No abstract

No abstract

Porosity is one of the most serious defects in Al alloy laser welding. The online detection of the porosity can identify the weak position of weld seam and take remedial measures accordingly. In this paper, a convolutional neural network (CNN) model where the input is the signal spectrum graphs extracted by wavelet packet decomposition (WPD) is constructed to identify the porosity during Al alloy laser welding in real-time. The porosity monitoring platform is set up to obtain the keyhole opening area signal and the keyhole depth signal in the welding process. The sliding window scanning algorithm is used to scan the signals and the weld seam, and the time-frequency spectrum graphs are obtained by WPD processing on the signals in each sliding window. Through analysis, when there is porosity in a small weld seam section of the current sliding window, the signal spectrum graphs are messy at this moment, while when there is no porosity, the signal spectrum graphs are clean and the frequency bands are concentrated in low-frequency part. The CNN model is constructed to classify the signal spectrum graphs under different porosity status, thereby it can identify the porosity in the weld seam.

No abstract

No abstract