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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.