Multi-sensing signals diagnosis and CNN-based detection of porosity defect during Al alloys laser welding

Abstract

Porosity is one of the most common defects in laser welding of aluminum (Al) alloys, which seriously affects the welding quality. Online monitoring of porosity defect can provide guidance for the adjustment of process parameters to reduce the occurrence of porosity, which has attracted increasing attention. This paper presents a novel method for recognizing and detecting porosity defect during Al alloys laser welding based on multi-sensing signals diagnosis and deep learning. A multi-sensor platform, including a high-speed camera and a coherent light measurement system, is established to measure the keyhole 3D morphological characteristics. The obtained keyhole depth signal is processed by ensemble empirical mode decomposition (EEMD), and it is found that the change of keyhole depth can be used to recognize the regions where the pores appear. Besides, the sliding window algorithm is used to scan the keyhole opening morphological characteristic signals, followed by the wavelet packet transform (WPT) processing on a small segment of the signals in each window to obtain the time-frequency spectrum graphs. The messy spectrum graphs show that the keyhole opening fluctuates violently, indicating the formation of porosity in the corresponding location of the weld seam. A convolutional neural network (CNN) is constructed to identify the spectrum graphs, realizing the online detection of porosity. The results show that the proposed method has high accuracy and good reliability.