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论文arXiv 2610.2193913 天前

通过物理基础数字孪生降低空间金属连接机器人焊接风险 ​

原文链接:De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins(arXiv 2610.21939 · 发布日期:2026-10-15)

De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins

面向在轨金属连接焊接表征不足,提出物理基础数字孪生原型,并在交叉验证、边界案例和仿真机械臂焊缝中评估。

核心要点 ​

  • 在轨服务、制造与装配需要可靠金属连接,但低重力、低压力下机器人焊接特性仍不充分。
  • 原型结合有限体积热焊池模型、GP与神经网络代理模型,以及仿真六轴机械臂。
  • 五折交叉验证中,GP在八项输出上误差低于神经网络;边界案例74/80落入90%区间。
  • 仿真L形焊缝研究支持在实验前筛选焊接条件和控制策略,但仍需进一步硬件相关研究。

验证范围与局限:验证范围为模型评估和仿真UR5 L形焊缝研究,仍需热真空、目标化低重力和机器人硬件在环研究。

英文摘要 (Abstract) ​

Growing interest in in-space servicing, manufacturing, and assembly has renewed attention on reliable metal joining for modular construction and on-orbit repair. Robotic welding is promising, but reduced-gravity and low-pressure operation remains poorly characterised and flight-like tests are limited. The UK Space Agency-supported ISPARK project is developing a physics-grounded digital twin for structured process de-risking. This paper evaluates a model-based prototype combining a finite-volume thermal weld-pool model, Gaussian-process (GP) and neural-network surrogates, and a simulated six-axis manipulator. The reduced model includes solid-liquid phase change, effective thermocapillary transport, and a local-pressure parameter. Gravity is assessed separately through a hydrostatic-to-capillary pressure ratio and does not enter the thermal energy equation. Across repeated five-fold cross-validation, the GP has lower normalised mean absolute error than the neural network for all eight outputs. In 10 held-out boundary cases, 74 of 80 solver responses fall within nominal 90% GP predictive intervals. A three-output GP query takes 249 microseconds, including scaling and predictive variance. In a simulated UR5 L-shaped-seam study, a proportional controller adjusts the synergic power-source setting as travel speed falls near the corner. Finite-volume re-evaluation finds all 12 closed-loop operating points and 9 of 12 open-loop points within an illustrative 1.0-3.0 mm root-side-width band. These simulation results support using the framework to screen welding conditions and control strategies before experimental tests. Thermal-vacuum, targeted reduced-gravity, and robotic hardware-in-the-loop studies are needed to assess the model and controller.

作者:Zhou Hao, Umer Masood Chaudry, Robert Shaw, Andrew Cheney, Nick Ludford, Mark Sims, Hongbiao Dong

来源

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  1. arXiv 2610.21939:De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins访问 2026-10-21