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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.