Defects diagnosis in laser brazing using near-infrared signals based on empirical mode decomposition

Real-time monitoring of laser welding plays a very important role in the modern automated production and online defects diagnosis is necessary to be implemented. In this study, the status of laser brazing was monitored in real time using an infrared photoelectric sensor. Four kinds of braze seams (including healthy weld, unfilled weld, hole weld and rough surface weld) along with corresponding near-infrared signals were obtained. Further, a new method called Empirical Mode Decomposition (EMD) was proposed to analyze the near-infrared signals. The results showed that the EMD method had a good performance in eliminating the noise on the near-infrared signals. And then, the correlation coefficient was developed for selecting the Intrinsic Mode Function (IMF) more sensitive to the weld defects. A more accurate signal was reconstructed with the selected IMF components. Simultaneously, the spectrum of selected IMF components was solved using fast Fourier transform, and the frequency characteristics were clearly revealed. The frequency energy of different frequency bands was computed to diagnose the defects. There was a significant difference in four types of weld defects. This approach has been proved to be an effective and efficient method for monitoring laser brazing defects.