阅读说明：本技术 一种夹杂物统计分析方法 (Impurity statistical analysis method ) 是由 宋明明 谢毓敏 门超奇 朱航宇 李建立 薛正良 于 2020-12-02 设计创作，主要内容包括：本发明属于非金属夹杂物分析技术领域,尤其涉及一种夹杂物统计分析方法,所述方法包括,获得待统计试样的夹杂物预估平均尺寸；扫描所述待统计试样,获得所述待统计试样中任意两个待确认夹杂物之间的距离；用所述任意两个待确认夹杂物之间的距离分别与所述夹杂物预估平均尺寸比较,获得待统计试样的夹杂物数量。采用本发明提供的方法夹杂物数量统计结果与人工统计夹杂物数量接近,误差小于3.99％,误差小,可以为技术研究提供数据支撑,以此为基础测量的夹杂物尺寸和成分也更准确。(The invention belongs to the technical field of non-metallic inclusion analysis, and particularly relates to an inclusion statistical analysis method, which comprises the steps of obtaining estimated average size of inclusions of a sample to be counted; scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted; and comparing the distance between any two inclusions to be determined with the estimated average size of the inclusions respectively to obtain the number of the inclusions of the sample to be counted. The statistical result of the number of the inclusions is close to the statistical result of the number of the inclusions manually, the error is less than 3.99 percent, the error is small, data support can be provided for technical research, and the sizes and the components of the inclusions measured on the basis of the error are more accurate.)

1. A method for statistical analysis of inclusions, comprising,

obtaining the estimated average size of inclusions of a sample to be counted;

scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted;

and comparing the distance between any two inclusions to be determined with the estimated average size of the inclusions respectively to obtain the number of the inclusions of the sample to be counted.

2. The method for the statistical analysis of inclusions according to claim 1, wherein the distance between any two inclusions to be confirmed is compared with the estimated average size of the inclusions to obtain the number of inclusions in the sample to be counted,

obtaining the distance between the ith inclusion to be confirmed and any remaining inclusions to be confirmed except the 1 st inclusion to the ith inclusion, and recording the distance as d_ii+1、d_ii+2……d_iNWith said d_ii+1、d_ii+2……d_iNAnd respectively comparing the average sizes of the inclusions with the estimated average size of the inclusions to obtain the number of the inclusions of the sample to be counted.

3. The method for the statistical analysis of inclusions as claimed in claim 1, wherein said d is used_ii+1、d_ii+2……d_iNRespectively comparing the measured average sizes of the inclusions with the estimated average sizes of the inclusions to obtain the number of the inclusions of the sample to be counted,

when said d is_ii+1、d_ii+2……d_iNWhen the average size of the inclusions is larger than the estimated average size of the inclusions, judging that the number of the inclusions of the sample to be counted is increased by 1;

when said d is_ii+1、d_ii+2……d_iNAnd when the average size of the inclusions is smaller than or equal to the estimated average size, judging that the number of the inclusions in the sample to be counted is unchanged.

4. The method for the statistical analysis of inclusions according to claim 1, wherein the distance between any two inclusions to be confirmed is the distance between the geometric centers of gravity of any two inclusions to be confirmed.

5. The method for the statistical analysis of inclusions as claimed in claim 1, wherein said obtaining of the estimated mean size of inclusions of the sample to be counted comprises,

randomly identifying 10-100 inclusions on a sample to be counted by using a scanning electron microscope or an optical microscope;

and counting the average sizes of the 10-100 identified inclusions to obtain the estimated average size of the inclusions of the sample to be counted.

6. The method for the statistical analysis of inclusions as claimed in claim 1, wherein the estimated mean size of inclusions is not less than 2 μm.

7. The inclusion statistical analysis method according to claim 1, wherein said scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted comprises,

scanning a sample to be counted by using a full-automatic inclusion analyzer to obtain coordinates of inclusions to be confirmed in the sample to be counted;

and obtaining the distance between any two inclusions to be confirmed in the sample to be counted according to the coordinates.

8. A method for the statistical analysis of inclusions according to any one of claims 1 to 7, further comprising,

the composition of the number of inclusions was measured separately.

9. A method for the statistical analysis of inclusions according to any one of claims 1 to 7, further comprising,

the sizes of the number of inclusions were measured separately.

Technical Field

The invention belongs to the technical field of non-metallic inclusion analysis, and particularly relates to an inclusion statistical analysis method.

Background

Non-metallic inclusions in steel are a general term for particles of various non-metallic substances, which may be derived from deoxidation products, and may also be slag, refractory materials, etc. The presence of inclusions in the steel deteriorates the continuity of the metal matrix, and deteriorates the quality of the steel. In general, the composition, size and amount of inclusions are quite detrimental to the mechanical, physical and chemical properties of the steel. The less the number of inclusions in the steel, the higher the cleanliness of the steel and the better the properties of the steel. Therefore, the analysis and research on the inclusions in the steel have important guiding significance for improving the performance of the steel.

The statistical method for researching the number, the composition, the size and the structure of the inclusions is one of the important methods for researching the non-metallic inclusions in the steel, and the statistical method is most widely applied. These statistical methods are mainly the sulfur printing method, the spectroscopic method and the electrolytic method. The sulfur printing method can only macroscopically provide sulfur-containing inclusions, and for steel with high steel cleanliness, the inclusions with the sizes in the micron level are difficult to count, and oxide inclusions cannot be counted. The spectroscopy is limited by the size of a light source excitation area, and has low treatment and analysis precision on dispersed inclusions with micron-sized sizes, and particularly has larger errors on steel grades with higher cleanliness. The electrolysis method requires the use of an acidic electrolyte solution, which causes the dissolution of inclusions, resulting in a deviation of the statistical result from the true level. In addition, the electrolysis method needs elutriation, the loss condition of inclusions is uncontrollable, the fluctuation of statistical errors is large, and the statistical result difference of different experimenters is large.

At present, due to the development of the automatic analysis technology of the scanning electron microscope and the energy spectrum, an automatic inclusion analysis system based on the scanning electron microscope and the energy spectrum, namely a full-automatic inclusion analyzer, is provided, the efficiency of counting the inclusions by using the automatic inclusion analysis system is high, and the automatic inclusion analysis system is widely applied in recent years. However, with the development of smelting technology, deoxidation is mostly a deoxidation mode of compounding different deoxidation elements, inclusions in steel have a layered structure, and the inclusions are analyzed by an inclusion automatic analysis system of a scanning electron microscope and an energy spectrum, so that the number of the inclusions in a sample is generally higher than the actual level, and accurate data cannot be provided for metallurgical research workers.

Disclosure of Invention

The invention provides an inclusion statistical analysis method, which aims to solve the technical problems that the statistical quantity of sample inclusions is large, deviates from the actual level and influences the research in the prior art.

In one aspect, the present invention provides a method for statistical analysis of inclusions, the method comprising,

obtaining the estimated average size of inclusions of a sample to be counted;

scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted;

and comparing the distance between any two inclusions to be determined with the estimated average size of the inclusions respectively to obtain the number of the inclusions of the sample to be counted.

Further, comparing the distance between any two inclusions to be confirmed with the estimated average size of the inclusions respectively to obtain the number of the inclusions of the sample to be counted,

obtaining the distance between the ith inclusion to be confirmed and any remaining inclusions to be confirmed except the 1 st inclusion to the ith inclusion, and recording the distance as d_ii+1、d_ii+2……d_iNWith said d_ii+1、d_ii+2……d_iNAnd respectively comparing the average sizes of the inclusions with the estimated average size of the inclusions to obtain the number of the inclusions of the sample to be counted.

Further, said using said d_ii+1、d_ii+2……d_iNRespectively comparing the measured average sizes of the inclusions with the estimated average sizes of the inclusions to obtain the number of the inclusions of the sample to be counted,

when said d is_ii+1、d_ii+2……d_iNWhen the average size of the inclusions is larger than the estimated average size, judging that the number of the inclusions of the sample to be counted is increasedAdding 1;

when said d is_ii+1、d_ii+2……d_iNAnd when the average size of the inclusions is smaller than or equal to the estimated average size, judging that the number of the inclusions in the sample to be counted is unchanged.

Further, the distance between any two inclusions to be confirmed is the distance between the geometric centers of gravity of any two inclusions to be confirmed.

Further, the obtaining of the estimated average size of the inclusions of the sample to be counted comprises,

randomly identifying 10-100 inclusions on a sample to be counted by using a scanning electron microscope or an optical microscope;

and counting the average sizes of the 10-100 identified inclusions to obtain the estimated average size of the inclusions of the sample to be counted.

Furthermore, the estimated average size of the inclusions is more than or equal to 2 μm.

Further, the scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted comprises,

scanning a sample to be counted by using a full-automatic inclusion analyzer to obtain coordinates of inclusions to be confirmed in the sample to be counted;

and obtaining the distance between any two inclusions to be confirmed in the sample to be counted according to the coordinates.

Further, the method may further comprise,

and respectively detecting the components of the number of the inclusions.

Further, the method may further comprise,

the sizes of the number of inclusions were measured separately.

One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

the invention provides an inclusion statistical analysis method, which comprises the steps of firstly obtaining the estimated average size of inclusions of a sample to be counted, then scanning the sample to be counted by adopting a full-automatic inclusion analyzer to obtain the distance between any two inclusions in the sample to be counted, and obtaining the number of the inclusions by comparing the distance with the average size of the inclusions. The statistical result of the number of the inclusions is close to the manual statistical result of the number of the inclusions, the error is less than 3.99%, the error is small, data support can be provided for technical research, and the sizes and components of the inclusions measured on the basis of the error are more accurate.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.

Fig. 1 is a process diagram of a method for statistical analysis of inclusions according to an embodiment of the present invention.

Detailed Description

The present invention will be described in detail below with reference to specific embodiments and examples, and the advantages and various effects of the present invention will be more clearly apparent therefrom. It will be understood by those skilled in the art that these specific embodiments and examples are for the purpose of illustrating the invention and are not to be construed as limiting the invention.

Throughout the specification, unless otherwise specifically noted, terms used herein should be understood as having meanings as commonly used in the art. Accordingly, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. If there is a conflict, the present specification will control.

Unless otherwise specifically stated, various raw materials, reagents, instruments, equipment and the like used in the present invention are commercially available or can be prepared by existing methods.

In order to solve the technical problems, the embodiment of the invention provides the following general ideas:

the invention provides a statistical method of nonmetallic inclusions, which comprises the following steps:

in one aspect, the present invention provides a method for statistical analysis of inclusions, which, in conjunction with fig. 1, comprises,

s1, obtaining the estimated average size of the inclusions of the sample to be counted;

s2, scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted;

and S3, comparing the distance between any two inclusions to be confirmed with the estimated average size of the inclusions respectively to obtain the number of the inclusions of the sample to be counted.

The automatic inclusion analysis system based on a scanning electron microscope and an energy spectrum, namely a full-automatic inclusion analyzer, has two working principles, one is based on that the contrast of inclusions and a matrix is different in a back scattering mode, the inclusions are positioned, and the components of the inclusions are further analyzed point by using the energy spectrum, so that the full-automatic inclusion analyzer considers that one composite inclusion is a plurality of inclusions due to layering of the composite inclusions and different contrast of different layers, and the quantity statistical result of the inclusions of a sample is higher than the actual level. And secondly, analyzing the components point by point, when the components are not Fe, the full-automatic analyzer considers that the impurities are hit, continuously analyzing the components by taking the hit impurities as a reference, and when composite impurities are encountered, because the impurities of the composite machine are layered and the components of different layers are different, the full-automatic analyzer considers that one composite impurity is a plurality of impurities, the statistical result of the number of the impurities in the sample is higher than the actual level, and the research work is not facilitated.

The method comprises the steps of firstly obtaining the estimated average size of inclusions of a sample to be counted as a threshold value, then scanning the sample to be counted by adopting a full-automatic inclusion analyzer to obtain the distance between any two inclusions in the sample to be counted, and obtaining the number of the inclusions by comparing the distance with the average size of the inclusions.

As an implementation manner of the embodiment of the present invention, comparing the distance between any two inclusions to be confirmed with the estimated average size of the inclusions respectively to obtain the number of inclusions of the sample to be counted, specifically including,

obtaining the distance between the ith inclusion to be confirmed and any remaining inclusions to be confirmed except the 1 st inclusion to the ith inclusion, and recording the distance as d_ii+1、d_ii+2……d_iNWith said d_ii+1、d_ii+2……d_iNAnd respectively comparing the average sizes of the inclusions with the estimated average size of the inclusions to obtain the number of the inclusions of the sample to be counted.

When the step is carried out, (1) whether the 1 st inclusion to be confirmed is a determined independent inclusion is confirmed, specifically, the distances between the 1 st inclusion to be confirmed and any remaining inclusions to be confirmed are obtained and are respectively marked as d₁₂、d₁₃……d_1NWith said d₁₂、d₁₃……d_1NRespectively comparing the estimated average sizes of the inclusions with the estimated average size of the inclusions to determine whether the 1 st inclusion to be determined is 1 independent inclusion; (2) obtaining the distance between the 2 nd inclusion to be confirmed and the remaining optional inclusions to be confirmed except the 1 st inclusion and the 2 nd inclusion to be confirmed, and respectively recording the distance as d₂₃、d₂₄……d_2NWith said d₂₃、d₂₄……d_2NRespectively comparing the estimated average sizes of the inclusions with the estimated average size of the inclusions to determine whether the 2 nd inclusion to be determined is 1 inclusion; (3) and (3) repeating the step (2) to confirm whether the ith inclusion to be confirmed is 1 inclusion or not until the nth inclusion is 1 inclusion or not, and obtaining the number of the inclusions of the sample to be counted.

As an implementation of the embodiment of the present invention, said step d is performed by_ii+1、d_ii+2……d_iNRespectively comparing the measured average sizes of the inclusions with the estimated average sizes of the inclusions to obtain the number of the inclusions of the sample to be counted,

when said d is_ii+1、d_ii+2……d_iNWhen the average size of the inclusions is larger than the estimated average size of the inclusions, judging that the number of the inclusions of the sample to be counted is increased by 1;

when said d is_ii+1、d_ii+2……d_iNAnd when the average size of the inclusions is smaller than or equal to the estimated average size, judging that the number of the inclusions in the sample to be counted is unchanged.

When d is_ii+1、d_ii+2……d_iNWhen the average size of the inclusions is larger than the estimated average size of the inclusions, the ith inclusion to be confirmed is regarded as 1 independent inclusion, and the number of the inclusions of the sample to be counted is increased by 1; when d is_ii+1、d_ii+2……d_iNAnd when the average size of the inclusions is less than or equal to the estimated average size of the inclusions, determining that the ith inclusion to be confirmed is not 1 independent inclusion, and judging that the ith inclusion to be confirmed is a composite inclusion, wherein the number of the inclusions of the sample to be counted is unchanged.

As an implementation manner of the embodiment of the present invention, the distance between any two inclusions to be confirmed is a distance between geometric barycenters of any two inclusions to be confirmed.

As an implementation manner of the embodiment of the present invention, the obtaining of the estimated average size of the inclusions of the sample to be counted includes randomly identifying 10 to 100 inclusions on the sample to be counted by using a scanning electron microscope or an optical microscope;

and counting the average sizes of the 10-100 identified inclusions to obtain the estimated average size of the inclusions of the sample to be counted.

The method comprises the steps of scanning a sample by using a scanning electron microscope or an optical microscope under manual conditions, counting the number and the size of inclusions to identify and obtain the size of 10-100 inclusions, and then calculating the average size of the 10-100 inclusions as a threshold value, namely the estimated average size of the inclusions. Too much amount of identification will reduce efficiency, and too little amount of identification will result in too large estimated average size error of the obtained inclusions and deviation from the actual level. Comprehensively considering, 10-100 inclusions are identified. The size of an inclusion in the present invention is understood to be the longest distance from one point on the edge of the inclusion to another point on the edge.

As an implementation of the embodiment of the present invention, the estimated average size of the inclusions is not less than 2 μm. The cross section diameter acted on the surface of a sample is about 1 mu m when the electron beam acceleration voltage of a scanning electron microscope is 15-20 kV, the cross section diameter of an EDS (energy spectrometer) acted sample is about equal to the action diameter of the electron beam, and under the limit condition, two points are struck on the EDS, namely the lower limit size of an inclusion is 2 mu m.

As an implementation manner of the embodiment of the present invention, the scanning the sample to be counted to obtain the distance between any two inclusions to be confirmed in the sample to be counted includes,

scanning a sample to be counted by using a full-automatic inclusion analyzer to obtain coordinates of inclusions to be confirmed in the sample to be counted;

and obtaining the distance between any two inclusions to be confirmed in the sample to be counted according to the coordinates.

As an implementation of the embodiment of the invention, the method further includes,

the composition of the number of inclusions was measured separately.

And (3) multiplying the composite inclusion by the energy spectrum components of the EDS (energy spectrometer) when the composite inclusion is automatically counted according to the area percentage of different component blocks in the inclusion, and finally adding all the products in the composite inclusion to obtain a result which is regarded as the components of the composite inclusion. For non-composite inclusions, the energy spectrum components of an EDS (energy dispersive spectrometer) in an automatic statistics system are directly used as the components of the inclusions.

As an implementation of the embodiment of the invention, the method further includes,

the sizes of the number of inclusions were measured separately.

In the present invention, the size of the inclusion is obtained by converting the total area of the inclusion into the area of a circle, and the diameter corresponding to the circle is defined as the size of the inclusion.

A method for statistical analysis of inclusions according to the present invention will be described in detail below with reference to examples, comparative examples, and experimental data.

Example 1

Sample preparation: smelting a steel sample by using a vacuum high-frequency induction furnace, adding titanium sponge and silicon-calcium alloy into the smelted molten steel to change the components of the molten steel, then pouring the molten steel into an ingot casting mold, cooling the molten steel to room temperature by air, then heating the ingot casting to 1200 ℃, and forging to obtain a steel plate; the steel plate was sampled and mechanically ground and polished to obtain a standard metallographic specimen having the chemical composition shown in table 1, and the balance of Fe and inevitable impurities.

Statistical analysis of inclusions:

(1) the sizes of 35 inclusions are obtained by manually scanning a standard metallographic sample through a scanning electron microscope, and the average value of the sizes of the 35 inclusions is 6 microns.

(2) Using an automatic inclusion analysis system of a scanning electron microscope, extracting 1.71mm immediately under a 500-time field under the condition of being scattered²Scanning a standard metallographic sample to obtain 660 coordinates of geometric gravity centers of inclusions to be confirmed;

(3) according to the coordinates of the geometric barycenter of each inclusion to be confirmed, the distances between the 1 st inclusion to be confirmed and the remaining 659 inclusions to be confirmed are obtained, and the distances are respectively d₁₂、d₁₃……d₁₆₅₉In μm, see Table 1, d₁₂、d₁₃……d₁₅₉₉Respectively, are compared with 6 μm, where d₁₁₀₅、d₁₂₀₈、d₁₃₅₆3.5 μm, 4 μm and 4.5 μm, respectively, are all < 6 μm, so that the 1 st inclusion to be confirmed is not 1 inclusion, and the statistical number of inclusions is unchanged and is zero.

(4) According to the coordinates of the geometric barycenter of each inclusion to be confirmed, the distances between the 2 nd inclusion to be confirmed and the 598 inclusions to be confirmed which are obtained by removing the 1 st inclusion and the 2 nd inclusion are respectively d₂₃、d₂₄……d₂₅₉₉D is mixing₂₃、d₂₄……d₂₅₉₉Compared with 6 μm, respectively, are larger than 6 μm, so that the 2 nd inclusion to be confirmed is1 inclusion, and adding 1 to the statistical number, wherein the statistical result of the inclusions in the statistical sample is 1.

(5) According to the coordinates of the geometric barycenter of each inclusion to be confirmed, the distances between the 3 rd inclusion to be confirmed and the 597 inclusions to be confirmed which are the rest except the 1 st, the 2 nd and the 3 rd inclusions are obtained and are respectively d₃₄、d₃₅……d₃₅₉₉D is mixing₃₄、d₃₅……d₃₅₉₉The number of inclusions to be confirmed at 3 is 1, and the statistical number is increased by 1, when the statistical result of the inclusions in the statistical sample is 2.

(6) According to the above method for comparing the 1 st, 2 nd and 3 rd inclusions, it is confirmed whether the 4 th to 1072 nd inclusions are 1 inclusion, the statistical number is increased by 1 when the i-th inclusion is confirmed to be 1 inclusion, and the statistical number is not changed when the i-th inclusion is confirmed not to be 1 inclusion. In example 1, the number of inclusions in the sample was 295.

And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Example 2

Example 2 referring to example 1, example 2 differs from example 1 in the chemical composition of the sample, and is specifically shown in table 1. In example 2, the statistical result of the number of inclusions in the sample was 596. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Example 3

Example 3 referring to example 1, example 3 differs from example 1 in the chemical composition of the sample, and is specifically shown in table 1. In example 3, the number of inclusions in the sample was 521 as a statistical result. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Comparative example 1

Comparative example 1 provides a statistical method of the number of inclusions, and the metallographic sample provided in example 1 is used for self-analysis by a scanning electron microscopeThe dynamic inclusion analysis system extracts 1.71mm immediately under the condition of scattering and under the 500-time field²The standard metallographic specimen was scanned in the area mode of (1) to obtain 660 inclusions. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Comparative example 2

Comparative example 2 provides a statistical method of the number of inclusions, using the metallographic specimen provided in example 2, and using an automatic inclusion analysis system of a scanning electron microscope under a scattering condition, under a 500-fold field, to extract 1.71mm immediately²Scanning a standard metallographic sample to obtain 1072 inclusions. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Comparative example 3

Comparative example 3 provides a statistical method of the number of inclusions, using the metallographic specimen provided in example 3, and using an automatic inclusion analysis system of a scanning electron microscope under a scattering condition, under a 500-fold field, to extract 1.71mm immediately²The standard metallographic specimen was scanned to obtain 1340 inclusions. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Comparative example 4

Comparative example 4 provides a statistical method of the number of inclusions, and the area of the metallographic specimen provided in example 1 was 1.71mm as photographed by an optical microscope²The number of inclusions is manually calculated one by ImageJ software according to the gray level threshold, and the statistical result is 284 inclusions. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

Comparative example 5

Comparative example 5 provides a statistical method of the number of inclusions, and the area of the metallographic specimen provided in example 2 was 1.71mm as photographed by an optical microscope²The number and the size of the inclusions are manually calculated one by ImageJ software according to the gray level threshold, and the statistical result is 612 inclusions. Using scanning electron microscope systemThe counted number of inclusions was subjected to size and composition tests.

Comparative example 6

Comparative example 6 provides a statistical method of the number of inclusions, and the area of the metallographic specimen provided in example 3 was 1.71mm as photographed by an optical microscope²The number and the size of the inclusions are manually calculated one by using ImageJ software according to the gray level threshold, and the statistical result is 501 inclusions. And detecting the sizes and components of the inclusions in the counted number by using a scanning electron microscope.

TABLE 1

Sample number	C/％	Si/％	Mn/％	P/％	S/％	O/％	N/％	Ce/％	Ti/％	Ca/％
											Example 1	0.16	0.36	1.50	0.025	0.011	0.0021	0.0075	0.012	0.013	-
Example 2	0.16	0.33	1.50	0.025	0.012	0.0031	0.0081	0.005	-	-
											Example 3	0.16	0.31	1.50	0.025	0.012	0.0022	0.0076	0.028	-	0.001

TABLE 2

The statistical results of inclusions of examples 1 to 3 and comparative examples 1 to 6 are shown in Table 2.

As can be seen from the data in Table 2, the statistical method for the number of inclusions provided in examples 1 to 3 of the present invention has a statistical result close to the artificially statistical number of inclusions provided in comparative examples 5 to 6, with an error of 2.61 to 3.99%, and a small error, and can provide data support for technical research, so that the sizes and components of inclusions measured on the basis of the error are more accurate.

The invention provides an inclusion statistical analysis method, which comprises the steps of firstly obtaining the estimated average size of inclusions of a sample to be counted, then scanning the sample to be counted by adopting a full-automatic inclusion analyzer to obtain the distance between any two inclusions in the sample to be counted, and obtaining the number of the inclusions by comparing the distance with the average size of the inclusions. The statistical result of the number of the inclusions is close to the statistical result of the number of the inclusions manually, the error is less than 3.99 percent, the error is small, data support can be provided for technical research, and the sizes and the components of the inclusions measured on the basis of the error are more accurate.

It should be noted that ImageJ is a java-based public image processing software developed by the National Institutes of Health. Can run on various platforms such as Microsoft Windows, Mac OS, Mac OS X, Linux, Sharp Zaurus and the like. Based on the characteristics of java, the program written by the method can be distributed in an applet mode and the like.

Finally, it should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.