CN102799938A - Optimizing method of 9% martensite steel pipeline postweld heat treatment heating width - Google Patents
Optimizing method of 9% martensite steel pipeline postweld heat treatment heating width Download PDFInfo
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Abstract
The invention relates to an optimizing method of 9% martensite steel pipeline postweld heat treatment heating width. According to the optimizing method, pipeline postweld heat treatment inner-outer wall temperature difference size data of T groups of pipelines with different sizes under different heating widths, different heat treatment environment temperature and different temperature control conditions are obtained through calculation. The minimum heating widths required by pipeline postweld heat treatment of the pipelines under different heat treatment environment temperatures, different temperature control temperatures and different preset inner-outer wall temperature differences are comprehensively considered, so that an error back propagation-based neural network is established for training and testing the minimum heating widths, pipeline size, heat treatment environment temperature, temperature control temperature and preset inner-outer wall temperature difference are used as inputs, and heating widths are used as outputs. By combining with actually measured data of the pipeline postweld heat treatment, a trained and tested network output threshold is corrected to obtain the optimizing method. According to the optimizing method, the minimum heating widths required by the postweld heat treatment can be rapidly calculated, a heat treatment process can be helped to be guided and optimized, and the heat treatment quality is improved.
Description
Technical field
The present invention relates to the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating.
Background technology
The novel martensite heat-resisting steel of 9%Cr mainly comprises T/P92, T/P91 and three kinds of novel martensite heat-resisting steel of E911; Be widely used in members such as heavy wall pipeline such as ultra-supercritical boiler main steam pipe, collection case, welding seam toughness is on the low side to be a subject matter that occurs in this Series Steel pipe welding seam installation process.In order to improve welding seam toughness, must carry out local heat treatmet by butt welded seam.Research both at home and abroad shows that the influence of post weld heat treatment temperature butt welded seam is very big, when heat treatment temperature (is annotated: receive the restriction of weld seam transformation temperature during at 760 ± 10 ℃; Heat treatment temperature is difficult to further improve), to handle through constant temperature in short-term, the ballistic work of weld seam just can reach more than the 41J; When about 740 ℃, heating, reach this index and must prolong constant temperature time, when heating-up temperature below 730 ℃ the time; Prolonging constant temperature time more not only has little effect; Ballistic work is difficult to reach the toughness index of 41J, and significantly increases installation cost, has a strong impact on construction speed.
At present; The post weld heat treatment technical regulation of bearing pipe has been proposed on the basis of traditional heat-resisting steel both at home and abroad; The novel martensite heat-resisting steel of 9%Cr is more harsh for the control of the inside and outside wall temperature difference; Therefore these standards not necessarily are suitable for for the post weld heat treatment of the novel martensite heat-resisting steel of 9%Cr, and promptly existing standard remains to be investigated to the applicability of the novel martensite heat-resisting steel of 9%Cr.
In addition, there is very big dispute in the post weld heat treatment standard for choosing of width of heating both at home and abroad, and is very big according to the width of heating numerical value difference of different specification gained.This has brought a difficult problem when giving on-the-spot thermal treatment, and there is query in the applicability of rules.
Artificial neural network is a nonlinear science that the 80's ends began to develop rapidly; Artificial nerve network model has very strong fault-tolerance, study property, adaptivity and nonlinear mapping ability, is particularly suitable for solving the complicated problems such as uncertainty reasoning, judgement, identification and classification of cause-effect relationship.At present, most widely used in the Ferrous Metallurgy field is the model (BP model) that has the Multi-layered Feedforward Networks structure and adopt reverse error propagation training method.
Summary of the invention
The present invention solves the existing in prior technology technical matters; A kind of method that not only can optimize the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment width of heating is provided, has had crucial meaning ensureing thermal treatment quality, raising heat treatment efficiency.
It is to solve the existing in prior technology problem that the present invention has a purpose again; A kind of otherness that domestic and international heat treatment technics rules are chosen for the novel martensite heat-resisting steel of 9%Cr pipeline post weld heat treatment width of heating that solved is provided.
Above-mentioned technical matters of the present invention mainly is able to solve through following technical proposals:
The optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating is characterized in that, may further comprise the steps:
Step 3; Forecast model is set up module; The data that obtain T group width of heating to step 1 obtain the forecast model that can predict 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment width of heating to carrying out training and testing based on the error back propagation neural network in the step 2;
Step 4, the model correcting module is revised the forecast model of gained in conjunction with 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment measured data of experiment;
Step 5, the width of heating optimal module, analysis conduit size (caliber and wall thickness), heat treatment environment temperature, control temperature, the preset inside and outside wall temperature difference are input to the minimum width of heating that revised model can obtain the pipeline post weld heat treatment.
Optimization method at above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating; In the described step 1; The heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature in the foundation; The utilization finite element software calculates the size of the pipeline post weld heat treatment inside and outside wall temperature difference under the different condition, and concrete grammar is:
According to the applicable cases of the novel martensite heat-resisting steel of 9%Cr, choose the line size scope; According to domestic and international heat treatment technics rules, for the pipeline calculating heating tape width of certain specification, the size of insulation width, choose the width of heating scope, the insulation width is chosen according to electric power standard; According to the control temperature and the heat treatment environment temperature conditions of the novel martensite heat-resisting steel of 9%Cr, select the scope of control temperature and heat treatment environment temperature.Set up the novel martensite heat-resisting steel of T group 9%Cr pipeline post weld heat treatment temperature field theoretical calculation model; Calculate line size (caliber and wall thickness), width of heating, control temperature and heat treatment environment temperature equivalent point position effects through the utilization finite element software, computing method are following:
Step 1.1 in finite element software, is set up the novel martensite heat-resisting steel of 9%Cr post weld heat treatment calculation model for temperature field;
Step 1.2, definition starting condition, boundary condition are found the solution;
Step 1.3, calculating is checked inner-walls of duct temperature and outside wall temperature after accomplishing in preprocessor, calculate the size of the inside and outside wall temperature difference.
At the optimization method of above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating, in the said step 2, the concrete grammar of setting up based on the error back propagation neural network is:
Step 2.1, definition input layer and output layer
The numerical value of choosing line size (caliber and wall thickness), preset the inside and outside wall temperature difference, control temperature and heat treatment environment temperature is as input variable, so the neuron number of this network input layer is 5; With the output of minimum width of heating required under the different condition as network model, so the output layer neuron number is 1.
Step 2.2 is selected the latent number of plies and latent layer unit number: adopt single latent layer, and definite the number of hidden nodes is 10.
Step 2.3, other Determination of Parameters: the transport function of the latent layer of latent layer is unipolarity S type function: f (x)=1/ (1+e
-x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and the error target is 0.5, and the selection sample number is T, N training sample wherein, T-N test sample book.
Optimization method at above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating; In the said step 2; Comprise an input layer, a middle layer and an output layer based on the error back propagation neural network; Input layer has 5 neurons, and there are 10 neurons in the middle layer, and output layer has 1 neuron; The transport function in the middle layer of said forecast model is a unipolarity S type function, and the transport function of output layer is a linear function, makes network export any value; It is following to carrying out the concrete steps of training and testing based on the error back propagation neural network in the step 2 that step 1 is obtained the T width of heating:
Step 3.1; Set weights and threshold value and frequency of training; And weights and threshold value are carried out initialization, and win T-N group sample in the T group sample at random as training sample, N group sample is as test sample book; Input T-N group training sample, said sample are that the T that obtains in the step 1 organizes the size of width of heating and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resisting steel pipeline post weld heat treatment;
Step 3.2, computational grid output obtains the weights and the threshold value of each layer in the reverse transmittance nerve network, and calculates the weights of each layer in the reverse transmittance nerve network and the modifying factor of threshold value, according to the T-N group A that obtains in the step 1
1Temperature computation value and network output computational grid output error, said network output error are the calculated value of the T-N group width of heating that obtains in the step 1 and the comparison difference of the network output that this step is calculated;
Step 3.3 judges whether to reach maximum frequency of training, and selects to carry out following steps according to whether reaching maximum frequency of training:
Select execution in step 1,, judge that whether the network output error is less than anticipation error in step 3.2 if do not reach maximum frequency of training as yet; If less than anticipation error; Then training finishes, and preserves in the step 3.2 weights of each layer and threshold value in the reverse transmittance nerve network simultaneously, obtains forecast model undetermined; If greater than anticipation error, revise after weights and the threshold value of each layer in the reverse transmittance nerve network step repeat 3.2. wherein modifying factor adopt the modifying factor of calculating in the step 3.2;
Select execution in step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training finishes;
Step 3.4; N is organized test sample book import the forecast model of selecting in the execution in step 1 undetermined one by one; If predicated error shows this forecast model undetermined and can be used in the required minimum width of heating of prediction 9%Cr martensite heat-resisting steel pipeline post weld heat treatment that promptly this forecast model undetermined promptly is a resulting forecast model in the step 3 when being lower than prescribed level; Otherwise this forecast model undetermined does not meet, and finishes whole steps.
Optimization method at above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating; In the described step 4; The data and the Model Calculation value of the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment experiment measuring are compared analysis, and correction model output threshold values.
Therefore, the present invention has following advantage: 1. can optimize the method for the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment width of heating, have crucial meaning to ensureing thermal treatment quality, raising heat treatment efficiency; 2. solved the otherness that domestic and international heat treatment technics rules are chosen for the novel martensite heat-resisting steel of 9%Cr pipeline post weld heat treatment width of heating.
Description of drawings
The BP neural network model figure that uses among Fig. 1 the present invention.
BP neural metwork training process flow diagram among Fig. 2 the present invention.
BP neural metwork training Error Graph among Fig. 3 the present invention.
Embodiment
Pass through embodiment below, and combine accompanying drawing, do further bright specifically technical scheme of the present invention.
The optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating of the present invention may further comprise the steps:
According to the applicable cases of the novel martensite heat-resisting steel of 9%Cr, choose the line size scope; According to domestic and international heat treatment technics rules, for the pipeline calculating heating tape width of certain specification, the size of insulation width, choose the width of heating scope, the insulation width is chosen according to electric power standard; According to the control temperature and the heat treatment environment temperature conditions of the novel martensite heat-resisting steel of 9%Cr, select the scope of control temperature and heat treatment environment temperature.Set up the novel martensite heat-resisting steel of T group 9%Cr pipeline post weld heat treatment temperature field theoretical calculation model; Calculate line size (caliber and wall thickness), width of heating, control temperature and heat treatment environment temperature equivalent point position effects through the utilization finite element software, computing method are following:
Step 1.1 in finite element software, is set up the novel martensite heat-resisting steel of 9%Cr post weld heat treatment calculation model for temperature field;
Step 1.2, definition starting condition, boundary condition are found the solution;
Step 1.3, calculating is checked inner-walls of duct temperature and outside wall temperature after accomplishing in preprocessor, calculate the size of the inside and outside wall temperature difference.
1) design of input layer and output layer
The numerical value of choosing line size (caliber and wall thickness), preset the inside and outside wall temperature difference, control temperature and heat treatment environment temperature is as input variable, so the neuron number of this network input layer is 5; With the output of the required minimum width of heating of pipeline post weld heat treatment under the different condition as network model, so the output layer neuron number is 1.
2) selection of the latent number of plies and latent layer unit number
1989, Robert Hecht-Nielson proved for a continuous function in any closed interval and can approach with the BP network of a latent layer.N ties up the Continuous Mappings that m ties up because one 3 layers BP network can be accomplished arbitrarily; So this model adopts single latent layer; And the selection of the number of hidden nodes is the problem of a more complicated, repeatedly attempts in conjunction with experimental formula and through the author, confirms that at last the number of hidden nodes is 10.
3) other Determination of Parameters
The transport function of the latent layer of latent layer is unipolarity S type function: f (x)=1/ (1+e
-x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and the error target is 1, and the selection sample number is T, N training sample wherein, T-N test sample book.
In this step, comprise an input layer, a middle layer and an output layer based on the error back propagation neural network, input layer has 5 neurons, and there are 10 neurons in the middle layer, and output layer has 1 neuron; The transport function in the middle layer of said forecast model is a unipolarity S type function, and the transport function of output layer is a linear function, makes network export any value, and structural drawing is shown in accompanying drawing 1.
Step 3; Forecast model is set up module; The data that obtain T group width of heating to step 1 obtain the forecast model that can predict 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment width of heating to carrying out training and testing based on the error back propagation neural network in the step 2; The T group width of heating data that obtain for step 1 are following to carrying out the concrete steps of training and testing based on the error back propagation neural network in the step 2:
Step 3.1; Set weights and threshold value and frequency of training; And weights and threshold value are carried out initialization, and win T-N group sample in the T group sample at random as training sample, N group sample is as test sample book; Input T-N group training sample, said sample are that the T that obtains in the step 1 organizes the size of width of heating and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resisting steel pipeline post weld heat treatment;
Step 3.2, computational grid output obtains the weights and the threshold value of each layer in the reverse transmittance nerve network, and calculates the weights of each layer in the reverse transmittance nerve network and the modifying factor of threshold value, according to the T-N group A that obtains in the step 1
1Temperature computation value and network output computational grid output error, said network output error are the calculated value of the T-N group width of heating that obtains in the step 1 and the comparison difference of the network output that this step is calculated;
Step 3.3 judges whether to reach maximum frequency of training, and selects to carry out following steps according to whether reaching maximum frequency of training:
Select execution in step 1,, judge that whether the network output error is less than anticipation error in step 3.2 if do not reach maximum frequency of training as yet; If less than anticipation error; Then training finishes, and preserves in the step 3.2 weights of each layer and threshold value in the reverse transmittance nerve network simultaneously, obtains forecast model undetermined; If greater than anticipation error, revise after weights and the threshold value of each layer in the reverse transmittance nerve network step repeat 3.2. wherein modifying factor adopt the modifying factor of calculating in the step 3.2;
Select execution in step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training finishes;
Step 3.4; N is organized test sample book import the forecast model of selecting in the execution in step 1 undetermined one by one; If predicated error shows this forecast model undetermined and can be used in the required minimum width of heating of prediction 9%Cr martensite heat-resisting steel pipeline post weld heat treatment that promptly this forecast model undetermined promptly is a resulting forecast model in the step 3 when being lower than prescribed level; Otherwise this forecast model undetermined does not meet, and finishes whole steps.
In the present embodiment; Training and test are meant that calculating under 3650 groups of different conditions of gained 3600 groups in the novel martensite heat-resisting steel of the 9%Cr pipeline post weld heat treatment width of heating data with the front finite element software tests the BP network that trains as test sample book with the novel martensite heat-resisting steel of 9%Cr pipeline post weld heat treatment width of heating data under 50 groups of different conditions of remainder as the model training of training sample to being set up.Network model network using error backpropagation algorithm is trained; The training flow process is shown in accompanying drawing 2;, the output error of neural network can stop training after the repetition training when reaching 0.5mm; Training error figure shows that network model can be used for predicting the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment width of heating when neural network is lower than prescribed level to the predicated error of 50 groups of test sample books shown in accompanying drawing 3.
Step 4, the model correcting module is revised correction model output layer threshold values in conjunction with 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment measured data of experiment to the forecast model of gained;
Step 5, the width of heating optimal module, analysis conduit size (caliber and wall thickness), heat treatment environment temperature, control temperature, the preset inside and outside wall temperature difference are input to the minimum width of heating that revised model can obtain the pipeline post weld heat treatment.
Choose line size (caliber and wall thickness), the preset inside and outside wall temperature difference, heat treatment environment temperature and control temperature among the present invention as input parameter, applicable scope is following:
Internal diameter of the pipeline (radius): 100mm-500mm;
Pipeline wall thickness: 30mm-140mm;
The preset inside and outside wall temperature difference: 0 ℃-50 ℃;
Heat treatment environment temperature :-10 ℃-30 ℃;
Control temperature: 750 ℃-780 ℃.
Embodiment:
The inner and outer walls of pipeline temperature difference size of BP Neural Network Optimization method involved in the present invention and actual measurement compares:
9%Cr martensite heat resisting pipe deferent road size (caliber and wall thickness), heat treatment environment temperature, control temperature and the preset inside and outside wall temperature difference of three kinds of specifications shown in analysis and the record sheet 1; The numerical value of each influence factor is input in the model calculates, can calculate the minimum width of heating of 9%Cr martensite heat-resisting steel pipeline post weld heat treatment under this condition fast.In addition through testing to verify the precision of this model.Result and measured result with gained of the present invention in this example are as shown in table 2 below.
Table 1
The post weld heat treatment parameter of 9%Cr martensite heat-resisting steel pipeline
| Pipeline specifications/mm | Width of heating/mm | Insulation width/mm | Environment temperature/
 |
Control temperature/
 |
The preset inside and outside wall temperature difference/
 |
| ID296*65 | 510 | 750 | 13 | 755 | 31 |
| ID430*90 | 937 | 1137 | 10 | 756 | 33 |
| ID288*110 | 866 | 1066 | 15 | 765 | 32 |
Table 2 adopts the inventive method and measured data to compare
| Pipeline specifications/mm | The inventive method/
 |
Measured value/
 |
Error/
 |
| ID296*65 | 515 | 510 | 5 |
| ID430*90 | 930 | 937 | -7 |
| ID288*110 | 870 | 866 | 3 |
Result of calculation shows that more consistent with the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment width of heating optimization method computed information and experimental data that the present invention proposes, the width of heating Error Absolute Value is less than 10mm.Comparing with experimental technique obviously has plurality of advantages, removes and calculates the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment width of heating quickly and easily, optimizes beyond the Technology for Heating Processing, can also solve the difference problem of domestic and international heat treatment technics rules.
Specific embodiment described herein only is that the present invention's spirit is illustrated.Person of ordinary skill in the field of the present invention can make various modifications or replenishes or adopt similar mode to substitute described specific embodiment, but can't depart from spirit of the present invention or surmount the defined scope of appended claims.
Claims (5)
- The optimization method of 1.9%Cr martensite steel pipeline post weld heat treatment width of heating is characterized in that, may further comprise the steps:Step 1; Set up the upward heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature by the temperature field computing module, adopt finite element analysis software to calculate the post weld heat treatment inside and outside wall temperature difference of each group model;Step 2 is set up module by neural network and is combined any specification pipeline under different heat treatment environment temperature, different control temperature and different preset inside and outside wall temperature difference condition, the width of heating of the required minimum of pipeline; Foundation is based on the error back propagation neural network;Step 3; Forecast model is set up module; The data that obtain T group width of heating to step 1 obtain the forecast model that can predict 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment width of heating to carrying out training and testing based on the error back propagation neural network in the step 2;Step 4, the model correcting module is revised the forecast model of gained in conjunction with 9%Cr martensite heat-resisting steel posted sides pipeline post weld heat treatment measured data of experiment;Step 5, the width of heating optimal module, analysis conduit size, heat treatment environment temperature, control temperature, the preset inside and outside wall temperature difference are input to the minimum width of heating that revised model can obtain the pipeline post weld heat treatment.
- 2. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1; It is characterized in that; In the described step 1; The heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature in the foundation, the utilization finite element software calculates the size of the pipeline post weld heat treatment inside and outside wall temperature difference under the different condition, and concrete grammar is:According to the applicable cases of the novel martensite heat-resisting steel of 9%Cr, choose the line size scope; According to domestic and international heat treatment technics rules, for the pipeline calculating heating tape width of certain specification, the size of insulation width, choose the width of heating scope, the insulation width is chosen according to electric power standard; Control temperature and heat treatment environment temperature conditions according to the novel martensite heat-resisting steel of 9%Cr; Select the scope of control temperature and heat treatment environment temperature; Set up the novel martensite heat-resisting steel of T group 9%Cr pipeline post weld heat treatment temperature field theoretical calculation model; Calculate line size, width of heating, control temperature and heat treatment environment temperature equivalent point position effects through the utilization finite element software, computing method are following:Step 1.1 in finite element software, is set up the novel martensite heat-resisting steel of 9%Cr post weld heat treatment calculation model for temperature field;Step 1.2, definition starting condition, boundary condition are found the solution;Step 1.3, calculating is checked inner-walls of duct temperature and outside wall temperature after accomplishing in preprocessor, calculate the size of the inside and outside wall temperature difference.
- 3. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1 is characterized in that, in the said step 2, the concrete grammar of setting up based on the error back propagation neural network is:Step 2.1, definition input layer and output layer:The numerical value of choosing line size, preset the inside and outside wall temperature difference, control temperature and heat treatment environment temperature is as input variable, so the neuron number of this network input layer is 5; With the output of minimum width of heating required under the different condition as network model, so the output layer neuron number is 1;Step 2.2 is selected the latent number of plies and latent layer unit number: adopt single latent layer, and definite the number of hidden nodes is 10;Step 2.3, other Determination of Parameters: the transport function of the latent layer of latent layer is unipolarity S type function: f (x)=1/ (1+e -x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and the error target is 0.5, and the selection sample number is T, N training sample wherein, T-N test sample book.
- 4. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1; It is characterized in that; In the said step 2, comprise an input layer, a middle layer and an output layer based on the error back propagation neural network, input layer has 5 neurons; There are 10 neurons in the middle layer, and output layer has 1 neuron; The transport function in the middle layer of said forecast model is a unipolarity S type function, and the transport function of output layer is a linear function, makes network export any value; It is following to carrying out the concrete steps of training and testing based on the error back propagation neural network in the step 2 that step 1 is obtained the T width of heating:Step 3.1; Set weights and threshold value and frequency of training; And weights and threshold value are carried out initialization, and win T-N group sample in the T group sample at random as training sample, N group sample is as test sample book; Input T-N group training sample, said sample are that the T that obtains in the step 1 organizes the size of width of heating and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resisting steel pipeline post weld heat treatment;Step 3.2, computational grid output obtains the weights and the threshold value of each layer in the reverse transmittance nerve network, and calculates the weights of each layer in the reverse transmittance nerve network and the modifying factor of threshold value, according to the T-N group A that obtains in the step 1 1Temperature computation value and network output computational grid output error, said network output error are the calculated value of the T-N group width of heating that obtains in the step 1 and the comparison difference of the network output that this step is calculated;Step 3.3 judges whether to reach maximum frequency of training, and selects to carry out following steps according to whether reaching maximum frequency of training:Select execution in step 1,, judge that whether the network output error is less than anticipation error in step 3.2 if do not reach maximum frequency of training as yet; If less than anticipation error; Then training finishes, and preserves in the step 3.2 weights of each layer and threshold value in the reverse transmittance nerve network simultaneously, obtains forecast model undetermined; If greater than anticipation error, revise after weights and the threshold value of each layer in the reverse transmittance nerve network step repeat 3.2. wherein modifying factor adopt the modifying factor of calculating in the step 3.2;Select execution in step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training finishes;Step 3.4; N is organized test sample book import the forecast model of selecting in the execution in step 1 undetermined one by one; If predicated error shows this forecast model undetermined and can be used in the required minimum width of heating of prediction 9%Cr martensite heat-resisting steel pipeline post weld heat treatment that promptly this forecast model undetermined promptly is a resulting forecast model in the step 3 when being lower than prescribed level; Otherwise this forecast model undetermined does not meet, and finishes whole steps.
- 5. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1; It is characterized in that; In the described step 4; The data and the Model Calculation value of the novel martensite heat-resisting steel of 9%Cr posted sides pipeline post weld heat treatment experiment measuring are compared analysis, and correction model output threshold values.
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| CN107688700A (en) * | 2017-08-22 | 2018-02-13 | 武汉大学 | A kind of 9%Cr refractory steel pipeline post weld heat treatment heating power computational methods |
| CN107881318A (en) * | 2017-11-15 | 2018-04-06 | 武汉大学 | A kind of method of optimization design 9%Cr refractory steel pipeline post weld heat treatment number of partitions |
| CN110309572A (en) * | 2019-06-24 | 2019-10-08 | 武汉大学 | The method for determining 9%Cr steel conduit local post weld heat treatment minimum width of heating |
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| CN107688700A (en) * | 2017-08-22 | 2018-02-13 | 武汉大学 | A kind of 9%Cr refractory steel pipeline post weld heat treatment heating power computational methods |
| CN107688700B (en) * | 2017-08-22 | 2020-08-11 | 武汉大学 | Method for calculating heating power of postweld heat treatment of 9% Cr hot-strength steel pipeline |
| CN107881318A (en) * | 2017-11-15 | 2018-04-06 | 武汉大学 | A kind of method of optimization design 9%Cr refractory steel pipeline post weld heat treatment number of partitions |
| CN107881318B (en) * | 2017-11-15 | 2019-11-26 | 武汉大学 | A kind of method of optimization design 9%Cr refractory steel pipeline post weld heat treatment number of partitions |
| CN110309572A (en) * | 2019-06-24 | 2019-10-08 | 武汉大学 | The method for determining 9%Cr steel conduit local post weld heat treatment minimum width of heating |
| CN110309572B (en) * | 2019-06-24 | 2020-12-18 | 武汉大学 | Method for determining minimum heating width of local postweld heat treatment of 9% Cr steel pipeline |
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