Study of Inclusion Characterization Method in IF Steel Based on the Spark Source Original Position Statistic Distribution Analytical Technology
LI Dong-ling1, 2, ZHAO Lei1, 2, SHEN Xue-jing2, 3, WANG Hai-zhou2, 3
1. The NCS Testing Technology Co., Ltd.，Beijing 100081, China
2. Beijing Key Laboratory of Metallic Materials Characterization, Beijing 100081, China
3. Central Iron & Steel Research Institute, Beijing 100081, China
Abstract：IF steel has been widely used in the field of automobile and appliance panel with the strict demand for surface quality. The existence of inclusion will greatly affect the surface quality and the performance of cold rolled sheet of IF steel. It is necessary for the IF slab to get rid of the surface layer contained a lot of inclusions. Because of the different manufacturing technology, there is a lot of uncertainty about the quantity, composition, and size distribution of inclusions in the surface of IF steel which is influenced by somereasons, such as insufficientfloating of the inclusions under the process of cast starting and slag involvement by pool level fluctuation under the acceleration process of the continuous casting machine.It is very important for the discovery of inclusion distribution rulein different depth beneath the surface of IF steel slab, identification of suitable cutting thickness in the slab surface and the inclusion control in a crystallizer to study the inclusion distribution characterization method in detail.Metal original position statistical distribution analytical technique can be used for the determination of inclusion content and size distribution within a large scale of the section by the high-speed data acquisition and analysis of spectrum signals excited by spark discharge with the mode of no pre-spark and continuous excitation on the scanning process. In this paper, the abnormal discharge behavior of inclusions in IF steel has been investigated and the suitable reference material of particle size distribution for IF steel was developed. The relationship of the abnormal spectrum signals produced by Al element with the size distribution of oxide inclusion was also discussed based on the spark source original position statistic distribution analytical technique combined with scanning electron microscope and energy dispersive spectrum. It was found that the linear correlation coefficient of the binary linear regression equation between the net intensity of the abnormal signal of inclusion components and the particle size of inclusion was good with the value above 0.99. So the inclusion characterization method of composition, content and size distribution in IF steel based on the spark source original position statistic distribution analytical technology has been developed. The variation rule of inclusion composition, content and size distributionin the depth of 0~3 mm beneath the surface of IF steel outer arc has been studied.It was found that the inclusion in IF steel consisted of two kinds of inclusions. One is the single inclusionof aluminum oxide produced in the deoxidization process. The other is the complex inclusion of AL, CA and Si produced by the slab involvement.The inclusion content in the depth of 0.5 and 1.0 mm beneath the surface was lower than the content from the depth of 1.5 to 2.5 mm beneath the surface. There were more complex inclusion of Al and Ca with a larger average particle size existed indepth from 1.5 to 2.5 mm beneath the surface, and the particle size decreased when the depth beneath the surface increase to 3 mm. It is of great importance for the technicalguidance of IF steelmanufacturing.
Key words：Inclusion; Spark source original position statistic distribution analytical technique; IF steel; Size distribution; Slab surface
李冬玲，赵 雷，沈学静，王海舟. 基于火花源原位统计分布分析技术的IF钢夹杂物表征方法研究[J]. 光谱学与光谱分析, 2020, 40(06): 1710-1715.
LI Dong-ling, ZHAO Lei, SHEN Xue-jing, WANG Hai-zhou. Study of Inclusion Characterization Method in IF Steel Based on the Spark Source Original Position Statistic Distribution Analytical Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1710-1715.
[1] LIU Jun-shan, ZHANG Hua, NI Hong-wei, et al(刘俊山，张 华，倪红卫，等). Special Steel（特殊钢），2018，39（4）：51.
[2] ZHAO Cheng-lin, TANG Fu-ping, ZHU Xiao-lei，et al(赵成林，唐复平，朱晓雷，等). Iron and Steel(钢铁)，2017，52（12）：42.
[3] YUAN Peng, SONG Li-na, LI Hai-bo，et al(苑 鹏，宋丽娜，李海波，等). Journal of Iron and Steel Research(钢铁研究学报)，2016，28（12）：27.
[4] TANG Ping, LI Yu-lin, JIA Cheng-cheng，et al(唐 萍，李玉林，贾成橙，等). Steelmaking(炼钢)，2016，32（4）：73.
[5] TIAN En-hua, CUI Heng, YOU Da-li，et al(田恩华，崔 衡，尤大利，等). Steelmaking(炼钢)，2014，30（1）：59.
[6] XIAO Peng-cheng, ZHAO Mao-guo, LIU Zeng-xun，et al(肖鹏程，赵茂国，刘增勋，等). Steelmaking(炼钢)，2018，34（2）：67.
[7] Hsu C, Chung H. Advanced Materials Research, 2014，939: 11.
[8] Zhang Qiaoying, Wang Litao, Wang Xinhua, et al. Journal of Iron and Steel Research International，2018, 15(1): 70.
[9] LI Dong-ling，LU Yu-hua，JIN Cheng，et al(李冬玲，卢毓华，金 呈，等). Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019, 39(1): 14.
[10] Li Dongling, Wang Haizhou. ISIJ International, 2014, 54(1): 160.
[11] ZHANG Ting-ting(张婷婷). Metallurgical Analysis（冶金分析）, 2017, 37(7): 6.
[12] LUO Qian-hua, LI Dong-ling, FAN Ying-ze, et al(罗倩华，李冬玲，范英泽，等). Metallurgical Analysis（冶金分析）, 2015, 35(10): 1.
