JP2509484B2 - Steel plate material prediction method - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a steel sheet material predicting method capable of predicting a structural material such as a thick steel sheet at a manufacturing stage without physically evaluating the product.

[0002]

2. Description of the Related Art For example, a user of a thick steel plate or the like may be required to attach a material inspection result at the time of product delivery. 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.

[0003]

However, the above-mentioned artificial characteristic measurement requires a great deal of time and affects the shipping and delivery of products.

At present, it is possible to know the material only after it is a finished product, but in the future, it is possible to predict the material before manufacturing and obtain the required material accurately and surely. The development of technology that sets conditions is desired.

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

[0006]

In order to achieve the above object, the present invention provides a slab thickness and steel composition, casting conditions, soaking / diffusion heat treatment conditions, preliminary rolling conditions, rolling conditions (inlet and outlet sides). Γ after rolling based on the steel plate thickness, time between passes, etc.)
The structure is calculated, and the unit of the metal structure after transformation (ferrite grain size, lath size of martensite, etc.) and structure fraction based on the calculation result and cooling conditions (water cooling / air cooling region, cooling zone strip speed, etc.) Calculate the calculation result and the tempering conditions (tempering temperature, holding time in tempering furnace, temperature rising rate, etc.) to calculate the precipitation state of precipitates in the tempered structure and the decomposition state of residual γ, From these, the material of the steel sheet is estimated.

[0007]

According to the above-mentioned means, the ferrite grain size, the microstructure fraction, etc., which are the keys to the judgment of the material (tensile strength, toughness, etc.), can be calculated from the input conditions necessary for each calculation to each process. Corresponding pre-calculations can be performed while sequentially executing. 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.

[0008]

DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 is a calculation flowchart showing a steel sheet 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. It should be noted that in the following, manufacturing of a thick steel plate will be described as an example.

As shown in FIG. 2, as the steel plate manufacturing equipment, there are a steel making step 102 for adjusting the components, a continuous casting equipment 103 for casting molten steel, or an equipment 104 for performing ingot casting. Furthermore, there are a soaking diffusion treatment step 105 for reducing segregation of the slab by high-temperature heat treatment, and a preliminary rolling affirmation 106 for agglomerating the ingot after the ingot or reducing the thickness of the continuously cast slab. This soaking diffusion process 10
5. In the preliminary rolling step 106, one or both of them may be used depending on the case, and either one may be used first. Moreover, it is not necessary to use it.

The rolling equipment comprises a heating furnace for heating 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, a rough rolling mill 3 for rough rolling, and a rough rolling. Finishing mill for rolling the rolled steel sheet to the required thickness 4, hot leveler (HL) 5 for adjusting the warpage of the steel sheet rolled by the finishing rolling machine 5, this hot leveler 5
Each of the cooling devices 6 that cools the thick steel plate 1 that has exited.

Each of the heating furnace 2, the rough rolling mill 3, the finish rolling mill 4, the hot leveler 5 and the cooling machine 6 is controlled by a process computer (hereinafter, referred to as a process computer) in order to control its driving and obtain information on its operation. It is connected).
In addition, a process computer is connected to control the driving of the steel making process, continuous casting, and ingot making process, and to obtain information during operation (steel making process control 101, heating process control 7, rolling process control 8, cooling process control 9). And heat treatment process control 2001). These process control stations are located in the central control room 10
Connected to a host computer (not shown) installed in
This host computer manages the steelmaking process control 101, the heating process control 7, the rolling process control 8, the cooling process control 9 and the heat treatment process control 2001 according to the production plan. Also,
A mechanical test system 11 for performing a mechanical test is provided on the thick steel plate 1 that has become a product, and the test result is sent to the main control room 10.

Next, the steel plate material prediction method shown in FIG. 1 will be described. In order to execute the processing of 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 a casting model, a soaking diffusion heat treatment model, a preliminary rolling model, a rolling model, a heat treatment model, and a structure / material model.

The casting model 107 is a model for calculating the state of precipitates, the state of segregation and the metallographic structure after continuous casting or ingot formation, based on the component conditions and the casting conditions. The details are shown in FIG. On the street.

The soaking diffusion heat treatment model 108 is based on the segregation concentration, precipitate state and metal structure calculated by the casting model, heating during soaking diffusion heat treatment, temperature holding, precipitation of elements through transformation, This is a model for calculating the state of diffusion and the metal structure, and the details are as shown in FIG.

The pre-rolling model 109 is based on the segregation concentration, the state of precipitates and the metal structure calculated by the casting model or the soaking diffusion heat treatment model, and heating, temperature holding, rolling and transformation during the pre-rolling or slab rolling. This is a model for calculating the precipitation of elements, the diffusion of elements, and the state of the metal structure through. The details are as shown in FIG.

The rolling model 110 is based on the segregation concentration, the state of precipitates, and the metal structure calculated by the casting model, the soaking diffusion heat treatment model, or the preliminary rolling model. This is a model for calculating the state of element precipitation, element diffusion through rolling and transformation, and the metal structure, and the details are the same as those shown in FIG.

The heat treatment model 1001 is based on the segregation concentration, precipitate state, and metallographic structure calculated by the casting model, the soaking diffusion heat treatment model, the preliminary rolling model and the rolling model.
This is a model for calculating the state of element precipitation, element diffusion, and metallographic structure through heating, holding, and transformation during heat treatment, and the details thereof are as shown in FIG.

The structure / material model 111 is a model provided for calculating the material by separating and formulating the effects of solid solution strengthening, precipitation hardening, and the size of the metal structure unit. This is as shown in FIG.

Next, the details of the calculation of each model are shown in FIG.
~ It demonstrates with reference to FIG.

FIG. 3 is a flow chart showing details of the processing of the casting model 107. Input ingredient 115,
Next, in the case of continuous casting, the temperature history 116 calculated by the casting temperature model 114 is based on the slab thickness and drawing speed, the cooling water amount density and the elapsed time after drawing, and in the case of ingot casting, based on the steel ingot size. Is input and an initial state required for calculation is set (step 118). Next, the state diagram is calculated.

The component condition 115 is represented by WT%,
Carbon (C), silicon (Si), manganese (Mn), phosphorus (P), sulfur (S), copper (Cu), nickel (Ni),
Chromium (Cr), molybdenum (Mo), niobium (N
b), vanadium (V), titanium (Ti), tantalum (Ta), aluminum (Al), boron (B), tungsten (W), cobalt (Co), calcium (C)
a), rare earth elements (Rem), nitrogen (N), oxygen (O), and the like.

Next, the solid solution amount of the precipitate and the particle size of the precipitate are calculated (step 121). Next, it is determined from the state diagram whether or not the γ / α transformation has started (step 133), and if not, it is determined whether or not the temperature has decreased to the γ1 phase (step 122). If not, the fractions of the liquid phase, the δ phase, and the γ phase are calculated (step 123), and the fractions of the equiaxed crystal and the columnar crystal are calculated for the γ phase (step 12).
4). In addition, the distribution of alloying elements among phases is calculated (step 126) and the segregation concentration is calculated (step 1).
27). This calculation is repeated until the temperature reaches the γ1 phase. When it is determined in step 122 that the temperature is not higher than the γ1 phase, grain growth of γ grain size (step 125) and change in segregation concentration due to diffusion of alloying elements (step 129) are calculated. This calculation is repeated until the temperature at which the γ / α transformation starts. Next, when it is determined in step 133 that the temperature has dropped to the γ / α transformation start temperature, the transformation model 38 determines the ferrite grain size, the microstructure fraction of each phase, the state of precipitates,
The segregation concentration is calculated (step 130). The details of this transformation model are as shown in FIG.

FIG. 8 is a flow chart showing the processing of the transformation model 38 in detail.

The transformation behavior of steel is affected by the γ state before transformation (γ grain size or grain boundary area per unit volume, residual dislocation density , solid solution / precipitation state of precipitate), and cooling rate. This model inputs these from 33, 37, 40, and
The influence of the component variation due to the slab position is input from the segregation state 128, and the progress of the transformation and the grain boundary ferrite, intragranular ferrite, pearlite, bainite, and martensite structure fractions, and the ferrite with a granular shape Is to calculate the particle size and fraction. 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. 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 obtained.

If ferrite is produced, it is judged whether the shape is acicular or granular. If it is granular, the number of produced particles obtained in step 382 is incremented by the number of granular ferrite particles, and in step 383. The increment of the obtained transformation 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 385). 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 the granular ferrite and the grain number. be able to. Further, after the calculation is completed for m points in the plate thickness direction (step 386), 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 388). 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,
This is because it is possible to accurately predict the material quality. The average generation temperature is necessary because the material differs depending on the generated temperature, and is used in the hardness calculation of the structure / material model 111 described above.

FIG. 4 is a flow chart showing the details of the soaking diffusion heat treatment model. The calculation mainly consists of calculation of heating, retention and transformation after extraction from the furnace.

Segregation concentration 20.2, state of precipitate 20.
3. Input the metallographic structure 20.4. Further, the temperature history calculated by the heating model 22 from the slab thickness, furnace atmosphere temperature, and time is input. Based on this, the heated γ grain size and the state of the precipitate are calculated by the initial state model 20, and the segregation concentration is calculated by the diffusion model 128. These calculation results 26 are input to the transformation model 38. The temperature calculated from the cooling model 39 is also input to the transformation model 38 to calculate the ferrite grain size, the microstructure fraction of each phase, the state of precipitates, the segregation concentration, and the average formation temperature (43). This transformation model is the same as that shown in FIG.

FIG. 9 is a flow chart showing details of the initial state model. Segregation concentration 25.2, precipitate state 2
Input 5.1 and metal structure 25.3.

Next, the slab heating history is input from the slab temperature / time information 23 or the heating conditions 21, and constants and initial values necessary for calculation are set (step 201). Next, 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, the γ grain growth is calculated within the set time. However, as is well known, in steel materials, α-grain state or θ
It transforms from (cementite) to the γ-grain state.

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

FIG. 5 is a flow chart showing the details of the preliminary rolling model. The calculation mainly consists of calculation of heating, retention, calculation of rolling process and calculation of transformation after rolling. The segregation concentration 20.2, the state of precipitate 20.3, and the metallographic structure 20.1 are input. Further, the temperature history calculated by the heating model 22 from the slab thickness, furnace atmosphere temperature, and time is input. Based on this, the heating γ grain size and the state of the precipitate are calculated by the initial state model 20, and the segregation concentration is calculated by the diffusion model 128. These calculation results 26 are input to the hot working model. This initial state model is the same as that described in FIG.

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

The hot working model 27 includes a γ grain size precipitate state, a segregation concentration 26, temperature / pass time information 29 based on a rolling temperature model 28, and equivalent strain / strain rate information 31 based on a strain model 30. The calculation result 33 (rolling γ grain size, transition density, strain) is calculated according to. In addition, the rolling temperature model 28 and the strain model 30 are rolling conditions 32 (inlet plate thickness,
It is calculated based on the outlet plate thickness, heating temperature, time between passes, roll diameter, and roll speed.

The precipitation model 35 is provided to separate nucleation and growth and to calculate the precipitation state in austenite during rolling and after rolling by considering the growth of individual precipitation particles. The rolling temperature model 2 is used to determine the precipitate state by the precipitation model 35.
8 based on the temperature information 34, the component information 36, and the calculation result 33 of the hot working model, the precipitation element (for example, Ti,
The solid solution amount of Ta, V, Nb), the precipitation amount, and the average particle size of the precipitates are calculated and output as the precipitate state 37.

FIG. 10 is a flow chart showing details of the processing of the hot working model 27.

This processing is performed with the heating γ grain size 26, the state of precipitate 26.1, the segregation concentration 26.2, the temperature / interpass time information 29 and the equivalent strain / strain rate information 31 as input conditions. When performing rolling by several passes of the steel sheet, between each pass, in the course of passing through the rolling → Recovery → recrystallization, rolling
The density changes as shown in FIG. 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 central portion at constant 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 the dynamic recrystallization has occurred, if occurring dislocations densely
The degree and the recrystallized grain size are calculated (step 273). When the dynamic recrystallization is not completed, the time until the recrystallization occurs after this is calculated, and further the reduction of dislocation density due to the recovery and the static recrystallization are calculated (recrystallization rate, recrystallized grain size) ( Step 274).

When the recrystallization is completed,
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 process until the final pass, the final pass information (the austenite grain boundary area at the thickness m point and its dislocation density)
Degree ) is obtained (step 277).

The calculation result 33 of the hot working model, the state of precipitate 37, and the temperature history 40 after the completion of rolling calculated by the cooling temperature model are input to the transformation model 38. The transformation model 38 is the same as that described with reference to FIG.

The flow chart showing the details of the rolling model is the same as that of the preliminary rolling model shown in FIG. The calculation mainly consists of heating, calculation during retention, calculation of positive rolling and calculation of transformation after completion of rolling. Figure 5 shows the model structure
Since it is the same as the pre-rolling model described above, the description thereof is omitted. .

FIG. 6 is a flowchart showing details of the processing of the heat treatment model 1001.

The heat treatment here means tempering and is influenced by the transformation structure / precipitation state, segregation state and heat treatment conditions until cooling. First, this model calculates the precipitation amount of carbon dissolved in supersaturation and the decomposition amount of retained austenite based on the calculation result 43 of the transformation model 38, the calculation result 37 of the precipitation model 35, and the temperature / time information 1003 of the heat treatment (step 6001). ). Then, the cementitization of the precipitated carbide and the growth of the precipitate are calculated (steps 6002, 600
3) Do. After the above calculation is completed for m points in the plate thickness direction (step 6004), final microstructure information and cementite / precipitate size are calculated (step 6005).

FIG. 7 is a flow chart showing details of the processing of the structure-material model 44.

Here, the purpose is to calculate the yield point, the tensile strength and the toughness which express the material of the steel sheet 1. First, the hardness of each of ferrite, bainite, pearlite, and martensite is calculated based on the input conditions of the component information 36 and the calculation result 1006 (structure and carbide / precipitate information) in the heat treatment model (step 4).
41).

Further, the heat treatment model calculation result 10 is also obtained.
The yield point is calculated based on 06 (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 the grain size information, the component information, the carbide / precipitate information, etc., which are the results of the heat treatment model calculation (step 44).
4). The above process is executed for m points (step 445), and when all the processes are executed, the process 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> FIG. 12 shows the chemical composition of the steel used in the present invention, FIG.
3 shows the manufacturing conditions, and FIG. 14 shows the measured values of the organization factors such as the particle size and the fraction and the material and the calculated values by the above prediction method.

As is apparent from FIG. 14, the measured value and the calculated value are close to each other, and it can be understood that the prediction can be performed with extremely high accuracy.

Since highly reliable prediction is possible in this way, in the future it will also be possible to easily calculate the manufacturing conditions of the product according to the material requested by the customer.

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 all hot rolled steel products and the slab direct-feeding process.

[0053]

Since the present invention is configured as described above, it has the following effects. In the steel sheet material prediction method according to claim 1, a steel slab produced by continuous casting or a steel ingot method is subjected to uniform diffusion heat treatment, pre-rolling, either or both, or rolling, cooling and heat treatment as they are after casting. For the steel sheet produced by applying, calculate the metallographic structure of the steel piece, the state of precipitates, the state of segregation based on the steel composition information and casting conditions, and the steel piece based on this calculation result and uniform diffusion heat treatment information. Of the metal structure, the state of precipitates, the state of segregation, the metal structure of the steel piece, the state of the precipitates, the state of segregation is calculated based on the calculation result and the pre-rolling conditions, Calculate the metal structure of the steel sheet based on the, the state of precipitates, the state of segregation, calculate the metallographic structure of the steel sheet, the state of precipitates, the state of segregation based on this calculation result and heat treatment conditions, by these Since the material of the plate is estimated, it is possible to predict the material at the manufacturing stage, and it is possible to set the manufacturing conditions that can surely realize the required material specifications. Inspection measurement becomes unnecessary.

[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.

FIG. 3 is a flowchart showing details of a casting model.

FIG. 4 is a flowchart showing details of a soaking diffusion heat treatment model.

FIG. 5 is a flowchart showing details of a preliminary rolling model and a rolling model.

FIG. 6 is a flowchart showing details of a heat treatment model.

FIG. 7 is a flowchart showing details of a texture-material model.

FIG. 8 is a flowchart showing details of a transformation model.

FIG. 9 is a flowchart showing details of an initial state model.

FIG. 10 is a flowchart showing details of a hot working model.

FIG. 11 is a characteristic diagram showing changes in dislocation density during rolling.

FIG. 12 is a diagram showing the chemical composition of steel used in the present invention.

FIG. 13 is a diagram showing manufacturing conditions of steel used in the present invention.

FIG. 14 is a diagram showing actually measured values of organization factors such as particle size and fraction and materials, and calculated values by the above prediction method.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 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 27 Hot working model 38 Transformation model 101 Steel making ingot process Computer 107 Casting model 108 Soaking diffusion heat treatment model 109 Pre-rolling model 110 Rolling model 111 Structure-material model 1001 Heat treatment model 2001 Heat treatment process computer 2002 Heat treatment equipment

 ─────────────────────────────────────────────────── ─── Continuation of front page (72) Inventor Shinichi Shimomura 1 Kimitsu, Kimitsu-shi, Chiba Nippon Steel Co., Ltd. Inside Kimitsu Works (56) Reference JP-A-4-369003 (JP, A) Flat 4-361158 (JP, A)

Claims (1)

(57) [Claims] 1. A steel slab produced by continuous casting or an ingot method is subjected to soaking / diffusion heat treatment, pre-rolling, or both, or is directly cast, then rolled, cooled and heat-treated. For the steel sheet, the composition of the billet, the metal structure of the steel sheet based on the manufacturing conditions, the precipitation state of precipitates, the concentration of segregation, in the steel sheet material prediction method of estimating the material of the steel sheet, prior to rolling Calculate the metal structure of the steel before heating, the precipitation state of precipitates, the concentration of segregation, the metal structure after continuous casting or ingot casting, the state of precipitates, the state of segregation based on the component conditions and casting conditions The metallographic structure, segregation concentration, calculated by the casting model that is the model,
Based on the state of precipitates, heating during soaking diffusion heat treatment, temperature maintenance, state of metal structure through transformation, state of precipitates, soaking diffusion heat treatment model which is a model for calculating segregation state, casting model and Based on the metallographic structure, segregation concentration, and precipitate state calculated by the soaking diffusion heat treatment model, the state of the metallographic structure and the state of precipitates during heating, temperature maintenance, rolling, and transformation during preliminary rolling or slabbing , A pre-rolling model that is a model for calculating the state of segregation, and further the metal structure of the steel material after tempering following rolling and cooling, the segregation concentration, the state of precipitates are the component conditions, the metal structure after cooling, segregation A steel sheet material prediction method, characterized in that it is calculated by a heat treatment model, which is a model calculated based on the concentration and the state of precipitates.