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PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPP0RT VECTOR MACHINE

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摘要 A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production. A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production.
作者 YeFeng SongYonglun LiDi LaiYizong
机构地区 ColiegeofMechanicalEngineering CollegeofMechatronicEngineering CollegeofMechanicalEngineering
出处 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2003年第4期387-390,共4页 中国机械工程学报（英文版）
基金 National Natural Science Foundation of China (No.59785004) Provincial Natural Science Foundation of Guangdong (No.000376)
关键词 WELDING Quality monitoring Support vector machine Welding Quality monitoring Support vector machine
分类号 TG444 [金属学及工艺—焊接]
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参考文献10

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Chinese Journal of Mechanical Engineering
2003年 第4期

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