Cooling rate control method and system in continuous directional solidification process of copper material

Abstract

The invention relates to the technical field of copper material cooling control, in particular to a copper material continuous directional solidification process cooling rate control method and system, and the method comprises the steps: obtaining multi-dimensional dynamic parameter data of a copper material solidification area; constructing a multi-field coupled solidification dynamic prediction model, and analyzing solidification instability characteristic quantity in the multi-dimensional dynamic parameter data; when the solidification instability characteristic quantity exceeds a preset dynamic critical threshold value, cooperative control of at least two cooling media is triggered, and a dynamic adjustment instruction set of cooling parameters is generated; thermodynamic safety verification is carried out on the dynamic adjustment instruction set, and gradient-limited parameter correction is carried out according to a verification result; and updating coupling parameters of the solidification dynamic prediction model according to solidification interface characteristic feedback data obtained by the corrected parameters. By means of the method, the problems of grain coarsening and thermal stress concentration caused by copper material continuous casting cooling regulation lag are effectively solved, dynamic cooperative control is achieved through multi-field coupling prediction and closed-loop optimization, and the casting blank quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of copper material cooling control, and particularly to a method and system for controlling the cooling rate during the continuous directional solidification of copper materials. Background Art

[0002] In the continuous directional solidification process of copper materials, the dynamic response hysteresis of the solidification interface has become the core technical bottleneck restricting the preparation of high-quality products. The traditional cooling control method is based on fixed sensing points and preset cooling gradients. Its fundamental defect lies in the inability to capture the transient evolution characteristics of the solidification front in real time. As the dynamic boundary of the liquid-solid phase change, the real-time matching of the migration rate, three-dimensional morphology fluctuation, and heat flux distribution of the solidification interface directly determines the grain orientation accuracy and the microstructure uniformity. However, the existing control system has double sensing defects: First, single-point temperature monitoring is difficult to reconstruct the real-time changes in the interface spatial morphology; second, the cooling parameter adjustment logic is mismatched with the dynamic characteristics of the interface migration rate, resulting in frequent problems such as cumulative grain orientation deviation and abnormal growth of equiaxed grains.

[0003] Although the industry has tried to improve by increasing sensing nodes or optimizing local control algorithms, it is still restricted by two essential contradictions: the structural conflict between the non-linear dynamic characteristics of the solidification interface evolution and the traditional linear control logic; the time-delay effect of cooling parameter adjustment and the mismatch with the millisecond-level response requirements of the process. These contradictions have led to the fact that the existing technology has always been unable to construct a high-frequency closed-loop mapping relationship between the "interface dynamic state - cooling parameters", resulting in the process control being long-term trapped in a vicious cycle of "perception lag → compensation failure → defect accumulation", seriously restricting the breakthrough and improvement of the performance of high-end copper material products. Summary of the Invention

[0004] The present invention provides a method and system for controlling the cooling rate during the continuous directional solidification of copper materials, thereby effectively solving the problems pointed out in the background art.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A method for controlling the cooling rate during the continuous directional solidification of copper materials, comprising: Obtaining multi-dimensional dynamic parameter data of the copper material solidification zone, where the multi-dimensional dynamic parameter data is physical field parameters characterizing the solidification dynamic evolution; Constructing a multi-field coupled solidification dynamic prediction model to analyze the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data; When the solidification instability characteristic quantity exceeds a preset dynamic critical threshold, triggering the coordinated control of at least two cooling media to generate a dynamic adjustment instruction set for cooling parameters; Performing a thermodynamic safety check on the dynamic adjustment instruction set and performing gradient-limited parameter correction according to the check result; Update the coupling parameters of the solidification dynamic prediction model based on the solidification interface characteristic feedback data obtained according to the corrected parameters to form a closed-loop optimization of the control strategy.

[0006] Further, construct a multi-field coupled solidification dynamic prediction model, including: Establish the interaction relationship of the control equations of the temperature field, phase field and stress field, dynamically correlate the spatio-temporal evolution of the temperature field through the phase field gradient term, and establish the mechanical constraint condition of the interface migration rate based on the thermal stress tensor; Discretize the temperature field diffusion term and the phase field stiffness term by the implicit difference method, and solve the interface migration convection term and the stress relaxation term by the explicit adaptive step method; Based on the real-time detection data of the solidification interface morphology, calculate the model prediction residual and trigger the update of the coupling parameters, and output the temperature gradient field, phase field gradient field and equivalent stress field. Further, discretize the temperature field diffusion term and the phase field stiffness term by the implicit difference method, and solve the interface migration convection term and the stress relaxation term by the explicit adaptive step method, including: Construct a global coefficient matrix including the coupling relationship between the temperature field and the phase field, and dynamically adjust the residual convergence threshold of the implicit difference according to the real-time monitoring value of the phase field gradient modulus; Solve the interface migration convection term and the stress relaxation term by the explicit difference method, and automatically adjust its calculation step based on the real-time feedback value of the solidification interface migration rate, and the explicit solution result performs data interaction with the implicit discretization process through the time domain interpolation method.

[0007] Further, analyze the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data, including: Extract the field variable data of the current space-time node based on the temperature gradient field, phase field gradient field and equivalent stress field; Calculate the spatial coordination degree of the temperature gradient and the phase field gradient, and correlate the acceleration component of the interface migration rate; Synthesize the spatial coordination degree and the equivalent stress constraint strength according to the dynamic weight to obtain the solidification instability characteristic quantity.

[0008] Further, obtain the multi-dimensional dynamic parameter data of the copper material solidification zone, including: Continuously capture the dynamic morphology image sequence of the copper material solidification zone; Generate the three-dimensional temperature field distribution data in the solidification direction based on the temperature measurement array; Perform spatio-temporal registration on the dynamic morphology image sequence and the three-dimensional temperature field distribution data, and fuse them to generate the physical field parameters.

[0009] Further, when the solidification instability characteristic quantity exceeds the preset dynamic critical threshold, trigger the coordinated control of at least two cooling media, and generate a dynamic adjustment instruction set of cooling parameters, including: Based on the solidification instability characteristic quantity, a cooperative combination is matched from a variety of cooling media, and the matching logic of the cooperative combination includes the corresponding relationship between the over-limit amplitude of the characteristic quantity and the regulation ability of the cooling medium; Map the degree of deviation of the characteristic quantity from the dynamic critical threshold to the adjustment instruction of the cooling parameter, perform multi-dimensional verification on the adjustment instruction, and output the dynamic adjustment instruction set that meets the multi-objective constraints.

[0010] Furthermore, the thermodynamic safety verification includes at least two of the temperature gradient change rate verification, stress increment analysis, and multi-physical field coupling verification.

[0011] Furthermore, according to the solidification interface characteristic feedback data obtained from the corrected parameters, update the coupling parameters of the solidification dynamic prediction model to form a closed-loop optimization of the control strategy, including: Obtain the measured values of the solidification interface migration rate and the grain orientation data under the action of the corrected parameters in real time; Calculate the dynamic error between the measured data and the predicted values of the solidification dynamic prediction model; Based on the dynamic error, adjust the temperature field and phase field coupling parameters of the solidification dynamic prediction model; Use the updated solidification dynamic prediction model to regenerate the cooling parameter optimization instruction and feedback it to the cooling control process.

[0012] A cooling rate control system for the continuous directional solidification process of copper materials, the system includes: A parameter acquisition module that acquires multi-dimensional dynamic parameter data of the copper material solidification zone, and the multi-dimensional dynamic parameter data is physical field parameters characterizing the dynamic evolution of solidification; A characteristic quantity and system module that constructs a solidification dynamic prediction model with multi-field coupling and analyzes the solidification instability characteristic quantity in the multi-dimensional dynamic parameter data; An instruction generation module that triggers the cooperative control of at least two cooling media and generates a dynamic adjustment instruction set of cooling parameters when the solidification instability characteristic quantity exceeds a preset dynamic critical threshold; A parameter correction module that performs thermodynamic safety verification on the dynamic adjustment instruction set and performs parameter correction with limited gradient according to the verification result; A model update module that updates the coupling parameters of the solidification dynamic prediction model according to the solidification interface characteristic feedback data obtained from the corrected parameters to form a closed-loop optimization of the control strategy.

[0013] Furthermore, the instruction generation module includes: A medium matching unit that matches a cooperative combination from a variety of cooling media based on the solidification instability characteristic quantity, and the matching logic of the cooperative combination includes the corresponding relationship between the over-limit amplitude of the characteristic quantity and the regulation ability of the cooling medium; The instruction verification unit maps the degree of deviation of the characteristic quantity from the dynamic critical threshold into an adjustment instruction for the cooling parameter, performs multi-dimensional verification on the adjustment instruction, and outputs the dynamic adjustment instruction set that meets multi-objective constraints.

[0014] Through the technical solution of the present invention, the following technical effects can be achieved: Effectively solve the problems of grain coarsening and thermal stress concentration caused by the lag of cooling regulation in copper continuous casting, and realize dynamic collaborative control through multi-field coupling prediction and closed-loop optimization, so as to improve the quality of the billet. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flow chart of a method for controlling the cooling rate during the continuous directional solidification of copper; Figure 2 It is a schematic flow chart of constructing a multi-field coupling solidification dynamic prediction model; Figure 3 It is a schematic flow chart of using the implicit difference method for discretization and the explicit adaptive step size method for solution; Figure 4 It is a schematic flow chart of analyzing the solidification instability characteristic quantity in multi-dimensional dynamic parameter data; Figure 5 It is a schematic flow chart of obtaining multi-dimensional dynamic parameter data of the copper solidification zone; Figure 6 It is a schematic flow chart of generating a dynamic adjustment instruction set for cooling parameters; Figure 7 It is a schematic flow chart of updating the coupling parameters of the solidification dynamic prediction model. Detailed Embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention pertains. The terms used in the description of this invention are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0019] Embodiment 1 As Figure 1 shown, the present invention provides a method for controlling the cooling rate during the continuous directional solidification of copper materials, and the method includes: S1: Obtain multi-dimensional dynamic parameter data of the copper material solidification zone, and the multi-dimensional dynamic parameter data are physical field parameters characterizing the dynamic evolution of solidification; Specifically, during the solidification process, information on the interface morphology and thermal distribution can be obtained simultaneously through means such as image acquisition and temperature monitoring, and by combining appropriate calibration and data fusion techniques, the time-series images, temperature measurement array feedback, and possible stress or phase distribution signals are placed in the same coordinate system. With the help of a high-speed industrial camera or line-scanning imaging device, the dynamic morphology evolution of the melt-solid interface can be captured at a high frame rate; a multi-channel temperature detection device can continuously record the thermal distribution around the melt pool and the solid-liquid interface. After registering the image and temperature information in a unified space-time manner, through data processing methods such as filtering and interpolation, accurate multi-dimensional dynamic parameters such as the thermal-morphology-phase structure presented by the solidification zone at different time and space positions can be obtained. These parameters can not only reflect key characteristics such as temperature gradient, interface migration speed, and microstructure growth, but also provide a detailed physical field basis for subsequent solidification dynamic prediction and cooling control strategies.

[0020] S2: Construct a multi-field coupled solidification dynamic prediction model to analyze the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data; Specifically, by establishing a prediction model that couples the temperature field, phase field, and stress field, the interaction of multiple physical quantities during the solidification process can be dynamically simulated at the numerical level, thereby more accurately depicting the interface morphology evolution and internal stress changes; by inputting the real-time obtained multi-dimensional dynamic parameter data and comparing it with the model output, the key signs of instability generated during the solidification process can be identified, the degree of solidification instability can be quantified, and a targeted adjustment basis for subsequent cooling control can be provided. The core of this model lies in simultaneously considering the coupling effects of temperature, phase change, and mechanical stress, making the prediction of the solidification process more accurate and forward-looking, thereby reducing defects and improving material quality.

[0021] S3: When the solidification instability characteristic quantity exceeds the preset dynamic critical threshold, trigger the coordinated control of at least two cooling media to generate a dynamic adjustment instruction set for cooling parameters; Specifically, by simultaneously activating at least two different types of cooling media, a wider range of cooling intensity adjustment can be achieved in a short time, and the local temperature gradient and interface migration rate can be optimized through multi-objective synergy, so as to pull the instability characteristic quantity back to the safe range. The generated dynamic adjustment instruction set usually includes refined settings in multiple dimensions such as cooling intensity, flow rate, or temperature, enabling the overall solidification process to obtain a more stable and controllable cooling environment, thereby reducing crystal defects and internal stress concentration, and ensuring the quality of the finished product and production efficiency.

[0022] S4: Conduct a thermodynamic safety check on the dynamic adjustment instruction set and perform gradient-limited parameter correction according to the check result; Specifically, by conducting a thermodynamic check on the temperature gradient change rate, stress increment, and multi-physical field coupling effect involved in the instruction set, it can be determined whether it will cause risks such as overcooling, thermal shock, or stress concentration; if there is an overlimit trend, a gradient-limited correction strategy is adopted to limit the change range of each adjustment amount within the allowable range of the equipment's bearing capacity and process window. This not only ensures the timeliness of the cooling response but also avoids cracks, warping, or equipment damage caused by excessive adjustment, thereby steadily pulling the instability characteristic quantity back to the controllable range on the premise of ensuring safety.

[0023] S5: Update the coupling parameters of the solidification dynamic prediction model based on the solidification interface characteristic feedback data obtained from the corrected parameters to form a closed-loop optimization of the control strategy.

[0024] Specifically, by collecting and analyzing the actual changes in the solidification interface generated by the corrected cooling parameters, comparing the measured data (such as interface morphology, temperature distribution, stress conditions, etc.) with the model prediction results, and then updating the key parameters of the multi-field coupling model, the model can fit the actual production situation after each execution. Such a closed-loop optimization mechanism enables the system to "learn" and adapt to various process fluctuations, gradually improving the prediction accuracy and control effect, and avoiding overcooling or undercooling caused by the deviation between parameters and reality, thereby achieving high-precision and high-stability control of the solidification process.

[0025] Through the present invention, the problems of grain coarsening and thermal stress concentration caused by the lag in cooling control during copper continuous casting are effectively solved, and dynamic collaborative control is achieved through multi-field coupling prediction and closed-loop optimization, improving the quality of the continuous casting billet.

[0026] As a preference of the above embodiment, as Figure 2 shown, a solidification dynamic prediction model with multi-field coupling is constructed, including: A10: Establish the interaction relationship of the control equations of the temperature field, phase field, and stress field, dynamically correlate the spatio-temporal evolution of the temperature field through the phase field gradient term, and establish the mechanical constraint conditions of the interface migration rate based on the thermal stress tensor; A20: The diffusion term of the temperature field and the rigidity term of the phase field are discretized by the implicit difference method, and the convection term of interface migration and the stress relaxation term are solved by the explicit adaptive step method; A30: Based on the real-time detection data of the solidification interface morphology, calculate the prediction residuals of the model and trigger the update of the coupling parameters, and output the temperature gradient field, the phase field gradient field and the equivalent stress field.

[0027] Specifically, first, the control equations of the temperature field, the phase field and the stress field need to be correlated. Through the gradient term of the phase field, the spatio-temporal evolution of the temperature field and the phase field is dynamically linked, so as to describe how the temperature change affects the position and morphology of the phase change interface during the solidification process. At the same time, based on the calculation of the thermal stress tensor, a mechanical constraint condition for the interface migration rate is established to ensure that the migration speed of the interface conforms to the mechanical limitations in the actual physical process. The change of the temperature field not only affects the phase change of the material, but may also cause the accumulation of thermal stress. Therefore, these influencing factors need to be comprehensively considered in the control equations; in order to solve the evolution of the temperature field and the phase field, it is necessary to discretize their mathematical models. In this model, the diffusion term of the temperature field and the rigidity term of the phase field are usually discretized by the implicit difference method. The implicit difference method can ensure the stability and accuracy of numerical calculation during high temperature gradients and phase change processes. Compared with the temperature field, the implicit method is more suitable for physical processes with longer time spans or slower changes. For the convection term of interface migration and the stress relaxation term, the explicit adaptive step method is used for solution. The explicit difference method can accurately capture the transient process of interface migration, and through the adjustment of the adaptive step, the calculation step can be dynamically optimized according to the interface migration speed and the stress state during the solidification process, so as to improve the calculation efficiency and accuracy; in order to improve the accuracy of the model, it is necessary to track the interface morphology during the solidification process in real time and compare its data with the model prediction results. By calculating the residuals between the predicted values and the actual measured values, the deviation degree of the current model in describing the solidification process can be judged. When the residual reaches the preset threshold, trigger the update of the coupling parameters in the model so that the model can reflect a more accurate physical process. For example, if there is a large deviation in the prediction of the interface morphology, parameters such as the gradient coefficient of the phase field and the thermal conductivity coefficient need to be adjusted. By updating the coupling parameters, the model can more accurately simulate the interaction of the temperature field, the phase field and the stress field, and then improve the prediction ability of the solidification process. The updated coupling model will output multiple physical field data including the temperature gradient field, the phase field gradient field and the equivalent stress field, providing an accurate prediction basis for subsequent cooling rate control and process adjustment.

[0028] As a preference of the above embodiment, as Figure 3 shown, step A20, discretizing the diffusion term of the temperature field and the rigidity term of the phase field by the implicit difference method, and solving the convection term of interface migration and the stress relaxation term by the explicit adaptive step method, includes: A201: Construct a global coefficient matrix that includes the coupling relationship between the temperature field and the phase field, and dynamically adjust the residual convergence threshold of implicit difference according to the real-time monitored value of the phase field gradient modulus; A202: Solve the interface migration convection term and the stress relaxation term using the explicit difference method. Its calculation step size is automatically adjusted based on the real-time feedback value of the solidification interface migration rate, and the explicit solution result interacts with the implicit discretization process through time domain interpolation.

[0029] Specifically, first, a global coefficient matrix needs to be constructed, which includes the coupling relationship between the temperature field and the phase field. This matrix is used to describe the interaction between the temperature field and the phase field in space and time, ensuring that the changes in both can be correctly coupled in the numerical solution. For example, when the temperature field changes, the solid-liquid interface of the phase field will shift, thereby affecting the phase change speed; similarly, the change in the phase field will also affect the heat transfer and distribution. Next, the implicit difference method is used to discretize the temperature field diffusion term and the phase field stiffness term. The implicit difference method has good stability, especially in high temperature gradients or more complex phase change processes, which can avoid the occurrence of numerical oscillation phenomena. To improve the accuracy of the model, the residual convergence threshold of the implicit difference method needs to be dynamically adjusted according to the real-time monitored value of the phase field gradient modulus. Specifically, the change in the phase field gradient can reflect the position and shape of the solid-liquid interface, which will affect the change in temperature distribution. Therefore, real-time monitoring of the phase field gradient and adjusting the residual convergence threshold of the implicit difference method according to its change helps to more precisely control the coupling process of the temperature field and the phase field. The interface migration convection term and the stress relaxation term usually have strong time-varying and transient behaviors, so the explicit difference method is used in the solution. The explicit difference method can quickly respond to these transient changes. Especially during the interface migration process, the convection term will be affected by the changes in temperature and phase field, resulting in a rapid movement of the interface position. In the explicit difference method, the calculation step size needs to be automatically adjusted according to the real-time feedback value of the solidification interface migration rate. The interface migration rate is closely related to the changes in the temperature field and the phase field. When the interface migration rate is fast, the calculation step size needs to be shortened to ensure the calculation accuracy; when the migration rate is slow, the step size can be appropriately increased to improve the calculation efficiency. This method of automatically adjusting the step size can improve the solution efficiency while ensuring the calculation accuracy. In addition, the calculation results of the explicit solution and the implicit discretization process are interacted through time domain interpolation. The calculation result given by the explicit difference method provides the instant information at the current moment, while the implicit difference method is a steady-state solution based on the entire time step. By combining the calculation results of the two through time domain interpolation, the model can take into account both short-term and long-term dynamic changes at each time step, thus more accurately simulating the physical phenomena during the solidification process.

[0030] As a preference of the above embodiment, as Figure 4As shown in the figure, the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data are analyzed, including: B10: Extract the field variable data of the current space-time node based on the temperature gradient field, phase field gradient field and equivalent stress field; B20: Calculate the spatial coordination degree between the temperature gradient and the phase field gradient, and correlate the acceleration component of the interface migration rate; B30: Synthesize the spatial coordination degree and the equivalent stress constraint strength according to the dynamic weight to obtain the solidification instability characteristic quantity.

[0031] Specifically, first, it is necessary to extract the field variable data of each space-time node based on the temperature gradient field, phase field gradient field and equivalent stress field. These data reflect the distribution states of temperature, phase change and stress during the solidification process. By analyzing these field variables, it is possible to comprehensively understand the heat conduction, phase change behavior and the influence of thermal stress on the casting during the solidification process; then, calculate the spatial coordination degree between the temperature gradient and the phase field gradient, and combine it with the acceleration component of the interface migration rate. The spatial coordination degree measures the interaction between the temperature field and the phase field. A higher coordination degree usually means that the temperature gradient and the phase field gradient have a stronger influence on the solidification interface. This relationship is crucial for predicting the stability of the interface. In addition, the acceleration component of the interface migration rate reflects the dynamic characteristics of the interface position change and can provide important information for predicting possible instability phenomena during the solidification process; finally, by synthesizing the spatial coordination degree and the equivalent stress constraint strength according to the dynamic weight, the solidification instability characteristic quantity is obtained. This characteristic quantity can comprehensively consider the influence of the temperature field, phase field and stress field during the solidification process and evaluate whether there is a risk of instability at the solidification interface. When the instability characteristic quantity is large, it indicates that problems such as grain coarsening, stress concentration or interface instability may occur during the solidification process, and it is necessary to adjust the cooling rate or process parameters in time to prevent the generation of defects. Through such a process, it is possible to monitor the possible instability risks during the solidification process in real time, provide data support for process adjustment, thereby optimizing the casting process and improving the quality of the casting.

[0032] As a preference of the above embodiment, as Figure 5 shown, obtain the multi-dimensional dynamic parameter data of the copper material solidification zone, including: C10: Continuously capture the dynamic morphology image sequence of the copper material solidification zone; C20: Generate the three-dimensional temperature field distribution data in the solidification direction based on the temperature measurement array; C30: Perform space-time registration on the dynamic morphology image sequence and the three-dimensional temperature field distribution data, and fuse them to generate physical field parameters.

[0033] Specifically, it is first necessary to arrange high-speed industrial cameras and light source devices in the solidification area to record the dynamic changes of the interface morphology at a high frame rate. The camera is usually installed at a suitable position where the solidification front or the surface of the ingot can be observed. By setting a suitable light source between the camera and the workpiece, the morphological features formed at the solidification interface can be recorded in a continuous shooting manner. After morphological reconstruction or edge detection of these continuous images, the morphological data of the solidification interface evolving over time can be obtained. In terms of temperature measurement, a multi-channel temperature measurement array can be arranged in the cross section of the ingot and in the solidification direction. The temperature value is monitored in real time with the help of infrared temperature probes or fiber grating sensors, and data is collected according to a unified time reference. The temperature value of each measuring point will change with position and time. By sequentially sampling in layers in the solidification direction, the three-dimensional temperature field distribution of the ingot during the solidification process can be obtained. In order to unify the image data and temperature data into the same coordinate system, the camera coordinate system and the temperature measurement array coordinate system need to be calibrated. Usually, external calibration parts or known size feature points are used to measure the relative position and posture between the two, and a unified clock signal or trigger device is used to synchronize the acquisition time. Subsequently, the image sequence and the temperature field are aligned through a spatiotemporal registration algorithm to achieve the mapping of the morphological data and the temperature data in the same coordinate system. After completing the coordinate calibration and time synchronization, the collected image and temperature data can be further filtered and interpolated to reduce the influence of external noise or sensor error on the results. At the same time, for missing or abnormal data points, conventional interpolation or prediction model-based methods can be used to compensate. Finally, the dynamic morphology of the solidification interface and the three-dimensional temperature field information are fused and processed to obtain the physical field parameters that characterize the dynamic evolution of solidification. The physical field parameters contain the change information of the solidification interface in the spatial and temporal dimensions, as well as the temperature distribution data corresponding to each spatiotemporal position, providing complete and high-resolution basic data for subsequent solidification dynamic prediction and cooling rate control.

[0034] As a preferred embodiment of the above, Figure 6 As shown, in step S3, when the solidification instability characteristic quantity exceeds a preset dynamic critical threshold, the coordinated control of at least two cooling media is triggered to generate a dynamic adjustment instruction set of cooling parameters, including: S31: Based on the solidification instability characteristic quantity, a synergistic combination is matched from a plurality of cooling media, and the matching logic of the synergistic combination includes a corresponding relationship between the characteristic quantity exceeding the limit amplitude and the cooling medium control capability; S32: Mapping the degree of deviation of the characteristic quantity from the dynamic critical threshold into an adjustment instruction of the cooling parameter, performing multi-dimensional verification on the adjustment instruction, and outputting a dynamic adjustment instruction set that satisfies multi-objective constraints.

[0035] Specifically, first, according to the magnitude of the solidification instability characteristic quantity, a suitable combination will be selected from multiple cooling media for collaborative control. The selection and combination of cooling media are based on the corresponding relationship between the over-limit amplitude of the characteristic quantity and the regulation ability of the cooling media. Specifically, when the solidification instability characteristic quantity exceeds the preset threshold, the system will select the corresponding cooling medium according to the over-limit amplitude. For example, if the over-limit amplitude of the instability characteristic quantity is small, cooling water and inert gas can be selected as a collaborative combination for mild cooling; if the over-limit amplitude is large, the system will select a stronger cooling combination, such as the combination of cooling water and cooling oil mist, to quickly reduce the temperature and reduce the instability of the solidification interface. The matching logic of the cooling media ensures that in the case of a large instability characteristic quantity, the abnormal behavior of the solidification interface can be quickly and effectively suppressed, thus ensuring the quality of the casting. The regulation ability of each cooling medium - such as cooling rate, heat transfer efficiency, gas pressure, etc. - will affect the selection of the combination, thus forming the most suitable cooling strategy. Mapping the degree of deviation of the solidification instability characteristic quantity from the dynamic critical threshold to the adjustment instruction of the cooling parameters means that the system will generate corresponding cooling parameter adjustment instructions according to the degree of deviation of the instability characteristic quantity from the threshold. For example, if the instability characteristic quantity deviates from the threshold by a small amount, the system may fine-tune the flow rate of the cooling water; while when the deviation is large, the system will greatly adjust the flow rate, pressure or injection angle of the cooling medium, or even increase the cooling intensity or change the type of the cooling medium. These adjustment instructions need to be verified in multiple dimensions to ensure that the adjusted cooling strategy meets multi-objective constraints. The verification content includes but is not limited to the verification of the temperature gradient change rate, the analysis of the thermal stress increment, and the coupling verification of the phase field. Only when these constraint conditions are met, the system will output the final dynamic adjustment instruction set. The adjustment instruction set will include, according to actual needs, the cooling water flow rate, gas pressure, cooling oil mist injection volume, etc., to ensure the cooling effect while avoiding cracks or other material defects caused by excessive cooling.

[0036] As a preference of the above embodiment, the thermodynamic safety verification includes at least two of the verification of the temperature gradient change rate, the stress increment analysis, and the multi-physical field coupling verification.

[0037] Specifically, first, the verification of the temperature gradient change rate and the analysis of stress increment have a certain complementarity in the cooling process. The verification of the temperature gradient change rate focuses on ensuring that the temperature change is not too drastic to avoid excessive thermal stress, while the stress increment analysis focuses on whether the thermal stress caused by the temperature difference exceeds the bearing range of the material. Since the goals of these two are similar, usually one of them is selected for key monitoring to reduce the computational burden while ensuring the stability of the cooling process and the integrity of the material. Secondly, the multi-physical field coupling verification can comprehensively evaluate various physical effects during the solidification process, covering the interactions between the temperature field, phase field, and stress field. Under some complex solidification processes or process conditions, choosing the multi-physical field coupling verification can already comprehensively cover potential risks. Therefore, there is no need to conduct the verification of the temperature gradient change rate and the stress increment analysis simultaneously, but only focus on two of the verifications. This method can not only reduce the computational complexity but also improve the real-time response ability. Especially in large-scale production, excessive calculations may affect the real-time performance of the system. Finally, which verification items to choose usually depends on the specific casting process and cooling conditions. Under rapid cooling or high temperature difference conditions, the temperature gradient and thermal stress may have a greater impact on the material. In this case, the combination of the temperature gradient verification and the stress increment analysis will be more effective. Under relatively stable cooling conditions, the multi-physical field coupling verification is sufficient to cover most potential risks. Therefore, it can be selected for verification together with the temperature gradient change rate verification. Through this flexible strategy of "choosing two out of three", it can be ensured that without increasing unnecessary computational burden and while guaranteeing the process stability, it can also respond to potential risks in a timely manner, optimize the cooling process, and ensure the quality of the casting.

[0038] As a preference of the above embodiment, as Figure 7 shown, in step S5, according to the solidification interface characteristic feedback data obtained from the corrected parameters, update the coupling parameters of the solidification dynamic prediction model to form a closed-loop optimization of the control strategy, including: S51: Obtain the measured value of the solidification interface migration rate and the grain orientation data under the action of the corrected parameters in real time; S52: Calculate the dynamic error between the measured data and the predicted value of the solidification dynamic prediction model; S53: Adjust the coupling parameters of the temperature field and phase field of the solidification dynamic prediction model based on the dynamic error; S54: Use the updated solidification dynamic prediction model to regenerate the cooling parameter optimization instruction and feedback it to the cooling control process.

[0039] Specifically, first, the migration rate of the solidification interface and the grain orientation data are monitored in real time to reflect the effect of the corrected cooling parameters in the actual solidification process. These data provide an accurate basis for model correction to ensure that the control strategy is consistent with the actual process. Next, by calculating the dynamic error between the measured data and the prediction model, the accuracy of the model prediction is evaluated. If there is a large deviation between the measured value and the predicted value, the system will determine that the model needs to be adjusted to improve the prediction accuracy. Based on the calculated dynamic error, the system will adjust the temperature field and phase field coupling parameters in the model to ensure that the predicted value is closer to the actual solidification process. The adjusted model will regenerate the cooling parameter optimization instructions, which will accurately calculate the cooling strategy according to the new coupling parameters and feedback them to the cooling control process. In this way, the cooling process will be continuously optimized according to the real-time feedback and model correction to ensure that the temperature, phase transformation behavior, and stress distribution during solidification can be accurately controlled, thereby improving the quality and stability of the casting. Through this closed-loop optimization mechanism, the control of the solidification process can be continuously adjusted and optimized to ensure a more efficient and stable production process.

[0040] Embodiment 2 Based on the same inventive concept as the cooling rate control method for the continuous directional solidification process of copper in the foregoing embodiment, the present invention also provides a cooling rate control system for the continuous directional solidification process of copper. The system includes: A parameter acquisition module that acquires multi-dimensional dynamic parameter data of the copper solidification zone. The multi-dimensional dynamic parameter data are physical field parameters characterizing the dynamic evolution of solidification; A feature quantity and system module that constructs a solidification dynamic prediction model with multi-field coupling and analyzes the solidification instability feature quantities in the multi-dimensional dynamic parameter data; An instruction generation module that triggers the coordinated control of at least two cooling media and generates a dynamic adjustment instruction set for cooling parameters when the solidification instability feature quantity exceeds a preset dynamic critical threshold; A parameter correction module that performs a thermodynamic safety check on the dynamic adjustment instruction set and performs gradient-limited parameter correction according to the check result; A model update module that updates the coupling parameters of the solidification dynamic prediction model according to the solidification interface feature feedback data obtained from the corrected parameters to form a closed-loop optimization of the control strategy.

[0041] The above control system in the present invention can effectively implement the cooling rate control method for the continuous directional solidification process of copper, and the technical effects that can be achieved are as described in the above embodiment, which will not be elaborated here.

[0042] As a preference of the above embodiment, the instruction generation module includes: The medium matching unit matches a cooperative combination from a variety of cooling media based on the solidification instability characteristic quantity, and the matching logic of the cooperative combination includes the corresponding relationship between the over-limit amplitude of the characteristic quantity and the cooling medium regulation ability; The instruction verification unit maps the degree of deviation of the characteristic quantity from the dynamic critical threshold into an adjustment instruction for the cooling parameter, performs multi-dimensional verification on the adjustment instruction, and outputs a dynamic adjustment instruction set that meets multi-objective constraints.

[0043] Similarly, for the above optimization solutions of the system, the corresponding optimization effects of the methods in the first embodiment can also be respectively achieved, and will not be elaborated here again.

[0044] Although the present application has been described in combination with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for controlling the cooling rate in the continuous directional solidification process of copper materials, characterized in that, Including: Obtaining multi-dimensional dynamic parameter data of the copper material solidification zone, where the multi-dimensional dynamic parameter data are physical field parameters characterizing the dynamic evolution of solidification; Constructing a solidification dynamic prediction model with multi-field coupling, and analyzing the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data; When the solidification instability characteristic quantity exceeds a preset dynamic critical threshold, triggering the coordinated control of at least two cooling media to generate a dynamic adjustment instruction set for cooling parameters; Performing a thermodynamic safety check on the dynamic adjustment instruction set, and performing gradient-limited parameter correction according to the check result; Updating the coupling parameters of the solidification dynamic prediction model based on the solidification interface characteristic feedback data obtained from the corrected parameters, forming a closed-loop optimization of the control strategy.

2. The method for controlling the cooling rate in the continuous directional solidification process of copper material according to claim 1, wherein Constructing a solidification dynamic prediction model with multi-field coupling, including: Establishing the interaction relationship of the control equations of the temperature field, phase field and stress field, dynamically correlating the spatio-temporal evolution of the temperature field through the phase field gradient term, and establishing the mechanical constraint condition of the interface migration rate based on the thermal stress tensor; Discretizing the temperature field diffusion term and the phase field stiffness term by the implicit difference method, and solving the interface migration convection term and the stress relaxation term by the explicit adaptive step method; Based on the real-time detection data of the solidification interface morphology, calculating the model prediction residual and triggering the update of the coupling parameters, and outputting the temperature gradient field, phase field gradient field and equivalent stress field.

3. The method for controlling the cooling rate in the continuous directional solidification process of copper materials according to claim 2, wherein Discretizing the temperature field diffusion term and the phase field stiffness term by the implicit difference method, and solving the interface migration convection term and the stress relaxation term by the explicit adaptive step method, including: Constructing a global coefficient matrix including the coupling relationship between the temperature field and the phase field, and dynamically adjusting the residual convergence threshold of the implicit difference according to the real-time monitoring value of the phase field gradient modulus; Solving the interface migration convection term and the stress relaxation term by the explicit difference method, automatically adjusting its calculation step based on the real-time feedback value of the solidification interface migration rate, and performing data interaction between the explicit solution result and the implicit discretization process through time domain interpolation.

4. The method for controlling the cooling rate in the continuous directional solidification process of copper material according to claim 2, wherein Analyzing the solidification instability characteristic quantities in the multi-dimensional dynamic parameter data, including: Based on the temperature gradient field, phase field gradient field and equivalent stress field, extracting the field variable data of the current space-time node; Calculating the spatial synergy degree of the temperature gradient and the phase field gradient, and correlating the acceleration component of the interface migration rate; Synthesizing the spatial synergy degree and the equivalent stress constraint strength according to the dynamic weight to obtain the solidification instability characteristic quantity.

5. The method for controlling the cooling rate in the continuous directional solidification process of copper materials according to claim 1, characterized in that, Obtaining multi-dimensional dynamic parameter data of the copper material solidification zone, including: Continuously capturing a dynamic morphology image sequence of the copper material solidification zone; Generating three-dimensional temperature field distribution data in the solidification direction based on the temperature measurement array; Performing spatio-temporal registration on the dynamic morphology image sequence and the three-dimensional temperature field distribution data, and fusing and generating the physical field parameters.

6. The method for controlling the cooling rate in the continuous directional solidification process of copper materials according to claim 1, characterized in that, When the solidification instability characteristic quantity exceeds a preset dynamic critical threshold, triggering the coordinated control of at least two cooling media to generate a dynamic adjustment instruction set for cooling parameters, including: Based on the solidification instability characteristic quantity, matching a coordinated combination from multiple cooling media, and the matching logic of the coordinated combination includes the corresponding relationship between the characteristic quantity overrun amplitude and the cooling medium regulation ability; Map the degree of deviation of the characteristic quantity from the dynamic critical threshold to the adjustment instruction of the cooling parameter, perform multi-dimensional verification on the adjustment instruction, and output the dynamic adjustment instruction set that meets the multi-objective constraints.

7. The method for controlling the cooling rate in the continuous directional solidification process of copper materials according to claim 1, characterized in that, The thermodynamic safety verification includes at least two of the verification of the temperature gradient change rate, the stress increment analysis, and the multi-physical field coupling verification.

8. The method for controlling the cooling rate in the continuous directional solidification process of copper materials according to claim 1, wherein, Based on the solidification interface characteristic feedback data obtained from the corrected parameters, update the coupling parameters of the solidification dynamic prediction model to form a closed-loop optimization of the control strategy, including: Obtain the measured values of the solidification interface migration rate and the grain orientation data under the action of the corrected parameters in real time; Calculate the dynamic error between the measured data and the predicted values of the solidification dynamic prediction model; Adjust the temperature field and phase field coupling parameters of the solidification dynamic prediction model based on the dynamic error; Use the updated solidification dynamic prediction model to regenerate the cooling parameter optimization instruction and feedback it to the cooling control process.

9. A cooling rate control system for the continuous directional solidification process of copper materials, characterized in that, The system includes: A parameter acquisition module that acquires multi-dimensional dynamic parameter data of the copper material solidification zone, and the multi-dimensional dynamic parameter data is a physical field parameter characterizing the solidification dynamic evolution; A characteristic quantity and system module that constructs a multi-field coupled solidification dynamic prediction model and analyzes the solidification instability characteristic quantity in the multi-dimensional dynamic parameter data; An instruction generation module that triggers the coordinated control of at least two cooling media when the solidification instability characteristic quantity exceeds the preset dynamic critical threshold, and generates a dynamic adjustment instruction set of the cooling parameter; A parameter correction module that performs thermodynamic safety verification on the dynamic adjustment instruction set and performs gradient-limited parameter correction according to the verification result; A model update module that updates the coupling parameters of the solidification dynamic prediction model based on the solidification interface characteristic feedback data obtained from the corrected parameters to form a closed-loop optimization of the control strategy.

10. The cooling rate control system for the continuous directional solidification process of copper materials according to claim 9, characterized in that, The instruction generation module includes: A medium matching unit that matches a coordinated combination from a variety of cooling media based on the solidification instability characteristic quantity, and the matching logic of the coordinated combination includes the corresponding relationship between the over-limit amplitude of the characteristic quantity and the regulation ability of the cooling medium; An instruction verification unit that maps the degree of deviation of the characteristic quantity from the dynamic critical threshold to the adjustment instruction of the cooling parameter, performs multi-dimensional verification on the adjustment instruction, and outputs the dynamic adjustment instruction set that meets the multi-objective constraints.

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