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Double-electrode gas metal arc welding (DE-GMAW) modifies conventional gas metal arc welding (GMAW) by adding a second electrode, allowing part of the current to flow directly from the wire back to the power supply. This configuration reduces the current flowing to the workpiece compared to that at the wire, and this reduction is freely controllable. This unique ability to separately control mass and heat input is particularly advantageous for applications requiring flexible heat management, such as additive manufacturing. In this innovative process, the positioning of the bypass electrode relative to the wire tip is critical for maintaining a stable arc and optimal metal transfer; however, designing an effective positioning rule can be tedious and challenging. A general solution is human-robot collaboration (HRC), which enables humans to directly operate robots and serves as real-time optimizers that can quickly develop effective rules through a few trials. Additionally, HRC allows for learning from human operation data to fully automate these rules. In this work, we designed a dual-robot HRC system that enables operators to make stable, real-time adjustments to electrode positions with ease. The HRC system incorporates a virtual reality (VR) environment, providing immersive, real-time process visualization to assist operators in accurately and safely perceiving the welding state. Efficient teleoperation of DE-GMAW is achieved by integrating high-quality camera visuals and precise robotic execution into a VR environment, eliminating hazards associated with on-site manual welding, such as welding fumes, arc radiation, and electric shock, while enhancing observation and operational accuracy. Experiments were conducted to evaluate the system's capability to support fast and precise human adjustments, demonstrating the effectiveness of the proposed system in implementing DE-GMAW. Furthermore, full automation provides a pathway for transitioning DE-GMAW into manufacturing applications. 

Double-Electrode Gas Metal Arc Welding (DE-GMAW) improves traditional GMAW by adding a non-consumable tungsten electrode, creating a bypass loop that decouples heat input and deposition rate. The bypass arc, critical for establishing the bypass loop, is affected by the bypass electrode position in both horizontal and vertical directions. However, the impact of the bypass electrode positioning has not been studied. This work focuses on monitoring human operations in DE-GMAW within a human-robot collaboration (HRC) setting, aiming to understand the process. Initially, the impact of bypass electrode position on arc morphology and metal transfer was studied, revealing the diversity of the process and the importance of precise electrode positioning. Subsequently, a convolutional neural network was trained using augmented data to accurately detect essential positional information from welding images, thereby determining the optimal operational positioning during human operation. Finally, the relationship between bypass arc voltage and position was quantified using Gaussian Process Regression (GPR), showing that this signal can effectively reflect the process state. This study advances the understanding of DE-GMAW and human operational intelligence, laying a foundational basis for automating the process. 

Gas Metal Arc Welding (GMAW) is a critical industrial technique known for its high productivity, flexibility, and adaptability to automation. Despite the significant advancements in robotic welding, challenges remain in fully automating the arc welding process, particularly due to the complex dynamics of the weld pool associated with GMAW. A human-robot collaborative (HRC) system where humans operate robots may conveniently provide the needed adaptive control to the complex GMAW. While in conventional HRC systems humans receive process feedback to make adaptive adjustments, we propose provide humans with predictive future feedback to further ease the human decision and reduce the needed skills/trainings. To this end, this study explores the integration of deep learning models, specifically Generative Adversarial Networks (GANs) combined with Gated Recurrent Units (GRUs), to model and predict the dynamic behavior of the weld pool during GMAW. By leveraging time-series data of torch movement and corresponding weld pool images, the proposed GRU-GAN model generates high-fidelity weld pool images, capturing the intricate relationship between speed variations and weld pool morphology. Through extensive experimentation, including the design of an acceptable Encoder-Decoder structure for the GAN, we demonstrate that incorporating both temporal and speed sequence information significantly enhances the model's predictive capabilities. The findings validate the hypothesis that dynamic torch speed adjustments, akin to those performed by skilled human welders, can be effectively modeled to improve the quality of automated welding processes. Future work will be devoted to human-based model predictive control (MPC) in an HRC environment. 

This study aims to extract critical scenes/continents in the weld pool region during gas metal arc welding (GMAW). The scenes considered include the wire, arc, and weld pool, while other secondary ones such as oxides are temporarily excluded. They are critical to understanding, analyzing, monitoring and controlling the welding process, in particular the critical correlation how the welding parameter, arc and weld pool are dynamically correlated. Unfortunately, such fundamental correlation has not been studied and lack of effective ways to simultaneously monitor/extract these scenes is responsible. With the development of optoelectronic devices, weld pool regions can be better imaged. However, because of the nature of the scenes in particular the arc which is formed by ionized gas without a clear boundary and highly dynamic, detecting them using computer vision is challenging. Deep learning is an effective method, but model training usually needs a large number of labels. As manually labeling is expensive, we propose an approach to address this challenge that can train a model from a small, inaccurately labeled dataset. This approach is designed, per the characteristics of the scenes and their dynamics All-in-One Network (AOD-Net) was deployed first for defogging, and then the YOLOX network was utilized to identify regions of interest to reduce the impact of molten metal splashes on image sharpness. Subsequently, we developed a timed segmentation network incorporating the Long Short-Term Memory (LSTM) mechanism into U-Net, which can be used to extract more accurate information about the weld pool by combining the temporal and spatial information in the continuous process of welding at a low cost because our scene of interest is in a continuous and dynamic evolutionary process. After defogging and removing the effects of molten metal spatter, we can obtain information on the dynamics of the weld pool and the weld arc at the same time. Experimental results verified that the trained network could extract the critical boundaries accurately under various welding conditions despite the highly dynamic changes and fuzziness of the views. 

We report the observation of an electronic reconstruction in dimensionally controlled ruthenate heterostructures synthesized by pulsed laser deposition. High structural and electronic quality of superlattices comprised of a single SrRuO3 layer inter-spaced with varying thicknesses of insulating SrTiO3 layers was verified by reflection high energy electron diffraction, atomic force microscopy, x-ray diffraction, reciprocal space mapping, and x-ray absorption spectroscopy. X-ray absorption spectroscopy evidences a confinement-driven evolution of the Ru electronic configuration from the d5L to the d4 state. Significant increases of the spin-orbit coupling are observed in connection with the configuration changes supporting recent works identifying large enhancement of the magnetic anisotropy. The growth of high quality two-dimensional confined ruthenate layers under precisely controlled environments highlights the potential to directly manipulate interlayer coupling and selectively perturb the electronic state in ruthenates in analogy to superconducting Sr2RuO4.