文献类型：期刊文献

英文题名：Intelligent Detection to Laser Welding Seam Defect Using Adaptive Joint Wavelet Dictionary Learning of PEC Signal

作者：He, Kuanfang[1];Yong, Jiangfeng[1];Zhou, Rongyu[1];Liang, Jiahe[1];Yang, Jingwei[1];Jiang, Mian[1];Shi, Wenqing[2];Qiao, Jiangwei[3];Lu, Qingbo[3]

机构：[1]Foshan Univ, Sch Mechatron Engn & Automat, 33 Guangyun Rd, Foshan 528200, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, 1 Haidu Rd, Zhanjiang 524088, Guangdong, Peoples R China;[3]Guangdong Jinggong Steel Struct Co Ltd, 19 5000 1 Jinxiang Rd, Southwest St, Foshan 528000, Guangdong, Peoples R China

年份：2023

卷号：51

期号：6

外文期刊名：JOURNAL OF TESTING AND EVALUATION

收录：SCI-EXPANDED(收录号：WOS:001012291400001)、、EI(收录号：20232614309896)、Scopus(收录号：2-s2.0-85162856942)、WOS

基金：ACKNOWLEDGMENTS This work is supported by the Guangdong Basic and Applied Basic Research Foundation (2022A1515140068) , the National Natural Science Foundation of China (51475159,62073089) , and the Guangdong innovation team (2020KCXTD015, 2022KCXTD029) , which are all gratefully acknowledged.

语种：英文

外文关键词：laser welding seam defect; pulsed eddy current; wavelet dictionary; feature parameters; support; vector machine; intelligent detection

外文摘要：The pulsed eddy current (PEC) is an effective method for the online detection of laser welding seam defects. The joint wavelet dictionary learning method is proposed for solving the separation problem of the broad frequency harmonic and local non-smooth distortion of the PEC signal. The Haar and Gabor wavelet is adopted to be the basic function, which is extended to be the over complete wavelet dictionary library by cyclic migration. The sparse representation of the defect PEC signal is obtained by combining the joint wavelet dictionary with the orthogonal matching pursuit algorithm. The feature parameters of the PEC signal are calculated and inputted into the support vector machine to detect the laser welding seam defect intelligently. The validity of the proposed method is further verified by the experimental results, demonstrating the effectiveness of the classification identification and quantitative assessment of the pore and crack.