Aluminum plate cast rolling control method and system

Abstract

The invention relates to an aluminum plate casting and rolling control method and system, and the method comprises the steps: collecting real-time technological parameters, including molten aluminum temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and surface quality information, in an aluminum plate casting and rolling process, and predicting aluminum plate temperature distribution, stress strain, thickness change and potential defects based on thermodynamics and mechanical models; the casting speed, the rolling speed, the rolling pressure and the cooling condition of the molten aluminum are dynamically adjusted, closed-loop control is achieved, the system comprises a data acquisition module, a data processing and analysis module, a control and execution module and a man-machine interaction and visualization module, and the system supports automatic correction of thickness deviation and surface defects, abnormal alarm and historical data analysis. The method adopts model predictive control, an artificial intelligence algorithm or a hybrid optimization algorithm to realize adaptive control. The method has the advantages that the thickness uniformity and the surface quality are optimized, the production efficiency and reliability are improved, and the safe, intelligent and efficient operation effect in the aluminum plate casting and rolling process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metallurgy and materials engineering and industrial automation, in particular to an aluminum plate casting and rolling control method and system. BACKGROUND

[0002] Aluminum plate, as a lightweight, high-strength, corrosion-resistant metal material, has been widely used in aerospace, transportation, construction engineering, electronic devices, automobile manufacturing, and packaging. With the increasing demand for high-performance aluminum alloy materials and lightweight design, aluminum plate products have higher requirements for thickness uniformity, surface quality, mechanical properties, and yield. The production of aluminum plate usually includes three key links: aluminum liquid melting, casting, and rolling. The casting and rolling process plays a decisive role in the final quality and production efficiency of aluminum plate.

[0003] Traditional aluminum plate casting and rolling processes rely on manual experience for control, including aluminum liquid pouring temperature, aluminum plate thickness setting, rolling speed and pressure, cooling water flow and cooling method, and other process parameters. However, due to the rapid change of aluminum liquid temperature, the complex stress distribution during rolling, and the large influence of cooling speed on environmental conditions, it is difficult to achieve precise control relying on manual experience, which can easily cause uneven thickness, warping, surface cracks, porosity, and other defects in aluminum plate. At the same time, in multi-variety production, the melting points, fluidity, and deformation characteristics of different aluminum alloys differ significantly, making it difficult for a single experience parameter to adapt to all materials, resulting in low production efficiency and high scrap rate.

[0004] Currently, some aluminum plate production enterprises use online measuring instruments such as thickness gauges, temperature sensors, and surface detection equipment, but most of them are only used for data recording and production report generation, lacking real-time feedback and dynamic adjustment capabilities for the casting and rolling process. Traditional closed-loop control systems are mostly based on simple PID algorithms or fixed parameter models. Such methods are prone to response lag or overshoot when faced with complex aluminum plate process conditions and multi-variable coupling, making it difficult to ensure thickness uniformity and surface quality.

[0005] In addition, with the increasing demand for environmental protection and energy saving, aluminum plate production also faces the challenges of energy consumption control and production sustainability. The aluminum liquid melting and rolling processes have high energy consumption, and if the process parameters are not optimized, not only will production costs increase, but also environmental pressure will increase. In addition, with the increasing market demand for high-quality, low-defect-rate aluminum plates, existing production control technologies have been difficult to meet the comprehensive requirements of enterprises in quality, efficiency, energy saving, and intelligentization.

[0006] In recent years, the application of intelligent manufacturing, industrial Internet of Things, and big data analysis in metal material production has gradually emerged. By collecting real-time key process parameters such as temperature, thickness, rolling speed, rolling pressure, cooling speed, and surface quality of aluminum plate casting and rolling, and combining thermodynamic, mechanical models, and advanced algorithms for prediction and optimization, it is expected to achieve precise control of aluminum plate production. This intelligent control not only can dynamically adjust production parameters, improve thickness uniformity and surface quality, but also can automatically correct deviations when abnormal situations occur, reduce scrap rate, and improve production efficiency and product reliability.

[0007] In addition, the production of multi-variety aluminum alloys puts forward new requirements for the self-adaptability of the process. Different alloy materials have different flow characteristics and mechanical responses during rolling. The existing fixed parameter control system is difficult to quickly adapt to changing production conditions, leading to frequent manual intervention and reducing automation level.

[0008] In view of the above related technologies, the inventors believe that the existing fixed parameter control system is difficult to quickly adapt to changing production conditions, leading to the technical problem of reducing automation level. SUMMARY

[0009] In order to improve the technical problem that the control system is difficult to quickly adapt to changes, the present application provides an aluminum plate casting and rolling control method and system.

[0010] The aluminum plate casting and rolling control method and system provided by the present application adopts the following technical solution: In a first aspect, an aluminum plate casting and rolling control method includes the following steps: Step one: parameter acquisition, collecting real-time process parameters in the aluminum plate casting and rolling process, including aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed, and aluminum plate surface quality data; Step two: model establishment and prediction, based on the real-time process parameters, establishing thermodynamic and mechanical models of the aluminum plate casting and rolling process, predicting the temperature distribution, stress and strain, thickness change, and potential defects of the aluminum plate during rolling; Step three: target presetting, dynamically adjusting the aluminum liquid pouring speed, rolling speed, rolling pressure, and cooling conditions according to the prediction results and preset quality targets, to optimize the thickness uniformity and surface quality of the aluminum plate; Step four: parameter feedback, monitoring, and processing, feeding the optimized parameters back to the control system to form a closed-loop control, realizing real-time monitoring and abnormal processing of the aluminum plate casting and rolling process, including automatically correcting thickness deviation or surface defects.

[0011] By adopting the above technical scheme, through real-time collection of key process parameters such as aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and surface quality, the system can obtain the dynamic state information of the aluminum plate casting and rolling process, provide accurate data support for subsequent prediction and optimization, based on thermodynamic and mechanical models, the system can predict the temperature distribution, stress and strain state, thickness change trend and potential defects of the aluminum plate in the rolling process, so as to find out the problems that may affect the product quality in advance, realize the early warning function of the production process, through dynamic adjustment of the aluminum liquid pouring speed, rolling speed, rolling pressure and cooling condition, the system optimizes the thickness uniformity and surface quality of the aluminum plate in real time, reduces the generation of warping, cracking, porosity and other defects, improves the qualified rate and reliability of the product, the method adopts a closed-loop control strategy, feeds back the optimized parameters to the control system, realizes automatic correction and abnormal processing, so that the aluminum plate production process can adapt to the changing process conditions, effectively reduces the frequency of manual intervention, combines model predictive control, artificial intelligence algorithm or hybrid optimization algorithm, realizes the adaptive production of multi-variety aluminum alloy, improves the intelligent level and control precision of the system.

[0012] Optionally, the dynamic adjustment in step 3 is realized by model predictive control (MPC), artificial intelligence algorithm or hybrid optimization algorithm.

[0013] By adopting the above technical scheme, the casting and rolling parameters of the aluminum plate are optimized in real time, the thickness uniformity and surface quality are accurately controlled, the changes of different aluminum alloy materials and production conditions are automatically responded, the defect rate is reduced, the production efficiency and reliability are improved, the closed-loop regulation and abnormal correction are supported, and the intelligentization, stability and high-efficiency operation ability of the aluminum plate production process are enhanced.

[0014] Optionally, the closed-loop control includes automatically generating alarm information and triggering production adjustment instructions according to real-time monitoring results.

[0015] By adopting the above technical scheme, immediate intervention and correction of the casting and rolling process are realized, automatic adjustment can effectively reduce thickness deviation and surface defects, improve aluminum plate yield and production stability, reduce the burden of manual monitoring, and improve production efficiency and intelligent level.

[0016] Optionally, the method is suitable for different aluminum alloy materials, and the multi-variety applicability of the aluminum plate production process is realized by adaptive adjustment of control parameters.

[0017] By adopting the above technical scheme, the thickness uniformity and surface quality of various aluminum alloys are optimized, the waste rate is reduced, the production efficiency and reliability are improved, manual intervention is reduced, the aluminum plate casting and rolling process is realized, and the flexible, efficient and intelligent operation is realized, Optionally, the method further includes data storage and historical record analysis for production optimization, statistical analysis and quality traceability.

[0018] By adopting the technical scheme, the system can save and statistically analyze the data such as the process parameters, thickness distribution, temperature curve and surface quality of the aluminum plate during the casting and rolling process, and realize optimization and improvement of the production process.

[0019] In another aspect, an aluminum plate casting and rolling control system comprises: A data acquisition module is configured to acquire process parameters of the aluminum plate casting and rolling process in real time, including the temperature of the molten aluminum, the thickness of the aluminum plate, the rolling speed, the rolling pressure, the cooling speed and the surface quality information of the aluminum plate. A data processing and analysis module is configured to establish a thermodynamic and mechanical model of the aluminum plate casting and rolling process according to the acquired process parameters, and predict the thickness, temperature distribution, stress and strain and surface defect of the aluminum plate. A control and execution module is configured to generate an optimized control instruction according to the prediction result and a preset target, dynamically adjust the casting speed of the molten aluminum, the rolling speed, the rolling pressure and the cooling condition, and realize closed-loop control and abnormal processing. A human-computer interaction and visualization module is configured to display the thickness distribution, temperature curve, rolling parameters, surface quality and abnormal alarm information of the aluminum plate in real time, and support historical data recording and statistical analysis.

[0020] By adopting the technical scheme, the data acquisition module can acquire the key process parameters such as the temperature of the molten aluminum, the thickness of the aluminum plate, the rolling speed, the rolling pressure, the cooling speed and the surface quality in real time, providing accurate real-time data support for subsequent analysis and control. The data processing and analysis module predicts the temperature distribution, stress and strain, thickness change and potential surface defects during the rolling process of the aluminum plate based on the thermodynamic and mechanical model, realizes early warning of possible quality problems in the production process. The control and execution module generates an optimized control instruction according to the prediction result and a preset target, dynamically adjusts the casting speed of the molten aluminum, the rolling speed, the rolling pressure and the cooling condition, realizes closed-loop control and abnormal processing, and ensures the thickness uniformity and surface quality of the aluminum plate. The human-computer interaction and visualization module can intuitively display the thickness distribution, temperature change, rolling parameters and surface quality of the aluminum plate, and provide real-time abnormal alarm information, which is convenient for operators to monitor and manage, and supports historical data recording and statistical analysis, providing a basis for production optimization and quality traceability.

[0021] Optionally, the data processing and analysis module comprises an artificial intelligence algorithm unit, a model predictive control unit or a combination of both, for adaptive optimization of the aluminum plate casting and rolling parameters.

[0022] By adopting the technical scheme, intelligent adjustment of the casting speed of the molten aluminum, the rolling speed, the rolling pressure and the cooling condition is realized, the thickness uniformity and the surface quality are improved, automatic adaptation to different aluminum alloy materials and production conditions is achieved, the closed-loop control precision is enhanced, the defect rate is reduced, the production efficiency and reliability are improved, and intelligent, efficient and stable operation of the aluminum plate casting and rolling process is realized.

[0023] Optionally, the data acquisition module further comprises an internal defect monitoring sensor and a surface detection device for real-time acquisition of internal and surface defect information of the aluminum plate.

[0024] By adopting the technical scheme, real-time acquisition of internal porosity, cracks and surface scratches, pits and other defect information of the aluminum plate is realized, full-range monitoring of the aluminum plate quality is achieved, combined with closed-loop control and optimization algorithms, the system automatically adjusts the casting speed of the molten aluminum, the rolling pressure and the cooling condition for correction in the early stage of defect occurrence, reduces the scrap rate, improves the product pass rate, provides accurate data support for production quality traceability, statistical analysis and process optimization, and enhances the intelligence and reliability of the aluminum plate casting and rolling process.

[0025] Optionally, the control and execution module automatically adjusts the casting speed of the molten aluminum, the rolling speed and the rolling pressure for correction when detecting thickness deviation, surface defects or other abnormal conditions, and generates corresponding alarm information.

[0026] By adopting the technical scheme, the automatic correction function can significantly reduce the scrap rate, improve the thickness uniformity and surface quality of the aluminum plate, ensure production stability and reliability, reduce manual intervention, improve production efficiency, and enhance the intelligence and safety of the aluminum plate casting and rolling process.

[0027] Optionally, the human-computer interaction and visualization module further provides historical data storage, production statistical analysis, report generation and quality traceability functions.

[0028] By adopting the technical scheme, the system can real-time display process parameters, thickness distribution, temperature changes and surface quality during the aluminum plate casting and rolling process, store and statistically analyze historical data, and generate production reports to support quality traceability and process evaluation, and provide decision-making basis for production management.

[0029] In summary, the present application includes at least one of the following beneficial technical effects: Real-time monitoring and intelligent control of the entire aluminum plate casting and rolling process are realized, key process parameters such as molten aluminum temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and surface quality are collected to provide accurate data support for production optimization; Based on thermodynamic and mechanical models, aluminum plate temperature distribution, stress and strain, thickness change and potential defects are predicted to realize early warning and abnormal handling of the production process, quality problems are found in advance, and the defect occurrence rate is reduced; By dynamically adjusting the casting speed, rolling speed, rolling pressure, and cooling conditions of the molten aluminum, the uniformity of the aluminum plate thickness and the surface quality are optimized, and the yield and reliability are improved. Adopting closed-loop control, model predictive control, artificial intelligence algorithm, or hybrid optimization algorithm, adaptive control and compatible production of multiple aluminum alloy varieties are realized, and the intelligent level and control precision of the system are improved. Automatic deviation correction and abnormality handling functions enable the system to intervene immediately when thickness deviation, surface defects, or other abnormal conditions occur, reducing the frequency of manual intervention and improving production efficiency and stability. Supporting internal defect monitoring and surface detection, the system realizes comprehensive real-time monitoring of internal and surface defects of the aluminum plate, and automatically corrects deviations combined with optimization algorithms to improve product quality and reliability. The human-computer interaction and visualization module provides real-time display, historical data storage, production statistical analysis, report generation, and quality traceability functions, providing reliable basis for production decision-making, process improvement, and quality management. The system can realize safe, efficient, and intelligent operation of the aluminum plate casting and rolling process, reduce waste and energy consumption, optimize the production process, and improve overall production efficiency and economic benefits. The comprehensive application of the above technical solutions enables flexible adjustment of the aluminum plate production process under different process conditions and material properties, achieving stable and high-quality production of multiple varieties and batches. The overall technical solution can improve the controllability, intelligent level, and reliability of the aluminum plate production process, providing technical support and data support for modern high-performance aluminum plate production. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a flowchart of the aluminum plate casting and rolling control method of the embodiments of the present application.

[0031] Figure 2 is a schematic diagram of the aluminum plate casting and rolling control system of the embodiments of the present application. DETAILED DESCRIPTION

[0032] The following will be described in detail in combination with the accompanying drawings. Figures 1-2 The present application is further described in detail.

[0033] Embodiment 1 Referring to Figure 1 An aluminum plate casting and rolling control method includes the following steps: Step one: Collecting real-time process parameters in the aluminum plate casting and rolling process, including aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and aluminum plate surface quality data; In the aluminum plate casting and rolling process, sensors arranged in the aluminum liquid pouring device, rolling mill and cooling system collect real-time key process parameters in the aluminum plate casting and rolling process, including but not limited to: aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and aluminum plate surface quality data, aluminum liquid temperature is measured by immersion type temperature sensor or non-contact infrared temperature sensor, the measurement accuracy is ±1 ℃, the collection frequency is once every 0.5 seconds; The thickness of the aluminum plate is measured by a laser thickness sensor in real time, with a resolution of 0.01 mm; The rolling speed is collected by the main shaft encoder of the rolling mill, with an accuracy of ±0.1 m / min; The rolling pressure is collected by the hydraulic system feedback or pressure sensor, with an accuracy of ±0.5%; The cooling speed is measured by a cooling water flow meter and a cooling roller temperature sensor, with a unit of ℃ / s or L / min; The aluminum plate surface quality data is obtained by a high-speed camera or a surface detection device, which can identify surface defects such as pores, cracks and pits, with a collection frequency of once every 1 second. All the collected data is transmitted to the central control system in real time through industrial Ethernet to form a data stream database, providing basic data for subsequent model prediction and control.

[0034] Step two: Model establishment and prediction phase: Establishing a thermodynamic and mechanical model of the aluminum plate casting and rolling process based on the real-time process parameters, predicting the temperature distribution, stress and strain, thickness change and potential defects of the aluminum plate in the rolling process; In specific implementation, the thermodynamic model is based on the heat conduction equation and cooling boundary conditions of the aluminum plate rolling process to predict the instantaneous temperature field of the aluminum plate at different positions in the rolling process; The mechanical model is based on the plastic deformation theory and elastic stress analysis of the aluminum plate to establish the stress-strain relationship and predict the thickness change and possible deformation anomaly in the rolling process; The defect prediction model combines surface quality data and temperature, pressure, speed and other information to predict possible cracks, pores and thickness deviation defects on the surface of the aluminum plate by using finite element simulation or machine learning method. The model calculation results are transmitted to the control module in real time for process parameter optimization; Step three: target preset, according to the prediction results and the preset quality target, dynamically adjust the casting speed of aluminum liquid, rolling speed, rolling pressure and cooling conditions, realize the optimization of aluminum plate thickness uniformity and surface quality; on the basis of model prediction, the user sets the thickness uniformity and surface quality target value according to the aluminum plate product quality standard, such as the thickness deviation control within ±0.05 mm, the surface defect rate is less than 1%, the control system dynamically adjusts the casting speed of aluminum liquid, rolling speed, rolling pressure and cooling conditions according to the model prediction results and the preset target value, realizes the optimal control of rolling process, when the model predicts that the middle thickness of aluminum plate is too large, the system automatically increases the rolling pressure or reduces the rolling speed; when the predicted surface temperature is too high and there is a risk of cracking, automatically increase the cooling speed or adjust the rolling step; when there is a risk of local porosity, appropriately reduce the casting speed of aluminum liquid or optimize the cooling intensity, dynamically optimize through closed loop control algorithm, without manual intervention, the process parameters can be corrected in real time during rolling.

[0035] Step four: parameter feedback, monitoring and processing, feedback the optimized parameters to the control system to form a closed loop control, realize real-time monitoring and abnormal treatment of the aluminum plate casting and rolling process, including automatic correction of thickness deviation or surface defects; the optimized parameters are fed back to the aluminum liquid pouring system, rolling mill and cooling system in real time through the industrial control network to realize closed loop control.

[0036] The method further comprises: real-time monitoring of various sensor data, when the thickness deviation or surface defect exceeds the allowed range, automatically trigger the correction measures, such as adjusting the rolling pressure, rolling speed or cooling intensity; record all the collected parameters and optimization process data, form a complete quality tracking file, for product quality analysis and production process optimization; When abnormal conditions occur, alarm or automatic shutdown function can be used to ensure production safety and reduce scrap rate; The method further comprises establishing a thermodynamic and mechanical model of the aluminum plate casting and rolling process based on the real-time process parameters collected in step one, which is used to predict the temperature distribution, stress and strain state, thickness change and potential defects of the aluminum plate during rolling.

[0037] The implementation principle of embodiment 1 is: in the aluminum plate casting and rolling process, the sensor arranged in the aluminum liquid pouring device, the rolling unit and the cooling system is used to collect the aluminum liquid temperature, the aluminum plate thickness, the rolling speed, the rolling pressure, the cooling speed and the aluminum plate surface quality data in real time, to form a whole-process process data flow, the collected parameters are transmitted to the central control system through the high-speed industrial Ethernet, a data database is constructed, and basic information is provided for model prediction, the system performs mathematical modeling on the aluminum plate rolling process through a thermodynamic model and a mechanical model, the thermodynamic model predicts the instantaneous temperature distribution at different positions of the plate through the heat conduction equation and the cooling boundary condition, the mechanical model predicts the stress and strain state and thickness change of the aluminum plate in the rolling process by combining the plastic deformation theory and the elastic stress analysis, and the surface quality data and the rolling parameters are combined to predict the possible surface defects and local thickness abnormalities through finite element simulation or machine learning method; On the basis of model prediction, the system compares the temperature field, the stress and strain state, the thickness change and the potential defect information of the aluminum plate with the preset product quality target, dynamically generates an optimized control strategy, including adjusting the aluminum liquid pouring speed, the rolling speed, the rolling pressure and the cooling condition. When the prediction result shows that the middle thickness of the plate is too large, the control system automatically increases the rolling pressure or reduces the rolling speed; when the prediction shows that the surface temperature is too high and there is a risk of cracking, the cooling intensity is automatically increased or the rolling step is adjusted; when the prediction shows that there is a local porosity risk, the aluminum liquid pouring speed is appropriately reduced. Through the closed-loop control algorithm, the system can correct the rolling parameters in real time, and realize the optimization of thickness uniformity and surface quality; In addition, the system forms a complete closed-loop control through real-time monitoring and data feedback, realizes online monitoring and abnormal treatment of the whole aluminum plate casting and rolling process, automatically triggers correction measures when the thickness deviation or surface defects exceed the allowable range, and ensures the stability of product quality; at the same time, the system records all the collected parameters and optimization process data, forms a traceable quality file, and provides data support for subsequent production optimization.

[0038] Embodiment 2 On the other hand, an aluminum plate casting and rolling control system comprises a data acquisition module, a data processing and analysis module, a control and execution module and a man-machine interaction and visualization module, the modules are connected through an industrial control bus or an industrial Ethernet to realize data communication and instruction interaction, and form a complete intelligent control system; The data acquisition module is used for real-time acquisition of process parameters of the aluminum plate casting and rolling process, including aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed and aluminum plate surface quality information. The data acquisition module includes a plurality of sensor units and a signal acquisition unit, which are used for real-time monitoring of various key parameters in the aluminum plate casting and rolling process. The data acquisition module includes a plurality of sensor units and a signal acquisition unit, which are used for real-time monitoring of various key parameters in the aluminum plate casting and rolling process. The sensor unit includes an aluminum liquid temperature sensor, a laser thickness measuring sensor, a speed sensor, a pressure sensor, a cooling flow and temperature sensor and a surface detection device. The aluminum liquid temperature sensor adopts an immersion type thermocouple or a non-contact infrared temperature measuring device, with an accuracy of ±1℃ and a sampling frequency of 2Hz, which is used to monitor the aluminum liquid tapping temperature and pouring temperature. The laser thickness measuring sensor is arranged at the rolling outlet, with a measurement accuracy of 0.01mm, which is used to detect the thickness distribution of the aluminum plate. The speed sensor is installed on the main shaft of the rolling mill, and the rolling line speed is obtained through the encoder, with an accuracy of ±0.1m / min. The pressure sensor is installed on the hydraulic system to monitor the rolling pressure in real time, with an accuracy of ±0.5%. The cooling flow and temperature sensor is used to collect the cooling water flow, temperature and spraying pressure to calculate the cooling rate. The surface detection device includes a high-speed camera and an image recognition module, which is used to detect the surface defects of the aluminum plate (such as pores, cracks, scratches, etc.) and generate surface quality grade data. The acquisition module communicates with the central control unit through industrial Ethernet to realize high-speed transmission and real-time updating of data. All collected data are stored in a database for model calculation and historical tracing. The data processing and analysis module is used to establish a thermodynamic and mechanical model of the aluminum plate casting and rolling process according to the collected process parameters, and to predict the thickness, temperature distribution, stress and strain of the aluminum plate and the surface defect situation. The data processing and analysis module is the core algorithm unit of the system, which mainly realizes data fusion, model calculation and defect prediction. The thermodynamic model is based on the heat conduction equation q=−k∇T to establish a temperature distribution model of the aluminum plate. Combined with the cooling boundary condition, the temperature field change of the aluminum plate at different positions and different times is predicted. The model considers the temperature gradient in the thickness direction and the length direction of the aluminum plate, and outputs a two-dimensional temperature distribution graph. The mechanical model is established according to the rolling mechanical equation and the stress-strain relationship to predict the thickness change, plastic deformation and residual stress distribution of the aluminum plate under the action of rolling force. The defect prediction model inputs the output parameters of the thermodynamic and mechanical models into the defect prediction algorithm, combines the surface detection image data, and uses the finite element analysis or the method based on machine learning (such as support vector machine or neural network) to predict the potential defect type and position. The calculation results include the aluminum plate temperature curve, the thickness prediction curve, the stress distribution cloud chart and the defect risk area, which are transmitted to the control and execution module in real time. A control and execution module is configured to generate an optimized control instruction according to the prediction result and a preset target, dynamically adjust the casting speed of the molten aluminum, the rolling speed, the rolling pressure and the cooling condition, and realize closed-loop control and abnormality processing. The control and execution module is configured to receive the model prediction and analysis result, compare the result with a preset target value, and automatically generate an optimized control strategy. The module includes a control algorithm unit, an execution driving unit and a feedback interface unit. The control algorithm unit is configured to dynamically calculate optimal combination parameters of the casting speed of the molten aluminum, the rolling speed, the rolling pressure and the cooling intensity based on the prediction data and a set product quality target (e.g., thickness deviation ±0.05 mm, surface defect rate <1%). When it is detected that the thickness of the aluminum plate is too large, the system automatically increases the rolling pressure or decreases the rolling speed. When it is detected that the surface temperature is too high and there is a risk of cracks, the system automatically increases the cooling flow or decreases the casting rate. When a surface defect trend is detected, the system adjusts the temperature of the molten aluminum or the rolling step to improve the metal fluidity and surface density. The execution driving unit is configured to realize real-time adjustment through a servo valve, a frequency converter and a hydraulic control mechanism according to the output result of the control algorithm. The response time of the instruction is less than 100 ms. The feedback interface unit is configured to collect the adjusted parameters in real time and feed back the parameters to the data acquisition module, so as to realize closed-loop control and ensure stable operation of the system. A man-machine interaction and visualization module is configured to display the thickness distribution curve of the aluminum plate, the temperature distribution cloud map, the rolling pressure change curve and the cooling parameter curve in real time, provide surface quality and abnormality alarm information, and provide a sound and light signal when an abnormal state occurs. The module also has functions of supporting historical data recording, statistical analysis and report generation. The module is configured to provide real-time monitoring and decision-making assistance functions for an operator, support historical data playback, statistical analysis and report export functions, and enable the operator to input quality target parameters or modify the control strategy, so as to realize combination of manual intervention and intelligent control.

[0039] The implementation principle of the embodiment 2 is that the aluminum plate casting and rolling control system realizes intelligent, dynamic and closed-loop control of the aluminum plate casting and rolling process through cooperation of multiple modules. When the system is running, the data acquisition module starts various sensor units to monitor the temperature of the molten aluminum, the rolling speed, the rolling pressure, the cooling speed and the surface quality in real time. The multiple source signals collected are converted into digital signals by the signal acquisition unit, transmitted to the central control unit at a high speed through an industrial Ethernet, and stored in a database, so as to provide basic data support for subsequent model calculation and historical tracing, and ensure high precision and high timeliness of the collected data, thereby providing real-time input for stable control of the aluminum plate casting and rolling process. The data processing and analysis module fuses and analyzes the above-mentioned collection parameters, establishes a thermodynamic and mechanical model of the aluminum plate casting and rolling process according to the heat conduction equation and the stress-strain relationship, calculates the internal temperature field and stress distribution of the aluminum plate, and through the analysis of the temperature gradient and plastic deformation behavior, the module can predict the thickness change and residual stress state of the aluminum plate under different working conditions. The system inputs the data output by the thermodynamic and mechanical model into the defect prediction algorithm, combines the surface detection image, and automatically identifies the potential surface defect risk by using machine learning or finite element analysis method, realizes the early warning of cracks, pores and other abnormalities in the rolling process, and provides optimization basis for the subsequent control link; In the control and execution module, the system compares the prediction results output by the analysis module with the preset quality targets (such as thickness tolerance, surface defect rate, etc.) in real time, generates optimized control instructions dynamically by using an adaptive control algorithm, automatically adjusts the rolling pressure or speed when detecting thickness deviation, adjusts the cooling intensity or casting rate when the temperature is too high, and modifies the aluminum liquid temperature or adjusts the rolling step when there is a potential defect trend on the surface. The control instructions are transmitted to the servo valve, frequency converter and hydraulic mechanism through the execution driving unit, realizing the rapid response and accurate adjustment of the process parameters. At the same time, the feedback interface unit continuously collects the adjusted parameters and returns them to the data collection module, forming a closed loop feedback, ensuring the stability and accuracy of the system control. The man-machine interaction and visualization module displays the aluminum plate temperature distribution cloud map, thickness change curve and cooling parameters and other core information in real time, which is convenient for operators to intuitively understand the production state. When the system detects an abnormality or deviation from the set range, it prompts with sound and light alarm, and provides traceable historical data records and statistical analysis functions. The operator can input new process targets or modify the control strategy through this module, realizing the fusion control of manual experience and intelligent algorithm. The coordinated operation of multiple modules realizes the whole-process closed-loop optimization of the aluminum plate casting and rolling process from data collection, model prediction to adaptive control, improves the consistency of the thickness and surface quality of the aluminum plate, reduces the influence of human intervention on the production stability, and ensures the safety, accuracy and efficiency of the casting and rolling process.

[0040] The above are preferred embodiments of the present application, which do not limit the protection scope of the present application, therefore: any equivalent changes made according to the structure, shape, principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A method of controlling cast-rolling of an aluminum sheet, characterized by: The method comprises the following steps: Step 1: Parameter collection, collecting real-time process parameters during the casting and rolling of aluminum plates, including aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed, and aluminum plate surface quality data; Step 2: Model establishment and prediction, establishing thermodynamic and mechanical models of the casting and rolling process of aluminum plates based on the real-time process parameters, and predicting the temperature distribution, stress and strain, thickness change, and potential defects of the aluminum plate during rolling; Step 3: Target presetting, dynamically adjusting the aluminum liquid casting speed, rolling speed, rolling pressure, and cooling conditions according to the prediction results and preset quality targets, to optimize the thickness uniformity and surface quality of the aluminum plate; Step 4: Parameter feedback, monitoring, and processing, feeding the optimized parameters back to the control system to form a closed-loop control, realizing real-time monitoring and abnormal processing of the casting and rolling process of the aluminum plate, including automatic correction of thickness deviation or surface defects.

2. The aluminum sheet cast rolling control method according to claim 1, characterized by: The dynamic adjustment in step 3 is realized by model predictive control (MPC), artificial intelligence algorithm, or hybrid optimization algorithm for adaptive control.

3. The aluminum sheet cast rolling control method according to claim 1 or 2, characterized by: The closed-loop control includes automatically generating alarm information and triggering production adjustment instructions based on real-time monitoring results.

4. The aluminum sheet and strip cast rolling control method according to any one of claims 1 to 3, characterized by: The method is suitable for different aluminum alloy materials and realizes multi-species applicability in the aluminum plate production process through adaptive adjustment of control parameters.

5. The aluminum sheet cast rolling control method according to any one of claims 1 to 4, characterized by: The method further comprises data storage and historical record analysis for production optimization, statistical analysis, and quality traceability.

6. A cast-rolling control system for aluminum sheet, characterized by: The method comprises: A data collection module for real-time collection of process parameters during the casting and rolling of aluminum plates, including aluminum liquid temperature, aluminum plate thickness, rolling speed, rolling pressure, cooling speed, and aluminum plate surface quality information; A data processing and analysis module for establishing thermodynamic and mechanical models of the casting and rolling process of aluminum plates based on the collected process parameters, and predicting aluminum plate thickness, temperature distribution, stress and strain, and surface defect conditions; A control and execution module for generating optimized control instructions based on prediction results and preset targets, dynamically adjusting aluminum liquid casting speed, rolling speed, rolling pressure, and cooling conditions, and realizing closed-loop control and abnormal processing; A human-computer interaction and visualization module for real-time display of aluminum plate thickness distribution, temperature curve, rolling parameters, surface quality, and abnormal alarm information, and supporting historical data record and statistical analysis.

7. The aluminum sheet and strip cast and roll control system of claim 6, wherein: The data processing and analysis module includes an artificial intelligence algorithm unit, a model predictive control unit, or a combination of both, for adaptive optimization of aluminum plate casting and rolling parameters.

8. The aluminum sheet and strip cast and roll control system according to claim 6 or 7, characterized in that: The data collection module further includes internal defect monitoring sensors and surface detection devices for real-time acquisition of aluminum plate internal and surface defect information.

9. The aluminum sheet and strip cast and roll control system of any of claims 6 to 8, wherein: The control and execution module automatically adjusts the aluminum liquid casting speed, rolling speed, and rolling pressure for correction when detecting thickness deviation, surface defects, or other abnormal conditions, and generates corresponding alarm information.

10. The aluminum sheet and strip cast and roll control system according to any one of claims 6 to 9, characterized in that: The human-computer interaction and visualization module also provides historical data storage, production statistical analysis, report generation, and quality traceability functions.

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