焊缝偏差RBF神经网络预测算法  

Prediction algorithm of weld seam deviation based on RBF neural network

作　　者：高向东[1] 莫玲[1] 游德勇[1] Katayama Seiji 

机构地区：[1]广东工业大学机电工程学院,广州510006 [2]大阪大学接合科学研究所

出　　处：《焊接学报》2012年第4期1-4,113,共4页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(51175095);广东省自然科学基金资助项目(10251009001000001;9151009001000020);高等学校博士学科点专项科研基金资助项目(20104420110001)

摘　　要：以10 kW大功率光纤激光焊接304奥氏体不锈钢板为试验对象,研究一种焊缝偏差预测算法.利用红外摄像机摄取焊接过程中的熔池红外图像,提取匙孔质心、匙孔形状参数和热堆积效应参数等反映激光束与焊缝位置偏差的特征量作为径向基函数RBF神经网络预测模型的输入量,建立焊缝偏差RBF神经网络预测模型.选择焊缝偏差特征量作为训练样本并对预测模型进行训练,建立焊缝偏差预测模型.结果表明,该模型能够对大功率光纤激光焊接过程中的激光束与焊缝位置之间的偏差进行有效预测.An algorithm was proposed to predict the weld seam deviation in high-power fiber laser（maximal laser power 10kW） welding of type 304 austenitic stainless steel.A high-speed camera was employed to capture the infrared-images of the molten pool in welding process.The eigenvectors such as keyhole centroid,keyhole configuration parameter,heat accumulation effect parameter and so on reflected the deviations between the laser beam and the weld seam position,which were applied as the inputs of a RBF（radial basis function） neural network,and a RBF neural network model was established to predict the weld deviations.The eigenvectors of weld deviations were sampled to train the prediction model,and the established prediction model was tested by the fiber laser welding data.Experimental results showed that the founded model could predict the deviations between the laser beam and the weld seam position during the high-power fiber laser welding.

关 键 词：大功率光纤激光焊 RBF神经网络 焊缝偏差 预测 

分 类 号：TG456.2[金属学及工艺—焊接]