Method and device for predicting structure property parameters of rolled bar

Abstract

The invention provides a method and device for predicting structure property parameters of a rolled bar. The corresponding method comprises the steps that the cooling rate of the rolled bar is obtained; wherein the diameter of the rolled bar is smaller than a preset value; and predicting structure property parameters of the rolled bar according to the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar. Compared with an existing method for predicting the structure property parameters of the rolled bar, the method has the advantages that the actually measured curve is matched with the temperature transformation curve of the material, the structure transformation result of the bar is calculated, an accurate temperature calculation curve and a phase change model are not needed in the calculation process, and the obtained result can better reflect the actual situation of the site and is more accurate and practical.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metal smelting, in particular to the technical field of steel rolling, and specifically relates to a method and device for predicting the microstructure and performance parameters of a rolled bar. BACKGROUND

[0002] Bar products are widely used in production and life. Ordinary bars are mainly used in the construction industry, and alloy steel bars are mainly used in many fields such as machinery, automobiles, railways, and mines. Optimizing production processes and improving product quality during rolling are the goals that all manufacturers strive to achieve.

[0003] In the prior art, bar product performance detection mainly involves rolling out finished products, then detecting product quality, and then adjusting the production process according to the detection results. This process has a long feedback cycle and a large time cost.

[0004] Specifically, the existing bar product microstructure and performance prediction method mainly uses an offline method to calculate the temperature field after rolling using the finite difference method or the finite element method, and then brings the microstructure transformation model into the temperature field to obtain the microstructure evolution result. This method requires strict calculation accuracy of the temperature curve and the phase change model. If the deviation is serious, the calculation result has no reference value. It can be understood that the field conditions are complex, and many factors will affect the rolling temperature. For example, the current environmental temperature, the opening degree of the cooling bed heat preservation cover, the temperature drop caused by the contact between the rolling piece and the roller or the cooling bed, and other factors will all affect the rolling temperature, and the numerical simulation technology is difficult to consider comprehensively. SUMMARY

[0005] One object of the present application is to provide a method for predicting the microstructure and performance parameters of a rolled bar. This method can predict the microstructure and performance parameters of the bar product online and in real time, and can shorten the intermediate trial process.

[0006] Another object of the present application is to provide a device for predicting the microstructure and performance parameters of a rolled bar. Another object of the present application is to provide an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for predicting the microstructure and performance parameters of a rolled bar when executing the computer program. Another object of the present application is to provide a readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the above-mentioned method for predicting the microstructure and performance parameters of a rolled bar.

[0007] To solve the technical problems in the background art of the present application, the present application provides the following technical solutions:

[0008] The first aspect of the present application provides a method for predicting the microstructure and performance parameters of a rolled bar, comprising:

[0009] obtaining a cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0010] predicting the microstructure and performance parameters of the rolled bar according to the cooling rate and a continuous cooling transformation model of the material corresponding to the rolled bar.

[0011] In some embodiments of the present application, the continuous obtaining of the cooling rate of the rolled bar comprises:

[0012] setting a plurality of thermometers at a preset distance interval on a cooling bed; wherein the cooling bed is used to transfer the rolled bar out of a rolling mill;

[0013] continuously measuring the temperature of the rolled bar by the plurality of thermometers;

[0014] obtaining the cooling rate according to the change in the temperature.

[0015] In some embodiments of the present application, the thermometer is a scanning temperature measuring instrument;

[0016] the setting of the plurality of thermometers on the cooling bed comprises:

[0017] setting the plurality of scanning temperature measuring instruments under the cooling bed; the plurality of scanning temperature measuring instruments are used to measure the corresponding temperatures of the rolled bar on a plurality of racks on the cooling bed.

[0018] In some embodiments of the present application, the microstructure and performance parameters comprise the microstructure proportion of the material corresponding to the rolled bar and the hardness of the rolled bar.

[0019] In some embodiments of the present application, the preset value is 60 mm.

[0020] The second aspect of the present application provides a device for predicting the microstructure and performance parameters of a rolled bar, comprising:

[0021] a cooling rate obtaining module, configured to obtain a cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0022] a performance parameter predicting module, configured to predict the microstructure and performance parameters of the rolled bar according to the cooling rate and a continuous cooling transformation model of the material corresponding to the rolled bar.

[0023] In some embodiments of the present application, the cooling rate obtaining module comprises:

[0024] The temperature measuring unit is configured to continuously measure the temperature of the rolled bar through the plurality of thermometers.

[0025] The temperature measuring unit is configured to continuously measure the temperature of the rolled bar through the plurality of thermometers.

[0026] The cooling rate obtaining unit is configured to obtain the cooling rate according to the temperature change.

[0027] In some embodiments of the present application, the thermometer is a scanning thermometer.

[0028] The thermometer setting unit comprises:

[0029] The thermometer setting subunit is configured to place the plurality of scanning thermometers under the cooling bed, and the plurality of scanning thermometers are configured to measure the corresponding temperatures of the rolled bar on the plurality of racks of the cooling bed.

[0030] In some embodiments of the present application, the microstructure performance parameter comprises a microstructure proportion of the material corresponding to the rolled bar and a hardness of the rolled bar.

[0031] In some embodiments of the present application, the preset value is 60 mm.

[0032] In a third aspect, the present application provides a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the microstructure performance parameter prediction method for a rolled bar.

[0033] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the microstructure performance parameter prediction method for a rolled bar when executing the program.

[0034] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the microstructure performance parameter prediction method for a rolled bar.

[0035] As can be seen from the above description, the embodiments of the present application provide a microstructure performance parameter prediction method and device for a rolled bar. The corresponding microstructure performance parameter prediction method for a rolled bar comprises the following steps: first, obtaining the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; and then, predicting the microstructure performance parameter of the rolled bar according to the cooling rate and a continuous cooling transformation model of the material corresponding to the rolled bar.

[0036] The organization performance parameter prediction device of the corresponding rolled bar comprises: a cooling rate acquisition module, configured to acquire the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; and a performance parameter prediction module, configured to predict the organization performance parameter of the rolled bar according to the cooling rate and a continuous cooling transformation model of the corresponding material of the rolled bar.

[0037] The embodiment of the present application provides a rolled bar organization performance parameter prediction method, which is different from the existing rolled bar organization performance parameter prediction method, adopts a measured curve and a temperature transformation curve of a material to calculate the organization transformation result of the rolled bar, does not need an accurate temperature calculation curve and a phase change model in the calculation process, and the obtained result can better reflect the actual situation on site and is more accurate and practical. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0039] Figure 1 The flowchart of the rolled bar organization performance parameter prediction method in the embodiment of the present application Figure 1

[0040] Figure 2 The flowchart of step 100 of the rolled bar organization performance parameter prediction method in the embodiment of the present application is shown in the figure;

[0041] Figure 3 The flowchart of step 102 of the rolled bar organization performance parameter prediction method in the embodiment of the present application is shown in the figure;

[0042] Figure 4 The flowchart of the rolled bar organization performance parameter prediction method in the embodiment of the present application is shown in the figure;

[0043] Figure 5 The schematic diagram of the installation position of the scanning type temperature measuring instrument in the embodiment of the present application is shown in the figure;

[0044] Figure 6 The logic diagram of the rolled bar organization performance parameter prediction method in the embodiment of the present application is shown in the figure;

[0045] Figure 7 The block diagram of the rolled bar organization performance parameter prediction device in the embodiment of the present application is shown in the figure;

[0046] Figure 8 ​A block diagram of the cooling rate acquisition module 10 in the embodiment of the present application;

[0047] Figure 9 A block diagram of the temperature setting unit 10a in the embodiment of the present application;

[0048] Figure 10 A structural schematic diagram of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0050] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the above-described drawings are intended to cover not exclusive inclusion, for example, a process, method, system, product or device containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0052] The embodiments of the present application provide a specific implementation of a method for predicting the microstructure performance parameters of a rolled bar, as shown in Figure 1 The method specifically includes the following contents:

[0053] Step 100: acquiring a cooling rate of a rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0054] Step 200: predicting the microstructure performance parameters of the rolled bar according to the cooling rate and a continuous cooling transformation model of a material corresponding to the rolled bar.

[0055] From the above description, the embodiment of the application provides a rolled bar microstructure performance parameter prediction method, which comprises the following steps: firstly, obtaining a cooling rate of a rolled bar; wherein the diameter of the rolled bar is less than a preset value; and then predicting a microstructure performance parameter of the rolled bar according to the cooling rate and a continuous cooling transformation model of a corresponding material of the rolled bar.

[0056] The rolled bar microstructure performance parameter prediction method provided by the embodiment of the application is different from the existing rolled bar microstructure performance parameter prediction method, adopts a measured curve and a temperature transformation curve of a material to calculate a microstructure transformation result of the bar, the calculation process does not need an accurate temperature calculation curve and a phase change model, and the obtained result can better reflect the actual situation on site and is more accurate and practical.

[0057] For step 100, the rolled bar refers to a bar-shaped product made of steel or other metal materials processed by a hot rolling or cold rolling process. Rolling is a process of changing the cross-sectional shape and size of metal materials by passing them through a pair of rotating rollers. According to the rolling temperature, rolling can be divided into hot rolling and cold rolling.

[0058] Hot rolled bar hot rolling is a rolling process performed above the recrystallization temperature of the metal (the research scope of the present application is hot rolled bar). This means that the metal becomes soft after heating and is easier to shape. The characteristics of hot rolled bar include:

[0059] Surface oxidation: Because the metal surface will react with air at high temperatures, a layer of oxide will be formed.

[0060] Large size tolerance: Because the material will have a large thermal expansion during the hot rolling process, the dimensional accuracy of the hot rolled product is poorer than that of the cold rolled product.

[0061] Loose internal organization: Rolling at high temperatures causes the grains inside the metal to grow, resulting in a slight decrease in the performance of the material.

[0062] Low cost: Because the hot rolling process is simpler than cold rolling, the energy consumption is also lower.

[0063] Cold rolling is a rolling process performed below the recrystallization temperature of the metal. This method does not require heating the material, but changes the shape and size of the material through physical force. The characteristics of cold rolled bar include:

[0064] Smooth surface: Cold rolling can obtain a smoother surface without the oxide skin produced during hot rolling.

[0065] High dimensional accuracy: Because rolling is performed at a lower temperature, the size control of the material is more accurate.

[0066] Good material performance: The cold rolling process will cause the material to harden, improving its strength and hardness.

[0067] Costly: Cold rolling requires more processing steps, and due to hardening, subsequent treatment such as annealing is needed to improve the plasticity of the material, thus it is costly.

[0068] In some embodiments of the present application, referring to Figure 2 , step 100 comprises:

[0069] Step 101: A plurality of thermometers are arranged on the cooling bed at preset distance intervals; wherein the cooling bed is used to transfer the rolled bar out of the rolling mill;

[0070] Step 102: The temperature of the rolled bar is continuously measured by the plurality of thermometers;

[0071] Step 103: The cooling rate is obtained according to the change of the temperature.

[0072] In steps 101 to 103, by arranging enough temperature detection devices on the rolling line, full coverage of rolling line temperature detection can be achieved. The temperature detection devices are arranged below the cooling bed straightening plate and detect the temperature of the bar on the straightening plate through the gap between the straightening plates. After sorting and summarizing, the cooling rate of the bar in the entire phase change interval can be obtained.

[0073] In addition, the cooling rate is used to describe the speed of the rolled bar cooling from high temperature to low temperature, expressed in °C / s (degrees Celsius per second). The cooling rate has an important influence on the microstructure and the final physical and chemical properties of the material. Different cooling rates will lead to the formation of different microstructures inside the material, such as grain refinement or coarsening, and different phase transformation products (such as the formation of pearlite, bainite, martensite, etc. in steel). Specifically, the effects of cooling rate on the rolled bar are as follows:

[0074] Microstructure: Fast cooling (quenching) usually leads to finer grain structure or the formation of non-equilibrium phases, such as martensite in steel. Slow cooling may form coarse grains and equilibrium phases, such as pearlite.

[0075] Mechanical properties: Fast cooling can improve the strength and hardness of the material, but often at the expense of toughness and ductility. Slow cooling tends to improve the toughness and plasticity of the material, but the strength and hardness are lower.

[0076] Residual stress: Fast cooling can cause large residual stress inside the material, which can reduce the performance of the material and even cause cracking. Slow cooling helps to reduce the generation of residual stress.

[0077] Phase transformation: Different cooling rates can lead to the formation of different microstructures in alloys such as steel, due to different phase transformation paths. For example, rapid cooling can promote martensitic transformation, while slow cooling can result in the formation of pearlite or ferrite.

[0078] Therefore, in actual production, the cooling rate can be controlled by adjusting parameters such as cooling medium (such as water, oil, air), temperature of cooling medium, contact mode of workpiece and cooling medium, etc., to obtain the desired material properties and microstructure.

[0079] In some embodiments of the present application, the thermometer is a scanning thermometer; scanning thermometer, also known as scanning infrared thermometer or infrared scanning temperature measurement system, is a kind of instrument that uses infrared technology to non-contact measure the surface temperature of object. This instrument is particularly suitable for measuring the temperature of moving objects, high temperature objects or difficult to access objects. The working principle of scanning thermometer is based on the measurement of infrared radiation energy of object surface, which is related to the surface temperature of object.

[0080] The scanning thermometer receives the infrared radiation energy emitted by the surface of the object through its built-in infrared sensor, and converts this energy into an electrical signal, and then calculates the corresponding temperature value through the internal microprocessor. This process is almost instantaneous, allowing the instrument to monitor and record temperature changes in real time. Specifically, the scanning thermometer has the following characteristics:

[0081] Non-contact measurement: Scanning thermometer can measure temperature without contacting the surface of object, avoiding direct contact with high temperature objects or moving objects, ensuring safety and convenience.

[0082] Fast response: This type of thermometer can quickly respond to temperature changes, suitable for situations that require real-time temperature monitoring.

[0083] Wide range of applications: Scanning thermometer is not limited by the state of the measured object (solid, liquid or gas), and can be widely used in various industries and fields.

[0084] High temperature measurement: For high temperature environments such as steel production, glass manufacturing, etc., scanning thermometer can measure temperatures up to several thousand degrees Celsius without damaging the instrument.

[0085] Accuracy and resolution: Modern scanning thermometers provide high accuracy and high resolution temperature measurement, but their accuracy and resolution are affected by many factors, such as measurement distance, environmental conditions, target object material and surface characteristics, etc.

[0086] In some embodiments of the present application, referring to Figure 3 , step 102 includes:

[0087] Step 1021: disposing the plurality of scanning thermometers under the cooling bed; the plurality of scanning thermometers are used to measure the corresponding temperature of the rolled bar on the plurality of racks on the cooling bed.

[0088] Specifically, after the rolled piece exits the rolling mill, a thermometer is arranged at a certain distance on the cooling bed entry roller way to ensure the continuity of temperature detection. After the cooling bed, a scanning thermometer is placed under the cooling bed to detect the temperature of the rolled piece on each rack.

[0089] In some embodiments of the present application, the microstructure performance parameters include the microstructure proportion of the corresponding material of the rolled bar and the hardness of the rolled piece.

[0090] The microstructure performance parameters of the rolled bar are used to describe the microstructure characteristics of the rolled bar and how these microstructures affect the macro performance of the material.

[0091] Preferably, the microstructure performance parameters also include strength, toughness, ductility, fatigue performance, and impact performance. First, the strength is divided into tensile strength and yield strength:

[0092] Tensile strength: the maximum stress that the rolled bar can withstand when subjected to tension, measured in MPa (mega pascal) or ksi (thousand pounds per square inch).

[0093] Yield strength: the stress level at which the rolled bar begins to permanently deform (yield).

[0094] Hardness is used to measure the ability of the rolled bar to resist being scratched or indented by a hard object. Toughness is used to describe the ability of the rolled bar to resist sudden impact or tearing. Ductility is the ability of the rolled bar to deform without breaking during stretching, which is measured by elongation and reduction of area. Fatigue performance is the ability of the rolled bar to resist crack generation and propagation under repeated or cyclic loading. Impact performance is the performance of the rolled bar under rapid dynamic loading, which is evaluated by impact testing (such as Charpy impact test).

[0095] In some embodiments of the present application, the diameter of the rolled bar is less than 60 mm.

[0096] It should be noted that the microstructure performance parameter prediction method of the rolled bar provided in the embodiments of the present application is only applicable to the production line of small-sized bars with a diameter of 60 mm or less. Because the temperature difference between the core and the surface of the rolled piece during cooling on the cooling bed is relatively small, the surface temperature can be used to approximate the core temperature. If it is a large-sized bar, the core-surface temperature difference is large, so the detected surface temperature cannot be used to replace the core temperature, and the microstructure obtained from the surface temperature drop curve cannot be used to replace the core microstructure.

[0097] From the above description, the embodiment of the application provides a rolled bar microstructure performance parameter prediction method, comprising the following steps: firstly, obtaining the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; then, predicting the microstructure performance parameter of the rolled bar according to the cooling rate and a continuous cooling transformation model of the material corresponding to the rolled bar.

[0098] Specifically, the embodiment of the application provides a rolled bar microstructure performance parameter prediction method, which firstly performs regional intensive multi-point temperature detection in a rolling bed area, then obtains the actual cooling rate of the rolled piece in a phase change temperature interval, and finally directly compares with a continuous cooling transformation curve to obtain the actual microstructure proportion, and realizes online microstructure and material hardness acquisition according to final microscopic detection.

[0099] In a specific embodiment, the application further provides a specific embodiment of a rolled bar microstructure performance parameter prediction method, which is described in detail with reference to Figure 4 , and specifically comprises the following steps.

[0100] S1: setting a temperature detection device.

[0101] Sufficient temperature detection devices are arranged on the rolling line to realize full coverage of rolling line temperature detection. The temperature detection devices are arranged below the cold bed straightening plate, and detect the temperature of the rolled bar on the straightening plate through the gap between the cold bed straightening plates. Specifically, referring to Figure 5 , thermometers are arranged on the cold bed input roller every certain distance after the rolled piece is discharged from the rolling mill to ensure the continuity of temperature detection, and a scanning temperature detector is arranged below the cold bed after the cold bed to detect the temperature of the rolled piece on each rack.

[0102] It should be noted that the temperature detection device can be placed in other positions, and other temperature detection methods can also be used, as long as the temperature drop rate of the rolled piece in the entire phase change interval can be effectively detected, and the above idea can be realized.

[0103] S2: calculating the cooling rate of the rolled bar in the entire phase change interval according to the temperature change obtained in step S1.

[0104] S3: generating a continuous cooling transformation curve of the material corresponding to the rolled bar.

[0105] The continuous cooling transformation curve shows the phase transformation behavior of a specific material (usually steel or other alloys) under continuous cooling conditions. The continuous cooling transformation curve shows the starting and finishing temperatures of different phases (such as ferrite, pearlite, bainite, martensite, etc.) in the material as the cooling rate changes. These curves are very valuable for understanding and predicting the microstructure changes of the material during the cooling process and its final performance.

[0106] Specifically, step S3 can be realized by the following way: first, the material composition of the bar is determined, and the corresponding database or model can be selected for calculation according to the chemical composition information of the target material. Then, the cooling rate range is determined: the possible cooling rate range of the material is determined according to the process conditions, usually from 0.1 °C / s to 100 °C / s. Then, numerical simulation is carried out: the phase change time-temperature curve under different cooling rates is calculated by using phase change dynamics model such as JMAK equation, diffusion control theory, etc.

[0107] Then, the critical cooling rate is determined: by analyzing the calculation results, the critical cooling rate, i.e. the lowest cooling rate required for complete martensite transformation, is determined. Draw the CCT curve: draw the CCT curve by connecting the time-temperature points of different phase change start and end with temperature as the vertical axis and cooling rate as the horizontal axis. Finally, add the phase change region: mark out the phase change regions of ferrite, pearlite, martensite, etc. on the CCT curve to form a complete CCT diagram.

[0108] S4: use the cooling rate to compare the continuous cooling transformation curve to obtain the final microstructure and hardness of the rolled piece.

[0109] Specifically, the temperature drop rate of the rolled piece is obtained by using the detected results, and then the microstructure proportion and hardness of the material are obtained by comparing with the continuous cooling transformation curve of the material.

[0110] S5: correct the microstructure and performance parameters of the rolled bar.

[0111] Specifically, the comparison results are compared and corrected with the on-site detection results, the detection results are corrected by using the correction coefficient, the results of the microstructure and performance prediction are continuously optimized, and finally the rolled microstructure is accurately predicted.

[0112] In steps S1 to S5, referring to Figure 6 The scanned temperature detector transmits the measured temperature signal to the first-level control system through an analog module, the control system sends the temperature signal to the process automation control computer system, the system calculates the cooling curve of the rolled piece after processing the temperature signal, and sends the result to the microstructure and performance prediction model for performance prediction. After comparing the prediction results with the test results, the prediction model is automatically corrected through self-learning.

[0113] From the above description, the embodiment of the present application provides a microstructure and performance parameter prediction method of a rolled bar, which comprises the following steps: first, obtaining the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; then, predicting the microstructure and performance parameters of the rolled bar according to the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar.

[0114] The prediction of the structure and performance parameters of the rolled bar after rolling is obtained by the cooling rate of the rolled bar after rolling. After the data is obtained, the existing material continuous cooling transformation curve can be referred to, and the structure proportion of the rolled material, the hardness of the rolled bar and other parameters can be directly obtained from the continuous cooling transformation curve according to the rolling temperature and the cooling rate.

[0115] In summary, the present application provides an online bar structure and performance parameter prediction method by a new detection method, which provides a basis for the opening degree of the rolling line water tank, the cold bed heat preservation cover and other equipment actions, thereby providing a fast adjustment roadmap for obtaining the optimal production process.

[0116] Based on the same inventive concept, the present application also provides a rolled bar structure and performance parameter prediction device, which can be used to implement the method described in the above embodiments, as follows. Since the principle of solving the problem of the rolled bar structure and performance parameter prediction device is similar to that of the rolled bar structure and performance parameter prediction method, the implementation of the rolled bar structure and performance parameter prediction device can be referred to the implementation of the rolled bar structure and performance parameter prediction method, and the repeated parts will not be described again. The term "unit" or "module" used below can be a combination of software and / or hardware that can implement a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is conceived.

[0117] The present application provides a specific implementation of a rolled bar structure and performance parameter prediction device capable of implementing a rolled bar structure and performance parameter prediction method, as follows. Figure 7 A rolled bar structure and performance parameter prediction device specifically includes the following contents:

[0118] The cooling rate acquisition module 10 is configured to acquire the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0119] The performance parameter prediction module 20 is configured to predict the structure and performance parameters of the rolled bar according to the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar.

[0120] In some embodiments of the present application, as follows, Figure 8 The cooling rate acquisition module 10 includes:

[0121] The thermometer setting unit 10a is configured to set a plurality of thermometers on the cooling bed at a preset distance interval; wherein the cooling bed is configured to transfer the rolled bar out of the rolling mill;

[0122] The temperature measurement unit 10b is configured to continuously measure the temperature of the rolled bar through the plurality of thermometers;

[0123] The cooling rate acquisition unit 10c is configured to acquire the cooling rate according to the change in the temperature.

[0124] In some embodiments of the present application, the temperature meter is a scanning temperature meter.

[0125] In some embodiments of the present application, referring to Figure 9 , the temperature meter setting unit 10a comprises:

[0126] The temperature meter setting subunit 10a1 is configured to set the plurality of scanning temperature meters under the cooling bed; the plurality of scanning temperature meters are configured to measure the corresponding temperatures of the rolled bar on the plurality of racks on the cooling bed.

[0127] In some embodiments of the present application, the organizational performance parameters comprise the organizational proportion of the corresponding material of the rolled bar and the hardness of the rolled piece.

[0128] In some embodiments of the present application, the preset value is 60 mm.

[0129] From the above description, it can be known that the embodiment of the present application provides a rolled bar organizational performance parameter prediction device, which comprises: a current review acquisition module configured to acquire the current review and download data of a target application; and an organizational performance parameter prediction module of a target rolled bar configured to update the target application according to the current review, the download data, a pre-generated review quantity prediction model, and a download abnormal result of the download data; wherein the review quantity prediction model is generated based on the time sequence of the current review, and the download abnormal result is generated according to the periodic component and the trend component of the download data.

[0130] The present application realizes the statistics and analysis of the user review data of a target product, identifies the user emotion, mines the user demand, and monitors and tracks the trend change, abnormal points, and the like of the review, so as to update the target product in a targeted manner.

[0131] The embodiment of the present application also provides a specific implementation of an electronic device capable of realizing all the steps in the rolled bar organizational performance parameter prediction method in the above-mentioned embodiments, referring to Figure 10 , the electronic device specifically comprises the following contents:

[0132] The electronic device specifically comprises the following contents:

[0132] The processor 1201, the memory 1202, the communications interface 1203, and the bus 1204.

[0133] The processor 1201, the memory 1202 and the communication interface 1203 perform communication with each other through the bus 1204; the communication interface 1203 is configured to realize information transmission between the server-side device and the user-side device and other related devices;

[0134] The processor 1201 is configured to call the computer program in the memory 1202, and when the processor executes the computer program, all steps in the method for predicting the microstructure performance parameter of the rolled bar in the above embodiment are realized, for example, when the processor executes the computer program, the following steps are realized:

[0135] Step 100: acquiring a cooling rate of a rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0136] Step 200: predicting a microstructure performance parameter of the rolled bar according to the cooling rate and a continuous cooling transformation model of a material corresponding to the rolled bar.

[0137] In some embodiments of the present application, the continuous acquisition of the cooling rate of the rolled bar comprises:

[0138] A plurality of thermometers are arranged on a cooling bed at a preset distance interval; wherein the cooling bed is configured to output the rolled bar from a rolling mill;

[0139] The temperature of the rolled bar is continuously measured by the plurality of thermometers;

[0140] The cooling rate is acquired according to the change of the temperature.

[0141] In some embodiments of the present application, the thermometer is a scanning temperature measuring instrument;

[0142] The plurality of thermometers arranged on the cooling bed comprise:

[0143] The plurality of scanning temperature measuring instruments are arranged below the cooling bed; the plurality of scanning temperature measuring instruments are configured to measure the corresponding temperatures of the rolled bar on a plurality of racks on the cooling bed.

[0144] In some embodiments of the present application, the microstructure performance parameter comprises a microstructure proportion of a material corresponding to the rolled bar and a hardness of the rolled bar.

[0145] In some embodiments of the present application, the preset value is 60 mm.

[0146] The embodiment of the present application further provides a computer readable storage medium capable of realizing all steps of the method for predicting the microstructure performance parameter of the rolled bar in the above embodiment, and the computer program is stored on the computer readable storage medium, and when the computer program is executed by the processor, all steps of the method for predicting the microstructure performance parameter of the rolled bar in the above embodiment are realized, for example, when the computer program is executed by the processor, the following steps are realized:

[0147] Step 100: obtaining the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value.

[0148] Step 200: predicting the microstructure performance parameter of the rolled bar according to the cooling rate and the continuous cooling transformation model of the material corresponding to the rolled bar.

[0149] In some embodiments of the present application, the continuous acquisition of the cooling rate of the rolled bar comprises:

[0150] A plurality of thermometers are arranged on the cooling bed at preset distance intervals; wherein the cooling bed is used for conveying the rolled bar out of the rolling mill.

[0151] The temperature of the rolled bar is continuously measured by the plurality of thermometers.

[0152] The cooling rate is obtained according to the change of the temperature.

[0153] In some embodiments of the present application, the thermometer is a scanning temperature measuring instrument.

[0154] The plurality of thermometers arranged on the cooling bed comprise:

[0155] The plurality of scanning temperature measuring instruments are arranged below the cooling bed; and the plurality of scanning temperature measuring instruments are used for measuring the corresponding temperatures of the rolled bar on a plurality of racks on the cooling bed.

[0156] In some embodiments of the present application, the microstructure performance parameter comprises the microstructure proportion of the material corresponding to the rolled bar and the hardness of the rolled bar.

[0157] In some embodiments of the present application, the preset value is 60 mm.

[0158] Each of the embodiments in the specification is described in a progressive manner, and the same parts between each of the embodiments can be referred to each other, and each of the embodiments mainly describes the difference from other embodiments. Especially, for the hardware+program type embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0159] The above described embodiments of the present specification have been described. Other embodiments are within the scope of the following claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. Also, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous.

[0160] While the present application provides method operational steps as in embodiments or flowcharts, more or fewer operational steps can be included based on conventional or non-creative labor. The order in which steps are listed in embodiments is only one of many ways to execute the steps, and does not represent the only way to execute the steps. In actual implementation, the device or user terminal product can be executed according to the method order shown in the embodiments or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0161] For the convenience of description, the above device is described as various modules described respectively in function. Of course, in the implementation of the embodiments of the present specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0162] Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer readable program code, the same function can also be implemented by logically programming the method steps to make the controller in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0163] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0164] Memory can include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory, etc. Memory is an example of computer-readable media.

[0165] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the description of the method embodiments. In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present specification. The illustrative representation of the above terms in the present specification does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, a person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0166] The above only describes the embodiments of the embodiments of the present specification and does not limit the embodiments of the present specification. Those skilled in the art can make various changes and modifications to the embodiments of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification shall be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A method for predicting the microstructure and performance parameters of a rolled bar, characterized in that: include: Obtaining a cooling rate of a rolled bar; wherein the diameter of the rolled bar is less than a preset value; The microstructure and performance parameters of the rolled bar are predicted according to the cooling rate and a continuous cooling transformation model of a material corresponding to the rolled bar.

2. The tissue performance parameter prediction method according to claim 1, characterized in that: The continuously obtaining the cooling rate of the rolled bar comprises: A plurality of thermometers are arranged on a cooling bed at predetermined intervals; wherein the cooling bed is used to transport the bent bars out of the rolling mill; continuously measuring the temperature of the rolled bar by the plurality of thermometers; The cooling rate is obtained according to the change in the temperature.

3. The tissue performance parameter prediction method according to claim 2, characterized in that: The thermometer is a scanning thermometer; The provision of multiple thermometers on the cooling bed includes: The plurality of scanning thermometers are placed under the cooling bed; The plurality of scanning thermometers are used to measure the corresponding temperatures of the rolled bars on the plurality of racks on the cooling bed.

4. The tissue performance parameter prediction method according to claim 1, characterized in that: The structural performance parameters include: the structural proportion of the material corresponding to the rolled bar and the hardness of the rolled piece after rolling.

5. The tissue performance parameter prediction method according to any one of claims 1 to 4, characterized in that: The preset value is 60 mm.

6. A device for predicting the microstructure and performance parameters of a rolled bar, characterized in that: include: A cooling rate acquisition module, configured to acquire a cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; A performance parameter prediction module is used to predict the microstructure and performance parameters of the rolled bar according to the cooling rate and a continuous cooling transformation model of the material corresponding to the rolled bar.

7. The tissue performance parameter prediction device according to claim 6, characterized in that: The cooling rate acquisition module includes: a thermometer setting unit, configured to set a plurality of thermometers at predetermined intervals on a cooling bed; wherein the cooling bed is used to transport bent bars out of the rolling mill; a temperature measuring unit, configured to continuously measure the temperature of the rolled bar using the plurality of thermometers; A cooling rate acquisition unit is used to acquire the cooling rate according to the change of the temperature.

8. The tissue performance parameter prediction device according to claim 7, characterized in that: The thermometer is a scanning thermometer; The thermometer setting unit includes: The thermometer setting subunit is used to place the multiple scanning thermometers under the cooling bed; the multiple scanning thermometers are used to measure the corresponding temperatures of the rolled bars on the multiple racks on the cooling bed.

9. The tissue performance parameter prediction device according to claim 6, characterized in that: The structural performance parameters include: the structural proportion of the material corresponding to the rolled bar and the hardness of the rolled piece after rolling.

10. The tissue performance parameter prediction device according to any one of claims 6 to 9, characterized in that: The preset value is 60 mm.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for predicting the microstructure and performance parameters of a rolled bar as claimed in any one of claims 1 to 5 are implemented.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for predicting the microstructure and performance parameters of the rolled bar according to any one of claims 1 to 5 are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the microstructure and performance parameters of a rolled bar as claimed in any one of claims 1 to 5 are implemented.

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