CN111933221A - Method for predicting dynamic recrystallization fraction of Nb microalloyed steel - Google Patents
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Abstract
The invention discloses a method for predicting dynamic recrystallization fraction of Nb microalloyed steel, belonging to the cross technical field of steel research and machine learning; according to the method, a data set of dynamic recrystallization behavior of the Nb microalloyed steel is constructed by using experimental data of the dynamic recrystallization type rheological stress of the existing C-Mn-Nb microalloyed steel, a model among chemical components, process parameters and rheological stress curve characteristics is established by using a BP neural network based on Bayesian regularization, and the dynamic recrystallization fraction is predicted with high precision through a dynamic recrystallization fraction mathematical model, so that the workload of a single-pass compression experiment and a quenching experiment is obviously reduced, and the efficiency of predicting the dynamic recrystallization fraction is improved.
Description
Technical Field
The invention belongs to the cross technical field of steel research and machine learning, and particularly relates to a method for predicting dynamic recrystallization fraction of Nb microalloyed steel.
Background
The austenite dynamic recrystallization during the high-temperature deformation of the Nb microalloy steel has great influence on the subsequent austenite phase transformation behavior and final performance, and the research on the dynamic recrystallization behavior can provide a basis for establishing an optimal controlled rolling process. At present, two methods are mainly used for researching the austenite dynamic recrystallization fraction, one method is to adopt single-pass hot compression experiment and then carry out quenching at different deformation amounts, and directly carry out metallographic observation to count the recrystallization fraction; the other is a mathematical model based on the recrystallization fraction. Recrystallization behavior can be described from quenching experiments, but the experimental effort is large. The recrystallization fraction can be predicted for different deformation according to a mathematical model of the recrystallization fraction, but the model has various forms, and model parameters are only suitable for specific components and process conditions. The machine learning can learn the characteristics in the data set according to the existing data set, and has the advantages of high precision and strong universality. However, in the aspect of dynamic recrystallization fraction prediction, machine learning has not been applied, and it is of great significance to develop work of machine learning in the aspect of dynamic recrystallization fraction prediction.
By searching a national intellectual property office database and an SOOPAT database, a patent CN106053754B authorizes a method for predicting the dynamic recrystallization fraction of a high-alloying material under a time-varying working condition, a metallographic microstructure is obtained through a large number of thermal simulation experiments and quenching experiments, and a traditional dynamic recrystallization kinetic model is improved into a new model capable of predicting the dynamic recrystallization fraction under the time-varying working condition, but the patent has two defects: firstly, a large amount of hot simulation and quenching experiments are needed, and secondly, the recrystallization fractional model is only suitable for specific components and process conditions. Patent CN110068507A discloses a method for correcting a traditional recrystallization model, which obtains samples under different deformation conditions through a large number of physical simulation tests, calculates the recrystallization fraction of a core part, and obtains the true strain of the core part of the sample by using numerical simulation software; obtaining model parameters by adopting a fitting method, fitting the obtained model parameters with Zener-Hollomon parameters, and obtaining recrystallization model parameters under other conditions, but the method has the defects that: firstly, the precision of the model parameters obtained by fitting is not high, secondly, a large number of thermal deformation experiments and numerical simulation experiments are needed, the cost is high, and the simulation time is long.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for predicting the dynamic recrystallization fraction of Nb microalloyed steel. The method is combined with a mathematical model and machine learning to predict the dynamic recrystallization fraction of the Nb microalloyed steel, has higher precision, wide application range and short consumed time, can obviously reduce a large amount of quenching experiment workload, and is suitable for predicting the dynamic recrystallization fraction of any steel or alloy.
A method of predicting the dynamic recrystallization fraction of Nb microalloyed steel, comprising the steps of:
step 1, constructing an initial data set of dynamic recrystallization behavior of Nb microalloyed steel by using experimental data of dynamic recrystallization rheological stress of the existing C-Mn-Nb microalloyed steel, wherein the data set comprises the following steps: C. mn and Nb contents, heating temperature, deformation temperature, strain rate and maximum strain amount;
step 2, screening a rheological stress curve conforming to the physical metallurgy rule to obtain a screening data set;
and 3, selecting a dynamic recrystallization fraction mathematical model form, wherein the dynamic recrystallization fraction mathematical model is as follows:
wherein f isdynThe dynamic recrystallization fraction, t is the time,
to the strain rate, b () and n () are variables related to strain.
Suppose that: first, the critical strain is reachedcWhen the ratio is high, the dynamic recrystallization fraction is 0.5%; ② reach steady state strainsAt the moment of time, moveThe fraction of recrystallization from the state was 99%. Then formulae (2) and (3) can be obtained:
obtained from the formulae (2) and (3),
wherein,
tcto achieve critical straincTime of (t)sTo achieve steady state strainsThe time of (a) is,cin order to obtain the critical strain,pin order to be the peak strain,sis steady state strain, k is a constant;
step 4, according to the flow stress curve in the screened data set, determining the actually measured critical strain of each flow stress curve in the data setcPeak strainpAnd steady state strains；
Step 5, according to the critical straincAnd peak strainpIn relation to (2)c＝kpCalculating the k value of each rheological stress curve;
step 6, establishing a nonlinear mapping network relation model among chemical components, process parameters and dynamic recrystallization rheological stress characteristics by adopting a BP neural network based on Bayesian regularization, and training the model to obtain a trained network model;
step 7, selecting at least one group of components and processes according to the trained network relation model, and predicting the rheological stress characteristics;
and 8, predicting the dynamic recrystallization fraction according to the rheological stress characteristics predicted in the step 7 and the dynamic recrystallization fraction mathematical model selected in the step 3.
In said step 4, each time is determinedCritical strain of the streamer stress curvecPeak strainpSteady state strainsThe specific process is as follows: determination of peak strain from peaks on the rheological stress curve (i.e. stress sigma-strain curve)p(ii) a Definition of the Strain hardening Rate
(delta sigma is a stress increment, delta is a strain increment), and the strain at which theta is first restored to a value of 0 is regarded as a steady-state strain from a strain hardening rate theta-strain curves(ii) a Obtaining the derivative of the strain hardening rate theta to the stress sigma
According to
Determination of the critical stress sigma at the peak of the curvecCritical straincDetermined from the stress sigma-strain curve.
In the step 6, a BP neural network based on Bayesian regularization is adopted to establish a nonlinear mapping network relation model among chemical components, process parameters and dynamic recrystallization rheological stress characteristics, and the model is trained by adopting the following specific processes: establishing a three-layer neural network relation by adopting a BP neural network based on Bayesian regularization, wherein input parameters of an input layer are C content, Mn content, Nb content, heating temperature, deformation temperature, strain rate and maximum strain; the output parameter of the output layer is the peak strainpSteady state strainsAnd k; the number of hidden layer neurons is 5.
In the step 7, at least one group of components and processes are selected according to the trained network relation model to predict the rheological stress characteristics, and the specific process is as follows: predicting the rheological stress characteristics for the components to be predicted and the process thereof: peak strainpSteady state strainsAnd k.
Compared with the prior art, the invention has the advantages that:
(1) the applicability is wide. The dynamic recrystallization type rheological stress curve 280 of the C-Mn-Nb microalloyed steel is collected, a C-Mn-Nb microalloyed steel data set is constructed, the data set contains more comprehensive chemical components and process parameter information of the Nb microalloyed steel, the defect of information loss under a single steel type or process condition is avoided, and the machine learning model has wider applicability;
(2) the precision is higher. According to the method, a network relation model among chemical components, process parameters and rheological stress characteristics is constructed by adopting a machine learning method, so that the defect of low precision of a traditional dynamic recrystallization fraction mathematical model is overcome, and the method has the advantage of high precision;
(3) a large amount of single-pass compression and subsequent quenching experiments can be reduced. According to the method, a network relation model among chemical components, process parameters and rheological stress characteristics of the Nb microalloyed steel series is established by adopting a machine learning algorithm, the rheological stress characteristics under different components and process conditions can be predicted, and meanwhile, the dynamic recrystallization fraction is predicted by the recrystallization fraction mathematical model, so that the workload of single-pass compression and quenching experiments is greatly reduced, and the prediction efficiency of the dynamic recrystallization fraction is improved.
Drawings
FIG. 1 is a flow chart of a method of predicting the dynamic recrystallization fraction of Nb microalloyed steel according to example 1 of the invention;
FIG. 2 is a graph comparing the predicted dynamic recrystallization fraction to the measured dynamic recrystallization fraction of example 1 of the present invention, wherein:
fig. 2(a) is a graph showing a comparison between the predicted dynamic recrystallization fraction and the actually measured dynamic recrystallization fraction in the a-component process, and fig. 2(B) is a graph showing a comparison between the predicted dynamic recrystallization fraction and the actually measured dynamic recrystallization fraction in the B-component process.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
Example 1
A method for predicting the dynamic recrystallization fraction of Nb microalloyed steel, the flow chart of which is shown in figure 1, comprises the following steps:
step 1, constructing an initial data set of dynamic recrystallization behavior of Nb microalloyed steel by using the experimental data of 410 conventional C-Mn-Nb microalloyed steel dynamic recrystallization type rheological stress curves, wherein the data set comprises the following steps: C. mn and Nb contents, heating temperature, deformation temperature, strain rate and maximum strain amount;
step 2, screening a rheological stress curve conforming to the physical metallurgy law, wherein the screening standard is as follows: firstly, judging whether the rheological stress curve conforms to the physical metallurgical rule or not under the condition of different deformation of the same component. For example, under the conditions of different deformation temperatures of the same component, the rheological stress is gradually increased with the same strain amount along with the reduction of the deformation temperature; under the conditions of the same component and different strain rates, the rheological stress is gradually increased with the increase of the strain rate and the same strain quantity; secondly, judging whether the rheological stress curve conforms to the physical metallurgical law when the same deformation condition has different components. For example, when the Nb content is different under the same deformation condition, the rheological stress is gradually increased along with the increase of the Nb content when the strain amount is the same; when the same deformation condition is different from Mn content, the rheological stress is gradually increased along with the increase of Mn content when the strain quantity is the same; when the content of C is different under the same deformation condition, the rheological stress is gradually reduced along with the increase of the content of C under the same strain quantity. After screening by the physical metallurgy principle, a screening data set is obtained, wherein the screening data set comprises 280 flow stress curves, and the table 1 shows screened steel grades and process information;
TABLE 1 Steel grades screened by the principles of physical metallurgy and Process information
Step 3, selecting a dynamic recrystallization fraction mathematical model form;
in the embodiment of the invention, the dynamic recrystallization fraction mathematical model form is selected as follows:
wherein f isdynThe dynamic recrystallization fraction, t is the time,
to the strain rate, b () and n () are variables related to strain.
Suppose that: first, the critical strain is reachedcWhen the ratio is high, the dynamic recrystallization fraction is 0.5%; ② reach steady state strainsWhen the ratio is 99%, the dynamic recrystallization fraction is obtained. Then formulae (2) and (3) can be obtained:
obtained from the formulae (2) and (3),
wherein,
tctime to reach critical strain, tsIn order to achieve the time to steady state strain,cin order to obtain the critical strain,pin order to be the peak strain,sthe strain k is constant for the steady state.
Step 4, according to the flow stress curve in the screened data set, determining the actually measured critical strain of each flow stress curve in the data setcPeak strainpAnd steady state strains；
In the embodiment of the invention, the peak strain is determined according to the peak value on the rheological stress curve (namely the stress sigma-strain curve)p(ii) a Definition of the Strain hardening Rate
(delta sigma is a stress increment, delta is a strain increment), and the strain at which theta is first restored to a value of 0 is regarded as a steady-state strain from a strain hardening rate theta-strain curves(ii) a Obtaining the derivative of the strain hardening rate theta to the stress sigma
According to
Determination of the critical stress sigma at the peak of the curvecCritical straincDetermined from the stress sigma-strain curve.
Step 5, according to the critical straincAnd peak strainpIn relation to (2)c＝kpCalculating the k value of each rheological stress curve, and determining the rheological stress characteristic information in the table 2;
table 2 screening data set rheological stress characterization information
Step 6, establishing a nonlinear mapping network relation model among chemical components, process parameters and dynamic recrystallization rheological stress characteristics by adopting a BP neural network based on Bayesian regularization, and training the model to obtain a trained network model;
in the embodiment of the invention, the specific process of the model training is as follows: establishing a three-layer neural network relation by adopting a BP neural network based on Bayesian regularization, wherein input parameters of an input layer are C content, Mn content, Nb content, heating temperature, deformation temperature, strain rate and maximum strain; the output parameter of the output layer is the peak strainpSteady state strainsAnd k; the number of hidden layer neurons is 5. And then training the model to obtain a trained network relation model.
Step 7, selecting at least one group of components and processes according to the trained network relation model, and predicting the rheological stress characteristics;
in the embodiment of the invention, the rheological stress characteristic prediction comprises the following specific processes: two groups of components are respectively selected, including:
component A: 0.1C-1.42Mn-0.035 Nb;
and B component: 0.117C-1.21Mn-0.041 Nb;
the corresponding process comprises the following steps:
a process: deformation temperature 1100 ℃ and strain rate 0.2s-1The heating temperature is 1400 ℃, and the maximum strain is 3.0;
and the process B comprises the following steps: deformation temperature 1050 ℃ and strain rate of 0.1s-1The heating temperature is 1200 ℃, and the maximum strain is 0.8.
Predicting the rheological stress characteristics for the components to be predicted and the process thereof: peak strainpSteady state strainsAnd k, the result is
The component A process comprises the following steps:p＝0.6211，s＝2.3629，k＝0.8048；
the component B process comprises the following steps:p＝0.2471，s＝0.5968，k＝0.8464。
and 8, predicting the dynamic recrystallization fraction according to the rheological stress characteristics predicted in the step 7 and the dynamic recrystallization fraction mathematical model selected in the step 3, wherein a comparison graph of the predicted dynamic recrystallization fraction and the actually measured dynamic recrystallization fraction in the component A process is shown in a figure 2(a), and a comparison graph of the predicted dynamic recrystallization fraction and the actually measured dynamic recrystallization fraction in the component B process is shown in a figure 2 (B).
Claims (4)
1. A method for predicting the dynamic recrystallization fraction of Nb microalloyed steel is characterized by comprising the following steps:
step 1, constructing an initial data set of dynamic recrystallization behavior of Nb microalloyed steel by using experimental data of dynamic recrystallization rheological stress of the existing C-Mn-Nb microalloyed steel, wherein the data set comprises the following steps: C. mn and Nb contents, heating temperature, deformation temperature, strain rate and maximum strain amount;
step 2, screening a rheological stress curve conforming to the physical metallurgy rule to obtain a screening data set;
and 3, selecting a dynamic recrystallization fraction mathematical model form, wherein the dynamic recrystallization fraction mathematical model is as follows:
wherein f isdynThe dynamic recrystallization fraction, t is the time,
to the strain rate, b () and n () are variables related to strain.
Suppose that: first, the critical strain is reachedcWhen the ratio is high, the dynamic recrystallization fraction is 0.5%; ② reach steady state strainsWhen the ratio is 99%, the dynamic recrystallization fraction is obtained. Then formulae (2) and (3) can be obtained:
obtained from the formulae (2) and (3),
wherein,
tcto achieve critical straincTime of (t)sTo achieve steady state strainsThe time of (a) is,cin order to obtain the critical strain,pin order to be the peak strain,sis steady state strain, k is a constant;
step 4, according to the flow stress curve in the screened data set, determining the actually measured critical strain of each flow stress curve in the data setcPeak strainpAnd steady state strains；
Step 5, according to the critical straincAnd peak strainpIn relation to (2)c＝kpCalculating the k value of each rheological stress curve;
step 6, establishing a nonlinear mapping network relation model among chemical components, process parameters and dynamic recrystallization rheological stress characteristics by adopting a BP neural network based on Bayesian regularization, and training the model to obtain a trained network model;
step 7, selecting at least one group of components and processes according to the trained network relation model, and predicting the rheological stress characteristics;
and 8, predicting the dynamic recrystallization fraction according to the rheological stress characteristics predicted in the step 7 and the dynamic recrystallization fraction mathematical model selected in the step 3.
2. The method for predicting the dynamic recrystallization fraction of Nb microalloyed steel according to claim 1, wherein in the step 4, the critical strain of each rheological stress curve is determinedcPeak strainpSteady state strainsThe specific process is as follows: determining peak strain from peaks on the rheological stress curvep(ii) a Definition of the Strain hardening Rate
Wherein, delta sigma is stress increment, and delta is strain increment; according to the strain hardening rate theta-strain curve, the strain when theta is recovered to 0 value for the first time is taken as the steady state strains(ii) a Obtaining the derivative of the strain hardening rate theta to the stress sigma
According to
Determination of the critical stress sigma at the peak of the curvecCritical straincDetermined from the stress sigma-strain curve.
3. The method for predicting the dynamic recrystallization fraction of the Nb microalloyed steel according to claim 1, wherein in the step 6, a BP neural network based on Bayesian regularization is adopted to establish a nonlinear mapping network relation model among chemical components, process parameters and dynamic recrystallization rheological stress characteristics, and the model is trained by the following specific processes: establishing a three-layer neural network relationship by adopting a BP neural network based on Bayesian regularization, wherein input parameters of an input layer are C content and Mn contentNb content, heating temperature, deformation temperature, strain rate, and maximum strain; the output parameter of the output layer is the peak strainpSteady state strainsAnd k; the number of hidden layer neurons is 5.
4. The method for predicting the dynamic recrystallization fraction of the Nb microalloyed steel according to claim 1, wherein in the step 7, at least one group of components and processes are selected according to a trained network relationship model to predict the rheological stress characteristics, and the specific process is as follows: predicting the rheological stress characteristics for the components to be predicted and the process thereof: peak strainpSteady state strainsAnd k.
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