HY-80 Intercritical Metallography

This repository of images was used for a thorough quantitative metallography investigation of the intercritical temperature range for HY-80. It also contains corresponding segmented images between ferrite and other phases (generally austenite/martensite and carbides) developed as a comparison of different methods for performing the investigation. Some images are higher quality than others; however, the results show that a simple, yet effective, machine-learning segmentation model outperformed simple image thresholding techniques. This machine-learning method is very accessible to other researchers without requiring special knowledge on developing and implementing machine-learning and computer vision algorithms that are often utilized for these types of investigations. The results of this investigation were also shown to be effective for adjusting CALPHAD models for predicting phase fractions following industrially relevant heat treatments. The CALPHAD phase fractions were modeled for HY-80 using Thermo-Calc 2022b with the TCFE12 database. DSC testing results are also included that were collected using a TA Instruments Q600 SDT. A sigmoidal baseline was fit and the phase fraction was calculated from the austenite transformation endotherm.

本图像库被用于对HY-80钢的临界温度区间进行深入的定量金相学调查。其中还包含了铁素体与其他相（通常为奥氏体/马氏体和碳化物）之间的对应分割图像，这些图像的开发旨在对比不同方法进行此类调查的效果。图像的质量参差不齐，然而，结果显示，一种简单而有效的机器学习分割模型在性能上优于简单的图像阈值化技术。该方法对于其他研究人员来说易于获取，无需具备开发与实施机器学习和计算机视觉算法的专门知识，这些算法通常用于此类调查。此外，该调查的结果亦被证明对于调整CALPHAD模型以预测工业相关热处理后的相分数具有有效性。利用Thermo-Calc 2022b和TCFE12数据库对HY-80的CALPHAD相分数进行了建模。此外，还包括了使用TA Instruments Q600 SDT收集的热重分析（DSC）测试结果。拟合了S型基线，并从奥氏体转变放热峰中计算了相分数。