CN115161445A - Method for optimizing heat treatment parameters after partial welding of 9-percent Cr hot-strength steel pipeline by medium-frequency induction heating - Google Patents
Method for optimizing heat treatment parameters after partial welding of 9-percent Cr hot-strength steel pipeline by medium-frequency induction heating Download PDFInfo
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Abstract
The invention discloses a method for optimizing local postweld heat treatment parameters of a 9-percent Cr hot-strength steel pipeline by medium-frequency induction heating. The method comprises the following steps: analyzing the influence of different heat treatment parameters on radial and axial temperature gradients of the pipeline by performing medium-frequency induction heating postweld heat treatment tests under different heat treatment parameter combinations, and finding out core parameters influencing the temperature gradients; meanwhile, a finite element transient and steady-state temperature field calculation model for the heat treatment after medium-frequency induction heating welding is established, the maximum axial temperature gradient and the radial temperature gradient in the constant temperature stage in the heat treatment process are calculated, a BP neural network model for parameter adjustment of a genetic algorithm is established for training, and finally, the input pipeline specification, the maximum axial temperature gradient and the radial temperature gradient are realized, namely, the heat treatment parameter optimization of pipelines with different specifications is completed. The invention can obtain the optimal heat treatment parameter combination on the basis of ensuring the axial and radial temperature gradients, thereby not only ensuring the heat treatment quality, but also improving the engineering efficiency and saving the cost.
Description
Technical Field
The invention belongs to the technical field of heat-resistant steel welding, and particularly relates to an optimization method for optimal combination of medium-frequency induction heating postweld heat treatment parameters of a 9-percent Cr hot-strength steel pipeline, which is applicable to optimization of the medium-frequency induction heating postweld heat treatment parameters of novel 9-percent Cr steel pipelines with different specifications, such as P91, P92, P93, G115 and the like.
Background
The novel 9-percent Cr hot-strength steel is an ideal material for manufacturing important thick-wall parts such as a header and a main steam pipe of a supercritical (super) critical thermal power generating unit due to good thermal conductivity, high-temperature oxidation resistance and high-temperature creep resistance. The pipeline structure of the thermal generator set is complex, the pipeline connection is usually carried out by adopting a fusion welding method, instantaneous high temperature generated in the welding process is concentrated on a welding part, the degradation of the structure of a pipeline joint is easily caused, the mechanical property is poor, and meanwhile, the safe operation of parts can also be influenced because the residual stress is generated by the temperature gradient between the welding joint and a nearby area. Therefore, the novel 9-% Cr hot-strength steel pipe must be heat-treated after welding. The current common local postweld heat treatment method during field welding is flexible ceramic resistance heating, and the method has the defects of small heating power, slow heating rate, poor heating uniformity and large heat damage to a base metal. The intermediate frequency induction heat treatment method has advantages of high temperature rise rate, good heating uniformity and little damage to the base metal, and has recently been applied to post-weld heat treatment of novel 9-% Cr steel thick-walled pipes.
In the heat treatment process after the intermediate frequency induction heating welding, the induction coil generates induction current on the outer surface of the pipeline, the induction current generates joule heat to heat the outer wall of the pipeline, and heat is transferred to the inner wall and other parts of the pipeline by a heat transfer method, so that a certain temperature gradient exists in the axial direction and the radial direction of the pipeline. As the constant temperature interval of the novel 9-percent Cr hot-strength steel heat treatment is narrow, the radial temperature gradient is too large, so that the heat treatment temperature of the inner wall is insufficient, and the heat treatment quality is influenced; and the excessive axial temperature gradient can generate new residual stress to influence the safe service of the pipeline. In order to meet the control requirement of the temperature gradient, the parameters (heating width, heat preservation width, alternating current frequency and the like) of the combined postweld heat treatment must be optimized. Although the electric power industry standard DL/T819-2019 recommends a selection method of part of heat treatment parameters, according to relevant researches, the standard recommended heating width is often large, the heating rate is often small, and recommended values are not given for parameters such as alternating current frequency, coil turns and coil gaps, so that the selection of parameters such as the heating width, the heat preservation width, the heating rate, the alternating current frequency, the coil turns and the coil gaps in engineering is too conservative, and the heat treatment cost is increased. Therefore, it is necessary to invent a novel method for optimizing the heat treatment parameters after the local welding of the medium frequency induction heating of the 9-percent Cr hot-strength steel pipeline, so as to guide the precise selection of the heat treatment parameters during the field welding construction.
Disclosure of Invention
The invention aims to provide an optimization method of heat treatment parameters after local welding of 9-percent Cr hot-strength steel pipelines by medium-frequency induction heating, to obtain an optimal heat treatment parameter combination, to guide engineering application and to reduce the heat treatment cost under the condition of ensuring the heat treatment quality.
In order to solve the technical problems, the invention adopts the following technical scheme:
there is provided a method of optimizing local post weld heat treatment parameters of medium frequency induction heating of a 9% cr hot strong steel pipe, comprising the steps of:
step 1, performing medium-frequency induction heating postweld heat treatment tests of N groups of pipelines with different specifications under different heat treatment parameters, recording the temperature of each characteristic point at different time, analyzing the influence of different heat treatment parameters on radial and axial temperature gradients of the pipelines through a contrast test, and obtaining main parameters influencing the temperature gradients, wherein the main parameters are as follows: pipeline diameter, pipeline wall thickness, heating width, heat preservation width and alternating current frequency;
step 2, establishing and verifying a medium-frequency induction heating postweld heat treatment temperature field model according to the postweld heat treatment test data obtained in the step 1, and calculating radial and axial temperature gradients of pipelines of different specifications under different heating widths, heat preservation widths and alternating current frequencies according to the temperature field model;
and 3, determining an optimization principle, namely that the axial temperature gradient is less than 2.1, the radial temperature gradient is less than 1.027, combining the calculation result obtained in the step 2 with the optimization principle, taking the pipeline specification, the pipeline radial temperature gradient, the axial temperature gradient and the radial temperature gradient as input vectors, the heating width, the heat preservation width and the alternating current frequency as output vectors, establishing a BP neural network model with parameters adjusted by a genetic algorithm, and finally optimizing the heat treatment parameters after local welding by medium-frequency induction heating of the pipelines with different specifications through the model.
According to the scheme, the characteristic points in the step 1 are as follows: the center point of the inner wall and the outer wall of the pipeline welding seam and the edge of the heating area of the outer wall.
According to the scheme, different heat treatment parameters in the step 1 are respectively as follows: heating width, induction coil turns, heat preservation width, alternating current frequency, coil and heat preservation layer gap, alternating current size, temperature rise and fall speed, constant temperature time.
According to the scheme, in the step 2, the specification of the Cr-9% hot-strength steel pipeline is selected as follows: inner diameter of the pipeline: 300-1200mm, pipe wall thickness: 30-140 mm; the alternating current frequency is 1kHz-8kHz, and the heating width and the heat preservation width are selected according to the DL/L-819 standard.
According to the scheme, in the step 3, the method for determining the optimization principle comprises the following steps: according to the control of the temperature difference between the inner wall and the outer wall and the maximum bending stress, the maximum allowable radial temperature gradient is determined to be 1.027, and the maximum allowable axial temperature gradient is determined to be 2.1.
According to the scheme, in the step 1, the after-welding heat treatment test of the medium-frequency induction heating local part under different heat treatment parameters is respectively carried out to obtain main parameters influencing the temperature gradient, and the specific method comprises the following steps:
step 1.1, under the condition that other heat treatment parameters are kept unchanged, selecting 3 groups of different heating widths by adjusting the turn intervals of the induction coils, and reading the temperatures of the central point of the inner wall and the outer wall of a pipeline welding seam and the edge of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature and stable state;
step 1.2, under the condition that other heat treatment parameters are kept unchanged, selecting 3 groups of different induction coil turns by adjusting the turn intervals of the induction coils, and reading the temperatures of the central point of the inner wall and the outer wall of a pipeline welding seam and the edge of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature and stable state;
step 1.3, under the condition of keeping other heat treatment parameters unchanged, respectively selecting 3 groups of different heat preservation widths, alternating current frequencies, gaps between coils and heat preservation layers, alternating current sizes, temperature rising and falling rates and constant temperature time parameters, and reading the temperatures of the central point of the inner wall and the outer wall of a welding seam of the pipeline and the edge of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state;
step 1.4, according to the test results of steps 1.1-1.3, obtaining heat treatment parameters which have obvious influence on the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state, specifically: heating width, heat preservation width, alternating current frequency.
According to the scheme, in the step 2, according to the postweld heat treatment test data obtained in the step 1, a medium-frequency induction heating postweld heat treatment temperature field model is established and verified, and finally, the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state under different heat treatment parameters are calculated through the model, and the specific method comprises the following steps:
step 2.1, coupling the electromagnetic model and the heat transfer model through finite element software, inputting transient time domain excitation to obtain a transient model of postweld heat treatment, reading the maximum axial temperature gradient in the heat treatment process according to the transient model, then verifying the maximum axial temperature gradient and a test result, adjusting related calculation parameters if the error is greater than 1%, and performing the next step if the error is less than 1%;
step 2.2, coupling the electromagnetic model and the heat transfer model through finite element software, performing steady state solution to obtain a steady state model of post-welding heat treatment, reading a radial temperature gradient when the heat treatment reaches a constant temperature steady state according to the steady state model, then verifying the radial temperature gradient with a test result, adjusting related calculation parameters if the error is more than 1%, and performing the next step if the error is less than 1%;
and 2.3, calculating N groups of pipelines with different specifications according to the transient model and the steady-state model of the postweld heat treatment temperature field in the steps 2.1 and 2.2, and calculating the axial temperature gradient and the radial temperature gradient under different heating widths, heat preservation widths and alternating current frequencies.
According to the scheme, in the step 3, a BP neural network model which is adjusted by a genetic algorithm is established according to the calculation result obtained in the step 2, and an optimization method for optimal combination of medium-frequency induction heating postweld heat treatment parameters of pipelines with different specifications is obtained through the model, and the specific method is as follows:
step 3.1, the weight and the threshold of the BP neural network are adjusted and optimized through a genetic algorithm, and the weight and the threshold suitable for the BP neural network are obtained through adjusting the population scale, the individual range, the intersection and variation probability and the iteration times of the genetic algorithm;
step 3.2, normalizing the calculation result in the step 2, taking the pipeline specification, the pipeline radial and axial temperature gradient as input vectors, the heating width, the heat preservation width and the alternating current frequency as output vectors, selecting B groups from the input vectors and the output vectors which correspond to one another as training data, and selecting the remaining C groups as test data;
step 3.3, setting a BP neural network by using the weight and the threshold obtained in the step 3.1, setting expected errors and dispersion constants, inputting B groups of training data into the BP neural network, training the network, finally inputting C groups of test data into the trained model, and performing error analysis, wherein if the errors reach a specified level, the obtained model is the optimal selection model of the required optimal process parameters; if the error is larger, returning to the step 3.1, and readjusting the related parameters of the genetic algorithm;
and 3.4, inputting the pipe diameter and the wall thickness of the target pipeline in the model, and selecting the radial temperature gradient and the axial temperature gradient to be controlled according to an optimization principle to obtain the optimal intermediate frequency induction heating local postweld heat treatment parameter combination.
The invention has the following beneficial effects:
the invention provides an optimization method of medium-frequency induction heating local postweld heat treatment parameters of a 9-percent Cr hot-strength steel pipeline, which comprises the steps of firstly analyzing the influence of different heat treatment parameters on radial and axial temperature gradients of the pipeline by performing medium-frequency induction heating postweld heat treatment tests under different heat treatment parameter combinations, and finding out core parameters influencing the temperature gradients; meanwhile, establishing a finite element transient and steady-state temperature field calculation model of the intermediate frequency induction heating postweld heat treatment by using data obtained by a postweld heat treatment test, and calculating the maximum axial temperature gradient (namely, axial temperature gradient) and the radial temperature gradient (namely, radial temperature gradient) of the constant temperature stage in the heat treatment process of N groups of pipelines with different specifications under different heat treatment parameters through the model; establishing a BP neural network model for genetic algorithm parameter adjustment by utilizing the data obtained by calculation, training the model, and finally realizing input of pipeline specifications, maximum axial temperature gradient and radial temperature gradient, namely completing the optimization of heat treatment parameters of pipelines with different specifications; the invention can obtain the optimal heat treatment parameter (heating width, heat preservation width and alternating current frequency) combination on the basis of ensuring the axial and radial temperature gradients, has high accuracy, can adopt more reasonable postweld heat treatment parameters on the premise of ensuring the safe service, not only ensures the heat treatment quality, but also improves the engineering efficiency and saves the cost.
Drawings
FIG. 1 is a diagram of the artificial neural network training results in an embodiment of the present invention.
Fig. 2 is a schematic diagram of the input and output of the BP neural network in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further specifically explained by the following examples and the accompanying drawings.
The embodiment of the invention provides an optimization method of local postweld heat treatment parameters by medium frequency induction heating of 9-percent Cr hot-strength steel pipelines, which comprises the following steps:
in step 1, selecting a P91 pipeline with OD40 mm × 85mm × 1300mm, wherein the initial heat treatment parameters are as follows: the heating width is 600mm, the heat preservation width is 900mm, the number of turns of the induction coil is 17, the turn-to-turn distance is 30mm, the gap between the coil and the heat preservation material is 40mm, the constant temperature time is 2.5 hours, the alternating current is 100A, the alternating current frequency is 1500HZ, and the temperature rise rate is 100 ℃/h. The method comprises the following steps of respectively carrying out medium-frequency induction heating local postweld heat treatment tests under different heat treatment parameters to obtain main parameters influencing temperature gradient, and specifically comprises the following steps:
step 1.1, under the condition of keeping other heat treatment parameters unchanged, selecting heating widths of 600mm, 800mm and 1000mm by adjusting the turn intervals of induction coils, and reading the central points of the inner wall and the outer wall of a pipeline welding seam and the edge temperature of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature and a stable state;
step 1.2, under the condition that other heat treatment parameters are kept unchanged, selecting 17, 25 and 33 turns of the induction coil by adjusting the turn interval of the induction coil, and reading the temperature of the central point of the inner wall and the outer wall of the welding line of the pipeline and the temperature of the edge of the heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature and stable state;
1.3, under the condition of keeping other heat treatment parameters unchanged, respectively selecting parameters such as the heat preservation width of 900mm, 1200mm and 1500mm, the alternating current frequency of 1500Hz, 2000Hz and 2500Hz, the gap between a coil and a heat preservation layer of 20mm, 40mm and 60mm, the alternating current size of 100A, 150A and 200A, the temperature rising and falling rate of 100 ℃/h, 150 ℃/h and 200 ℃/h, the constant temperature time of 2.5h, 3.5h and 4.5h, and the like, reading the temperature of the central point of the inner wall and the outer wall of the welding line of the pipeline and the edge of the heating zone of the outer wall in real time, and obtaining the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state;
step 1.4, according to the test results of steps 1.1-1.3, obtaining heat treatment parameters which have obvious influence on the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state, specifically: heating width, heat preservation width, alternating current frequency.
Step 2, establishing a medium-frequency induction heating postweld heat treatment temperature field model according to the postweld heat treatment test data obtained in the step 1, verifying, and finally calculating the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state under different heat treatment parameters through the model, wherein the specific method comprises the following steps:
step 2.1, coupling an electromagnetic model and a heat transfer model through finite element software, inputting transient time domain excitation to obtain a transient model of post-welding heat treatment, reading a maximum axial temperature gradient in the heat treatment process according to the transient model, then verifying the maximum axial temperature gradient with a test result, adjusting relevant calculation parameters (such as heat dissipation coefficients, air flow rate, grid size, excitation frequency and other finite element model parameters) if the error is more than 1%, and performing the next step if the error is less than 1%;
step 2.2, coupling the electromagnetic model and the heat transfer model through finite element software, performing steady state solution to obtain a steady state model of post-welding heat treatment, reading a radial temperature gradient when the heat treatment reaches a constant temperature steady state according to the steady state model, then verifying the radial temperature gradient with a test result, adjusting related calculation parameters if the error is more than 1%, and performing the next step if the error is less than 1%;
and 2.3, calculating the pipe diameter of the pipeline to be 300-1200mm (10 groups) and the wall thickness of the pipeline to be 30-140mm (12 groups) according to the transient model and the steady-state model of the temperature field of the postweld heat treatment in the steps 2.1 and 2.2, wherein the alternating current frequency is 1kHz-8kHz (8 groups), the heating width and the heat preservation width are selected according to the DL/L-819 standard, and the axial temperature gradient and the radial temperature gradient of the postweld heat treatment are measured. Based on the calculation results, 960 sets of data were obtained.
Step 3, establishing a BP neural network model for adjusting parameters through a genetic algorithm according to the calculation result (namely 960 groups of data) obtained in the step 2, and obtaining an optimization method of the medium-frequency induction heating local postweld heat treatment parameters of pipelines with different specifications through the model, wherein the specific method comprises the following steps:
step 3.1, the weight and the threshold of the BP neural network are adjusted and optimized through a genetic algorithm, and the weight and the threshold suitable for the BP neural network are obtained through adjusting the population scale, the individual range, the intersection and variation probability and the iteration times of the genetic algorithm;
step 3.2, normalizing the calculation result in the step 2, wherein the normalization equation is as follows:
in the formula x 0 For normalized data, x p As a vector of data sets, x max Is the maximum value of the vector, x min For the minimum value of the vector,
is the vector average. Selecting 880 groups of input vectors and output vectors which correspond to each other one by one as training data, and selecting the remaining 80 groups of input vectors and output vectors as test data, wherein the pipeline specification, the pipeline radial temperature gradient and the pipeline axial temperature gradient are used as input vectors, the heating width, the heat preservation width and the alternating current frequency are used as output vectors;
step 3.3, setting a BP neural network by using the weight and the threshold obtained in the step 3.1, setting an expected error of 0.0001 and a dispersion constant of 0.8, inputting 880 groups of training data obtained in the step 3.2 into the BP neural network, training the network (the artificial neural network training result is shown in a figure 1), finally inputting 80 groups of test data obtained in the step 3.2 into a trained model, and performing error analysis, wherein if the error is less than 1%, the obtained model is the optimal model of the required optimal process parameters; if the error is larger, the step 3.1 is repeated to readjust the relevant parameters of the genetic algorithm.
Step 3.4, inputting the pipe diameter and the wall thickness of a target pipeline in a BP neural network model, and determining that the maximum allowable radial temperature gradient is 1.027 and the axial temperature gradient is 2.1 according to the control of the temperature difference between the inner wall and the outer wall and the maximum bending stress, so that an intermediate frequency induction heating local postweld heat treatment parameter optimization model can be obtained; the input and output diagram of the BP neural network is shown in fig. 2.
In the invention, the specification (pipe diameter and wall thickness) of the pipeline and the alternating current frequency are selected as variable parameters, and the applicable range is as follows:
the pipeline material: novel 9% Cr hot strength steel;
inner diameter of the pipeline: 300-1200 mm;
pipe wall thickness: 30-140 mm;
alternating current frequency: 1-8 kHz.
Examples
For a P91 pipeline with the OD540 x 85mm specification, the optimal heat treatment parameter (heating width, heat preservation width and alternating current frequency) combination of the local postweld heat treatment of the medium-frequency induction heating is calculated according to the method of the invention. And performing a post-welding heat treatment test on the pipeline by using the optimal heat treatment parameter set, actually measuring the maximum axial temperature gradient and the radial temperature gradient of the pipeline, and comparing the maximum axial temperature gradient and the radial temperature gradient with the calculated values to verify the accuracy of the method. The comparison results are shown in table 1, and it can be seen that the differences between the predicted values and the test values of the axial and radial temperature gradients are small, which indicates that the optimization results of the thermal treatment parameters obtained by the invention are very accurate. By using the method, on the premise of ensuring safe service, more reasonable postweld heat treatment parameters can be adopted, thereby being beneficial to saving cost and improving efficiency.
TABLE 1 verification of the accuracy of the optimization results of the postweld heat treatment parameters of the present invention
TABLE 2 comparison of the present invention with the existing Standard recommendations
| Parameters of heat treatment | Heating width/mm | Insulation width/mm | AC frequency/kHz |
| Obtained by the invention | 584 | 821 | 5.3 |
| DL/T819-2019 recommendation | 850 | 1190 | Without recommended value |
The result shows that the heating width and the heat preservation width obtained by the method are both smaller than the electric power industry standard, the recommended value of the alternating current frequency is given, and the reduction range of the pipeline with the specification is very obvious. Therefore, the invention can reduce the damage to the pipe, reduce the cost and achieve the aim of safety and energy saving.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (8)
1. A method of optimizing local post-weld heat treatment parameters of medium frequency induction heating of a 9% cr hot strong steel pipe, comprising the steps of:
step 1, performing medium-frequency induction heating postweld heat treatment tests of N groups of pipelines with different specifications under different heat treatment parameters, recording the temperature of each characteristic point at different time, analyzing the influence of different heat treatment parameters on radial and axial temperature gradients of the pipelines through a contrast test, and obtaining main parameters influencing the temperature gradients, wherein the main parameters are as follows: pipeline diameter, pipeline wall thickness, heating width, heat preservation width and alternating current frequency;
step 2, establishing and verifying a medium-frequency induction heating postweld heat treatment temperature field model according to the postweld heat treatment test data obtained in the step 1, and calculating radial and axial temperature gradients of pipelines of different specifications under different heating widths, heat preservation widths and alternating current frequencies according to the temperature field model;
and 3, determining an optimization principle, namely that the axial temperature gradient is less than 2.1, the radial temperature gradient is less than 1.027, combining the calculation result obtained in the step 2 with the optimization principle, taking the pipeline specification, the pipeline radial temperature gradient, the axial temperature gradient and the radial temperature gradient as input vectors, the heating width, the heat preservation width and the alternating current frequency as output vectors, establishing a BP neural network model with parameters adjusted by a genetic algorithm, and finally optimizing the heat treatment parameters after local welding by medium-frequency induction heating of the pipelines with different specifications through the model.
2. The method according to claim 1, wherein the feature points in step 1 are: the center point of the inner wall and the outer wall of the pipeline welding seam and the edge of the heating area of the outer wall.
3. The method according to claim 1, wherein the different heat treatment parameters in step 1 are respectively: heating width, induction coil turns, heat preservation width, alternating current frequency, coil and heat preservation layer gap, alternating current size, temperature rise and fall speed, constant temperature time.
4. The method of claim 1, wherein in step 2, the specification for 9-% cr-reducible steel conduit is selected as: inner diameter of the pipeline: 300-1200mm, pipe wall thickness: 30-140 mm; the alternating current frequency is 1kHz-8kHz, and the heating width and the heat preservation width are selected according to the DL/L-819 standard.
5. The method according to claim 1, wherein in step 3, the optimization principle determining method is: according to the control of the temperature difference between the inner wall and the outer wall and the maximum bending stress, the maximum allowable radial temperature gradient is determined to be 1.027, and the maximum allowable axial temperature gradient is determined to be 2.1.
6. The method according to claim 1, wherein in step 1, the after-welding heat treatment test of the medium-frequency induction heating is performed respectively under different heat treatment parameters to obtain main parameters influencing the temperature gradient, and the specific method comprises the following steps:
step 1.1, under the condition that other heat treatment parameters are kept unchanged, selecting 3 groups of different heating widths by adjusting the turn intervals of the induction coils, and reading the temperatures of the central point of the inner wall and the outer wall of a pipeline welding seam and the edge of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature and stable state;
step 1.2, under the condition of keeping other heat treatment parameters unchanged, selecting 3 groups of different induction coil turns by adjusting the turn intervals of the induction coils, and reading the temperature of the central point of the inner wall and the outer wall of the pipeline welding seam and the temperature of the edge of the heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state;
step 1.3, under the condition of keeping other heat treatment parameters unchanged, respectively selecting 3 groups of different heat preservation widths, alternating current frequencies, gaps between coils and heat preservation layers, alternating current sizes, temperature rising and falling rates and constant temperature time parameters, and reading the temperatures of the central point of the inner wall and the outer wall of a welding seam of the pipeline and the edge of a heating area of the outer wall in real time to obtain the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state;
step 1.4, according to the test results of steps 1.1-1.3, obtaining heat treatment parameters which have obvious influence on the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state, specifically: heating width, heat preservation width, alternating current frequency.
7. The method according to claim 1, wherein in the step 2, a medium frequency induction heating postweld heat treatment temperature field model is established according to the postweld heat treatment test data obtained in the step 1, verification is performed, and finally the maximum axial temperature gradient in the heat treatment process and the radial temperature gradient when the heat treatment reaches a constant temperature steady state under different heat treatment parameters are calculated through the model, and the specific method is as follows:
step 2.1, coupling the electromagnetic model and the heat transfer model through finite element software, inputting transient time domain excitation to obtain a transient model of postweld heat treatment, reading the maximum axial temperature gradient in the heat treatment process according to the transient model, then verifying the maximum axial temperature gradient and a test result, adjusting related calculation parameters if the error is greater than 1%, and performing the next step if the error is less than 1%;
step 2.2, coupling the electromagnetic model and the heat transfer model through finite element software, performing steady state solution to obtain a steady state model of post-welding heat treatment, reading a radial temperature gradient when the heat treatment reaches a constant temperature steady state according to the steady state model, then verifying the radial temperature gradient with a test result, adjusting related calculation parameters if the error is more than 1%, and performing the next step if the error is less than 1%;
and 2.3, calculating N groups of pipelines with different specifications according to the transient model and the steady-state model of the postweld heat treatment temperature field in the steps 2.1 and 2.2, and calculating the axial temperature gradient and the radial temperature gradient under different heating widths, heat preservation widths and alternating current frequencies.
8. The method according to claim 1, wherein in the step 3, a BP neural network model which is adjusted by a genetic algorithm is established according to the calculation result obtained in the step 2, and an optimization method for obtaining the optimal combination of the medium-frequency induction heating postweld heat treatment parameters of pipelines with different specifications is obtained through the model, and the specific method is as follows:
step 3.1, the weight and the threshold of the BP neural network are adjusted and optimized through a genetic algorithm, and the weight and the threshold suitable for the BP neural network are obtained through adjusting the population scale, the individual range, the intersection and variation probability and the iteration times of the genetic algorithm;
step 3.2, normalizing the calculation result in the step 2, taking the pipeline specification, the pipeline radial and axial temperature gradient as input vectors, the heating width, the heat preservation width and the alternating current frequency as output vectors, selecting a group B from the input vectors and the output vectors which correspond to each other one by one as training data, and taking the remaining group C as test data;
step 3.3, setting a BP neural network by using the weight and the threshold obtained in the step 3.1, setting expected errors and dispersion constants, inputting B groups of training data into the BP neural network, training the network, finally inputting C groups of test data into the trained model, and performing error analysis, wherein if the errors reach a specified level, the obtained model is the optimal selection model of the required optimal process parameters; if the error is larger, returning to the step 3.1, and readjusting the related parameters of the genetic algorithm;
and 3.4, inputting the pipe diameter and the wall thickness of the target pipeline in the model, and selecting the radial temperature gradient and the axial temperature gradient to be controlled according to an optimization principle to obtain the optimal intermediate frequency induction heating local postweld heat treatment parameter combination.
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