JPH045551A - Emission spectrochemical analysis of steel - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention relates to an emission spectroscopic analysis method for analyzing the components of steel materials, and in particular to an emission spectroscopic analysis of steel for quantitatively analyzing inclusions in steel. Regarding the method.

[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, AN) that is dissolved in steel, and acid-insoluble aluminum (hereinafter referred to as In5o1.Aj), which is an alumina-based compound.
There are two forms: J).

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 l1 falls within the standard range. Most of the added aluminum becomes solid solution and becomes Sol, AI, but some of it becomes In5o1. Becomes Al1. In5o1. When AN is present in large amounts in steel, alumina inclusions are formed, making the product more likely to have defects such as surface scratches. Therefore, it is necessary to accurately grasp the amount of alumina inclusions in steel at each stage of the steelmaking process.

Generally, methods for quantifying inclusions in steel include sand analysis and microscopy. In the sand analysis method, a sample is dissolved in acid and the amount of alumina in the residue is determined.

[Problems to be Solved by the Invention] However, the sand analysis method requires complicated operations, and furthermore, the time required for analysis is 2 to 5 days, making it impractical.

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, but they are prone to errors due to polishing scratches and adhesion of 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.

1987-3989 (233) describes a method for estimating the number of alumina inclusions with a particle size of 10 μm or more by emission spectroscopic analysis. However, this method is limited to alumina inclusions with a particle size of 10 μm or more, and cannot determine particles with other particle sizes.

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. The correlation between the emission intensities is determined, the state quantity of the abnormal value pulse of the target element in the sample is determined from this correlation, and the content rate of inclusions containing the target element is determined using the state quantity of the abnormal value pulse. Characterized by seeking.

In this case, it is preferable to determine the number of abnormal value pulses of the aluminum element as the state quantity of the abnormal value pulses, and to determine the content rate of aluminum oxide inclusions based on the number of abnormal value pulses.

Further, it is preferable to obtain the sum of the degrees of abnormality of the abnormal value pulses of the aluminum element as the state quantity of the abnormal value pulses, and to determine the content rate of aluminum oxide inclusions based on the sum of the degrees of abnormality of the abnormal value pulses.

[The action sample contains soluble aluminum and insoluble aluminum (
(alumina inclusions) coexist, or the two exhibit different behavior in luminescence intensity. In general, the excitation efficiency of insoluble aluminum is higher than that of soluble aluminum, so the presence of insoluble aluminum causes a positive error in the PDA method.

In the optical emission spectroscopic analysis method according to the present invention, a part of the sample is melted by irradiation with an electron beam, and inclusions inside the sample are floated to the surface. It becomes possible to perform luminescence analysis.

If a pulse discharge is performed after the electron beam irradiation area has solidified, there will be more inclusions than other surface areas, so
A large number of outlier pulses are detected. 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 state quantity of the abnormal value pulse is grasped. The state quantity of the abnormal value pulses may be the number of abnormal value pulses, or may be the sum of the degrees of abnormality of the abnormal value pulses. The number of abnormal value pulses is determined by using a formula that is the slope of the lower tangent multiplied by 0 (or a formula that multiplies the intercept by m) as the upper tangent, and using this upper tangent as a threshold value, pulses that exceed this are used as the upper tangent. Find it by counting. In addition, the degree of abnormality of the abnormal value pulse is
Abnormality in how the aluminum strength value deviates from the upper tangent (
The abnormality degree b/a) is determined, and the insoluble aluminum correction amount is determined as a quadratic function of the number of abnormal value plots or the abnormality degree b/a. This quadratic function is determined in advance using a standard sample with a known amount of insoluble aluminum. The content of alumina inclusions is determined from the sum of these abnormalities (indexes).

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

In this example, continuous casting molten steel (element tM) and a sample in the pot during refining are analyzed by emission spectroscopy. Samples were taken from inside the continuous casting tandash and the inside of the pot during refining.
After solidification, it is cut and polished to prepare a sample.

As shown in FIG. 1, the polished surface of the sample 3 is irradiated with an electron beam for a predetermined period of time to partially melt a predetermined volume of the sample 3, thereby forming a molten solidified portion 4.

As shown in FIG. 2, in the melting and solidifying section 4, the sample 3
The alumina inclusions 5 contained inside float 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 pulsed earth 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, but in reality, there are 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 (intensity increases from left to right on the screen), and the vertical axis shows the luminescence intensity of aluminum element (intensity increases from the bottom of the screen to the top). 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 procedure for finding a regression line for a plot group will be explained.

Two-point regression method [I] Obtain 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 obtain the average value AV of the iron intensity.

[II] Extract the fifth plot from the one where the iron intensity exists in the region above the average value AV and the emission intensity of the aluminum element (hereinafter referred to as "aluminum intensity") is small, and calculate the plots of these five iron The average value FH of strength and the average value AH of aluminum strength are determined respectively.

[I [I] Extract the plots that exist in the region where the iron intensity is less than the average value AV and the luminescence intensity of the aluminum element (hereinafter referred to as "aluminum intensity") is the lowest, and extract these five plots. The average value FL of the iron strength and the average value AL of the aluminum strength 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)XA, +Bl −=(1
) However, (1) is the aluminum strength, (Fe) is the iron strength, A is the slope of the straight line at the XYY elevation, and B is the XYY
The intercepts of the straight lines on the Y mark are shown.

Iron Column Least Squares Regression Method [I]] The sum of the emission intensities of the elements (hereinafter referred to as "iron intensity") is determined, and this is divided by the number of pulses (2000) to determine 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 L1 to L1°.

[m]] Iron strength average value FVI and aluminum strength average value AVI of the nth plots from the lowest aluminum strength among the plots existing in 1 column L1
seek.

rlV] For M2 column L2 to 10th column LIO, the iron strength average values FV2 to F are obtained by the same procedure, respectively.
VIO and aluminum strength average value AV2 ~ AVIO
seek.

[V] Average value (FVI) representing each cikaram.

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.

(AN) - (Fe) x A2 + 82 ・ (2) However, A2 is the slope of the straight line on the XYY standard, and B2 is the XYY
Each shows the intercept of a straight line at the elevation.

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).

(Afri - (Fe)XA3 +83 - (3) where A is the slope of the straight line on the
Each shows the intercept of a straight line at the elevation. In this case,
The correlation coefficient representing the degree of dispersion of the plot group is small.

[I[] Discard the plot groups existing in the area above the straight line corresponding to the above equation (3), 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, Find the following equation (4).

This increases the correlation coefficient.

(All) - (Fe) x A4 + B4 - (4) However,
(Ail) is aluminum strength, (Fe) is iron strength, A
4 indicates the slope of the straight line on the XY locus, and B4 indicates the intercept of the straight line on the XYY coordinate.

[ml As described above, the correlation coefficient is increased by repeating the operations of linear regression and upward rejection, 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.

CAD ) = (Fe) Find the formula.

(A, Q) - (Fe) x A6 +86 -16) However, A6 is the slope of the straight line on the XYY altitude, and B6 is
The intercepts of the straight lines on the YY mark are shown. In this case, the correlation coefficient representing the degree of dispersion of the plot group is small.

[I[] 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, Find the following equation (7).

This increases the correlation coefficient.

(Ajl') - (F e)XA7 +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.

[ml As described above, the correlation coefficient is increased by repeating the operations of linear regression and upward rejection, 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 σ6 is determined using the following equation (8). However, N is the number of residual plots.

σ, -Σd2/ (N-1) ... (8) [
IV] Plots with a standard deviation exceeding twice the standard deviation σ6 are rejected as abnormal values, or plots with a distance d from the provisional regression line that is 10% from the farthest 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).

(AX')" (Fe)XAl 1Be −(9
) However, A indicates the slope of the final regression line on the XYY elevation, and B 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).

(AfI)-(Fe)XAIO+B,. -(10) However, Alo is the slope of the straight line on the XY seat, B, O
indicate the intercept of a straight line on the XYY reference plane, respectively.

[■]] Discard the plot groups that exist in the area above the straight line corresponding to equation 10) 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 (11).

(Ail)-(Fe)XA1+B14-(11
) However, A11 is the slope of the straight line on the XY seat, and B
ll indicates the intercept of a straight line on the XYY standard.

[■] Reduce the number of residual plots by repeating the linear regression and upward rejection operations as described above. When the number of plots becomes less than a predetermined number (for example, 100), the iterative calculation is stopped and the regression line at that time is seek. This final regression line is expressed by the following equation (12).

(/l)-(Fe)xA, □+B,, □-(12)
However, A12 indicates the slope of the final regression line on the XYY elevation, and B12 indicates the intercept of the final regression line on the XYY elevation.

Regression convergence pulse (plot) bedding constant abnormal pulse rejection method [11 From the correlation diagram, iron strength and aluminum strength are linearly regressed by the least squares method to obtain the following equation (13).

(Aff) - (Fe)XA13+ BI3 -
(13) However, ABB indicates the slope of the straight line on the XY locus, and B13 indicates the intercept of the straight line on the XYY base.

[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.

(Aj!)-(F e)XA14+B14-(1
4) r However, A and 4 indicate the slope of the straight line on the XYY standard, and B14 indicates the intercept of the straight line on the XYY standard, respectively.

[m]] 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 provisional regression at that time is performed. Find the line. The distance d from the provisional regression line to each residual plot is determined, and the standard deviation σ 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).

(Aj))=(F e)xA,,+B,,-(15)
However, A15 indicates the slope of the final regression line on the XYY coordinate, and BI5 indicates the intercept of the final regression line on the XY locus.

Determine the regression line (lower tangent line) of the plot group using one of the six methods mentioned above, create a total aluminum calibration curve based on these regression lines, and determine the total aluminum content in the sample from the calibration curve. .

Next, the above regression line (hereinafter, general formula (AN) - (Fe)
A case will be described in which alumina inclusions are quantified using one of the following methods using xA+B).

Example 1' (abnormal value pulse (plot) enemy side standard method) [,
I] Find the number of abnormal value pulses existing in the upper region using either method (a) or (b) below.

(a) Calculate the number of plots that satisfy the inequality (AN)>(F e)XAXN+B. However, N is a constant.

The region that satisfies this inequality is the region above the straight line G in FIG.

(b) Calculate the number of plots that satisfy the inequality (All) > (F e) XAXN0C. However, N is a constant, C
-FHXA+B.

The region that satisfies this inequality is the region above straight line H in FIG.

[11] The amount of alumina inclusions is determined as a quadratic function f (n) of the number n of abnormal value pulses determined in (a) or (b) above. In this case, the quadratic function f (n) is determined in advance from a standard sample with a known amount of alumina inclusions.

In Figure 5, the horizontal axis shows the analytical value (ppm) of alumina inclusions that was accurately quantitatively analyzed using the Sandoz analysis method, and the vertical axis shows the analytical value (ppm) of alumina inclusions that was calibrated using the abnormal value pulse number measurement method.
It is a graph diagram obtained by examining and plotting the analysis accuracy of the above example by taking the data (ppIll).

In both cases, the same sample (nine samples) was investigated. As a result, the two agree well, and the repeatability σ
M was 3.4 ppm, proving that this analytical method has high reproducibility and accuracy.

According to the abnormal value pulse number measurement method described above, the time required from the start to the end of the analysis is approximately 15 seconds, and even including sample preparation (electron beam dissolution) time, it is approximately 1 hour, which is the time required for quantitative analysis of alumina inclusions. It is possible to sufficiently respond to requests for speeding up.

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

■ Find equation (A11)=(Fe)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 according to the degree of abnormality b/a. Abnormality level b/a
is determined by the following equation (16).

b/a-Σ ”+-+[(AN)-1(Fe)x ^X
N+BlCobaFe) (16) However, for i, only the abnormal value pulses existing in the region above the straight line G are added.

(2) Find equation (1)-(Fe)xAxN+C.

However, N is a constant, C-FH x A and B. , the above equation is shown by the 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. The abnormality level b/a is as follows (
17) Calculate using formula.

b/a-Σ", -, [(A1)) -f(Fe) X
Ax N+ CI]/(Fe)... (17) Here, l is assumed to add only the abnormal value pulses existing in the region above the straight line H.

[11] Obtain the amount of alumina inclusions as a quadratic function f(b/a) of the degree of abnormality b/a. In this case, the quadratic function f
(b/a) was determined in advance from a standard sample with a known amount of alumina inclusions.

According to the above abnormal value pulse abnormality measurement method, the time required from the start to the end of analysis is approximately 15 seconds, and sample preparation (
It takes about 1 hour including electron beam melting time.
This can fully meet the demand for rapid quantitative analysis of alumina inclusions.

[Effects of the Invention] According to the present invention, the amount of inclusions in steel can be quantitatively analyzed 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.

[Brief explanation of drawings]

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 is a diagram for explaining the method for measuring the number of abnormal value pulses according to the first embodiment and the method for measuring the abnormality degree of abnormal value pulses according to the second embodiment, and FIG. 5 is for explaining the effect of the method for measuring the number of abnormal value pulses. FIG. 2 is a graph diagram showing variations in analysis results.

Claims (3)

[Claims] (1) A part of the sample is melted by irradiation with an electron beam, the inclusions contained in the sample are floated to the sample surface, and after solidification, this is subjected to emission analysis. The emission intensity of each spectroscopic spectral line is detected, and the correlation between the emission intensity of the iron element and the emission intensity of the analyte element is determined using a statistical method for the detected emission intensity, and from this correlation, the analyte element in the sample is determined. A method for optical emission spectrometry analysis of steel, characterized in that the state quantity of an abnormal value pulse is determined, and the content rate of inclusions containing an analysis target element is determined using the state quantity of the abnormal value pulse. (2) The light emission of the steel according to claim 1, characterized in that the number of abnormal value pulses of aluminum element is determined as the state quantity of the abnormal value pulses, and the content rate of aluminum oxide inclusions is determined based on the number of abnormal value pulses. Spectroscopic analysis method. (3) A claim characterized in that the sum of the degrees of abnormality of the abnormal value pulses of aluminum element is determined as the state quantity of the abnormal value pulses, and the content rate of aluminum oxide inclusions is determined based on the sum of the degrees of abnormality of the abnormal value pulses. 1. The method for optical emission spectroscopic analysis of steel according to 1.