KR101746990B1

KR101746990B1 - Steal cleanness measurement apparatus and method

Info

Publication number: KR101746990B1
Application number: KR1020150185608A
Authority: KR
Inventors: 신용태; 류창우; 김완근; 문종호
Original assignee: 주식회사 포스코
Priority date: 2015-12-24
Filing date: 2015-12-24
Publication date: 2017-06-28

Abstract

A steel cleanliness evaluating apparatus according to an embodiment of the present invention includes a steel sample measuring device that scans a steel sample to measure a distribution of inclusions contained in the steel sample and derive an inclusion concentration from the measured inclusion distribution, And a correlation derivation device for deriving a correlation between the concentration and the crack index of the steel sample.

Description

Technical Field [0001] The present invention relates to a steel cleanliness measuring apparatus and method,

The present invention relates to a steel cleanliness evaluation apparatus and method.

HIC (Hydrogen Induced Cracking) tests are required to evaluate the cleanliness. For example, API steel is an intermediate product when producing pipe products in the hot rolled state. Even if the strength of the pipe product meets the required standard, the HIC standard of the pipe product does not satisfy the required standard, have. Therefore, it is necessary to perform the HIC test of the API lecture to satisfy the pipe HIC criterion. However, currently producers are improving the inclusions in the hot-rolled API steel, but there is no inclusion management standard and they do not know how to improve it.

Currently, HIC control standards are based on the NACE (TM) TM0284 test method for NACE (H2O) saturated water solution for 96 hours. After the immersion experiment, the HIC crack sensitivity and localization are detected using an ultrasonic flaw detector. The index is usually divided into four categories: Crack area ratio (CAR), Crack length ratio (CLR), Crack height ratio (CTR), and Crack sensitivity ratio (CSR). To obtain such a crack index, it is difficult to manage because it has to undergo rigorous testing. In addition, since the evaluation time takes 96 hours or more, it is almost impossible to evaluate the material so as to routinely perform such a test.

Therefore, if there is a quick and accurate measurement method, it is possible to know the crack index in the product state of the hot-rolled material in advance, and it is possible to know the product's acceptance and rejection before shipment, thereby enhancing the reliability of the product.

Published Patent Application No. 10-2011-0076423

An embodiment of the present invention provides an apparatus and method for evaluating steel cleanliness.

A steel cleanliness evaluation apparatus according to an embodiment of the present invention scans a first group of steel samples containing steel samples each having a measured crack index to determine the distribution of inclusions contained in the steel samples of the first group A steel sample measuring device which measures a first inclusion concentration group from the measured inclusion distribution; And a correlation derivation device for deriving a correlation equation from the first inclusion concentration group and the measured crack index; Wherein the steel signal measuring device scans a steel sample of a second group to measure a distribution of inclusions contained in each of the steel samples of the second group and derives a second group of inclusion concentration groups from the measured inclusion distribution And acquiring an acquired crack index of each of the second group of steel samples by applying the correlation equation to each inclusion concentration of the second inclusion concentration group.

For example, the steel sample measurement apparatus includes a steel sample preparation device for machining the surfaces of the first and second groups of steel samples before scanning the first and second groups of steel samples; A signal generating device for obtaining a spectrum of each element contained in the first and second groups of steel samples from sparks generated by applying a voltage to the first and second groups of steel samples; A steel sample transfer device for sequentially transferring the first and second groups of steel samples one by one to the signal generator; And a signal processing device for obtaining the inclusion information contained in each of the first and second groups of strong samples based on the spectrum obtained by the signal generating device and deriving an inclusion concentration degree by combining the respective inclusion information; . &Lt; / RTI >

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For example, the correlation derivation apparatus receives the measured crack index, and the first and second groups of steel samples may be hot-rolled API steel samples.

For example, the steel sample transferring apparatus may include a steel sample holder for transferring at least two of the first and second groups of steel samples in close contact with each other.

For example, the signal processing apparatus obtains a raw signal of the inclusion from a spectrum scanned for 50 to 400 占 퐏 from the time when the spark occurs, measures the type and size of the inclusion from the low signal, and maps , The size of the inclusion can be determined based on the intensity of the low signal of another element determined to be included in the inclusion in the same time zone as the time zone in which the inclusion is determined to contain oxygen.

For example, the information processing apparatus may further include a display device for visually displaying the inclusion information and the correlation equation by visualizing the inclusion information and the correlation equation, wherein the inclusion information includes types, sizes, and positional information of the inclusions, can do.

For example, the steel sample transfer device continuously moves the first and second groups of steel samples so that sparks occur in a plurality of rows of the first and second groups of steel samples, and the signal processing device The inclusion area of each row of the first and second groups of steel samples is obtained on the basis of the spectrum obtained by the signal generator, and based on the inclusion area of each row, the first and second groups of steel samples The inclusion concentration can be derived.

For example, the signal processing apparatus measures the intensity of a spectrum with respect to a plurality of elements contained in each row of the plurality of steel samples, acquires data of occurrence frequency with respect to the intensity of the spectrum, It is possible to determine the inclusion area of each row by multiplying the intensity of the spectrum out of the normal distribution of the pure iron by the frequency of occurrence of the intensity of the spectrum and the intensity of the spectrum in the data and determine the inclusion concentration of each row as the inclusion concentration.

A method for evaluating steel cleanliness according to an embodiment of the present invention includes the steps of: measuring a crack index for a steel sample of a first group; Measuring a distribution of inclusions contained in the first group of steel samples by scanning the first group of samples of steel and deriving an inclusion concentration from the measured inclusions; Deriving a correlation equation between the inclusion concentration and the crack index of the first group of steel samples; Measuring the distribution of inclusions contained in the second group of steel samples by scanning the second group of steel samples and deriving the inclusion concentration from the measured inclusion distributions; And applying the inclusion concentration degree of the second group to the correlation equation to obtain a crack index of the steel sample of the second group; . &Lt; / RTI >

For example, deriving the inclusion concentration may include: machining a surface of the first or second group of steel samples before scanning the first or second group of steel samples; Obtaining a spectrum of each element contained in the first or second group of steel samples from sparks generated by applying a voltage to the first or second group of steel samples; And acquiring the inclusion information contained in each of the first or second group of strong samples based on the spectrum and deriving an inclusion concentration degree by synthesizing the respective inclusion information; And the first and second groups of steel samples may be hot-rolled API steel samples.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to easily and quickly evaluate the cleanliness degree by using the correlation between the inclusion concentration of the steel sample and the crack index.

In addition, the kind, size, and position information of the inclusions present in the steel sample can be visually displayed so that they can be visually recognized, thereby making it possible to identify at a glance which composition is distributed in which part of the steel sample.

1 is a view showing a steel cleanliness evaluation apparatus according to an embodiment of the present invention.
FIG. 2 is a view showing the steel sample measurement apparatus shown in FIG. 1 in detail.
FIG. 3 is a time chart illustrating the optical signals output from the slits of FIG.
FIG. 4 is a schematic view showing a spark sequence occurring in the steel sample measurement apparatus of FIG. 2;
Fig. 5 is a photograph showing the damage mode of the present invention after spark generation. Fig.
Fig. 6 is a photograph showing a damage pattern after 3000 sparks of the present invention have occurred. Fig.
FIG. 7 shows signal processing of a signal processing apparatus for a low signal measured by the steel sample measuring apparatus shown in FIG.
FIG. 8 shows a low signal when the steel sample measuring apparatus of FIG. 1 measures inclusions between 0 and 400 usec after spark generation.
FIG. 9 shows a low signal when the steel sample measuring apparatus of FIG. 1 measures inclusions between 50 and 400 usec after spark generation.
FIG. 10 shows a low signal for an inclusion of each element in each intensity pipe in the steel sample measuring apparatus of FIG.
11 shows the inclusion map information of the signal processing apparatus shown in Fig.
Fig. 12 shows that damage patterns generated by sparks in the steel samples of the present invention are formed in 12 rows.
13 is a cross-sectional photograph of a steel sample in the case where a large number of large inclusions exist uniformly in the steel sample of the present invention.
Fig. 14 is a cross-sectional photograph of a steel sample in the case where a large inclusion is uniformly present in the steel sample of the present invention.
15 is a cross-sectional photograph of a steel sample in the case where a large inclusion is present in the steel sample of the present invention.
16 is a view for explaining a crack index appearing after a HIC (Hydrogen Induced Cracking) test.
17 shows the appearance of the specimen subjected to the HIC test.
18 is a cross-sectional photograph of a place where a real crack occurred.
Fig. 19 is a view for explaining a steel sample holder included in the steel sample transferring device of Fig. 1;
Fig. 20 shows a section of the steel sample section divided into a plurality of steel samples having an area of 100 x 50 mm.
Fig. 21 shows a screen displaying the inclusion information of a steel sample.
FIG. 22 shows an HMI screen in which signal processing is performed on a plasma signal that appears when electrical sparks are sequentially generated in a steel sample.
23 shows a contour diagram showing inclusion concentration of a steel sample with a crack index of " 0 ".
24 is a contour diagram showing inclusion concentration of a steel sample with a crack index of " 1.93 ".
Figure 25 shows the results of six measurements to implement Figure 24.
Fig. 26 shows the inclusion concentration contour map of a steel sample with a crack index of "0" and a steel sample with a crack index of "1.99".
Fig. 27 shows an ultrasonic image of a steel sample with a crack index of " 0 ".
28 shows an ultrasonic image of a steel sample with a crack index of " 1.99 ".
29 is a graph showing the correlation between the crack index and the portion occupying an inclusion concentration of 5,000 탆 ² or more.
30 is a flowchart showing a method for evaluating the cleanliness of a steel according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the embodiments of the present invention can be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. The shape and the size of the elements in the drawings may be exaggerated for clarity and the same elements are denoted by the same reference numerals in the drawings.

1 is a view showing a steel cleanliness evaluation apparatus according to an embodiment of the present invention.

1, a steel cleanliness evaluation apparatus according to an embodiment of the present invention may include a steel sample measurement apparatus 100 and a correlation derivation apparatus 200, and may further include a display apparatus 300 have.

The steel sample measurement apparatus 100 can scan a steel sample to measure the distribution of the inclusions contained in the steel sample and derive the inclusion concentration from the measured inclusion distribution. For example, a steel sample can be a steel sample that has been rolled to a specific size, such as a [100 x 50] mm size, like a hot-rolled API steel sample.

Here, the inclusion concentration is a value that quantitatively indicates how uniform the distribution of the inclusions is in the steel sample. In other words, a high concentration of inclusions means that a mass of inclusions exists in the local region. A more simple explanation is as follows. Consider two samples of the same area of steel. Assume that the number and area of the inclusions contained in the two steel samples are the same. [A] If the inclusions are very uniformly distributed in the steel sample, the concentration of the inclusions may be low because the [A] steel samples are not concentrated in the inclusions. [B] Steel samples are concentrated in three places, and [B] steel specimens may have a high concentration of inclusions because the inclusions are concentrated. The inclusion concentration can be used for deriving the correlation of the correlation derivation apparatus 200.

For example, the steel sample measuring apparatus 100 may include a signal generating apparatus 110, a steel sample transferring apparatus 120, a signal processing apparatus 130, and a steel sample preparing apparatus 140.

The signal generator 110 can acquire the spectrum of each element contained in the steel sample from sparks generated by applying a voltage to the steel sample. For example, the signal generator 110 may be implemented as an OES (Optical Emission Spectrometer).

The steel sample transferring device 120 can sequentially transfer a plurality of steel samples one by one to the signal generating device 110. For example, the steel sample transfer device 120 can be configured as a robotic arm to pick up and transfer a steel sample, and the steel sample can be transferred 42 mm to a steel sample width of 50 mm from left to right. Thereafter, the steel sample transfer device 120 can transfer the steel sample from the right to the left.

For example, the steel sample transfer device 120 may include a steel sample holder for transferring at least two of the plurality of steel samples in close contact with each other. The steel sample holder will be described later with reference to Fig.

The signal processing apparatus 130 can obtain the inclusion information contained in each of the plurality of steel samples based on the spectrum acquired by the signal generating apparatus 110 and derive the inclusion concentration degree by synthesizing the respective inclusion information. Details of the signal processing will be described later with reference to Figs. 3, 7 to 11 and 20 to 26. Fig.

Al-Ca-Mg-O, Al-Ca-Mg-Si-O, and Al-Ca are the most commonly found inclusions, -Mg-Si-Ti-O, and Al-Mg-O. These complex inclusions may be composed of oxides and most of them may contain Al elements. Since the inclusions are distributed in the rolled steel samples, the extensible inclusions tend to stretch in the rolling direction, and the non-extensible inclusions are likely to be crushed and scattered in the rolling direction.

For example, the signal processing device 130 derives the inclusion concentration of the first and second groups of steel samples, derives the correlation between the inclusion concentration and the crack index from the crack index of the first group of steel samples, And the crack index of the steel sample of the second group can be obtained based on the correlation.

The steel sample preparation apparatus 140 can process the surfaces of a plurality of steel samples before scanning for a plurality of steel samples. The surface processing of the steel sample may be a milling process for reducing noise and acquiring data precisely at the time of spectrum acquisition due to spark generation of the signal generating device 110.

The correlation derivation apparatus 200 can derive the correlation between the inclusion concentration of the steel sample and the crack index of the steel sample. Considering that the inclusions that are concentrated in the steel samples can cause cracks, it can be deduced that the correlation between the inclusion concentration of the steel samples and the crack index of the steel samples is high. The correlation derivation apparatus 200 can specify such correlation through precise measurement and calculation.

When the correlation between the inclusion concentration and the crack index is derived for some steel samples, the rest of the steel sample can be easily obtained by knowing only one of the inclusion concentration index and the crack index. Here, the time required for the measurement of the crack index may be longer than the time required for the measurement of the inclusion concentration. Therefore, when the inclusion concentration is measured for all the steel samples, the crack index of all the steel samples can be obtained even if the crack index is measured only for some steel samples.

Thus, the time to obtain the crack index for all steel samples can be reduced. That is, the cleanliness evaluation of the steel can be easily and quickly performed.

For example, the correlation derivation apparatus 200 can obtain the correlation by receiving the crack index of the first group of steel samples. Thereafter, the correlation can be used to derive the crack index of the second group of steel samples. A specific example and technical significance of the correlation will be described later with reference to FIG. 29, FIG. 30, and Table 1. FIG.

The display device 300 can visually display the correlation between the inclusion information of the steel sample and the crack index. Here, the inclusion information includes the type, size, and position information of the inclusions, and colors can be displayed differently according to the types of inclusions.

For example, the display device 300 may include a human machine interface (HMI) system. Here, the HMI system not only provides a display but also can control the steel sample measuring apparatus 100. Specific examples of the display can be seen from FIGS. 7 to 11 and FIG. 22 to FIG. 26.

FIG. 2 is a view showing the steel sample measurement apparatus shown in FIG. 1 in detail.

2, the steel sample measuring apparatus 100 may include a high voltage generating device 121, an electrode rod 122, a grating 123, a slit 124, and a light pipe 125.

When the high voltage generator 121 applies a high voltage of 300 Hz per second, a spark is generated between the steel specimen A and the electrode rod 122 adjacent to the steel specimen A. This spark is present in the steel specimen A To generate fluorescence including the spectrum of all the elements. This fluorescence spreads to the width of the grating 123 while traveling in the direction of the grating 123, and is split by the grating 123 into the slit 124. The slit 124 is located at a specific position where the spectrum of the elements is incident by the number of elements to be analyzed. At this time, since the respective wavelengths are determined according to the characteristic spectrum of the element, the desired number of slits 124 can be set.

Figure 2 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 spectra is determined according to a 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, and slits for calcium measurement can be sequentially arranged.

Each of the slits 124 is connected to a light pipe 125. Each spectrum incident through the light pipe 125 may be converted into an electrical signal and input to the signal processor 130. The signal processing apparatus 130 can analyze the inclusion of the steel sample A using the input electrical signal.

FIG. 3 is a time chart illustrating the optical signals output from the slits of FIG.

In Fig. 3, the spectrum of each element of the inclusions measured for 10 seconds by the signal generating device 110 is shown as an embodiment. In order to quantitatively analyze metallic specimens using conventional OES, the current signal input to each channel was added for 10 seconds, and the intensity was calculated and expressed as a quantitative value. However, in the present invention, as shown in FIG. 3, the spectrum of 10 seconds is represented by the x-axis as the time axis and the y-axis as the spectral intensity. Since the spectrum is displayed on the time axis, a spark is generated between the steel specimen and the electrode, so that the surface of the steel specimen is eroded and the signal is transmitted for 10 seconds. As a result, the inclusions in the surface of the steel specimen can be detected over time will be. Generally, the depth of erosion by a discharge of about 10 seconds is about 0.1 mm, and the size of the inclusions is from several mu m to several tens of mu m, so new inclusions at the position where erosion can be measured can be measured.

Referring to FIG. 3, spectral results are shown in a slit for measuring five slits, i.e., oxygen, aluminum, manganese, calcium, and iron wavelengths. The first graph at the top of the graph represents the spectrum of oxygen (O), marked with an asterisk (*) (1-9) where the oxygen peak along the time axis. In the actual spectrum, there are numerous small signals between the peak value and the peak value, but they are omitted and only the oxygen spectrum intensity is above the set value.

An asterisk (*) is used for the peak value of oxygen because most of the nonmetallic inclusions exist in the form of oxides. Therefore, when peaks of other elements exist simultaneously in the time zone in which the oxygen peak value exists, It is judged to exist in the sample. More specifically, oxygen (O) and manganese (Mn) are found simultaneously in the first star (1) with the first peak in the oxygen spectrum and in the second star (2) Oxygen (O) and manganese (Mn) in the third asterisk 3 having the third peak value, oxygen (O) and manganese (Mn) (O), and calcium (Ca) in the fifth star table 6 and oxygen (O) and calcium (Ca) in the sixth star table 6, (O) and manganese (Mn) in the eighth star table (8) and oxygen (O) in the ninth star table (9) , Aluminum (Al) and calcium (Ca), respectively, can be found at the same time.

Based on the spectrum peak value, it can be determined that manganese oxide is present in the first star (1), and alumina, manganese oxide, and calcium oxide are present in the second star (2). In addition, calcium oxide is present in the third star (3), alumina is present in the fourth star (4), manganese oxide and calcium oxide are present in the fifth star (5), and calcium oxide is present in the sixth star (6) It can be judged. In the seventh star table (7), only oxygen (O) is found, so it can be judged that bubbles exist. Further, it can be judged that manganese oxide is present in the eighth star (8), alumina and calcium oxide are present in the ninth star (9), respectively.

On the other hand, even when the oxygen peak value does not appear in the spectrum, the peak values of Al, Ca, Mg, Si, Ti and the like may occur singly or simultaneously. Therefore, the presence of inclusions can be judged on the basis of the peak values of Al, Ca, Mg, Si and Ti generated in the time zone different from the time zone in which the oxygen peak value exists.

In order to measure the size of the inclusion, the size of the inclusion can be determined based on the strength of the found element. For example, in the case of aluminum, the levels of a, b and c are set to a level in the fourth star table 4, b level in the second star table 2, and c level in the ninth star table (9) And the size of the inclusions can be evaluated. The a level can be evaluated as a large alumina inclusion, the b level can be evaluated as a medium-sized alumina inclusion, and the c-level can be evaluated as a small-sized alumina inclusion. The type and the size of the inclusions in a steel sample are entered into the database of the signal processing apparatus by setting the level and the inclusions are evaluated by this principle in parallel with the inclusion analysis work carried out offline in advance, It is possible to evaluate the kind and size of the inclusions in a short period of time and also to analyze the inclusions existing in the surface and the steel samples.

Thus, for each inclusion element to be evaluated, the spectrum is measured on a time axis and the peak value of the existing element is found to confirm that it is an inclusion of the element. Further, when a plurality of elements are simultaneously found, a composite inclusion or independent inclusion If it is judged that it is analyzed and only oxygen peak value is found, it can be judged as bubble inside the metal.

In addition, the size of the inclusions can be determined by setting the level to the intensity of the peak for each element, and the spectrum information measured on the time axis can be used to measure inclusions in deeper regions on the surface of the steel sample.

In FIG. 3, the aluminum (Al) channel shows a lot of peak values. However, as mentioned above, only a relatively large peak value is indicated and numerous small peak values are omitted. This is a problem that needs to set the offset correctly. This is because the offset determines the point at which aluminum (Al) and alumina (Al 2 O 3) are distinguished.

Fig. 4 is a schematic view showing a spark sequence occurring in the steel sample measuring apparatus of Fig. 2, Fig. 5 is a photograph showing a damage mode after spark generation according to the present invention, Fig. 6 is a graph showing a damage type FIG.

4, a steel sample 10 is placed on a stand 20, and a voltage is applied to the electrode rod 122 by a high voltage generator 121. When the frequency is applied at 300 Hz per second, the electrode rod 122 generates a spark in the steel sample 10, and electrical sparks occur in the order of 1 to 6.

Referring to FIG. 5, an electric spark is generated once to form a damage pattern, and the diameter is about 50 탆.

Referring to FIG. 6, a damaged pattern having a diameter of about 4 mm is generated when 3000 electric sparks occur. In the circle having a diameter of 4 mm, there are 3000 damaged patterns having a diameter of 50 μm shown in FIG. 5. As described above, signals are displayed for each channel of each element during 3,000 times of light emission. This arc generates fluorescence that contains the spectrum of all the elements present in the steel sample.

FIG. 7 shows signal processing of a signal processing apparatus for a low signal measured by the steel sample measuring apparatus shown in FIG.

Referring to FIG. 7, (A) shows a raw signal, (B) shows the y axis of (A) to correspond to the x axis, (D) shows the information converted into the size distribution information of the inclusions.

Specifically, (A) graphs the intensity of a low signal for an Al inclusion for a specified time. (B) shows a graph in which the horizontal axis indicates the intensity and the vertical axis indicates the frequency. Here, (B) is the sum of signals of Al dissolved in pure iron and alumina (Al2O3) composed of inclusions. (D) is a graph showing the size distribution of the inclusions compared with the inclusion size distribution measured by an actual microscope using (C). Accordingly, the steel sample measuring apparatus can measure the size distribution of inclusions.

FIG. 8 shows a low signal when the steel sample measuring apparatus of FIG. 1 measures inclusions between 0 and 400 usec after spark generation.

Referring to FIG. 8, in the process of measuring a low signal, the noise distribution is excessively higher than the inclusion peak signal. In the state where the steel sample does not move, a line spark is generated to remove the oxidized site on the surface of the steel sample to generate the main spark, thereby obtaining a stable inclusion peak. However, when sparks are generated while moving the steel sample, The same noise may be caused. Such an unstable signal can not accurately process an inclusion signal, and it may be difficult to measure an inclusion map.

FIG. 9 shows a low signal when the steel sample measuring apparatus of FIG. 1 measures inclusions between 50 and 400 usec after spark generation.

Referring to FIG. 9, when the measurement time is delayed by 50 usec behind, a stable inclusion signal can be obtained. When the inclusion signal processing is performed using such a stable inclusion signal, it is convenient to obtain the inclusion map.

FIG. 10 shows a low signal for an inclusion of each element in each intensity pipe in the steel sample measuring apparatus of FIG.

Referring to FIG. 10, a low signal for oxygen, a low signal for aluminum, a low signal for manganese, and a low signal for iron may be individually processed by the signal processor.

11 shows the inclusion map information of the signal processing apparatus shown in Fig.

Referring to FIG. 11, the left screen shows the inclusion map information that can give the user the size and composition information of the inclusion three-dimensionally, and the right screen shows the kind, size, and the like of the inclusions. Thus, the distribution and positional information of the inclusions present on the cross section of the steel sample can be accurately realized.

Fig. 12 shows that damage patterns generated by sparks in the steel samples of the present invention are formed in 12 rows.

Referring to FIG. 12, the signal processing apparatus can process the position information, the size, the composition, and the like of the inclusions existing in the steel sample by analyzing the optical signal generated in the 12th row, , The position, size, and composition of the inclusions can be visualized and displayed on the display device, thereby enabling the user to identify at a glance which composition is distributed in which part of the steel sample. Further, the signal processing apparatus can calculate the inclusion concentration degree by synthesizing these information.

Fig. 13 is a cross-sectional photograph of a steel sample in which a large number of large inclusions are uniformly present in the steel sample of the present invention, and Fig. 14 is a cross-sectional photograph of a steel sample in the case where a large number of large inclusions are uniformly present in the steel sample of the present invention And Fig. 15 is a cross-sectional photograph of a steel sample when a large inclusion is present in the steel sample of the present invention.

16 is a view for explaining a crack index appearing after a HIC (Hydrogen Induced Cracking) test.

Referring to FIG. 16, CSR represents a crack sensitivity ratio, CLR represents a crack length ratio, and CTR represents a crack thickness ratio. Where a is the crack length, b is the crack thickness, W is the section width, and T is the specimen thickness.

Also, the crack area ratio (CAR) can be defined in consideration of information on the area of the crack. CAR is the area of the total crack in the molecule, the denominator is the total area of the measurement, multiplied by 100, and expressed as a percentage of the area of the crack. Usually CAR is considered a lot because crack area is an important factor.

17 shows the appearance of the specimen subjected to the HIC test.

Referring to FIG. 17, it is a point where cracks are generated in a circular spot in the outer shape of a specimen taken out after immersing a steel specimen in an acidic solution of pH 5.2 or less for 96 hours.

18 is a cross-sectional photograph of a place where a real crack occurred.

Referring to FIG. 18, it can be seen that the inclusions contained in the hot-rolled API steel cause cracks in the acid solution. In order to prevent such cracks from occurring, it is necessary to produce steel having no inclusions in the hot-rolled API steel. However, when steel is produced, it is difficult to produce steel without inclusions because it can not measure information on inclusions in real time in the steelmaking process. Therefore, the steel cleanliness evaluation apparatus according to an embodiment of the present invention can quickly measure information on inclusions against hot-rolled API steel.

Fig. 19 is a view for explaining a steel sample holder included in the steel sample transferring device of Fig. 1;

19, (A) is a plan view, (B) is a front view, (C) is a side view, and (D) is a real picture.

For example, a steel specimen holder can have a structure in which two steel specimens [100 length x 20 x thickness 20] mm are stacked to hold two steel samples at the same time. The length of the steel sample holder is 102 mm, the width is 56 mm, and the height is 30 mm. Here, the height can be variously designed according to the thickness of the steel sample.

In addition, the steel sample holder may have a structure in which a compression spring is provided to push the aggregate inward to hold a steel sample. Thereby, a gap may not be generated between the two steel samples. When a clearance is formed in the steel sample, air enters into the gap, normal plasma is not generated, abnormal plasma is generated, and intense light is generated along with high sound. When such a signal enters the steel sample measuring device, it can not catch a normal inclusion signal to be.

Based on this principle, a steel sample holder is manufactured, and two steel samples for HIC are caught in a steel sample holder and the inclusions are measured. In addition, since it is possible to evaluate in the thickness direction of a steel sample, the crack index can be more accurately derived by using bulk inclusion information when the inclusion information in the thickness direction is synthesized and analyzed. In the steel sample holder, since the thickness of the hot-melt API steel is varied from 4.5 mm to 19 mm, it can be processed into a specimen to facilitate measurement of inclusions. In addition, since it is convenient for the steel sample holder to use a steel sample made of a steel sample for HIC as it is, it is convenient to make it unnecessary to prepare a steel sample separately.

Fig. 20 shows a section of the steel sample section divided into a plurality of steel samples having an area of 100 x 50 mm. Thus, by detecting the inclusion information for each steel sample and synthesizing the data for each steel sample, it is possible to grasp the inclusion information for a part or all of the steel sample.

In order to grasp the inclusion information of the steel sample, when a steel sample is put into the apparatus, the steel sample transferring device lifts the steel sample from the steel sample mounting table to process the surface of the steel sample and transfers it to the steel sample preparation apparatus, The surface of the steel sample is milled.

When the steel sample transferring device elevates the steel sample to the stand of the signal generating device after the surface of the steel sample is milled, the spark is continuously generated in the signal generating device. While the sparking occurs, the steel sample transferring device transfers the steel sample to one side For example, from left to right. When moving, move the 42 mm of the 50 mm length of the steel sample minus the left margin, and then move the remaining 42 mm of the right side again to generate a spark.

At this time, since the spark is generated while continuously moving the steel sample, the inclusion low signal is continuously measured.

FIG. 21 is a view illustrating a display device that visually displays the kind, size, and position information of inclusions present on the entire cross-sectional area of the steel sample so as to be visually recognizable. In the display screen, the kinds of inclusions can be visually recognized easily by displaying different colors according to the types of inclusions. Thus, the user can identify at a glance which composition is distributed in which part of the steel sample.

As shown in Fig. 21, the display device not only displays the inclusion information for the entire area of the steel sample but also display the inclusion information for a part of the area of the steel sample. The user can select some areas of interest from among the entire area, and the display device can enlarge and display the inclusion information for the selected part of the area.

As described above, according to the present invention, from the step of transferring a plurality of steel samples one by one to the signal generating device, the previous step of displaying the inclusion information on a plurality of steel samples (part or all of the steel samples) So that the user can quickly acquire and grasp comprehensive information of the inclusion information.

FIG. 22 shows an HMI screen in which signal processing is performed on a plasma signal that appears when electrical sparks are sequentially generated in a steel sample.

22, (1) is information showing the size, position and composition of the inclusions in the form of a map, (2) shows the size distribution of the inclusion of Al 2 O 3 type, (3) (4) represents information corresponding to 10um or more of the information on the size, area, and number of the inclusions. (5) is information indicating how much interstitial oxygen exists in the steel using the inclusion information. (6) is information indicating the content and composition ratio of the inclusions in the inclusion map shown in (1).

As such, the signal processing device serves to represent the size and position of the inclusions on the surface of the steel sample in the form of a map.

Fig. 23 is a contour diagram showing inclusion concentration of a steel sample having a crack index of " 0 ", and Fig. 24 is a contour diagram showing inclusion concentration of a steel sample having a crack index of " 1.93 ".

Referring to FIG. 23, it is shown that there is almost no region where inclusions are concentrated in the steel sample.

Referring to FIG. 24, there is a region where inclusions are concentrated in the steel sample. This means that cracks can occur because the inclusion concentration region forms a link.

Figure 25 shows the results of six measurements to implement Figure 24.

Referring to Fig. 25, the signal processing apparatus can represent the contour map of the steel sample in the depth direction.

In order to signal one layer, it divides into 20 rows and 9 columns and divides into 180 sections, and this individual section becomes [4 x 4] mm section. The area of the inclusion present in this individual section is represented by contour lines, and the closer the contour lines are, the higher the concentration of inclusions is.

Fig. 26 shows the inclusion concentration contour map of a steel sample with a crack index of "0" and a steel sample with a crack index of "1.99".

Referring to FIG. 26, it can be seen that a steel sample with a crack index of "0" has almost no contour line, and a steel sample with a crack index of "1.99" can easily see that the contour lines are densely formed.

Fig. 27 shows an ultrasonic image of a steel sample with a crack index of " 0 ".

Referring to Fig. 27, the ultrasonic image proves that there is almost no crack in the steel sample.

28 shows an ultrasonic image of a steel sample with a crack index of " 1.99 ".

Referring to FIG. 28, the ultrasonic image proves that cracks are present in a large number of steel samples.

Steel grade thickness Name of river Number of Occurrences Share
(%) correction
Share
(%) Reference
CAR index
(%) X80 19mmT B 5 2.8 1.25 - 1B 3 1.7 0.75 - 2B 12 6.7 3.00 - D 4 2.2 1.00 - 1D 8 4.4 2.00 - 2D 9 5.0 2.25 - 12mmT G 6 3.3 1.5 0 1G 4 2.2 One 0 2G 4 2.2 One 0 X70 12.7 mmT N 3 1.7 0.75 0.03 1N 2 1.1 0.5 0 2N 6 3.3 1.5 0.06 19mmT O One 0.6 0.25 0.06 1O 6 3.3 1.5 0.03 2O 6 3.3 1.5 0 I 2 1.1 0.5 0.23 1I 12 6.7 3.00 3.3 2I 10 5.6 2.50 1.93

Table 1 is a table showing the relationship between a specimen having a crack index and a portion occupying an inclusion concentration of 5,000 μm ² or more measured by the present apparatus. We will explain the correlation only with the data of the area having the CAR index. Counting the number of points with an inclusion area of 5,000 ㎛ 2 or more and counting the total number of measurements [20 columns X 9 rows] is calculated as a percentage divided by 180 points. This share can be expressed as the correction share by multiplying by the correction factor. This value is comparable to the CAR index. We do not know CAR index of "B" series and "D" series, but considering CAR index of "I" series, CAR index is estimated to have a value near "0.75 ~ 3". From this, it is considered that the 19mmT steel samples of the X80 steel grade show a high inclusion concentration phenomenon. As the CAR index of the "G" series steel is "0" and the corrected share is about 1 ~ 1.5, the difference between the CAR index steel samples and the steel samples Seems to be. The "N" series steel samples also had a CAR index between 0 and 0.06, and the adjusted share was slightly higher than the value. The "O" series steel samples have a thickness of 19mmT and the CAR index has a value between 0 and 0.06, but the correction share can be around 0.25 ~ 1.5. This is supported by the fact that the larger the thickness, the larger the number of large inclusions and the CAR index will be somewhat higher. In the case of "I" series steel samples, the CAR index is widely distributed in W / 4 and W / 2, and the CAR index is high in the CAR index of 3.3 and the adjusted share is about "3". Thus, it can be seen that the inclusion area has a tendency when compared with the CAR index with an occupied area of 5,000 탆 2 or more.

29 is a graph showing the correlation between the crack index and the portion occupying an inclusion concentration of 5,000 탆 ² or more.

29, the horizontal axis represents the inclusion concentration, and the vertical axis represents the crack index. From this, it can be seen that there is a correlation between the crack index and the inclusion concentration. When the number of entrances is rapidly measured, the CAR Index, which is a crack index, can be quickly obtained, thereby preventing cracking of the hot-rolled API steel.

For example, the correlation may be represented in three dimensions as shown in equation ^{[y = 0.0042x 3 -0.0303x 2 +} 0.0504x].

30 is a flowchart showing a method for evaluating the cleanliness of a steel according to an embodiment of the present invention.

Referring to FIG. 30, a method for evaluating steel cleanliness according to an embodiment of the present invention includes a step (S10) of measuring a crack index of a first group of steel samples, a step of deriving an inclusion concentration of the first group of steel samples A step S40 of deriving a inclusion concentration degree of the second group of steel samples and a step S50 of obtaining a crack index of the second group of steel samples, .

Here, the first group steel sample may be ten steel samples having a crack index by performing HIC test. The second group of steel samples may be ten or more steel samples that do not know the crack index.

The inclusions can be measured 6 times in the depth direction by the same method after removing the electric spark marks by cutting 0.6 mm of the surface of the steel sample after one measurement.

In the HIC test, when a steel sample is placed in a strong acid for 96 hours, cracks are generated in the area where the inclusions are present. Large cracks grow in the area with large inclusions and small cracks occur in the areas with small inclusions. These cracks are measured by ultrasound to measure the CAR (Crack Area Ratio) index, which is an index representing the sum of the crack areas per measurement area as a percentage.

The larger the crack index, the higher the concentration of inclusions in the steel samples. The high concentration of inclusions means that there is a mass of inclusions in the local region. A more simple explanation is as follows. Consider two samples of the same area of steel. Assume that the number and area of the inclusions contained in the two steel samples are the same. [A] Assuming that the inclusions are very uniformly distributed, and [B] the steel samples are concentrated in three places, [A] the steel samples have almost zero crack indexes because the inclusions are not concentrated However, since [B] steel samples are inclined only in three places, it is easy to generate cracks there, and when cracks are measured, they are high.

That is, inclusions concentrated in a steel sample may cause cracks. Therefore, the method for evaluating steel cleanliness according to an embodiment of the present invention can determine the crack index without having to perform the HIC test for most of the steel by deriving the correlation between the inclusion concentration and the crack index using such a characteristic have. Accordingly, the time for evaluating the cleanliness degree can be greatly shortened.

The present invention is not limited to the above-described embodiments and the accompanying drawings. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be self-evident.

100: Steel sample measuring device
110: Signal generator
120: Steel sample transfer device
121: High voltage generator
122: Electrode
123: Grid
124: slit
125: Madness piping
130: Signal processing device
140: Steel sample preparation device
150: Steel sample holder
200: correlation derivation device
300: display device

Claims

A first group of steel samples each including a steel sample having a measured crack index is scanned to measure the distribution of the inclusions contained in the first group of steel samples and the first inclusion concentration group is derived from the measured inclusion distribution A steel sample measuring device; And
A correlation derivation device for deriving a correlation equation from the first inclusion concentration group and the measured crack index; Lt; / RTI >
Wherein the steel sample measuring device scans a steel sample of a second group to measure a distribution of inclusions contained in each of the steel samples of the second group, derives a second group of inclusion concentration groups from the measured inclusion distribution, And the inclusion concentration of each inclusion concentration group is obtained by applying the correlation equation to each inclusion concentration of the inclusion concentration group.

The apparatus according to claim 1,
A steel sample preparation device for machining the surfaces of the first and second groups of steel samples before scanning the first and second groups of steel samples;
A signal generating device for obtaining a spectrum of each element contained in the first and second groups of steel samples from sparks generated by applying a voltage to the first and second groups of steel samples;
A steel sample transfer device for sequentially transferring the first and second groups of steel samples one by one to the signal generator; And
A signal processing device for acquiring the inclusion information contained in each of the first and second groups of strong samples on the basis of the spectrum acquired by the signal generating device and deriving an inclusion concentration degree by combining the respective inclusion information; The steel cleanliness evaluation apparatus comprising:

delete

The method according to claim 1,
The correlation derivation apparatus receives the measured crack index,
Wherein the first and second groups of steel samples are hot-rolled API steel samples.

3. The method of claim 2,
Wherein the steel sample transferring device comprises a steel sample holder for transferring at least two of the first and second group of steel samples in close contact with each other.

3. The method of claim 2,
Wherein the signal processing apparatus obtains a raw signal of the inclusion from a spectrum scanned for 50 to 400 占 퐏 from the point of time when the spark occurs and measures the type and size of the inclusion from the low signal and maps the inclusion, And determining the size of the inclusion on the basis of the intensity of the low signal of the other element determined to be included.

The method according to claim 6,
Further comprising a display device for visually displaying the inclusion information and the correlation equation in a visualized manner,
Wherein the inclusion information includes type, size, and position information of the inclusions, and displays different colors according to types of inclusions.

3. The method of claim 2,
The steel sample transferring device continuously moves the first and second groups of steel samples so as to generate sparks in a plurality of rows of the first and second groups of steel samples,
Wherein the signal processing apparatus obtains an inclusion area of each row of the first and second groups of strong samples on the basis of the spectrum acquired by the signal generating apparatus, A steel cleanliness evaluation device that derives inclusion concentration of a steel sample of a group.

9. The method of claim 8,
The signal processing apparatus includes:
Measuring intensities of spectra with respect to a plurality of elements contained in each row of the first and second groups of steel samples,
Acquiring data of occurrence frequency with respect to the intensity of the spectrum,
Wherein the inclusion area of each row is obtained by multiplying the intensity of the spectrum out of the normal distribution of the pure iron by the frequency of occurrence of the intensity of the spectrum in the data.

Measuring a crack index for a first group of steel samples;
Measuring a distribution of inclusions contained in the first group of steel samples by scanning the first group of samples of steel and deriving an inclusion concentration from the measured inclusions;
Deriving a correlation equation between the inclusion concentration and the crack index of the first group of steel samples;
Measuring the distribution of inclusions contained in the second group of steel samples by scanning the second group of steel samples and deriving the inclusion concentration from the measured inclusion distributions; And
Applying the inclusion concentration of the second group to the correlation equation to obtain a crack index of the steel sample of the second group; Wherein the steel cleanliness evaluation method comprises:

The method of claim 10, wherein deriving the inclusion concentration comprises:
Machining a surface of the first or second group of steel samples before scanning the first or second group of steel samples;
Obtaining a spectrum of each element contained in the first or second group of steel samples from sparks generated by applying a voltage to the first or second group of steel samples; And
Obtaining inclusion information included in each of the first or second group of strong samples based on the spectrum and deriving an inclusion concentration degree by integrating each of the inclusion information; Lt; / RTI >
Wherein the first and second groups of steel samples are hot-rolled API steel samples.