可解释的MA岛聚类与指纹：将贝氏体微观组织与成分及加工温度相关联
Interpretable MA-island clusters and fingerprints relating bainite microstructures to composition and processing temperature
AI总结：
本文针对贝氏体微观组织中MA岛几何特征难以关联成分与加工温度的问题，提出对单个MA岛进行深度学习聚类，获得五类可解释形貌聚类，并基于其分布构建微观组织指纹，从而有效建立成分、加工条件与微观组织之间的对应关系。
摘要：
由于组成相的复杂分布，认识成分和加工条件对贝氏体微观组织的影响往往具有挑战性。本工作对含马氏体-奥氏体（MA）岛的非等温转变贝氏体的扫描电子显微照片进行分析，以将微观组织与成分和淬火停止温度相关联。从一开始，通过Kullback-Leibler（KL）散度即可明显看出MA岛的几何特征——即长宽比、多边形面积和致密度——在建立这种关系方面的不足。在特征提取和降维相结合之后对贝氏体微观组织进行聚类，进一步未能实现成分和加工温度的影响识别。与贝氏体微观组织整体分析不同，对单个MA岛的深度学习分析产生了具有特征性不同尺寸和形貌的可解释聚类。这五个聚类，分别称为细枝晶状、粗枝晶状、细多边形状、粗多边形状和细长状，具有极高的可辨识性，可用于描述任意MA岛。基于可解释MA岛聚类的分布来表征贝氏体微观组织，生成了能够充分将成分和加工条件与微观组织相关联的“指纹”。
英文摘要：
Realising the affect of composition and processing condition on bainite microstructures is often challenging, owing to the intricate distribution of the constituent phases. In this work, scanning electron micrographs of non-isothermally transformed bainite, with martensite-austenite (MA) islands, are analysed to relate the microstructures to the composition and quench-stop temperature. The inadequacy of the MA-islands' geometric features, namely aspect ratio, polygon area and compactness, in establishing this relation is made evident from Kullback-Leibler (KL) divergence at the outset. Clustering the bainite microstructures, following a combination of feature extraction and dimensionality reduction, further fails to realise the affect of composition and processing temperature. Deep-learning analysis of the individual MA islands, in contrast to the bainite microstructures, yields interpretable clusters with characteristically distinct size and morphology. These five clusters, referred to as fine- and coarse-dendrite, fine- and coarse-polygon and elongated, are exceptionally discernible and can be adopted to describe any MA island. Characterising the bainite microstructures, based on the distribution of the interpretable MA-island clusters, generates \textit{fingerprints} that sufficiently relates the composition and processing conditions with the microstructures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
