KR19990057388A - Determination of the correction function to improve the prediction accuracy of the final pass rolling load during steel sheet rolling - Google Patents
Determination of the correction function to improve the prediction accuracy of the final pass rolling load during steel sheet rolling Download PDFInfo
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- KR19990057388A KR19990057388A KR1019970077439A KR19970077439A KR19990057388A KR 19990057388 A KR19990057388 A KR 19990057388A KR 1019970077439 A KR1019970077439 A KR 1019970077439A KR 19970077439 A KR19970077439 A KR 19970077439A KR 19990057388 A KR19990057388 A KR 19990057388A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/58—Roll-force control; Roll-gap control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B2265/00—Forming parameters
- B21B2265/12—Rolling load or rolling pressure; roll force
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Abstract
본 발명은 가역식 압연기를 이용하는 후강판 압연중 제품의 두께편차 감소를 위해 가장 중요하게 요구되는 최종 패스에서의 압연하중 예측정도를 개선하기 위해 회귀식을 이용하여 각 압연인자들의 상관계수를 구하여 압연하중 예측치를 보정하는 방법에 관한 것으로 그 목적은 단순화된 압연하중 예측식에 의해서 발생되는 압연하중 실적치와의 오차를 시험을 통하지 않고 압연실적 데이터를 이용하여 압연하중 예측정도를 향상시킴으로써 실험을 통한 시간 및 경비부담의 절감은 물론 실험에 비해 신뢰독 훨씬 높은 압연하중 예측정도 개선방법을 제공하는데 있다.In order to improve the degree of prediction of the rolling load in the final pass, which is most important for reducing the thickness variation of the steel product during the steel sheet rolling using the reversible rolling mill, the correlation coefficient of each rolling factor is calculated using a regression equation, The present invention relates to a method of correcting load predictions, and more particularly, to a method of correcting load predictions by improving the rolling load prediction accuracy using rolling performance data without performing an error between actual rolling load actual values generated by a simplified rolling load prediction equation And to provide a method for improving the prediction accuracy of the rolling load, which is much higher than that of the experiment.
상기와 같은 목적을 달성하기 위하여 본 발명은 전체 압연실적 데이터로 부터 조업조건 그룹별 분류에 따라 최종 pass 압연하중 실적을 이용하여 회귀분석하므로써, 각 인자별 보정계수(A1)를 구하는 단계 : 구하여진 보정계수를 해당 그룹별 분류조건의 함수로 하여 보정함수(β)를 구하는 단계: 구하여진 보정함수를 이용하여 최종 pass의 압연하중 예측치를 보정한 후, 실제 데이터로 검정하는 단계 : 이와 같은 일련의 작업을 반복하여 모델정도를 더욱 증가시키는 단계를 포함하여 구성되는 후강판 압연중 최종 패스 압연하중 예측정도 개선을 위한 보정함수 결정방법을 요지로 한다.In order to achieve the above object, the present invention provides a step of obtaining a correction coefficient (A 1 ) for each factor by performing regression analysis using final pass rolling load performance according to classification according to operating condition group from total rolling performance data: Calculating a correction function (β) by using the true correction coefficient as a function of the classification condition for each group: correcting the rolling load prediction value of the final pass by using the obtained correction function, The present invention relates to a method for determining a correction function for improving the predicted degree of final pass rolling load during rolling of a steel sheet, comprising the steps of:
Description
본 발명은 가역식 압연기를 이용하는 후강판 압연중, 제품의 두께편차 감소를 위해 가장 중요하게 요구되는 최종 패스에서의 압연하중 예측정도를 개선하기 위해 회귀식을 이용하여 각 압연인자들의 상관계수를 구하여 압연하중 예측치를 보정하는 방법에 관한 것으로, 특히 실험을 통하지 않고 압연실적을 이용하여 압연하중 예측정도를 향상시킴으로써 실험을 통한 시간 및 경비부담의 절감은 물론 실험에 비해 신뢰도가 훨씬 높은 압연하중 예측정도 개선방법에 관한 것이다.In order to improve the degree of prediction of the rolling load in the final pass, which is most important for reducing the thickness deviation of the product, the correlation coefficient of each rolling factor is obtained by using a regression equation during the steel sheet rolling using the reversible rolling mill The present invention relates to a method of correcting rolling load predictions and, more particularly, to a method for correcting rolling load predictions, And an improvement method.
일반적인 후강판 압연에서 각 패스별 출측두께를 제어하기 위한 방법은 각 패스의 압연하중을 예측하고 이를 이용하여 압하위치(roll gap)을 설정하여 출측두께를 제어하고 있다.In order to control the exit thickness of each pass in general post-roll rolling, the roll thickness is controlled by predicting the rolling load of each pass and setting a roll gap using the predicted rolling load.
압하위치 설정식은 하기식 (1)과 같이 표현된다.The push-down position setting equation is expressed by the following equation (1).
RG = he + So - RF(cal.)(1/M + B) ·······.······(1)RG = he + So - RF (cal.) (1 / M + B)
(여기서, RG : 압하위치(roll gap)(Here, RG: roll gap)
he : 압축두께he: compression thickness
So : 영점보정(zero point calibration)So: zero point calibration
RF(cal.) : 압연하중 예측치RF (cal.): Rolling load predicted value
M : 밀상수(Mill Modulus)M: Mill Modulus
B : 폭 보상계수)B: Width compensation coefficient)
식 (1)에서 알 수 있듯이, 압연중 각 패스별 두께를 제어하고, 최종제품의 두께를 목표치에 적중시키기 위해서는 압연하중 예측치의 적중률이 크게 영향을 주게된다. 그 중에서도 최종 패스의 압연하중 예측정도는 최종 후강판 제품의 두께적중률을 위해서 가장 중요하게 확보되어야 된다.As can be seen from Equation (1), the hit ratio of rolling load predictions greatly influences the thickness of each pass during rolling and the final product thickness to the target value. In particular, the prediction of the rolling load of the final pass should be most important for the thickness hit ratio of the final finished steel product.
후강판 압연에서 압연하중 예측식은 공정상의 계산시간과 계산과정의 단순화를 위해서 하기식 (2)와 같이 각 항목이 단순 1차항으로 곱해져서 표현된다.In order to simplify the calculation time and calculation process in the rolling process, the rolling load prediction equation in the post-rolling process is expressed by multiplying each item by a simple first order as shown in the following equation (2).
RF(cal.) = α * Km * ε * f ··················(2)RF (cal.) = 留 * Km * 竜 * f (2)
(여기서, RF(cal.) : 압연하중 예측치(Here, RF (cal.): Rolled load prediction value
α : 학습계수α: learning coefficient
Km : 열간변형저항Km: Hot deformation resistance
ε : 상대 압하량(relative reduction)ε: relative reduction (relative reduction)
f : 롤 편평계수(roll flattening factor))f: roll flattening factor)
상기식 (2)에서 각 패스별로 열간변형저항과 상대 압하량, 그리고 롤 편평계수를 계산하고 전 패스의 압연하중 오차를 이용하여 학습계수를 계산하여 적용함으로써 압연하중을 예측하고 있다. 그러나, 각 인자별 계산이 단순화되어 있어 정도가 높지 않으므로, 이를 학습계수를 통하여 어떻게 보상해 주느냐가 압연하중 예측에 크게 영향을 미친다.In the equation (2), the rolling load is predicted by calculating the hot deformation resistance, the relative rolling reduction, and the rolling flat coefficient for each pass and calculating the learning coefficient using the rolling load error of the entire pass. However, since the calculations for each factor are simplified, the degree of compensation is not so high. Therefore, how to compensate it through learning coefficients greatly affects rolling load prediction.
본 발명은 상기와 같은 압연하중 예측시의 문제점을 보완, 개선하기 위해서 안출된 것으로써, 압연실적 및 기존의 압연하중 예측식의 인자계산을 그대로 이용하여 회귀식을 통한 보상계수를 계산하고 이를 새롭게 첨가함으로써 압연하중 예측정도를 높이는 것이 목적이다.The present invention has been devised in order to overcome and solve the problems in predicting the rolling load as described above. The present invention calculates a compensation coefficient through a regression equation using the rolling performance and the factor calculation of the existing rolling load prediction equation as it is, To increase the prediction of the rolling load.
본 발명은 상술한 목적을 달성하기 위하여, 전체 압연실적 데이터로 부터 조업조건 그룹별 분류에 따라 최종 패스 압연하중 실적과 각 인자별 보정계수(A1)를 구하는 단계와 ; 구하여진 보정계수를 해당 그룹별 분류조건의 함수로 하여 보정함수(β)를 구하는 단계와 ; 구하여진 보정함수를 이용하여 최종 pass의 압연하중 예측치를 보정한 후, 실제 데이터로 검정하는 단계와 ; 이와 같은 일련의 작업을 반복하여 모델정도를 더욱 증가시키는 단계를 포함하여 이루어지는 후강판 압연중 최종 패스 압연하중 예측정도 개선을 위한 보정함수 결정방법을 제공하는 것을 특징으로 한다.In order to achieve the above-mentioned object, the present invention provides a method of calculating a final pass rolling load performance and a correction coefficient (A 1 ) for each factor according to classification by operating condition group from total rolling performance data; Obtaining a correction function (?) By using the obtained correction coefficient as a function of the classification condition for each group; Correcting the rolling load prediction value of the final pass using the obtained correction function, and then performing a test using actual data; The present invention provides a method of determining a correction function for improving the predicted degree of final pass rolling load during rolling of a steel sheet including a step of repeating such a series of operations to further increase the degree of the model.
도 1은 전체 압연실적 데이터를 조업조건 그룹인 두께그룹별로 나누어서, 압연하중 실적과 압연하중 예측식에 사용되는 각 인자 계산치를 자연로그로 취하여 회귀분석을 통하여 구한 각 인자의 보정계수인 A0- A4의 값을 도시한 도면,1 is a correction coefficient of each factor obtained by a regression analysis by dividing the total rolling performance data by operating condition group with a thickness group, taking each factor calculated for use in rolling load performance and rolling load prediction formula to the natural logarithm of A 0 - A 4 ,
도 2 및 도 3은 각 조업조건 그룹별 보정함수(β)를 구하고, 이 함수를 이용하여 보정된 압연하중 예측치를 구하여, 압연하중 실적치와의 차이를 상대적으로 나타낸 Delta RF=(RF(meas.)-RF(cal.))/RF(meas.)를 구해 오차구간별로 발생빈도를 나타낸 막대그래프로서.FIGS. 2 and 3 show a correction function (?) For each operating condition group, and a corrected rolling load prediction value is obtained by using this function, and a difference Delta RF = (RF (meas. ) -RF (cal.)) / RF (meas.) As a bar graph showing frequency of occurrence by error interval.
(a)는 보정전의 압연하중 예측치와 실적치를 가지고 구한 Delta RF에 관한 막대그래프,(a) is a bar graph relating to Delta RF obtained with the predicted rolling load before correction and the actual value,
(b)는 보정후의 압연하중 예측치와 실적치를 가지고 구한 Delta RF에 관한 막대그래프이다.(b) is a bar graph relating to the Delta RF obtained with the predicted rolling load after correction and the actual value.
진하게 표시된 막대는 압연하중 예측치가 압연하중 실적치에 비해 ±10%이상의 오차를 가지는 부분이다.)A thickly marked bar is the part where the predicted rolling load has an error of more than ± 10% compared to the rolling load actual value.)
본 발명을 상세하게 설명하면 다음과 같다.The present invention is described in detail as follows.
본 발명에서의 후강판 압연중 최종 패스 압연하중 예측정도 개선을 위한 보정계수 결정방법은 전체 압연실적 데이터를 조업조건 그룹별로 분류하여 최종 pass 압연하중 실적과 계산된 각 인자간에 회귀분석을 통하여 보정계수(A1)를 하기식(3)과 같이 구하는 단계와 ;In the present invention, the correction factor determination method for improving the prediction accuracy of the final pass rolling load during the rolling of the final pass is classified by the operating condition group and the total rolling performance data is classified by the operating condition group, (A 1 ) as shown in the following equation (3);
Ln(RF(meas.)) = A0+ A1Ln(α) + A2Ln(Km) + A3Ln(ε) + A4Ln(f) ··········(3)Ln (RF) = A 0 + A 1 Ln (α) + A 2 Ln (Km) + A 3 Ln (ε) + A 4 Ln (f) 3)
(여기서, RF(meas.) : 압연하중 실적치)(Here, RF (meas.): Rolling load actual value)
각 해당 그룹별 보정계수(A1)를 이용하여 구하여지는 압연하중 회귀식을 압연하중 예측치로 나누는 보정함수(β)를 하기식(7)과 같이 구하는 단계와 ;Obtaining a correction function (?) For dividing the rolling load regression equation obtained by using the correction coefficient (A 1 ) for each corresponding group by the rolling load prediction value as shown in the following equation (7);
Ln(RF(meas.)) - Ln(RF(cal.)) = Ln(RF(meas.)/RF(cal.)) ·············(4)Ln (RF (meas)) - Ln (RF (cal.)) = Ln (RF (meas.) / RF (cal.))
Ln(RF(meas.)/RF(cal.))Ln (RF (meas.) / RF (cal.))
=[A0+A1Ln(α)+A2Ln(Km)+A3Ln(ε)+A4Ln(f)]-[Ln(α)+Ln(Km)+Ln(ε)+Ln(f)] = [A 0 + A 1 Ln (α) + A 2 Ln (Km) + A 3 Ln (ε) + A 4 Ln (f)] - [Ln (α) + Ln (Km) + Ln (ε) + Ln (f)]
=[A0+(A1-1)Ln(α)+(A2-1)Ln(Km)+(A3-1)Ln(ε)+(A4-1)Ln(f)] ············(5) = [A 0 + (A 1 -1) Ln (α) + (A 2 -1) Ln (Km) + (A 3 -1) Ln (ε) + (A 4 -1) Ln (f)] · (5)
RF(meas.)/RF(cal.) = β ···················(6)RF (meas.) / RF (cal.) =? ... (6)
∴ β = Exp[A0+(A1-1)Ln(α)+(A2-1)Ln(Km)+(A3-1)Ln(ε)+(A4-1)Ln(f)]··························(7) ∴ β = Exp [A 0 + (A 1 -1) Ln (α) + (A 2 -1) Ln (Km) + (A 3 -1) Ln (ε) + (A 4 -1) Ln (f )] · · · · · · · · · · · · · · · · · · · · · · · · · (7)
구하여진 보정함수(β)를 이용하여 최종 패스의 압연하중 예측치를 하기식(8)과 같이 보정한 후, 실제 데이터로 검정하는 단계와 ;Correcting the rolling load prediction value of the final pass by using the correction function?
RF(new)=β * RF(cal.)RF (new) =? * RF (cal.)
RF(new)=Exp[A0+(A1-1)Ln(α)+(A2-1)Ln(Km)+(A3-1)Ln(ε)+(A4-1)Ln(f)]*RF(cal.)······················(8)RF (new) = Exp [A 0 + (A 1 -1) Ln (α) + (A 2 -1) Ln (Km) + (A 3 -1) Ln (ε) + (A 4 -1) Ln (f)] * RF (cal.) ... (8)
마지막으로 이와 같은 일련의 작업을 압연실적 데이터를 축적하면서 지속적으로 반복하여 모델정도를 더욱 증가시키는 단계로 이루어 진다.Finally, this series of work is repeated as the rolling performance data is accumulated, further increasing the degree of the model.
상기에 서술한 단계를 통한 보정방법은 압연하중 실적치를 근거로 각 인자별 상관계수를 구하는 회귀분석을 통하여 구하여진 보정함수(β)를 이용함으로써, 압연하중 예측식이 간단화 됨으로써 발생하는 오차들을 각 보정계수와 각 인자별 계산치를 이용한 보정함수를 통하여 감소시킬 수 있다.The correction method based on the above-described steps uses the correction function (β) obtained through the regression analysis for obtaining the correlation coefficient for each factor based on the rolling load actual value, thereby correcting the errors caused by the simplification of the rolling load prediction equation Can be reduced through the correction function using the correction factor and the calculated value of each factor.
또한, 조업조건 그룹별로 보정계수(A1)를 구하여 보정함수(β)를 구하므로써, 압연하중 예측식의 각 인자별 정도향상을 통해서 전체 데이터의 정도향상을 꾀하는 방법에 비해, 그 안정성 및 신뢰도를 크게 할 수 있다.Compared with the method of improving the accuracy of the entire data by improving the degree of each factor of the rolling load prediction equation by obtaining the correction coefficient (?) By calculating the correction coefficient (A 1 ) for each group of operating conditions, Can be increased.
이하, 첨부된 도면에 의거하여 본 발명의 실시예를 설명하면 다음과 같다.Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.
(실시예)(Example)
도 1은 전체 압연실적 데이터를 조업조건 그룹인 두께그룹별로 나누어서, 압연하중 실적과 압연하중 예측식에 사용되는 각 인자 계산치를 자연로그로 취하여 각 인자의 보정계수인 A0- A4를 구한 것이다.FIG. 1 is a graph obtained by dividing total rolling performance data by thickness group, which is a group of operating conditions, and taking the values of the factors used in the rolling load history and rolling load prediction equation as natural logarithms, and calculating the correction coefficients A 0 - A 4 for each factor .
도 2 및 도 3은 구하여진 보정계수(A1)값을 이용하여 각 조업조건 그룹별 보정함수(β)를 구하고, 이 함수에 의해서 계산되는 보정값을 압연하중 예측식에 곱하여 나오는 보정된 압연하중 예측치를 구하여, 압연하중 실적치와의 차이를 상대적으로 나타낸 Delta RF=(RF(meas.)-RF(cal.))/RF(meas.)를 구해 오차구간별로 발생빈도를 나타낸 것으로, 왼쪽은 보정전의 압연하중 예측치와 실적치를 가지고 구한 Delta RF이고, 오른쪽은 보정후의 Delta RF로써, 진하게 표시된 막대는 압연하중 예측치가 압연하중 실적치에 비해 ±10%이상의 오차를 가지는 부분이다.FIGS. 2 and 3 show a correction function (?) Obtained by obtaining a correction function (?) For each operation condition group by using the obtained correction coefficient (A 1 ) value and multiplying the correction value calculated by this function by the rolling load prediction formula (Measured) - RF (meas.) - RF (meas.), Which shows the difference between the measured value and the rolling load actual value, Delta RF obtained with the predicted rolling load before correction and actual value, and on the right is Delta RF after correction. The thickly marked rod is a portion where the rolling load prediction value has an error of ± 10% or more with respect to the rolling load actual value.
도 2 및 도 3에서 알 수 있듯이, 압연하중 예측식에 사용되는 각 인자와 보정계수로 이루어지는 보정함수에 의한 보정된 압연하중 예측치는 ±10%이상의 오차범위에서의 발생빈도가 보정전의 압연하중 예측치보다 크게 감소된 것을 알 수 있다.As can be seen from Figs. 2 and 3, the corrected rolling load prediction value corrected by the correction function including each factor used in the rolling load prediction equation and the correction coefficient is calculated as the rolling load prediction value before correction As shown in FIG.
상술한 바와 같이 본 발명에 의한 방법은 종전의 실험을 통한 압연하중 예측식의 정도향상 보다 시간 및 경비부담 차원에서 그 효과가 더욱 크다고 볼 수 있다.As described above, the method according to the present invention is more effective in terms of time and expense than the improvement of the rolling load prediction equation through the previous experiments.
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| KR1019970077439A KR100325539B1 (en) | 1997-12-29 | 1997-12-29 | Calibration function determinating method for improving prediction accuracy of roll force of final pass during rolling of steel plate |
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| KR1019970077439A KR100325539B1 (en) | 1997-12-29 | 1997-12-29 | Calibration function determinating method for improving prediction accuracy of roll force of final pass during rolling of steel plate |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100519846B1 (en) * | 2001-12-27 | 2005-10-06 | 주식회사 포스코 | Method for prediction of rolling forces during hot rolling of stainless steel |
| KR100854361B1 (en) * | 2002-05-24 | 2008-09-02 | 주식회사 포스코 | Mill constant measuring method of continuous rolling mill |
| KR100880952B1 (en) * | 2008-07-31 | 2009-02-04 | 유넷웨어(주) | Rolling process control apparatus for improving material characteristic |
| KR100929015B1 (en) * | 2002-12-23 | 2009-11-26 | 주식회사 포스코 | Prediction of rolling load by calibrating plasticity factor of rolled material |
| KR100953622B1 (en) * | 2002-12-26 | 2010-04-20 | 주식회사 포스코 | A Method for On-line Rolling Force Prediction |
| KR101105900B1 (en) * | 2008-12-26 | 2012-01-17 | 주식회사 포스코 | Method of roll force prediction in hot plate rolling |
Families Citing this family (1)
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| CN113182363B (en) * | 2021-05-12 | 2022-08-19 | 南京钢铁股份有限公司 | Force arm coefficient calculation method of single-stand wide and thick plate rolling mill |
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1997
- 1997-12-29 KR KR1019970077439A patent/KR100325539B1/en not_active IP Right Cessation
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100519846B1 (en) * | 2001-12-27 | 2005-10-06 | 주식회사 포스코 | Method for prediction of rolling forces during hot rolling of stainless steel |
| KR100854361B1 (en) * | 2002-05-24 | 2008-09-02 | 주식회사 포스코 | Mill constant measuring method of continuous rolling mill |
| KR100929015B1 (en) * | 2002-12-23 | 2009-11-26 | 주식회사 포스코 | Prediction of rolling load by calibrating plasticity factor of rolled material |
| KR100953622B1 (en) * | 2002-12-26 | 2010-04-20 | 주식회사 포스코 | A Method for On-line Rolling Force Prediction |
| KR100880952B1 (en) * | 2008-07-31 | 2009-02-04 | 유넷웨어(주) | Rolling process control apparatus for improving material characteristic |
| KR101105900B1 (en) * | 2008-12-26 | 2012-01-17 | 주식회사 포스코 | Method of roll force prediction in hot plate rolling |
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| KR100325539B1 (en) | 2002-08-09 |
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