作　　者： ; ; ;

机构地区： 华南理工大学机械与汽车工程学院

出　　处： 《华南理工大学学报（自然科学版）》 2009年第9期88-92,共5页

摘　　要： 为了提高焊缝偏差识别精度,首先对基于旋转电弧传感的焊接电流信号进行小波滤波,预处理后构建样本数据集.然后建立基于支持向量回归机的Laplace特征映射外延算法,对样本数据集和新样本进行维数约简,利用维数约简后的样本数据集训练支持向量回归机,并对新样本进行偏差识别.最后与不进行维数约简而是直接利用支持向量回归机进行偏差识别的方法进行对比试验.结果表明,利用特征映射进行维数约简能使焊缝偏差识别的精度平均提高25%. In order to improve the identification precision of welding seam offset,first,the welding current signals based on the rotational arc sensor are filtered by wavelet,followed by the reconstruction of a sample data set via the pretreatment.Next,an extension algorithm of Laplace feature mapping is proposed based on the support vector regression（SVR） machine,which is applied to the dimensionality reduction of the sample data set and the new sample.Then,the sample data set after the dimensionality reduction is used to train the SVR machine and identify the offset for the new sample. Finally, the proposed identification method is compared with the traditional method without dimensionality reduction. Experimental results indicate that the dimensionality reduction based on Laplace feature mapping may result in an average increase of identification precision by 25 %.

关 键 词： 焊缝 偏差识别 小波滤波 特征映射 外延算法 支持向量回归机

领　　域： [金属学及工艺]