JP2563844B2 - Steel plate material prediction method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention is
The present invention relates to a steel plate material predicting method capable of predicting a structural material such as a thick steel plate at a manufacturing stage.

[Prior Art] For example, a user of a thick steel plate or the like may be required to attach a material inspection result together with delivery of a product. In response to this requirement, manufacturers have conventionally cut out a part of the product and measured physical properties (tensile strength, toughness, etc.) of the product.

[Problems to be Solved by the Invention] However, the above-mentioned artificial characteristic measurement requires a great deal of time and affects the shipping and delivery of products.

In addition, at present, the material can only be known after it is a finished product, but in the future, the material will be predicted before manufacturing, and the manufacturing conditions will be set so that the required material can be obtained accurately and reliably. The development of such technology is desired.

Therefore, the object of the present invention is, according to the given conditions,
It is to provide a steel plate material predicting method capable of automatically predicting material quality.

[Means for Solving the Problems] In order to achieve the above-mentioned object, the present invention performs rolling based on the thickness and the steel composition of a steel slab and the rolling conditions (the steel thickness on the inlet and outlet sides, the time between passes, etc.). After that, the γ grain size is calculated, and the α grain size and the microstructure fraction of ferrite grains and the like are calculated based on the calculation result and cooling conditions (water cooling / air cooling region, cooling zone threading temperature, etc.), and I try to estimate the material.

In addition, when passing through a heating furnace, it is desirable to add heating conditions (heating temperature, holding temperature, charging time, etc.) to the input conditions in order to consider the effect of heating.

[Operation] According to the above means, the material (tensile strength, toughness, etc.)
The ferrite grain size, the microstructure fraction, and the like, which are the keys to the determination, can be obtained by sequentially performing the pre-calculation corresponding to each process from the input conditions required for the calculation. As a result, it is possible to predict the material quality at the manufacturing stage, and it is possible to set the manufacturing conditions that can reliably achieve the required material specifications, which eliminates the need for inspecting and measuring the finished product as in the past.

[Embodiment] FIG. 1 is a calculation flowchart showing a steel plate material prediction method according to the present invention. Further, FIG. 2 is an equipment configuration diagram showing an example of a steel sheet production line to which the present invention is applied. In addition,
In the following, production of thick steel plate will be described as an example.

As shown in FIG. 2, the rolling equipment includes a heating furnace 2 that heats a slab (for example, a length of 2 to 4 m, a width of 1 to 2.5 m, and a thickness of about 250 mm) before rolling, and rough rolling for rough rolling. Machine 3, finish rolling machine 4 for rolling rough rolled steel plate to required thickness, hot leveler (HL) 5 for adjusting warpage of steel plate rolled by this finishing rolling machine 4, thickness of hot leveler 5 Each of the cooling devices 6 that cools the steel plate 1 is provided.

Each of the heating furnace 2, the rough rolling mill 3, the finishing rolling mill 4, the hot leveler 5, and the cooling machine 6 is controlled by its process computer (hereinafter,
(Referred to as a process control) is connected (heating process control 7, rolling process control 8, and cooling process control 9). These process control computers are connected to a host computer (not shown) installed in the main control room 10, and these host computers operate according to the production plan such as heating process control 7, rolling process control 9 and cooling process control 9.
Manage. Further, a mechanical test system 11 for performing a mechanical test is provided on the thick steel plate 1 as a product, and the test result is sent to the central control room 10.

Next, the steel plate material prediction method shown in FIG. 1 will be described. In order to execute the processing shown in FIG. 1, software that realizes this may be created and loaded into a computer.

The steel sheet material prediction method according to the present invention is roughly classified into an initial state model, a hot working model, a precipitation model, a transformation model, and a microstructure / material model.

The initial state model 20 is heated along the heating curve by the component condition 20.1, heating condition 21 (slab thickness, heating rate, holding temperature, holding time) or slab temperature / time information 23 based on the heating model 22. The particle size 26 is calculated for a plurality of points from the surface of the plate thickness toward the center. This makes it possible to know not only the surface but also the internal state.

The heating model 22 has input conditions 24 (furnace atmosphere temperature,
Time and slab thickness). In addition, the component condition 20.1 is represented by WT%, and carbon (C), silicon (S
i), manganese (Mn), phosphorus (P), sulfur (S), copper (C
u), nickel (Ni), chrome (Cr), molybdenum (M
o), niobium (Nb), vanadium (V), titanium (T
i), tantalum (Ta), aluminum (Al) and nitrogen (N).

The hot working model 27 clarifies recovery and recrystallization by formulating the latent period of recrystallization, and determines the austenite such as grain size (grain boundary area per unit volume) and residual dislocation density during and after rolling. It is provided to stably calculate the state.

This hot working model 27 has a γ grain size of 26 and rolling temperature model 2
The calculation result 33 (rolling γ grain size, dislocation density, strain) is calculated from the temperature / pass time information 29 based on 8 and the equivalent strain / strain rate information 31 based on the strain model 30. The rolling temperature model 28 and the strain model 30 are calculated based on the rolling conditions 32 (inlet plate thickness, outlet plate thickness, heating temperature, time between passes, roll diameter, roll rotation speed).

The precipitation model 35 is provided to separate the nucleation and the growth, and further to calculate the precipitation state in the austenite during rolling and by taking into consideration the growth of individual precipitated particles. When determining the precipitate state by this precipitation model 35, the temperature information 3 by the rolling temperature model 28 is used.
4, based on the component information 36 and the calculation result 33 of the hot working model, the solid solution amount and precipitation amount of the precipitation element (for example, Ti, Ta, V, Nb),
The average particle size of the precipitates is calculated and output as the precipitate state 37.

The transformation model 38 separates nucleation and growth, and formulates each as a function of the precipitate state (precipitation amount, precipitate average grain size) and dislocation density. It is also provided to estimate the post-transformation microstructure such as grain size, pearlite and bainite fraction.

The transformation model 38 outputs a calculation result 43 (ferrite grain size, microstructure fraction, average generation temperature) based on the temperature information 40 based on the cooling temperature model 39. The cooling temperature model
39 is the cooling condition 42 (air cooling / water cooling classification, water amount density, stripping speed in the cooling device, composition) and the transformation amount 41 according to the transformation model 38.
Is calculated based on each of the above.

The structure / material model 44 is provided to calculate the material by separating and formulating the effects of solid solution strengthening, precipitation hardening, and ferrite grain size, and the precipitate state 37, the calculation result 43, and the component information 36 The material is output based on each.

Next, details of the calculation of each model will be described with reference to FIGS. 3 to 7.

FIG. 3 is a flowchart showing details of the processing of the initial state model 20.

Input component 25, then slab temperature / time information 23
Or, input the slab reheating from heating condition 21,
Set constants and initial values required for calculation (step 20)
1). Then, the state diagram is calculated (step 202).

Next, it is judged whether or not the heating time exceeds the set value (step 203), and if not, the solid solution amount of the precipitate and the particle size of the precipitate are calculated (step 204).

After that, if it is within the set time, the γ grain growth is calculated. However, as is well known, a steel material transforms from an α-grain state or θ (cementite) to a γ-grain state due to a change in crystal structure as the temperature rises.

Therefore, the growth state of this γ grain is calculated by a method different for each state calculated in step 202. That is, depending on the temperature, in addition to the γ single phase region, γ + α region, γ + α + θ
The γ grain growth is calculated for each of the regions (step 205).

FIG. 4 is a flowchart showing details of the processing of the hot working model 7.

This process consists of heating γ particle size 26, temperature / pass time information 29
And the equivalent strain / strain rate information 31 are input conditions.
When the steel sheet is passed through multiple passes for rolling, the dislocation density changes as shown in FIG. 5 in the process of rolling → recovery → recrystallization between passes. Therefore, it is necessary to calculate recrystallization and recovery for each pass. The calculation of γ grain size, average dislocation density, etc. for each pass and after rolling is performed as follows.

First, in order to know the internal state of the steel sheet, m positions are determined from the surface toward the center at regular intervals (step 271). Then, for each of these, the grain boundary area per unit volume of γ after rolling is calculated based on the input conditions (step 272).

When the rolling reduction amount is large, recrystallization, that is, dynamic recrystallization occurs instantaneously. Therefore, it is determined whether dynamic recrystallization has occurred, and if it has occurred, the dislocation density and the recrystallized grain size are calculated (step 273). If dynamic recrystallization is not completed, calculate the time until recrystallization occurs after this,
Further, the recovery time and static recrystallization are calculated (recrystallization rate, recrystallized grain size) (step 274).

When recrystallization is completed (recrystallization rate =
4) The grain growth is calculated (step 275), and the average grain size and average dislocation density of crystal grains are calculated (step 276). By repeating this up to the final pass, final pass information (austenite grain boundary area at plate thickness m point and its dislocation density) is obtained (step 277).

FIG. 6 is a flow chart showing the processing of the transformation model 38 in detail.

The meaning of the phase diagram calculation in the present invention is to first calculate thermodynamically in advance which phase may exist at each temperature based on component information including solid solution / precipitation information after rolling. Then, it is intended to reduce the calculation time. That is, if the temperature does not reach the temperature at which the phase change (transformation) occurs thermodynamically, the process does not proceed to the next step. When the temperature calculated sequentially reaches the temperature at which the phase change can occur, the process proceeds to the next step. If the thermodynamic transformation is performed for each temperature that is sequentially calculated, the calculation takes too much time, and therefore the state diagram is calculated after the rolling model is completed. The temperature to be calculated may be given at arbitrary intervals,
It is not necessary to use temperatures that are calculated sequentially.

The transformation behavior of steel is γ state before transformation (γ grain size or intergranular interfacial area per unit volume, residual dislocation density, solid solution of precipitates,
Precipitation state) and cooling rate. This model inputs these from 33, 37, 40, and the progress of transformation and grain boundary ferrite, intragranular ferrite, pearlite, bainite, martensite, and each microstructure, The particle size and the fraction are calculated. The calculation method is as follows. First, the state diagram of the component is calculated (step 381) to obtain the condition (temperature region) in which each tissue can be thermodynamically generated. The calculation of the state diagram in step 381 is the calculation of the state diagram in the process of the transformation model 38 in FIG.
This is different from the calculation of the state diagram in the processing of the initial state model 20 in the figure (step 202 in FIG. 3). Next, the increment of the transformation amount within an arbitrary minute time (step 383) for the structure determined to be producible and the increment of the number of grains formed during this period for ferrite (step 382) are determined.

If ferrite is formed, it is judged whether the shape is acicular or granular. If it is granular, the number of grains formed in step 382 is incremented by the number of grains of granular ferrite, and the transformation obtained in step 383 is performed. The increment of the amount is taken as the increment of the amount of granular ferrite, and if it is acicular, only the increment of the transformation amount is obtained (step 384). Next, in order to feed back the heat generated by the transformation to the cooling temperature model, the temperature change according to the transformation amount obtained in step 383 is calculated (step 38).
Five). The above calculation is repeated for each plate thickness position until the end of cooling, and the transformation amount and the increment of the granular ferrite grain number are added to obtain each microstructure fraction of the final structure, the fraction of granular ferrite and the grain number. be able to. Furthermore, after the calculation is completed for the m points in the plate thickness direction (step 38).
In 6), the grain size of the granular ferrite is obtained from the number of grains and the fraction (step 387). Further, based on the results of steps 383 and 385, the average temperature (average generation temperature) generated by each of ferrite, pearlite, and bainite is calculated (step 38).
8). The reason for separating the ferrite into granular and acicular shapes in the above calculation is that the granular and acicular shapes are related to the material, and it is possible to predict the material with high accuracy. This is because Also, the average generation temperature is required because the material differs depending on the generated temperature,
It is used in the hardness calculation of the structure and material model 44 described later.

FIG. 7 is a flowchart showing details of the processing of the structure / material model 44.

Here, hardness, tensile strength, which expresses the material of the steel plate 1,
And to calculate toughness. First, the hardness of each of ferrite, bainite, and pearlite is calculated based on the input conditions of the component information 36, the solid solution Nb information 37, and the calculation result 43 (step 441).

Further, the yield point is calculated based on the particle size information and the component information (step 442), and then the tensile strength is calculated using the hardness calculation value obtained in step 441 (step 443). Further, the toughness is calculated based on each of the particle size information, the component information and the calculated hardness value (step 444). The above processing is executed for m points (step 445), and when all the processing is executed, the processing ends, and the material prediction can be performed. The result is stored in a recording medium such as a floppy disk and is printed by a printer.

<Test Example> FIGS. 8A and 8B show the results of a test example according to the present invention.

In FIGS. 8 (a) and 8 (b), 6 lots of thick steel plates are taken as samples and the length direction (L) and the width direction (C) for each are taken.
Comparisons are made on samples cut into pieces. Here, we show a comparison of the actual measurement value with a microscope and the calculated value by the above-mentioned prediction method, but as is clear from the figure, the actual measurement value and the calculated value were close and it was possible to predict with extremely high accuracy. I understand.

As described above, since highly reliable prediction is possible, in the future, it becomes possible to easily calculate the manufacturing conditions of the product according to the material requested by the customer.

Figures 9, 10 and 11 show the relationship between the calculated and measured values of the yield point (YP), tensile strength (TS) and toughness (vTrs) when an integrated simulation was performed. There is.

In the above description, the slab reheating process for thick steel plates is taken as an example, but the present invention can be applied to the conventional hot rolled steel and the slab direct-feeding process.

[Advantages of the Invention] Since the present invention is configured as described above, it has the effects described below.

In the steel sheet material prediction method according to claim (1), with respect to a steel sheet manufactured by rolling and cooling a steel slab produced by continuous casting or a steel ingot method, the thickness of the slab, steel component information, and rolling Calculate the γ grain size after rolling based on the conditions,
Based on this calculation result and cooling conditions, the α grain size, the microstructure fraction, and the average generation temperature of each microstructure are calculated, and the material of the steel sheet is estimated based on these, so the material prediction should be performed at the manufacturing stage. In addition, the manufacturing conditions that can surely realize the required material specifications can be set, and inspection and measurement for the finished product are not required unlike the conventional case.

In the steel sheet material prediction method according to claim (2), since the heating condition by the heating furnace is added to the input condition in addition to the rolling condition, the effect of passing through the heating furnace can be added to the material prediction. It enables more accurate material prediction.

[Brief description of drawings]

FIG. 1 is a calculation flowchart showing a steel sheet material prediction method according to the present invention, FIG. 2 is an equipment configuration diagram showing an outline of a steel sheet production line to which the present invention is applied, and FIG. 3 shows details of processing of an initial state model. Flow chart, FIG. 4 is a flow chart showing details of processing of the hot working model, FIG. 5 is a characteristic diagram showing change of dislocation density during rolling, FIG. 6 is a flow chart showing details of processing of transformation model, FIG. Is a flow chart showing the details of the processing of the structure / material model, FIGS. 8 (a) and 8 (b) are comparison diagrams showing the results of the embodiment of the present invention, and FIGS. 9, 10, and 11 are consistent simulations. It is a characteristic view which shows the relationship between each calculated value of yield point, tensile strength, and toughness when it performed, and an actual value. In the figure, 1: thick steel plate 2: heating furnace 3: rough rolling mill 4: finishing rolling mill 6: cooling device 7: heating process computer 8: rolling process computer 9: cooling process computer 10: central control room 20: initial state model 21: Heating condition 27: Hot working model 32: Rolling condition 36: Composition information 38: Transformation model 42: Cooling condition 44: Structure / material model

Front Page Continuation (72) Inventor Kiyoshi Nishioka 1 Kimitsu, Kimitsu-shi, Chiba Nippon Steel Stock Company Kimitsu Works (72) Inventor Kazuo Funato 1 Kimitsu, Chiba New Japan Stock Company Kimitsu In-house (72) Inventor Atsuhiko Yoshie 1-1-1, Emitsu, Hachiman-to-ku, Kitakyushu, Fukuoka Prefecture Inside the 3rd Technical Research Laboratory, Nippon Steel Corporation (72) In-house Masaaki Fujioka 1-Emitsu, Ewata, Hachiman-ku, Kitakyushu, Fukuoka 1-1 Nippon Steel Co., Ltd. 3rd Technical Laboratory (72) Inventor Hirofumi Morikawa 1618 Ida, Nakahara-ku, Kawasaki-shi, Kanagawa Pref. Nippon Steel Co., Ltd. 1st Technical Laboratory (56) Reference JP 62- 158816 (JP, A)

Claims (2)

(57) [Claims] 1. A steel sheet produced by rolling and cooling a steel slab produced by continuous casting or a steel ingot method, and after the rolling, γ after rolling based on the thickness of the slab and the steel composition information and rolling conditions. The particle size is calculated, the phase diagram of the component is calculated based on this calculation result and cooling conditions, and it is determined whether or not each structure can be thermodynamically transformed at any minute time, and transformation is possible. In that case, the increment of the transformation amount for the structure is calculated, and at the same time, the transformation latent heat corresponding to the transformation amount is calculated and the cooling temperature is corrected until the transformation is completed. A steel plate material prediction method, comprising estimating a material of the steel plate by performing a series of calculations for calculating an average generation temperature of each structure for a plurality of positions in the thickness direction of the plate thickness. 2. The steel sheet material prediction method according to claim 1, wherein, in addition to the rolling condition, a heating condition by a heating furnace is used for calculating the γ grain size.