KR101325458B1 - Slab cleanness measurement apparatus and method - Google Patents

Abstract

The present invention relates to an apparatus and method for evaluating slab cleanliness, and more particularly, to a signal generator (120) for obtaining a spectrum of each element contained in a slab sample from a spark generated by applying a voltage to the slab sample, and a plurality of slabs. Based on the spectrum obtained by the sample transfer device 160 and the signal generator 120 to sequentially transfer the samples to the signal generator 120 one by one, the inclusion information contained in the slab sample is obtained and each slab is obtained. A signal processing device 140 for acquiring inclusion information on a plurality of slab samples transferred by combining the inclusion information of the sample, and a display device 190 displaying the inclusion information acquired by the signal processing device 140. It is characterized by including.

According to the present invention, the cleanliness evaluation can be easily and quickly performed by dividing the slab end surface into a plurality of slab samples, and incorporating the inclusion information of each slab sample to obtain the inclusion information for the entire slab.

Slab, inclusion oxygen index, inclusion nonuniformity index

Description

Slab cleanliness evaluation device and method {SLAB CLEANNESS MEASUREMENT APPARATUS AND METHOD}

The present invention relates to an apparatus and method for evaluating slab cleanliness.

API steel is a material that can be manufactured mainly by pipes, and then slab is rolled into hot-rolled steel sheets and then made into pipes. In this case, when an inclusion cluster exists at the edge of the pipe, the pipe bursts. Therefore, if it is possible to prevent the flow of the product downstream by determining the presence or absence of such inclusions at the edge of the slab in the slab step, only normal products can be sent to the consumer.

However, since there is no such equipment yet, it is not possible to measure the inclusion clusters in the slab cross section, so that the slab flows into the hot rolling process with the inclusion clusters, which are supplied to the consumer and made into pipes to test at the product level. In doing so, defects caused by inclusions occur. In such a case, the hot rolled product supplier will be compensated for the consumer, and therefore will suffer losses.

The present invention is designed to solve the problems of the prior art, and to provide an apparatus and method that can easily and quickly obtain the inclusion information present in the slab to evaluate the slab cleanness of the end surface of the entire slab.

According to a first aspect of the present invention for achieving the above object, a signal generator for obtaining the spectrum of each element contained in the slab sample from a spark generated by applying a voltage to the slab sample, a plurality of slab samples one by one A plurality of slab samples transferred by acquiring inclusion information contained in the slab sample on the basis of the spectrum obtained by the signal generation device and the signal generation device by combining the inclusion information of each slab sample; A slab cleanliness evaluation device is provided that includes a signal processing device for obtaining inclusion information about a and a display device for displaying inclusion information acquired by the signal processing device.

The display apparatus visually displays the inclusion information on the plurality of slab samples transferred.

The inclusion information includes the type, size, and location information of the inclusion, and displays colors differently for each type of inclusion.

And a sample preparation device for processing the surfaces of the plurality of slab samples, wherein the sample transfer device transfers the slab samples processed by the sample preparation device one by one to the signal generator.

The sample transfer device continuously moves the slab sample so that sparks occur in a plurality of rows of the slab sample, and the signal processing device determines an area of inclusions in each row of the slab sample based on a spectrum obtained by the signal generator. The inclusion oxygen index and the inclusion nonuniformity index of the slab sample are obtained based on the inclusion area of each row.

The signal processing apparatus measures intensity of a spectrum over time for a plurality of elements contained in the slab sample for each row of the slab sample, obtains data of the frequency of occurrence for the intensity of the spectrum, and generates pure iron from the data. The inclusion area of each row is obtained by multiplying the intensity of the spectrum outside the normal distribution of by the frequency of occurrence for the intensity of the spectrum.

The signal processing apparatus obtains the sum of the inclusion areas present in the slab sample by adding the inclusion areas of each row, and obtains the inclusion oxygen index of the slab sample using the correlation between the preset sum of the inclusion areas and the inclusion oxygen index.

The signal processing apparatus determines the standard deviation of the inclusion area of each row as an inclusion nonuniformity index.

If the inclusion nonuniformity index is greater than or equal to the reference value, cleanliness is bad. If the inclusion nonuniformity index is less than the reference value, it is determined that the cleanliness is good.

According to a second aspect of the present invention, a step of transferring a plurality of slab samples one by one to a signal generator, each element contained in each slab sample from the spark generated by applying a voltage to the slab sample in the signal generator Acquiring the spectrum information of the slab sample based on the spectrum obtained by the signal generator, and collecting the inclusion information of each slab sample to collect the inclusion information of the plurality of slab samples transferred. A slab cleanliness evaluation method comprising the step of obtaining and visually displaying the inclusion information on the plurality of slab samples is visually displayed.

The inclusion information includes the type, size, and location information of the inclusion, and displays colors differently for each type of inclusion.

In the obtaining of the spectrum, while generating a spark by applying a voltage to the slab sample while continuously moving the slab sample in a plurality of rows, and obtaining the spectrum of each element contained in each row of the slab sample from the generated spark In the obtaining of the inclusion information, the inclusion area of each row of the slab is obtained based on the acquired spectrum, and the inclusion oxygen index and the inclusion nonuniformity index of the slab sample are obtained based on the inclusion area of each row.

The sum of the inclusion areas present in the slab sample is obtained by adding the inclusion areas in each row, and the inclusion oxygen index of the slab sample is obtained using the correlation between the preset sum of the inclusion areas and the inclusion oxygen index.

The standard deviation of the inclusion area of each row is determined by the inclusion nonuniformity index of the slab sample.

According to the present invention, the slab end face is divided into a plurality of slab samples, and the inclusion information of each slab sample is synthesized to obtain inclusion information on some or all areas of the slab, thereby making it easier and faster to evaluate cleanliness. Can be.

In addition, the type and size of the inclusions in the slab, and the location information to visually recognize the display so that it can be identified at a glance what composition is distributed in which part of the slab.

In addition, it is possible to quickly determine the quality of the slab by obtaining the inclusion oxygen index and the inclusion nonuniformity index of the slab sample to know the cleanliness of the slab.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Usually, when casting steel, molten steel is cast out of the mold into slabs. Inside the mold there is a nozzle from which the molten steel descends from the tundish, through which the molten steel enters the mold. The nozzles take the form of a pipe, the ends of which are closed and holes are formed on both sides near the ends. As the molten steel is discharged through both holes, the molten steel is circular in the mold. At this time, the molten steel comes down in the downward direction of the mold with the slag existing in the upper part of the mold. As the molten steel strikes the side of the mold, the slab solidifies as it exits the mold in accordance with vibration of the mold. At this time, the slag is sandwiched between the molten steel in the mold and the mold wall surface, and there are many slag inclusions in the vicinity of the slab edge 100 mm. Therefore, it is important to measure the inclusions near the slab edges.

In the slab cleanliness evaluation apparatus according to the embodiment of the present invention for measuring the cleanliness of the slab by measuring the inclusions near the edge of the slab, as shown in Figure 1, the slab end surface divided into a plurality of predetermined size A signal generator 120 for obtaining a spectrum of each element contained in the slab sample from a spark generated by applying a voltage to the slab sample of the sample, a sample preparation device 180 for processing the surfaces of the plurality of slab samples, and a plurality of slab samples Obtains the inclusion information contained in the slab sample based on the spectrum obtained by the sample transfer device 160 and the signal generator 120 sequentially transferring the signal generator 120 or the sample preparation device 180 one by one. And a signal processing device 140 for synthesizing the inclusion information of each slab sample to obtain the inclusion information for a part or the whole area of the slab, and It includes a display device 190 for displaying the inclusion information obtained by the signal processing device 140. Although not shown in the drawings, a dividing device is provided to divide the end face of the slab into a plurality of slab samples having a predetermined size.

The sample transfer device 160 may be configured as a robotic arm and transfer the slab sample loaded on the sample mount 162 to the signal generator 120 or the sample preparation device 180.

2 shows the slab end surface divided into a plurality of slab samples 100 having an area of 100 × 50 mm. As such, when the inclusion information is identified for each sample 100 and the data for each sample 100 are combined, the inclusion information for a partial area or the entire area of the slab may be determined.

In order to grasp the inclusion information of the slab sample 100, when the slab sample 100 is put into the device, the sample transport device 160 lifts the slab sample 100 from the sample mounting table 162 to process the sample surface. The sample preparation device 180 is transferred to the sample preparation device 180, and the sample preparation device 180 mills the sample surface.

After the sample surface is milled, when the sample transfer device 160 raises the slab sample 100 to the stand of the signal generator 120, sparks are continuously generated in the signal generator 120 and sparks are generated. The sample transport device 160 moves the slab sample 100 from one side to the other side, for example, from left to right. When moving the 42mm minus the left margin of the 50mm length of half the width of the slab sample 100, and then move again 42mm, the remaining right portion to generate a spark.

At this time, since the spark is generated while continuously moving the slab sample 100, the inclusion low signal is continuously measured. FIG. 3 shows that the damage pattern generated by the spark generated while continuously moving is 12 rows. The signal processing device 140 may rapidly obtain information on the correct inclusions by analyzing the optical signals generated in the 12 rows and processing the position information, the size, and the composition of the inclusions present in the slab sample 100. By visualizing the location, size, and composition of the inclusions and displaying them on the display device 190, the user can identify at a glance which composition is distributed in which part of the slab. In addition, the slab cleanliness can be evaluated by combining the information to calculate the inclusion oxygen index and the inclusion heterogeneity index.

The signal generator 120 will be described in more detail. The signal generator 120 is implemented by an optical emission spectrometer (OES), and FIG. 4 illustrates a configuration of the signal generator 120. As shown in FIG. 4, the signal generator 120 includes a high voltage generator 121, an electrode rod 122, a grating 123, a slit 124, and a photomultiplier tube 125.

When the high voltage generator 121 applies the voltage at 300 Hz per second, a spark is generated between the slab sample 100 and the electrode 122 proximate to the slab sample 100, and the spark exists in the slab sample 100. To generate fluorescence including the spectrum of all elements. The fluorescence spreads about the width of the grating 123 while traveling in the direction of the grating 123, and is spectralized by the grating 123 to enter the slit 124. The slit 124 is placed at a specific position where the spectrum of elements is incident by the number of elements to be analyzed. At this time, since each wavelength is determined according to the characteristic spectrum of the element, a desired number of slits 124 can be set.

4 shows only six slits for convenience. Usually, the characteristic wavelength of oxygen is 130 nm, the characteristic wavelength of aluminum is 256 nm, the characteristic wavelength of manganese is 293 nm, the characteristic wavelength of iron is 322 nm, and the characteristic wavelength of calcium is 396 nm. The diffraction angle of the spectroscopic spectrum is determined according to the specific wavelength of the spectrum of such an element, and the position of the slit 124 is determined by each diffraction angle. Therefore, slits for oxygen wavelength measurement, slits for aluminum measurement, slits for manganese measurement, slits for iron measurement, slits for calcium measurement are sequentially arranged.

In addition, an optical multiplier 125 is connected to each slit 124, and each spectrum incident through the optical multiplier 125 is converted into an electrical signal and input to the signal processing device 140. The signal processing device 140 analyzes the inclusions of the slab sample 100 using the input electrical signal.

FIG. 5 is a diagram showing a time axis for converting an optical signal from each slit 124 of the signal generator 120 into an electrical signal in the optical multiplier 125 for each channel. In FIG. 5, the spectrum of each element of the inclusion measured by the signal generator 120 for 10 seconds is shown as an example. For the quantitative analysis of metal specimens using the conventional OES, the current signal entering each channel was summed for 10 seconds, and the intensity was calculated and expressed as a quantitative value. However, in the present invention, as shown in Fig. 5, the spectrum for 10 seconds is represented by the x axis as the time axis and the y axis as the intensity of the spectrum. Since the spectrum is displayed on the time axis, a spark is generated between the slab sample 100 and the electrode 122 and the surface of the slab sample 100 is eroded, thereby sending a signal for 10 seconds. Can also be found over time. Typically, the depth to be eroded by the discharge of about 10 seconds is about 0.1mm, the new inclusion at the position of erosion can be measured because the size of the inclusion ranges from several micrometers to several tens of micrometers.

Referring to FIG. 5, the spectral results are shown in five slits, namely slits for measuring oxygen, aluminum, manganese, calcium and iron wavelengths. The first graph at the top represents the spectrum of oxygen (O), where asterisks (1-9) are indicated where oxygen peaks along the time axis. The actual spectrum shows a myriad of small signals between the peaks and peaks, but omits them, and only shows that the intensity of the oxygen spectrum is above the set point.

The asterisk (*) on the peak of oxygen means that most of the nonmetallic inclusions are in the form of oxides, so if there are peaks of other elements in the time zone at which the oxygen peaks exist, it means that the oxides of that element, that is, the inclusions It is believed to exist inside. More specifically, oxygen (O) and manganese (Mn) are found simultaneously in the first asterisk (1) having the first peak in the oxygen spectrum, and oxygen (M) in the second asterisk (2) with the second peak. O), aluminum (Al), manganese (Mn) and calcium (Ca) are oxygen (O) and manganese (Mn) in the third asterisk (3) having a third peak, and oxygen in the fourth asterisk (4). (O) and aluminum (Al), oxygen (O), manganese (Mn) and calcium (Ca) in the fifth asterisk (5), oxygen (O) and calcium (Ca) in the sixth asterisk (6) At the same time, each is found, only oxygen (O) is found in the seventh asterisk (7), oxygen (O) and manganese (Mn) in the eighth asterisk (8), and oxygen (O) in the ninth asterisk (9). , Aluminum (Al) and calcium (Ca) are found simultaneously.

Based on such spectral peak values, it can be judged that manganese oxide is present in the first asterisk 1, and it can be determined that alumina, manganese oxide, calcium oxide are present in the second asterisk (2). In addition, calcium oxide is present in the third asterisk (3), alumina in the fourth asterisk (4), manganese oxide and calcium oxide in the fifth asterisk (5), and calcium oxide in the sixth asterisk (6). You can judge. Since only oxygen (O) is found in the seventh asterisk (7), it is determined that bubbles exist. Furthermore, it can be determined that manganese oxide is present in the eighth asterisk 8, and alumina and calcium oxide are present in the ninth asterisk 9, respectively.

In addition, in order to measure the size of the inclusions, the size of the inclusions is determined based on the strength of the elements simultaneously found in the time zone where oxygen is found. For example, in the case of aluminum, the levels of a, b, and c are set, respectively, so that the level a in the fourth asterisk 4, the level b in the second asterisk 2, and the level c in the ninth asterisk 9 are determined. Each correspondence can be used to evaluate the size of the inclusions. The level a can be evaluated as a large alumina inclusion, the level b as a medium alumina inclusion and the level c as a small size alumina inclusion. In parallel with the inclusion analysis carried out offline beforehand, the type and size of the inclusions in a sample are input to the database of the signal processing device 140, and the level is determined. Not only can the type and size of inclusions be assessed quickly, but also the inclusions present on the surface and inside of the sample can be analyzed to reduce errors in inclusion evaluation.

In this way, the spectrum is measured on the time axis of each inclusion element to be evaluated, and the oxygen peak value and the peak value of the element existing at the same time period are confirmed to be an inclusion of that element. Furthermore, when oxygen and several elements are found simultaneously, It is judged that complex inclusions or independent inclusions are analyzed at the same time. If only oxygen peaks are found, it is considered as bubbles inside the metal.

In addition, it is possible to determine the size of the inclusions by setting a level in the intensity of the peak for each element, the information of the spectrum measured on the time axis can measure the inclusions in the deeper region on the sample surface.

Looking at the aluminum (Al) channel in Figure 5 shows a lot of peak values. However, as mentioned above, only relatively large peak values are shown and numerous small peak values are omitted. This is a matter of setting the offset correctly. This is because the offset determines the point at which aluminum (Al) and alumina (Al ₂ O ₃ ) are distinguished.

FIG. 6 is data (screen display) measuring inclusions using OES for a sample manufactured using pure iron as a raw material, and FIG. 7 illustrates OES for a sample prepared by adding alumina powder to pure iron. It is data that measured the inclusions using. Figure 6 shows the results of measuring the inclusions through the OES by making a sample using 20kg of pure iron powder without actually added alumina at all, Figure 7 is a sample produced by adding 800mg of alumina powder to 20kg of pure iron powder The results of measurement of inclusions using OES are shown.

FIG. 6A shows that there is no peak value that greatly jumps up as the time axis data. FIG. 6B shows the y-axis intensity in the data of (A) as the x-axis in (B) and the frequency representing the same intensity in the y-axis, which is almost normal. Samples not containing alumina inclusions are present in a form in which the normal distribution shape of FIG. 6B is well maintained. However, in FIG. 7A, there are a number of peak values, and in FIG. 7B, the right side of the normal distribution jumps out, not the normal distribution shape. This is because the y-axis intensity of the data in FIG. 7A is placed on the x-axis of (B). If only data corresponding to the alumina inclusions of FIG. 7 is to be obtained, the shape as shown in FIG. 6B may be omitted from FIG. 7B. This will give you an accurate offset. FIG. 7C shows the subtraction of FIG. 6B from FIG. 7B.

That is, FIG. 7C is data of alumina inclusions, where the x-axis is the intensity of the signal of the alumina inclusions, and the y-axis is the frequency with respect to the intensity of the inclusion signal. The strength of this signal is related to the size of the inclusions. The larger the size radius of the alumina, the larger the intensity is in proportion to the second or third power of the radius. This is because the shape of the alumina inclusions is composed of small grains. The volume or area of the grain is given by R ² or R ³ for the radius R. (D) of FIG. 7 shows root x square roots on the x-axis of (C) and shows values substantially similar to those of actual inclusions. The unit which shows the diameter of the inclusion of the x-axis of (D) is micrometer.

Table 1 below shows the results obtained by calculating the sum of the area of the inclusions by multiplying the squared size of the inclusions measured for 15 kinds of samples by the frequency. The sum of the area of the inclusions thus represents the values actually measured using the OES device applied to the present invention. The value of the total oxygen amount (inclusion oxygen index) is the cleanliness value measured by the sample melting method. It is a reliable value because the average value was calculated as the sum of the area of the inclusions after 50 or more experiments per sample to calculate each value.

Table 1

sample # Sum of Areas of Inclusions Inclusion Oxygen Index [ppm] One 239 9.2 2 438 15 3 458 17 4 603 24 5 868 37 6 2903 140 7 2506 110 8 1165 47 9 1048 48 10 1262 50 11 1263 55 12 1347 62 13 1028 56 14 1193 54 15 1265 52

Referring to Table 1 above as a graph, the correlation line graph shown in FIG. 8 may be derived. Figure 8 shows the relationship between the sum of the area of the alumina inclusions and the inclusion oxygen index measured by using the OES sample taken from the product produced in the steelmaking process. As can be seen in FIG. 8, the area sum of inclusions and the inclusion oxygen index have a proportional relationship. In the present invention, if the correlation is stored in a database and any sample sample is measured only by the sum of the area of the inclusions using the OES apparatus, cleanliness, that is, the inclusion oxygen index can be obtained by applying the correlation database.

In order to quickly measure the inclusion oxygen index of an arbitrary sample sample, for example, the signal for Al in an aluminum (Al) channel is measured on a time axis (FIG. 7A), and then the intensity of the signal is measured on the x axis. If the frequency is set to the y-axis, the information on the alumina inclusions appears as shown in (B) of FIG. 7, and if the normal distribution form is omitted, only the information about the alumina inclusions is given in the form of FIG. 7C. In addition, when the root of the root of the x-axis of FIG. Then, the sum of the area of the inclusions is obtained by multiplying the squared size of the inclusions by the frequency, and finally, the oxygen index of the inclusions can be obtained using a predetermined correlation. On the other hand, since the magnitude of the inclusions is squared to give the strength of the inclusion signal, the area sum of the inclusions may be obtained by multiplying the frequency of the inclusion signal by the frequency without determining the inclusion size.

The signal processing device 140 calculates the inclusion area for each row of the slab sample 100. Inclusion area for each row was calculated, and then the sum of the inclusion area was added to each other, and the inclusion oxygen of the slab sample 100 was calculated using the correlation of the sum of the predetermined inclusion area and the inclusion oxygen index (see Table 1 and FIG. 8). Can get index

In addition, by obtaining the standard deviation of the inclusion area obtained by each row, that is, the area of 12 inclusions, it is possible to know the nonuniformity of the inclusion distribution. In other words, if the standard deviation is large, the degree of inclusion unevenness is large, and if the standard deviation is small, the degree of inclusion unevenness is small. For example, when the inclusion nonuniformity index is 150 or more, the slab cleanness may be judged not to be good, and when it is less than that, the cleanness may be determined to be good.

FIG. 9 illustrates that the display apparatus 190 visualizes and displays the type, size, and location information of the inclusions present in the entire slab end surface. The display screen displays colors differently according to the types of inclusions, so that the types of inclusions can be visually easily identified. Thus, the user may be able to identify at a glance which composition is distributed in which part of the slab.

As shown in FIG. 9, the display device 190 may display inclusion information on all regions of the slab, and may also display inclusion information on some regions of the slab. The user may select a region of interest from all regions, and the display apparatus 190 may enlarge and display the inclusion information on the selected region.

As described above, according to the present invention, the step of transferring the plurality of slab samples one by one to the signal generating device sequentially and the previous step of displaying the inclusion information on the plurality of slab samples (part or all of the slab) on the display device is automatically performed. The user can quickly obtain and grasp the comprehensive information of the inclusion information.

10 shows in a flow chart a method of evaluating the cleanliness of a slab sample. First, when the slab sample is put into the device, the sample transfer device transfers the sample to the sample preparation device to process the sample surface, and the sample preparation device mills the sample surface. The surface-treated slab sample generates an inclusion signal while generating an electrical spark in 12 rows in the signal generator, and transmits the signal to the signal processor. The signal processing apparatus maps the type, position, and size of the inclusions to be displayed on the screen. In this case, the inclusion information obtained from the plurality of samples may be aggregated to display inclusion information on the full width of the slab. On the other hand, the signal processing apparatus calculates the inclusion oxygen index and the inclusion nonuniformity index of the slab sample and displays them together. Thus, the user can quickly determine the cleanliness of the slab. If the slab cleanliness is good, the slab is put directly into the post-process. However, if the cleanliness of the slab is not good, the slab cannot be put into the post-process and precision measurement is requested.

1 is a block diagram of a slab cleanliness evaluation apparatus according to an embodiment of the present invention.

FIG. 2 shows the slab end face divided into a plurality of slab samples having an area of 100 × 50 mm.

3 illustrates a damage pattern caused by a spark generated while continuously moving.

4 shows the configuration of a signal generator.

FIG. 5 is a diagram showing a time axis for each channel converting an optical signal from each slit of the signal generator into an electrical signal in a photomultiplier tube.

6 is data obtained by measuring inclusions in a sample produced using pure iron as a raw material.

7 is data obtained by measuring inclusions in a sample prepared by adding alumina powder to pure iron.

8 shows the relationship between the area sum of the alumina inclusions and the inclusion oxygen index.

9 shows a screen displaying the inclusion information of the slab.

10 shows in a flow chart a method of evaluating the cleanliness of a slab.

Description of the Related Art [0002]

100 slab sample 120 signal generator

121 High voltage generator 122 Electrode

123 Lattice 124 Slit

125 photomultiplier pipe 140 signal processing unit

160 Sample transfer unit 180 Sample preparation unit

190 display device

Claims (14)

A signal generator 120 for obtaining a spectrum of each element contained in the slab sample from the spark generated by applying a voltage to the slab sample, A sample transfer device 160 which sequentially transfers a plurality of slab samples to the signal generator 120 one by one, Signal processing for acquiring the inclusion information contained in the slab sample based on the spectrum obtained by the signal generator 120 and integrating the inclusion information of each slab sample to obtain the inclusion information for the plurality of slab samples transferred. Device 140, and Slab cleanliness evaluation device including a display device for displaying the inclusion information obtained by the signal processing device (140). The method according to claim 1, The display device 190 is a slab cleanliness evaluation device, characterized in that to visually display the inclusion information on the plurality of transported slab samples. The method according to claim 2, The inclusion information includes the type, size, and location information of the inclusions, and the slab cleanliness evaluation device, characterized in that to display different colors for each type of inclusions. The method according to claim 1, Further comprising a sample preparation device 180 for processing the surface of the plurality of slab samples, The slab cleanliness evaluation device, characterized in that the sample transfer device 160 transfers the slab samples processed by the sample preparation device (180) one by one to the signal generator (120) one by one. The method according to claim 1, The sample transfer device 160 continuously moves the slab sample so that sparks occur in a plurality of rows of the slab sample, The signal processing device 140 calculates the area of inclusions in each row of the slab sample based on the spectral signal strength obtained by the signal generating device 120 increasing in proportion to the second or third power of the inclusion radius. Based on the area of inclusions in each row, Obtain the inclusion oxygen index of the slab sample based on the correlation database with the measured oxygen index, A slab cleanliness evaluation device, characterized in that the inclusion nonuniformity index is calculated based on a standard deviation from the measured inclusion area. The method of claim 5, The signal processing device 140, For each row of the slab sample, the intensity of the spectrum over time is measured for a plurality of elements contained in the slab sample, Obtain data of the frequency of occurrence for the intensity of the spectrum, And the inclusion area of each row is obtained by multiplying the intensity of the spectrum outside the normal distribution of pure iron from the data and the frequency of occurrence of the intensity of the spectrum. The method according to claim 5 or 6, The signal processing device 140 obtains the sum of the inclusion areas present in the slab sample by adding the inclusion areas of each row, and uses the correlation equation of the sum of the inclusion areas and the inclusion oxygen index to determine the inclusion oxygen index of the slab sample. Slab cleanliness evaluation device, characterized in that obtaining. The method according to claim 5 or 6, The signal processing device (140) is a slab cleanliness evaluation device, characterized in that for determining the standard deviation of the inclusion area of each row as inclusion index. The method of claim 8, The slab cleanliness evaluation device, characterized in that the cleanliness is bad if the inclusion nonuniformity index is more than the reference value, and that the cleanliness is good if the inclusion nonuniformity index is less than the reference value. Transferring a plurality of slab samples one by one to a signal generator, Obtaining a spectrum of each element contained in each slab sample from the spark generated by applying a voltage to the slab sample in the signal generator; Acquiring inclusion information contained in the slab sample based on the spectrum obtained by the signal generator, and incorporating the inclusion information of each slab sample to obtain inclusion information on the plurality of slab samples transferred; and Slab cleanliness evaluation method comprising the step of visually displaying the inclusion information for the plurality of slab samples to visually display. The method of claim 10, The inclusion information includes the type, size, and location information of the inclusions, and the slab cleanliness evaluation method characterized in that the color is displayed differently for each type of inclusions. The method of claim 10, In the obtaining of the spectrum, while generating a spark by applying a voltage to the slab sample while continuously moving the slab sample in a plurality of rows, and obtaining the spectrum of each element contained in each row of the slab sample from the generated spark , In the step of obtaining the inclusion information, the area of inclusions in each row of the slab is obtained based on the obtained spectral signal strength increasing in proportion to the second or third power of the inclusion radius. Based on the inclusion area of each row above, Obtain the inclusion oxygen index of the slab sample based on the correlation database with the measured oxygen index, A method for evaluating slab cleanliness, characterized in that the inclusion nonuniformity index is calculated based on a standard deviation from the measured inclusion area. The method of claim 12, The sum of the inclusions present in the slab sample is obtained by adding the inclusion areas of each row, and the inclusion oxygen index of the slab sample is calculated using the correlation of the sum of the predetermined inclusion area and the inclusion oxygen index. Degree evaluation method. The method of claim 12, The slab cleanliness evaluation method, characterized in that the standard deviation of the inclusion area of each row is determined by the inclusion nonuniformity index of the slab sample.

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