典型热连轧产品成分、工艺、氧化行为数据集

本数据集是“机理模型与生产数据协同的动态数字孪生模型”的核心成果的集成，容量1.5GB，综合地呈现了用于进行氧化行为预测的主要数据。数据生成于2022年11月～2025年10月，主要来源自山东钢铁集团2250mm热轧产线，整合了包括Q235B、Q355B、SPHC、510L等典型钢种在热轧过程中的化学成分、关键工艺，使用电镜类设备检测得到的对应的氧化铁皮显微组织，以及使用的模型库及实验设计。基于上述数据进行人工智能预测建模，实现氧化行为的预测。数据以.xlsx，.docx, .bmp, .pdf等格式进行储存，建立了热轧过程氧化行为预测过程的完整流程，为热轧钢材的氧化行为的预测提供了关键的实验依据。

This dataset is an integration of core achievements from the dynamic digital twin model that collaboratively combines mechanistic models and production data, with a total size of 1.5 GB. It comprehensively showcases the primary datasets used for oxidation behavior prediction. The data was generated between November 2022 and October 2025, primarily sourced from the 2250mm hot rolling production line of Shandong Iron and Steel Group. It incorporates chemical compositions, key processing parameters, corresponding oxide scale microstructures measured via electron microscopy-related equipment, as well as employed model libraries and experimental designs for typical steel grades including Q235B, Q355B, SPHC, 510L during the hot rolling process. AI-based predictive modeling was developed using these datasets to realize accurate oxidation behavior prediction. The datasets are stored in formats such as .xlsx, .docx, .bmp, and .pdf. This work establishes a complete workflow for the oxidation behavior prediction process in hot rolling, providing critical experimental evidence for predicting the oxidation behavior of hot-rolled steel products.

搜集汇总

背景与挑战

背景概述

该数据集是动态数字孪生模型的核心成果，容量为1.5GB，整合了Q235B、Q355B等典型钢种在热轧过程中的成分、工艺及氧化铁皮显微组织数据，用于氧化行为预测建模。数据来源于山东钢铁集团2250mm热轧产线，覆盖2022年11月至2025年10月，以多种格式存储，为热轧钢材氧化行为预测提供了关键实验依据。