Estimation of the probable maximum size of inclusions using statistics of extreme values and particle size distribution methods

被引:8
作者
Wang, Yong [1 ,2 ]
Bai, Hong [3 ]
Liu, Chengsong [1 ]
Zhang, Hua [1 ]
Ni, Hongwei [1 ]
Jonsson, Par [2 ]
机构
[1] Wuhan Univ Sci & Technol, State Key Lab Refractories & Met, Wuhan 430081, Peoples R China
[2] KTH Royal Inst Technol, Dept Mat Sci & Engn, SE-10044 Stockholm, Sweden
[3] Northeastern Univ, Sch Mat Sci & Engn, Key Lab Anisotropy & Texture Mat, Shenyang 110819, Peoples R China
来源
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T | 2022年 / 20卷
基金
中国国家自然科学基金;
关键词
Electrolytic extraction; Statistics of extreme values; Particle size distribution; Maximum size; GENERALIZED PARETO DISTRIBUTION; FATIGUE-STRENGTH PREDICTION; CLEAN STEELS; QUALITY-CONTROL; SIMULATION;
D O I
10.1016/j.jmrt.2022.07.177
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Prediction of the maximum size of inclusion in a large weight of steel using data from a small volume steel sample is an important topic for steelmakers. Therefore, the probable maximum sizes (PMS) of inclusions in three steel grades were evaluated by the statistics of extreme values (SEV) and the particle size distributions (PSD) methods based on three-dimensional (3D) investigations of inclusions using the electrolytic extraction (EE) method. The effect of number of measurements and size of unit area on the PMS of in-clusions were investigated. The results showed that at least 80 measurements of NMIs should be done in the SEV method, while in the PSD method the number of measurements has little influence when the number of inclusions in the observed areas was large enough. The effect of unit area size on the PMS of inclusions in the SEV method can be ignored for small-sized inclusions (less than 10 mm). The PMS of inclusions determined from the SEV method agreed with that from the PSD method by considering the 95% confidence interval. The SEV method was preferred when predicting the PMS of inclusions because of its simplicity by reducing the cost and labour involved compared to the PSD method.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:2454 / 2465
页数:12
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