KR20220026155A - System of estimating residual scale in rolled steel sheet - Google Patents

Abstract

The present invention provides a residual scale prediction system of a steel sheet for rapidly and quantitatively predicting a residual scale amount. According to an embodiment of the present invention, the residual scale prediction system of a steel sheet comprises: a pickling processing unit for pickling a steel sheet; a colorimetric value measuring unit measuring a colorimetric value of the surface of the steel sheet and including a non-contact colorimeter disposed spaced apart from the steel sheet; and a data processing unit for predicting a residual scale amount from the colorimetric value.

Description

System of estimating residual scale in rolled steel sheet}

The technical idea of the present invention relates to steel manufacturing, and more particularly, to a method for predicting residual scale of a steel plate and a system for predicting residual scale of a steel plate.

The slab is hot rolled and cold rolled to form a cold rolled steel sheet. In a hot-rolled steel sheet formed by hot rolling, iron on the surface reacts with oxygen under high temperature to form an iron oxide film, and this iron oxide film is referred to as a scale. In order to secure the surface quality of the cold-rolled steel sheet, the hot-rolled steel sheet is subjected to a pickling process before performing cold rolling, and the scale is removed by chemical reaction with an acid such as hydrochloric acid. Even after the pickling process, residual scale remaining on the surface of the steel sheet may cause surface quality problems such as deterioration of the appearance quality of the cold-rolled steel sheet, deterioration of plating properties, and deterioration of corrosion resistance. Therefore, in order to improve the surface quality of cold-rolled steel sheets, it is essential to accurately measure and manage the amount of residual scale remaining after the pickling process. In particular, the quality of the steel sheet after the pickling treatment can determine the surface quality of the cold-rolled steel sheet. If a scale defect occurs in the cold-rolled steel sheet and responds afterward, there is a risk of expanding to a mass defect of the cold-rolled steel sheet, so it is necessary to evaluate the pickling quality in real time during the rolling process.

As a conventional method for evaluating the amount of residual scale, there are a surface gloss measurement method, an image analysis method, a visual discrimination method, and the like. The surface glossiness measurement method has a limit in that the glossiness value obtained from the glossmeter varies greatly depending on the surface roughness of the material. The image analysis method has a disadvantage in that it takes a long time to pre-process the specimen. The visual discrimination method has a problem in that it is difficult to present a quantitative judgment standard.

Korean Patent Application No. 10-2010-0137220

The technical problem to be achieved by the technical idea of the present invention is to provide a system for predicting residual scale of a steel sheet for rapidly and quantitatively predicting the amount of residual scale.

However, these tasks are exemplary, and the technical spirit of the present invention is not limited thereto.

According to one aspect of the present invention, there is provided a system for predicting residual scale of a steel sheet.

According to an embodiment of the present invention, the residual scale prediction system of the steel sheet, the pickling processing unit for pickling the steel sheet; a colorimetric value measuring unit that measures the colorimetric value of the surface of the steel sheet and includes a non-contact colorimeter that is spaced apart from the steel plate; and a data processing unit for predicting a residual scale amount from the color space value.

According to an embodiment of the present invention, the data processing unit may predict the residual scale amount from the color space value by using a correlation between the color space value and the residual scale amount.

According to an embodiment of the present invention, a database unit for storing the correlation between the color space value and the residual scale amount and providing it to the data processing unit may be further included.

According to an embodiment of the present invention, the pickling processing unit, the scale crushing zone for crushing the scale formed on the surface of the steel sheet; a pickling zone for pickling the steel plate; a washing zone for washing the steel plate with water; and a drying zone for drying the steel sheet.

According to an embodiment of the present invention, the color space value measured by the color space value measuring unit may include at least one of a white value, a red-green value, and a yellow-blue value.

According to an embodiment of the present invention, the colorimetric value measuring unit may include: a colorimeter reciprocally transferred in the width direction of the steel sheet; and a transfer means for reciprocating the colorimeter.

According to an embodiment of the present invention, the colorimetric value measuring unit includes: a central colorimeter fixed to the center with respect to the width of the steel sheet; and a pair of moving colorimeters disposed on both sides of the central colorimeter and reciprocally transferred in the width direction of the steel sheet. and a transfer means for reciprocating the pair of moving colorimeters.

According to an embodiment of the present invention, the colorimetric value measuring unit may include a plurality of colorimeters arranged in a line in the width direction of the steel sheet.

According to the technical idea of the present invention, the residual scale prediction system of the steel sheet acquires the correlation between the color color value of the steel sheet and the residual scale amount, and uses the correlation using the measured color color value of the steel sheet to be predicted. The residual scale amount can be predicted. Therefore, it is possible to provide the effect of quantitatively predicting the amount of residual scale of the steel sheet to be predicted simply and quickly in real time. Therefore, it is possible to strengthen the quality inspection of the steel sheet to be predicted, and it is possible to prevent quality loss.

According to the technical idea of the present invention, it is possible to quantify the quality of the steel using a colorimeter after the pickling process. The colorimeter can be applied to a frame that measures while reciprocating in the width direction of the steel, or it can be measured by fixing it at a specific location. Alternatively, the colorimeter in the center may be fixed and a total of three areas may be measured together by configuring movable colorimeters that can measure the width change according to the width of the steel sheet at both edge portions. In addition, according to the measurement area of the colorimeter, it is also possible to measure the entire width of the steel sheet at the same time. In addition, in order to constantly manage the measured colorimetric value for each steel type, pickling conditions such as acid concentration, iron concentration, inhibitor concentration, temperature, speed, etc. may be linked and controlled. In addition, by measuring the measurement distance using a laser rangefinder, it is possible to automatically correct the measurement value error for the measurement distance deviation.

According to the technical idea of the present invention, since it is possible to evaluate the quality of the steel sheet according to the degree of pickling, the surface quality can be measured, and the quality of the final steel sheet can be predicted in advance through the surface quality measurement of the steel sheet, or a pre-response to defects is possible. As a result, quality costs can be reduced and mass defects can be prevented. In addition, it is possible to reduce costs by optimizing the pickling conditions of the steel sheet, and by deriving the sensitivity for each steel type to pickling temperature and acid concentration, etc., pickling treatment under the optimal conditions can save energy. In addition, since an appropriate pickling rate can be derived, the pickling rate can be improved, and productivity of the steel sheet can be improved. In addition, since the portion used for visually observing the surface quality of the steel sheet is improved, operator fatigue can be reduced, and measurement fluctuations caused by manual work can be minimized.

The above-described effects of the present invention have been described by way of example, and the scope of the present invention is not limited by these effects.

1 is a flowchart illustrating a method for predicting residual scale of a steel sheet according to an embodiment of the present invention.
2 is a flowchart illustrating a step of providing a steel sheet in the method for predicting residual scale of the steel sheet of FIG. 1 according to an embodiment of the present invention.
3 is a schematic diagram illustrating an image analysis method used to quantify the amount of residual scale in the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.
4 is a photograph showing the white value and the residual scale amount of the steel plate obtained by using the method for predicting residual scale of the steel plate according to an embodiment of the present invention.
5 is a schematic diagram illustrating a system for predicting residual scale of a steel sheet according to an embodiment of the present invention.
6 is a schematic diagram of the implementation of the pickling processing unit and the color space measurement unit of the system for predicting residual scale of a steel sheet according to an embodiment of the present invention.
7 is a schematic diagram illustrating an arrangement of a colorimeter included in a colorimetric value measurement unit of a system for predicting residual scale of a steel sheet according to an embodiment of the present invention.
8 and 9 are photographs showing the surface color of the steel sheet according to the pickling immersion time obtained using the method for predicting residual scale of the steel sheet according to an embodiment of the present invention.
10 is a graph showing the residual scale amount according to the pickling immersion time obtained using the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.
11 is a graph illustrating a relationship between an amount of residual scale and a white value obtained by using the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.
12 to 16 are graphs comparing the measured residual scale amount and the predicted residual scale amount obtained using the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The embodiments of the present invention are provided to more completely explain the technical idea of the present invention to those of ordinary skill in the art, and the following examples may be modified in various other forms, The scope of the technical idea is not limited to the following examples. Rather, these embodiments are provided so as to more fully and complete the present disclosure, and to fully convey the technical spirit of the present invention to those skilled in the art. In this specification, the same reference numerals refer to the same elements throughout. Furthermore, various elements and regions in the drawings are schematically drawn. Accordingly, the technical spirit of the present invention is not limited by the relative size or spacing drawn in the accompanying drawings.

The technical idea of the present invention is to provide a technique for measuring the degree of pickling that can quickly and quantitatively predict the amount of residual scale remaining on the surface of a steel sheet after the pickling process through surface color measurement.

1 is a flowchart illustrating a method (S100) for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to FIG. 1 , the method for predicting residual scale of the steel sheet ( S100 ) includes providing a steel sheet ( S110 ); measuring a first colorimetric value of the surface of the steel sheet (S120); measuring the residual scale amount on the surface of the steel sheet (S130); obtaining a correlation between the first colorimetric value and the residual scale amount (S140); measuring a second colorimetric value of the surface of the steel sheet to be predicted (S150); and predicting the residual scale amount of the prediction target steel sheet by substituting the second colorimetric value into the correlation (S160).

The step of providing the steel plate (S110) may be performed according to FIG. 2 below.

2 is a flowchart illustrating a step (S110) of providing a steel plate in the method (S100) for predicting residual scale of the steel plate of FIG. 1 according to an embodiment of the present invention.

Referring to FIG. 2 , the step of providing the steel sheet (S110) includes: providing a hot-rolled steel sheet (S111); and immersing the hot-rolled steel sheet in acid for pickling treatment (S112).

Referring back to FIG. 1 , the steps of measuring the first colorimetric value ( S120 ) and measuring the second colorimetric value ( S150 ) may be performed using a non-contact colorimeter.

In the case of using a contact colorimeter, it is difficult to measure the entire width of the steel sheet, and during the steel sheet manufacturing process, it is almost impossible to contact the contact colorimeter to a continuously moving steel sheet, and thus real-time measurement is impossible. there is On the other hand, in the case of using a non-contact colorimeter, measurement is possible while moving in the width direction of the steel plate, and measurement is possible by extending the sensor to the length of the width of the steel plate. It has the advantage of being able to measure in real time on the moving steel plate.

The first color space value and the second color space value may include a white value. Alternatively, the first colorimetric value and the second colorimetric value may include at least one of a white value (L ^* ), a red-green value (a ^* ), and a yellow-blue value (b ^* ).

The white value (L ^* ), the red-green value (a ^* ), and the yellow-blue value (b ^* ) may be set according to CIE L ^* a ^* b ^* standards. The white value (L ^* ) may be a value obtained by classifying brightness by setting pure black to 0 and pure white to 100. The red-green value (a ^* ) may be a value obtained by classifying red-green saturation by setting a positive (+) value to red and a negative (-) value to green. The yellow-blue value (b ^* ) may be a value obtained by classifying yellow-blue saturation by setting a positive (+) value to yellow and a negative (-) value to blue.

Referring back to FIG. 1 , the step of measuring the amount of residual scale on the surface of the steel plate ( S130 ) may be performed by photographing the surface of the steel plate with a digital camera, and then quantifying the residual scale through an image analysis method. there is. However, this is an example, and a case of applying various methods capable of quantifying the residual scale is also included in the technical spirit of the present invention.

3 is a schematic diagram illustrating an image analysis method used to quantify a residual scale amount in a method for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to FIG. 3 , an original photo of the steel sheet is obtained, and the original photo is converted into an 8-bit image. Then, a colorimetric measurement area is extracted from the 8-bit image. Then, black and white are divided based on a specific value in the colorimetric measurement area, and black pixels are counted. The counted black pixels correspond to the residual scale amount.

4 is a photograph showing the white value and the residual scale amount of the steel plate obtained by using the method for predicting residual scale of the steel plate according to an embodiment of the present invention.

Referring to FIG. 4 , by changing the pickling treatment conditions of the steel sheet, the residual scale amount and white value on the surface of the steel sheet were changed. In the black region, the white value was the lowest at 46.95, and the residual scale amount was the highest at 42.3%. In the blue region, the white value was 59.24, which was an intermediate value, and the residual scale amount was 26.6%, which was an intermediate value. In the red region, the white value was the highest at 61.09, and the residual scale amount was the lowest at 11.4%. Accordingly, the white value and the residual scale amount are related to each other, for example, may have an inverse relationship. Therefore, it is analyzed that if the pickling immersion time is increased, the white value is increased and the residual scale amount is decreased.

Referring back to FIG. 1 , in the step of acquiring the correlation between the first colorimetric value and the residual scale amount ( S140 ), the correlation is linear with respect to the residual scale amount (%) and the first colorimetric value. correlation can be shown.

The correlation may be established using only a white value among the first colorimetric values, for example, the correlation may be

[Residual scale amount (%)] = X - Y [L ^* ]

(here, 180 < X < 500, 2.5 < Y < 9, and L ^* is a white value).

In addition, the correlation may be established by using at least one or both of a white value, a red-green value, and a yellow-blue value in the first colorimetric value, for example, the correlation is

[Residual scale amount (%)] = C - D [L ^* ] + E[a ^* ] + F [b ^* ]

(here, 150 < C < 300, 2.8 < D < 4.5, -10 < E < 4, -10 < F < 3, L ^* is the white value, a ^* is the red-green value, and b ^* is the yellow-blue value) can have a relationship.

In addition, the method for predicting residual scale of a steel sheet according to an embodiment of the present invention may establish a correlation between colorimetric values and residual scale amounts for steel sheets having various compositions as a database. Using the database, it is possible to easily and simply predict the amount of residual scale in real time by measuring the colorimetric value of a desired steel sheet.

Hereinafter, a system for predicting residual scale of a steel plate implementing the method for predicting residual scale of a steel plate according to an embodiment of the present invention will be described. However, the residual scale prediction system described below is exemplary, and the spirit of the present invention is not limited thereto.

5 is a schematic diagram illustrating a system 100 for predicting residual scale of a steel sheet according to an embodiment of the present invention.

5, the residual scale prediction system 100 of the steel sheet, the pickling processing unit 110 for pickling the steel sheet; a colorimetric value measuring unit 120 for measuring a colorimetric value of the surface of the steel sheet; and a data processing unit 130 for predicting a residual scale amount from the color space value.

The system 100 for predicting residual scale of a steel sheet may further include a database unit 140 that stores the correlation between the color space value and the residual scale amount and provides it to the data processing unit 130 .

The color space measurement unit 120 may include a colorimeter for measuring the colorimetric value of the surface of the steel sheet, and may include a colorimetric value measurer including a non-contact colorimeter disposed apart from the steel sheet. The color space value measurement unit 120 may provide the measured color space value information to the data processing unit 130 . To this end, the color space measurement unit 120 and the data processing unit 130 may be electrically connected to each other in a wired or wireless manner.

The data processing unit 130 may receive information on the color space value from the color space value measurement unit 120 . The data processing unit 130 may predict the residual scale amount from the color space value by using the correlation between the color space value and the residual scale amount. The correlation may be input by a user or may be provided by data stored in the following database unit 140 .

The correlation may be obtained by the method for predicting residual scale of the steel sheet described above with reference to FIG. 1 .

The data processing unit 130 and the database unit 140 may be electrically connected to each other in a wired or wireless manner. Alternatively, the data processing unit 130 and the database unit 140 may be configured as a single computer system, or may be configured as various types of computer systems.

6 is a schematic diagram of the implementation of the pickling processing unit 110 and the color space measurement unit 120 of the system 100 for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to FIG. 6 , the steel plate M continuously transports the pickling processing unit 110 and the color space measurement unit 120 . Although not shown, the steel sheet M may reach the pickling treatment unit 110 by a continuous process after being hot-rolled in a hot rolling unit (not shown). In addition, the steel sheet M may be cold rolled in a cold rolling unit (not shown) by a continuous process after passing through the color space measurement unit 120 .

The pickling processing unit 110, the scale crushing zone 111 for crushing the scale formed on the surface of the steel plate (M); Pickling section 112 for pickling the steel plate (M); a washing zone 113 for washing the steel plate M with water; and a drying zone 114 for drying the steel plate M; may be configured to include.

The color space value of the surface of the steel plate M pickled by the pickling processing unit 110 may be measured by the color space value measuring unit 120 . The color space measurement unit 120 may include the colorimeter 150 , and may include, for example, a non-contact colorimeter disposed to be spaced apart from the steel plate M . The color space value measured by the color space measurement unit 120 may include at least one of a white value, a red-green value, and a yellow-blue value.

7 is a schematic diagram illustrating an arrangement of a colorimeter included in the colorimetric value measurement unit 120 of the system 100 for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to (a) of FIG. 7 , the color space measurement unit 120 includes a color meter 151 that is reciprocally transferred in the width direction of the steel sheet M; and a transfer unit 160 for reciprocating the colorimeter 151 . The width direction of the steel plate M may be a direction perpendicular to the moving direction of the steel plate. The conveying means 160 may include various conveying means. In this way, since the colorimeter 151 measures the color space value of the steel plate M while reciprocally transferred in the width direction of the steel plate M by the transfer means 160, the color space value for the entire surface of the steel plate M is calculated. It can be measured in real time.

Referring to FIG. 7B , the color space measurement unit 120 includes a central colorimeter 152 fixed to the center with respect to the width of the steel plate M; a pair of moving colorimeters 153 and 154 disposed on both sides around the central colorimeter 152 and reciprocally transferred in the width direction of the steel plate M; and a transport unit 160 for reciprocally transporting the pair of moving colorimeters 153 and 154 . In this way, the central colorimeter 152 is fixed to the center in the width direction of the steel plate M to measure and measure the colorimetric value of the central portion of the steel plate M, and a pair of moving colorimeters 153 and 154 are transported Since the color value of the steel plate M is measured while reciprocating in the width direction of the steel plate M with respect to the half region of the width of the steel plate M by means 160, the color space value for the entire surface of the steel plate M is calculated It can be quickly measured in real time.

Referring to FIG. 7C , the color space measurement unit 120 includes a plurality of color meters 155 arranged in a line in the width direction of the steel sheet M. As shown in FIG. The plurality of colorimeters 155 may simultaneously measure the color space value of the steel sheet M in the width direction of the steel sheet M as a whole. Therefore, it is possible to quickly measure the color space value of the entire surface of the steel sheet (M) in real time. In this case, since the conveying means 160 is not included, the apparatus can be relatively simple.

The arrangement of the colorimeter in the colorimetric value measuring unit as described above is exemplary, and the technical spirit of the present invention is not limited thereto.

Experimental example

Hereinafter, preferred experimental examples are presented to help the understanding of the present invention. However, the following experimental examples are only for helping understanding of the present invention, and the present invention is not limited by the following experimental examples.

First, a hot-rolled steel sheet is prepared. Table 1 shows the steel type and thickness of the hot-rolled steel sheet.

division steel grade thickness (mm) Experimental Example 1 very low soft material 4.5 Experimental Example 2 35K class 4.0 Experimental Example 3 45K class 3.4 Experimental Example 4 60K class 3.0 Experimental Example 5 80K class 3.6

Then, the hot-rolled steel sheet was immersed in hydrochloric acid to perform a pickling simulation experiment. In the pickling simulation experiment, the degree of scale removal on the surface of the hot-rolled steel sheet was changed by changing the immersion time, and the amount of residual scale was changed accordingly. In general, increasing the immersion time reduces the amount of residual scale. In the pickling simulation experiment, the hydrochloric acid concentration was 100 g/L, the pickling temperature was 85 °C, the inhibitor was 0.3 wt%, and the immersion time was 2 to 30 seconds.

Then, the color value of the surface of the steel sheet is measured using a non-contact colorimeter. The color space value may include a white value (L ^* ) or a white value (L ^* ), a red-green value (a ^* ), and a yellow-blue value (b ^* ) together. The separation distance of the non-contact colorimeter from the steel sheet was 90 mm, and the measurement area was the area of a circle with a diameter of 25.4 mm.

In the case of the non-contact colorimeter, since the light intensity of the light source and the light intensity reflected from the sample are simultaneously measured, the intensity of the light source according to time and wavelength, changes in detector sensitivity, etc. can be automatically corrected, and changes in ambient brightness and environment It can be measured without influence. However, the non-contact colorimeter is exemplary, and a case of using a contact-type colorimeter operating in contact with the surface of a steel sheet is also included in the technical spirit of the present invention.

Then, the surface image of the steel sheet, which was measured with the colorimeter, was taken with a high-performance digital camera, and the amount of residual scale was quantified through an image analysis method for the captured image. The image analysis method is as described above with reference to FIG. 3 .

Then, the correlation between the color space value and the residual scale amount was obtained by using a regression analysis method. The correlation may be a correlation between the residual scale amount and a white value. Alternatively, the correlation may be a correlation between the residual scale amount and a combination of a white value, a red-green value, and a yellow-blue value.

Next, the color color value of the surface of the steel sheet to be predicted, which is another steel sheet, was measured, and the color value was substituted into the correlation to predict the residual scale amount of the steel sheet to be predicted. The prediction target steel sheet may be a hot-rolled steel sheet subjected to hot rolling and pickling treatment.

8 and 9 are photographs showing the surface color of the steel sheet according to the pickling immersion time, obtained using the method for predicting residual scale of the steel sheet according to an embodiment of the present invention.

8 and 9, as the pickling immersion time increases, it can be seen that the surface color approaches from black to white. The experiment was repeated 3 times for each pickling immersion time on the same steel plate. After repeated execution, the average value of the residual scale amount was obtained.

10 is a graph showing the residual scale amount according to the pickling immersion time obtained using the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to FIG. 10 , as the pickling immersion time increased, the amount of residual scale decreased. In particular, when the pickling immersion time was between 10 seconds and 15 seconds, or between 5 seconds and 10 seconds, the amount of residual scale decreased sharply. These results are consistent with the results in which the brightness of the surface sharply increased in the time range in FIGS. 9 and 10 .

11 is a graph illustrating a relationship between a residual scale amount and a white value obtained using a method for predicting residual scale of a steel sheet according to an embodiment of the present invention.

Referring to FIG. 11 , it can be seen that the residual scale amount and the white value have a linear relationship with a negative slope. That is, it can be seen that as the residual scale amount increases, the white value decreases.

In Experimental Example 1, the relationship was [L ^* ] = 73.14 - 0.3159 x [residual scale amount (%)], and R ² = 96%. In Experimental Example 2, the relationship was [L ^* ] = 67.37 - 0.3453 x [residual scale amount (%)], and was calculated as R ² = 96%. In Experimental Example 3, the relationship was [L ^* ] = 64.33 - 0.3231 x [residual scale amount (%)], and was calculated as R ² = 96%. In Experimental Example 4, the relationship was [L ^* ] = 52.99 - 0.09032 x [residual scale amount (%)], and R ² = 96%. In Experimental Example 5, the relationship was [L ^* ] = 51.65 - 0.2004 x [residual scale amount (%)], and was calculated as R ² = 96%. From the above-described relationships, a correlation in Table 2 can be derived.

Table 2 shows the correlation between the measured residual scale amount and the white value and the correlation between the measured residual scale amount and the combination of the white value, the red-green value, and the yellow-blue value from the result of FIG. 11 . In Table 2, L ^* is a white value, a ^* is a red-green value, and b ^* is a yellow-blue value.

division correlation Experimental Example 1 White [Residual scale amount (%)] = 225 - 3.04 [L ^* ] Combination [Residual scale amount (%)] = 219-3.15[L ^* ]+2.67[a ^* ]+2.15[b ^* ] Experimental Example 2 White [Residual scale amount (%)] = 195 - 2.84 [L ^* ] Combination [Residual scale amount (%)] = 166-3.19[L ^* ]+3.98[a ^* ]-2.27[b ^* ] Experimental Example 3 White [Residual scale amount (%)] = 195 - 3.01 [L ^* ] Combination [Residual scale amount (%)] = 202-2.95[L ^* ]-1.09[a ^* ]+0.97[b ^* ] Experimental Example 4 White [Residual scale amount (%)] = 480 - 8.9 [L ^* ] Combination [Residual scale amount (%)] = 298-4.1[L ^* ]-9.94[a ^* ]-9.78[b ^* ] Experimental Example 5 White [Residual scale amount (%)] = 248 - 4.75 [L ^* ] Combination [Residual scale amount (%)] = 246-4.47[L ^* ]-2.08[a ^* ]-1.51[b ^* ]

Referring to Table 2, the correlation between the measured residual scale amount and the white value is as Equation 1 below.

<Equation 1>

[Residual scale amount (%)] = X - Y [L ^* ]

(where 180 < X < 500, 2.5 < Y < 9, and L ^* is the white value)

In addition, the correlation between the measured residual scale amount and the white value, the red-green value, and the yellow-blue value is as shown in Equation 2 below.

<Equation 2>

[Residual scale amount (%)] = C - D [L ^* ] + E[a ^* ] + F [b ^* ]

(Where, 150 < C < 300, 2.8 < D < 4.5, -10 < E < 4, -10 < F < 3, L ^* is the white value, a ^* is the red-green value, and b ^* is the yellow-blue value)

12 to 16 are graphs comparing the measured residual scale amount and the predicted residual scale amount obtained using the method for predicting residual scale of a steel sheet according to an embodiment of the present invention.

12 to 16 , (a) is a case where a white value is applied as a color space value, and (b) is a case where a combination of a white value, a red-green value, and a yellow-blue value is applied as a color space value.

Referring to (a) of each of FIGS. 12 to 16 , it can be seen that the actually measured residual scale amount and the predicted residual scale amount predicted using the white value according to the embodiment of the present invention match very well.

In addition, referring to (b) of each of FIGS. 12 to 16, the actually measured residual scale amount and the predicted residual scale amount predicted using the white value, the red-green value, and the yellow-blue value according to the embodiment of the present invention are very good. coincidence can be seen.

Table 3 is a table showing R ² and root mean square deviation (RMSE) for the measured residual scale amount and the predicted residual scale amount in the experimental examples of FIGS. 12 to 16 .

division R ² Root Mean Squared Deviation (RMSE) Experimental Example 1
(Fig. 12) White 95.0% 7.1% Combination 96.4% 6.8% Experimental Example 2
(Fig. 13) White 97.6% 6.4% Combination 98.8% 4.1% Experimental Example 3
(Fig. 14) White 97.1% 6.2% Combination 98.4% 4.5% Experimental Example 4
(Fig. 15) White 80.4% 19.9% Combination 97.5% 7.1% Experimental Example 5
(Fig. 16) White 95.1% 9.1% Combination 95.3% 8.9%

The technical spirit of the present invention described above is not limited to the above-described embodiments and the accompanying drawings, and it is the technical spirit of the present invention that various substitutions, modifications and changes are possible within the scope without departing from the technical spirit of the present invention. It will be apparent to those of ordinary skill in the art to which this belongs.

100: residual scale prediction system;
110: pickling processing unit;
111: scale crushing zone, 112: pickling zone,
113: washing area, 114: drying area,
120: color space measurement unit;
130: data processing unit, 140: database unit;
150, 151, 152, 153, 154, 155: colorimeter,
160: means of transportation, M: steel plate,

Claims (8)

Pickling processing unit for pickling the steel sheet;
a colorimetric value measuring unit that measures the colorimetric value of the surface of the steel sheet and includes a non-contact colorimeter that is spaced apart from the steel plate; and
A data processing unit for predicting a residual scale amount from the color space value;
Steel plate residual scale prediction system. The method of claim 1,
wherein the data processing unit predicts the residual scale amount from the color space value using a correlation between the color space value and the residual scale amount;
Steel plate residual scale prediction system. 3. The method of claim 2,
Further comprising a database unit for storing the correlation between the color space value and the residual scale amount and providing it to the data processing unit,
Steel plate residual scale prediction system. The method of claim 1,
The pickling processing unit,
a scale crushing zone for crushing the scale formed on the surface of the steel sheet;
a pickling zone for pickling the steel plate;
a washing zone for washing the steel plate with water; and
A drying zone for drying the steel sheet; including,
Steel plate residual scale prediction system. The method of claim 1,
The color space value measured by the color space value measurement unit includes at least one of a white value, a red-green value, and a yellow-blue value,
Steel plate residual scale prediction system. The method of claim 1,
The colorimetric value measurement unit,
a color meter reciprocating in the width direction of the steel sheet; and
Containing; conveying means for reciprocating the colorimeter
Steel plate residual scale prediction system. The method of claim 1,
The colorimetric value measurement unit,
a central colorimeter fixed to the center with respect to the width of the steel plate; and
a pair of moving colorimeters disposed on both sides around the central colorimeter and reciprocally transferred in the width direction of the steel plate; and
Containing, conveying means for reciprocating the pair of moving colorimeters
Steel plate residual scale prediction system. The method of claim 1,
The colorimetric value measurement unit,
Containing; a plurality of color meters arranged in a line in the width direction of the steel sheet;
Steel plate residual scale prediction system.

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