SteelBench v1.0: A Benchmark Dataset for Steel Mechanical Property Prediction with Grade-Shift Evaluation

SteelBench is an open benchmark for steel mechanical property prediction that links heat-level chemistry, heat-treatment parameters, and tensile properties with grade identity and data-origin labels. The dataset contains 1,636 samples across 594 steel grades and 17 steel families, aggregated from public and semi-public sources: MMPDS, NIMS, EMK, Kaggle, and laboratory measurements. Two variants are included: steelbench_core.csv — strict publication variant (original reported values only) steelbench_full.csv — filled training variant (missing heat-treatment parameters imputed using documented assumptions) Key features: 11 input features: C, Mn, Si, Cr, Ni, Mo, V, Cu, Al, austenitize_T, temper_T Targets: tensile_strength, yield_strength, elongation Grade-shift evaluation protocols: RandomKFold, GKF-grade, LOFO (Leave-One-Family-Out), LOSO (Leave-One Source-Out) Data-origin (provenance) labels for each filled field Associated paper: "SteelBench: A Physics-Aware Benchmark for Steel Mechanical Property Prediction" (under review at KDD 2026). Reference code: https://github.com/cornada/steelbenchSteelBench是一款面向钢材力学性能预测的开源基准数据集，它关联了炉次级化学成分、热处理工艺参数、拉伸性能指标与钢种标识及数据来源标签。该数据集共包含1636个样本，覆盖594种钢种与17个钢族，数据汇总自公开及半公开渠道：MMPDS、NIMS、EMK、Kaggle，以及实验室实测数据。 本数据集包含两个变体： steelbench_core.csv —— 严格发布版（仅保留原始报告值） steelbench_full.csv —— 完整训练版（缺失的热处理参数基于已文档化的假设完成插补） 核心特征： 11项输入特征：C、Mn、Si、Cr、Ni、Mo、V、Cu、Al、奥氏体化温度（austenitize_T）、回火温度（temper_T） 目标变量：抗拉强度（tensile_strength）、屈服强度（yield_strength）、断后伸长率（elongation） 钢种偏移评估协议：随机K折交叉验证（RandomKFold）、GKF-钢种评估（GKF-grade）、留一钢族法（Leave-One-Family-Out，简称LOFO）、留一数据源法（Leave-One Source-Out，简称LOSO） 为每个插补字段配备数据来源（溯源，provenance）标签 关联论文：《SteelBench：面向钢材力学性能预测的物理感知基准数据集》（目前投稿至KDD 2026并处于审稿阶段） 参考代码：https://github.com/cornada/steelbench