Spectral Characterization For The Distribution Of Nonmetallic Inclusions In Super Large Steels

Non-metallic inclusions play a crucial role in determining the quality of steel,as they directly impact the performance of steel materials.The composition,size,and distribution of these inclusions are key factors in assessing steel products.However,current measurement methods,such as scanning electron microscopy or metallography,only provide information on small sample areas and fail to capture the overall inclusion level of steel or billets.As a result,there is currently no existing quantitative characterization method for the content and distribution of inclusions in the entire region of super large（≥300 mm）steel（billet）.This paper proposes a novel approach called the Generalized Pareto After Normal Distribution analysis method（GPAND）,which aims to provide a comprehensive solution.This study utilizes a method called single spark original position statistical distribution analysis to analyze the entire surface of super-large steel.The aim of the study is to obtain the relative frequency distribution of spectral intensity from the solid solution and inclusion.To achieve this,our employed a composite base function consisting of a normal function and a generalized Pareto function to fit the entire distribution map.This approach allowed them to obtain the spectral intensity relative frequency distribution map of non-metallic inclusions by fitting process.Additionally,scanning electron microscopy with energy dispersive spectroscopy was used to analyze steel samples,providing information on the quantity and size distribution of non-metallic inclusions.The maximum size of non-metallic inclusions was determined using the extremum method.By correlating the size distribution information of non-metallic inclusions with spectral intensity distribution data,the study established the corresponding relationship between inclusion size and spectral intensity.Based on the intensity of inclusions on the whole surface of super-large steel,the content and distribution of various inclusions were accurately determined in this study.Compared to the traditional threshold method,the GPAND method considered the interference caused by the right trailing part of the normal function during the inclusion spectral intensity separation process of non-metallic inclusions.This interference affected the spectral intensity frequency of the non-metallic inclusions.In order to ensure the accuracy of the non-metallic inclusion content results,the GPAND method completely excluded this part of the data.Additionally,the extremum method was introduced,which enhanced the correlation between the maximum inclusion size and the maximum spectral intensity.It established a spectral calibration curve that spans from low to high spectral intensity,addressing the limitations of traditional threshold methods that require extrapolation of the calibration curve.The GPAND method was applied to analyze low alloy steel billets or components in ultra-large bearing steel,high-speed rail wheels,and high-speed rail axle billets.This method enabls a comprehensive and quantitative characterization of the content and distribution of Al₂O₃ and Mn S inclusions throughout the entire surface.By using this method,it becomes possible to improve the quality evaluation system for inclusions in super-large steels such as bearing steel,high-speed train wheels,and axle blanks.Moreover,it contributes to enhancing the safety and reliability of these materials.Additionally,the data obtained through this method can improve preparation processes and help us investigate the relationship between inclusion distribution and performance.