Rozman Kyle A, Doğan Ömer N, Chinn Richard, Jablonksi Paul D, Detrois Martin, Gao Michael C
National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321, USA.
NETL Support Contractor, 1450 Queen Ave SW, Albany, OR 97321, USA.
Data Brief. 2022 Oct 29;45:108714. doi: 10.1016/j.dib.2022.108714. eCollection 2022 Dec.
The microstructure of steel greatly influences the mechanical properties. For 9 wt% Cr steels, which are widely used in the power generation industry, the steels have a ferritic and martensitic microstructure which can be altered by heat treating and chemical composition variations. Fully martensitic steels typically having high yield strengths but low ductility. Tempering can reduce the amount of martensite in the steel lowering the yield strength but increasing the ductility of the alloy. Alloying can alter the time required for a martensitic transformation. In authors' previously published research, the authors used machine learning methodology to predict room temperature tensile properties from scanning electron microscopy (SEM) images of the initial steel microstructures from a wide range of steel compositions. This data-in-brief supplies the raw image files and the associated tensile properties for the authors' previously published research utilized to predict tensile properties of steels [1].
钢的微观结构对其力学性能有很大影响。对于在发电行业广泛使用的9 wt% Cr钢,其具有铁素体和马氏体微观结构,可通过热处理和化学成分变化来改变。完全马氏体钢通常具有高屈服强度但低延展性。回火可减少钢中的马氏体含量,降低屈服强度,但增加合金的延展性。合金化可以改变马氏体转变所需的时间。在作者之前发表的研究中,作者使用机器学习方法,根据来自多种钢成分的初始钢微观结构的扫描电子显微镜(SEM)图像来预测室温拉伸性能。本简要数据提供了作者之前发表的用于预测钢拉伸性能的研究所使用的原始图像文件及相关拉伸性能[1]。