Rolling pressure forecasting method suitable for high-speed rolling process of wire mill

Abstract

The invention discloses a rolling pressure forecasting method applicable to a high-speed rolling process of a wire mill, which comprises the following steps of: establishing a rolling pressure calculation model Pic by utilizing a friction coefficient model in the high-speed rolling process of the wire mill, and establishing an optimized objective function F (E); and solving the working condition influence coefficient in the rolling pressure calculation model Pic through field actual production data by utilizing a big data regression method to obtain the rolling pressure P of the high-speed rolling unit of the wire mill under the specific incoming material. According to the method, the rolling pressure forecasting model is established based on the equipment of the wire unit and the technological characteristics of high-speed wire production, and the rolling stability and the wire size precision are guaranteed.

Description

Technical Field

[0001] The invention relates to a high-speed wire rolling technology, and more specifically to a rolling pressure prediction method suitable for a wire rolling mill in a high-speed rolling process. Background Art

[0002] In recent years, with the rapid development of the construction industry, the demand for wire products has gradually increased. Continuous rolling is a rolling method widely used in modern rod and wire production, which can greatly improve production efficiency and produce better economic benefits. During continuous rolling, a billet is rolled in multiple stands at the same time. The equal metal flow rate between each stand is the basic principle for hole design and rolling procedure setting in continuous rolling. During the continuous rolling process, the front and rear hole profiles should alternately press down the height and width of the rolled piece, and the rolling force setting is an extremely important link. It is the core of the computer setting model of the wire rolling mill. Its setting accuracy directly affects the setting of the roll gap, which in turn affects the stability and dimensional accuracy of the wire rolling, thereby affecting the final quality. During the wire rolling process, the roll grooves of the 13H and 14V stand rolls will crack or even peel off due to cold and hot fatigue. Therefore, it is necessary to start with the rolling force setting and calibrate the rolling force setting model in combination with the actual production on site. In this way, how to accurately set the rolling force of 13H and 14V racks for specific units has become the focus of on-site research.

[0003] At present, most of the research on wire rods focuses on the influence of temperature and other aspects on mechanical properties, while there are almost no studies on the setting of wire rod rolling force. For example, Chinese Patent Application No. 201911125887.6 proposes a rolling force energy verification calculation method for hot-rolled high-speed wire rods. Starting from the basic parameter setting and rolled piece temperature calculation, combined with the existing theoretical calculation formula, through parameter correction, a set of simple and effective rolling force energy calculation methods are proposed. For example, Chinese Patent Application No. 202011047949.9 discloses a hot-rolled bar and wire rolling force energy verification and gearbox ratio calculation system based on B / S architecture, which also uses the existing theoretical calculation formula to verify the force energy parameters. Through the above two patent literature studies, it can be seen that there are few studies on optimizing the calculation of rolling force. Summary of the invention

[0004] In view of the defects existing in the prior art, the purpose of the present invention is to provide a rolling pressure prediction method suitable for the high-speed rolling process of a wire rolling mill. Based on the equipment of the wire rod unit and the process characteristics of high-speed wire production, a rolling pressure prediction model is established to ensure the stability during rolling and the wire size accuracy.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A rolling pressure prediction method suitable for high-speed rolling of wire rod mills:

[0007] The rolling pressure calculation model P is established by using the friction coefficient model during high-speed rolling of wire rod mill. ic , establish the optimization objective function F(E), and use the big data regression method to analyze the rolling pressure calculation model P through the actual production data on site. ic The operating condition influence coefficient in is solved to obtain the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials.

[0008] Preferably, the rolling pressure calculation model P is established ic The specific steps include:

[0009] (A) collecting equipment parameters and process parameters of the high-speed rolling unit of the material rolling mill;

[0010] (B) grouping the collected data;

[0011] (C) Establishing the rolling pressure calculation model P ic .

[0012] Preferably, the equipment parameters and process parameters in step (A) include the roller ring diameter D, radius R, rolling temperature t, wire rolling speed v, carbon content C in the wire, manganese content Mn in the wire, chromium content Cr in the wire, absolute reduction Δh, wire inlet height H, wire outlet height h, roller circumferential speed V, roller material, and rolling pressure P.

[0013] Preferably, the data grouping collected in step (B) specifically includes:

[0014] The n sets of input parameters after rolling are defined as X = {t i 、v i , R, H i 、h i 、V i}, and the corresponding n groups of actual average rolling pressure Where i=1, 2,…, n.

[0015] Preferably, in step (C), a rolling pressure calculation model P is established. ic Specifically include:

[0016]

[0017] In the formula, m is the coefficient of influence of external friction on the average unit pressure,

[0018] f is the friction coefficient,

[0019] C is the material coefficient. When the material is steel roll, C=1; when the material is cast iron roll, C=0.8;

[0020] K is the influence coefficient of wire composition, K = (14-0.01t)(1.4+C+Mn+0.3Cr);

[0021] is the average deformation rate,

[0022] η is the influence coefficient of deformation rate on deformation resistance, η = 0.01(14-0.01t)c;

[0023] c is the rolling speed influence coefficient, which is 0.6;

[0024] c′ is the speed correction coefficient, c′=a0+a1v, a0 and a1 are the operating condition influence coefficients.

[0025] Preferably, the step of establishing the optimization objective function F(E) specifically includes the following steps:

[0026] (D) Introducing the objective function

[0027] (E) Define the initial target value F0, intermediate variables m0, m1, and set m0 = 0;

[0028] (F) Given a search step of a0, let a0 = 0.500 + m0 * step0, and let m1 = 0;

[0029] (G) Given the search step size step1 of a1, let a1 = 0.001 + m1 * step1;

[0030] (H) Using the rolling pressure calculation model in step (C), calculate the average rolling pressure values ​​under the current E = {a0, a1} condition.

[0031] (I) Calculate the specific value of the objective function under the current situation

[0032] Preferably, the working condition influence coefficient E = {a0, a1}, the smaller the F(E), the closer the calculation model value of the average rolling pressure is to the actual value on site, and a set of optimal E = {a0, a1} is sought, so that The group E = {a0, a1} is the influence coefficient that makes the rolling force calculation model satisfy the optimal working condition of the current unit.

[0033] Preferably, the rolling pressure calculation model P is calculated by using the big data regression method through the actual production data on site. ic Solving the working condition influence coefficient in the specific steps includes the following steps:

[0034] (J) Define the optimal working condition influence coefficient a 0y , a 1y , determine whether F<F0 is true? If so, let F0=F, a 0y =a0,a 1y = a1, then go to step (K); if not, go directly to step (K);

[0035] (K) Determine whether the inequality m1≤1.000 / step1 holds. If so, set m1=m1+1 and proceed to step (G). If not, proceed to step (L).

[0036] (L) Determine whether the inequality m0≤0.5000 / step0 holds. If so, set m0=m0+1 and proceed to step (F); if not, proceed to step (M);

[0037] (M) Output optimal working condition influence coefficient a 0y , a 1y .

[0038] Preferably, the rolling pressure P of the high-speed rolling unit of the wire rod rolling mill under specific incoming materials is obtained specifically including:

[0039] (N) 0y , a 1y Substitute into the rolling pressure calculation model P in step (C) ic , the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials can be obtained.

[0040] The present invention provides a rolling pressure prediction method suitable for the high-speed rolling process of a wire rod rolling mill. The method combines the theoretical model with the actual production data on site, establishes a rolling pressure calculation model that conforms to the actual production on site, and more finely ensures the stability during rolling and the wire size accuracy. At the same time, the method can be applied to other steel grades and production lines to better serve high-speed wire production. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a rolling pressure prediction method applicable to a wire rod rolling mill during high-speed rolling. DETAILED DESCRIPTION

[0042] In order to better understand the above technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0043] Combination Figure 1 As shown, the present invention provides a method for predicting rolling pressure during high-speed rolling of a wire rod rolling mill:

[0044] The rolling pressure calculation model P is established by using the friction coefficient model during high-speed rolling of wire rod mill. ic , establish the optimization objective function F(E), and use the big data regression method to calculate the rolling pressure calculation model P based on the actual production data on site. ic The working condition influence coefficient in is solved to obtain the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials, ensuring the stability during rolling and the wire rod size accuracy. Specifically, the following steps are included:

[0045] (A) Equipment parameters and process parameters of the high-speed rolling unit of the material mill, including the roller ring diameter D, radius R, rolling temperature t, wire rolling speed v, carbon content C in the wire, manganese content Mn in the wire, chromium content Cr in the wire, absolute reduction Δh, wire inlet height H, wire outlet height h, roller circumferential speed V, roller material, and rolling pressure P;

[0046] (B) The collected data are grouped and the n groups of input parameters after rolling are defined as X = {t i 、v i , R, H i 、h i 、V i}, and the corresponding n groups of actual average rolling pressure Where i = 1, 2, ..., n;

[0047] (C) Establishing the rolling pressure calculation model P ic ,in

[0048] In the formula, m is the coefficient of influence of external friction on the average unit pressure,

[0049] f is the friction coefficient,

[0050] C is the material coefficient. When the material is steel roll, C=1; when the material is cast iron roll, C=0.8;

[0051] K is the influence coefficient of wire composition, K = (14-0.01t)(1.4+C+Mn+0.3Cr);

[0052] is the average deformation rate,

[0053] η is the influence coefficient of deformation rate on deformation resistance, η = 0.01(14-0.01t)c;

[0054] c is the rolling speed influence coefficient, which is 0.6;

[0055] c′ is the speed correction coefficient, c′=a0+a1v, a0 and a1 are the working condition influence coefficients;

[0056] (D) Introducing the objective function The working condition influence coefficient E = {a0, a1}, then, the smaller F(E) is, the closer the calculation model value of the average rolling pressure is to the actual value on site, so we can find an optimal set of E = {a0, a1} so that The minimum, this group E = {a0, a1} is the influence coefficient of the optimal working condition that makes the rolling force calculation model meet the current unit;

[0057] (E) Define the initial target value F0, intermediate variables m0, m1, and set m0 = 0;

[0058] (F) Given a search step of a0, let a0 = 0.500 + m0 * step0, and let m1 = 0;

[0059] (G) Given the search step size step1 of a1, let a1 = 0.001 + m1 * step1;

[0060] (H) Using the rolling pressure calculation model in step (C), calculate the average rolling pressure values ​​under the current E = {a0, a1} condition.

[0061] (I) Calculate the specific value of the objective function under the current situation

[0062] (J) Define the optimal working condition influence coefficient a 0y , a 1y , determine whether F<F0 is true? If so, let F0=F, a 0y =a0,a 1y = a1, then go to step (K); if not, go directly to step (K);

[0063] (K) Determine whether the inequality m1≤1.000 / step1 holds. If so, set m1=m1+1 and proceed to step (G). If not, proceed to step (L).

[0064] (L) Determine whether the inequality m0≤0.5000 / step0 holds. If so, set m0=m0+1 and proceed to step (F); if not, proceed to step (M);

[0065] (M) Output optimal working condition influence coefficient a 0y , a 1y ;

[0066] (N) 0y , a1y Substitute into the rolling pressure calculation model P in step (C) ic , the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials can be obtained.

[0067] Example 1

[0068] This embodiment 1 provides a rolling pressure prediction method applicable to a wire rod rolling mill during high-speed rolling, comprising the following steps:

[0069] (A) The equipment and process parameters of the high-speed wire rolling mill are collected, the roller ring diameter D = 400 mm, the radius R = 200 mm, the rolling temperature t = 840 ° C, the rolling wire speed v = 6 m / s, the carbon content of the wire C = 0.05%, the manganese content of the wire Mn = 0.12%, the chromium content of the wire Cr = 0.12, the absolute reduction Δh = 2 mm, the wire inlet height H = 30 mm, the wire outlet height h = 28 mm, the roller circumferential speed V = 6 m / s, the roller material is steel, and the rolling pressure P;

[0070] (B) The collected data are grouped, and the 200 sets of input parameters after rolling are defined as X = {t1 = 840, v1 = 6, R = 200, H1 = 30, h1 = 28, V1 = 6}, and the corresponding 200 sets of actual average rolling pressure Take i=1;

[0071] (C) Establishing the rolling pressure calculation model P c ,in

[0072] (D) Introducing the objective function Working condition influence coefficient E = {a0, a1};

[0073] (E) Define the initial target value F0, set F0 = 10000. At the same time, define two intermediate variables m0 and m1, and set m0 = 0;

[0074] (F) Given the search step of a0, step0=0.001, and let a0=0.500+m0*step0, and let m1=0;

[0075] (G) Given the search step size of a1, step1=0.00005, and let a1=0.001+m1*step1;

[0076] (H) Calculate the average rolling pressure values ​​under the current conditions using the rolling pressure calculation model in step C Take i=1;

[0077] (I) Calculate the specific value of the objective function under the current situation

[0078] (J) Define the optimal working condition influence coefficient a 0y , a 1y , judge that F=6.27<F0=10000 holds, let F0=6.27, a 0y =0.05, a 1y =0.001, proceed to step (K);

[0079] (K) If the inequality m1=0≤1.000 / 0.00001 holds, set m1=m1+1 and proceed to step (G). After a loop calculation, proceed to step (L).

[0080] (L) If the inequality m0=0≤0.5000 / 0.001 holds, set m0=m0+1 and proceed to step (F). After multiple cycles of calculation, finally obtain a0 and a1 that satisfy the minimum F(E), and proceed to step (M);

[0081] (M) According to the calculation process described in this question, a rolling pressure prediction software was compiled, and the optimal working condition influence coefficient a was obtained using the software. 0y =0.96, a 1y =0.0065;

[0082] (N) 0y , a 1y Substituting into the rolling pressure formula of step (C), the rolling pressure P=22.89 of the unit under the parameters shown in step (A) can be calculated.

[0083] It can be seen from the simulation calculation that before optimizing the working condition influence coefficients a0 and a1, the deviation between the predicted rolling pressure and the actual rolling pressure is large. After optimizing the coefficients a0 and a1, the rolling pressure obtained by simulation calculation is basically consistent with the actual rolling pressure, and the error is only ξ1=(22.89-22.6) / 22.6=1.26%, which fully meets the production requirements.

[0084] Example 2

[0085] This embodiment 2 provides a rolling pressure prediction method applicable to a wire rod rolling mill during high-speed rolling, comprising the following steps:

[0086] (A) The equipment and process parameters of the high-speed wire rolling mill are collected, the roller ring diameter D = 400 mm, the radius R = 200 mm, the rolling temperature t = 840 ° C, the rolling wire speed v = 5 m / s, the carbon content of the wire C = 0.05%, the manganese content of the wire Mn = 0.12%, the chromium content of the wire Cr = 0.12, the absolute reduction Δh = 3 mm, the wire inlet height H = 30 mm, the wire outlet height h = 27 mm, the roller circumferential speed V = 5 m / s, the roller material is steel, and the rolling pressure P;

[0087] (B) The collected data are grouped, and the 200 sets of input parameters after rolling are defined as X = {t2 = 850, v2 = 5, R = 200, H2 = 30, h2 = 27, V2 = 5}, and the corresponding 200 sets of actual average rolling pressure Take i=2;

[0088] (C) Establishing the rolling pressure calculation model P c ,in

[0089] (D) Introducing the objective function Working condition influence coefficient E = {a0, a1};

[0090] (E) Define the initial target value F0, set F0 = 10000. At the same time, define two intermediate variables m0 and m1, and set m0 = 0;

[0091] (F) Given the search step size of a0, step0=0.0001, let a0=0.5+m0*step0, and let m1=0;

[0092] (G) Given the search step size of a1, step1=0.00001, and let a1=0.001+m1*step1;

[0093] (H) Calculate the average rolling pressure values ​​under the current conditions using the rolling pressure calculation model in step C Take i=2;

[0094] (I) Calculate the specific value of the objective function under the current situation

[0095] (J) Define the optimal working condition influence coefficient a 0y , a 1y , judge that F=3.62<F0=10000 holds, let F0=3.62, a 0y =0.5, a 1y =0.001, proceed to step (K);

[0096] (K) If the inequality m1=0≤1.000 / 0.00001 holds, set m1=m1+1 and proceed to step (G). After a loop calculation, proceed to step (L).

[0097] (L) If the inequality m0=0≤0.5000 / 0.001 holds, set m0=m0+1 and proceed to step (F). After multiple cycles of calculation, finally obtain a0 and a1 that satisfy the minimum F(E), and proceed to step (M);

[0098] (M) According to the calculation process described in this question, a rolling pressure prediction software was compiled, and the optimal working condition influence coefficient a was obtained using the software. 0y =0.97, a 1y =0.0059;

[0099] (N) 0y , a 1y Substituting into the rolling pressure formula of step (C), the rolling pressure P=29.26 of the unit under the parameters shown in step (A) can be calculated.

[0100] It can be seen from the simulation calculation that before optimizing the working condition influence coefficients a0 and a1, the deviation between the predicted rolling pressure and the actual rolling pressure is large. After optimizing the coefficients a0 and a1, the rolling pressure obtained by simulation calculation is basically consistent with the actual rolling pressure, with an error of only ξ2=(29.5-29.26) / 29.5=0.81%, which fully meets the production requirements.

[0101] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A method for predicting rolling pressure during high-speed rolling of a wire rod rolling mill, characterized in that: The rolling pressure calculation model P is established by using the friction coefficient model during high-speed rolling of wire rod mill. ic , establish the optimization objective function F(E), and use the big data regression method to analyze the rolling pressure calculation model P through the actual production data on site. ic The operating condition influence coefficient in is solved to obtain the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials.

2. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 1, characterized in that: The rolling pressure calculation model P is established ic The specific steps include: (A) collecting equipment parameters and process parameters of the high-speed rolling unit of the material rolling mill; (B) grouping the collected data; (C) Establishing the rolling pressure calculation model P ic .

3. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 2, characterized in that: The equipment parameters and process parameters in the step (A) include the roller ring diameter D, radius R, rolling temperature t, wire rolling speed v, carbon content C in the wire, manganese content Mn in the wire, chromium content Cr in the wire, absolute reduction Δh, wire inlet height H, wire outlet height h, roller circumferential speed V, roller material, and rolling pressure P.

4. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 3 is characterized in that: The data grouping collected in step (B) specifically includes: The n sets of input parameters after rolling are defined as X = {t i 、v i , R, H i 、h i 、V i }, and the corresponding n groups of actual average rolling pressure Where i=1, 2,…, n.

5. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 4, characterized in that: In the step (C), a rolling pressure calculation model P is established. ic Specifically include: In the formula, m is the coefficient of influence of external friction on the average unit pressure, f is the friction coefficient, C is the material coefficient. When the material is steel roll, C=1; when the material is cast iron roll, C=0.8; K is the influence coefficient of wire composition, K = (14-0.01t)(1.4+C+Mn+0.3Cr); is the average deformation rate, η is the influence coefficient of deformation rate on deformation resistance, η = 0.01(14-0.01t)c; c is the rolling speed influence coefficient, which is 0.6; c′ is the speed correction coefficient, c′=a0+a1v, a0 and a1 are the operating condition influence coefficients.

6. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 2, characterized in that: The establishment of the optimization objective function F(E) specifically comprises the following steps: (D) Introducing the objective function (E) Define the initial target value F0, intermediate variables m0, m1, and set m0 = 0; (F) Given a search step of a0, let a0 = 0.500 + m0 * step0, and let m1 = 0; (G) Given the search step size step1 of a1, let a1 = 0.001 + m1 * step1; (H) Using the rolling pressure calculation model in step (C), calculate the average rolling pressure values ​​under the current E = {a0, a1} condition. (I) Calculate the specific value of the objective function under the current situation 7. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 6, characterized in that: The working condition influence coefficient E = {a0, a1}, the smaller F (E) is, the closer the calculation model value of the average rolling pressure is to the actual value on site, and a set of optimal E = {a0, a1} is sought so that The group E = {a0, a1} is the influence coefficient that makes the rolling force calculation model satisfy the optimal working condition of the current unit.

8. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 7, characterized in that: The rolling pressure calculation model P is analyzed by using the big data regression method through the actual production data on site. ic Solving the working condition influence coefficient in the specific steps includes the following steps: (J) Define the optimal operating condition influence coefficient a 0y , a 1y , determine whether F < F0 holds? If it holds, then let F0 = F, a 0y = a0, a 1y = a1, and then go to step (K); if it does not hold, then directly go to step (K); (K) Determine whether the inequality m1≤1.000 / step1 holds. If so, set m1=m1+1 and proceed to step (G). If not, proceed to step (L). (L) Determine whether the inequality m0≤0.5000 / step0 holds. If so, set m0=m0+1 and proceed to step (F); if not, proceed to step (M); (M) Output optimal working condition influence coefficient a 0y , a 1y .

9. The rolling pressure prediction method applicable to the high-speed rolling process of a wire rod rolling mill according to claim 8, characterized in that: The method of obtaining the rolling pressure P of the high-speed rolling unit of the wire rod rolling mill under specific incoming materials specifically includes: (N) 0y , a 1y Substitute into the rolling pressure calculation model P in step (C) ic , the rolling pressure P of the wire rod mill high-speed rolling unit under specific incoming materials can be obtained.

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