JPH045553A - Emission spectrochemical analysis of steel - Google Patents

Abstract

PURPOSE:To rapidly analyze the grain size distribution of inclusions by irradiating a sample with an electron beam to melt a part of the sample and discharging pulses, then using the average of the degree of abnormality of the emission intensities of spectral rays. CONSTITUTION:The polished surface of the sample 3 is irradiated with the electron beam to melt the prescribed part and to form a melted and solidified part 4. Alumina inclusions 5 float on the solidified part 4 and are exposed on the surface. The pulses are then discharged between the solidified part 4 and electrode, and the emission intensities of the iron element and aluminum element of the spectral rays are respectively detected. The detected pulse groups are processed by a regression analysis and the pulses exceeding the upper tangent G are decided as the abnormal value pulses. The degree of abnormality of every abnormal value pulses is obtained. The average grain size of the inclusions is rapidly measured with high accuracy in this way.

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial Field of Application] This invention relates to an emission spectroscopic analysis method for analyzing the components of steel materials, and particularly relates to an emission spectroscopic analysis method for analyzing the components of steel materials, and in particular, for measuring the average particle size of inclusions in steel. Concerning spectroscopic analysis methods.

[Prior Art] The addition of aluminum to steel serves as an important factor in determining the quality of the steel, in addition to its function as a deoxidizing agent. The forms of aluminum in steel are acid-soluble aluminum (hereinafter referred to as Sol, A, 17) dissolved in steel, and acid-insoluble aluminum (hereinafter referred to as In5o1.
There are two forms: All).

In the steelmaking process, the amounts of Sol and A1) are determined for each type of steel, and in actual operations, Sol and A1) are determined based on the analysis results.
, the amount of aluminum added is adjusted so that the amount of Q falls within the standard range. Most of the added aluminum dissolves into Sol and Ag, but some of it becomes In5o1. Become All. In5o1. When A and Q are present in large amounts in steel, alumina inclusions are generated, making the product more likely to have defects such as surface flaws.

Particularly, in steel products such as deep-drawn materials, if a large number of large-grain alumina inclusions are present in the steel, defects are likely to occur, resulting in a decisive quality deterioration. Therefore, it is necessary to accurately grasp the average particle size of alumina inclusions in steel at each stage of the steelmaking process.

Generally, methods for measuring the average particle size of inclusions in steel include sand analysis and microscopy. In the sand analysis method,
The sample is dissolved in acid, inclusions such as alumina in the residue are screened out, and the average particle size of these is measured. However, the Sandoz analysis method requires complicated operations and requires analysis time of 2 to 5 days, making it impractical.

[Problem to be solved by the invention] The microscopy method is specified in JIS standard GO555.

In this method, the sample must be mirror-finished, and it takes one to two days for sample preparation and measurement, making it impossible to obtain analytical results quickly.

In recent years, computer image analysis methods have been developed to speed up analysis, or they are prone to errors due to polishing scratches and dust. Furthermore, computer image analysis methods have drawbacks such as difficulty in determining the type of inclusions.

JP-A-64-70134 describes a method in which inclusions are floated to the surface of the sample by melting a portion of the sample using an electron beam (EB), and the inclusions are quantified by microscopic observation. However, even with this method, the time required for analysis is as long as 2 to 3 hours, and rapid measurement results cannot be obtained.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide an emission spectroscopic analysis method for steel that can quickly and accurately analyze the amount of inclusions in steel.

[Means for Solving the Problems] The method for optical emission spectroscopic analysis of steel according to the present invention melts a part of the sample by irradiating it with an electron beam, floats inclusions contained in the sample to the sample surface, and solidifies the sample. This is then subjected to emission analysis, and the emission intensities of the spectroscopic lines of the iron element and the target element are detected for each pulse discharge, and the detected emission intensities are used to calculate the emission intensity of the iron element and the target element using statistical methods. Determine the correlation between the emission intensities, use this correlation to determine the degree of abnormality for each abnormal value pulse of the element to be analyzed in the sample, and use the average of these degrees of abnormality to determine the average particle size of inclusions containing the element to be analyzed. It is characterized by

[Effect] The sample contains soluble aluminum and insoluble aluminum (
Alumina inclusions) coexist, but the behavior of the two in terms of emission intensity is different. In general, the excitation efficiency of insoluble aluminum is higher than that of soluble aluminum, so the presence of a large amount of insoluble aluminum causes a positive error in the PDA method.

In the steel emission spectroscopic analysis method according to the present invention, an electron beam is irradiated to melt a part of the sample and cause inclusions inside the sample to float to the surface. It becomes possible to analyze. If a pulse discharge is performed after the electron beam irradiation area has solidified, a large number of abnormal value pulses will be detected because there are more inclusions than other surface areas. In these abnormal value pulses, the emission intensity of the element to be analyzed becomes abnormally higher than the emission intensity of the iron element.

The pulse group detected in this manner is processed by a regression method, and the lower tangent line of the pulse group is determined as a correlation equation. Furthermore, this downward tangent correlation equation is transformed using statistical analysis methods,
Find the upper tangent. This upper tangent provides a threshold value for distinguishing abnormal value pulses from other normal value pulses, and a pulse that exceeds the upper tangent is determined to be an abnormal value pulse.

Next, the degree of abnormality is determined for each abnormal value pulse.

The abnormality of the abnormal value pulse is the abnormality of the deviation of the emission intensity value of the analysis target element from the upper tangent (abnormality degree b / a)
is the hail index. The average particle size of inclusions is calculated based on the abnormality degree b / a of all abnormal value pulses (:). Then, these abnormal value pulses are used as reference values.The average particle size of inclusions in the sample is determined by comparing these reference values with the actually measured abnormal value pulses.

[Embodiments] Hereinafter, various embodiments of the present invention will be specifically described with reference to the accompanying drawings.

In this example, samples taken from RH degassing refining molten steel, continuous casting molten steel (raw steel), ladle refining molten steel, and converter-extracted molten steel are analyzed by emission spectroscopy. A predetermined amount of molten steel is sampled, and after solidification, it is cut and polished to create a sample.

As shown in FIG. 1, the polished surface of the sample 3 is irradiated with an electron beam to partially melt a predetermined volume of the sample 3, thereby forming a molten solidified portion 4. As shown in FIG.
In this case, alumina inclusions 5 contained inside sample 3
floats up, and the inclusions 5 are exposed on the surface.

Next, a pulse discharge is generated between the melting solidification part 4 and the electrode for only 5 seconds, and the spectral lines are received by a photomultiplier tube to detect the emission intensities of iron element and aluminum element, respectively. In this case, the frequency of the discharge is 400 Hertz.

The photomultiplier tube of the emission spectrometer is connected to the input side of the data processing device. FIG. 3 shows the relationship between the luminescence intensities of iron element and aluminum element obtained by discharging the sample. In Figure 3, the number of plots is simplified for convenience, or there are actually 200 luminescence plots in one analysis.
It consists of 0 plot groups. The horizontal axis of the screen shows the luminescence intensity of the iron element (the intensity increases from left to right on the screen), and the vertical axis shows the luminescence intensity of the aluminum element (the intensity increases from the bottom to the top of the screen). show.

The CPU of the data processing device has various programs for executing statistical analysis. All the data of the luminescence plots are temporarily stored in the memory section of the data processing device, and the data are analyzed using various statistical analysis techniques.

Next, the detected data is analyzed using various statistical methods,
The procedure for finding a regression line for a group of plots will be explained.

Two-point regression method [I] Find the sum of the emission intensities of iron elements (hereinafter referred to as "iron intensity"), and divide this by the number of pulses (2000) to find the average value Av of the iron intensity.

[n] Exists in a region where the iron strength is equal to or higher than the average value Av, and
Plots of the fifth smallest emission intensity of the aluminum element (hereinafter referred to as "aluminum intensity") are extracted, and the average value FH of these five iron intensities and the average value AH of aluminum intensities are determined, respectively.

[m] Exists in a region where the iron strength is less than the average value AV, and
The fifth plots with the smallest emission intensity of the aluminum element (hereinafter referred to as "aluminum intensity") are extracted, and the average value FL of these five iron intensities and the average value AL of the aluminum intensities are determined, respectively.

[IV] The intersection point P of the average values (FH, AH) representing the upper region and the average value (F L) representing the lower region.

Find the equation of the straight line that passes through the intersection Q of AL) and the two points. This linear equation can be expressed as the following equation (1) as a correlation equation representing the lower tangent of the iron strength and aluminum strength.

(AN) - (Fe)
1 indicates the slope of the straight line on the XY coordinates, and B1 indicates the intercept of the straight line on the XY coordinates.

Iron Column Least Squares Regression Method [I] Find the sum of the emission intensities of iron elements (hereinafter referred to as "iron intensity"), and divide this by the number of pulses (2000) to find the average value Av of the iron intensity.

[■] Divide the average value AV by 10 to find the column width.

The plot group existing in the region where the iron strength is below the average value Av is equally divided into 10 columns L, ~LIO.

[I[[] Iron strength average value FVI and aluminum strength average value A of the n-th plots from the lowest aluminum strength among the plots existing in the first column L1
Find VI.

[IV] For the second column L2 to the tenth column LIO, the iron strength average values FV2 to FV are determined by the same procedure, respectively.
VI O and aluminum strength average value AV2 ~ AVI
Find O.

[Vl Average value representing each column (FVl.

10 intersection points corresponding to AVI) to (FVIO, AVIO) are subjected to linear regression using the method of least squares to obtain a straight line equation. This linear equation can be expressed as the following equation (2) as a correlation equation representing the lower tangent line of iron strength and aluminum strength.

(A I ) = (F e ) X A 2 + 8
2...(2) However, A2 indicates the slope of the straight line on the XYY coordinate, and B2 indicates the intercept of the straight line on the XY locus.

Regression convergence correlation coefficient determination method [I] From the correlation diagram, iron strength and aluminum strength are subjected to linear regression using the least squares method to obtain the following equation (3).

(AN)-(Fe)XA3 +B3-(3
) However, A3 is the slope of the straight line on the XYY standard, B3
indicate the intercept of a straight line on the XYY reference plane, respectively. In this case, the correlation coefficient representing the degree of dispersion of the plot group is small.

[II] Discard the plot groups that exist in the area above the straight line corresponding to equation (3) above, perform linear regression on the iron strength and aluminum strength using the least squares method for the plot groups that exist in the area below the straight line, and calculate the following: Find equation (4).

This increases the correlation coefficient.

(A,Q)-(Fe)XA4 +84 ・-(
4) T, (Aρ) is aluminum strength, (F e)
is the steel strength, A4 is the slope of the straight line on the XYY elevation, B4
indicate the intercept of a straight line on the XYY reference plane, respectively.

[m] The correlation coefficient is increased by repeating the linear regression and upward rejection operations as described above, and when the correlation coefficient exceeds a predetermined value, the repeated calculation is stopped and the regression equation at that time is determined. The final regression equation is shown in equation (5) below.

(AN) = (Fe) ) Find the formula.

(Aff) - (Fe)XA6 +B6 -
(6) However, A6 is the slope of the straight line on the XYY standard,
B6 indicates the intercept of a straight line on the XYY standard.

In this case, the correlation coefficient representing the degree of dispersion of the plot group is small.

[■C] Discard the plot groups that exist in the area above the straight line corresponding to the above equation (6), perform linear regression on the iron strength and aluminum strength using the least squares method for the plot groups that exist in the area below the straight line, and calculate the following Find equation (7). This increases the correlation coefficient.

(AII)-(Fe)XA, +B7-= (7)
However, A7 represents the slope of the straight line on the XYY standard, and B7 represents the intercept of the straight line on the XYY standard.

[III] As described above, the correlation coefficient is increased by repeating the linear regression and upward rejection operations, and when the correlation coefficient exceeds a predetermined value, the repeated calculation is stopped and the regression line at that time is determined. The distance d from this provisional regression line to each plot (remaining plot) is determined, and its standard deviation σ is determined using the following equation (8). However, N is the number of residual plots.

σ6- Σd2/ (N-1) ... (8)
[IV] Plots that exceed twice the standard deviation σ are rejected as abnormal values, or plots that are 10% of the distance d from the provisional regression line, whichever is farther, are rejected. After rejecting abnormal values, linear regression is performed again using the least squares method to obtain a regression line. This final regression line is expressed by the following equation (9).

(AN) = (Fe)XAQ 189 ・-(9
), A9 indicates the slope of the final regression line on the XYY elevation, and B9 indicates the intercept of the final regression line on the XYY elevation.

Regression convergence pulse (plot) bedding method [1] From the correlation diagram, iron strength and aluminum strength are linearly regressed by the least squares method to obtain the following equation (10).

(AΩ)”” (F e)XA+o+ Boo ・-
(10) However, AHO represents the slope of the straight line on the XYY standard, and BIO represents the intercept of the straight line on the XYY standard.

[II] Discard the plot groups that exist in the area above the straight line corresponding to the above equation (10), linearly regress the iron strength and aluminum strength using the least squares method for the plot groups that exist in the area below the straight line, and calculate the following: Find equation (11).

(Ag) ”” (F e) xAll+EllI
・+・(11) where A11 indicates the slope of the straight line on the XY coordinates, and Bll indicates the intercept of the straight line on the XY coordinates.

[ml As mentioned above, reduce the number of residual plots by repeating the operations of linear regression and upward rejection, and when the number of plots becomes less than a predetermined number (for example, 100), stop the repeated calculations and calculate the regression line at that time. demand. This final regression line is expressed by the following equation (12).

(Aff) = (F e) XAl2+B12
=-(12) However, A12 indicates the slope of the final regression line on the XY coordinates, and B and □ indicate the intercepts of the final regression line on the XY coordinates, respectively.

Regression Convergence Pulse (Plot) Bedding Constant Abnormal Pulse Rejection Method [I] From the correlation diagram, the iron strength and aluminum strength are linearly regressed by the least squares method to obtain the following equation (13).

(Ag)-(Fe)XA13+B13-(1B)
However, A13 is the slope of the straight line on the XY coordinates, B1
3 indicates the intercept of a straight line on the XY coordinates.

[I[] Discard the plot groups existing in the area above the straight line corresponding to the above equation (13), linearly regress the iron strength and aluminum strength using the least squares method for the plot groups existing in the area below the straight line, The following equation (14) is obtained.

(Ag)=' (F e)XA14+B14 '''
(14) However, A14 indicates the slope of the straight line on the XY coordinates, and B14 indicates the intercept of the straight line on the XY coordinates.

[I [[] As described above, the number of residual plots is reduced by repeating the linear regression and upward rejection operations, and when the number of plots becomes less than a predetermined number (for example, 100), the iterative calculation is stopped and the Find a provisional regression line. The distance d from the provisional regression line to each residual plot is determined, and the standard deviation σ4 is determined using the above equation (8).

[IV] Plots that exceed twice the standard deviation σ are rejected as abnormal values. After rejecting outliers, linear regression is performed on the remaining plot groups using the least squares method to obtain a final regression line. This final regression line is expressed by the following equation (15).

(A, Q) −(F e) ×A+5+ B+s
-(15) where A15 indicates the slope of the final regression line on the XY coordinates, and B15 indicates the intercept of the final regression line on the XY coordinates.

Determine the regression line (lower tangent) of the plot group using one of the six methods described above.

Next, the above regression line (hereinafter, general formula (AN) - (Fe)
A case will be described in which the average particle size of alumina inclusions is determined by the following method using xA+B).

Example 1 (Average [I] by abnormal value pulse abnormality degree determination method
The threshold value of the abnormal value pulse existing in the upper region is determined by either method (1) or (2) below.

■ Find the equation (Aff)=(F e)xAxN+B.

However, N is a constant.

The above equation is shown by straight line G in FIG. The degree of abnormality b/a is determined for each abnormal value pulse, and each abnormal value pulse is ranked based on the degree of abnormality b/a. Abnormality level b/a
is determined by the following equation (16).

b/a-Σ'+-+[(A, C') f(Pe)XA
xN+Bl]/(Fe) (16) However, l is assumed to be added only to the abnormal value pulses existing in the region above the straight line G.

■ Find the equation (AN)-(Fe)XAXN+C.

However, N is a constant, C-FHXA10B.

The above equation is shown as straight line H in FIG. The degree of abnormality b/a is determined for each abnormal value pulse, and each abnormal value pulse is ranked based on the degree of abnormality b/a. Abnormality level b/a
is determined by the following equation (17).

b/a-Σ'+-+[(Ail)-1(Fe)x A
x N+ C1]/(Fe) (17) where i is assumed to add only the abnormal value pulses existing in the region above the straight line H.

[II] Find the sum of the abnormality degrees b/a and divide this by the number of abnormal value pulses to find the average value. This average value of the degree of abnormality is compared with a reference value (a value determined in advance using a standard sample in which the average particle size of inclusions is known) to determine the average particle size of alumina inclusions in the sample.

According to the method using the above abnormal value pulse abnormality degree determination method, the time required from the start to the end of analysis is approximately 30 seconds (2
(in the case of multiple analysis), and the analysis can be completed in about one hour, including the sample preparation (electron beam melting) time, and can fully meet the demand for speeding up the measurement of the average particle size of alumina inclusions.

In Figure 5, the horizontal axis shows the average particle size of inclusions measured by the sand analysis method, and the vertical axis shows the average particle size of inclusions measured by the method of the present invention, and the correlation between the two measurement results is investigated. FIG. In the figure, white circles show the results of RH degassed molten steel samples, black circles show the results of continuous cast molten steel samples, white triangles show the results of ladle refined molten steel samples, and black triangles show the results of molten steel samples drawn from the converter. The results for each sample are shown below. As is clear from the figure, the two measurement results show good agreement for various types of molten steel.

In Figure 6, the horizontal axis shows the average particle size of inclusions measured by microscopy, and the vertical axis shows the average particle size of inclusions measured by the method of the present invention, and the correlation between the two measurement results was investigated. It is a graph diagram. In the figure, white circles show the results of the RH degassed molten steel sample, black circles show the results of the continuous cast molten steel sample,
The white triangles show the results for samples of ladle-refined molten steel, and the black triangles show the results for samples of molten steel extracted from a converter. As is clear from the figure, the two measurement results show good agreement for various types of molten steel.

[Effects of the Invention] According to the present invention, the average particle size of inclusions in steel can be measured with high accuracy. Further, according to the present invention, the time required from the start to the end of analysis can be shortened, and analysis results can be obtained more quickly than with conventional methods. Therefore, when refining clean steel such as deep-drawn steel, it is particularly effective in quality control, and can greatly contribute to improving the quality of steel products.

[Brief explanation of the drawing]

Figure 1 is a perspective view showing a sample irradiated with an electron beam, Figure 2 is a longitudinal section showing a portion melted and solidified by electron beam irradiation, and Figures 3 and 4 schematically show a group of light emission pulses. , FIG. 5 and FIG. 6 are graphs for explaining the present invention in detail, respectively. Figure 1 Figure 2 Figure 3 Applicant's agent Patent attorney Takehiko Suzue Figure 4

Claims (1)

[Claims] A part of the sample is melted by irradiation with an electron beam, and the inclusions contained in the sample float to the surface of the sample. After solidification, this is subjected to luminescence analysis, and each pulse discharge produces spectroscopic spectral lines of the iron element and the element to be analyzed. The detected luminescence intensities are used to find the correlation between the luminescence intensity of iron element and the luminescence intensity of the target element, and from this correlation, abnormal values of the target element in the sample are determined. A method for optical emission spectrometry analysis of steel, characterized in that the degree of abnormality is determined for each pulse, and the average particle size of inclusions containing the element to be analyzed is determined using the average of these degrees of abnormality.