Seamless steel pipe based on stress gradient processing

Abstract

The application provides a seamless steel pipe based on stress gradient processing, and relates to the technical field of metal material processing and intelligent manufacturing. The method for processing the seamless steel pipe based on the stress gradient comprises the following steps: extracting a first stress gradient, a first maximum residual stress and a first stress concentration coefficient from an axial stress distribution of a previous batch of seamless steel pipes, and calculating a current batch of straightening press-down amount adjustment values through a preset multivariate linear function relationship; obtaining a second stress gradient, a second maximum residual stress, a second stress concentration coefficient and a circumferential stress range of the current batch of steel pipes, inputting a multivariate linear annealing temperature model with engineering constraints to obtain an annealing temperature correction amount, and adjusting an annealing furnace temperature for annealing treatment. The application realizes adaptive regulation and control of cross-batch feedforward straightening and annealing temperature, effectively improves the precision of residual stress elimination in continuous production and the consistency of product performance between batches.

Description

Technical Field

[0001] This application relates to the fields of metal material processing and intelligent manufacturing technology, and in particular to a seamless steel pipe based on stress gradient processing. Background Technology

[0002] Seamless steel pipes require straightening after cold rolling to eliminate bending deformation. During the straightening process, the steel pipe undergoes uneven plastic deformation under repeated bending, introducing residual stress into the material. The spatial distribution of residual stress along the axial direction of the steel pipe often exhibits significant non-uniformity. The stress concentration in local areas and the rate of change of axial stress directly affect the dimensional stability, deformation resistance, and resistance to stress corrosion cracking during subsequent processing.

[0003] In related technologies, the conventional method for eliminating residual stress is stress-relief annealing. Its process parameters (such as annealing temperature, holding time, and cooling rate) are usually set based on experience or fixed standards according to the material and specifications of the steel pipe, without considering the differences in the actual magnitude and distribution characteristics of residual stress in each steel pipe. The straightening and annealing processes operate independently in existing production lines, which can easily lead to some steel pipes having excessively high residual stress due to insufficient annealing, or some steel pipes experiencing energy waste and material performance degradation due to over-annealing, making it difficult to balance stress relief effectiveness with production economy. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first aspect of this application proposes a method for processing seamless steel pipes based on stress gradient, comprising the following steps:

[0006] The first stress gradient, the first maximum residual stress, and the first stress concentration factor are extracted from the axial stress distribution curve of the previous batch of seamless steel pipes. Based on the first stress gradient, the first maximum residual stress, the first stress concentration factor, and a preset multivariate linear function relationship, the straightening machine reduction adjustment value for the current batch of seamless steel pipes is obtained, and the current batch of seamless steel pipes is straightened based on the straightening machine reduction adjustment value; the multivariate linear function relationship includes the mapping relationship between the maximum residual stress, stress gradient, stress concentration factor, and straightening machine reduction adjustment value; Obtain the second stress gradient, second maximum residual stress, second stress concentration factor, and circumferential stress range of the current batch of seamless steel pipes; The annealing temperature correction is obtained based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints. The temperature inside the annealing furnace is adjusted according to the annealing temperature correction amount to perform annealing treatment on the current batch of seamless steel pipes.

[0007] In some embodiments of this application, the axial stress distribution curve of the previous batch of seamless steel pipes is obtained by the following method: at the outlet of the straightening machine, the previous batch of seamless steel pipes is scanned with an LCR wave to obtain the residual stress values ​​of multiple measuring points along the axial direction of the previous batch of seamless steel pipes, and the axial stress distribution curve is constructed.

[0008] In some embodiments of this application, the multivariate linear function relationship is represented as follows:

[0009] in, This is the adjustment value for the straightening machine reduction of the current batch of seamless steel pipes. The first stress gradient, As the reference stress gradient, The first maximum residual stress, As the reference maximum residual stress, The first stress concentration factor is... The reference stress concentration factor is... , , is a coefficient.

[0010] In some embodiments of this application, the method further includes: calculating the number of extreme points and the skewness coefficient of the peak region of the axial stress distribution curve; identifying the stress distribution pattern of the current batch of seamless steel pipes based on the number of extreme points and the skewness coefficient; and correcting the multivariate linear function relationship and / or the multivariate linear annealing temperature model based on the stress distribution pattern.

[0011] In some embodiments of this application, the multivariate linear annealing temperature model is represented as follows:

[0012] in, This is the correction amount for the annealing temperature. The second stress gradient, This is the second maximum residual stress. This is the second stress concentration factor. For the aforementioned circumferential stress difference, , , , , The coefficients used to satisfy engineering constraints.

[0013] In some embodiments of this application, the method further includes: when the circumferential stress difference is greater than or equal to a preset circumferential stress difference threshold, extending the annealing holding time of the current batch of seamless steel pipes and reducing the annealing cooling rate of the current batch of seamless steel pipes.

[0014] In some embodiments of this application, the method further includes: performing stress testing on the current batch of seamless steel pipes after annealing to obtain the stress relief rate of the current batch of seamless steel pipes; and storing the straightening machine reduction adjustment value, the annealing temperature correction amount, and the stress relief rate into a historical database.

[0015] In some embodiments of this application, the method further includes: updating the coefficients in the multivariate linear function relationship by at least one of the following methods every preset period or when the average stress relief rate of multiple consecutive steel pipes is lower than a preset threshold: outlier removal, weighted regression, multi-objective optimization, and parameter smoothing.

[0016] A second aspect of this application provides a seamless steel pipe processing apparatus based on stress gradient, comprising: The first acquisition module is used to extract the first stress gradient, the first maximum residual stress, and the first stress concentration factor from the axial stress distribution curve of the previous batch of seamless steel pipes. The straightening adjustment module is used to obtain the straightening machine reduction adjustment value for the current batch of seamless steel pipes based on the first stress gradient, the first maximum residual stress, the first stress concentration factor, and a preset multivariate linear function relationship, and to perform straightening processing on the current batch of seamless steel pipes based on the straightening machine reduction adjustment value; the multivariate linear function relationship includes the mapping relationship between the maximum residual stress, stress gradient, stress concentration factor, and straightening machine reduction adjustment value; The second acquisition module is used to acquire the second stress gradient, the second maximum residual stress, the second stress concentration factor, and the circumferential stress range of the current batch of seamless steel pipes. The annealing adjustment module is used to obtain the annealing temperature correction amount based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints. The annealing control module is used to adjust the temperature inside the annealing furnace according to the annealing temperature correction amount, and to perform annealing treatment on the current batch of seamless steel pipes.

[0017] The third aspect of this application provides a seamless steel pipe, which is obtained by processing the seamless steel pipe based on the stress gradient processing method described in the first aspect above.

[0018] The stress gradient-based seamless steel pipe processing method provided in this application calculates the straightening machine reduction adjustment value for the current batch of seamless steel pipes by extracting the first stress gradient, first maximum residual stress, and first stress concentration factor from the axial stress distribution of the previous batch of seamless steel pipes. This achieves cross-batch feedforward control based on the measured stress spatial distribution characteristics, enabling the straightening parameters to be adaptively corrected according to the stress state of the preceding workpiece, avoiding control delays caused by offline detection lag. Furthermore, the method obtains the second stress gradient, second maximum residual stress, second stress concentration factor, and circumferential stress range of the current batch of steel pipes. An annealing temperature correction is generated using a multivariate linear annealing temperature model with engineering constraints, and the annealing furnace temperature is adjusted accordingly. This allows the annealing process to fully respond to the multidimensional stress characteristics of the current steel pipe, thereby effectively improving the accuracy of residual stress elimination and the consistency of product performance between batches during continuous production.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A schematic flowchart illustrating a method for processing seamless steel pipes based on stress gradient, provided for an embodiment of this application; Figure 2 This is a schematic diagram of a seamless steel pipe processing device based on stress gradient, provided for an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] Specifically, the following describes an embodiment of the seamless steel pipe based on stress gradient processing according to the accompanying drawings.

[0023] Figure 1 This is a schematic flowchart illustrating a method for processing seamless steel pipes based on stress gradients, provided as an embodiment of this application. Figure 1 As shown, the stress gradient-based seamless steel pipe processing method may include the following steps: Step 101: Extract the first stress gradient, the first maximum residual stress, and the first stress concentration factor from the axial stress distribution curve of the previous batch of seamless steel pipes.

[0024] In one implementation, the axial stress distribution curve of the previous batch of seamless steel pipes can be obtained as follows: at the outlet of the straightening machine, the previous batch of seamless steel pipes is scanned using an LCR wave to obtain the residual stress values ​​at multiple measuring points along the axial direction of the previous batch of seamless steel pipes, thus constructing the axial stress distribution curve. .

[0025] The first stress gradient of the previous batch of seamless steel pipes was extracted based on the axial stress distribution curve. : First maximum residual stress The maximum residual stress in the axial stress distribution curve, and the first stress concentration factor. , This represents the average residual stress of the previous batch of seamless steel pipes. Stress concentration factor. The stress concentration factor characterizes the degree of distribution and concentration of residual stress. The larger the value, the more concentrated the stress is in a local area (such as a single peak). The smaller the value, the more uniform the stress distribution (e.g., uniform type).

[0026] Step 102: Based on the first stress gradient, the first maximum residual stress, the first stress concentration factor, and the preset multivariate linear function relationship, obtain the straightening machine reduction adjustment value for the current batch of seamless steel pipes, and perform straightening processing on the current batch of seamless steel pipes based on the straightening machine reduction adjustment value.

[0027] The multivariate linear function relationship includes the mapping relationship between maximum residual stress, stress gradient, stress concentration factor, and straightening machine reduction adjustment value. In some embodiments of this application, the multivariate linear function relationship can be expressed as follows:

[0028] in, This is the adjustment value for the straightening machine reduction of the current batch of seamless steel pipes. The first stress gradient, The reference stress gradient is the statistical average value under normal production conditions. The first maximum residual stress, The reference maximum residual stress (statistical average under normal production conditions) is used. The first stress concentration factor is... The baseline stress concentration factor (statistical average value under normal production conditions) is used. , , The coefficient. In some embodiments, it can be obtained from historical data ( , , The coefficients were obtained by fitting the multiple linear regression of the sample pairs. ,coefficient And satisfy <0 (the greater the stress gradient, the smaller the reduction needs to be). <0 (the greater the residual stress, the smaller the reduction needs to be); coefficient It can be determined through regression analysis of historical data, and meets the following conditions: <0 (The greater the stress concentration, the smaller the reduction needs to be).

[0029] This application employs a two-dimensional feature space composed of stress gradient and maximum residual stress to characterize the intensity and spatial non-uniformity of residual stress. Compared to the peak or average stress value, the stress gradient characterizes the rate of spatial change of residual stress, reflecting the concentration of stress along the axial direction and the degree of non-uniformity of plastic deformation within the material. It is the dominant factor leading to subsequent deformation and cracking. The maximum residual stress reflects the amplitude level of stress. These two factors are independent and complementary, and this two-dimensional combination effectively describes the influence of residual stress on subsequent deformation and cracking.

[0030] Since the straightening process precedes the annealing process, and residual stress testing can only be performed after straightening is completed, forcibly performing secondary straightening or online adjustment on already straightened steel pipes may cause surface damage, dimensional deviations, or introduce new additional stress. Therefore, this application adjusts the straightening parameters of the next batch (i.e., the current batch) of seamless steel pipes based on the straightening machine reduction adjustment value obtained from the previous batch of seamless steel pipe data, forming a cross-workpiece feedforward control mechanism. This overcomes the timing contradiction of testing following and straightening preceding, smoothly distributing the adjustment action to subsequent batches of seamless steel pipes without requiring any backtracking processing of the current steel pipes, thus ensuring the continuity of production rhythm and the surface quality of the steel pipes.

[0031] Step 103: Obtain the second stress gradient, second maximum residual stress, second stress concentration factor, and circumferential stress range of the current batch of seamless steel pipes.

[0032] The second stress gradient, second maximum residual stress, and second stress concentration factor can be found in the relevant descriptions in step 101, and will not be repeated here. Circumferential stress range The maximum value of the circumferential range of each axial section is taken, and the maximum value of this difference is taken for all axial sections to characterize the degree of non-uniformity of the circumferential stress of the steel pipe.

[0033] Step 104: Based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints, obtain the annealing temperature correction amount.

[0034] During the straightening process, the steel pipe is subjected to bending and compression, resulting in uneven plastic deformation of the cross-section and the introduction of residual stress. The spatial distribution characteristics of the residual stress (stress gradient, stress concentration factor, and circumferential stress range) determine the driving force and path of stress relaxation during subsequent annealing. Specifically, a larger stress gradient indicates a greater difference in plastic strain between adjacent regions, leading to a stronger tendency for stress redistribution driven by dislocation movement during annealing, requiring higher temperatures or longer holding times for homogenization. A larger concentration factor indicates that stress is highly concentrated in a localized area, where stress relaxation preferentially occurs during annealing. However, excessively high local stress may lead to hot cracking, thus requiring a reduced heating rate or extended holding time. A larger circumferential range indicates asymmetrical circumferential stress in the steel pipe, making it prone to bending deformation during annealing, requiring a lower cooling rate and longer holding time to mitigate this. The aforementioned three independent stress parameter characteristics are mapped to a multivariate linear annealing temperature model using a mathematical model.

[0035] In some embodiments of this application, the multivariate linear annealing temperature model can be expressed as follows:

[0036] in, This is the correction amount for the annealing temperature. For the second stress gradient, The second maximum residual stress, This is the second stress concentration factor. For extreme circumferential stress, , , , , The coefficients used to satisfy engineering constraints. The unit is ℃·m / MPa. The unit is ℃ / MPa. and Dimensionless, the unit of d is °C. All terms have the same dimensions, and the calculation results... The unit is ℃. Coefficient , , , It can be determined through multivariate regression of historical data, and meets the following engineering constraints: b>0: The greater the stress gradient, the higher the annealing temperature needs to be; c>0: The greater the maximum residual stress, the higher the annealing temperature needs to be; e>0: The greater the stress concentration, the higher the annealing temperature needs to be; f>0: The larger the circumferential range, the higher the annealing temperature needs to be.

[0037] This constraint relationship is based on the physical mechanism of straightening-annealing: the stress gradient directly reflects the degree of uneven plastic deformation and is the primary factor determining the stress relaxation effect of annealing, while the reduction adjustment and the maximum stress amplitude only play an auxiliary corrective role.

[0038] Step 105: Adjust the temperature inside the annealing furnace according to the annealing temperature correction amount, and perform annealing treatment on the current batch of seamless steel pipes.

[0039] Actual controlled temperature inside the annealing furnace ,in, The reference annealing temperature (set according to the steel pipe material and specifications).

[0040] Optionally, in some embodiments of this application, when the circumferential stress difference is extremely large... A stress level greater than or equal to a preset circumferential stress difference threshold can extend the annealing holding time of the current batch of seamless steel pipes and reduce the annealing cooling rate of the current batch of seamless steel pipes. As an example, the preset circumferential stress difference threshold can be set to 30 MPa. When the pressure is greater than or equal to 30 MPa, except for the calculation in step 104. In addition, it can automatically extend the heat preservation time by 15% to 20% and reduce the cooling rate by 10% to 15% to enhance the circumferential stress relaxation effect.

[0041] In some embodiments of this application, the number of extreme points and the skewness coefficient of the peak region of the axial stress distribution curve can also be calculated; the stress distribution pattern of the current batch of seamless steel pipes can be identified based on the number of extreme points and the skewness coefficient; and the multivariate linear function relationship and / or multivariate linear annealing temperature model can be corrected based on the stress distribution pattern.

[0042] One approach is to calculate the axial stress distribution curve. first derivative ,statistics Number of extreme points where 0 = And its location (peak region), further calculate the skewness coefficient of the peak region. ,in, The average stress of the current batch of seamless steel pipes, Let E be the stress standard deviation and E be the expected value. Based on the number of extreme points and the skewness coefficient Skew of the peak region, the stress distribution patterns can be divided into the following four categories: Single-peak type: =1 and Skew>0.5, the physical characteristic is a single stress concentration area; Bimodal: =2 and the peak spacing >0.2L, indicating two independent stress concentrations; End-concentrated type: The peak value is located within 0.1L from the head end, indicating excessive end stress; Uniform type: coefficient of variation CV < 0.2 (coefficient of variation CV = stress standard deviation s / stress mean μ), stress distribution is uniform.

[0043] When the function is identified as unimodal or bimodal, the coefficients in the multivariate linear function relationship in step 102 will be... Multiply by the morphological coefficient k M (single-peak type k) M =1.2, bimodal k M =1.1), to enhance the effect of the stress concentration factor; When the condition is identified as end-concentrated, an additional term is added to the multivariate linear annealing temperature model in step 104. ,in, This is a quantified value of the degree of concentration at the ends (such as the ratio of peak value to average value). For coefficients; When the data is identified as uniform, the baseline parameters can be used directly without adjustment.

[0044] Stress gradient and stress concentration factor can describe the numerical characteristics of stress, while stress distribution pattern can describe the spatial structure of stress. Complementing and correcting the pattern recognition results with stress gradient and stress concentration factor allows for more precise matching of control strategies. Adjustments to multivariate linear function relationships and / or multivariate linear annealing temperature models are used to correct the straightening parameters of the next batch of seamless steel pipes, but not applied to the current batch, thus forming a cross-workpiece feedforward control mechanism.

[0045] In some embodiments of this application, stress testing can also be performed on the current batch of seamless steel pipes after annealing to obtain the stress relief rate of the current batch of seamless steel pipes. Adjust the straightening machine's pressing amount. Annealing temperature correction amount and stress relief rate The steel pipe is stored in the historical database according to its unique code. Optionally, stress-related parameters collected from each batch and each processing stage can also be stored in the historical database.

[0046] Furthermore, in some embodiments of this application, every preset period (e.g., every 500 steel pipes) or when the average stress relief rate of multiple consecutive steel pipes is lower than a preset threshold (e.g., the average stress relief rate of the most recent 100 steel pipes is lower than 80%), the coefficients in the multivariate linear function relationship can be updated in at least one of the following ways: outlier removal, weighted regression, multi-objective optimization, and parameter smoothing.

[0047] Outlier removal includes removing samples with an elimination rate of less than 60% or more than 95%, as well as outliers with stress gradients or maximum residual stresses exceeding 3 times the standard deviation. Weighted regression: assigns higher weights to recent data, with the weighting function being... , The weight of the i-th historical data point. The time decay coefficient is dimensionless. A value of 0.1 to 0.2 is acceptable. For the current time (unit and) Consistent (e.g., seconds or hours). Let be the time of data collection for the i-th historical data point. When using the least squares method to fit the coefficients, the squared residual of each sample is multiplied by its corresponding weight. This means minimizing the weighted sum of squared residuals, thereby making recent data have a greater impact on the regression results.

[0048] Multi-objective optimization: Simultaneously maximizing the elimination rate and the improvement rate of circumferential uniformity while minimizing energy consumption, the optimal combination of coefficients is searched through a multi-objective optimization method; Parameter smoothing: To prevent process oscillations caused by sudden parameter changes, exponential smoothing is used for updating; based on multivariate linear function relationships... coefficients in For example: , For the updated coefficients , The coefficients before the update , These are the new coefficients obtained from regression calculations. As a smoothing factor, A value of 0.3 to 0.5 is acceptable.

[0049] The same applies to the other coefficients in the multivariate linear function relationship and the multivariate linear annealing temperature model; the updated coefficients are automatically deployed to the online control system, and the version number is recorded for traceability.

[0050] By implementing the embodiments of this application, the adjustment value of the straightening machine reduction for the current batch of seamless steel pipes is calculated by extracting the first stress gradient, the first maximum residual stress, and the first stress concentration factor from the axial stress distribution of the previous batch of seamless steel pipes. This achieves cross-batch feedforward control based on the measured stress spatial distribution characteristics, enabling the straightening parameters to be adaptively corrected according to the stress state of the preceding workpiece, avoiding control delays caused by offline detection lag. Furthermore, the second stress gradient, the second maximum residual stress, the second stress concentration factor, and the circumferential stress range of the current batch of steel pipes are obtained. An annealing temperature correction is generated using a multivariate linear annealing temperature model with engineering constraints, and the temperature inside the annealing furnace is adjusted accordingly, allowing the annealing process to fully respond to the multidimensional stress characteristics of the current steel pipe. By acquiring effective information characterizing the spatial distribution of residual stress online and achieving coordinated control of the straightening and annealing processes, the accuracy of residual stress elimination and the consistency of product performance between batches can be effectively improved during continuous production.

[0051] This application also provides a seamless steel pipe based on stress gradient processing, which is obtained by processing the seamless steel pipe based on stress gradient processing method of any of the foregoing embodiments. Since the processing method can calculate the straightening machine reduction adjustment value based on the axial stress gradient, maximum residual stress, and stress concentration factor of the previous batch of steel pipes to perform cross-batch feedforward control of the current batch of steel pipes, and generate annealing temperature correction based on the multidimensional stress characteristics of the current batch of steel pipes through a multivariate linear annealing temperature model with engineering constraints to implement adaptive annealing treatment, the resulting seamless steel pipe exhibits more accurate and stable residual stress elimination during continuous production, and significantly improved uniformity of axial stress distribution between batches, thus possessing excellent dimensional stability and resistance to stress corrosion cracking.

[0052] Figure 2 This is a schematic diagram of a seamless steel pipe processing apparatus based on stress gradient, provided as an embodiment of this application. Figure 2 As shown, the stress gradient-based seamless steel pipe processing device may include: a first acquisition module 201, a straightening adjustment module 202, a second acquisition module 203, an annealing adjustment module 204, and an annealing control module 205.

[0053] The first acquisition module 201 is used to extract the first stress gradient, the first maximum residual stress, and the first stress concentration factor from the axial stress distribution curve of the previous batch of seamless steel pipes.

[0054] The straightening adjustment module 202 is used to obtain the straightening machine reduction adjustment value for the current batch of seamless steel pipes based on the first stress gradient, the first maximum residual stress, the first stress concentration factor and the preset multivariate linear function relationship, and to perform straightening processing on the current batch of seamless steel pipes based on the straightening machine reduction adjustment value; the multivariate linear function relationship includes the mapping relationship between the maximum residual stress, stress gradient, stress concentration factor and the straightening machine reduction adjustment value.

[0055] The second acquisition module 203 is used to acquire the second stress gradient, the second maximum residual stress, the second stress concentration factor, and the circumferential stress range of the current batch of seamless steel pipes.

[0056] Annealing adjustment module 204 is used to obtain the annealing temperature correction amount based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints.

[0057] Annealing control module 205 is used to adjust the temperature inside the annealing furnace according to the annealing temperature correction amount to perform annealing treatment on the current batch of seamless steel pipes.

[0058] In some embodiments of this application, such as Figure 2Based on the illustrated embodiment, the seamless steel pipe processing device based on stress gradient may further include a stress detection module. The stress detection module is used to: scan the previous batch of seamless steel pipes at the outlet of the straightening machine using an LCR wave, obtain the residual stress values ​​at multiple measuring points along the axial direction of the previous batch of seamless steel pipes, and construct an axial stress distribution curve.

[0059] In some embodiments of this application, such as Figure 2 Based on the illustrated embodiment, the seamless steel pipe processing device based on stress gradient may further include a parameter adjustment module. This parameter adjustment module is used to: calculate the number of extreme points and the skewness coefficient of the peak region of the axial stress distribution curve; identify the stress distribution pattern of the current batch of seamless steel pipes based on the number of extreme points and the skewness coefficient; and correct the multivariate linear function relationship and / or the multivariate linear annealing temperature model based on the stress distribution pattern.

[0060] In some embodiments of this application, the parameter adjustment module is further configured to: update the coefficients in the multivariate linear function relationship by at least one of the following methods every preset period or when the average stress relief rate of multiple consecutive steel pipes is lower than a preset threshold: outlier removal, weighted regression, multi-objective optimization, and parameter smoothing.

[0061] In some embodiments of this application, the annealing control module 205 is further configured to: extend the annealing holding time of the current batch of seamless steel pipes and reduce the annealing cooling rate of the current batch of seamless steel pipes when the circumferential stress difference is greater than or equal to a preset circumferential stress difference threshold.

[0062] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0063] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0064] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0065] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0066] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above 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 one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0067] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0068] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0070] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0071] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0072] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0073] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for processing seamless steel pipes based on stress gradient, characterized in that, Includes the following steps: The first stress gradient, the first maximum residual stress, and the first stress concentration factor are extracted from the axial stress distribution curve of the previous batch of seamless steel pipes. Based on the first stress gradient, the first maximum residual stress, the first stress concentration factor, and a preset multivariate linear function relationship, the straightening machine reduction adjustment value for the current batch of seamless steel pipes is obtained, and the current batch of seamless steel pipes is straightened based on the straightening machine reduction adjustment value; the multivariate linear function relationship includes the mapping relationship between the maximum residual stress, stress gradient, stress concentration factor, and straightening machine reduction adjustment value; Obtain the second stress gradient, second maximum residual stress, second stress concentration factor, and circumferential stress range of the current batch of seamless steel pipes; The annealing temperature correction is obtained based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints. The temperature inside the annealing furnace is adjusted according to the annealing temperature correction amount to perform annealing treatment on the current batch of seamless steel pipes.

2. The method according to claim 1, characterized in that, The axial stress distribution curve of the previous batch of seamless steel pipes was obtained in the following way: At the exit of the straight-line machine, LCR wave is used to scan the previous batch of seamless steel pipes to obtain the residual stress values ​​of multiple measuring points along the axial direction of the previous batch of seamless steel pipes, and to construct the axial stress distribution curve.

3. The method according to claim 1, characterized in that, The multivariate linear function relationship is expressed as follows: in, This is the adjustment value for the straightening machine reduction of the current batch of seamless steel pipes. The first stress gradient, As the reference stress gradient, The first maximum residual stress, As the reference maximum residual stress, The first stress concentration factor is... The reference stress concentration factor is... , , is a coefficient.

4. The method according to claim 3, characterized in that, Also includes: Calculate the number of extreme points and the skewness coefficient of the peak region of the axial stress distribution curve; The stress distribution pattern of the current batch of seamless steel pipes is identified based on the number of extreme points and the skewness coefficient. The multivariate linear function relationship and / or the multivariate linear annealing temperature model are modified according to the stress distribution pattern.

5. The method according to claim 1, characterized in that, The multivariate linear annealing temperature model is expressed as follows: in, This is the correction amount for the annealing temperature. The second stress gradient, This is the second maximum residual stress. This is the second stress concentration factor. For the aforementioned circumferential stress difference, , , , , The coefficients used to satisfy engineering constraints.

6. The method according to claim 1, characterized in that, Also includes: When the circumferential stress difference is greater than or equal to the preset circumferential stress difference threshold, the annealing holding time of the current batch of seamless steel pipes is extended, and the annealing cooling rate of the current batch of seamless steel pipes is reduced.

7. The method according to any one of claims 1-6, characterized in that, Also includes: Stress testing is performed on the current batch of seamless steel pipes after annealing to obtain the stress relief rate of the current batch of seamless steel pipes; The straightening machine reduction adjustment value, the annealing temperature correction value, and the stress relief rate are stored in the historical database.

8. The method according to claim 7, characterized in that, Also includes: Every preset period or when the average stress relief rate of multiple consecutive steel pipes is lower than a preset threshold, the coefficients in the multivariate linear function relationship are updated in at least one of the following ways: outlier removal, weighted regression, multi-objective optimization, and parameter smoothing.

9. A seamless steel pipe processing device based on stress gradient, characterized in that, include: The first acquisition module is used to extract the first stress gradient, the first maximum residual stress, and the first stress concentration factor from the axial stress distribution curve of the previous batch of seamless steel pipes. The straightening adjustment module is used to obtain the straightening machine reduction adjustment value for the current batch of seamless steel pipes based on the first stress gradient, the first maximum residual stress, the first stress concentration factor, and a preset multivariate linear function relationship, and to perform straightening processing on the current batch of seamless steel pipes based on the straightening machine reduction adjustment value; the multivariate linear function relationship includes the mapping relationship between the maximum residual stress, stress gradient, stress concentration factor, and straightening machine reduction adjustment value; The second acquisition module is used to acquire the second stress gradient, the second maximum residual stress, the second stress concentration factor, and the circumferential stress range of the current batch of seamless steel pipes. The annealing adjustment module is used to obtain the annealing temperature correction amount based on the second stress gradient, the second maximum residual stress, the second stress concentration factor, the circumferential stress range, and the multivariate linear annealing temperature model with engineering constraints. The annealing control module is used to adjust the temperature inside the annealing furnace according to the annealing temperature correction amount, and to perform annealing treatment on the current batch of seamless steel pipes.

10. A seamless steel pipe, characterized in that, The seamless steel pipe is obtained by processing the seamless steel pipe processing method based on stress gradient as described in any one of claims 1-8.

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