CN115954065B - Austenite grain size prediction method for micro-alloyed steel TSCR process - Google Patents
Austenite grain size prediction method for micro-alloyed steel TSCR process Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 126
- 230000008569 process Effects 0.000 title claims abstract description 103
- 229910001566 austenite Inorganic materials 0.000 title claims abstract description 77
- 229910000742 Microalloyed steel Inorganic materials 0.000 title claims abstract description 20
- 230000005012 migration Effects 0.000 claims abstract description 27
- 238000013508 migration Methods 0.000 claims abstract description 27
- 238000005266 casting Methods 0.000 claims abstract description 23
- 238000009749 continuous casting Methods 0.000 claims abstract description 20
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 18
- 239000010959 steel Substances 0.000 claims abstract description 18
- 238000002791 soaking Methods 0.000 claims abstract description 11
- 230000008859 change Effects 0.000 claims description 33
- 239000000523 sample Substances 0.000 claims description 27
- 238000004321 preservation Methods 0.000 claims description 22
- 238000010438 heat treatment Methods 0.000 claims description 10
- 238000005260 corrosion Methods 0.000 claims description 6
- 230000007797 corrosion Effects 0.000 claims description 6
- 230000036962 time dependent Effects 0.000 claims description 6
- 230000004913 activation Effects 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 5
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- 230000000171 quenching effect Effects 0.000 claims description 4
- 229920000742 Cotton Polymers 0.000 claims description 3
- 238000001816 cooling Methods 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 3
- 230000003628 erosive effect Effects 0.000 claims description 3
- 238000000227 grinding Methods 0.000 claims description 3
- 238000005498 polishing Methods 0.000 claims description 3
- 239000012496 blank sample Substances 0.000 claims description 2
- 238000011534 incubation Methods 0.000 claims description 2
- 239000006104 solid solution Substances 0.000 claims 2
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- 238000001556 precipitation Methods 0.000 claims 1
- 230000001737 promoting effect Effects 0.000 claims 1
- 238000005096 rolling process Methods 0.000 abstract description 7
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- 230000001276 controlling effect Effects 0.000 abstract description 5
- 238000002474 experimental method Methods 0.000 abstract description 5
- 229910045601 alloy Inorganic materials 0.000 abstract description 3
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- 238000005516 engineering process Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 229910001018 Cast iron Inorganic materials 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical group [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 229910052786 argon Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- NUHSROFQTUXZQQ-UHFFFAOYSA-N isopentenyl diphosphate Chemical compound CC(=C)CCO[P@](O)(=O)OP(O)(O)=O NUHSROFQTUXZQQ-UHFFFAOYSA-N 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000001953 recrystallisation Methods 0.000 description 1
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- 239000010936 titanium Substances 0.000 description 1
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Abstract
The invention discloses a method for predicting austenite grain size in the process of continuous casting and rolling (TSCR) of microalloy steel sheet billet, belonging to the field of tissue performance prediction methods in the process of continuous casting and rolling. According to the method, the relation between the grain boundary migration driving force and the grain boundary migration rate is introduced on the basis of an isothermal classical model, the influence of micro-alloy elements 'solute drag action' and 'second phase pinning action' on the growth of austenite grains is comprehensively considered, and a prediction model of the growth of the austenite grains of the steel in isothermal and non-isothermal processes is established through a limited isothermal experiment. The model can accurately predict the austenitic grain size in the microalloy steel casting blank at different process nodes in the continuous casting and soaking processes of the TSCR process, and provides a reference basis and a model foundation for regulating and controlling the austenitic grain size, optimizing the process parameters and improving the performance and quality of the casting blank.
Description
Technical Field
The invention relates to the field of microstructure performance prediction methods in the continuous casting and rolling processes of microalloyed steel, and provides an austenite grain size prediction method in the continuous casting and rolling (TSCR) process of microalloyed steel sheet billets.
Background
The austenite grain size in the casting blank has important influence on the quality of the casting blank, the recrystallization behavior in the rolling process, the phase transformation behavior in the cooling process after rolling and the grain size after phase transformation. Compared with the traditional process, the thin slab continuous casting and rolling (TSCR) process has unique metallurgical characteristics, and the characteristics cause the austenitic growth rule to be obviously different from that of the traditional process. The cast blank structure before the TSCR process is still a coarse original austenite structure, which is far larger than that of the traditional process. Therefore, the TSCR technology has higher demand for refining the casting blank structure than the traditional technology. Therefore, the method has important guiding significance for accurately predicting the austenitic grain size of the TSCR casting blank and controlling the casting blank structure and improving the product performance.
Related patent technologies in the field of austenite growth prediction are relatively deficient. Only two related patent application reports are searched by a patent search engine, namely a method for forecasting the size of original austenite grains after casting blank heating in 2020 and a method for forecasting the growth behavior of austenite grains during continuous casting blank heating in 2021 in CN 113791009A.
The isothermal prediction model of the austenitic grain growth is derived on the basis of a classical isothermal model, and further the austenitic grain size prediction in the temperature change process is expanded. The present prediction method is also divided into isothermal process austenite grain size prediction and austenite grain size prediction, but differs from the [ CN 113791009A ] method in that: ① In the derivation of the isothermal prediction model, not only is the isothermal classical model cited, but also the change relation between the grain boundary migration driving force and the grain boundary migration rate is considered, and the more accurate isothermal prediction model is derived and established. ② In [ CN 113791009A ], parameters n, M 0 and Q in the temperature change prediction model still adopt the fitted constant values in the isothermal model when the temperature change process is predicted. In fact, the addition of microalloying elements makes the austenitic grain growth behavior more complex. The microalloy elements dissolved in the steel matrix are enriched in the grains near the grain boundary, so that a solute drag effect is achieved on the migration of the austenite grain boundary, and the migration of the grain boundary is prevented; the micro-alloy precipitated at the grain boundaries acts as a "pinning effect" to the migration of the austenite grain boundaries, and also has an inhibiting effect on the migration of the austenite grain boundaries. As the composition and temperature of the steel grade change, the solute "drag" and the second phase "pinning" also change. Thus, n, M 0, Q should vary with temperature during the temperature change process rather than a constant value. Therefore, in the prediction method, the change relation of n, M 0 and Q along with the temperature T is determined by comparing and analyzing parameters in the isothermal prediction model at different temperatures, so that the accuracy of the temperature change model prediction is improved. ③ The prediction range is wider, [ CN 113791009A ] mainly aims at predicting the austenite grain size in the casting blank heating process and the heat preservation process, and the method can accurately predict the austenite grain size in the steel continuous casting cooling process, the heating process and the heat preservation process including TSCR.
In [ CN 11152146A ], the grain size of austenite and the steel grade composition in the heat preservation process are simply fitted through Matlab software, and the prediction method is only suitable for simple prediction of the isothermal process and cannot predict the temperature change process.
The model is further deduced on the basis of a classical isothermal model and the relation between the migration driving force of the grain boundary and the migration rate of the grain boundary, so that an isothermal process prediction model of the model is obtained. The changes in solute "drag" and second phase "pinning" with steel grade composition and temperature are all attributed to changes in time index n, grain boundary migration parameter M 0, and grain boundary migration activation energy Q. The change relation between the model and the temperature is determined through finite isothermal experiment fitting, so that the accuracy of a prediction model and the universality of steel grades are greatly improved. The prediction model can accurately predict the austenite grain size at each moment in the heat treatment process of any component steel, thereby providing guidance for controlling the austenite grain size.
Disclosure of Invention
The invention provides a method for predicting the austenitic grain size in a TSCR process of microalloy steel, which introduces the relation between the migration driving force of grain boundary and the migration rate of grain boundary on the basis of an isothermal classical model, comprehensively considers the influence of microalloy elements 'solute drag action' and 'second phase pinning action' on the austenitic grain growth, and establishes a prediction model of the austenitic grain growth of the steel in isothermal and non-isothermal processes through a limited isothermal experiment. The model can accurately predict the austenitic grain size in the microalloy steel casting blank at different process nodes in the continuous casting and soaking processes of the TSCR process, and provides a reference basis and a model foundation for regulating and controlling the austenitic grain size, optimizing the process parameters and improving the performance and quality of the casting blank.
The invention comprises the following steps:
step 1): isothermal process simulation
Simulating heat preservation processes at different temperatures by using a high-temperature confocal microscope or other heating furnaces capable of accurately controlling the temperature, obtaining casting blank samples at different temperatures and different heat preservation times, and quenching;
step 2): grain boundary corrosion of original austenite
Grinding and polishing a sample, adding the sample into an etchant, carrying out hot erosion for 0-3 min at 50-55 ℃, taking out the sample after the surface of the sample turns black, rapidly wiping off a black film on the surface of the sample by using absorbent cotton, drying, putting the sample into an etchant, and repeating the above processes until the grain boundary is clear and distinguishable;
Step 3): austenitic grain size statistics
The average grain size of austenite grains in each sample was counted by observation with a metallographic microscope. Taking the average austenite grain size in a sample which is preserved for 0s at each temperature as the initial austenite grain size D 0-T in the heat preservation process;
Step 4): austenitic grain size isothermal prediction model establishment
By deducting and correcting the classical isothermal austenite grain prediction model, an austenite grain size prediction model in the isothermal process of the microalloyed steel is obtained and is expressed as a formula (1)
Wherein D t-T is austenite grain size when preserving heat at temperature T, μm; d 0-T is the austenite initial grain size, μm, when held at temperature T for 0 seconds; n is a time index constant; m 0 is the grain boundary migration coefficient; t is the heat preservation time, s; r is a gas constant, 8.314J/(mol.times.K); t is the heating temperature, K; q is grain boundary migration activation energy, J/(mol); gamma is grain boundary energy, 0.679J/m 2;
Bringing the average sizes of austenite grains at different moments in the heat preservation process into a formula (1), and determining values of n, M 0 and Q to obtain an austenite grain size prediction model of the microalloyed steel in the heat preservation process at the temperature;
step 5): influence relation determination of temperature on n, Q and M 0
Fitting and determining the time-dependent change relation of the n, the Q and the M 0 according to the values of the n, the Q and the M 0 determined in the thermal insulation model at different temperatures:
n*＝f(T) (2)
M0＝g(T) (3)
Q＝h(T) (4)
Step 6): defining the change rule of austenite grain growth speed along with temperature in non-isothermal process
Deriving the time t in the formula (1) to obtain the austenite growth rate:
The change relation of the austenitic grain growth rate along with the temperature in the temperature changing process can be obtained by introducing the change relation (2-4) of n, Q and M 0 along with the time into the formula (5):
step 7): prediction of austenitic grain size of microalloy steel casting blank during TSCR process
Carrying out high-temperature confocal microscopic observation to obtain an austenite average grain size D 0 of the casting blank austenite initial grain size of the TSCR process when the molten steel is completely solidified; bringing the time-dependent temperature change relation t=p (T) of the TSCR process continuous casting and soaking into formula (6), and obtaining the time-dependent austenitic grain growth rate change relation in the process:
at this time, the austenite grain size at any time t in the continuous casting and soaking processes of the TSCR process is as follows:
The invention has the following beneficial effects:
1. The method for predicting the austenite grain size of the micro-alloy steel in the TSCR process can be used for accurately predicting the austenite grain size of the casting blank at any time in the continuous casting and soaking processes of the TSCR process, and fills the blank of a prediction model of the austenite grain size of the casting blank in the TSCR process.
2. The model introduces the relation between the migration driving force of the grain boundary and the migration rate of the grain boundary on the basis of an isothermal classical model, comprehensively considers the influence of micro-alloy elements 'solute drag action' and 'second phase pinning action' on the growth of austenite grains, and greatly improves the accuracy of a prediction model.
3. The changes of the microalloy elements "solute drag effect" and "second phase pinning effect" with temperature are comprehensively represented by the changes of the time index n, the grain boundary migration parameter M 0, and the grain boundary migration activation energy Q with temperature. The change relation of n, M 0 and Q along with the temperature T is determined through a limited isothermal experiment, so that the applicable process and steel grade range of the model can be improved.
Drawings
FIG. 1 is a schematic diagram showing the simulation of the heat preservation process at different temperatures.
FIG. 2 is an austenite morphology after corrosion.
FIG. 3 shows the austenite grain size over time during incubation at various temperatures.
FIG. 4 is a schematic diagram of the thermal history and sampling of the TSCR process.
FIG. 5 is a graph showing the comparison of austenite grain size measurements and predictions at various time nodes during the TSCR process.
Detailed Description
The invention provides a method for predicting the austenitic grain size of a microalloyed steel casting blank in a TSCR (cast iron reduction) process, and the invention is further described below with reference to the accompanying drawings and examples.
The selection of the characterization apparatus in this example was an XD30M inverted optical metallographic microscope.
The steel grade selected in the embodiment is titanium microalloyed 22MnB5 steel, and the main chemical components in the casting blank and the mass fraction thereof are as follows ：Fe-0.21wt.％C-0.27wt.％Si-1.18wt.％Mn-0.008wt.％P-0.001wt.％S-0.23wt.％Cr-0.033wt.％Ti-0.0027wt.％B-0.0028wt.％N.
Step 1) isothermal simulation
Sampling at 1/4 of the thickness and width of the TSCR continuous casting billet and processing intoIs a cylinder. And simulating the heat preservation processes at different temperatures by using a high-temperature confocal microscope, wherein argon protection is realized in the whole simulation process. Rapidly quenching to prepare samples with different temperatures and different heat preservation times. The simulated temperature regime is shown in figure 1.
Step 2) sample Corrosion
And (3) grinding and polishing the sample, adding the sample into an etchant, carrying out thermal erosion for 0-3 min at 50-55 ℃, taking out the sample after the surface of the sample turns black, rapidly wiping off a black film on the surface of the sample by using absorbent cotton, drying, putting the sample into an etchant, and repeating the above processes until the grain boundary is clear and discernible. The austenite morphology after corrosion is shown in fig. 2.
Step 3) sample Austenitic grain size statistics
And placing the corroded sample into a metallographic microscope to take 10 photos with different view fields. The average grain size of austenite in the ten views was counted as the austenite size of the sample by IPP image processing software. The diameter of the equal area circle is taken as the size of the crystal grain. The statistics of the austenite average grain size in samples from different temperature holding processes are shown in figure 3.
Step 4) determination of isothermal model parameters (n× Q, M 0)
Fitting the statistics of fig. 3 with isothermal prediction model pattern (1) to obtain austenite isothermal prediction models of the heat preservation process at 1100, 1150, 1200, 1250 and 1300 ℃ respectively:
Determination of the relation between step 5) n, Q and M 0 and the temperature T
Fitting analysis is carried out on the values of n, Q, M 0 determined in the heat preservation process of different temperatures in the step 4), and the functional relation between n, Q and M 0 and the temperature T is determined as follows:
n*＝-0.0003T+2.6982 (14)
M0＝2.95×105 (15)
Q＝-2.026T +66185 (16)
step 6) determining the change relation of the growth speed of austenite grains in the non-isothermal process along with the temperature
Bringing the formulas (14-16) into the formula (5) gives the austenitic growth rate as a function of temperature as follows:
step 7) Austenite grain size prediction at different process nodes of TSCR process
The change rule of the casting blank temperature along with time in the continuous casting and soaking process of the TSCR process is as follows:
substituting the formula (18) into the formula (17) to obtain the time-dependent change relation of the austenitic growth rate in the continuous casting and soaking processes of the TSCR process. Thereby combining the formulas (8), (17) and (18) to obtain the austenite grain size in the TSCR process casting blank at any time.
Step 8) verification of prediction results
The continuous casting and soaking processes of the TSCR process are simulated by a high-temperature confocal microscope, the sampling quenching is carried out at different process moments, and the temperature system of the TSCR process is shown in figure 4. And carrying out corrosion statistics on samples at different moments of the TSCR process through the step 2) and the step 3), and comparing and verifying the statistical result with the predicted result of the formula (8), wherein the verification result is shown in figure 5. The verification result of fig. 5 shows that the prediction model is close to the data measured by the experiment, the average error is 0.0323, and the production can be well guided.
Claims (4)
1. The austenitic grain size prediction method for the TSCR process of the microalloyed steel is characterized by comprising the following steps of:
step 1): isothermal process simulation
Simulating heat preservation processes at different temperatures by using a high-temperature confocal microscope or other heating furnaces capable of accurately controlling the temperature, obtaining casting blank samples at different temperatures and different heat preservation times, and quenching;
step 2): grain boundary corrosion of original austenite
Grinding and polishing a sample, adding the sample into an etchant, carrying out hot erosion for 0-3 min at 50-55 ℃, taking out the sample after the surface of the sample turns black, rapidly wiping off a black film on the surface of the sample by using absorbent cotton, drying, putting the sample into an etchant, and repeating the above processes until the grain boundary is clear and distinguishable;
Step 3): austenitic grain size statistics
Counting the average grain size of austenite grains in each sample by observation of a metallographic microscope, and taking the average grain size of austenite in a sample which is subjected to heat preservation for 0s at each temperature as the initial grain size D 0-T of austenite in the heat preservation process;
Step 4): austenitic grain size isothermal prediction model establishment
By deducting and correcting the classical isothermal austenite grain prediction model, an austenite grain size prediction model in the isothermal process of the microalloyed steel is obtained and is expressed as a formula (1)
Wherein D t-T is austenite grain size when preserving heat at temperature T, μm; d 0-T is the austenite initial grain size, μm, when held at temperature T for 0 seconds; n is a time index constant; m 0 is the grain boundary migration coefficient; t is the heat preservation time, s; r is a gas constant, 8.314J/(mol.times.K); t is the heating temperature, K; q is grain boundary migration activation energy, J/(mol); gamma is grain boundary energy, 0.679J/m 2;
Bringing the average sizes of austenite grains at different moments in the heat preservation process into a formula (1), and determining values of n, M 0 and Q to obtain an austenite grain size prediction model of the microalloyed steel in the heat preservation process at the temperature;
step 5): influence relation determination of temperature on n, Q and M 0
The microalloy element exists in the steel mainly in a second phase or solid solution state in the steel, the solid solution amount of the microalloy element in the steel and the precipitation amount of the microalloy second phase change along with the change of temperature, and further the solute drag force of the microalloy element to the grain boundary and the pinning force of the second phase particles to the grain boundary change along with the change of temperature, so that the time index n, the grain boundary migration parameter M 0 and the grain boundary migration activation energy Q change along with the change of temperature; according to the values of n, Q and M 0 at different temperatures determined in the step 4), the change relation of n, Q and M 0 along with the temperature is determined by fitting:
n*＝f(T) (2)
M0＝g(T) (3)
Q＝h(T) (4)
Step 6): defining the change rule of austenite grain growth speed along with temperature in non-isothermal process
Deriving the time t in the formula (1) to obtain the austenite growth rate:
the change relation of the austenitic grain growth rate along with the temperature in the temperature changing process can be obtained by introducing the change relation (2-4) of n, Q and M 0 along with the time into the formula (2):
step 7): prediction of austenitic grain size of microalloy steel casting blank during TSCR process
Carrying out high-temperature confocal microscopic observation to obtain an austenite average grain size D 0 of the casting blank austenite initial grain size of the TSCR process when the molten steel is completely solidified; bringing the time-dependent temperature change relation t=p (T) of the TSCR process continuous casting and soaking into formula (6), and obtaining the time-dependent austenitic grain growth rate change relation in the process:
at this time, the austenite grain size at any time t in the continuous casting and soaking processes of the TSCR process is as follows:
And substituting the time t to determine the austenite grain size of any time t in the continuous casting and soaking processes of the TSCR process.
2. A method for predicting the austenite grain size of a microalloyed steel TSCR process according to claim 1, wherein: in said step 4), an austenite grain size prediction isothermal model is derived from an isothermal classical model and an austenite grain growth rate-grain boundary migration driving force relationship, wherein the isothermal classical model of austenite growth during the incubation is shown in formula (a):
the growth rate of austenite obtained by deriving time t from both sides of the equal sign is as follows:
the relationship between the austenite grain growth rate and the driving force for grain boundary migration is shown in the formula (c):
Wherein, the calculation formulas of the grain boundary migration rate M and the grain growth promoting force Δf are as follows:
the combination formulas (a) - (e) are:
m= (1/n) -1 (g) substituting formula (f) and formula (g) into formula (a), and letting n=1/n, the growth model of austenite grains in isothermal process, i.e. formula (1), can be obtained.
3. A method for predicting the austenite grain size of a microalloyed steel TSCR process according to claim 1, wherein: the austenite grain size in the step 3) is the average grain size in the sample, and the diameter of the equal-area circle is adopted as the size of each grain.
4. A method for predicting the austenite grain size of a microalloyed steel TSCR process according to claim 1, wherein: the prediction method is suitable for all steel grade components which can be produced by the TSCR process, and is suitable for the continuous casting cooling process, the heating process and the heat preservation process of the TSCR process.
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