Method and apparatus for predicting microstructural property parameters of rolled bar

Abstract

Provided in the present disclosure are a method and apparatus for predicting microstructural property parameters of a rolled bar. The corresponding method comprises: acquiring the cooling rate of a rolled bar, wherein the diameter of the rolled bar is less than a preset value; and on the basis of the cooling rate, and a continuous cooling transformation model of a material corresponding to the rolled bar, predicting microstructural property parameters of the rolled bar. Different from an existing method for predicting microstructural property parameters of a rolled bar, the present disclosure predicts a microstructural transformation result of a bar by using an actual measurement curve together with a temperature transformation curve of a material, no accurate temperature calculation curve and phase change model are required during the prediction, and an obtained result can better reflect the actual situation on a field, and is more accurate and practical.

Description

A method and device for predicting microstructure and performance parameters of rolled bars

[0001] Related applications

[0002] This application claims priority to the Chinese invention patent application with application number 202410392248.0 filed on April 2, 2024, and cites the entire disclosure of the above patent application as part of this application. Technical Field

[0003] The present disclosure belongs to the field of metal smelting technology, in particular to the field of steel rolling technology, and specifically relates to a method and device for predicting the microstructure and performance parameters of a rolled bar. Background Art

[0004] Bar products are widely used in production and daily life. Ordinary bars are primarily used in the construction industry, while alloy steel bars are primarily used in machinery, automobiles, railways, mining, and other fields. Utilizing various methods during the rolling process to optimize production processes and improve product quality is a goal that manufacturers strive for.

[0005] In related technologies, bar product performance testing primarily involves rolling the finished product, testing its quality, and then adjusting the production process based on the test results. This process is repeated repeatedly to ultimately achieve a qualified product. This process has a long feedback cycle and is time-consuming and costly.

[0006] Specifically, existing methods for predicting the microstructure and properties of bar products mainly use an offline approach, using the finite difference method or the finite element method to calculate the temperature field after rolling, and then bring the microstructure transformation model into the temperature field to obtain the microstructure evolution results. This method has strict requirements on the calculation accuracy of the temperature curve and the calculation accuracy of the phase transformation model. If the deviation is serious, the calculation result will have no reference value. It is understandable that the on-site working conditions are complex and many factors will affect the temperature of the rolled piece. For example, the ambient temperature at the time, the degree of opening of the cooling bed insulation cover, the temperature drop caused by the contact between the rolled piece and the roller or cooling bed, etc., all of these factors will have an impact on the temperature of the rolled piece, and numerical simulation technology is difficult to consider so comprehensively. Summary of the Invention

[0007] One purpose of the present disclosure is to provide a method for predicting the microstructure and performance parameters of rolled bars. This method can obtain approximate microstructure and performance parameters of bar products online through online and real-time microstructure and performance prediction of rolled bars, thereby shortening the intermediate repeated testing process.

[0008] Another object of the present disclosure is to provide a device for predicting the microstructure and performance parameters of a rolled bar. A further object of the present disclosure is to provide an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for predicting the microstructure and performance parameters of a rolled bar are implemented. A further object of the present disclosure is to provide a readable medium storing the computer program, and when the processor executes the computer program, the steps of the method for predicting the microstructure and performance parameters of a rolled bar are implemented.

[0009] To solve the technical problems in the background technology of this disclosure, the present disclosure provides the following technical solutions:

[0010] In a first aspect, the present disclosure provides a method for predicting microstructure and performance parameters of a rolled bar, comprising:

[0011] Obtaining a cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0012] The microstructure and performance parameters of the rolled bar are predicted based on the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar.

[0013] In some embodiments of the present disclosure, continuously obtaining the cooling rate of the rolled bar includes:

[0014] A plurality of thermometers are arranged on the cooling bed at predetermined intervals; wherein the cooling bed is used to transport the rolled bars out of the rolling mill;

[0015] The temperature of the rolled bar is continuously measured by multiple thermometers;

[0016] Get the cooling rate based on the temperature change.

[0017] In some embodiments of the present disclosure, the thermometer is a scanning thermometer;

[0018] Setting up multiple thermometers on the cooling bed includes:

[0019] A plurality of scanning thermometers are arranged 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.

[0020] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

[0021] In some embodiments of the present disclosure, the preset value is 60 mm.

[0022] In a second aspect, the present disclosure provides a device for predicting microstructure and performance parameters of a rolled bar, the device comprising:

[0023] A cooling rate acquisition module is used to acquire the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0024] The performance parameter prediction module is used to predict 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.

[0025] In some embodiments of the present disclosure, the cooling rate acquisition module includes:

[0026] a thermometer setting unit, for setting a plurality of thermometers at predetermined intervals on a cooling bed; wherein the cooling bed is used to transport rolled bars out of the rolling mill;

[0027] A temperature measuring unit, used to continuously measure the temperature of the rolled bar using multiple thermometers;

[0028] The cooling rate acquisition unit is used to acquire the cooling rate according to the temperature change.

[0029] In some embodiments of the present disclosure, the thermometer is a scanning thermometer;

[0030] The thermometer setting unit includes:

[0031] The thermometer setting subunit is used to set up multiple scanning thermometers under the cooling bed; the multiple scanning thermometers are used to measure the corresponding temperatures of the rolled bars on multiple racks on the cooling bed.

[0032] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

[0033] In some embodiments of the present disclosure, the preset value is 60 mm.

[0034] In a third aspect, the present disclosure provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of a method for predicting microstructure and performance parameters of a rolled bar.

[0035] In a fourth aspect, the present disclosure provides 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 a method for predicting the microstructure and performance parameters of a rolled bar are implemented.

[0036] In a fifth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for predicting the microstructure and performance parameters of a rolled bar.

[0037] From the above description, it can be seen that the embodiment of the present disclosure provides a method and device for predicting the microstructure and performance parameters of a rolled bar. The corresponding method for predicting the microstructure and performance parameters of a rolled bar includes: 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 based on the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar.

[0038] The corresponding microstructure and performance parameter prediction device of the rolled bar includes: a cooling rate acquisition module, used to obtain the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value; a performance parameter prediction module, used to predict the microstructure and performance parameters of the rolled bar based on the cooling rate and the continuous cooling transformation model of the corresponding material of the rolled bar.

[0039] The disclosed embodiment provides a method for predicting the microstructure and performance parameters of a rolled bar. Different from the existing method for predicting the microstructure and performance parameters of a rolled bar, the method uses a measured curve in combination with the temperature transformation curve of the material to infer the microstructure transformation result of the bar. The inference process does not require an accurate temperature calculation curve and phase change model, and the obtained results can better reflect the actual situation on site and are more accurate and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] FIG1 is a flow chart of a method for predicting microstructure and performance parameters of a rolled bar according to an embodiment of the present disclosure;

[0042] FIG2 is a flow chart of step 100 of a method for predicting microstructure and performance parameters of a rolled bar in an embodiment of the present disclosure;

[0043] FIG3 is a flow chart of step 102 of a method for predicting microstructure and performance parameters of a rolled bar in an embodiment of the present disclosure;

[0044] FIG4 is a schematic flow chart of a method for predicting microstructure and performance parameters of a rolled bar in a specific embodiment of the present disclosure;

[0045] FIG5 is a schematic diagram of the installation position of a scanning thermometer in a specific embodiment of the present disclosure;

[0046] FIG6 is a logic diagram of a method for predicting microstructure and performance parameters of a rolled bar in a specific embodiment of the present disclosure;

[0047] FIG7 is a block diagram of a device for predicting microstructure and performance parameters of a rolled bar in an embodiment of the present disclosure;

[0048] FIG8 is a block diagram of the cooling rate acquisition module 10 in an embodiment of the present disclosure;

[0049] FIG9 is a block diagram of a thermometer setting unit 10 a in an embodiment of the present disclosure;

[0050] FIG10 is a schematic structural diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0052] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present disclosure and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices. The embodiments of the present disclosure and the features described in the embodiments may be combined with each other unless there is a conflict. The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0054] The embodiment of the present disclosure provides a specific implementation of a method for predicting microstructure and performance parameters of a rolled bar. Referring to FIG1 , the method specifically includes the following contents:

[0055] Step 100: Obtaining a cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0056] Step 200: predicting the microstructure and performance parameters of the rolled bar according to the cooling rate and the continuous cooling transformation model of the material corresponding to the rolled bar.

[0057] From the above description, it can be seen that an embodiment of the present disclosure provides a method for predicting the microstructure and performance parameters of a rolled bar, comprising: 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 based on the cooling rate and a continuous cooling transformation model of the corresponding material of the rolled bar.

[0058] The disclosed embodiments provide a method for predicting the microstructure and performance parameters of a rolled bar. This method is different from existing methods for predicting the microstructure and performance parameters of a rolled bar in that it uses a measured curve in combination with the temperature transformation curve of the material to infer the microstructure transformation result of the bar. The inference process does not require an accurate temperature calculation curve and phase change model, and the obtained results can better reflect the actual situation on site and are more accurate and practical.

[0059] Regarding step 100, rolled bar refers to a bar-shaped product made from steel or other metal materials after hot or cold rolling. Rolling involves passing a metal material through a pair of rotating rollers to change its cross-sectional shape and size. Depending on the rolling temperature, rolling can be categorized as hot rolling or cold rolling.

[0060] Hot rolling is a rolling process that is performed above the recrystallization temperature of the metal (the scope of this disclosure is hot-rolled bar). This means that the metal becomes softer and easier to shape after heating. Characteristics of hot-rolled bar include:

[0061] There is oxide scale on the surface: because the metal surface will react with air at high temperature to form a layer of oxide.

[0062] Larger dimensional tolerances: Due to the large thermal expansion of the material during the hot rolling process, the dimensional accuracy of hot-rolled products is worse than that of cold-rolled products.

[0063] Loose internal structure: When rolled at high temperature, the grains inside the metal will grow, resulting in a slight decrease in the performance of the material.

[0064] Lower cost: Because the hot rolling process is simpler than cold rolling, energy consumption is also lower.

[0065] Cold rolling is a rolling process performed below the recrystallization temperature of the metal. This method does not require heating the material, but rather changes the shape and size of the material through the action of physical forces. Characteristics of cold rolled bars include:

[0066] Smooth surface: Cold rolling can obtain a smoother surface without the oxide scale produced by hot rolling.

[0067] High dimensional accuracy: Since rolling is carried out at a lower temperature, the dimensional control of the material is more precise.

[0068] Good material properties: The cold rolling process causes the material to harden, increasing its strength and hardness.

[0069] Higher cost: Cold rolling requires more processing steps, and because hardening requires subsequent treatments such as annealing to improve the plasticity of the material, the cost is higher.

[0070] In some embodiments of the present disclosure, referring to FIG2 , step 100 includes:

[0071] Step 101: multiple thermometers are arranged at predetermined intervals on a cooling bed, wherein the cooling bed is used to transport rolled bars out of a rolling mill;

[0072] Step 102: Continuously measuring the temperature of the rolled bar using a plurality of thermometers;

[0073] Step 103: Obtain the cooling rate according to the temperature change.

[0074] In steps 101 through 103, sufficient temperature monitoring devices are deployed throughout the rolling line to achieve full coverage of the rolling line temperature. Temperature monitoring devices are located below the cooling bed straightening plates. Through the gaps between the plates, these temperature monitoring devices measure the temperature of the bar on the straightening plates. This data is then aggregated to provide the cooling rate of the bar throughout the entire phase transition range.

[0075] In addition, the cooling rate is used to describe the speed at which the rolled bar cools from a high temperature to a low temperature, and is expressed in degrees Celsius per second. The cooling rate has an important influence on the microstructure and final physical and chemical properties of the material. Different cooling rates will lead to different microstructures in 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 the cooling rate on the rolled bar are as follows:

[0076] Microstructure: Rapid cooling (quenching) generally results in a finer grain structure or the formation of non-equilibrium phases, such as martensite in steel. Slow cooling may result in the formation of coarse grains and equilibrium phases, such as pearlite.

[0077] Mechanical properties: Rapid cooling can increase the strength and hardness of the material, but often at the expense of toughness and ductility. Slow cooling tends to increase the toughness and plasticity of the material, but the strength and hardness are lower.

[0078] Residual stress: Rapid cooling can cause large residual stresses within the material, which can reduce its performance and even cause cracking. Slow cooling helps reduce the generation of residual stress.

[0079] Phase transformation: Different cooling rates lead to different microstructures in alloys such as steel due to different phase transformation paths. For example, rapid cooling promotes martensitic transformation, while slow cooling may lead to the formation of pearlite or ferrite.

[0080] Therefore, in the actual production process, the cooling rate can be controlled by adjusting parameters such as the cooling medium (such as water, oil, air), the temperature of the cooling medium, and the contact method between the workpiece and the cooling medium to obtain the desired material properties and microstructure.

[0081] In some embodiments of the present disclosure, the thermometer is a scanning thermometer. A scanning thermometer, also known as a scanning infrared thermometer or infrared scanning temperature measurement system, is an instrument that uses infrared technology to measure the surface temperature of an object without contact. This instrument is particularly suitable for measuring the temperature of moving objects, high-temperature objects, or objects that are difficult to access. The operating principle of a scanning thermometer is based on the measurement of infrared radiation energy emitted from an object's surface, which is related to the object's surface temperature.

[0082] A scanning thermometer uses its built-in infrared sensor to receive infrared radiation energy emitted by an object's surface, converts this energy into an electrical signal, and then uses an internal microprocessor to calculate the corresponding temperature value. This process is almost instantaneous, allowing the instrument to monitor and record temperature changes in real time. Specifically, a scanning thermometer has the following features:

[0083] Non-contact measurement: The scanning thermometer can measure temperature without touching the surface of the object, avoiding direct contact with high-temperature objects or moving objects, ensuring safety and convenience.

[0084] Fast response: This type of thermometer can respond quickly to temperature changes and is suitable for situations where real-time temperature monitoring is required.

[0085] Wide range of applications: Scanning thermometers are not restricted by the material state (solid, liquid or gas) of the measured object and can be widely used in various industries and fields.

[0086] High temperature measurement: For high temperature environments, such as steel production and glass manufacturing, scanning thermometers can measure temperatures up to several thousand degrees Celsius without damaging the instrument.

[0087] Accuracy and resolution: Modern scanning thermometers provide high-precision and high-resolution temperature measurements, but their accuracy and resolution are affected by many factors, such as measurement distance, environmental conditions, and the material and surface characteristics of the target object.

[0088] In some embodiments of the present disclosure, referring to FIG3 , step 102 includes:

[0089] Step 1021: multiple scanning thermometers are placed 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.

[0090] Specifically, after the rolled piece leaves the rolling mill, thermometers are placed at regular intervals on the cooling bed input roller table to ensure continuous temperature detection. After the piece is on the cooling bed, a scanning thermometer is placed under the cooling bed to detect the temperature of the rolled piece on each rack.

[0091] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

[0092] The microstructure and performance parameters of the rolled bar are used to describe the microstructural characteristics of the rolled bar and how these microstructures affect the macroscopic properties of the material.

[0093] Optionally, the tissue performance parameters also include: strength, toughness, ductility, fatigue performance, and impact performance. First, strength is divided into tensile strength and yield strength:

[0094] Tensile Strength: The maximum stress that a rolled bar can withstand when stretched, measured in MPa (megapascals) or ksi (thousand pounds per square inch).

[0095] Yield Strength: The stress level at which a rolled bar begins to permanently deform (yield).

[0096] Hardness measures the ability of a rolled bar to resist scratching or intrusion by hard objects. Toughness describes the ability of a rolled bar to resist sudden impact or tearing. Ductility, the ability of a rolled bar to deform without breaking during tension, is measured by elongation and reduction of area. Fatigue resistance, the ability of a rolled bar to resist crack initiation and propagation under repeated or cyclic loading, is also measured. Impact resistance, such as the Charpy impact test, is used to evaluate the performance of a rolled bar under rapid dynamic loading.

[0097] In some embodiments of the present disclosure, the diameter of the rolled bar is less than 60 mm.

[0098] It should be noted that the method for predicting the microstructure and performance parameters of rolled bars provided in the embodiments of the present disclosure is only applicable to small-sized bar production lines, for example, small-sized bars with diameters of 80 mm, 70 mm, or 60 mm (i.e., bars with a diameter of 60 mm) or less. Because the temperature difference between the core and the surface of small bars is relatively small during cooling on the cooling bed, the surface temperature can be used to approximately replace the core temperature. If the bar is large, the temperature difference between the core and the surface is large, and the detected surface temperature cannot be used to replace the core temperature, nor can the microstructure obtained from the surface temperature drop curve be used to replace the core microstructure.

[0099] From the above description, it can be seen that an embodiment of the present disclosure provides a method for predicting the microstructure and performance parameters of a rolled bar, comprising: 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 based on the cooling rate and a continuous cooling transformation model of the corresponding material of the rolled bar.

[0100] Specifically, the embodiment of the present disclosure provides a method for predicting the microstructure and performance parameters of a rolled bar. First, a dense multi-point temperature detection is performed in the post-rolling cooling bed area. For example, the post-rolling cooling bed area can be divided into several grids, and one or more temperature detection points are set in each grid. Optionally, a more densely distributed temperature detection point can be set for key cooling areas or locations where the temperature changes significantly during the cooling process. Then, the actual cooling rate of the rolled piece in the phase change temperature range is obtained, and finally it is directly compared with the continuous cooling transformation curve to obtain the actual microstructure ratio, which is appropriately corrected according to the final microscopic detection to achieve online microstructure and material hardness acquisition.

[0101] In a specific embodiment, the present disclosure also provides a specific embodiment of a method for predicting microstructure and performance parameters of a rolled bar, see FIG4 , which specifically includes the following steps.

[0102] S1: Set up the temperature detection device.

[0103] Installing sufficient temperature monitoring devices along the rolling line ensures full coverage of the rolling line's temperature. Temperature monitoring devices are placed beneath the cooling bed's straightening plates, detecting the bar's temperature on the straightening plates through the gaps between them. Specifically, as shown in Figure 5, after the rolled product exits the rolling mill, thermometers are placed at regular intervals on the cooling bed's input rollers to ensure continuous temperature monitoring. After the product enters the cooling bed, a scanning thermometer is placed beneath the bed to monitor the temperature of the rolled product on each rack.

[0104] It should be pointed out that the temperature detection device can be placed in other locations, and other temperature detection methods can be used. As long as the temperature drop rate of the rolled piece in the entire phase change range can be effectively detected, the above idea can be realized.

[0105] S2: Calculate the cooling rate of the rod in the entire phase transformation range based on the temperature change obtained in step S1.

[0106] S3: Generate a continuous cooling transformation curve of the corresponding material of the rolled bar.

[0107] Continuous cooling transformation curves illustrate the phase transformation behavior of a specific material (such as steel or other alloys) under continuous cooling conditions. They show the start and finish temperatures of the formation of different phases (such as ferrite, pearlite, bainite, martensite, etc.) in the material as the cooling rate changes. These curves are invaluable for understanding and predicting the microstructural changes of a material during cooling and its ultimate properties.

[0108] Specifically, step S3 can be implemented as follows: First, determine the material composition of the rod. Based on the target material's chemical composition information, select a corresponding database or model for calculation. Next, determine the cooling rate range: Based on the process conditions, determine the possible cooling rate range for the material, typically from 0.1°C / s to 100°C / s. Then, perform numerical simulations: Utilizing phase transition kinetic models such as the JMAK equation and diffusion-controlled theory, calculate the phase transition time-temperature curves at different cooling rates.

[0109] Next, determine the critical cooling rate: By analyzing the calculated results, determine the critical cooling rate, the minimum cooling rate required for complete martensitic transformation. Draw the CCT curve: Using temperature as the vertical axis and cooling rate as the horizontal axis, connect the time-temperature points at the start and end of different phase transformations to draw the CCT curve. Finally, add the phase transformation regions: Annotate the ferrite, pearlite, martensite, and other phase transformation regions on the CCT curve to form a complete CCT diagram.

[0110] S4: Use the cooling rate to compare the continuous cooling transformation curve to obtain the final microstructure and hardness of the rolled piece.

[0111] Specifically, the temperature drop rate of the rolled piece is obtained using the test results, and then compared with the continuous cooling transformation curve of the material to obtain the material's microstructure ratio and hardness.

[0112] S5: Correct the microstructure and performance parameters of the rolled bar.

[0113] Specifically, the control results are compared and corrected with the on-site test results, and the correction coefficient of the test results is used to continuously optimize the results of the microstructure performance prediction, ultimately achieving accurate prediction of the post-rolling microstructure.

[0114] In steps S1 to S5, see Figure 6, the scanning thermometer transmits the measured temperature signal to the primary control system through the analog module, and the control system sends the temperature signal to the process automation control computer system. The system processes the temperature signal and calculates the cooling curve of the rolled piece, and sends the result to the microstructure performance prediction model for performance prediction. After the prediction result is compared with the inspection and test results, the prediction model will be automatically corrected through self-learning.

[0115] From the above description, it can be seen that a specific embodiment of the present disclosure provides a method for predicting the microstructure and performance parameters of a rolled bar, comprising: 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 and performance parameters of the rolled bar based on the cooling rate and a continuous cooling transformation model of the corresponding material of the rolled bar.

[0116] The prediction of the microstructure and performance parameters of the rolling line disclosed in the present invention is achieved by obtaining the cooling rate of the rolled piece after rolling. After the data is obtained, the existing material continuous cooling transformation curve can be compared to directly obtain the microstructure ratio of the rolled material, the hardness of the rolled piece and other parameters from the continuous cooling transformation curve according to the post-rolling temperature and cooling rate.

[0117] In summary, the present disclosure provides an online method for predicting the microstructure and performance parameters of bar materials through a new detection method, which provides a basis for the operation of equipment such as the opening degree of the rolling line water tank and the cooling bed insulation cover, thereby providing a quick adjustment roadmap for obtaining the optimal production process.

[0118] Based on the same inventive concept, the embodiments of the present disclosure also provide a device for predicting the microstructure and performance parameters of rolled bars, which can be used to implement the methods described in the above embodiments, such as the following embodiments. Since the principle of solving the problem by the device for predicting the microstructure and performance parameters of rolled bars is similar to that of predicting the microstructure and performance parameters of rolled bars, the implementation of the device for predicting the microstructure and performance parameters of rolled bars can refer to the implementation of the method for predicting the microstructure and performance parameters of rolled bars, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0119] The embodiments of the present disclosure provide a specific implementation of a device for predicting microstructure and performance parameters of a rolled bar that can implement a method for predicting microstructure and performance parameters of a rolled bar. Referring to FIG. 7 , the device for predicting microstructure and performance parameters of a rolled bar specifically includes the following contents:

[0120] A cooling rate acquisition module 10 is used to acquire the cooling rate of the rolled bar; wherein the diameter of the rolled bar is less than a preset value;

[0121] The performance parameter prediction module 20 is used to predict the microstructure and performance parameters of the rolled bar according to the cooling rate and the continuous cooling transformation model of the material corresponding to the rolled bar.

[0122] In some embodiments of the present disclosure, referring to FIG8 , the cooling rate acquisition module 10 includes:

[0123] The thermometer setting unit 10a is used to set a plurality of thermometers at predetermined intervals on a cooling bed, wherein the cooling bed is used to transport the rolled bars out of the rolling mill;

[0124] The temperature measuring unit 10b is used to continuously measure the temperature of the rolled bar using a plurality of thermometers;

[0125] The cooling rate acquisition unit 10c is used to acquire the cooling rate according to the temperature change.

[0126] In some embodiments of the present disclosure, the thermometer is a scanning thermometer;

[0127] In some embodiments of the present disclosure, referring to FIG9 , the thermometer setting unit 10a includes:

[0128] The thermometer setting subunit 10a1 is used to set up multiple scanning thermometers under the cooling bed; the multiple scanning thermometers are used to measure the corresponding temperatures of the rolled bars on multiple racks on the cooling bed.

[0129] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

[0130] In some embodiments of the present disclosure, the preset value is 60 mm.

[0131] The embodiments of the present disclosure also provide a specific implementation of an electronic device capable of implementing all steps of the method for predicting microstructure and performance parameters of a rolled bar in the above embodiment. Referring to FIG10 , the electronic device specifically includes the following contents:

[0132] Processor 1201, memory 1202, communications interface 1203, and bus 1204;

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

[0134] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all steps of the method for predicting the microstructure and performance parameters of the rolled bar in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

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

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

[0137] In some embodiments of the present disclosure, continuously obtaining the cooling rate of the rolled bar includes:

[0138] A plurality of thermometers are arranged on the cooling bed at predetermined intervals; wherein the cooling bed is used to transport the rolled bars out of the rolling mill;

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

[0140] Get the cooling rate based on the temperature change.

[0141] In some embodiments of the present disclosure, the thermometer is a scanning thermometer;

[0142] Setting up multiple thermometers on the cooling bed includes:

[0143] A plurality of scanning thermometers are arranged 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.

[0144] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

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

[0146] The embodiments of the present disclosure also provide a computer-readable storage medium capable of implementing all steps of the method for predicting the microstructure and performance parameters of a rolled bar in the above-mentioned embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program implements all steps of the method for predicting the microstructure and performance parameters of a rolled bar in the above-mentioned embodiment. For example, when the processor executes the computer program, the following steps are implemented:

[0147] Step 100: Obtaining a 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 and performance parameters 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 disclosure, continuously obtaining the cooling rate of the rolled bar includes:

[0150] A plurality of thermometers are arranged on the cooling bed at predetermined intervals; wherein the cooling bed is used to transport the rolled bars out of the rolling mill;

[0151] The temperature of the rolled bar is continuously measured by multiple thermometers;

[0152] Get the cooling rate based on the temperature change.

[0153] In some embodiments of the present disclosure, the thermometer is a scanning thermometer;

[0154] Setting up multiple thermometers on the cooling bed includes:

[0155] A plurality of scanning thermometers are arranged 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.

[0156] In some embodiments of the present disclosure, the microstructure performance parameters include: the microstructure ratio of the corresponding material of the rolled bar and the hardness of the rolled piece after rolling.

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

[0158] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the hardware + program embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0159] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0160] Although the present disclosure provides method operation steps such as embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many steps and does not represent the only execution order. When an actual device or user-end product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0161] For the convenience of description, the above devices are described in terms of functions divided into various modules. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0162] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by logically programming the method steps in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered 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 may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0165] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple. For relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the embodiments in this specification. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0166] The above description is merely an example of the embodiments of this specification and is not intended to limit the embodiments of this specification. For those skilled in the art, various modifications and variations of the embodiments of this specification are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of this specification shall be included within the scope of the claims of the embodiments of this 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 rolled 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 rolled 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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