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LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting — arXiv:2611.00619

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00619
License (as archived): CC0-1.0
Archived: 2026-11-03T10:42:00.601Z
SHA-256 of the archived text: 23edc134146d6fc330c4738066c847ee29e4ff52e91200d2dc2989228a04e0f9
Stable URL of this page: https://metraipolis.com/documento/dc_fbda4d8d4fff

How to cite

LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting — arXiv:2611.00619 — arXiv (en), https://arxiv.org/abs/2611.00619 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fbda4d8d4fff (Metraipolis archive dc_fbda4d8d4fff, sha256 23edc134146d6fc330c4738066c847ee29e4ff52e91200d2dc2989228a04e0f9)

BibTeX

@misc{dcfbda4d8d4fff,
  title = {LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting — arXiv:2611.00619},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00619},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fbda4d8d4fff; sha256 23edc134146d6fc330c4738066c847ee29e4ff52e91200d2dc2989228a04e0f9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00619. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Transient thermal fields during solidification connect casting process conditions and cooling behaviour to the formation of defect-prone regions in aluminum cast ings. Repeatedly solving this thermal problem across geometries and process conditions is computationally expensive, motivating data-driven surrogate models. However, existing machine-learning studies on casting thermal behaviour rely on study-specific simulation databases that do not support reproducible geometry-level benchmarking. We introduce LM6-CastBench, an open simulation dataset for geometry-aware learning of transient thermal fields in LM6 aluminum sand casting, combining three geometry families, ten variants per family, five pouring tempera tures, and three mould temperatures (450 geometry-process cases in total). Each case retains CAD geometry, unstructured mesh connectivity, spatial descriptors, process metadata, and time-resolved nodal temperatures. We benchmark a point wise MLP against a spatially structured U-Net under geometry-level data splits; the U-Net reduces temperature-field errors by a large margin, with the biggest gains on geometries with localized features and abrupt section changes. LM6-CastBench gives researchers a reproducible dataset and benchmark for geometry-aware surro gate modelling of casting thermal behaviour.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Materials Science (cond-mat.mtrl-sci).
Pagina del lavoro: https://arxiv.org/abs/2611.00619 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:39:50.184Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).