CN114054511B - Rolled piece organization performance control system, method, medium and electronic terminal - Google Patents
Rolled piece organization performance control system, method, medium and electronic terminal Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
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- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
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Abstract
The invention provides a system, a method, a medium and an electronic terminal for controlling the organization performance of rolled pieces, wherein the system comprises: the data acquisition module is used for acquiring heat transfer related parameters and organization performance related parameters on a rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data; the first prediction module is used for inputting heat transfer related parameters into a preset heat transfer mathematical model to predict, and obtaining the overall dimensions, running speed and temperature field distribution of a rolled piece at a plurality of positions on a production line; the second prediction module is used for inputting the overall dimension, the running speed and the temperature field distribution into a preset rolled piece microstructure evolution model to predict the tissue state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data; the control module is used for controlling and adjusting the working parameters of each component part according to the target organization and performance data of the finished product and the predicted organization and performance data; the system well realizes the control of the organization and the performance of the rolled piece, and has strong universality.
Description
Technical Field
The invention relates to the field of steel rolling, in particular to a system, a method, a medium and an electronic terminal for controlling the tissue performance of rolled pieces.
Background
In recent years, with the increasing level of intellectualization and informatization of steel rolling lines, methods or systems for controlling the organization and performance of rolled pieces, such as methods for controlling the organization and performance of wire and bar materials, have come into play. At present, the existing rolled piece organization and performance control method or system has the defects that the core model is a pure theoretical model based on physical metallurgy or a data model based on big data driving, however, the pure theoretical model has poor universality and cannot cover a plurality of wire rod steel types, the model based on data driving has strong uncertainty and poor adaptability to new sample data, and the two methods have higher requirements on more parameters and data quality, and corresponding sensing devices and data processing devices are additionally added or installed on a steel rolling production line under partial conditions.
Disclosure of Invention
The invention provides a rolled piece organization performance control system, a method, a medium and an electronic terminal, which are used for solving the problems that the rolled piece organization and performance control method in the prior art is poor in universality, cannot cover a plurality of wire and bar steel types, is poor in new sample data adaptability, needs more parameters and has higher data quality requirements.
The invention provides a rolled piece organization performance control system, which comprises:
the data acquisition module is used for acquiring heat transfer related parameters and organization performance related parameters on a rolling production line, and the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data;
the first prediction module is used for inputting the heat transfer related parameters into a preset heat transfer mathematical model to predict, and obtaining the overall dimension, the running speed and the temperature field distribution of the rolled piece at a plurality of positions on the production line;
the second prediction module is used for inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line;
and the control module is used for controlling and adjusting the working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data, so as to control the organization and performance of the rolled piece.
Optionally, the finished product target organization and performance data includes at least one of: the method comprises the steps of obtaining a target finished product yield strength, a target tensile strength, a target elongation, a target microstructure composition and a target grain size, wherein the target microstructure composition comprises components of a target finished product structure and the proportion of each component;
the predicted organization and performance data includes at least one of: the method comprises the steps of predicting yield strength, predicting tensile strength, predicting elongation, predicting microstructure composition and predicting grain size of a finished product, wherein the predicting microstructure composition comprises components of the predicted finished product structure and the proportion of each component.
Optionally, the real-time blank parameters include: blank overall dimension and blank temperature;
the real-time arrangement parameters of each component part of the rolling production line comprise: the length of the rolling line, the length of each component part and the absolute position of each component part, wherein the component parts at least comprise one of the following components: the device comprises a descaling box, a cooling water tank, a nozzle, a rolling mill roller, an air cooling line fan, coil collecting equipment, coil conveying equipment and a roller way;
the real-time working parameters of each component part of the rolling production line comprise: the operating parameters of the individual components of the wire rod rolling line and/or the operating parameters of the individual components of the bar rolling line.
The invention also provides a method for controlling the tissue performance of the rolled piece, which comprises the following steps:
collecting heat transfer related parameters and tissue performance related parameters on a rolling production line, wherein the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data;
inputting the heat transfer related parameters into a preset heat transfer mathematical model for prediction, and obtaining the overall dimensions, running speeds and temperature field distribution of rolled pieces at a plurality of positions on a production line;
inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line;
and controlling and adjusting working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data, so as to control the organization and performance of the rolled piece.
Optionally, the step of constructing the thermal conductivity model includes:
collecting sample data, the sample data comprising: sample blank parameters, sample arrangement parameters of each component part of a rolling production line, sample working parameters of each component part of the rolling production line, outline dimension sample data, running speed sample data and temperature field distribution sample data of rolled pieces at a plurality of positions on the production line;
acquiring the heat transfer mathematical model according to the sample data and a preset heat transfer mathematical function;
or, the sample data is input into a preset first neural network for training, so that the heat transfer mathematical model is obtained.
Optionally, the step of obtaining the thermal conductivity model according to the sample data and a preset thermal conductivity function includes:
carrying out regression fitting on the sample blank parameters, sample arrangement parameters of each component part of a rolling production line, sample working parameters of each component part of the rolling production line, outline dimension sample data, running speed sample data and temperature field distribution sample data of a rolled piece at a plurality of positions on the production line according to a preset heat transfer mathematical function, determining regression fitting coefficients, and further obtaining a heat transfer mathematical model;
the step of obtaining the thermal conductivity model by inputting sample data into a preset first neural network for training comprises the steps of:
inputting the sample blank parameters, the sample arrangement parameters of each component part of the rolling production line and the sample working parameters of each component part of the rolling production line into the first neural network for prediction, and obtaining outline dimension prediction data, running speed prediction data and temperature field distribution prediction data of rolled pieces at a plurality of positions on the production line;
and training the first neural network according to the outline dimension sample data, the running speed sample data, the temperature field distribution sample data, the outline dimension prediction data, the running speed prediction data and the temperature field distribution prediction data to obtain the heat transfer mathematical model.
Optionally, the step of obtaining the microstructure evolution model of the rolled piece includes:
obtaining overall dimension sample data, running speed sample data, temperature field distribution sample data and finished product actual organization and performance data of rolled pieces at a plurality of positions on a rolling production line;
inputting the overall dimension sample data, the running speed sample data and the temperature field distribution sample data of the rolled piece at a plurality of positions on a rolling production line into a preset second neural network for prediction, and obtaining a prediction result;
and training the second neural network according to the prediction result, the actual organization of the finished product and the performance data to obtain a microstructure evolution model of the rolled piece.
Optionally, the step of controlling and adjusting the working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data comprises the following steps:
obtaining a comparison result by comparing the target organization and performance data of the finished product with the predicted organization and performance data;
according to the comparison result, a better working parameter control strategy is matched;
and feeding back the working parameter control strategy, and/or controlling and adjusting the working parameters of all the component parts of the rolling production line according to the working parameter control strategy to finish the control of the organization and the performance of the rolled piece.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described in any of the above.
The invention also provides an electronic terminal, comprising: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so as to cause the terminal to perform the method according to any one of the above.
The invention has the beneficial effects that: according to the system, the method, the medium and the electronic terminal for controlling the tissue performance of the rolled piece, disclosed by the invention, the heat transfer related parameters and the tissue performance related parameters on a rolling production line are collected, and the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, and tissue performance related parameters comprise: product target organization and performance data; inputting heat transfer related parameters into a preset heat transfer mathematical model for prediction, and obtaining the overall dimensions, running speeds and temperature field distribution of rolled pieces at a plurality of positions on a production line; inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line; according to the target organization and performance data of the finished product and the predicted organization and performance data, the working parameters of each component part of the rolling production line are controlled and adjusted, the control of the organization and performance of the rolled piece is completed, the control of the organization and performance of the rolled piece, such as wire rod rolled piece and the like, is better realized, the universality is stronger, the defects of more wire rod steel types can be covered, the adaptability to the newly-appearing sample data is stronger, and the implementation is more convenient.
Drawings
FIG. 1 is a schematic diagram of a system for controlling the tissue properties of a rolled piece in accordance with an embodiment of the present invention.
FIG. 2 is a flow chart of a method for controlling the tissue properties of a rolled piece in accordance with an embodiment of the present invention.
FIG. 3 is a schematic flow chart of a method for controlling tissue properties of a rolled piece for constructing a thermal model in accordance with an embodiment of the present invention.
FIG. 4 is a schematic flow chart of a method for controlling the tissue properties of a rolled piece to obtain a model of the microstructure evolution of the rolled piece according to an embodiment of the present invention.
FIG. 5 is a schematic flow chart of controlling the organization and properties of a rolled piece in a method for controlling the organization and properties of a rolled piece according to an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
The inventors found that, in recent years, with the increasing level of intelligence and informatization of steel rolling lines, a method or system for controlling the organization and performance of rolled pieces, such as a method for controlling the organization and performance of wire and rod materials, has come to emerge. At present, the existing rolled piece organization and performance control method or system has the defects that the pure theoretical model is poor in universality and cannot cover a plurality of wire rod steel types, the data-driven model is strong in uncertainty and poor in new sample data adaptability, more parameters and data quality requirements are required for the two methods, and corresponding sensing devices and data processing devices are additionally added or installed on a steel rolling production line under partial conditions. Therefore, the inventor proposes a system, a method, a medium and an electronic terminal for controlling the tissue performance of a rolled piece, wherein the heat transfer related parameters and the tissue performance related parameters on a rolling production line are collected, and the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, and tissue performance related parameters comprise: product target organization and performance data; inputting heat transfer related parameters into a preset heat transfer mathematical model for prediction, and obtaining the overall dimensions, running speeds and temperature field distribution of rolled pieces at a plurality of positions on a production line; inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line; according to the target organization and performance data of the finished product and the predicted organization and performance data, the working parameters of each component part of the rolling production line are controlled and adjusted, the control of the organization and performance of the rolled piece is completed, the control of the organization and performance of the rolled piece, such as wire rod rolled piece and the like, is better realized, the universality is stronger, the defects of more wire rod steel types can be covered, the adaptability to the newly-appearing sample data is stronger, the implementation is more convenient, the degree of automation is higher, and the cost is lower.
As shown in fig. 1, the system for controlling tissue properties of rolled piece in this embodiment includes:
the data acquisition module is used for acquiring heat transfer related parameters and organization performance related parameters on a rolling production line, and the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data;
the first prediction module is used for inputting the heat transfer related parameters into a preset heat transfer mathematical model to predict, and obtaining the overall dimension, the running speed and the temperature field distribution of the rolled piece at a plurality of positions on the production line;
the second prediction module is used for inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line;
the control module is used for controlling and adjusting working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data to finish the control of the organization and performance of the rolled piece; the data acquisition module, the first prediction module, the second prediction module and the control module are connected. The system for controlling the tissue performance of the rolled piece better realizes the control on the tissue and performance of the rolled piece, such as wire and bar rolled pieces, has strong universality, can cover the defects of more wire and bar steel types, has strong adaptability to newly-appearing sample data, is convenient to implement, has strong feasibility, strong epitaxial capability, lower cost and higher degree of automation.
In some embodiments, the finished target organization and performance data includes at least one of: the product comprises a target finished product yield strength, a target tensile strength, a target elongation, a target microstructure composition and a target grain size, wherein the target microstructure composition comprises components of the target finished product composition and the proportion of each component.
In some embodiments, the real-time blank parameters include: blank overall dimension and blank temperature;
the real-time arrangement parameters of each component part of the rolling production line comprise: the length of the rolling line, the length of each component part and the absolute position of each component part, wherein the component parts at least comprise one of the following components: the device comprises a descaling box, a cooling water tank, a nozzle, a rolling mill roller, an air cooling line fan, coil collecting equipment, coil conveying equipment and a roller way;
the real-time working parameters of each component part of the rolling production line comprise: the operating parameters of the individual components of the wire rod rolling line and/or the operating parameters of the individual components of the bar rolling line.
Specifically, the working parameters of each component part of the wire rod rolling production line comprise: the water quantity or water pressure of the descaling box and the cooling water tank, the form of nozzles, the size of the nozzles, the number of the nozzles, the opening and closing state of the nozzles, the diameter of the rolling mill roller, the size of the pass, the water quantity of the cooling water of the roller, the form of an air cooling line fan, the opening and closing state of the fan, the air quantity of the fan, the collecting time of the collecting device, the running speed of the coil conveying device and the running speed of each roller way; the coil collecting device is a double-core rod or a vertical coil core frame and the like, and the running speed of the coil conveying device is PF line running speed.
The working parameters of each component part of the bar rolling production line comprise: the working parameters of each component part of the wire rod rolling production line comprise: the water quantity or water pressure of the descaling box and the cooling water tank, the form of nozzles, the size of the nozzles, the number of the nozzles, the opening and closing states of the nozzles, the diameter of a rolling mill roller, the size of a pass, the water quantity of roller cooling water and the friction coefficient of a transmission passage from the double-length flying shear to the cooling bed; the tooth pitch of the cooling bed, the number of racks of the cooling bed, the stepping cycle of the cooling bed and the friction coefficient of the surface of the cooling bed.
In some embodiments, the finished target organization and performance data includes at least one of: the product comprises a target finished product yield strength, a target tensile strength, a target elongation, a target microstructure composition and a target grain size, wherein the target microstructure composition comprises components of the target finished product composition and the proportion of each component.
In some embodiments, the predicted organization and performance data includes at least one of: the method comprises the steps of predicting yield strength, predicting tensile strength, predicting elongation, predicting microstructure composition and predicting grain size of a finished product, wherein the predicting microstructure composition comprises components of the predicted finished product structure and the proportion of each component.
As shown in fig. 2, the method for controlling tissue performance of rolled piece in this embodiment includes:
s201: collecting heat transfer related parameters and tissue performance related parameters on a rolling production line, wherein the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: and (5) final product target organization and performance data. The heat transfer related parameters and the organization performance related parameters on the rolling production line can facilitate the follow-up prediction of the whole line temperature of the rolled piece, the organization and performance of the rolled piece and the like according to the heat transfer related parameters and the organization performance related parameters on the rolling production line. The rolled piece in this embodiment may be a wire rod rolled piece or the like.
S202: and inputting the heat transfer related parameters into a preset heat transfer mathematical model to predict, and obtaining the overall dimensions, the running speed and the temperature field distribution of the rolled piece at a plurality of positions on the production line. Because the production flow and working procedures of the rolling production line are more and more complex, the inventor proposes that one or more heat transfer mathematical models can be adopted, a plurality of heat transfer mathematical models respectively correspond to different heat transfer flow or heat transfer working procedures, and the external dimension, the running speed and the temperature field distribution of a rolled piece at a plurality of positions on the production line can be accurately obtained by inputting the heat transfer related parameters into the corresponding heat transfer mathematical models for prediction, so that the precision is higher, the adaptability is higher, and the implementation is more convenient.
S203: inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line. The method has the advantages that the physical dimension, the running speed and the temperature field distribution of the rolled piece at any position on the rolling production line are input into the microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, so that the predicted microstructure and property data of the key position or any position of the rolled piece on the rolling production line, namely the predicted value of the microstructure and property data, can be accurately obtained.
In some embodiments, the predicted organization and performance data includes at least one of: the method comprises the steps of predicting yield strength, predicting tensile strength, predicting elongation, predicting microstructure composition and predicting grain size of a finished product, wherein the predicting microstructure composition comprises components of the predicted finished product structure and the proportion of each component.
S204: and controlling and adjusting working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data, so as to control the organization and performance of the rolled piece. For example: and comparing the target organization and performance data of the finished product with the predicted organization and performance data, matching corresponding working parameter control strategies according to comparison results, obtaining a better working parameter control strategy, and further adjusting and controlling working parameters of all component parts of the rolling production line according to the working parameter control strategy, so that the control of the organization and performance of the rolled piece is realized, the accuracy is higher, the feasibility is stronger, the new steel variety can be well adapted, the universality is stronger, the epitaxial capability is stronger, and the cost is lower.
In order to improve the prediction accuracy of the thermal transfer model, the inventors propose that, as shown in fig. 3, the construction step of the thermal transfer model includes:
s301: collecting sample data, the sample data comprising: sample blank parameters, sample arrangement parameters of each component part of a rolling production line, sample working parameters of each component part of the rolling production line, outline dimension sample data of rolled pieces at a plurality of positions on the production line, running speed sample data and temperature field distribution sample data. By collecting sample data, a subsequent construction of a thermal model from the sample data is facilitated.
S302: acquiring the heat transfer mathematical model according to the sample data and a preset heat transfer mathematical function; or, the sample data is input into a preset first neural network for training, so that the heat transfer mathematical model is obtained. The first neural network may be a deep neural network or a convolutional neural network, etc.
In some embodiments, the step of obtaining the thermal conductivity model from the sample data and a preset thermal conductivity function comprises:
and carrying out regression fitting on the sample blank parameters, the sample arrangement parameters of each component part of the rolling production line, the sample working parameters of each component part of the rolling production line, the overall dimension sample data, the running speed sample data and the temperature field distribution sample data of the rolled piece at a plurality of positions on the production line according to a preset heat transfer mathematical function, and determining regression fitting coefficients so as to obtain a heat transfer mathematical model. The heat transfer function may be set according to practical situations, and will not be described here again.
For example: acquiring temperature field distribution sample data of the surface of a rolled piece in any technological process, and acquiring the temperature distribution of the cross section of the rolled piece before a target technological process according to the temperature field distribution sample data and by combining the thermophysical parameters of the material of the rolled piece, wherein the thermophysical parameters comprise specific heat capacity, heat transfer coefficient and the like; obtaining overall dimension sample data of a rolled piece, obtaining deformation and deformation rate parameters of each position of a rolled piece section in the target process according to the overall dimension sample data, and obtaining temperature change of each position of the rolled piece section, wherein if the deformation process is a deformation process, thermal deformation and temperature rise occur, and if the deformation process is other process, the thermal conduction process is a heat conduction process; and according to the obtained temperature distribution, deformation and deformation rate parameters of the rolled piece before the target technological process, combining heat radiation, heat convection and heat conduction processes, obtaining a temperature evolution model of the rolled piece in the target technological process, and taking the temperature evolution model as a heat transfer mathematical model of the rolled piece.
In some embodiments, the step of obtaining the thermal conductivity model by training by inputting sample data into a preset first neural network comprises:
inputting the sample blank parameters, the sample arrangement parameters of each component part of the rolling production line and the sample working parameters of each component part of the rolling production line into the first neural network for prediction, and obtaining outline dimension prediction data, running speed prediction data and temperature field distribution prediction data of rolled pieces at a plurality of positions on the production line;
and training the first neural network according to the outline dimension sample data, the running speed sample data, the temperature field distribution sample data, the outline dimension prediction data, the running speed prediction data and the temperature field distribution prediction data to obtain the heat transfer mathematical model. By acquiring the difference between the outline dimension sample data and the outline dimension prediction data, the difference between the operation speed sample data and the operation speed prediction data and the difference between the temperature field distribution sample data and the temperature field distribution prediction data, iterative training is carried out on the first neural network, so that the prediction accuracy of the heat transfer mathematical model can be improved well.
In order to improve the accuracy of the microstructure evolution model of the rolled piece, the inventors propose that, as shown in fig. 4, the step of obtaining the microstructure evolution model of the rolled piece includes:
s401: and obtaining appearance dimension sample data, running speed sample data, temperature field distribution sample data and finished product actual organization and performance data of the rolled piece at a plurality of positions on a rolling production line.
S402: inputting the overall dimension sample data, the running speed sample data and the temperature field distribution sample data of the rolled piece at a plurality of positions on a rolling production line into a preset second neural network for prediction, and obtaining a prediction result; the second neural network may be a deep neural network or a convolutional neural network, etc.
S403: and training the second neural network according to the prediction result, the actual organization of the finished product and the performance data to obtain a microstructure evolution model of the rolled piece. The prediction results, the actual tissues of the finished products and the performance data are compared to obtain differences between the prediction results and the corresponding actual tissues and the performance data of the finished products, and according to the differences, iterative training or deep learning is carried out on the second neural network to obtain a relatively good microstructure evolution model of the rolled piece, so that the prediction accuracy of the microstructure evolution model of the rolled piece is improved.
In some embodiments, the regression fit is performed on the overall dimension sample data, the running speed sample data, the temperature field distribution sample data and the actual tissue and performance data of the finished product at a plurality of positions on the production line according to a preset microstructure evolution rule of the rolled piece, so that a better microstructure evolution model of the rolled piece is obtained. The microstructure evolution rule of the rolled piece can be set according to actual conditions, and is not described herein.
For example: acquiring temperature field distribution sample data of the surface of a rolled piece in any process, and acquiring temperature distribution of the section of the rolled piece in the process according to the temperature field distribution sample data; obtaining overall dimension sample data of a rolled piece, and obtaining deformation and deformation rate parameters of each position of a section of the rolled piece in the process according to the overall dimension sample data; according to the obtained temperature distribution, deformation and deformation rate parameters of the rolled piece in the process, the grain growth process of the rolled piece in the process is obtained, wherein the grain growth process relates to an austenite recovery process, an austenite recrystallization process, an austenite growth process, an austenite-to-low temperature tissue transformation process and a low temperature tissue growth process, and finally the final microstructure of the rolled piece is obtained through multi-step coupling, so that a microstructure evolution model of the rolled piece is obtained.
As shown in fig. 5, the steps of controlling and adjusting the working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data include:
s501: obtaining a comparison result by comparing the target organization and performance data of the finished product with the predicted organization and performance data;
s502: according to the comparison result, a better working parameter control strategy is matched; i.e. a policy repository is preset, said policy repository comprising: and (3) working parameter control strategies under different conditions, wherein the different conditions refer to different degrees of difference between the target organization and the performance data of the finished product and the predicted organization and the performance data, and the comparison result is matched with the working parameter control strategies in the strategy library to obtain a better working parameter control strategy.
S503: and feeding back the working parameter control strategy, and/or controlling and adjusting the working parameters of all the component parts of the rolling production line according to the working parameter control strategy to finish the control of the organization and the performance of the rolled piece. By controlling and adjusting the working parameters of each component part of the rolling production line according to the working parameter control strategy, the good organization and performance of the rolled piece can be ensured, the unqualified quality of the rolled piece is avoided, and the yield of the rolled piece is improved.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods of the present embodiments.
The embodiment also provides an electronic terminal, including: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so that the terminal executes any one of the methods in the present embodiment.
The computer readable storage medium in this embodiment, as will be appreciated by those of ordinary skill in the art: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and complete communication with each other, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic terminal performs each step of the above method.
In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. A system for controlling tissue properties of a rolled piece, comprising:
the data acquisition module is used for acquiring heat transfer related parameters and organization performance related parameters on a rolling production line, and the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data;
the first prediction module is used for inputting the heat transfer related parameters into a preset heat transfer mathematical model to predict, and obtaining the overall dimension, the running speed and the temperature field distribution of the rolled piece at a plurality of positions on the production line;
the second prediction module is used for inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line;
and the control module is used for controlling and adjusting the working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data, so as to control the organization and performance of the rolled piece.
2. The product tissue performance control system of claim 1, wherein the product target tissue and performance data comprises at least one of: the method comprises the steps of obtaining a target finished product yield strength, a target tensile strength, a target elongation, a target microstructure composition and a target grain size, wherein the target microstructure composition comprises components of a target finished product structure and the proportion of each component;
the predicted organization and performance data includes at least one of: the method comprises the steps of predicting yield strength, predicting tensile strength, predicting elongation, predicting microstructure composition and predicting grain size of a finished product, wherein the predicting microstructure composition comprises components of the predicted finished product structure and the proportion of each component.
3. The product tissue performance control system of claim 1, wherein the real-time billet parameters comprise: blank overall dimension and blank temperature;
the real-time arrangement parameters of each component part of the rolling production line comprise: the length of the rolling line, the length of each component part and the absolute position of each component part, wherein the component parts at least comprise one of the following components: the device comprises a descaling box, a cooling water tank, a nozzle, a rolling mill roller, an air cooling line fan, coil collecting equipment, coil conveying equipment and a roller way;
the real-time working parameters of each component part of the rolling production line comprise: the operating parameters of the individual components of the wire rod rolling line and/or the operating parameters of the individual components of the bar rolling line.
4. A method for controlling tissue properties of a rolled piece, comprising:
collecting heat transfer related parameters and tissue performance related parameters on a rolling production line, wherein the heat transfer related parameters comprise: real-time blank parameters, real-time arrangement parameters and real-time working parameters of all components of the rolling production line, wherein the organization performance related parameters comprise: product target organization and performance data;
inputting the heat transfer related parameters into a preset heat transfer mathematical model for prediction, and obtaining the overall dimensions, running speeds and temperature field distribution of rolled pieces at a plurality of positions on a production line;
inputting the overall dimension, the running speed and the temperature field distribution into a preset microstructure evolution model of the rolled piece to predict the microstructure state and the mechanical property of the rolled piece, and obtaining predicted tissue and performance data of the rolled piece at a plurality of positions on a production line;
and controlling and adjusting working parameters of each component part of the rolling production line according to the target organization and performance data of the finished product and the predicted organization and performance data, so as to control the organization and performance of the rolled piece.
5. The method of controlling tissue properties of a rolled piece according to claim 4, wherein said constructing step of said thermal conductivity model comprises:
collecting sample data, the sample data comprising: sample blank parameters, sample arrangement parameters of each component part of a rolling production line, sample working parameters of each component part of the rolling production line, outline dimension sample data, running speed sample data and temperature field distribution sample data of rolled pieces at a plurality of positions on the production line;
acquiring the heat transfer mathematical model according to the sample data and a preset heat transfer mathematical function;
or, the sample data is input into a preset first neural network for training, so that the heat transfer mathematical model is obtained.
6. The method of claim 5, wherein the step of obtaining the thermal conductivity model based on the sample data and a predetermined thermal conductivity function comprises:
carrying out regression fitting on the sample blank parameters, sample arrangement parameters of each component part of a rolling production line, sample working parameters of each component part of the rolling production line, outline dimension sample data, running speed sample data and temperature field distribution sample data of a rolled piece at a plurality of positions on the production line according to a preset heat transfer mathematical function, determining regression fitting coefficients, and further obtaining a heat transfer mathematical model;
the step of obtaining the thermal conductivity model by inputting sample data into a preset first neural network for training comprises the steps of:
inputting the sample blank parameters, the sample arrangement parameters of each component part of the rolling production line and the sample working parameters of each component part of the rolling production line into the first neural network for prediction, and obtaining outline dimension prediction data, running speed prediction data and temperature field distribution prediction data of rolled pieces at a plurality of positions on the production line;
and training the first neural network according to the outline dimension sample data, the running speed sample data, the temperature field distribution sample data, the outline dimension prediction data, the running speed prediction data and the temperature field distribution prediction data to obtain the heat transfer mathematical model.
7. The method of claim 4, wherein the step of obtaining a model of microstructure evolution of the rolled stock comprises:
obtaining overall dimension sample data, running speed sample data, temperature field distribution sample data and finished product actual organization and performance data of rolled pieces at a plurality of positions on a rolling production line;
inputting the overall dimension sample data, the running speed sample data and the temperature field distribution sample data of the rolled piece at a plurality of positions on a rolling production line into a preset second neural network for prediction, and obtaining a prediction result;
and training the second neural network according to the prediction result, the actual organization of the finished product and the performance data to obtain a microstructure evolution model of the rolled piece.
8. The method of claim 4, wherein the step of controlling and adjusting the operating parameters of each component of the rolling line based on the product target organization and performance data, the predicted organization and performance data comprises:
obtaining a comparison result by comparing the target organization and performance data of the finished product with the predicted organization and performance data;
according to the comparison result, a better working parameter control strategy is matched;
and feeding back the working parameter control strategy, and/or controlling and adjusting the working parameters of all the component parts of the rolling production line according to the working parameter control strategy to finish the control of the organization and the performance of the rolled piece.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the method according to any of claims 4 to 8 when executed by a processor.
10. An electronic terminal, comprising: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, to cause the terminal to perform the method according to any one of claims 4 to 8.
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