Continuous casting and rolling process regulation and control method and system based on copper rod sub-grain boundary

Abstract

The invention relates to the technical field of non-ferrous metal processing, in particular to a continuous casting and rolling process regulation and control method and system based on copper rod sub-grain boundaries, and the method comprises the steps of collecting process parameters, constructing process parameter vectors, and setting positions in a continuous casting crystalline region and a continuous rolling section, a casting blank surface temperature field, a sub-grain boundary orientation consistency index and rolling traction tension are collected respectively; a casting blank temperature-stress field is discretely calculated based on a finite element method, a coupling sub-grain boundary model is constructed, and parameters of the coupling sub-grain boundary model are corrected online; constructing a multi-target collaborative optimization model based on the coupled subboundary model, and performing optimization solution by using a hybrid algorithm based on cuckoo search and a teaching optimization algorithm to obtain an optimal process parameter vector; and applying the optimal process parameter vector to the continuous casting and rolling system, and triggering re-optimization or alarming according to the online monitoring data and the predicted deviation. And the sub-boundary size and orientation consistency can be optimized at the same time, and the mechanical property and the conductivity of the copper rod are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nonferrous metal processing, and in particular to a continuous casting and rolling process control method and system based on copper rod subgrain boundaries. Background Art

[0002] The continuous casting and rolling (SCR) process is a key step in the efficient production of copper rods. Its basic principle is that in a five-wheel copper continuous casting machine, molten copper passes through the ladle, tundish, and mold cavity, where it rapidly solidifies under the action of a carbon coating and cooling water. A fine equiaxed crystal shell first forms on the surface, and then grows into columnar crystals inside along the direction of heat flow to form a cast embryo. After demolding, the embryo enters the continuous rolling mill, where it is rolled into shape through heating and traction tension control. Adjusting the casting speed, cooling intensity (carbon coating thickness, cooling water volume, and nozzle layout), rolling temperature, and tension can influence the size, orientation, and distribution of the embryo's subgrain boundaries. The parameters of the subgrain boundaries are highly correlated with the mechanical properties, electrical conductivity, and tensile properties of the copper rod.

[0003] In the existing technology, there is uneven cooling, which leads to insufficient local cooling strength of the embryo, coarse grains, and bent and offset crystallization lines, resulting in unbalanced distribution of equiaxed crystal areas and columnar crystal areas of the embryo, affecting the torsional performance and elongation performance after rolling. The traditional empirical adjustment of process parameters is difficult to take into account both the continuous casting and continuous rolling processes of the embryo, and lacks control and feedback on the evolution of subgrain boundaries, resulting in large production fluctuations and insufficient qualified rate.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for controlling a continuous casting and rolling process based on copper rod subgrain boundaries, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a continuous casting and rolling process control method based on copper rod subgrain boundaries, comprising the following steps:

[0007] Collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and collect the surface temperature field of the casting, subgrain boundary orientation consistency index and rolling traction tension respectively;

[0008] Discretely calculating the temperature-stress field of the casting embryo based on the finite element method, constructing a coupled subgrain boundary model, and online correcting the parameters of the coupled subgrain boundary model based on the casting embryo surface temperature field, subgrain boundary orientation consistency index and rolling traction tension;

[0009] A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model, and the multi-objective collaborative optimization model is optimized and solved using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain an optimal process parameter vector;

[0010] The optimal process parameter vector is applied to the continuous casting and rolling system, and re-optimization or alarm is triggered according to the deviation between the online monitoring data and the prediction.

[0011] Furthermore, the discrete calculation of the temperature-stress field of the casting embryo based on the finite element method and the construction of the coupled subgrain boundary model include the following steps:

[0012] Construct a three-dimensional model of the geometric shape of the cast billet in the continuous casting and rolling section;

[0013] Dividing the three-dimensional model grid into a plurality of finite element units and setting boundary conditions in finite element software;

[0014] Performing finite element temperature field and stress field simulation based on the process parameter vector through heat transfer equations and mechanical equilibrium equations;

[0015] At the center of each finite element, the subgrain boundary evolution is driven by the local temperature-stress field to construct a coupled subgrain boundary model.

[0016] Furthermore, the coupled subgrain boundary model includes:

[0017] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0018]

[0019] Where f(t;x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; k(T,σ) is the temperature- and stress-dependent diffusion coefficient, T(t;x) is the temperature at the finite element unit position x at time t, σ(t;x) is the stress at the finite element unit position x at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; n is the growth exponent;

[0020] Furthermore, the online correction of the evolutionary coupling model parameters includes:

[0021] Comparing the surface temperature predicted by the finite element method with the surface temperature field of the casting embryo at every first set time;

[0022] comparing the orientation consistency predicted by the model with the subgrain boundary orientation consistency index every second set time;

[0023] If the deviation after comparison is greater than the set condition, the parameter re-identification is triggered, including:

[0024] A model parameter vector is constructed, the model parameter vector is updated online using a recursive least squares method, and the updated model parameter vector is applied to the coupled subgrain boundary model.

[0025] Furthermore, the construction of the multi-objective collaborative optimization model includes:

[0026] The process parameter vector is recorded as p = [v, ΔT, T r ,F] T ;

[0027] Where: v is the casting speed, ΔT is the degree of supercooling, T r is the rolling temperature, F is the traction tension;

[0028] The average subgrain boundary size and orientation consistency at a given p are predicted based on the coupled subgrain boundary model, and the objective function is constructed:

[0029] F(p)=w1F1(p)+w2F2(p);

[0030]

[0031] Where F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function respectively, w1 and w2 are the first weighting coefficient and the second weighting coefficient respectively, is the average subgrain boundary size, C o (p) is the subgrain boundary orientation consistency, which is obtained by statistics based on the coupled subgrain boundary model;

[0032] in, Calculated by the coupled subgrain boundary model:

[0033]

[0034] Where d(t; x) represents the characteristic size of the subgrain boundary at the finite element unit position x at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t; x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0035] Furthermore, the method of optimizing and solving the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm comprises the following steps:

[0036] Using the process parameters as decision variables, a global search is first performed using the Cuckoo Search CS algorithm to generate several individuals;

[0037] The best individual is taken as the teacher individual, and each of the remaining individuals is learned and the fitness is calculated. If there is improvement, the teacher individual is replaced, iterated and the optimal process parameter vector is output.

[0038] Furthermore, the cuckoo search CS algorithm is used to perform a global search to generate a number of individuals, including the following steps:

[0039] Randomly generate n feasible solutions:

[0040]

[0041] Make it meet the process safety constraints and physical feasibility constraints;

[0042] Calculate the fitness of each individual:

[0043]

[0044] For each solution Perform a Lévy flight to generate candidate solutions:

[0045]

[0046] Where g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size.

[0047] Randomly select another solution like:

[0048]

[0049] Then the solution Replaced by the candidate solution p i ';

[0050] With probability p a Discard the worst p a n solutions, supplemented by new random solutions;

[0051] Iteratively generate several solutions, which are the individuals.

[0052] Furthermore, the method of taking the best individual as the teacher individual, learning and calculating the fitness of each of the remaining individuals, and replacing the teacher individual if there is improvement, includes the following steps:

[0053] Calculate the number of individuals in the current population The mean of:

[0054]

[0055] Take the best individual as the teacher individual

[0056] For each solution renew:

[0057]

[0058] where r∈(0,1),q∈{1,2};

[0059] If the fitness of the updated solution is less than that of the original solution, it is replaced;

[0060] Randomly pair the population, each pair (i, j):

[0061]

[0062] right Calculate the fitness and replace it if it is improved;

[0063] Select the one with the lowest fitness in the updated population

[0064] like If there is no significant improvement after several consecutive generations, the iteration is terminated;

[0065] Output the optimal process parameter vector.

[0066] Furthermore, triggering re-optimization or alarming based on the deviation between the online monitoring data and the prediction includes:

[0067] Based on the coupled subgrain boundary model, the surface temperature field of the casting embryo, the subgrain boundary orientation consistency index and the rolling pulling tension are pre-calculated under a given P;

[0068] At each sampling moment, the temperature field deviation, subgrain boundary orientation consistency deviation and rolling traction tension deviation are calculated;

[0069] If at any time there is a temperature field deviation, subgrain boundary orientation consistency deviation or rolling traction tension deviation greater than the set temperature field deviation threshold, orientation consistency threshold or tension deviation threshold, it is determined to be a working condition drift;

[0070] If operating condition drift occurs at N consecutive sampling points, the hybrid algorithm optimization process is re-executed with the last optimal process parameter vector as the population center. If communication timeout, model solution failure, or actuator response abnormality occurs during the re-optimization process, an alarm is issued.

[0071] The new process parameter vector obtained by the solution is issued and executed;

[0072] If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state and re-optimization or alarm is canceled.

[0073] The present invention also includes a continuous casting and rolling process control system based on copper rod subgrain boundaries, using the above method, the system comprising:

[0074] The acquisition unit is used to collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and respectively collect the surface temperature field of the casting embryo, the subgrain boundary orientation consistency index and the rolling traction tension;

[0075] a modeling unit for discretely calculating the temperature-stress field of the casting embryo based on the finite element method, constructing a coupled subgrain boundary model, and performing online correction of the coupled subgrain boundary model parameters based on the casting embryo surface temperature field, the subgrain boundary orientation consistency index, and the rolling traction tension;

[0076] A solving unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model, and optimize and solve the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain an optimal process parameter vector;

[0077] An application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and trigger re-optimization or alarm according to the deviation between the online monitoring data and the prediction.

[0078] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0079] The present invention also includes a storage medium storing a computer program, which implements the above method when executed by a processor.

[0080] The present invention has the following beneficial effects: based on the finite element method, the temperature-stress field of the casting embryo is discretely calculated to construct a coupled subgrain boundary model. The coupled subgrain boundary model parameters are online corrected based on the casting embryo surface temperature field, the subgrain boundary orientation consistency index, and the rolling traction tension. Real-time monitoring is combined with simulation prediction to ensure that the model is highly consistent with the actual working conditions, accurately predict the subgrain boundary distribution under different process parameters, avoid blind trial and error, and continuously improve the model prediction accuracy through online correction. A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector. The cuckoo search CS algorithm ensures a global jump search to avoid falling into the local optimum, and the teaching optimization TLBO algorithm improves local accuracy and accelerates convergence. The hybrid algorithm does not require gradients, is suitable for complex nonlinear coupling models, has few parameters, is easy to adjust, and is more suitable for industrial field applications. It can simultaneously optimize multiple indicators to achieve balanced control of subgrain boundary size and orientation, and can simultaneously optimize subgrain boundary size and orientation consistency, significantly improving the mechanical properties and electrical conductivity of copper rods. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0082] Figure 1 is a flow chart of the method of the present invention;

[0083] Figure 2 Schematic diagram of the structure of the system of the present invention;

[0084] Figure 3 This is a comparison chart of tensile strength before and after optimization;

[0085] Figure 4 This is a comparison chart of the copper rod elongation before and after optimization;

[0086] Figure 5 The conductivity comparison chart before and after optimization;

[0087] Figure 6 Schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0089] Example 1:

[0090] like Figure 1 As shown: A continuous casting and rolling process control method based on copper rod subgrain boundaries, comprising the following steps:

[0091] Collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and collect the surface temperature field of the casting, subgrain boundary orientation consistency index and rolling traction tension respectively;

[0092] The temperature-stress field of the casting embryo is discretely calculated using the finite element method, and a coupled subgrain boundary model is constructed. The coupled subgrain boundary model parameters are then calibrated online based on the casting embryo surface temperature field, subgrain boundary orientation consistency index, and rolling traction tension.

[0093] A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector.

[0094] The optimal process parameter vector is applied to the continuous casting and rolling system, and re-optimization or alarm is triggered based on the deviation between online monitoring data and prediction.

[0095] Based on the finite element method, the temperature-stress field of the cast embryo is discretely calculated, and a coupled subgrain boundary model is constructed. The coupled subgrain boundary model parameters are then online corrected based on the surface temperature field of the cast embryo, the subgrain boundary orientation consistency index, and the rolling traction tension. Real-time monitoring is combined with simulation prediction to ensure high consistency between the model and actual working conditions, accurately predict the subgrain boundary distribution under different process parameters, and avoid blind trial and error. Through online correction, the model prediction accuracy is continuously improved. A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithms is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector. The cuckoo search algorithm (CS) ensures a global jump search to avoid falling into local optima, while the teaching optimization algorithm (TLBO) improves local accuracy and accelerates convergence. The hybrid algorithm does not require gradients and is suitable for complex nonlinear coupling models. With its low number of parameters and easy adjustment, it is more suitable for industrial field applications. It can simultaneously optimize multiple indicators to achieve balanced control of subgrain boundary size and orientation, and can simultaneously optimize subgrain boundary size and orientation consistency, significantly improving the mechanical and electrical properties of copper rods.

[0096] In this embodiment, discrete calculation of the temperature-stress field of the embryo is performed based on the finite element method, and a coupled subgrain boundary model is constructed, including the following steps:

[0097] Construct a three-dimensional model of the geometric shape of the cast billet in the continuous casting and rolling section;

[0098] In the finite element software, the 3D model mesh is divided into several finite element units and boundary conditions are set. Tetrahedral or hexahedral units are appropriately densified along the length (for example, 100–200 units per meter), densified along the diameter according to a gradient, and several circumferential units are divided on the cross section.

[0099] Set the boundary conditions:

[0100] Thermal boundary: The initial temperature field is set to the copper liquid temperature; Crystallizer section: The convection heat transfer coefficient between the surface and the carbon coating layer / cooling water is hc≈5000–8000 W / (m 2 K); atmospheric section: natural convection and radiation, combined heat transfer coefficient ha≈50–100 W / (m 2 K);

[0101] Mechanical boundaries: Axial traction: a time-varying tension F(t) is applied to the tail of the billet; Rolling contact: contact pressure and friction are applied in the roller-bill contact area.

[0102] Based on the process parameter vector, finite element temperature field and stress field simulation is performed through heat transfer equation and mechanical equilibrium equation;

[0103] Heat transfer equation:

[0104]

[0105] Where, P is the density of copper, c p is the specific heat capacity, k is the thermal conductivity, Q latent (t) is the latent heat of solidification, which is interpolated based on the variation of solid fraction with temperature;

[0106] Mechanical equilibrium equation:

[0107] ▽·σ+b=0,σ=D:(ε-ε th );

[0108] Where, ε th =α th (TT ref )I is the thermal expansion strain; D is the elastic or elastoplastic constitutive matrix;

[0109] Time stepping and alternating iterations:

[0110] The time step Δt ≈ 0.1–1 s was selected;

[0111] At each time step, the heat transfer equation is first solved to obtain T(x, t), and then the mechanical equilibrium is solved based on the new temperature field;

[0112] Iterate until the strand is completely solidified and passes through all rolling mill units.

[0113] At the center of each finite element, the subgrain boundary evolution is driven by the local temperature-stress field to construct a coupled subgrain boundary model.

[0114] Among them, the coupled subgrain boundary model includes:

[0115] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0116]

[0117] Where f(t;x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; k(T,σ) is the temperature- and stress-dependent diffusion coefficient, T(t;x) is the temperature at the finite element unit position x at time t, σ(t;x) is the stress at the finite element unit position x at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; n is the growth exponent;

[0118] As a preferred embodiment of the above embodiment, online correction of the evolutionary coupling model parameters includes:

[0119] Comparing the surface temperature predicted by the finite element with the surface temperature field of the casting embryo at every first set time;

[0120] The orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index every second set time;

[0121] If the deviation after comparison is greater than the set condition, the parameter re-identification is triggered, including:

[0122] The model parameter vector is constructed, updated online using the recursive least squares method, and then applied to the coupled subgrain boundary model.

[0123] The model parameter vector θ = [lnk0, Q, β, n] is updated online using the recursive least squares (RLS) method;

[0124] Construct a linear regression form:

[0125] y(t)=φ(t) T θ+ε(t);

[0126]

[0127] Where: y(t) = ln[-ln(1-f meas )] by measuring the local f meas Back calculation; ε(t) is noise;

[0128] RLS update formula:

[0129]

[0130] Where λ∈(0.95,1) is the forgetting factor; P is the initial value of the covariance matrix, which can be set to a large number;

[0131] Through high-precision finite element solution, field drive is provided for microevolution; the JMAK equation is seamlessly connected with local temperature and stress to obtain size and orientation distribution; with the help of online monitoring data, key model parameters are continuously updated to ensure high consistency between simulation and on-site working conditions, ultimately improving prediction and control accuracy.

[0132] Among them, the construction of a multi-objective collaborative optimization model includes:

[0133] The process parameter vector is recorded as p = [v, ΔT, T r ,F] T ;

[0134] Where: v is the casting speed, ΔT is the degree of supercooling, T r is the rolling temperature, F is the traction tension;

[0135] The average subgrain boundary size and orientation consistency at a given p are predicted based on the coupled subgrain boundary model, and the objective function is constructed:

[0136] F(p)=w1F1(p)+w2F2(p);

[0137]

[0138] Where F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function respectively, w1 and w2 are the first weighting coefficient and the second weighting coefficient respectively, is the average subgrain boundary size, C o (p) is the subgrain boundary orientation consistency, which is obtained by statistics of the coupled subgrain boundary model;

[0139] in, Calculated by the coupled subgrain boundary model:

[0140]

[0141] Where d(t; x) represents the characteristic size of the subgrain boundary at the finite element unit position x at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t; x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0142] In this embodiment, a hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model, including the following steps:

[0143] Taking the process parameters as decision variables, the cuckoo search CS algorithm is first used to perform a global search to generate several individuals;

[0144] The best individual is taken as the teacher individual, and each of the remaining individuals is learned and the fitness is calculated. If there is improvement, the teacher individual is replaced, iterated and the optimal process parameter vector is output.

[0145] Use the Cuckoo Search CS algorithm to perform a global search and generate several individuals, including the following steps:

[0146] Randomly generate n feasible solutions:

[0147]

[0148] Make it meet the process safety constraints and physical feasibility constraints;

[0149] Process safety constraints:

[0150] v min n≤v≤v max ,

[0151] △T min ≤△T≤△T max ,

[0152] T r，min ≤T r ≤T r，max ,

[0153] F min ≤F≤F max .

[0154] Physical feasibility constraints: The billet should be completely solidified in the mold: the cooling time τ is determined by the finite element model c (p)≥τ req ;Matching of rolling mill tension and temperature: ensuring that rolling stress does not exceed the yield stress of the material.

[0155] Calculate the fitness of each individual:

[0156]

[0157] For each solution Perform a Lévy flight to generate candidate solutions:

[0158]

[0159] Where g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size.

[0160] Randomly select another solution like:

[0161]

[0162] Then the solution Replace with candidate solution p i ';

[0163] With probability p a Discard the worst p a n solutions, supplemented by new random solutions;

[0164] Iteratively generate several solutions, and the solutions are individuals.

[0165] The best individual is selected as the teacher individual, and each of the remaining individuals is learned and the fitness is calculated. If there is improvement, the teacher individual is replaced, including the following steps:

[0166] Calculate the number of individuals in the current population The mean of:

[0167]

[0168] Take the best individual as the teacher individual

[0169] For each solution renew:

[0170]

[0171] where r∈(0,1),q∈{1,2};

[0172] If the fitness of the updated solution is less than that of the original solution, it is replaced;

[0173] Randomly pair the population, each pair (i, j):

[0174]

[0175] right Calculate the fitness and replace it if it is improved;

[0176] Select the one with the lowest fitness in the updated population

[0177] like If there is no significant improvement after several consecutive generations, the iteration is terminated;

[0178] Output the optimal process parameter vector.

[0179] The CS algorithm provides a multi-point jump global search, while the TLBO algorithm improves local search accuracy. Requiring only a few parameters, such as the Lévy step size, nest abandonment rate, and teaching factor, it facilitates on-site debugging. It is suitable for highly nonlinear and non-differentiable problems such as evolutionary coupled models. Through rapid exploration using the CS algorithm and refined convergence using the TLBO algorithm, the overall number of iterations can be controlled to less than 150. This model and algorithmic process allows for the efficient determination of optimal process parameters based on online monitoring and numerical simulation, enabling precise control of copper rod subgrain boundaries.

[0180] In this embodiment, re-optimization or alarming is triggered based on the deviation between the online monitoring data and the prediction, including:

[0181] Based on the coupled subgrain boundary model, the surface temperature field of the casting, the subgrain boundary orientation consistency index and the rolling pulling tension are pre-calculated under a given P;

[0182] At each sampling moment, the temperature field deviation, subgrain boundary orientation consistency deviation and rolling traction tension deviation are calculated;

[0183] If at any time there is a temperature field deviation, subgrain boundary orientation consistency deviation or rolling traction tension deviation greater than the set temperature field deviation threshold, orientation consistency threshold or tension deviation threshold, it is determined to be a working condition drift;

[0184] If operating condition drift occurs at N consecutive sampling points, the hybrid algorithm optimization process is re-executed with the last optimal process parameter vector as the population center. If communication timeout, model solution failure, or actuator response abnormality occurs during the re-optimization process, an alarm is issued.

[0185] The new process parameter vector obtained by the solution is issued and executed;

[0186] If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state and re-optimization or alarm is canceled.

[0187] When the operation deviates but does not meet the re-optimization conditions (such as isolated outliers), only log records are made and historical data is retained for offline analysis;

[0188] Through real-time data collection, model-synchronized prediction, and deviation quantification, production conditions are highly consistent with the simulation model. A hierarchical triggering logic is employed: isolated deviations are recorded, continuous deviations trigger optimization, and advanced anomalies trigger alarms, ensuring both rapid self-adaptation and safety alarms. This closed-loop system, from parameter distribution to feedback and optimization, forms an end-to-end automated control system, maximizing quality stability and production efficiency in copper rod continuous casting and rolling.

[0189] like Figure 2As shown, this embodiment also includes a continuous casting and rolling process control system based on copper rod subgrain boundaries, using the above method, the system includes:

[0190] The acquisition unit is used to collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and respectively collect the surface temperature field of the casting embryo, the subgrain boundary orientation consistency index and the rolling traction tension;

[0191] Modeling unit, used to discretely calculate the temperature-stress field of the casting embryo based on the finite element method, construct a coupled subgrain boundary model, and perform online correction of the coupled subgrain boundary model parameters based on the casting embryo surface temperature field, subgrain boundary orientation consistency index and rolling traction tension;

[0192] A solving unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model, and to optimize and solve the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain the optimal process parameter vector;

[0193] The application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and trigger re-optimization or alarm according to the deviation between the online monitoring data and the prediction.

[0194] Example 2:

[0195] On a copper rod production line (equipment models: CC-800 continuous casting machine and CRP-500 continuous rolling mill), the method of this embodiment was used to perform online process control on Φ20 mm copper rods.

[0196] 1. Online monitoring;

[0197] 1.1 As Figure 3 As shown, a temperature detection sensor is installed on the casting wheel with a measuring frequency of 100 Hz to monitor the surface temperature field of the casting billet;

[0198] 1.2 An LDS-OB3000 laser backscattering grain orientation detector is placed between the 2nd and 4th rolling mills in the continuous rolling section. The subgrain boundary orientation distribution data is collected every 10 seconds, and the orientation consistency index C is calculated in real time by the software. o ;

[0199] 1.3 Install the GT-Tension1000 tension sensor at the front end of the traction mechanism at the rolling mill exit to obtain the traction tension F at a frequency of 1 kHz.

[0200] 2. Initial process parameters and model verification;

[0201] Initial casting speed v0 = 1.2 m / min; initial mold cooling intensity corresponds to undercooling △T0 = 25 °C; initial rolling temperature T ro=450°C; initial traction tension F0 = 12kN. Using finite element software (ANSYS Mechanical), the continuous casting and rolling sections were discretely divided under the above parameters, with a total of 100 units. The temperature-stress field distribution was simulated and substituted into the JMAK equation:

[0202] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0203]

[0204] Where f(t;x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; k(T,σ) is the temperature- and stress-dependent diffusion coefficient, T(t;x) is the temperature at the finite element unit position x at time t, σ(t;x) is the stress at the finite element unit position x at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; n is the growth exponent;

[0205] The calculated initial average subgrain boundary size d0 = 15 pum, orientation consistency index C o =0.72.

[0206] Comparing the surface temperature predicted by the finite element with the surface temperature field of the casting embryo at every first set time;

[0207] The orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index every second set time;

[0208] If the deviation after comparison is greater than the set condition, the parameter re-identification is triggered, including:

[0209] The model parameter vector is constructed, updated online using the recursive least squares method, and then applied to the coupled subgrain boundary model.

[0210] Construct a multi-objective collaborative optimization model, including:

[0211] The process parameter vector is recorded as p = [v, ΔT, T r ,F] T ;

[0212] Where: v is the casting speed, ΔT is the degree of supercooling, T r is the rolling temperature, F is the traction tension;

[0213] The average subgrain boundary size and orientation consistency at a given p are predicted based on the coupled subgrain boundary model, and the objective function is constructed:

[0214] F(p)=w1F1(p)+w2F2(p);

[0215]

[0216] Where F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function respectively, w1 and w2 are the first weighting coefficient and the second weighting coefficient respectively, is the average subgrain boundary size, C o (p) is the subgrain boundary orientation consistency, which is obtained by statistics of the coupled subgrain boundary model;

[0217] in, Calculated by the coupled subgrain boundary model:

[0218]

[0219] Where d(t; x) represents the characteristic size of the subgrain boundary at the finite element unit position x at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t; x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0220] 3. Hybrid CS–TLBO optimization;

[0221] 3.1 Algorithm parameters:

[0222] Population size n = 30;

[0223] Maximum iteration G max =150;

[0224] Lévy step size factor α = 1.0;

[0225] Nest abandonment probability pa = 0.2;

[0226] Target weight w1=w2=0.5.

[0227] 3.2 Execution process;

[0228] Generation 1: Randomly generate 30 sets of process vectors p = [v, ΔT, T r ,F] T , calculate the respective objective function values; CS exploration: apply Levy flight to each vector, abandon the worst 6 substitute new solutions; TLBO refinement: use the current optimal vector as the "teacher", perform the teacher phase and learner phase to update all individuals; repeat iterations until the 150th generation or the optimal value changes by <0.001 within 20 consecutive generations.

[0229] 3.3 Optimization results;

[0230] At the end of the iteration, the optimal process parameters were obtained: {1.05 m / min, 30 °C, 470 °C, 14 kN};

[0231] The model predicts that under this condition:

[0232] The average subgrain boundary size is 9 μm; the orientation consistency index is 0.85.

[0233] 4. Parameter distribution and closed-loop feedback;

[0234] 4.1 Send {1.05, 30, 470, 14} to the continuous casting and rolling control units through PLC / SCADA;

[0235] 4.2 The continuous casting machine automatically adjusts the casting speed to 1.05m / min, increases the cooling water flow by 10%, controls the roller temperature of the continuous rolling mill at 470°C, and adjusts the tension of the traction device to 14kN;

[0236] 4.3 After running for 5 minutes, the online monitoring data is updated to:

[0237] Surface average temperature error △T err <2℃; measured d meas =9.3um, △d<0.3um; measured C omeas =0.83, △C o <0.02.

[0238] 4.4 Deviations are all within the set threshold (∈d=0.5um,∈C o =0.05), no further optimization is triggered.

[0239] 5. Effect evaluation;

[0240] like Figures 3 to 6 As shown, the copper rod sample after the process control of this embodiment was subjected to a tensile test. Figure 3 The following is a comparison of the tensile strength before and after optimization. The yellow broken line in the figure is the tensile strength of the copper rod sample before optimization, and the red broken line is the tensile strength after optimization. It can be seen from the figure that the tensile strength of the copper rod is increased by 5% after optimization.

[0241] Figure 4 This is a comparison chart of the copper rod elongation before and after optimization. The yellow broken line in the figure is the elongation of the copper rod sample before optimization, and the red broken line is the elongation after optimization. It can be seen from the figure that the elongation is increased by 8%;

[0242] Figure 5 This is a comparison chart of conductivity before and after optimization. The yellow broken line in the figure is the conductivity of the copper rod sample before optimization, and the red broken line is the conductivity after optimization. It can be seen from the figure that the conductivity test shows that the conductivity is increased by 2%.

[0243] See Figure 6The computer device 400 provided in the embodiment of the present application includes a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the method described above is performed.

[0244] The embodiment of the present application further provides a storage medium 430 , on which a computer program is stored. When the computer program is run by the processor 410 , the above method is executed.

[0245] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0246] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. "Multiple" means two or more, unless otherwise specifically defined.

[0247] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0248] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0249] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0250] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0251] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0252] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0253] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for controlling continuous casting and rolling processes based on copper rod subgrain boundaries, characterized in that: The steps include: Collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and collect the surface temperature field of the casting, subgrain boundary orientation consistency index and rolling traction tension respectively; Discretely calculating the temperature-stress field of the casting embryo based on the finite element method, constructing a coupled subgrain boundary model, and online correcting the parameters of the coupled subgrain boundary model based on the casting embryo surface temperature field, subgrain boundary orientation consistency index and rolling traction tension; A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model, and the multi-objective collaborative optimization model is optimized and solved using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain an optimal process parameter vector; The optimal process parameter vector is applied to the continuous casting and rolling system, and re-optimization or alarm is triggered according to the deviation between the online monitoring data and the prediction.

2. The continuous casting and rolling process control method based on copper rod subgrain boundaries according to claim 1, characterized in that: The discrete calculation of the temperature-stress field of the casting embryo based on the finite element method and the construction of the coupled subgrain boundary model include the following steps: Construct a three-dimensional model of the geometric shape of the cast billet in the continuous casting and rolling section; Dividing the three-dimensional model grid into a plurality of finite element units and setting boundary conditions in finite element software; Performing finite element temperature field and stress field simulation based on the process parameter vector through heat transfer equations and mechanical equilibrium equations; At the center of each finite element, the subgrain boundary evolution is driven by the local temperature-stress field to construct a coupled subgrain boundary model.

3. The method for controlling the continuous casting and rolling process based on the copper rod subgrain boundary according to claim 2, characterized in that: The coupled subgrain boundary model includes: f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ]; Where f(t;x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; k(T,σ) is the temperature- and stress-dependent diffusion coefficient, T(t;x) is the temperature at the finite element unit position x at time t, σ(t;x) is the stress at the finite element unit position x at time t; k0 is the prefactor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; and n is the growth exponent.

4. The method for controlling the continuous casting and rolling process based on the copper rod subgrain boundary according to claim 3, characterized in that: The online correction of the evolutionary coupling model parameters includes: Comparing the surface temperature predicted by the finite element method with the surface temperature field of the casting embryo at every first set time; comparing the orientation consistency predicted by the model with the subgrain boundary orientation consistency index every second set time; If the deviation after comparison is greater than the set condition, the parameter re-identification is triggered, including: A model parameter vector is constructed, the model parameter vector is updated online using a recursive least squares method, and the updated model parameter vector is applied to the coupled subgrain boundary model.

5. The method for controlling continuous casting and rolling process based on copper rod subgrain boundaries according to claim 1, characterized in that: The multi-objective collaborative optimization model is constructed, including: The process parameter vector is recorded as p = [v, ΔT, T r ,F] T ; Where: v is the casting speed, ΔT is the degree of supercooling, T r is the rolling temperature, F is the traction tension; The average subgrain boundary size and orientation consistency at a given p are predicted based on the coupled subgrain boundary model, and the objective function is constructed: F(p)=w1F1(p)+w2F2(p); Where F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function respectively, w1 and w2 are the first weighting coefficient and the second weighting coefficient respectively, is the average subgrain boundary size, C o (p) is the subgrain boundary orientation consistency, which is obtained by statistics based on the coupled subgrain boundary model; in, Calculated by the coupled subgrain boundary model: Where d(t; x) represents the characteristic size of the subgrain boundary at the finite element unit position x at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t; x) represents the evolution fraction of the subgrain boundary at the finite element unit position x at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

6. The method for controlling continuous casting and rolling process based on copper rod subgrain boundaries according to claim 5, characterized in that: The method of optimizing and solving the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm comprises the following steps: Using the process parameters as decision variables, a global search is first performed using the Cuckoo Search CS algorithm to generate several individuals; The best individual is taken as the teacher individual, and each of the remaining individuals is learned and the fitness is calculated. If there is improvement, the teacher individual is replaced, iterated and the optimal process parameter vector is output.

7. The method for controlling the continuous casting and rolling process based on the copper rod subgrain boundary according to claim 6, characterized in that: The method of using the cuckoo search CS algorithm to perform a global search and generate several individuals includes the following steps: Randomly generate n feasible solutions: Make it meet the process safety constraints and physical feasibility constraints; Calculate the fitness of each individual: For each solution Perform a Lévy flight to generate candidate solutions: Where g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size. Randomly select another solution like: Then the solution Replaced by the candidate solution p i '; With probability p a Discard the worst p a n solutions, supplemented by new random solutions; Iteratively generate several solutions, which are the individuals.

8. The method for controlling continuous casting and rolling process based on copper rod subgrain boundaries according to claim 6, characterized in that: The method of taking the best individual as the teacher individual, learning and calculating the fitness of each of the remaining individuals, and replacing the teacher individual if there is improvement, includes the following steps: Calculate the number of individuals in the current population The mean of: Take the best individual as the teacher individual For each solution renew: where r∈(0,1),q∈{1,2}; If the fitness of the updated solution is less than that of the original solution, it is replaced; Randomly pair the population, each pair (i, j): right Calculate the fitness and replace it if it is improved; Select the one with the lowest fitness in the updated population like If there is no significant improvement after several consecutive generations, the iteration is terminated; Output the optimal process parameter vector.

9. The method for controlling continuous casting and rolling process based on copper rod subgrain boundaries according to claim 5, characterized in that: The triggering of re-optimization or alarm based on the deviation between the online monitoring data and the prediction includes: Based on the coupled subgrain boundary model, the surface temperature field of the casting embryo, the subgrain boundary orientation consistency index and the rolling pulling tension are pre-calculated under a given P; At each sampling moment, the temperature field deviation, subgrain boundary orientation consistency deviation and rolling traction tension deviation are calculated; If at any time there is a temperature field deviation, subgrain boundary orientation consistency deviation or rolling traction tension deviation greater than the set temperature field deviation threshold, orientation consistency threshold or tension deviation threshold, it is determined to be a working condition drift; If operating condition drift occurs at N consecutive sampling points, the hybrid algorithm optimization process is re-executed with the last optimal process parameter vector as the population center. If communication timeout, model solution failure, or actuator response abnormality occurs during the re-optimization process, an alarm is issued. The new process parameter vector obtained by the solution is issued and executed; If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state and re-optimization or alarm is canceled.

10. A continuous casting and rolling process control system based on copper rod subgrain boundaries, characterized in that: Using the method according to any one of claims 1 to 9, the system comprises: The acquisition unit is used to collect process parameters, construct process parameter vectors, set positions in the continuous casting crystallization zone and continuous rolling section, and respectively collect the surface temperature field of the casting embryo, the subgrain boundary orientation consistency index and the rolling traction tension; a modeling unit for discretely calculating the temperature-stress field of the casting embryo based on the finite element method, constructing a coupled subgrain boundary model, and performing online correction of the coupled subgrain boundary model parameters based on the casting embryo surface temperature field, the subgrain boundary orientation consistency index, and the rolling traction tension; A solving unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model, and optimize and solve the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain an optimal process parameter vector; An application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and trigger re-optimization or alarm according to the deviation between the online monitoring data and the prediction.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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