LM6-CastBench：面向 LM6 铝砂型铸件瞬态温度场预测的开源数据集
LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting
做工业仿真或代理模型的朋友可以看看，450 个铸造热场仿真案例开源，U-Net 在这类几何任务上明显比 MLP 强。
研究团队发布 LM6-CastBench，一个用于 LM6 铝合金砂型铸造瞬态热场预测的开源仿真数据集。数据集覆盖 3 类几何家族、每类 10 个变体、5 种浇注温度和 3 种模具温度，共 450 个几何-工艺组合案例。每个案例包含 CAD 几何、非结构网格连接关系、空间描述符、工艺元数据和随时间变化的节点温度。在几何级别数据划分下，U-Net 相比逐点 MLP 大幅降低温度场误差，在具有局部特征和截面突变的几何上提升最明显。
LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting
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.