In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

Tungsten inert gas (TIG) arc welding of 2219 aluminum alloy was assisted with a trailing periodic ultrasonic vibration, which was output from a trailing roller behind the welding torch. It was found that the weld appearance was periodically convex due to the periodic input of ultrasonic vibration. With the addition of ultrasonic vibration, the columnar grains in the weld zone transformed to equiaxed grains, so along the longitudinal direction, the equiaxed grains and the columnar grains were alternately distributed due to the periodic ultrasonic vibration. The effects of different ultrasonic powers were investigated. The penetration depth and the amount of the melting metal both increased as the ultrasonic power increased. The coarse precipitated phases in the weld zone tended to disperse uniformly under ultrasonic vibration. Compared with conventional TIG welded joints, the hardness of the weld zone of the ultrasonic assisted TIG welding increased by 8.43%, and the tensile strength increased by 29.02%. The ultrasonic cavitation could decrease the nucleation radius and break the dendrites, which led to the grains’ refinement and the final mechanical properties’ improvement.