Continuous casting method based on digital twinning

Abstract

The invention provides a continuous casting method based on digital twinning. The continuous casting method comprises the steps that a material performance database of various metal materials under different alloy element components is established; a computer simulation model is established according to different ingot casting information, casting information, cold area information of continuous casting equipment and environment information, calculation is conducted, and post-processing results are sorted; collecting production test data in the continuous casting test; adopting a machine learning method, taking the obtained result data as a training and verification data set, and training a continuous casting digital twin machine learning model; automatic calculation and real-time adjustment of continuous casting process parameters are achieved through the digital twinborn model obtained after machine learning, and in the machining process, ingot casting information is collected and automatically calculated to obtain the internal structure, stress and temperature information of an ingot casting. According to the method, the required process can be rapidly calculated, linkage adjustment of other processes is achieved by adjusting a single process, the internal structure of the cast ingot can be rapidly calculated, and digital twinning is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous casting, and in particular to a continuous casting method based on digital twin. Background Art

[0002] Continuous casting technology occupies an important position in the production of steel and non-ferrous metals. During casting, metal materials such as steel, iron, copper alloy, aluminum alloy, magnesium alloy, etc. pass through the continuous casting machine, and the liquid metal is continuously poured into the crystallizer with cooling water to form a continuous ingot. After subsequent processing, these ingots can be made into various types of metal profiles, which are widely used in the fields of construction, automobiles, ships, home appliances, etc. Due to the continuous casting production, the continuous ingots formed are relatively long, and the longest can even reach 8000mm. In order to ensure the quality of subsequent molded parts, the ingots are required to be free of defects such as shrinkage cavities, shrinkage, and fractures. However, during the ingot molding process, it is impossible to intuitively observe the solid-liquid phase distribution, stress distribution, shell thickness and other information inside the ingot. Therefore, the production control and process design of continuous casting are relatively difficult, which also leads to excessively high design costs for continuous casting. Modern computer simulation methods, such as finite element simulation, phase field simulation, and machine learning, can use technical means and computer equipment to evaluate the quality of continuous casting ingots (casting stress, solid-liquid phase distribution, and shell thickness) in order to select better continuous casting process solutions.

[0003] However, during production, factors such as the temperature, flow rate, stability of the cooling water, and changes in ambient temperature during the casting process often cannot fully match the boundary conditions set during computer simulation, resulting in errors in the actual production process, and the internal and stress distribution of the ingots that cannot be directly observed during the actual production process. Therefore, before determining the casting process, a trial production process will be added to determine the final production process. However, when this method is used to change the ingot size, shorten the production period, and urgently need to speed up production efficiency, it is necessary to manually redesign and test the production process, and the method has poor versatility. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a continuous casting method based on digital twins. On the basis of traditional continuous casting, in view of the problem that the solid-liquid area inside the ingot is difficult to observe and the casting process is difficult to adjust during the continuous casting process, a digital twin continuous casting model is established according to a method combining computer simulation and experiment, and then a temperature measuring device is added to the continuous casting machine and combined with the measuring device existing in the equipment itself to realize the real data input of the digital twin model. The model is then used to automatically calculate the solid-liquid phase distribution, shell thickness, stress distribution and defects inside the ingot. At the same time, the established continuous casting machine learning model is used to automatically calculate the optimal cooling water flow rate and casting speed and other parameters that match it when increasing the ingot pulling speed and improving production efficiency. The internal structure of the ingot is visualized, the casting process is quickly adjusted, and the production quality and production efficiency of continuous casting are improved.

[0005] Specifically, the present invention provides a continuous casting method based on digital twin, comprising the following steps: S1: Establish a material performance database for various metal materials with different alloy element compositions; S2: Establish a computer simulation model based on different ingot information, casting information, cold zone information of continuous casting equipment, and environmental information, perform calculations, and organize post-processing results; S3: A temperature detection device is installed in the first cooling zone of the continuous casting equipment, and a temperature detection device and an alloy composition analysis detection device are installed in the second cooling zone of the crystallizer, and the existing measuring devices on the equipment are combined to realize the collection of production and test data; S4: Using machine learning methods, the result data obtained from S1, S2 and S3 are used as training and verification data sets to train the continuous casting digital twin machine learning model, and mathematical statistics methods are used to select computer simulation schemes under different processes, conduct experimental verification, collect data after the experiment, calibrate and train the model, and obtain the digital twin model after machine learning; S5: The digital twin model after machine learning is used to realize the automatic calculation and real-time adjustment of continuous casting process parameters. During the processing, the ingot information is collected to automatically calculate the internal structure, stress and temperature information of the ingot.

[0006] As a further illustration of the present invention, in step S1, based on different alloy compositions, a material properties database of various metal materials with different alloy element compositions is established by using the first principle of materials or metal phase diagram calculation and material properties simulation method.

[0007] As a further explanation of the present invention, in step S2, the ingot information includes ingot shape and ingot size; the casting information includes casting temperature and casting speed; the cold zone information includes the first cold zone information and the second cold zone information of the crystallizer, wherein the first cold zone information and the second cold zone information both include cooling water flow rate and cooling water temperature; the environmental information includes ambient temperature and mold temperature.

[0008] As a further illustration of the present invention, in step S2, the post-processing results specifically include: statistical distribution of solid-liquid phases, stress distribution, temperature distribution, defect distribution, and microstructure in the ingot under different boundary conditions.

[0009] As a further illustration of the present invention, in step S3, the process of collecting production and test data specifically includes: S301: Collect ingot information, casting information, cooling information and environmental information, wherein the ingot information includes ingot type, first cooling zone temperature, second cooling zone temperature and ingot bottom temperature; casting information includes casting temperature and casting speed; cooling information includes cooling water flow rate and cooling water temperature; environmental information includes ambient temperature and mold temperature; S302: Test the material to obtain the material constitutive parameters; S303: Count the test results obtained in step S301 and step S302, and detect the shell thickness of the ingot through the continuous casting equipment, and detect the residual stress, defects, material properties and microstructure of the ingot after production.

[0010] As a further illustration of the present invention, in step S4, a machine learning model is used to train alloy composition-microstructure-material performance-energy consumption-cost-quality, and a mapping relationship between process-alloy composition-microstructure-material performance-energy consumption-cost-quality is constructed to obtain the optimal matching model.

[0011] As a further illustration of the present invention, in step S5, the digital twin model after machine learning is used to realize automatic calculation and real-time adjustment of continuous casting process parameters, including: According to the set ingot size, shell thickness, defect requirement parameters and the measured ambient temperature and cooling water temperature, the trained model in step S4 automatically calculates the required ingot pulling speed, casting temperature, casting speed, cooling water flow rate, and sends the calculated speed and temperature data to the setting end of the continuous casting equipment.

[0012] As a further illustration of the present invention, in step S5, the digital twin model after machine learning is used to realize the automatic calculation and real-time adjustment of continuous casting process parameters, which also includes: When the ingot pulling speed changes, the model trained in step S4 will use the changed ingot pulling speed as data input, maintain the set ingot size, shell thickness, defect requirement parameters, and the ambient temperature and cooling water temperature measured by the continuous casting equipment, automatically calculate the required casting temperature, casting speed, cooling water flow rate, and send the calculated speed and temperature data to the setting end of the continuous casting equipment.

[0013] As a further illustration of the present invention, in step S5, the digital twin model after machine learning is used to collect ingot information during the processing to automatically calculate the internal structure, stress and temperature information of the ingot, including: During continuous casting production, the continuous casting equipment will collect the temperature of the first cooling zone of the ingot, the temperature of the second cooling zone, the temperature of each area at the bottom of the ingot, the casting speed, the casting temperature, the cooling water flow rate, the cooling water temperature, the ambient temperature, and the ingot pulling speed, and use the collected information as the data input of the model trained in step S4, automatically calculate the internal result information and stress distribution of the ingot, and display them in the display window of the continuous casting equipment.

[0014] As a further illustration of the present invention, in step S5, the digital twin model after machine learning is used to collect ingot information during the processing to automatically calculate the internal structure, stress and temperature information of the ingot, and further includes: Based on the metal composition information obtained by detection, the model trained in step S4 will calculate the actual thermal conductivity, specific heat capacity, latent heat, thermal expansion coefficient, plasticity, and elasticity of the material in the current state, and use the above material constitutive structure as the corrected parameter and import it into the model described in step S4 again, and correct the calculation results of the model in real time to make the calculated internal information and stress-strain conditions of the ingot more accurate.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: 1. The existing finite element simulation trial production method requires a high time cost in terms of finite element model establishment and calculation. The present invention can quickly calculate the required process, reduce process design costs, and improve efficiency.

[0016] 2. The existing finite element simulation trial production method has low model versatility. The present invention adopts a machine learning model that includes training for multiple ingot sizes. The model has high versatility, reduces pre-processing time, and improves production efficiency.

[0017] 3. The existing finite element simulation trial production method cannot intuitively observe the internal structure of the ingot during the production process. The present invention adopts a model trained by machine learning, which can quickly calculate the internal structure of the ingot based on the real data input by the equipment, realize digital twin, and improve the stability of continuous casting processing.

[0018] 4. The existing finite element simulation trial production method cannot achieve rapid adjustment of the casting process. The present invention can achieve linkage adjustment of other processes by adjusting a single process on the basis of maintaining the original production quality, thereby improving production efficiency.

[0019] Other features and advantages of the technical solution will be described in the subsequent description, and partly become apparent from the description, or understood by implementing the technical solution. The purpose and other advantages of the technical solution can be achieved and obtained through the structures specifically pointed out in the written description and the drawings.

[0020] The technical solution of the present technical solution is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the technical solution and constitute a part of the specification. Together with the embodiments of the technical solution, they are used to explain the technical solution and do not constitute a limitation of the technical solution. In the accompanying drawings: Figure 1 It is a schematic diagram of the installation of a temperature detection device and an alloy composition analysis detection device on a continuous casting device in an embodiment of the present invention; Figure 2 This is a schematic diagram of the machine learning process in an embodiment of the present invention; Figure 3 The temperature fields at different times in the embodiment of the present invention; Figure 4 The solid-liquid phase changes at different times in the embodiments of the present invention; Figure 5 This is a cloud diagram of the equivalent stress distribution of the ingot in the embodiment of the present invention. DETAILED DESCRIPTION

[0022] The preferred embodiments of the present technical solution are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present technical solution, and are not used to limit the present technical solution.

[0023] The embodiment of the present invention provides a continuous casting method based on digital twin, comprising the following steps: S1: Establish a material properties database for various metal materials with different alloy element compositions.

[0024] Specifically, according to different alloy compositions, we can adopt the methods of material first principles or metal phase diagram calculation and material performance simulation to establish a material performance database for various metal materials with different alloy element compositions. The material properties mainly include elastic modulus, Poisson's ratio, plasticity, thermal expansion coefficient, specific heat capacity, latent heat of fusion, liquidus temperature, solidus temperature, etc.

[0025] S2: Establish a computer simulation model based on different ingot information, casting information, cold zone information of continuous casting equipment, and environmental information, perform calculations, and organize post-processing results.

[0026] The above-mentioned computer simulation methods include finite element simulation methods, phase field simulation methods, machine learning methods and other methods that use computers to perform simulation calculations.

[0027] Specifically, the ingot information includes the ingot shape and the ingot size; the casting information includes the casting temperature and the casting speed; the cold zone information includes the first cold zone information and the second cold zone information of the crystallizer, wherein the first cold zone information and the second cold zone information both include the cooling water flow rate and the cooling water temperature; the environmental information includes the ambient temperature and the mold temperature.

[0028] The post-processing results specifically include: statistics on the distribution of solid and liquid phases in the ingot under different boundary conditions, stress distribution, temperature distribution, defect distribution, microstructure, etc.

[0029] S3: A temperature detection device is installed in the first cooling zone of the continuous casting equipment, and a temperature detection device and an alloy composition analysis detection device are installed in the second cooling zone of the crystallizer, and the existing measuring devices on the equipment are combined to realize the collection of production and test data; The temperature detection device and alloy composition analysis detection device are commercially available and installed at the following locations: Figure 1 shown.

[0030] The above process of collecting production and test data specifically includes: S301: Collect ingot information, casting information, cooling information and environmental information, wherein the ingot information includes ingot type, first cooling zone temperature, second cooling zone temperature and ingot bottom temperature; casting information includes casting temperature and casting speed; cooling information includes cooling water flow rate and cooling water temperature; environmental information includes ambient temperature and mold temperature; S302: Testing the material to obtain the material constitutive parameters, wherein the material constitutive parameters refer to the parameters of the material constitutive model. Taking the hyperbolic sine model as an example (also called the Arrhennius constitutive model, as shown in the following formula), the constitutive parameters are the parameters in the formula, which can be obtained by statistically analyzing the results of the mechanical test and processing the result data;

[0031] S303: Count the test results obtained in step S301 and step S302, and detect the shell thickness of the ingot through the continuous casting equipment, and detect the residual stress, defects, material properties and microstructure of the ingot after production.

[0032] S4: Figure 2As shown, the machine learning method is adopted, and the result data obtained from S1, S2 and S3 are used as training and verification data sets to train the continuous casting digital twin machine learning model. The mathematical statistics method is used to select computer simulation schemes under different processes, and experimental verification is carried out. The data after the experiment is collected, the model is calibrated and trained, and the digital twin model after machine learning is obtained.

[0033] Specifically, during the experimental verification process, the orthogonal test method can be used to select different processes to achieve a wider range of results with fewer simulation times.

[0034] It should also be noted that in step S4, a machine learning model is used to train the alloy composition-microstructure-material performance-energy consumption-cost-quality, and a mapping relationship between process-alloy composition-microstructure-material performance-energy consumption-cost-quality is constructed to obtain the optimal matching model.

[0035] S5: The digital twin model after machine learning is used to realize the automatic calculation and real-time adjustment of continuous casting process parameters. During the processing, the ingot information is collected to automatically calculate the internal structure, stress and temperature information of the ingot.

[0036] Specifically, step S5 includes the following parts: S501: Based on the set ingot size, shell thickness, defect requirement parameters and the measured ambient temperature and cooling water temperature, the model trained in step S4 automatically calculates the required ingot pulling speed, casting temperature, casting speed, cooling water flow rate, and sends the calculated speed and temperature data to the setting end of the continuous casting equipment.

[0037] S502: During continuous casting production, the continuous casting equipment will collect the temperature of the first cooling zone of the ingot, the temperature of the second cooling zone, the temperature of each area at the bottom of the ingot, the casting speed, the casting temperature, the cooling water flow rate, the cooling water temperature, the ambient temperature, and the ingot pulling speed, and use the collected information as the data input of the model trained in step S4, automatically calculate the internal result information and stress distribution of the ingot, and display them in the display window of the continuous casting equipment. Figure 3 is the temperature condition of aluminum alloy ingot as an example, Figure 4 is the cloud diagram of the solid-liquid phase distribution inside the obtained ingot, Figure 5 is the stress distribution.

[0038] S503: Based on the metal composition information obtained by detection, the model trained in step S4 will calculate the actual thermal conductivity, specific heat capacity, latent heat, thermal expansion coefficient, plasticity, and elasticity of the material in the current state, and use the above material constitutive structure as the corrected parameter and import it into the model of step S4 again, and correct the calculation results of the model in real time to make the calculated internal information and stress-strain conditions of the ingot more accurate.

[0039] S504: When the ingot pulling speed changes, the model trained in step S4 will use the changed ingot pulling speed as data input, maintain the set ingot size, shell thickness, defect requirement parameters, and the ambient temperature and cooling water temperature measured by the continuous casting equipment, automatically calculate the required casting temperature, casting speed, cooling water flow rate, and send the calculated speed and temperature data to the setting end of the continuous casting equipment.

[0040] The above step S5 can automatically calculate the required process parameters based on the set product requirements and the model obtained by machine learning. During the processing, the ingot information is collected to automatically calculate the internal structure, stress and temperature information of the ingot. At the same time, if a single process parameter is adjusted, the remaining processes will be adjusted in conjunction, thereby improving the design and production efficiency.

[0041] In summary, the present invention adopts a method combining computer simulation with experimental data to train the continuous casting digital twin model. A temperature detection device is added to the first cold zone of the crystallizer of the continuous casting equipment, and a temperature detection device and an alloy composition analysis detection device are added to the second cold zone, and the measurement device of the equipment itself is combined to realize the real data input of the digital twin model. Using various detection equipment on the continuous casting equipment as the data input of the digital twin model, the internal microstructure, material properties, stress distribution, defect distribution, etc. of the ingot are automatically calculated in real time. When the ingot pulling speed is changed, the model will automatically calculate the remaining production processes that match the changed ingot pulling speed based on the set production quality parameters, and send the process to the execution end of the continuous casting equipment to achieve rapid response during the casting process.

[0042] Obviously, those skilled in the art can make various changes and modifications to the technical solution without departing from the spirit and scope of the technical solution. Thus, if these modifications and variations of the technical solution fall within the scope of the claims of the technical solution and their equivalents, the technical solution is also intended to include these modifications and variations.

Claims

1. A continuous casting method based on digital twin, characterized in that: The steps include: S1: Establish a material performance database for various metal materials with different alloy element compositions; S2: Establish a computer simulation model based on different ingot information, casting information, cold zone information of continuous casting equipment, and environmental information, perform calculations, and organize post-processing results; S3: A temperature detection device is installed in the first cooling zone of the continuous casting equipment, and a temperature detection device and an alloy composition analysis detection device are installed in the second cooling zone of the crystallizer, and the existing measuring devices on the equipment are combined to realize the collection of production and test data; S4: Using machine learning methods, the result data obtained from S1, S2 and S3 are used as training and verification data sets to train the continuous casting digital twin machine learning model, and mathematical statistics methods are used to select computer simulation schemes under different processes, conduct experimental verification, collect data after the experiment, calibrate and train the model, and obtain the digital twin model after machine learning; S5: The digital twin model after machine learning is used to realize the automatic calculation and real-time adjustment of continuous casting process parameters. During the processing, the ingot information is collected to automatically calculate the internal structure, stress and temperature information of the ingot.

2. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S1, according to different alloy compositions, a material performance database of various metal materials with different alloy element compositions is established by using the material first principle or metal phase diagram calculation and material performance simulation method.

3. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S2, the ingot information includes ingot shape and ingot size; the casting information includes casting temperature and casting speed; the cold zone information includes the first cold zone information and the second cold zone information of the crystallizer, wherein the first cold zone information and the second cold zone information both include cooling water flow rate and cooling water temperature; the environmental information includes ambient temperature and mold temperature.

4. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S2, the post-processing results specifically include: statistics on the distribution of solid and liquid phases in the ingot under different boundary conditions, stress distribution, temperature distribution, defect distribution, and microstructure.

5. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S3, the process of collecting production and test data specifically includes: S301: Collect ingot information, casting information, cooling information and environmental information, wherein the ingot information includes ingot type, first cooling zone temperature, second cooling zone temperature and ingot bottom temperature; casting information includes casting temperature and casting speed; cooling information includes cooling water flow rate and cooling water temperature; environmental information includes ambient temperature and mold temperature; S302: Test the material to obtain the material constitutive parameters; S303: Count the test results obtained in step S301 and step S302, and detect the shell thickness of the ingot through the continuous casting equipment, and detect the residual stress, defects, material properties and microstructure of the ingot after production.

6. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S4, a machine learning model is used to train alloy composition-microstructure-material performance-energy consumption-cost-quality, and a mapping relationship between process-alloy composition-microstructure-material performance-energy consumption-cost-quality is constructed to obtain the optimal matching model.

7. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S5, the digital twin model after machine learning is used to realize automatic calculation and real-time adjustment of continuous casting process parameters, including: According to the set ingot size, shell thickness, defect requirement parameters and the measured ambient temperature and cooling water temperature, the trained model in step S4 automatically calculates the required ingot pulling speed, casting temperature, casting speed, cooling water flow rate, and sends the calculated speed and temperature data to the setting end of the continuous casting equipment.

8. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S5, the digital twin model after machine learning is used to realize automatic calculation and real-time adjustment of continuous casting process parameters, which also includes: When the ingot pulling speed changes, the model trained in step S4 will use the changed ingot pulling speed as data input, maintain the set ingot size, shell thickness, defect requirement parameters, and the ambient temperature and cooling water temperature measured by the continuous casting equipment, automatically calculate the required casting temperature, casting speed, cooling water flow rate, and send the calculated speed and temperature data to the setting end of the continuous casting equipment.

9. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S5, the digital twin model after machine learning is used to collect ingot information during the processing to automatically calculate the internal structure, stress and temperature information of the ingot, including: During continuous casting production, the continuous casting equipment will collect the temperature of the first cooling zone of the ingot, the temperature of the second cooling zone, the temperature of each area at the bottom of the ingot, the casting speed, the casting temperature, the cooling water flow rate, the cooling water temperature, the ambient temperature, and the ingot pulling speed, and use the collected information as the data input of the model trained in step S4, automatically calculate the internal result information and stress distribution of the ingot, and display them in the display window of the continuous casting equipment.

10. The continuous casting method based on digital twinning according to claim 1, characterized in that: In step S5, the digital twin model after machine learning is used to collect ingot information during the processing to automatically calculate the internal structure, stress and temperature information of the ingot, and also includes: Based on the metal composition information obtained by detection, the model trained in step S4 will calculate the actual thermal conductivity, specific heat capacity, latent heat, thermal expansion coefficient, plasticity, and elasticity of the material in the current state, and use the above material constitutive structure as the corrected parameter and import it into the model described in step S4 again, and correct the calculation results of the model in real time to make the calculated internal information and stress-strain conditions of the ingot more accurate.

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