Abstract
This study attempted to estimate the maximum size of inclusions in the ultra-low carbon Bake Hardening (BH) steels of automobile exposed panel. The Probable Maximum Sizes (PMS) of inclusions at the different steelmaking stages for BH steel with different sulfur contents were predicted by two methods of Statistics of Extreme Values (SEV) and Particle Size Distribution (PSD). The S content does not show a relationship with the PMS prediction of inclusions in the molten steel in which Al2O3 is the main inclusion, while the higher content of S leads to a larger PMS value in the slab, due to more number of large-sized Al2O3-MnS inclusions formed during solidification. The PMS value in the slab is greater than that in the molten steel for BH steel. Thus, the PMS of inclusions in the slab cannot be estimated from the molten steel samples. The SEV can be used to predict well the PMS values at different steelmaking stages for BH steels. However, the PSD of exponential function cannot predict well the PMS value in the slab for BH steel when considering all kinds of inclusions due to the large influence of small-sized MnS with high number density on the PSD of exponential function. When only considering Al2O3-MnS inclusions, the PSD of exponential function can make a reasonable PMS prediction in the slab, because the size distribution of Al2O3-MnS with large size can follow the exponential function.
Highlights
The requirements on the surface quality of automobile exposed panel become stricter with the increasing demands of advanced automotive users
Since the precipitation temperature of MnS is between the solidus and liquidus temperatures, MnS will precipitate during the solidification of the molten steel
The Probable Maximum Sizes (PMS) of inclusions in Ruhstahl Hausen (RH), tundish and slab were predicted by Statistics of Extreme Values (SEV) and Particle Size Distribution (PSD) methods for ultra-low carbon
Summary
The requirements on the surface quality of automobile exposed panel become stricter with the increasing demands of advanced automotive users. It is well known that large-sized inclusions are the key factors affecting the surface quality of steel products, especially for the ultra-low carbon steel of automobile exposed panel [1,2,3,4,5]. The quantitative characterization on the composition, size, quantity and distribution of large-sized inclusions in steel is necessary to control the surface defect. The large-sized inclusions with relatively low incidence are difficult to be detected by conventional analysis ways. If a method can be used to estimate the maximum size of inclusions in the steel, it will be of great significance to evaluate the property and quality of the steel for automobile exposed panel. The Statistics of Extreme Values (SEV) is an effective method to predict the Probable Maximum
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