EMD- PNN based welding defects detection using laser-induced plasma electrical signals

The plasma electrical signal has gained extensive attention for characterizing the behavior of the laser-induced plasma due to the advantages of easy acquisition and feedback control. In this paper, the electrical signals were measured by a passive probe based on the principle of plasma sheath effect. To explore the mutation characteristics of plasma electrical signals during defect generation in laser deep penetration welding, wavelet packet transform (WPT) and empirical mode decomposition (EMD) were used to compress data and extract features, respectively. Based on the analysis of the time-frequency spectrum of a typical plasma electrical signal, the approximate coefficients of 0˜390 Hz frequency range were reconstructed. The residual term which characterizes the change trend of electrical signal was obtained by the further adaptive decomposition. For better identifying weld defects, another two statistical features, mean value and standard deviation, were extracted by carrying out statistical analysis in the time domain. The feature database is built with above features and used as inputs of the predictive model based on the probabilistic neural network (PNN). The result showed the average prediction accuracy was as high as 90.16% when recognizing five statuses of weld seam, including sound weld and four kinds of weld defects.