光谱学与光谱分析, 2020, 40 (6): 1710, 网络出版: 2020-12-07  

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

Study of Inclusion Characterization Method in IF Steel Based on the Spark Source Original Position Statistic Distribution Analytical Technology
李冬玲 1,2,*赵雷 1,2沈学静 2,3王海舟 2,3
作者单位
1 钢研纳克检测技术股份有限公司, 北京 100081
2 金属材料表征北京市重点实验室, 北京 100081
3 钢铁研究总院, 北京 100081
摘要
IF钢被广泛应用于汽车及家电面板领域, 对表面质量要求非常严格, 夹杂物的存在对IF钢冷轧板的表面质量和性能具有显著影响, 因此必须对铸坯表面进行清理。 由于生产工艺的差别, 开浇过程钢水夹杂物未充分上浮, 以及连铸机升速过程中液面波动引起的保护渣被卷入等诸多原因导致了IF钢表层夹杂物数量、 种类和尺寸分布的不确定性, 因此系统研究IF钢铸坯中夹杂物分布的定量表征方法, 对于探寻IF钢表层夹杂物分布规律, 确定合适的铸坯表层清理深度, 实现对结晶器内夹杂物的控制具有重要意义。 金属原位统计分布分析技术通过对经无预燃、 连续扫描激发的火花放电所产生的元素光谱信号进行高速数据采集和解析, 可实现大尺寸金属样品中夹杂物含量和尺寸的快速定量分布分析, 将火花源-原位统计分布分析技术与扫描电镜能谱分析相结合, 研究了IF钢中夹杂物的异常放电行为, 制备了与待测试样匹配的IF钢粒度分布参考物质, 探讨了IF钢中Al的异常光谱信号与氧化物夹杂粒度分布的相关性, 发现夹杂物组成元素异常信号净强度与夹杂物粒径的二元线性回归方程具有良好的线性相关性, 并由此建立了基于火花源原位统计分布分析技术的IF钢夹杂物组成、 含量和粒度分布表征方法。 研究了IF钢外弧处皮下0~3 mm处的夹杂物含量、 组成和粒度分布的变化规律, 发现IF钢中的氧化物夹杂主要由两类组成: 一种是脱氧产物, 主要为单一的氧化铝, 另一种是卷渣夹杂物, 主要为Al, Ca和Si的复合夹杂物, 靠近表层的0.5和1.0 mm处的夹杂物含量较低, 皮下1.5~2.5 mm处夹杂物含量相对较高, 而且存在较多的Al和Ca的复合夹杂物, 平均粒径也较大, 但至皮下3.0mm处夹杂物含量和粒径都有所下降。 该表征方法的建立对于改进IF钢生产工艺具有重要指导作用。
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.

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(6): 1710. 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[J]. Spectroscopy and Spectral Analysis, 2020, 40(6): 1710.

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