CN111570532B - Method for predicting influence of hot rolling coiling temperature and finish rolling temperature on flattening friction coefficient - Google Patents

Method for predicting influence of hot rolling coiling temperature and finish rolling temperature on flattening friction coefficient Download PDF

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CN111570532B
CN111570532B CN202010425912.9A CN202010425912A CN111570532B CN 111570532 B CN111570532 B CN 111570532B CN 202010425912 A CN202010425912 A CN 202010425912A CN 111570532 B CN111570532 B CN 111570532B
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CN111570532A (en
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白振华
魏宝民
何召龙
王孝剑
华长春
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Yanshan University
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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
    • B21B37/28—Control of flatness or profile during rolling of strip, sheets or plates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2261/00—Product parameters
    • B21B2261/02—Transverse dimensions
    • B21B2261/04—Thickness, gauge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2261/00—Product parameters
    • B21B2261/02—Transverse dimensions
    • B21B2261/06—Width
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2261/00—Product parameters
    • B21B2261/20—Temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2265/00—Forming parameters
    • B21B2265/02—Tension
    • B21B2265/04—Front or inlet tension
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2265/00—Forming parameters
    • B21B2265/02—Tension
    • B21B2265/08—Back or outlet tension
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2267/00—Roll parameters
    • B21B2267/02—Roll dimensions
    • B21B2267/06—Roll diameter

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  • Mechanical Engineering (AREA)
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Abstract

The invention provides a method for predicting the influence of hot rolling coiling temperature and finishing rolling temperature on a flat friction coefficient based on big data. The invention comprises the following steps: collecting the mechanical property parameters of the strip steel with certain production frequency, and collecting n groups of flattening unit process parameters in a certain production period. And calculating a leveling initial friction coefficient, a leveling friction coefficient, any group of theoretical rolling force and rolling power based on the acquired parameters. An optimization objective function is calculated based thereon. The method can obtain the coefficient of influence of the finish rolling temperature and the curling temperature on the flattening friction coefficient by fully combining the equipment characteristics of the flattening unit according to the field production condition of the flat rolling of the strip steel and analyzing the data of certain production frequency of the product, and can predict the friction coefficient of the product in the flat rolling process when the corresponding product is encountered in the subsequent production process, thereby effectively solving the problem of predicting the friction coefficient in the flat rolling process and laying a foundation for the mechanical property control of the field flattening unit.

Description

Method for predicting influence of hot rolling coiling temperature and finish rolling temperature on flattening friction coefficient
Technical Field
The invention relates to the field of temper rolling in a steel rolling process, in particular to a method for predicting the influence of hot rolling coiling temperature and finish rolling temperature on a temper rolling friction coefficient based on big data.
Background
In the temper rolling process, the friction coefficient is the basis of rolling pressure prediction, the calculation precision of the friction coefficient directly influences the prediction and setting precision of parameters such as rolling pressure, plate shape, elongation and the like, and the friction coefficient has a great influence on the quality of finished strips. For temper rolling, the final rolling temperature and the coiling temperature of the upstream process will affect the coefficient of friction to some extent.
The artificial neural network is an artificial intelligent mode recognition method established by simulating the learning process of cranial nerves to an external environment, has the characteristics of self-adaptive learning function and processing complex nonlinear phenomena, realizes the direct mapping of final rolling temperature and curling temperature parameters and friction coefficients in temper rolling, and can improve the accuracy of a forecast result.
In the leveling production process of the strip steel, the influence of the coiling temperature and the finishing rolling temperature of an upstream process on the friction coefficient is large, so that the actual production condition of a leveling rolling field must be fully combined to improve the product quality of the strip steel, and on the premise of fully utilizing field actual production data, the influence of the hot rolling coiling temperature and the finishing rolling temperature on the friction coefficient is combined to search out a set of prediction method for the influence of the hot rolling coiling temperature and the finishing rolling temperature on the leveling friction coefficient, which can be fully utilized.
Disclosure of Invention
According to the technical problems, the influence of the hot rolling coiling temperature and the finish rolling temperature on the flat friction coefficient is predicted based on big data. The technical means adopted by the invention are as follows:
a method for predicting the influence of hot rolling coiling temperature and finishing rolling temperature on a flat friction coefficient based on big data comprises the following steps:
A) collecting the mechanical property parameters of the strip steel with a certain production frequency (frequency is n), including the thickness h of the strip steel inlet0iOutlet thickness h of strip steel1iStrip steel inlet deformation resistance sigma0iStrip steel outlet deformation resistance sigma1iWidth of strip Bi(i＝1,2,3,···,i,···,n)；
B) Collecting n sets of temper mill process parameters including the exit rolling speed v of the stand in a production cycleiCoefficient of influence of frame speed on coefficient of friction BviAttenuation coefficient k of gantry velocity versus friction coefficientViThe rolling kilometers of the strip steel after the working rolls of the frame are changediInfluence coefficient B of rolling kilometers on friction coefficient after roll change of working rolls of machine frameliFlow rate Q of emulsion in the machine frameiCoefficient of influence of the flow of emulsion in the frame on the coefficient of friction BQiMachine for drillingDamping coefficient k of flow of emulsion to friction coefficientQiElongation of the frame εiCoefficient of influence of ith frame elongation on coefficient of friction BεiCoefficient of influence of the thickness of the strip inlet and outlet of the frame strip on the coefficient of friction
Figure GDA0002958596600000021
Front tension F of the frame0iRear tension F of the frame1iCoefficient of influence of the strip inlet and outlet tension on the coefficient of friction
Figure GDA0002958596600000022
Weighting coefficient k of front and rear tension of rack0i,k1iFrame model correction factor aiCoefficient of frame strain rate a0iThe influence coefficient a of the i frame for leveling steel grade1i(-10.0≤a1iLess than or equal to 10.0), the influence coefficient a of the ith frame working condition2i(-6.0≤a2iLess than or equal to 6.0), the influence coefficient of the average deformation resistance of the strip steel of the frame on the friction coefficient
Figure GDA0002958596600000023
Strip steel average deformation resistance K of framei,
Figure GDA0002958596600000024
Diameter D of working roll of machine frameiInfluence coefficient k of frame deformation resistance3iRadius R of the working roll of the frameiStandard hot rolling coiling temperature TCmiActual hot rolling coiling temperature TCaciStandard hot rolling finishing temperature TFmiActual finishing temperature T of hot rollingFaciActual rolling force P of the standi', actual rolling power of stand Ni'；
C) Calculating the initial coefficient of friction mu of the flatness0i：
Figure GDA0002958596600000025
D) Defining friction coefficient temperature influence coefficient array X ═ beta1，β2，γ1，γ2Giving an array initial value X0＝{β10，β20，γ10，γ20Given an initial search step size Δ X ═ Δ β1i，Δβ2i，Δγ1i，Δγ2i}, convergence accuracy α;
E) calculating the flat friction coefficient:
Figure GDA0002958596600000026
F) calculating any group of theoretical rolling force piAnd rolling power Ni:
F1) Let i equal to 1;
F2) calculating the equivalent deformation resistance sigma of the strip steelsi：
σsi＝k3σ1i-(k1F0i+k2F1i)
F3) Calculating the contact arc length L of the roller and the strip in the rolling deformation zonei：
Figure GDA0002958596600000031
F4) Calculating the unit width rolling force fi：
Figure GDA0002958596600000032
F5) Calculating the theoretically calculated rolling force Pi：
Pi＝fiBi
F6) Rolling moment M of calculation theoryi：
Figure GDA0002958596600000033
F7) Rolling power N of the calculation theoryi：
Figure GDA0002958596600000034
F8) Judging that i is less than N, and if so, turning to step F2 if i is equal to i + 1); if not, turning to the step G);
G) calculating an optimization objective function f (x):
Figure GDA0002958596600000035
H) determine whether Powell conditions hold? If yes, turning to the step I); if not, updating the array X and the search step length delta X thereof, and turning to the step E);
I) and outputting the influence coefficient of the hot rolling characteristic of the temper mill unit on the friction coefficient, and completing the prediction of the influence of the hot rolling characteristic of the temper mill unit based on a big data theory on the friction coefficient.
The invention has the following advantages:
the method can obtain the coefficient of influence of the finish rolling temperature and the curling temperature on the flattening friction coefficient by fully combining the equipment characteristics of the flattening unit according to the field production condition of the flat rolling of the strip steel and analyzing the data of certain production frequency of the product, and can predict the friction coefficient of the product in the flat rolling process when the corresponding product is encountered in the subsequent production process, thereby effectively solving the problem of predicting the friction coefficient in the flat rolling process and laying a foundation for the mechanical property control of the field flattening unit.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting the influence of hot rolling coiling temperature and finishing rolling temperature on the coefficient of flat friction based on big data.
FIG. 2 is a flow chart of a method for calculating theoretical rolling force and rolling power of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Next, a method for predicting the influence of the hot rolling coiling temperature and the finish rolling temperature on the leveling friction coefficient based on the big data will be described in detail with reference to fig. 1 by taking a certain leveling machine group as an example.
Example 1
Taking a certain temper mill as an example, according to a calculation flow chart of a method for predicting influence of hot rolling coiling temperature and finishing rolling temperature on the temper rolling friction coefficient based on big data shown in fig. 1 and fig. 2, firstly, in step (a), collecting strip steel mechanical property parameters and the like with certain production frequency (frequency is n), including strip steel inlet thickness h0iOutlet thickness h of strip steel1iStrip steel inlet deformation resistance sigma0iStrip steel outlet deformation resistance sigma1iWidth of strip Bi(i＝1,2,3,···,i,···,n)；
Subsequently, in step (B), n sets of temper mill process parameters including exit rolling speed v of the stand for a production cycle are collectediCoefficient of influence of frame speed on coefficient of friction
Figure GDA0002958596600000041
Attenuation coefficient k of gantry velocity to friction coefficientViThe rolling kilometers of the strip steel after the working rolls of the frame are changediInfluence coefficient B of rolling kilometers on friction coefficient after roll change of working rolls of machine frameliFlow rate Q of emulsion in the machine frameiCoefficient of influence of the flow of emulsion in the frame on the coefficient of friction BQiDamping coefficient k of flow of emulsion on friction coefficient of frameQiElongation of the frame εiCoefficient of influence of ith frame elongation on coefficient of friction BεiCoefficient of influence of the thickness of the strip inlet and outlet of the frame strip on the coefficient of friction
Figure GDA0002958596600000042
Front tension F of the frame0iRear tension F of the frame1iCoefficient of influence of the strip inlet and outlet tension on the coefficient of friction
Figure GDA0002958596600000043
Weighting coefficient k of front and rear tension of rack0i,k1iFrame model correction factor aiCoefficient of frame strain rate a0iThe influence coefficient a of the i frame for leveling steel grade1i(-10.0≤a1iLess than or equal to 10.0), the influence coefficient a of the ith frame working condition2i(-6.0≤a2iLess than or equal to 6.0), the influence coefficient of the average deformation resistance of the strip steel of the frame on the friction coefficient
Figure GDA0002958596600000051
Strip steel average deformation resistance K of framei,
Figure GDA0002958596600000052
Diameter D of working roll of machine frameiInfluence coefficient k of frame deformation resistance3iRadius R of the working roll of the frameiStandard hot rolling coiling temperature TCmiActual hot rolling coiling temperature TCaciStandard hot rolling finishing temperature TFmiActual finishing temperature T of hot rollingFaciActual rolling force P of the standi', actual rolling power of stand Ni'；
Subsequently, in step (C), a flat initial friction coefficient is calculated:
calculating the initial coefficient of friction mu of the flatness0i：
Figure GDA0002958596600000053
Subsequently, in step (D), the friction coefficient temperature influence coefficient array X ═ β is defined1，β2，γ1，γ2Giving an array initial value X0＝{β10，β20，γ10，γ20Given an initial search step size Δ X ═ Δ β1i，Δβ2i，Δγ1i，Δγ2i}, convergence accuracy α;
subsequently, in step (E), a coefficient of flat friction is calculated:
Figure GDA0002958596600000054
F) calculating any group of theoretical rolling force piAnd rolling power Ni:
First, in step F1), let i be 1;
subsequently, in a step F2), the strip equivalent deformation resistance σ is calculatedsi：
σsi＝k3σ1i-(k1F0i+k2F1i)
Subsequently, in step F3), the arc length L of the contact of the rolls with the strip in the rolling deformation zone is calculatedi：
Figure GDA0002958596600000055
Subsequently, in step F4), the unit width rolling force F is calculatedi：
Figure GDA0002958596600000056
Subsequently, in step F5), the theoretically calculated rolling force P is calculatedi：
Pi＝fiBi
Subsequently, in step F6), the theoretical rolling moment M is calculatedi：
Figure GDA0002958596600000057
Subsequently, in step F7), the theoretical rolling power N is calculatedi：
Figure GDA0002958596600000058
Finally, in step F8), it is determined that i < N, and if yes, the process proceeds to step F2 where i +1 is set to i + 1); if not, turning to the step G);
subsequently, in step (G), an optimization objective function f (x):
Figure GDA0002958596600000061
subsequently, in step (H), it is determined whether the Powell condition is satisfied? If yes, turning to the step I); if not, updating the array X and the search step length delta X thereof, and turning to the step E);
and finally, in the step (I), outputting an influence coefficient of the hot rolling characteristic of the temper mill set on the friction coefficient, and completing the influence prediction of the hot rolling characteristic of the temper mill set on the friction coefficient based on a big data theory.
Finally, for the convenience of comparison, the friction coefficient prediction result of the prediction method of the influence of the hot rolling coiling temperature and the finish rolling temperature on the flattening friction coefficient, which are based on the big data, of the flattening unit in the embodiment 1 is shown in the table 1.
In the table, the predicted value of the temper rolling coefficient is the result of step I, and the measured value of the temper rolling coefficient is the result of back calculation of the rolling pressure and the rolling power measured in the actual production process.
Table 1 leveling machine set friction coefficient prediction results in example 1
Figure GDA0002958596600000062
Example 2
The specific flow of the embodiment 2 is the same as that of the embodiment 1, and a table 2 shows the prediction result of the leveling unit in the embodiment 2 after the influence prediction method of the hot rolling characteristic based on the big data theory on the leveling deformation resistance is adopted, and the results of the embodiment 1 and the embodiment 2 are integrated, so that the influence coefficients of the finish rolling temperature and the curling temperature on the leveling friction coefficient are obtained through data analysis of a certain production frequency of the product, the friction coefficient in the leveling rolling process of the product can be predicted when the corresponding product is encountered in the subsequent production process, the prediction problem of the friction coefficient in the leveling rolling process is effectively solved, and a foundation is laid for the mechanical property control of the field leveling unit.
Table 2 prediction results of friction coefficient of leveling machine set in example 2
Figure GDA0002958596600000063
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. A method for predicting the influence of hot rolling coiling temperature and finishing rolling temperature on a flat friction coefficient based on big data is characterized by comprising the following steps:
A) collecting the strip steel mechanical property parameters of a certain production frequency, including the strip steel inlet thickness h0iOutlet thickness h of strip steel1iStrip ofResistance to deformation of steel inlet sigma0iStrip steel outlet deformation resistance sigma1iWidth of strip BiWherein, the data group i is 1,2,3, … i, …, n, n is frequency;
B) collecting n sets of temper mill process parameters including the exit rolling speed v of the stand in a production cycleiCoefficient of influence of frame speed on coefficient of friction
Figure FDA0002958596590000017
Attenuation coefficient k of gantry velocity to friction coefficientViThe rolling kilometers of the strip steel after the working rolls of the frame are changediInfluence coefficient B of rolling kilometers on friction coefficient after roll change of working rolls of machine frameliFlow rate Q of emulsion in the machine frameiCoefficient of influence of the flow of emulsion in the frame on the coefficient of friction BQiDamping coefficient k of flow of emulsion on friction coefficient of frameQiElongation of the frame εiCoefficient of influence of ith frame elongation on coefficient of friction BεiCoefficient of influence of the thickness of the strip inlet and outlet of the frame strip on the coefficient of friction
Figure FDA0002958596590000011
Front tension F of the frame0iRear tension F of the frame1iCoefficient of influence of the strip inlet and outlet tension on the coefficient of friction
Figure FDA0002958596590000012
Weighting coefficient k of front and rear tension of rack0i,k1iFrame model correction factor aiCoefficient of frame strain rate a0iThe influence coefficient a of the i frame for leveling steel grade1iI th rack condition influence coefficient a2iCoefficient of influence of the strip steel average deformation resistance of the frame on the friction coefficient
Figure FDA0002958596590000013
Strip steel average deformation resistance K of framei,
Figure FDA0002958596590000014
Diameter D of working roll of machine frameiInfluence coefficient k of frame deformation resistance3iRadius R of the working roll of the frameiStandard hot rolling coiling temperature TCmiActual hot rolling coiling temperature TCaciStandard hot rolling finishing temperature TFmiActual finishing temperature T of hot rollingFaciActual rolling force P of the standi', actual rolling power of stand Ni'；
C) Calculating the initial coefficient of friction mu of the flatness0i：
Figure FDA0002958596590000015
D) Defining friction coefficient temperature influence coefficient array X ═ beta1，β2，γ1，γ2Giving an array initial value X0＝{β10，β20，γ10，γ20Given an initial search step size Δ X ═ Δ β1i，Δβ2i，Δγ1i，Δγ2i}, convergence accuracy α;
E) calculating the flat friction coefficient:
Figure FDA0002958596590000016
F) calculating any group of theoretical rolling force piAnd rolling power Ni；
F1) Let i equal to 1;
F2) calculating the equivalent deformation resistance sigma of the strip steelsi：
σsi＝k3σ1i-(k1F0i+k2F1i)
F3) Calculating the contact arc length L of the roller and the strip in the rolling deformation zonei：
Figure FDA0002958596590000021
F4) Calculating the unit width rolling force fi：
Figure FDA0002958596590000022
F5) Calculating the theoretically calculated rolling force Pi：
Pi＝fiBi
F6) Rolling moment M of calculation theoryi：
Figure FDA0002958596590000023
F7) Rolling power N of the calculation theoryi：
Figure FDA0002958596590000024
F8) Judging that i is less than N, and if so, turning to step F2 if i is equal to i + 1); if not, turning to the step G);
G) calculating an optimization objective function f (x):
Figure FDA0002958596590000025
H) determine whether Powell conditions hold? If yes, turning to the step I); if not, updating the array X and the search step length delta X thereof, and turning to the step E);
I) and outputting the influence coefficient of the hot rolling characteristic of the temper mill unit on the friction coefficient, and completing the prediction of the influence of the hot rolling characteristic of the temper mill unit based on a big data theory on the friction coefficient.
2. The big data based prediction method of influence of hot rolling coiling temperature and finishing rolling temperature on coefficient of flat friction according to claim 1Characterized in that-10.0 is not less than a1i≤10.0。
3. The big data based prediction method of influence of hot rolling coiling temperature and finishing rolling temperature on flat friction coefficient as claimed in claim 1, characterized in that-6.0 ≤ a2i≤6.0。
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