from auto parts welding
production line, the learning machine with a radial basis
function kernel shows good performance. And this method can be
feasible to identify defect online in welding production.
Key words: Welding Quality monitoring Support vector machine |