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Prediction of Inclusion State in Molten Steel by Morphology and Appearance of Inclusions in Liquid Steel Samples

机译:液钢样品中夹杂物形态及外观预测钢水中的预测

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摘要

> Inclusions are unwanted but to some extent inevitable in molten and solid steel. Usually solid inclusions are considered to be the most harmful. Inclusions can be converted into a less detrimental form with calcium treatment. The success of calcium treatment can be evaluated by analyzing the state of the inclusion population. The state of inclusions is usually determined by computational thermodynamics making use of the chemical composition of inclusions and system conditions. In this process, liquid and solid inclusions are usually distinguished. Herein, a classification procedure which combines computational thermodynamics and data‐driven reasoning is presented. The objective of this work is to study the predictability of the inclusion state based on its appearance and morphological properties. As a result, Al 2 O 3 –CaO–MgO–CaS inclusions are classified as liquid and solid ones based on their aspect ratio, equivalent circle diameter, and mean gray value with a recall of 82.7% and precision of 84.9%, by making use of a logistic regression‐based classifier.
机译: <第XML:ID =“SRIN201900424-SEC-0001”> > 夹杂物不受欢迎,但在一定程度上是不可避免的熔融和固体钢。通常,固体夹杂物被认为是最有害的。夹杂物可以转化为钙处理的不利形式。可以通过分析包涵体的状态来评估钙处理的成功。夹杂物状态通常由计算热力学来利用夹杂物和系统条件的化学成分来确定。在该过程中,通常区分液体和固体夹杂物。这里,呈现了组合计算热力学和数据驱动推理的分类过程。本作作品的目的是基于其外观和形态学研究包涵体的可预测性。结果,al 2 O. 3 -CaO-MgO-CAS夹杂物基于它们的纵横比,当量圆直径和平均灰色值归类为液体和固体,并且通过利用基于逻辑回归的分类器,召回的召回量为82.7%和精度为84.9% 。

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