Method for forecasting zinc layer thickness transverse distribution in automobile sheet hot galvanizing process
By collecting equipment process parameters and strip steel properties, introducing stress models and correcting thickness models, the problem of uneven zinc layer thickness was solved, achieving more accurate prediction, higher protective performance and processing quality, and reducing production costs.
Patent Information
- Application Number
- CN202511544537.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-03-24
AI Technical Summary
During the hot-dip galvanizing process, uneven lateral distribution of zinc layer thickness leads to decreased protective performance, affects appearance and processing quality, and increases production costs.
By collecting equipment process parameters and strip material properties, introducing normal stress and shear stress models, the original thickness model of the unit is modified, the objective function and optimization variables are set, and the parameters are iteratively adjusted to accurately predict the zinc layer thickness.
It improves the accuracy of zinc layer thickness prediction, reduces non-uniformity, enhances protective performance and processing quality, and lowers production costs.
Smart Images
Figure CN121723637A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of automotive manufacturing technology, and in particular to a method for predicting the lateral distribution of zinc layer thickness during the hot-dip galvanizing process of automotive steel sheets. Background Technology
[0002] Hot-dip galvanizing is a key technology for improving the corrosion resistance and surface quality of steel. For hot-dip galvanized products, the thickness and uniformity of the zinc layer are important quality indicators. Currently, the air-blowing method is commonly used in hot-dip galvanizing production both domestically and internationally to control the zinc layer thickness. The equipment used is known as an "air knife." It utilizes the jet principle, employing a rectangular nozzle that spans the entire width of the strip to blow out a flat airflow, scraping away excess zinc from the strip surface, thus making the galvanized surface smoother and the zinc layer thickness more uniform.
[0003] In the hot-dip galvanizing process, uneven lateral distribution of the zinc layer thickness on the strip can cause a series of problems. First, uneven zinc layer thickness leads to a decrease in protective performance. The main function of the zinc layer is to protect the steel sheet from corrosion. If the zinc layer is too thin in some areas, it may lead to localized corrosion, thus affecting the entire material. Secondly, uneven zinc layer thickness affects the appearance of the strip, especially in products requiring high-quality appearance. Obvious color differences or defects on the surface will affect the aesthetics of the final product. Unevenness can also affect the coating strength, particularly in areas with thinner zinc layers. These areas are more prone to coating defects, which can cause cracking or other defects in subsequent processing techniques such as stamping or bending, thus affecting processing quality. Furthermore, to avoid corrosion in thin zinc layer areas, it may be necessary to increase the overall zinc layer thickness, resulting in wasted areas and increased production costs. This invention proposes a method for predicting the lateral distribution of zinc layer thickness in the hot-dip galvanizing process of automotive steel sheets. Summary of the Invention
[0004] This application provides a method for predicting the lateral distribution of zinc layer thickness during the hot-dip galvanizing process of automotive steel sheets, in order to solve the problems mentioned above.
[0005] This application provides a method for predicting the lateral distribution of zinc layer thickness during the hot-dip galvanizing process of automotive steel sheets, including the following steps:
[0006] S1. Collect equipment process parameters;
[0007] S2. Collect the properties of incoming steel strip;
[0008] S3. Introduce the normal stress and shear stress model for the strip surface;
[0009] S4. Correct the original thickness model of the unit;
[0010] S5. Introduce the objective function and optimization variables;
[0011] S6. Set initial values for iteration variables;
[0012] S7. Calculate the zinc layer thickness and objective function F(X) under the current operating conditions;
[0013] S8. Determine whether the condition has been met;
[0014] S9. Output the corrected forecast model.
[0015] Preferably, the process parameters of the collection equipment include the distance Z from the air knife to the strip surface, the air knife opening degree D, the air knife opening degree convexity a at the edge and the middle, the air knife blowing pressure P0, the zinc liquid density ρ, the gravitational acceleration g, the zinc liquid dynamic viscosity μ, and the strip running speed V.
[0016] Preferably, the collection of incoming steel strip includes the width B of the incoming steel strip, the target zinc layer thickness h(y) of the steel strip, and the zinc layer thickness convexity b at the edge and center.
[0017] Preferably, the introduced normal stress and shear stress model for the strip surface includes normal stress p, shear stress τ, and maximum normal stress P. max and maximum shear stress τ max ,in:
[0018]
[0019] Where: P max τ max These represent the maximum normal stress and shear stress on the strip surface, respectively. P0 represents the air knife blowing pressure, Z represents the distance between the cutter lip and the strip surface, D represents the cutter lip opening, and ξ represents the characteristic number of the pressure curve, ξ = y / b. p ,in:
[0020] b p =0.38*D-0.0025P0+0.7.
[0021] Preferably, the correction of the original thickness model of the unit specifically includes introducing the existing thickness calculation model of the unit:
[0022]
[0023] Where h is the weight of zinc layer per unit area, ρ is the density of zinc liquid, g is the acceleration due to gravity, μ is the dynamic viscosity of zinc liquid, and V is the running speed of strip steel;
[0024] The above formula contains errors for the actual zinc layer thickness. A correction factor is added based on actual data, and the corrected zinc layer thickness calculation model is as follows:
[0025]
[0026] Where c, d, and e are correction coefficients.
[0027] Preferably, the objective function and optimization variables are introduced, using the degree of closeness between the calculated and actual zinc layer thickness as the objective function to regress the parameters in the model, i.e.:
[0028]
[0029] Let the optimization variable X = {c, d, e}. The smaller F(X) is, the closer the calculated value of the thickness model prediction is to the back-calculated value, and the optimal optimization variable X is.
[0030] Preferably, the initial value of the iterative variable is set, and the initial value of the objective function is defined as F(X) = 10. 6 Given initial values for the optimization variables X0 = {c0 = 1, d0 = 2, e0 = 2}.
[0031] Preferably, the determination of whether the condition is met includes: if the condition is met, proceeding to step S9; otherwise, readjusting X and proceeding to step S6.
[0032] The technical solutions provided in this application have the following advantages compared with the prior art:
[0033] The overall structure provided in this application embodiment involves various complex factors in the hot-dip galvanizing process, including the characteristics of the unit equipment and the influence of the production environment. Therefore, the surface stress obtained from simulation results cannot be completely accurate. Correction coefficients are added to three important components of the model: c and e are used to adjust the effects of viscosity, flow rate, and shear stress on the strip; d is mainly used to adjust the combined effects of normal pressure and shear stress. By repeatedly adjusting the weights of these parameters and verifying them in simulations, a corrected zinc layer thickness calculation model is finally obtained, which can more accurately predict zinc layer thickness than the previous model. Attached Figure Description
[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1 This is a schematic diagram of the overall principle structure of the present invention;
[0037] Figure 2 This is the strip cross-line distribution curve of Embodiment 1 of the present invention;
[0038] Figure 3 This is the strip cross distribution curve of Embodiment 2 of the present invention. Detailed Implementation
[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0040] Various embodiments of this application may exist in the form of a range. It should be understood that the description in the form of a range is merely for convenience and brevity and should not be construed as a rigid limitation on the scope of this application. Therefore, it should be considered that the range description has specifically disclosed all possible sub-ranges and single numerical values within that range. For example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and single numbers within the range, such as 1, 2, 3, 4, 5, and 6, regardless of the range. In addition, whenever a numerical range is indicated in this application, it means including any referenced number (fraction or integer) within the indicated range. Unless otherwise specified, all raw materials, reagents, instruments, and equipment used in this application can be purchased commercially or prepared using existing equipment.
[0041] In this application, unless otherwise stated, directional terms such as "upper" and "lower" specifically refer to the drawing directions in the accompanying drawings. Furthermore, in this application, the terms "comprising," "including," etc., mean "including but not limited to." In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In this application, "and / or" describes the relationship between related objects, indicating that three relationships may exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. In this application, "at least one" means one or more, and "more than one" means two or more. "At least one," "at least one of the following," or similar expressions refer to any combination of these items, including any combination of a single item or a plural item. For example, "at least one of a, b, or c", or "at least one of a, b, and c", can both mean: a, b, c, ab, i.e., a and b, ac, bc, or abc, where a, b, and c can be a single or multiple.
[0042] like Figures 1 to 3 As shown: This application provides a method for predicting the lateral distribution of zinc layer thickness during the hot-dip galvanizing process of automotive steel sheets, including the following steps:
[0043] S1. Collect equipment process parameters;
[0044] S2. Collect the properties of incoming steel strip;
[0045] S3. Introduce the normal stress and shear stress model for the strip surface;
[0046] S4. Correct the original thickness model of the unit;
[0047] S5. Introduce the objective function and optimization variables;
[0048] S6. Set initial values for iteration variables;
[0049] S7. Calculate the zinc layer thickness and objective function F(X) under the current operating conditions;
[0050] S8. Determine whether the condition has been met;
[0051] S9. Output the corrected forecast model.
[0052] The process parameters of the collection equipment include the distance Z from the air knife to the strip surface, the air knife opening degree D, the air knife opening degree convexity a at the edge and the middle, the air knife blowing pressure P0, the zinc liquid density ρ, the gravitational acceleration g, the zinc liquid dynamic viscosity μ, and the strip running speed V.
[0053] The collection of incoming steel strip includes the following attributes: the width B of the incoming steel strip, the target zinc layer thickness h(y) of the steel strip, and the zinc layer thickness convexity b at the edges and center.
[0054] The introduced model for normal stress and shear stress on the strip surface includes normal stress p, shear stress τ, and maximum normal stress P. max and maximum shear stress τ max ,in:
[0055]
[0056] Where: P max τ max These represent the maximum normal stress and shear stress on the strip surface, respectively. P0 represents the air knife blowing pressure, Z represents the distance between the cutter lip and the strip surface, D represents the cutter lip opening, and ξ represents the characteristic number of the pressure curve, ξ = y / b. p ,in:
[0057] b p =0.38*D-0.0025P0+0.7.
[0058] The modification of the original thickness model of the unit specifically includes introducing the existing thickness calculation model of the unit:
[0059]
[0060] Where h is the weight of zinc layer per unit area, ρ is the density of zinc liquid, g is the acceleration due to gravity, μ is the dynamic viscosity of zinc liquid, and V is the running speed of strip steel;
[0061] The above formula contains errors for the actual zinc layer thickness. A correction factor is added based on actual data, and the corrected zinc layer thickness calculation model is as follows:
[0062]
[0063] Where c, d, and e are correction coefficients.
[0064] The introduced objective function and optimization variables use the degree of closeness between the calculated and actual zinc layer thickness as the objective function to regress the parameters in the model, i.e.:
[0065]
[0066] Let the optimization variable X = {c, d, e}. The smaller F(X) is, the closer the calculated value of the thickness model prediction is to the back-calculated value, and the optimal optimization variable X is.
[0067] The initial values of the iterative variables are set, and the initial value of the objective function is defined as F(X) = 10. 6Given initial values for the optimization variables X0 = {c0 = 1, d0 = 2, e0 = 2}.
[0068] The determination of whether the condition is met includes: if the condition is met, proceed to step S9; otherwise, readjust X and proceed to step S6.
[0069] Example 1:
[0070] The process parameters for the collection equipment include: distance from the air knife to the strip surface Z = 15 mm; air knife opening D = 1.47 mm; air knife opening convexity a = 0.15 mm at the edge and center; air knife blowing pressure P0 = 15 kPa; and zinc liquid density ρ = 6.7 g / cm³. 3 The acceleration due to gravity is g = 10 m / s². 2 The dynamic viscosity of zinc liquid is μ = 3.5 * 10⁻⁶. 3 The strip running speed V = 120 m / min;
[0071] The collected steel strip material includes steel strip with a width B = 1000 mm and a target zinc coating thickness h(y) = 40 g / m. 2 The zinc layer thickness convexity b at the edges and center is 0.5 g / m. 2 .
[0072] The introduced model for normal stress and shear stress on the strip surface includes normal stress p, shear stress τ, and maximum normal stress P. max and maximum shear stress τ max ,in:
[0073]
[0074] Where: P max τ max These represent the maximum normal stress and shear stress on the strip surface, respectively. P0 represents the air knife blowing pressure, Z represents the distance between the cutter lip and the strip surface, D represents the cutter lip opening, and ξ represents the characteristic number of the pressure curve, ξ = y / b. p ,in:
[0075] b p =0.38*D-0.0025P0+0.7.
[0076] The modification of the original thickness model of the unit specifically includes introducing the existing thickness calculation model of the unit:
[0077]
[0078] Where h is the weight of zinc layer per unit area, ρ is the density of zinc liquid, g is the acceleration due to gravity, μ is the dynamic viscosity of zinc liquid, and V is the running speed of strip steel;
[0079] The above formula contains errors for the actual zinc layer thickness. A correction factor is added based on actual data, and the corrected zinc layer thickness calculation model is as follows:
[0080]
[0081] Where c, d, and e are correction coefficients.
[0082] The introduced objective function and optimization variables use the degree of closeness between the calculated and actual zinc layer thickness as the objective function to regress the parameters in the model, i.e.:
[0083]
[0084] Let the optimization variable X = {c, d, e}. The smaller F(X) is, the closer the calculated value of the thickness model prediction is to the back-calculated value, and the optimal optimization variable X is.
[0085] The initial values of the iterative variables are set, and the initial value of the objective function is defined as F(X) = 10. 6 Given initial values for the optimization variables X0 = {c0 = 1, d0 = 1, e0 = 1}.
[0086] The determination of whether the condition is met includes: if the condition is met, proceeding to step S9; otherwise, readjusting X and proceeding to step S6.
[0087] Calculate the zinc coating thickness and objective function F(X) under the current operating conditions. When D = 1.47, h(y) = 41.7 g / m. 2 When D = 1.62, h(y) = 42.57 g / m 2 .
[0088] Determine if the condition is met. If the condition is not met, readjust X and proceed to step S6.
[0089] The revised forecast model, after iterative calculations, outputs c = 0.9, d = 1.3, e = 1.3. When D = 1.47, h(y) = 39.6 g / m³. 2 When D = 1.62, h(y) = 40.39 g / m 2 .
[0090]
[0091] The strip cross-line distribution curve of Example 1 is as follows: Figure 2 As shown.
[0092] Example 2:
[0093] The process parameters for the collection equipment include: distance from the air knife to the strip surface Z = 15 mm; air knife opening D = 1.12 mm; air knife opening convexity a = 0.20 mm at the edge and center; air knife blowing pressure P0 = 16 kPa; and zinc liquid density ρ = 6.7 g / cm³. 3 The acceleration due to gravity is g = 10 m / s². 2 The dynamic viscosity of zinc liquid is μ = 3.5 * 10⁻⁶. -3 The strip running speed V = 120 m / min;
[0094] The collected steel strip material includes steel strip with a width B = 800 mm and a target zinc coating thickness h(y) = 59 g / m. 2 The zinc layer thickness convexity at the edges and center is b = 1 g / m 2 .
[0095] The introduced model for normal stress and shear stress on the strip surface includes normal stress p, shear stress τ, and maximum normal stress P. max and maximum shear stress τ max ,in:
[0096]
[0097] Where: P max τ max These represent the maximum normal stress and shear stress on the strip surface, respectively. P0 represents the air knife blowing pressure, Z represents the distance between the cutter lip and the strip surface, D represents the cutter lip opening, and ξ represents the characteristic number of the pressure curve, ξ = y / b. p ,in:
[0098] b p =0.38*D-0.0025P0+0.7.
[0099] The modification of the original thickness model of the unit specifically includes introducing the existing thickness calculation model of the unit:
[0100]
[0101] Where h is the weight of zinc layer per unit area, ρ is the density of zinc liquid, g is the acceleration due to gravity, μ is the dynamic viscosity of zinc liquid, and V is the running speed of strip steel;
[0102] The above formula contains errors for the actual zinc layer thickness. A correction factor is added based on actual data, and the corrected zinc layer thickness calculation model is as follows:
[0103]
[0104] Where c, d, and e are correction coefficients.
[0105] The introduced objective function and optimization variables use the degree of closeness between the calculated and actual zinc layer thickness as the objective function to regress the parameters in the model, i.e.:
[0106]
[0107] Let the optimization variable X = {c, d, e}. The smaller F(X) is, the closer the calculated value of the thickness model prediction is to the back-calculated value, and the optimal optimization variable X is.
[0108] The initial values of the iterative variables are set, and the initial value of the objective function is defined as F(X) = 10. 6 Given initial values for the optimization variables X0 = {c0 = 1, d0 = 1, e0 = 1}.
[0109] The determination of whether the condition is met includes: if the condition is met, proceeding to step S9; otherwise, readjusting X and proceeding to step S6.
[0110] Calculate the zinc layer thickness and objective function F(X) under the current operating conditions. When D = 1.12, h(y) = 59.55 g / m. 2 When D = 1.32, h(y) = 61.28 g / m 2 .
[0111] Determine if the condition is met. If the condition is not met, readjust X and proceed to step S6.
[0112] The revised forecast model, after iterative calculations, outputs c = 0.9, d = 1.5, e = 1.8. When D = 1.12, h(y) = 58.3 g / m². 2 When D = 1.32, h(y) = 59.9 g / m³ 2 .
[0113]
[0114] The strip cross-line distribution curve of Example 2 is as follows: Figure 3 As shown.
[0115] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed in this application.
Claims
1. A method for predicting the lateral distribution of zinc layer thickness during the hot-dip galvanizing process of automotive steel sheets, characterized in that, Includes the following steps: S1. Collect equipment process parameters; S2. Collect the properties of incoming steel strip; S3. Introduce the normal stress and shear stress model for the strip surface; S4. Correct the original thickness model of the unit; S5. Introduce the objective function and optimization variables; S6. Set initial values for iteration variables; S7. Calculate the zinc layer thickness and objective function F(X) under the current operating conditions; S8. Determine whether the condition has been met; S9. Output the corrected forecast model.
2. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The process parameters of the collection equipment include the distance Z from the air knife to the strip surface, the air knife opening degree D, the air knife opening degree convexity a at the edge and the middle, the air knife blowing pressure P0, the zinc liquid density ρ, the gravitational acceleration g, the zinc liquid dynamic viscosity μ, and the strip running speed V.
3. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The collection of incoming steel strip includes the width B of the incoming steel strip, the target zinc layer thickness h(y) of the steel strip, and the zinc layer thickness convexity b at the edge and center.
4. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The introduced model for normal stress and shear stress on the strip surface includes normal stress p, shear stress τ, and maximum normal stress P. max and maximum shear stress τ max ,in: Where: P max τ max These represent the maximum normal stress and shear stress on the strip surface, respectively. P0 represents the air knife blowing pressure, Z represents the distance between the cutter lip and the strip surface, D represents the cutter lip opening, and ξ represents the characteristic number of the pressure curve, ξ = y / b. p ,in: b p =0.38*D-0.0025P0+0.7。 5. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The modification of the original thickness model of the unit specifically includes introducing the existing thickness calculation model of the unit: Where h is the weight of zinc layer per unit area, ρ is the density of zinc liquid, g is the acceleration due to gravity, μ is the dynamic viscosity of zinc liquid, and V is the running speed of strip steel; The above formula contains errors for the actual zinc layer thickness. A correction factor is added based on actual data, and the corrected zinc layer thickness calculation model is as follows: Where c, d, and e are correction coefficients.
6. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The introduced objective function and optimization variables use the degree of closeness between the calculated and actual zinc layer thickness as the objective function to regress the parameters in the model, i.e.: Let the optimization variable X = {c, d, e}. The smaller F(X) is, the closer the calculated value of the thickness model prediction is to the back-calculated value, and the optimal optimization variable X is.
7. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The initial values of the iterative variables are set, and the initial value of the objective function is defined as F(X) = 10. 6 Given initial values for the optimization variables X0 = {c0 = 1, d0 = 2, e0 = 2}.
8. The method for predicting the lateral distribution of zinc layer thickness during hot-dip galvanizing of automotive steel sheets according to claim 1, characterized in that: The determination of whether the condition is met includes: if the condition is met, proceed to step S9; otherwise, readjust X and proceed to step S6.