{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21939",
  "author_total": 7,
  "authors": [
    {
      "name": "Zhou Daniel Hao"
    },
    {
      "name": "Umer Masood Chaudry"
    },
    {
      "name": "Robert Shaw"
    },
    {
      "name": "Andrew Cheney"
    },
    {
      "name": "Nick Ludford"
    },
    {
      "name": "Mark Sims"
    },
    {
      "name": "Hongbiao Dong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0b21wm30k7aqx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21939",
  "fulltext_url": null,
  "id": "0b21wm30k7aqx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21939",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.RO"
    ],
    [
      "arxiv",
      "physics.app-ph"
    ],
    [
      "openalex",
      "2210|Welding Techniques and Residual Stresses"
    ]
  ],
  "openalex": "W7222533644",
  "pdf_url": "https://arxiv.org/pdf/2610.21939",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0b21wm30k7aqx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:50Z",
      "native_id": "2610.21939",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21939"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins",
  "title_text": "De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:50Z",
  "url": "https://research.draconitic.com/paper/0b21wm30k7aqx",
  "venue": "arXiv",
  "versions": []
}