Digital Twin-Driven TD3 Reinforcement Learning for Welding Path Optimization and Deformation Control of Large Mechanical Components

To tackle both welding deformation in large mechanical components and the poor dynamic adaptability of traditional open-loop path planning, this study proposed a digital twin-driven Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning framework for online collaborative welding path optimization and deformation control. The framework integrated data from multiple sensing sources to build a high-dimensional state space and continuous 3D action space for welding parameters. A multi-objective reward function was designed, and the framework employed a finite, element–experiment hybrid training mechanism together with the TD3 algorithm to reduce the simulation-to-reality gap and mitigate action-value overestimation. Experiments on Q345 steel box girders showed that the framework reduced maximum angular deformation to 1.82 mm/m, increased the weld qualification rate to 96.2%, and improved both energy consumption and invalid path ratio. The system also demonstrated strong robustness under disturbances, while multi-field state fusion accelerated strategy convergence and enabled closed-loop intelligent welding for high-precision manufacturing.