Monitoring and controlling of weld joint penetration are essential issues in variable polarity plasma arc welding (VPPAW). In this paper, we develop a flexible visual sensor system to measure the backside keyhole characteristic parameters such as keyhole length, width and area. Further, data analysis from dynamic welding experiments reveals a nonlinear correlation of the keyhole features and the backside beam width. To provide accurate feedback information, an extreme learning machine (ELM) method is applied to predict the weld width with sufficient accuracy and less computing time. Then a novel model-free adaptive control (MFAC) is designed to control the weld penetration evaluated by the weld width. Closed-loop experimental results confirm that the MFAC system can simultaneously adjust the welding current and plasma gas flow rate to control the VPPAW process for obtaining a full-penetrated weld under various initial welding conditions and disturbances.

Accurate characterization of keyhole pool behavior is important to improve the penetration recognition and quality detection during the variable polarity plasma arc welding (VPPAW) of aluminum alloy. However, the low-level hand-crafted visual/acoustic features are often incapable of sufficiently representing the dynamic characteristics of keyhole pool under complex welding conditions. In this paper, we developed an end-to-end visual-acoustic penetration recognition (VAPR) framework based on a hybrid convolutional neural network (CNN) and extreme learning machine (ELM), which consists of three consecutive phases: (1) visual-acoustic data preparation, (2) multi-features extraction, and (3) penetration classification. Specifically, we applied a flexible dual-sensor acquisition system for synchronously collecting/preprocessing the visual-acoustic signals and exploring the internal correlation between the visual-acoustic features and keyhole pool behavior as well as weld penetration status. Then we established two individual CNNs for learning high-level visual-acoustic features directly from the raw visual images and 2-D time-frequency spectrograms. With the visualization of CNN-based deep learning features, we also explained the physical meanings of visual-acoustic features. To further improve the prediction performance of CNN model, we employed the ELM model as a strong tool to classify penetration status. By comparing with other state-of-the-art methods, our hybrid CNN-ELM approach with visual-acoustic fusion has a superior performance in terms of the classifying accuracy. The VAPR framework in this paper will provide some guidance for post-process quality inspection and realize an adaptive control of VPPAW process.

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.

With the synergistic effect of laser and arc heat sources, laser-arc hybrid welding (LAHW) technology can improve welding speed and penetration depth, and enhance gap-bridging ability. This review describes the fundamental concepts and characteristics of droplet transfer behavior in LAHW. Emphasis was placed on the physical interaction between the laser and arc and the effect of the combined laser/arc heat sources on the welding process. However, the physical understanding of these multivariable and complex interactions is still evolving. Through numerous research findings and summary, it is found that there are several critical factors, including the laser-to-arc distance, heat source leading mode, shielding gas composition, and laser power, affecting the droplet transfer characteristics. This review critically interprets the latest development in the basic understanding of LAHW. It lays great stress on the coupling effect of laser and arc in droplet transfer dynamic process of LAHW, and offers a direction for the future study and progress of LAHW. Significant fields for future research are also confirmed.