JPH045552A - Emission spectrochemical analysis of steel - Google Patents

Abstract

PURPOSE:To rapidly analyze the grain sizes of inclusions by discharging pulses in a sample, detecting the correlation of the emission intensities of the spectral rays between an iron element and the element to be analyzed, and using the average of the degree of abnormality of abnormal value pulses. CONSTITUTION:The pulses are generated between the sample 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 lower tangent of the pulse groups is determined as a correction equation. The correlation equation of the lower tangent is deformed to obtain the upper tangent G. The pulses exceeding the upper tangent G is decided as the abnormal value pulses. Then the degree of abnormality of every abnormal value pulses is obtained. The average grain size of the inclusions is determined in accordance with the degree of abnormality of all the abnormal value pulses. The abnormal value pulses are previously detected by discharging the pulses in a standard sample of the known average grain size of the inclusions in this case. These abnormal value pulses are used as standard values. The average grain size of the inclusions are rapidly measured with the high accuracy in this way.

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 particularly relates to an emission spectrometry 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, AjJ) solidly dissolved in steel, and acid-insoluble aluminum (hereinafter referred to as In5ol, AjJ), which is an alumina-based compound.
There are two forms: All).

In the steelmaking process, Sol and AiI amounts are determined for each steel type, and in actual operation, Sol and Ai are determined based on analysis results.
The amount of aluminum added is adjusted so that the amount of jJ falls within the standard range. Most of the added aluminum dissolves into Sol, A, and Q, but some of it becomes In5o1. Become All. In5o1. If AjJ is present in large quantities in steel,
This results in the formation of alumina inclusions, making the product more susceptible to defects such as surface scratches.

In particular, when a large number of large-grain alumina inclusions are present in steel, cracks are likely to occur starting from the inclusions, and the fatigue properties of the product are significantly reduced. For this reason, steel products such as deep-drawn materials require high cleanliness, so it is necessary to accurately grasp the average particle size of alumina inclusions in steel at each stage of the steel manufacturing 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, but they are prone to errors due to adhesion of abrasive paper and dust. Furthermore, computer image analysis methods have drawbacks such as difficulty in determining the type of inclusions.

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 spectrometry analysis of steel according to the present invention involves applying a pulse discharge to a sample, detecting the emission intensities of the spectral lines of the iron element and the element to be analyzed for each pulse discharge, Using statistical methods for the detected luminescence intensity, we determined the correlation between the luminescence intensity of the iron element and the luminescence intensity of the element to be analyzed.
The method is characterized in that the degree of abnormality is determined for each abnormal value pulse of the element to be analyzed in the sample from this correlation, and the average particle size of inclusions containing the element to be analyzed is determined using the average of these degrees of abnormality.

[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 a large amount of insoluble aluminum (
This results in a positive error.

In the steel emission spectroscopic analysis method according to the present invention, when a pulse discharge is applied to a sample, a large number of abnormal value 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 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 degree of abnormality of the abnormal value pulse is the degree of abnormality in how the emission intensity value of the target element deviates from the upper tangent (degree of abnormality b/a)
is the hail index. The average particle size of inclusions is determined based on the abnormality degree b/a of all abnormal value pulses. In this case, abnormal value pulses are detected in advance by performing pulse discharge on a standard sample whose average particle size of inclusions is known, and these abnormal value pulses are used as reference values. The average particle size of inclusions in the sample is determined by comparing this reference value with the actually measured abnormal value pulse.

[Embodiments] Various embodiments of the present invention will be specifically described below 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.

Next, a pulse discharge was applied between the sample and the electrode for 5 seconds,
The spectral lines are received by a photomultiplier tube, and the emission intensities of iron and aluminum elements are detected. 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. 1 shows the relationship between the luminescence intensities of iron element and aluminum element obtained by discharging the sample. In Figure 1, the number of blots may be simplified for convenience, or there may actually be 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 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 [1] 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
The fifth plots with the lowest 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 the aluminum intensities are determined, respectively.

[I [I] Extract the plots from the fifth to lowest in the region where the iron intensity is less than the average value AV and the emission intensity of the aluminum element (hereinafter referred to as "aluminum intensity") is 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) X A 1+ B 1・
...(1) However, (Ai) ) is aluminum strength,
(Fe) indicates the iron strength, A indicates the slope of the straight line on the XY coordinates, and B indicates the intercept of the straight line on the XY coordinates.

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.

[11] 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.

[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.

[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.
VIO and aluminum strength average value AV2 ~ AVIO
seek.

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

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.

(Ai)-(F e)XA2 +B2 ,,- (2)
However, A2 indicates the slope of the straight line on the XY coordinates, and B2 indicates the intercept of the straight line on the XY coordinates.

Regression convergence correlation coefficient determination method [1] From the correlation diagram, iron strength and aluminum strength are subjected to linear regression using the least squares method to obtain equation (3) below.

(AN) −(Fe) XA3 +B3
-(3) However, A3 indicates the slope of the straight line on the XY coordinates, and B indicates the intercept of the straight line on the XY coordinates. In this case, the correlation coefficient representing the degree of dispersion of the plot group is small.

[11] 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.

(AN) - (Fe)XA4 +B4 - (4)
However, (1) is the aluminum strength, (Fe) is the iron strength, A4 is the slope of the straight line on the XYY elevation, and B4 is the XYY
Each shows the intercept of a straight line at the elevation.

[II[] 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.

(A, &) - (Fe) ) Find the formula.

(AN) = (Fe) XA6 +B6 −
(6) Where, A6 represents the slope of the straight line on the XYY standard, and B6 represents the intercept of the straight line on the XYY standard. In this case, the correlation coefficient representing the degree of dispersion of the plot group is small.

[n] Discard the plot groups that exist in the area above the straight line corresponding to equation (6) 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 (7). This increases the correlation coefficient.

(A Ω) = (F e) XA7 +
B7 - (7) where 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] 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 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.

σ, -Σd2/ (N-1) ... (8) [
IV] Reject plots that exceed twice the standard deviation σ6 as abnormal values, or reject plots that are 10% of the distance Md from the provisional regression line, whichever is farther. 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).

(8Ω) = (F e) x A9 + 89 - (9) Add, A9 is the slope of the final regression line on the XYY elevation, B
, respectively indicate 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).

(Al') = (F e) XA+o+B ro
-(10) However, Alo indicates the inclination of the straight line on the XY locus, and Blo indicates the intercept of the straight line on the XYY base, respectively.

[11] 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).

(AN)-(F e)xA,,+B,,-(11) However, A1□ is the slope of the straight line on the
indicate the intercept of a straight line on the XYY reference plane, respectively.

[m] Reduce the number of residual plots by repeating the linear regression and upward rejection operations as described above, and stop the repeated calculations when the number of plots becomes less than a predetermined number (for example, 100), and draw the regression line at that time. seek. This final regression line is expressed by the following equation (12).

(Al2) −(Fe) XAl2+ B12 −
(12) Where, A1□ represents the slope of the final regression line on the XYY standard, and B12 represents the intercept of the final regression line on the XYY standard.

Regression convergence pulse (plot) bedding constant abnormal pulse rejection method [1] From the correlation diagram, iron strength and aluminum strength are linearly regressed by the least squares method to obtain equation (13) below.

(AN)-(Fe)XA15+B15-(1B
) However, A13 is the slope of the straight line on the XY coordinates, B
13 indicates the sections of straight lines on the XY coordinates.

[n] Discard the Boulot group that exists 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 group that exists in the area below the straight line, and calculate the following: Find equation (14).

(AN)-(F e)XA140B+4-(14) However, Al1 is the slope of the straight line on the
indicate the intercept of a straight line on the XY coordinates.

[m] Reduce the number of residual plots by repeating the linear regression and upward rejection operations as described above, stop the iterative calculation when the number of plots becomes less than a predetermined number (for example, 100), and perform the provisional regression at that time. Find the line. The distance d from the provisional regression line to each residual plot is determined, and the standard deviation σ6 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).

(AN2) -(Fe) XA15+B15-(15
) However, 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 (1)-(Fe)XA
A case will be described in which the average particle size of alumina inclusions is determined by the following method using +B (expressed using +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.

(2) Find the equation (Ai)-(Fe)xAxN+B.

However, N is a constant.

The above equation is shown as 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”, −+ [(AN) −1(Fe) x
Ax N+ Bl ]/(Pe) (16) However, i is assumed to add only the abnormal value pulses existing in the region above the straight line G.

(2) Find equation (A11)-(Fe)XAxN+C.

However, N is a constant and C-FHXA+B.

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”+−+[(AL)) −1(Pe)X A
x N+ C1,/(Fe) (17) However, only the abnormal value pulses existing in the area above the straight line H are added to i.

[Calculate the sum of the IN abnormality degrees b/a, divide this by the number of abnormal value pulses, and calculate 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 it takes approximately 15 minutes including the sample preparation time.
The analysis can be completed in minutes, which satisfies the need for faster measurement of the average particle size of alumina inclusions.

In Figure 3, 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, both l'llJ determination results show good agreement for various types of molten steel.

In Figure 4, 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 results of both measurements show good agreement for various types of molten steel.

In addition, although the above-mentioned example explained the case where the average particle diameter of alumina inclusions was measured, the present invention is not limited to this only, and MnS.

It can also be used to measure the average particle size of other types of inclusions such as S iO21T IO2.

[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 Description of the Drawings] Figures 1 and 2 schematically show a group of light emission pulses, respectively, and are diagrams for explaining the procedure for measuring the average particle diameter by the abnormal value pulse abnormality determination method, and Figures 3 and 2 respectively. FIG. 4 is a graph diagram for explaining the present invention in detail. Applicant's representative Patent attorney Takehiko Suzue Ll = 1 Hinato Toyu Fig. 1 Kinru t, Gonrei Toki Fig. 2 Intermediate KL (pm) Fig. 3 Fig. 4

Claims (1)

[Claims] A pulse discharge is applied to the sample, and the emission intensities of the spectral lines of the iron element and the target element are detected for each pulse discharge, and the emission intensity of the iron element and the luminescence of the target element are calculated using statistical methods for the detected luminescence intensities. Determine the correlation between the 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. An optical emission spectroscopic analysis method for steel.