CN118080578B - Rolling control device and method based on artificial intelligence and finite element analysis - Google Patents
Rolling control device and method based on artificial intelligence and finite element analysis Download PDFInfo
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Abstract
The application provides a rolling control device and a rolling control method based on artificial intelligence and finite element analysis, which are used for acquiring initial rolling temperatures of various points on the surface of strip steel in the rolling process by starting an artificial intelligence client to obtain a strip steel rolling temperature array; determining a discrete sparse matrix of the strip steel rolling temperature array, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array by the discrete sparse matrix; performing finite element analysis according to the obtained roll shifting amount and the obtained roll temperature to obtain strip steel roll bending resistance, and determining roll bending deformation temperature rise degree through the strip steel roll bending resistance; carrying out temperature polymerization by using a strip steel sparse temperature domain, a bending roller deformation temperature rise degree and a strip steel medium heat exchange coefficient to obtain a rolling temperature polymerization value; when the rolling temperature polymerization value is not in the preset rolling temperature interval, the artificial intelligent control center automatically adjusts the roller temperature of the strip steel rolling machine, so that the fluctuation degree of the strip steel rolling temperature is effectively controlled, and bad products caused by temperature fluctuation can be reduced.
Description
Technical Field
The application relates to the technical field of rolling control, in particular to a rolling control device and method based on artificial intelligence and finite element analysis.
Background
Rolling is a metal working process in which a metal blank is pressed, stretched or extruded by a rolling mill to be plastically deformed to obtain a finished product of a desired size, shape and surface quality, and a rolling control device is an apparatus for monitoring and controlling a metal rolling process, which generally includes a sensor, a data acquisition system, analysis software and a feedback control system.
The rolling control device generally comprises a sensor arranged on a rolling mill, and can collect data such as temperature, pressure, mechanical deformation and the like in the rolling process in real time, then the data acquired by the sensor is collected through a data acquisition system and transmitted to a data processing unit, and then the collected data is analyzed and processed in real time by using a machine learning or deep learning algorithm and the like to realize control and regulation of the rolling mill, but in the prior art, the surface temperature of the strip steel material is unevenly distributed in the rolling process of the strip steel material through the rolling mill, the temperature of the roller is difficult to control due to the influence of a plurality of factors when the strip steel material is rolled through a roller, and the temperature of the roller can influence the surface temperature of the strip steel material, so that the temperature of the strip steel is changed, and the deviation from a preset rolling temperature value is large, and the rolling quality of the strip steel is influenced.
Disclosure of Invention
The application provides a rolling control device and a rolling control method based on artificial intelligence and finite element analysis, which can reduce bad products caused by temperature fluctuation by effectively controlling the fluctuation degree of strip steel rolling temperature in the strip steel rolling process.
In a first aspect, the present application provides a rolling control method based on artificial intelligence and finite element analysis, comprising the steps of:
Starting an artificial intelligent client to collect initial rolling temperatures of all points on the surface of the strip steel in the rolling process, and further obtaining a strip steel rolling temperature array;
Determining a discrete vector of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vector, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix;
acquiring roll shifting quantity and roll temperature, and performing finite element analysis according to the roll shifting quantity and the roll temperature to obtain strip steel roll bending resistance, so as to determine roll deformation temperature rise degree through the strip steel roll bending resistance;
determining a medium heat exchange coefficient of the strip steel, and performing temperature polymerization by using the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value;
When the rolling temperature polymerization value is not in the preset rolling temperature range, the artificial intelligent control center automatically adjusts the roller temperature of the strip steel rolling machine.
In some embodiments, starting the artificial intelligence client to collect initial rolling temperatures of various points on the surface of the strip steel in the rolling process, and further obtaining the strip steel rolling temperature array specifically comprises:
For each strip steel surface point, determining a temperature mutation value corresponding to the strip steel surface point according to initial rolling temperatures corresponding to the strip steel surface point at all acquisition moments, and further obtaining the temperature mutation value corresponding to each strip steel surface point;
Removing all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values higher than a preset mutation threshold value, so as to obtain strip steel surface temperature defect points;
And filling the temperature missing points of the strip steel surface temperature through all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values lower than a preset mutation threshold value, and further forming a strip steel rolling temperature array by the initial rolling temperatures corresponding to all strip steel surface points.
In some embodiments, determining, by the discrete sparse matrix, a sparse band steel temperature domain corresponding to the sparse band steel rolling temperature array specifically includes:
performing eigenvalue decomposition on the discrete sparse matrix to obtain eigenvalues and eigenvectors;
constructing a rolling temperature projection matrix according to the characteristic values and the characteristic vectors;
and projecting the strip steel rolling temperature array through the rolling temperature projection matrix, so as to obtain a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array.
In some embodiments, performing finite element analysis according to the roll shifting amount and the roll temperature to obtain the roll bending resistance of the strip steel specifically includes:
Obtaining a finite element model of strip steel rolling in finite element analysis software;
And inputting the roll shifting amount and the roll temperature into a finite element model for strip steel rolling to simulate strip steel rolling stress, thereby obtaining strip steel roll bending resistance.
In some embodiments, performing temperature polymerization by using the strip steel sparse temperature domain, the bending roller deformation temperature rise degree and the medium heat exchange coefficient to obtain a rolling temperature polymerization value specifically includes:
determining the rolling temperature of the strip steel according to the strip steel sparse temperature domain;
converting the deformation temperature rise of the bending roller into strip steel temperature rise through the medium heat exchange coefficient;
and polymerizing the rolling temperature of the strip steel and the temperature rise of the strip steel to obtain a rolling temperature polymerization value.
In some embodiments, when the rolling temperature aggregate value is not within the preset rolling temperature range, the automatic adjustment of the roller temperature of the strip mill by the artificial intelligence control center specifically includes:
when the rolling temperature polymerization value is higher than the upper limit of a preset rolling temperature interval, the roller temperature of the strip steel rolling machine is increased;
and when the rolling temperature polymerization value is lower than the lower limit of the preset rolling temperature interval, reducing the roller temperature of the strip steel rolling machine.
In some embodiments, the roll-shifting amount is obtained by a laser rangefinder and the roll temperature is obtained by an infrared thermometer.
In a second aspect, the present application provides a rolling control device based on artificial intelligence and finite element analysis, which comprises a data processing unit, wherein the data processing unit comprises:
The acquisition module is used for starting the artificial intelligent client to acquire initial rolling temperatures of all points on the surface of the strip steel in the rolling process, so as to obtain a strip steel rolling temperature array;
The processing module is used for determining discrete vectors of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vectors, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix;
the processing module is also used for acquiring roll shifting quantity and roll temperature, carrying out finite element analysis according to the roll shifting quantity and the roll temperature to obtain strip steel roll bending resistance, and further determining roll bending deformation temperature rise degree through the strip steel roll bending resistance;
The processing module is also used for determining the medium heat exchange coefficient of the strip steel, and performing temperature polymerization by the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value;
And the automatic adjusting module is used for automatically adjusting the roller temperature of the strip steel rolling machine by the artificial intelligent control center when the rolling temperature polymerization value is not in the preset rolling temperature interval.
In a third aspect, the present application provides a computer device comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, such that the computer device performs the above rolling control method based on artificial intelligence and finite element analysis.
In a fourth aspect, the present application provides a computer readable storage medium having instructions or code stored therein, which when executed on a computer, cause the computer to perform the above-described rolling control method based on artificial intelligence and finite element analysis.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
Firstly, starting an artificial intelligent client to collect initial rolling temperatures of all points on the surface of strip steel in the rolling process, and further obtaining a strip steel rolling temperature array; determining discrete vectors of a strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vectors, identifying a region and factors affecting the accuracy of rolling temperature data, conveniently identifying and eliminating unreliable or disturbed rolling temperature data, improving the accuracy of the rolling temperature data, then carrying out temperature polymerization by a strip steel sparse temperature domain, a bending roller deformation temperature rise degree and a medium heat exchange coefficient to obtain a rolling temperature polymerization value, combining the strip steel sparse temperature domain, the bending roller deformation temperature rise degree and the medium heat exchange coefficient, comprehensively considering the influence of different factors on the strip steel temperature, improving the comprehensiveness and accuracy of the rolling temperature polymerization value, optimizing rolling parameters and a control strategy to more accurately control the rolling temperature of the strip steel, and finally, when the rolling temperature polymerization value is not in a preset rolling temperature interval, automatically adjusting the roller temperature of a rolling machine by an artificial intelligent control center, wherein the strip steel is in the rolling process of strip steel materials, the strip steel is contacted with the roller temperature of the rolling machine at the moment, the strip steel surface temperature is far higher than the roller temperature, heat is transmitted from the material to the roller temperature, the strip steel is reduced by the strip steel, the strip steel is automatically reduced, the preset temperature is reduced when the strip steel temperature is automatically adjusted, the strip steel temperature is reduced, the rolling temperature of the strip steel is reduced in the preset rolling temperature interval is reduced, and the rolling temperature fluctuation of the strip steel is not normally occurs due to the rolling temperature fluctuation is reduced, and the rolling temperature is reduced in the rolling temperature interval is controlled, and the rolling temperature fluctuation is reduced, and the rolling temperature is not is controlled, and the rolling temperature is in the rolling temperature range is controlled.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is an exemplary flow chart of a rolling control method based on artificial intelligence and finite element analysis, according to some embodiments of the application;
FIG. 2 is an exemplary flow chart for determining an array of strip rolling temperatures according to some embodiments of the application;
FIG. 3 is an exemplary flow chart for determining a sparse temperature domain of a strip in accordance with some embodiments of the present application;
FIG. 4 is a schematic diagram of exemplary hardware and/or software of a temperature control unit shown according to some embodiments of the application;
fig. 5 is a schematic structural diagram of a computer device implementing a rolling control method based on artificial intelligence and finite element analysis, according to some embodiments of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides a rolling control device and a rolling control method based on artificial intelligence and finite element analysis, wherein the core is that an artificial intelligence client is started to collect initial rolling temperatures of all points on the surface of strip steel in the rolling process, so as to obtain a strip steel rolling temperature array; determining a discrete vector of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vector, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix; acquiring roll shifting quantity and roll temperature, and performing finite element analysis according to the roll shifting quantity and the roll temperature to obtain strip steel roll bending resistance, so as to determine roll deformation temperature rise degree through the strip steel roll bending resistance; determining a medium heat exchange coefficient of the strip steel, and performing temperature polymerization by using the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value; when the rolling temperature polymerization value is not in the preset rolling temperature interval, the artificial intelligent control center automatically adjusts the roller temperature of the strip steel rolling machine, and in the strip steel rolling process, the fluctuation degree of the strip steel rolling temperature is effectively controlled so as to reduce bad products caused by temperature fluctuation.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of an artificial intelligence and finite element analysis-based rolling control method 100 according to some embodiments of the present application, the artificial intelligence and finite element analysis-based rolling control method 100 mainly includes the steps of:
in step 101, an artificial intelligent client is started to collect initial rolling temperatures of all points on the surface of the strip steel in the rolling process, and then a strip steel rolling temperature array is obtained.
Preferably, in some embodiments, reference is made to FIG. 2, which is an exemplary flow chart for determining a strip rolling temperature array in some embodiments of the application, which may be implemented by:
firstly, in step 1011, for each strip steel surface point, determining a temperature mutation value corresponding to the strip steel surface point according to initial rolling temperatures corresponding to all the acquisition moments of the strip steel surface point, thereby obtaining a temperature mutation value corresponding to each strip steel surface point;
Secondly, in step 1012, all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values higher than a preset mutation threshold are removed, so as to obtain strip steel surface temperature defect points;
finally, in step 1013, temperature filling is performed on the missing points of the surface temperature of the strip steel through all initial rolling temperatures corresponding to the surface points of the strip steel with the temperature abrupt change value lower than the preset abrupt change threshold value, and then the strip steel rolling temperature array is formed by the initial rolling temperatures corresponding to the surface points of all strip steel.
In the above embodiment, the determining the temperature abrupt change value corresponding to the strip steel surface point according to the initial rolling temperatures corresponding to the strip steel surface point at all the acquisition moments specifically may adopt the following modes:
Determining initial rolling temperature average values corresponding to the strip steel surface points at all acquisition moments;
Determining the dispersion of initial rolling temperatures corresponding to the strip steel surface points at all acquisition moments;
Determining a temperature mutation value corresponding to a strip steel surface point according to the initial rolling temperature average value corresponding to all the acquisition moments and the dispersion of the initial rolling temperature corresponding to all the acquisition moments, wherein the temperature mutation value can be determined according to the following formula when the method is concretely implemented:
Si＝exp(γi)+μi
Wherein S i represents a temperature abrupt change value corresponding to an ith strip steel surface point, μ i represents an initial rolling temperature average value corresponding to the ith strip steel surface point at all collection times, γ i represents a dispersion of initial rolling temperatures corresponding to the ith strip steel surface point at all collection times, and it is to be noted that, in the present application, the temperature abrupt change value represents a deviation degree of initial rolling temperatures corresponding to the strip steel surface point at all collection times relative to a conventional rolling temperature value, the dispersion represents a difference degree between initial rolling temperatures corresponding to the strip steel surface point at all collection times, and the variance of initial rolling temperatures corresponding to the strip steel surface point at all collection times can be used as the dispersion of initial rolling temperatures corresponding to the strip steel surface point at all collection times.
In the specific implementation, an artificial intelligence client is started, intelligent temperature control of rolling mill equipment can be realized by utilizing an artificial intelligence technology, sensor data are monitored, parameters in the rolling process are adjusted in real time, the size, shape and quality of a strip steel product at the rolling temperature are guaranteed to reach expectations, firstly, the initial rolling temperature of each point of the strip steel surface in the rolling process can be acquired through an infrared thermometer in the artificial intelligence client, the initial rolling temperature of each point of the strip steel surface is acquired for a plurality of times through the infrared thermometer, namely, each strip steel surface point corresponds to a plurality of initial rolling temperatures at different acquisition moments, secondly, for each strip steel surface point, temperature mutation values corresponding to the strip steel surface point are determined according to the initial rolling temperature corresponding to the strip steel surface point at all acquisition moments, then the temperature mutation values corresponding to each strip steel surface point are obtained, the temperature mutation values are lower than preset mutation thresholds, the temperature mutation values corresponding to all the initial rolling temperature values corresponding to the strip steel surface point are in accordance with conventional temperature values in the rolling process, the temperature mutation values are higher than the preset mutation thresholds, the temperature mutation values corresponding to all the initial rolling temperatures corresponding to the strip steel surface point of the strip steel surface point are not in accordance with the conventional temperature values in the rolling process, the preset temperature mutation values are not met, the temperature mutation values corresponding to all the initial mutation values are not corresponding to the initial mutation values of the initial rolling temperature values, and the strip steel surface point values are used as the temperature mutation values corresponding to the initial mutation values, and the strip steel surface point temperature corresponding to the strip steel surface point temperature is not caused by the temperature mutation values, and the temperature mutation values are used as the temperature threshold-fault point threshold, and the temperature threshold is detected, the average value of all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values lower than the preset mutation threshold value can be filled into the strip steel surface temperature missing points, so that all the strip steel surface points have corresponding initial rolling temperatures, a strip steel rolling temperature array is formed by the initial rolling temperatures corresponding to all the strip steel surface points, for example, the number of the strip steel surface points is n, and the number of the strip steel surface points is m at the acquisition time, and the strip steel rolling temperature array is a data matrix containing n x m of the initial rolling temperatures.
By the steps, abnormal rolling temperature data caused by noise or other interference can be removed, accuracy and reliability of the rolling temperature data are improved, the finally obtained strip steel rolling temperature array is more stable and reliable, the strip steel rolling temperature array is more accurate, real-time monitoring of the strip steel rolling process is facilitated, adjustment of rolling parameters is more accurate, and accuracy of strip steel rolling temperature is improved.
In step 102, determining a discrete vector of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vector, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix.
In some embodiments, a discrete vector of the strip rolling temperature array is determined, in specific implementation, for each column of the strip rolling temperature array, a standard deviation of all initial rolling temperatures corresponding to the column is taken as a dispersion of the column, for each row of the strip rolling temperature array, a standard deviation of all initial rolling temperatures corresponding to the row is taken as a dispersion of the row, and a vector formed by all dispersions obtained through the steps is taken as a discrete vector of the strip rolling temperature array, and it is to be noted that, in the application, the dispersion represents a fluctuation degree of initial rolling temperatures relative to an average rolling temperature in each row or each column of the strip rolling temperature array.
In some embodiments, the discrete sparse matrix of the strip rolling temperature array is obtained by using discrete vectors specifically in the following manner:
determining a covariance matrix H of a strip steel rolling temperature array;
acquiring a rolling temperature penalty function delta (i, j);
Determining a discrete sparse matrix theta of the strip steel rolling temperature array according to the discrete vector, the covariance matrix H of the strip steel rolling temperature array and the rolling temperature penalty function delta (i, j), wherein the discrete sparse matrix theta of the strip steel rolling temperature array can be determined according to the following formula when the discrete sparse matrix theta is concretely realized:
In the application, the discrete sparse matrix is a matrix for improving the precision of the strip steel rolling temperature array, the rolling temperature penalty function delta (i, j) is a function for restraining the discrete sparse matrix, when i and j are equal in actual implementation, the value of the rolling temperature penalty function delta (i, j) takes 1, otherwise, the value of the rolling temperature penalty function delta (i, j) is 0.
Preferably, in some embodiments, reference is made to fig. 3, which is an exemplary flowchart for determining a sparse temperature domain of a strip in some embodiments of the present application, where the determining of the sparse temperature domain of the strip may be implemented by:
Firstly, in step 1021, performing eigenvalue decomposition on the discrete sparse matrix to obtain eigenvalues and eigenvectors;
next, in step 1022, a rolling temperature projection matrix is constructed according to the eigenvalues and the eigenvectors;
Finally, in step 1023, the strip steel rolling temperature array is projected through the rolling temperature projection matrix, so as to obtain a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array.
When the method is specifically implemented, firstly, the discrete sparse matrix of the strip steel rolling temperature array is subjected to eigenvalue decomposition to obtain eigenvalues and eigenvectors of the discrete sparse matrix, the eigenvalues and eigenvectors are in one-to-one correspondence, then, the corresponding eigenvectors are ordered according to the magnitude of the eigenvalues, so that the eigenvectors corresponding to the first K largest eigenvalues are selected, a matrix formed by the K eigenvectors is used as a rolling temperature projection matrix, the rolling temperature projection matrix is a linear transformation matrix containing the most important rolling temperature information and is used for mapping an original strip steel rolling temperature array to a new eigenvspace, finally, the original strip steel rolling temperature array is subjected to projection operation through the rolling temperature projection matrix to obtain a strip steel sparse temperature domain in the new eigenvector, the data dimension of the strip steel sparse temperature domain is lower, the proportion of the contained important rolling temperature information is higher, and the strip steel sparse temperature domain is a data set containing rolling temperature.
It should be noted that, by constructing the discrete sparse matrix through the dispersion, the discrete sparse matrix can identify the region and the factors affecting the accuracy of the rolling temperature data, which is helpful for identifying and eliminating unreliable or interfered rolling temperature data and improving the quality of the rolling temperature data, and by determining the strip steel sparse temperature domain through the discrete sparse matrix, the rolling temperature data set considered according to the accuracy can be more confidently relied on in the strip steel sparse temperature domain, thereby improving the accuracy of the strip steel rolling temperature.
In step 103, obtaining roll shifting amount and roll temperature, and carrying out finite element analysis according to the roll shifting amount and the roll temperature to obtain strip steel roll bending resistance, so as to determine roll deformation temperature rise degree through the strip steel roll bending resistance.
In the method, the roll shifting amount of the roll can be obtained through a laser range finder, and the roll temperature can be obtained through an infrared thermometer, and the roll shifting amount of the roll refers to the thickness reduction amount of a strip steel blank when the strip steel blank passes through a roll gap between the rolls, and the position and displacement of the roll can be monitored by the laser range finder so as to obtain the roll shifting amount of the roll, the roll temperature refers to the temperature of the surface of the roll, the temperature of the surface of the roll can be measured through the infrared thermometer in a non-contact mode, and when the strip steel passes through the roll of a rolling machine, the roll can exert pressure on the strip steel to cause the shaping deformation of the strip steel material, so that deformation heat is generated, the rolling temperature of the strip steel is increased, and the rolling temperature of the roll is far lower than the initial rolling temperature of the surface of the strip steel, so that the rolling temperature of the strip steel surface is reduced when the roll is contacted with the strip steel material.
In some embodiments, finite element analysis is performed according to the roll shifting amount and the roll temperature, and the following specific ways can be adopted to obtain the roll bending resistance of the strip steel, namely:
Obtaining a finite element model of strip steel rolling in finite element analysis software;
And inputting the roll shifting amount and the roll temperature into a finite element model for strip steel rolling to simulate strip steel rolling stress, thereby obtaining strip steel roll bending resistance.
When the method is specifically implemented, firstly, a finite element model of strip steel rolling is established in finite element analysis software, the finite element model should consider the geometric shape, material mechanical properties and boundary conditions in the rolling process, such as acting force, temperature distribution and the like of a roller, then, the obtained roller channeling amount and roller temperature data are input into the finite element model to serve as boundary conditions or material parameters, the finite element analysis software is utilized to simulate the stress condition of the strip steel in the rolling process, the stress condition is simulated based on the input roller channeling amount and roller temperature data, the influence of the pressure and temperature of the roller on the strip steel is considered, the finite element analysis can calculate parameters such as deformation, stress distribution and the like of the strip steel in the rolling process, and the bending resistance of the strip steel in the rolling process, namely the bending resistance of the strip steel, which is the bending resistance of the strip steel caused by the deformation resistance caused by blanks in the roller gap of a rolling mill, can be calculated based on simulation results.
In some embodiments, the following method can be adopted to determine the bending deformation temperature rise degree through the strip steel bending resistance, namely:
Obtaining a stress state influence coefficient R of a strip steel deformation zone;
acquiring the heat conduction efficiency beta, the specific heat c and the density lambda of the strip steel material;
obtaining roll shifting quantity eta of a roll;
Determining a bending deformation temperature rise delta T according to strip steel bending resistance kappa, a strip steel deformation zone stress state influence coefficient R, roll channeling amount eta, strip steel material heat conduction efficiency beta, strip steel material specific heat c and strip steel material density lambda, wherein the bending deformation temperature rise delta T can be determined according to the following formula when the method is concretely implemented:
In the application, the bending roller deformation temperature rise refers to a temperature rise value of a strip steel material in a roller rolling process due to shaping deformation, and the stress state influence coefficient, the strip steel material heat conduction efficiency, the strip steel material specific heat and the strip steel material density of a strip steel deformation zone can be obtained through historical experience and a strip steel material manual and are not repeated herein.
The deformation condition of the strip steel in the rolling process can be predicted by calculating the strip steel roll bending resistance through finite element analysis, the accurate calculation of the strip steel roll bending deformation temperature rise is beneficial to improving the strip steel production quality, and the strip steel roll bending deformation temperature rise can effectively measure the rolling temperature change of the strip steel.
And 104, determining a medium heat exchange coefficient of the strip steel, and performing temperature polymerization by the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value.
In some embodiments, the medium heat exchange coefficient of the strip steel can be determined specifically by the following modes:
acquiring a radiation heat exchange coefficient G s;
Obtaining the dimension h of the strip steel material;
Determining a nucelotet number N u;
Determining a medium heat conductivity coefficient L 0 of the strip steel material;
the medium heat exchange coefficient G of the strip steel is determined through the radiation heat exchange coefficient G s, the strip steel material size h, the Knudsen number N u and the medium heat conduction coefficient L 0 of the strip steel material, and in particular, the medium heat exchange coefficient G of the strip steel can be determined according to the following formula:
The medium heat exchange coefficient of the strip steel represents the transfer efficiency of heat transferred to the strip steel in the rolling process, the radiation heat exchange coefficient is a parameter describing the heat transfer of the surface of the strip steel through radiation, the radiation heat exchange coefficient represents the ratio of the radiation heat flow density to the temperature difference on the unit surface area, the Nuzier number is a dimensionless number used for describing the heat transfer in the fluid flow, the Nuzier number represents the ratio of the heat transfer capacity of the fluid in the flow process to the relative contribution of the convection heat transfer and the conduction heat transfer, and the radiation heat exchange coefficient and the Nuzier number can be obtained through historical experience in specific implementation and are not repeated here.
In some embodiments, the temperature polymerization is performed by the strip steel sparse temperature domain, the bending roller deformation temperature rise degree and the medium heat exchange coefficient, and the rolling temperature polymerization value can be obtained by the following specific modes:
determining the rolling temperature of the strip steel according to the strip steel sparse temperature domain;
converting the deformation temperature rise of the bending roller into strip steel temperature rise through the medium heat exchange coefficient;
and polymerizing the rolling temperature of the strip steel and the temperature rise of the strip steel to obtain a rolling temperature polymerization value.
When the method is specifically implemented, firstly, the rolling temperature of the strip steel is determined according to a strip steel sparse temperature domain, the ratio of the sum of all initial rolling temperatures in the strip steel sparse temperature domain to the total number of initial rolling temperatures in the strip steel sparse temperature domain can be used as the rolling temperature of the strip steel, the strip steel rolling temperature is used for representing the temperature value of the strip steel before rolling through a roller, then, the bending roller deformation temperature rise is converted into the strip steel temperature rise through a medium heat exchange coefficient, the product between the medium heat exchange coefficient and the bending roller deformation temperature rise can be used as the strip steel temperature rise, the strip steel Wen Shenglai is used for representing the temperature rise value of the strip steel after rolling through a roller, and finally, the strip steel rolling temperature and the strip steel temperature rise are polymerized into a rolling temperature polymerization value, wherein the sum of the strip steel rolling temperature and the strip steel temperature rise is used as the rolling temperature polymerization value, and the rolling temperature polymerization value represents the final temperature value of the strip steel after rolling through the roller.
The method combines the sparse temperature domain of the strip steel, the deformation temperature rise of the bending roller and the medium heat exchange coefficient, comprehensively considers the influence of different factors on the strip steel temperature, improves the comprehensiveness and accuracy of the rolling temperature polymerization value, and is beneficial to optimizing rolling parameters and control strategies so as to control the strip steel rolling temperature more accurately.
In step 105, when the rolling temperature polymerization value is not in the preset rolling temperature interval, the artificial intelligent control center automatically adjusts the roller temperature of the strip steel rolling machine.
In some embodiments, when the rolling temperature aggregate value is not within the preset rolling temperature range, the artificial intelligence control center can specifically adjust the roller temperature of the strip steel rolling mill in the following manner:
When the rolling temperature polymerization value is higher than the upper limit of a preset rolling temperature interval, reducing the roller temperature of the strip steel rolling machine;
and when the rolling temperature polymerization value is lower than the lower limit of the preset rolling temperature interval, increasing the roller temperature of the strip steel rolling machine.
When the rolling temperature polymerization value is lower than the lower limit of the preset rolling temperature interval, the temperature of the strip steel material is too low, rolling resistance is increased at the moment, rolling quality is reduced, the temperature of the roll of the strip steel rolling machine can be increased by sending a control instruction through an artificial intelligent control center, other modes can be adopted, and the situation that the rolling resistance is increased, rolling quality is reduced, the temperature of the roll of the strip steel rolling machine is increased by sending a control instruction through the artificial intelligent control center, so that the rotating speed of the roll is increased.
It should be noted that, during the rolling process of the strip steel material, the strip steel can be contacted with the roller of the rolling machine, at this time, the surface temperature of the strip steel is far higher than the roller temperature, at this time, the contact of the strip steel and the roller can lead to heat transfer from the strip steel material to the roller, thereby the rolling temperature of the strip steel is reduced, and the reduction amplitude of the rolling temperature of the strip steel can be changed by increasing or reducing the roller temperature, so that the rolling temperature of the strip steel is prevented from deviating from a preset rolling temperature interval, and the accuracy of the rolling temperature of the strip steel is effectively improved during the rolling process of the strip steel.
In addition, in another aspect of the present application, in some embodiments, the present application provides a rolling control device based on artificial intelligence and finite element analysis, the device further including a temperature control unit, referring to fig. 4, which is a schematic diagram of exemplary hardware and/or software of the temperature control unit according to some embodiments of the present application, the temperature control unit 400 includes: the acquisition module 401, the processing module 402, and the automatic adjustment module 403 are respectively described as follows:
The acquisition module 401 is mainly used for starting an artificial intelligent client to acquire initial rolling temperatures of all points on the surface of the strip steel in the rolling process, so as to obtain a strip steel rolling temperature array;
The processing module 402 is mainly used for determining discrete vectors of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vectors, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix;
The processing module 402 is further configured to obtain a roll shifting amount and a roll temperature, perform finite element analysis according to the roll shifting amount and the roll temperature, obtain a strip steel roll bending resistance, and further determine a roll deformation temperature rise degree according to the strip steel roll bending resistance;
The processing module 402 is further configured to determine a medium heat exchange coefficient of the strip steel, and perform temperature polymerization by using the strip steel sparse temperature domain, the bending deformation temperature rise degree and the medium heat exchange coefficient to obtain a rolling temperature polymerization value;
The automatic adjustment module 403 is mainly used for automatically adjusting the roller temperature of the strip steel rolling mill by the artificial intelligent control center when the rolling temperature aggregate value is not in a preset rolling temperature interval.
The above describes in detail examples of the rolling control device and method based on artificial intelligence and finite element analysis provided by the embodiments of the present application, and it can be understood that, in order to implement the above functions, the corresponding device includes a corresponding hardware structure and/or software module for executing each function. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In some embodiments, the present application also provides a computer device comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, such that the computer device performs the above rolling control method based on artificial intelligence and finite element analysis.
In some embodiments, reference is made to fig. 5, in which the dashed lines indicate that the unit or the module is optional, which is a schematic diagram of a computer device for a rolling control method based on artificial intelligence and finite element analysis, provided according to an embodiment of the application. The rolling control method based on artificial intelligence and finite element analysis described above in the above embodiments may be implemented by a computer device shown in fig. 5, the computer device 500 including at least one processor 501, a memory 502 and at least one communication unit 505, the computer device 500 may be a terminal device or a server or a chip.
The processor 501 may be a general purpose processor or a special purpose processor. For example, the processor 501 may be a central processing unit (central processing unit, CPU) which may be used to control the computer device 500, execute software programs, process data of the software programs, and the computer device 500 may further comprise a communication unit 505 for enabling input (receiving) and output (transmitting) of signals.
For example, the computer device 500 may be a chip, the communication unit 505 may be an input and/or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, which may be an integral part of a terminal device or a network device or other devices.
For another example, the computer device 500 may be a terminal device or a server, the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.
The computer device 500 may include one or more memories 502 having a program 504 stored thereon, the program 504 being executable by the processor 501 to generate instructions 503 such that the processor 501 performs the methods described in the method embodiments above in accordance with the instructions 503. Optionally, data (e.g., a goal audit model) may also be stored in memory 502. Alternatively, the processor 501 may also read data stored in the memory 502, which may be stored at the same memory address as the program 504, or which may be stored at a different memory address than the program 504.
The processor 501 and the memory 502 may be provided separately or may be integrated together, for example, on a System On Chip (SOC) of the terminal device.
It should be appreciated that the steps of the above-described method embodiments may be accomplished by logic circuitry in the form of hardware or instructions in the form of software in the processor 501. The processor 501 may be a central processing unit, a Digital Signal Processor (DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), a field programmable gate array (field programmable GATE ARRAY, FPGA) or other programmable logic device such as discrete gates, transistor logic or discrete hardware components.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
For example, in some embodiments, the present application also provides a computer-readable storage medium having instructions or code stored therein, which when executed on a computer, cause the computer to perform the above-described rolling control method based on artificial intelligence and finite element analysis.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the application
Clear spirit and scope. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. The rolling control method based on artificial intelligence and finite element analysis is characterized by comprising the following steps of:
Starting an artificial intelligent client to collect initial rolling temperatures of all points on the surface of the strip steel in the rolling process, and further obtaining a strip steel rolling temperature array;
Determining a discrete vector of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vector, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix;
acquiring roll shifting quantity and roll temperature, and performing finite element analysis according to the roll shifting quantity and the roll temperature to obtain strip steel roll bending resistance, so as to determine roll deformation temperature rise degree through the strip steel roll bending resistance;
determining a medium heat exchange coefficient of the strip steel, and performing temperature polymerization by using the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value;
when the rolling temperature polymerization value is not in a preset rolling temperature interval, automatically adjusting the roller temperature of the strip steel rolling machine by an artificial intelligent control center;
the method for acquiring the initial rolling temperature of each point on the surface of the strip steel in the rolling process by starting the artificial intelligence client, and further obtaining the strip steel rolling temperature array specifically comprises the following steps:
For each strip steel surface point, determining a temperature mutation value corresponding to the strip steel surface point according to initial rolling temperatures corresponding to the strip steel surface point at all acquisition moments, and further obtaining the temperature mutation value corresponding to each strip steel surface point;
Removing all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values higher than a preset mutation threshold value, so as to obtain strip steel surface temperature defect points;
filling the temperature missing points of the strip steel surface temperature through all initial rolling temperatures corresponding to the strip steel surface points with the temperature mutation values lower than a preset mutation threshold value, and further forming a strip steel rolling temperature array by the initial rolling temperatures corresponding to all strip steel surface points;
the determining the strip steel sparse temperature domain corresponding to the strip steel rolling temperature array by the discrete sparse matrix specifically comprises the following steps:
performing eigenvalue decomposition on the discrete sparse matrix to obtain eigenvalues and eigenvectors;
constructing a rolling temperature projection matrix according to the characteristic values and the characteristic vectors;
Projecting the strip steel rolling temperature array through the rolling temperature projection matrix, and further obtaining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array;
The method for obtaining the bending resistance of the strip steel specifically comprises the following steps of:
Obtaining a finite element model of strip steel rolling in finite element analysis software;
inputting the roll shifting amount and the roll temperature into a finite element model of the strip steel rolling to simulate the strip steel rolling stress so as to obtain the strip steel roll bending resistance;
wherein, the temperature polymerization is carried out by the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient, and the rolling temperature polymerization value is obtained specifically comprises:
determining the rolling temperature of the strip steel according to the strip steel sparse temperature domain;
converting the deformation temperature rise of the bending roller into strip steel temperature rise through the medium heat exchange coefficient;
polymerizing the strip steel rolling temperature and the strip steel temperature rise to rolling temperature polymerization values;
When the rolling temperature polymerization value is not in a preset rolling temperature interval, the automatic adjustment of the roller temperature of the strip steel rolling machine by the artificial intelligent control center specifically comprises the following steps:
when the rolling temperature polymerization value is higher than the upper limit of a preset rolling temperature interval, the roller temperature of the strip steel rolling machine is increased;
and when the rolling temperature polymerization value is lower than the lower limit of the preset rolling temperature interval, reducing the roller temperature of the strip steel rolling machine.
2. The method of claim 1, wherein the roll shifting amount is obtained by a laser range finder and the roll temperature is obtained by an infrared temperature detector.
3. A rolling control device based on artificial intelligence and finite element analysis, which is controlled by the method of claim 1, characterized in that the rolling control device based on artificial intelligence and finite element analysis comprises a temperature control unit, the temperature control unit comprises:
The acquisition module is used for starting the artificial intelligent client to acquire initial rolling temperatures of all points on the surface of the strip steel in the rolling process, so as to obtain a strip steel rolling temperature array;
The processing module is used for determining discrete vectors of the strip steel rolling temperature array, obtaining a discrete sparse matrix of the strip steel rolling temperature array through the discrete vectors, and determining a strip steel sparse temperature domain corresponding to the strip steel rolling temperature array through the discrete sparse matrix;
the processing module is also used for acquiring roll shifting quantity and roll temperature, carrying out finite element analysis according to the roll shifting quantity and the roll temperature to obtain strip steel roll bending resistance, and further determining roll bending deformation temperature rise degree through the strip steel roll bending resistance;
The processing module is also used for determining the medium heat exchange coefficient of the strip steel, and performing temperature polymerization by the strip steel sparse temperature domain, the bending roller deformation temperature rise and the medium heat exchange coefficient to obtain a rolling temperature polymerization value;
And the automatic adjusting module is used for automatically adjusting the roller temperature of the strip steel rolling machine by the artificial intelligent control center when the rolling temperature polymerization value is not in the preset rolling temperature interval.
4. A computer device, characterized in that it comprises a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the artificial intelligence and finite element analysis based rolling control method according to any one of claims 1 to 2.
5. A computer readable storage medium having instructions or code stored therein which, when run on a computer, cause the computer to perform the artificial intelligence and finite element analysis based rolling control method according to any one of claims 1 to 2.
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| US5787746A (en) * | 1994-07-25 | 1998-08-04 | Alcan Aluminum Corporation | Multi-stand hot rolling mill tension and strip temperature multivariable controller |
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| CN111079275B (en) * | 2019-12-05 | 2023-04-28 | 中冶南方工程技术有限公司 | Method and device for acquiring temperature of rolled piece in strip hot rolling production line |
| CN110765671B (en) * | 2019-12-05 | 2022-07-08 | 中冶南方工程技术有限公司 | Method and device for acquiring temperature distribution of hot rolling working roll |
| CN115846423B (en) * | 2022-09-14 | 2023-06-30 | 东北大学 | Method for calculating roller temperature in plate and strip rolling process |
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| CN101178746A (en) * | 2007-12-18 | 2008-05-14 | 东北大学 | Method for forecasting finite element of hot rolling process plate belt temperature field |
| CN101221416A (en) * | 2007-12-28 | 2008-07-16 | 东北大学 | Finite element method for on-line board temperature calculation in course of hot rolling |
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