Laser welding processes with high energy density, precision, and efficiency are widely employed in the automotive, aerospace, and medical industries. They have significant advantages in joining materials, such as aluminum alloys, stainless steel, magnesium alloys, and dissimilar metals.
The welding penetration state is one of the most critical indicators for the quantitative evaluation of laser welding quality. The precise identification of the weld penetration state in real-time is a key bottleneck in monitoring and controlling dynamic laser welding processes. The complex physical-chemical metallurgical interaction between the laser beam and the metal workpiece releases intense optical, thermal, and acoustic radiation. The acoustic information is derived from the thermal vibration under a high heat input, and the pressure shock wave generated when the keyhole is internally stressed. Its acoustic characteristics (sound pressure amplitude and frequency characteristics) are closely related to the state of the keyhole. In this paper, we present a new method for quantitatively assessing weld penetration state based on acoustic time-frequency characteristics and deep learning for pulsed laser welding of thin-walled aluminum alloys. This study will contribute to research on the high correlation between acoustic information and weld penetration state in laser welding.
First, as shown in Fig. 1, a visual-acoustic-emission multi-information real-time synchronous sensing system was developed to acquire visual images and acoustic signals reflecting the dynamic behavior of the keyhole, and the acoustic signals were preprocessed using frame splitting and wavelet-packet threshold denoising methods. Second, a smoothed pseudo-Wigner-Ville distribution (SPWVD) was used to extract time-frequency domain images from each frame of acoustic signal, and a gray-level co-occurrence matrix (GLCM) was introduced to extract the time-frequency domain texture features and feed them into the back-propagation neural network (BPNN) for prediction. Finally, a convolutional neural network (CNN)-based weld penetration state classification model was established using the SPWVD acoustic time-frequency map as the original input.
During the preprocessing of the acoustic signal, wavelet-packet threshold denoising effectively filters out some burrs in the signal, and the denoised signal is framed in one pulse period, as shown in Fig 2. Second, the time-frequency maps of the acoustic signals extracted using the SPWVD method exhibit significant differences in the texture features with different penetration states, as shown in Fig 6. Here, the four texture features of the SPWVD time-frequency maps extracted from the GLCM show a clear trend as they change from non penetration to partial penetration and then to full penetration states. Finally, we constructed GLCM-BPNN and SPWVD-CNN classification models and compared the advantages and disadvantages of both classification models. Despite the high correlation between the acoustic time-frequency map and the dynamic behavior of the keyhole and welding penetration, the CNN classification model based on the SPWVD time-frequency map shows a higher accuracy (98.8%) than the traditional BPNN classification model (85%). This indicates that the deep learning model based on the SPWVD time-frequency map as a direct input to the CNN model yields improved recognition results.
(1) The preprocessing method of acoustic signals using frame splitting and wavelet packet thresholding can effectively intercept useful segments and obtain a signal with good denoising results.
(2) The SPWVD method extracts a time-frequency map of the pulsed laser welding acoustic signal. The texture features of SPWVD time-frequency map is highly correlated with the dynamic behavior of the laser-welded keyhole and the weld penetration state.
(3) The SPWVD-CNN deep learning weld penetration state recognition model has a classification accuracy exceeding 98%. The proposed model provides a new approach and technical path for reliable monitoring of the thin-walled aluminum alloys laser welding process.
