JP2672572B2 - Manufacturing method of hot rolled steel - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial field of application> The present invention relates to a method for producing a steel material such as a thick plate and a hot strip by hot rolling based on the relationship between the required material and the structural constitution that produces it. is there.

<Prior Art> Generally, the material quality of steel can be expressed as the sum of the terms determined by the microstructure, the grain size, and the other strengthening mechanism terms.

For example, the tensile strength (TS) can be displayed as in equation (28).

TS = f (σ _f , σ _p , σ _b , σ _m , V _f , V _p V _b , V _m , d _f , β)-(28) where σ is a parameter indicating the strength of each tissue, V
Is a parameter indicating the volume fraction of each tissue, and d is the particle size.

The subscripts f, p, b and m indicate ferrite, pearlite, bainite and martensite, respectively. Note that β
Are other strengthening mechanisms (eg precipitation strengthening, work strengthening, etc.)
Is a parameter that represents.

<Problems to be Solved by the Invention> Conventionally, as to the strength estimation model, there is only a simple multiple regression in which a component, a hot rolling end temperature, a winding or cooling stop temperature are variables, and a microstructure,
The influence of ferrite grain size etc. is not considered.

The use of such an inexact multiple regression model is as follows.
The model targets only products in one rolling mill, the manufacturing conditions are also started from constant heating conditions, and the rolling conditions including the rolling end temperature that determines the austenite grain size before transformation include the product thickness and composition, and the transformation This is because the cooling temperature range and cooling rate that control the behavior are used under strong high-speed conditions such that they are automatically determined from the rolling end temperature and the winding temperature.

For this reason, the conventional model can be used only under the specific conditions as described above, and the range of the material of the rolled material can be changed by applying it to other lines or by widely changing the rolling conditions and the subsequent cooling conditions. I could not meet the request to expand.

Also, there have already been attempts to adjust the material using a model in which the material of the steel material is described in association with the microstructure, for example, Japanese Patent Publication No. 58-2246 and Japanese Patent Publication No. 59-67324. Proposals are being made.

Japanese Examined Patent Publication No. 58-2246 describes a method for obtaining a transformation structure volume ratio from a cooling curve and predicting the material quality of a steel material from this transformation structure volume ratio.The hardness of the structure, the grain size, and the effect of hot rolling are described. Is not considered at all.

In JP-A-59-67324, the final reached austenite grain size and residual strain are calculated from the rolling load of the actual rolling mill,
The method of calculating the ferrite grain size in the subsequent cooling process and estimating the strength from the structure strengthening parameter obtained from the ferrite grain size and cooling rate is described, but it is possible to predict the hardness and volume fraction of the microstructure. Absent.

As a solution to these problems, the inventors of the present invention provide a manufacturing method that can be applied to the general manufacturing of steel products by hot rolling in view of the relationship between the required material and the structural constitution that gives rise to it, and JP-A-62-158816. We are proposing the publication of the issue.

However, the inventors of the present invention have found that the following research experiment
It has been discovered that the proposal of the above-mentioned Japanese Patent Laid-Open No. 62-158816 includes a portion with poor accuracy in the region where the content of Si is 0.1% by weight or more.

The present invention has been made as a result of an experimental study based on this new knowledge, and is more accurate for carbon steel containing Si in the vicinity of 0.1% by weight, and for carbon steel containing 0.2% or more of Si at all. It provides a new applicable manufacturing method.

<Means for Solving the Problems> The present invention is based on the above-mentioned findings, and solves the above-mentioned conventional drawbacks by controlling the essential factors that govern the material quality of hot-rolled steel products. It is to provide a method for manufacturing, to calculate the austenite grain size and residual strain immediately before cooling from the rolling conditions not performed in the prior art for this, each microstructure from the subsequent cooling conditions (ferrite, pearlite, Bainite, martensite, etc.) volume ratio and hardness and ferrite grain size are calculated accurately, and the material of the steel material is calculated from these micro factors, and based on this, the target material of the steel material after hot rolling. The manufacturing conditions of the steel materials are adjusted so that (1) a carbon steel containing 0.1% by weight or more of Si is rolled at an Ar ₃ point temperature or higher and then cooled by the formula obtained from the actual condition. To manufacture And at least a heating rate of the heating furnace exit side average γ grain size α (K / min), the heating temperature T ₀ (K), based on the heating conditions consisting of isothermal holding time t ₀ (min) (1) equation in Calculated, dynamic recrystallization, static recrystallization, unrecrystallized space factor
f _D , f _S , f _N and grain sizes of dynamic recrystallization, static recrystallization and unrecrystallized, d _D (μm) and d _S (μm) and d _N (μm), respectively, at least at the processing temperature. Ti (K), additional strain ε _i , strain rate
_{When the} additional strain ε given by the equation (2) is larger than the critical strain ε _C of the dynamic recrystallization based on the processing condition consisting of _i (S ⁻¹ ), dynamic recrystallization, static recrystallization, Divided into 3 groups of unrecrystallized and divided into 2 groups of static recrystallized and unrecrystallized when the additional strain ε is smaller than the critical strain ε _C of dynamic recrystallization (3).
(4), (5), (6), (7), and (8) are calculated, and grain growth between passes is calculated using Eq. (9) for dynamic recrystallization and Eq. (10) for static recrystallization. Then, calculate the average austenite grain size immediately before the next pass, ▲ ▼ _i (μm) (11)
In the equation, Δε _i which is the residual strain amount immediately before the next pass is obtained by the equation (12), and in the next pass, the additional strain ε _{i + 1} is added to the residual strain amount Δε _i of the previous pass to obtain the execution strain, The recrystallization behavior was calculated from this execution strain and the average austenite grain size ▲ ▼ _i (μm) immediately before the next pass in the same manner as above, and this was repeated for a predetermined number of passes, and the average austenite grain size before cooling was started.
(Μm) and residual strain amount Δε are calculated, and the average austenite grain size ▲ ▼ (μm) before the start of cooling and residual strain amount Δε are used as initial conditions, and the average consumption of the latent period for transformation starts in Eq. (16). Obtain the transformation temperature Ae ₃ (K), find the boundary between the transformation temperature of ferrite and pearlite and the transformation temperature of bainite with TPE (K) using equation (17), and estimate the isothermal transformation rate of ferrite, pearlite, and bainite. Formula (13) (1
4) Applying the transformation rate addition rule to the cooling curve already obtained by the equation (15), and calculating the volume ratio of the final ferrite structure, pearlite structure, and bainite structure from 1 to the ferrite and pearlite The volume ratio of the martensite structure is obtained by subtracting the total volume ratio of each structure of bainite. In the above calculation of the transformation rate, the hardness H _f0 of the ferrite that appeared at 923K or higher is expressed by equation (18), and is less than 923K 723K
The hardness H _f0 of the ferrite that appears above is calculated along each cooling curve by equation (19), and the hardness H _f of the final ferrite is calculated by equation (20), and the final hardness H _p of pearlite is calculated by equation (21). The final hardness H _b of the bainite is calculated by the formula (22), and the final ferrite grain size d _f (μm) is calculated by the formulas (23) and (24).

From the above, ferrite particle size d _f (μm), ferrite volume fraction X _f , ferrite hardness H _f , pearlite volume fraction X _p , pearlite hardness H _p , bainite volume fraction X _b , bainite hardness
The tensile strength TS (kgf / mm ² ) and the yield strength YS (kgf / mm ² ) are calculated from _Eqs. (25) to (27) based on H _b and martensite volume ratio X _fm.
And calculate the total elongation T.E1 (%), compare these with the target material and adjust the manufacturing conditions consisting of at least the rolling conditions and cooling conditions in the above actual conditions so that the difference is always within the required range. It is characterized by doing.

d _γ0 ² = (k1 · α ^-a ) ² + A1 · exp (−A ₂ / T ₀ ) t
₀ ^b (1) k ₁ = B [(T ₀ −1173) / 100] ³ + C a, b, A ₁ , A ₂ , B and C are experimentally determined.

α is the heating rate, T ₀ is the heating temperature, and t ₀ is the holding time.

ε _C = A ₃ · d ₀ ^{C 1} · exp (A ₄ / RT _i ) (2) T _i is the rolling temperature and R is the gas constant.

C ₁ , A ₃ , and A ₄ are experimentally determined.

f _D = 1-exp · _{[- (ε i -ε C / ε} S -ε i) m ]
(3) ε _S = A ₅ · [1-exp (−d ₀ / k ₂ )] k ₂ = A ₆ · ^-d · exp (−A ₇ / Ti) m = A ₈ · exp (A ₉ / Ti ) d, A ₅ ~A ₉ is determined by experiments.

 Is the strain rate.

_{f S = (1-f D} ) {1-exp [-t / τ _{S) ^e]} (4) τ S = A} 10 · ε -f · exp (A 11 / RT i) t between the path time.

e, f, A ₁₀ and A ₁₁ are experimentally determined.

f _N = 1-f _D −f _S (5) d _D = k ₃ · Z ^-g (6) k ₃ = A ₁₂ · Q ₀ −A ₁₃ Q ₀ = A ₁₄ −A ₁₅ · C _eq C _eq = [% C] + [% Mn] / 6 Z = · exp (Q ₀ / RT) g, A _{12 to} A ₁₅ are determined by experiments.

d _S = k ₁₆ · d ₀ ^h · ε ^-i (7) h, i, A ₁₆ are obtained by experiments.

j, k, l, m, m ₁ , A _{17 to} A ₂₀ are experimentally determined.

Δε = (f _D · ε _C + f _D · ε) · exp [− (t / τ _k ) ⁿ ]
(12) τ _k = A ₂₁ · exp (A ₂₂ / RT _i ) n, A ₂₁ , and A ₂₂ are obtained by experiments.

X _f / X _fmax = 1- [1 + k ₁ / p (t-τ ₁ )] ^-p (13) q = 1/2 [r ² + β · γ ² (α ² −γ ² ) ⁻¹ / ² · F + β (α ² −γ ² ) ^1/2 · E] α = e ^Δε , β = 1, γ = e ^−Δε Γ = [α ² (β ² −γ ² ) / β ² (α ² −γ ² )] ^1/2 μ = arccos (γ / α) K _f = exp {B ₄ + B ₅ ln (% Si) + B ₆ [ln (% Si)] _{^{2 + B 7 (% C)}} + B 8 (% Mn) + B 9 (T-273) + B 10 (T-273) 2} τ _f = exp {B ₁₁ + B ₁₂ ln (% Si) + B ₁₃ [ln (% Si)] ² + B ₁₄ (% C) + B ₁₅ (% Mn) + B ₁₆ (T-273) + B ₁₇ (T-273) ^2} X _fmax = 1 - [% C] / _{[B 18 + 19 (T-273} ) + B 20 (T-273) 2 ] (T ≧ 993k) = 1 - [% C] / [B ₁₈ + B ₁₉ · 720 + B ₂₀ · (720) ² ] (T <993k) p = B ₂₁ + B ₂₂ ln (% Si) + B ₂₃ [ln (% Si)] ² B _{1 to} B ₂₃ are obtained by experiments.

X _p = 1-exp [1 + k ₁ / p (t−τ ₁ )] ^-p (14) _{K b = exp [B 24 +} B 25 (% Si) 2 + B 26 (% C) + B 27 (% Mn) + B 28 (T-273) + B 29 (T-273) 2] τ _b = exp [B ₃₀ + B ₃₁ (% Si) + B ₃₂ (% Si)] ² + B ₃₃ (% C) + B ₃₄ (% Mn) + B ₃₅ (T-273) + B ₃₆ (T-273) ² ] P ₁ = 1.4 B ₂₄ ~B ₃₆ is determined by experiment.

_{TPE = B 41 + B 42 (} % C) + B 43 (% Mn) + B 44 (% Si) (17) P 2, B 37 ~B 44 is determined by experiment.

H _f0 = C ₁ + C ₂ (% C) + C ₃ (% Mn) + C ₄ (% Si) + C ₄ lnt
(18) (T ≧ 993k) _{H f = H f0 + C 8} · exp (C 9 / T) (20) P 3, C 1 ~C 9 is determined by experiments.

H _p = Σ [ΔX _p · H (T)] / ΣΔX _p (21) H (T) = C ₁₀ + C ₁₁ (Ae ₁ −T) ⁻¹ Ae ₁ = C ₁₂ + C ₁₃ (% Mn) + C ₁₄ ( % Si) ΔX _p is the amount of pearlite that appears at each temperature _{P 4, P 5, C 11} ~C 19 is determined by experiment.

d _f = d _f0 (23) d _f0 = exp [f ₁ + f ₂ · lnX _f + f ₃ · k ₃ + f ₄ · k ₄ + f ₅ · ln (1 + B ₃ · Δε) + f ₆ · ln (% C) + f ₇・ Ln (% Mn)] (T ≦ 723k) k ₃ = ln {[f ₈ + f ₉・ (% Si) + f ₁₀・ (% Si) ² ] ³ × [(2.24 / 40) / (2.24 / 40 + B ₂ )]} d _f = d _f0 + d ₁₁ · exp (f ₁₂ / T) (24) (T> 723k) f _{1 to} f ₁₂ are obtained by experiments.

TS = g ₁ + g ₂ · X _f · d _f ^−1/2 + g ₃ · X _b · d _γ ^−1/2 + g ₄ · Xm ^1/2 + g ₅ (H _f · X _f + Hp · X _p + H _b · _{X b) (25) YS =} g 6 + g 7 · X f · d f -1/2 + g 8 · X b · d γ -1/2 + g 9 · Xm 1/2 + g 10 (H f · X f + Hp・ X _p + H _b・ X _b ) (26) T.El ＝ g ₁₁ + g ₁₂・ X _f・ d _f ^−1/2 + g ₁₃・ X _b・ d _γ ^−1/2 + g ₁₄・ Xm ^1/2 + g _{15 (H f · X f +} Hp · X p + H b · X b) (27) g 1 ~g 15 is determined by experiment.

<Operation> The following is a description of the knowledge for constructing the model formula used in the invention and the operation thereof.

The material of the hot-rolled steel material varies depending not only on the composition but also on manufacturing conditions such as rolling conditions and cooling conditions.

The inventors of the present invention focused on the fact that the material quality of the steel material is associated with the microstructure of the steel material, and constructed a model for predicting the material quality from the manufacturing conditions through the microstructure based on actual analysis data.

This model consists of two models, one for predicting the microstructure and one for predicting the material from the microstructure. The model for predicting the microstructure is the austenite grain size model for calculating the austenite grain size and residual strain before cooling. Based on these cooling conditions as the initial conditions, a transformation model for calculating a microstructure such as a transformation structure volume fraction, hardness, and ferrite grain size after completion of cooling is constructed.

The overall calculation flow is shown in FIG. 1, and will be described below with reference to FIG.

 The model is described below.

Average austenite grain size immediately after cooling after rolling is completed ▲
The ▼ and the residual strain Δε have a great influence on the transformation structure fraction, hardness, and grain size generated by the subsequent cooling, and influence the final steel material.

▲ ▼ and Δε are determined from the processing conditions (processing temperature T _i , additional strain ε _i , strain rate _i ) in the austenite region and the initial grain size dγ ₀ .

d ₀ is the austenite grain size at the inlet of the first rolling mill, and in the case of a reheated material, it is determined by the heating temperature T ₀ , the heating rate α, and the isothermal holding time t ₀ and expressed using the equation (1). ,
Under normal heating conditions, the influence of the heating temperature is the largest, and the low temperature heating temperature fine particles are obtained.

When austenite with an initial grain size d _o is processed (processing temperature T _i , additional strain ε _i , strain rate _i ), recrystallization occurs when the strain ε _i is small (or when the temperature T _i is low). Although difficult, if ε _i is large (or high T _i ), static recrystallization occurs after processing and the grains become finer.

When a larger strain is applied, recrystallization (dynamic recrystallization) occurs even during processing, and the grains become finer.

The grain size change at this time can be divided into two regions with the critical strain ε _c of the dynamic recrystallization given by the equation (2) as a boundary, and the dynamic recrystallization can be considered in the region of ε _i ≧ ε _c. , Static recrystallization and non-recrystallization, and when ε _i <ε _c , it is divided into two groups, static recrystallization and non-recrystallization.
_c becomes smaller as the temperature is higher and as the initial grains are smaller.

Although space factor f _D of the dynamic recrystallization is epsilon _{i <the} epsilon _c zero, increases with epsilon _i is increased, epsilon _i is the temperature, the initial particle size, it exceeds the strain epsilon _s determined by the strain rate It becomes 1 and can be expressed by equation (3).

The higher the temperature, the lower the strain rate, and the smaller the initial austenite grain size, the smaller the value of ε _s , indicating that the progress of dynamic recrystallization is slow.

Further, the dynamic recrystallized grain size d _D obtained at this time does not depend on the additional strain and the initial grain size at all, and is expressed by the Zener-Hollomon factor Z determined by the temperature and the strain rate as in the equation (5). The characteristic is that the lower the temperature and the higher the strain rate (high Z), the finer the particles.

If dynamic fine crystals do not occur, the entire region is targeted, and if dynamic recrystallization occurs, the remaining parts are targeted, and static recrystallization occurs after processing.

The static recrystallization space factor f _s at this time increases with time, and the higher the additional strain, the higher the temperature, and the higher the speed, the higher the higher the additional strain, the more the rate is represented by the formula (4).

Also, the static recrystallized grain size d _s obtained at this time does not depend on the temperature and strain rate as shown in Eq. (7) symmetrically with the dynamic recrystallization, and it depends on the initial grain size d ₀ and the additional strain ε. It has a characteristic that it is determined, and the smaller d ₀ and the larger ε _i , the finer the particles.

In this way, the dynamic and static recrystallized grains generated in one processing are
Coarse grains are formed by grain growth before the next processing or the start of cooling.

At this time, the dynamic recrystallized grain is a kind of work structure that has a large amount of dislocations in the grain, and since the dislocation density is different for each grain, a large driving force including the interface energy at the grain boundary and the dislocation density as well. Forces very fast grain growth.

On the other hand, since the dislocation velocity in the grains of the statically recrystallized grains is extremely low, the driving force for grain growth is almost exclusively the interface energy, and relatively slow grain growth is performed.

 These are expressed by equations (9) and (10).

The portion that has not undergone dynamic and static recrystallization is an unrecrystallized portion, and its space factor f _N can be expressed by equation (5), and the grain size d _N can be expressed by equation (8) by incorporating the flattening effect. Can be expressed.

By using the above equation, the average austenite grain size ▲ ▼ or ▲ ▼ _i after one processing time t has elapsed (before the next processing or immediately before the start of cooling) is the grain size of each recrystallization morphology. A two-dimensional average is calculated using the space factor and the grain size of each grain grown during the time t, and is calculated by equation (11).

Regarding the release of strain during this time t, ε _c was averaged in the dynamically recrystallized grains, and
o, additional strain remains in the non-recrystallized grains, and the residual strain Δε can be calculated by the equation (12) assuming that it is statically recovered during the time t.

By repeating the above process for the actual number of machining,
Immediately before the start of cooling, the average γ grain size ▲ ▼, residual strain Δε
Can be obtained.

Next, regarding the cooling process, the isothermal transformation curve (TTT curve)
Based on, the calculation is performed by the following method.

A steel material that has been worked in the austenite region is in a state where it can be transformed when the temperature becomes equal to or lower than the equilibrium transformation temperature Ae ₃ shown in equation (15).

When kept isothermally (Ae ₃ temperature or lower) at this time, the transformation starts after consuming the incubation period τ determined by the components and temperature, and thereafter the transformation amount increases with time.

Kinetics of transformation progress at this time can be expressed by the formula (a) in the case of ferrite and pearlite, and by the formula (b) in the case of bainite.

X (t) = 1- (1 + k / p (t-τ)) ^-p (b) X (t) = 1-exp (-k (t-τ) ^p (b) (where t ≧ τ) where τ is the incubation period, k and n are constants obtained from experiments or theory, and X (t) is the time t during the isothermal transformation.
It is the amount of transformation in.

k and n are important factors that can express the kinetics of transformation, the effect of processing, etc., and must be determined for each structure (ferrite, pearlite, bainite) that appears due to transformation.

The effect of residual strain Δε due to processing in the γ region is similar to that of Umemoto et al. (Iron and steel vol70 (1984) N06), and the transformation nucleation site due to the increase in the grain boundary area caused by the flattening of the γ grain It is necessary to comprehensively incorporate the three effects of increase, increase of new transformation nucleation sites such as deformation zones generated in grains, and increase of nucleation rate itself.

According to the method of Umemoto et al., Can be expressed by using the grain boundary area increase index q of the γ grain elongated by rolling, can be expressed by the term of square of strain, and can be expressed by the first term of strain.

By taking these effects into consideration and expressing equation (a), equations (13), (14), and (15) can be expressed for ferrite, pearlite, and bainite, respectively.

These equations (13), (14) and (15) and k _f and k _b are based on the newly known transformation behavior of the steel materials containing 0.1% or more of Si by the present inventors. (13), (14), and (1) of Japanese Patent Application Laid-Open No. 62-158816 proposed by the inventors.
5) changed the formula and _{k 4, k 10, k 13} , it is to predict more accurately the material along the actual conditions of transformation behavior.

In the present invention as well, the fact that each structure must consume and finish the latent period indicated by τ ₁ and τ ₂ before the transformation starts at the temperature T remains unchanged.

The maximum transformation rate of each structure can be predicted from the equilibrium diagram. For ferrite, X _fmax can be described in terms of carbon content and transformation temperature, and the rest are X _Pmax and X _Bmax .

When there is such an isothermal transformation curve (TTT curve), the transformation progress can be calculated using the addition rule in the normal cooling process.

That is, the temperature change is decomposed into a minute temperature ΔT along the cooling curve, and the progress of the isothermal transformation at each temperature is added.

Below Ae ₃ , the consumption W of ferrite during the incubation period is (C)
As shown by the formula, when W exceeds 1, ferrite transformation starts.

W = ΣM _i (c) However, W _i is the latent period consumed in one step from T to T−ΔT, and is expressed by the equation (d).

 However, CR is the average cooling rate between Δ.

W _i = ΔT / CR / τ (T) (d) When the ferrite transformation is completed, the pearlite transformation amount is subsequently obtained, and the ferrite transformation is completed up to the pearlite transformation end temperature TPE shown in equation (17). If not, the transformation amount of bainite transformation is calculated thereafter.

Each structure transformed during cooling softens during cooling after transformation and determines the final hardness.

Regarding the ferrite expressed by the expression (17) above the pearlite transformation end temperature TPE, the contained elemental components ([C
%], [Mn%], [Si%]), the time up to TPE softens according to formula (18), but below TPE it softens according to temperature and time according to formula (19). .

At this time, the solid solution carbon amount SC (T) that equilibrates at each temperature is taken into consideration and calculated up to 450 ° C by the equation (19). Calculate the ferrite hardness H _f .

For pearlite, the pearlite hardness H (T) determined by the production temperature is multiplied by the production amount ΔX _p , and the total is averaged.

The hardness of pearlite formed at each temperature is considered to be determined by the lamellar spacing of pearlite, and this spacing is described by supercooling from the Ae ₁ equilibrium transformation temperature determined by [Mn%]. Therefore, the final pearlite hardness is expressed by equation (21).

The hardness of bainite can be calculated by the equation considering the softening shown in equation (22) for the entire temperature range where bainite is generated.

Next, the grain size of ferrite that appeared during this cooling was (23)
It can be calculated by a formula.

In addition, grain growth occurs due to coalescence of ferrite grains with each other when cooling is performed after cooling is stopped (or after winding), but since it is cooling, the finally grown grain size is the same as CT when cooling is stopped (or winding temperature). It is uniquely determined by the initial ferrite grain size d _f0 and can be expressed by equation (24).

It is considered that a mixing rule similar to that used by Tomoda et al. (Iron and Steel vol 61 (1976) No. 1) is established between the microstructure obtained in this way and the material of the final hot-rolled steel.

The tensile strength TS in the tensile test can be described by decomposing and describing the relationship between the material and microstructure of steel materials containing ferrite, pearlite, bainite, and martensite in various proportions into parts that deform with constant strain and parts that deform with constant stress. , Yield stress YS, Total elongation T · El (25) (26)
It becomes the formula (27), and the material of the final hot rolled steel can be calculated by substituting the microstructure factor.

In this way, the microstructure is predicted from each factor in the manufacturing process,
The material of the hot rolled steel can be estimated by adopting the method of determining the material of the steel from the microstructure.

By adjusting the rolling conditions and cooling conditions based on the characteristics of each manufacturing line and changes in the manufacturing method so that this predicted material matches the target material, the required steel material can be efficiently and efficiently produced. Can be manufactured.

<Examples> Table 1 shows the materials (tensile strength, yield strength, total elongation) of hot-rolled steel materials set as basic components, cooling rates, and target values when the present invention was carried out in the hot-rolling process, Predicted required microstructure factors (structure fraction of ferrite, pearlite, bainite, hardness, and ferrite grain size) and finally measured values of those of the steel material obtained in the hot rolling process are shown. Table 2 shows the rolling temperature, rolling speed, and winding temperature applied in the simulation for each test number shown in Table 1. In the present embodiment, (1) to (2
The specific numerical values used in equation (7) are shown below.

In Table 1, 1 to 11 are examples of the present invention, and 12 and 13 are JP-A-62-1588.
It is a comparative example showing the material prediction and the actual product value proposed by Japanese Patent No. 16 publication.

As is clear from Table 1, according to the method of the present invention, the microstructure required for the target material of each steel material can be estimated more accurately than in the comparative example, and the hot rolled steel material having the target material can be obtained with high accuracy. .

<Effects of the Invention> The present invention described above has a high range of accuracy of prediction of hardness, grain size prediction, structure volume fraction prediction, and combination of microstructures of hot-rolled steel containing Si in an amount of 0.1% by weight or more. Since it has been further improved over the past, by setting the composition and manufacturing conditions based on the estimated value of this material, the hot rolled steel material in which the material is accurately controlled to the target value can be used efficiently, with high yield and at low cost. For example, the effect of producing the steel material by hot rolling is great.

[Brief description of the drawings]

 FIG. 1 shows an overall calculation flow chart of the present invention.

Continuation of the front page (72) Inventor Isaka Esaka 1-chome Nishinosu, Oita City, Oita Prefecture Shin-Nippon Steel Co., Ltd. Oita Works (56) Reference JP 62-158816 (JP, A) JP 61 -163211 (JP, A) Iron and steel 70th year (1984) No. 6 Umemoto, Otsuka, Tamura P. 557 ~ 564

Claims (1)

(57) [Claims] 1. When manufacturing a steel product by rolling a carbon steel containing 0.1% by weight or more of Si at a temperature of Ar ₃ point or higher and cooling it by using a formula obtained from actual conditions in advance, the average γ grain size on the outlet side of the heating furnace is set. (1) on the basis of the heating conditions including at least the heating rate α (K / min), the heating temperature T ₀ (K), and the isothermal holding time t ₀ (min)
Calculated by the formula, the space factors f _D , f _S , and f _N of dynamic recrystallization, static recrystallization, and non-recrystallization and the grain sizes of dynamic recrystallization, static recrystallization, and non-recrystallization , D _D (μm) and d _S (μm) and d _N (μm), respectively, based on the processing conditions including at least the processing temperature Ti (K), the additional strain ε _i , and the strain rate _i (S ⁻¹ ). When the additional strain ε given by the equation (2) is larger than the critical strain ε _C of dynamic recrystallization, it is divided into three groups of dynamic recrystallization, static recrystallization, and non-recrystallization, and the additional strain ε is dynamic. When the critical strain of recrystallization is smaller than ε _C, static recrystallization and non-recrystallization are divided into two groups, and calculations are performed using equations (3), (4), (5), (6), (7), and (8), and The average austenite grain size immediately before the next pass is calculated by calculating the grain growth in Eq. (9) for dynamic recrystallization and Eq. (10) for static recrystallization ▲ ▼ _i (μm)
Is calculated by Eq. (11), Δε _i , which is the residual strain amount immediately before the next pass, is calculated by Eq. (12), and the additional strain ε _{i + 1} is added to the residual strain amount Δε _i of the previous pass in the next pass. The strain is calculated, and the effective strain and the average austenite grain size immediately before the next pass ▲ ▼ _i (μ
m), the recrystallization behavior is calculated in the same manner as described above, and this is repeated a predetermined number of times to obtain the average austenite grain size ▲ ▼ (μm) before the start of cooling and the residual strain amount Δε, and the average austenite grain before the start of cooling. With the diameter ▲ ▼ (μm) and the residual strain Δε as initial conditions, the average transformation temperature Ae ₃ (K) at which consumption of the latent period for transformation starts is calculated using equation (16), and the temperature at which ferrite and pearlite transform and bainite The TPE (K), which indicates the boundary of the temperature at which the transformation takes place, is calculated using Equation (17), and the cooling curve that has already been calculated using Equations (13), (14), and (15) for estimating the isothermal transformation rate of ferrite, pearlite, and bainite The transformation ratio addition rule is applied to calculate the final volume fraction of the ferrite structure, the pearlite structure, and the bainite structure, and the total volume ratio of the ferrite, pearlite, and bainite structures is subtracted from 1 to calculate the volume ratio. Determine the volume rate of ten site organization. In the above calculation of the transformation rate, the hardness H _{f0 of} ferrite that appears above 923 K is expressed by equation (18), and the hardness H _{f0 of} ferrite that appears below 923 K and above 723 K is expressed by equation (19) along each cooling curve. Calculated from the hardness of the final ferrite
The H _f final hardness H _p (20) perlite determined by the equation obtained in (21), each seeking final hardness H _b of bainite in (22), the final ferrite grain size d _f ([mu] m) (23) (24) is calculated. From the above, ferrite grain size d _f (μm) and ferrite volume ratio
X _f , ferrite hardness H _f , pearlite volume fraction X _p , pearlite hardness H _p , bainite volume fraction X _b , bainite hardness
The tensile strength TS (kgf / mm ² ) and the yield strength YS (kgf / mm ² ) are calculated from _Eqs. (25) to (27) based on H _b and martensite volume ratio X _fm.
And calculate the total elongation T.E1 (%), compare these with the target material and adjust the manufacturing conditions consisting of at least the rolling conditions and cooling conditions in the above actual conditions so that the difference is always within the required range. A method for producing a hot rolled steel material, comprising: d _γ0 ² = (k1 · α ^-a ) ² + A1 · exp (−A ₂ / T ₀ ) t
₀ ² (1) k ₁ = B [(T ₀ −1173) / 100] ³ + C a, b, A ₁ , A ₂ , B and C are experimentally determined. α is the heating rate, T ₀ is the heating temperature, and t ₀ is the holding time. ε _C = A ₃ · d ₀ ^{C 1} · exp (A ₄ / RT _i ) (2) T _i is the rolling temperature and R is the gas constant. C ₁ , A ₃ , and A ₄ are experimentally determined. f _D = 1-exp · _{[- (ε i -ε C / ε} S -ε i) m ]
(3) ε _S = A ₅ · [1-exp (−d ₀ / k ₂ )] k ₂ = A ₆ · ^-d · exp (−A ₇ / Ti) m = A ₈ · exp (A ₉ / Ti ) d, A ₅ ~A ₉ is determined by experiments. Is the strain rate. _{f S = (1-f D} ) {1-exp [-t / τ _{S) ^e]} (4) τ S = A} 10 · ε -f · exp (A 11 / RT i) t between the path time. e, f, A ₁₀ and A ₁₁ are experimentally determined. f _N = 1-f _D −f _S (5) d _D = k ₃ · Z ^-g (6) k ₃ = A ₁₂ · Q ₀ −A ₁₃ Q ₀ = A ₁₄ −A ₁₅ · C _eq C _eq = [% C] + [% Mn] / 6 Z = · exp (Q ₀ / RT) g, A _{12 to} A ₁₅ are determined by experiments. d _S = k ₁₆ · d ₀ ^h · ε ^-i (7) h, i, A ₁₆ are obtained by experiments. d _N = d ₀ · exp (−ε / 4) (8) d _DG ² = d _D ² + A ₁₇ · exp (−A ₁₈ / RT) · t ^j (9) j, k, l, m, m ₁ , A _{17 to} A ₂₀ are experimentally determined. Δε = (f _D · ε _C + f _D · ε) · exp [− (t / τ _k ) ⁿ ]
(12) τ _k = A ₂₁ · exp (A ₂₂ / RT _i ) n, A ₂₁ , and A ₂₂ are obtained by experiments. X _f / X _fmax = 1- [1 + k ₁ / p (t-τ ₁ )] ^-p (13) q = 1/2 [r ² + β · γ ² (α ² −γ ² ) ⁻¹ / ² · F + β (α ² −γ ² ) ^1/2 · E] Γ = [α ² (β ² −γ ² ) / β ² (α ² −γ ² )] ^1/2 μ = arccos (γ / α) k _f = exp {B ₄ + B ₅ ln (% Si) + B ₆ [ln (% Si)] _{^{2 + B 7 (% C)}} + B 8 (% Mn) + B 9 (T-273) + B 10 (T-273) 2} τ _f = exp {B ₁₁ + B ₁₂ ln (% Si) + B ₁₃ [ln (% Si)] ² + B ₁₄ (% C) + B ₁₅ (% Mn) + B ₁₆ (T-273) + B ₁₇ (T-273) ^2} X _fmax = 1 - [% C] / _{[B 18 + 19 (T-273} ) + B 20 (T-273) 2 ] (T ≧ 993k) = 1 - [% C] / [B ₁₈ + B ₁₉ · 720 + B ₂₀ · (720) ² ] (T <993k) p = B ₂₁ + B ₂₂ ln (% Si) + B ₂₃ [ln (% Si)] ² B _{1 to} B ₂₃ are obtained by experiments. X _p = 1-exp [1 + k ₁ / p (t−τ ₁ )] ^-p (14) k _b = exp [B ₂₄ + B ₂₅ (% Si) ² + B ₂₆ (% C) + B ₂₇ (% Mn) + B ₂₈ (T-273) + B ₂₉ (T-273) ² ] τ _b = exp [B ₃₀ + B ₃₁ (% Si) + B ₃₂ (% Si)] ² + B ₃₃ (% C) + B ₃₄ (% Mn) + B ₃₅ (T-273) + B ₃₆ (T-273) ² ] P ₁ = 1.4 B ₂₄ ~B ₃₆ is determined by experiment. _{TPE = B 41 + B 42 (} % C) + B 43 (% Mn) + B 44 (% Si) (17) P 2, B 37 ~B 44 is determined by experiment. H _f0 = C ₁ + C ₂ (% C) + C ₃ (% Mn) + C ₄ (% Si) + C ₄ lnt
(18) (T ≧ 993k) _{H f = H f0 + C 8} · exp (C 9 / T) (20) P 3, C 1 ~C 9 is determined by experiments. H _p = Σ [ΔX _p · H (T)] / ΣΔX _p (21) H (T) = C ₁₀ + C ₁₁ (Ae ₁ −T) ⁻¹ Ae ₁ = C ₁₂ + C ₁₃ (% Mn) + C ₁₄ ( % Si) ΔX _p is the amount of pearlite that appears at each temperature _{P 4, P 5, C 11} ~C 19 is determined by experiment. d _f = d _f0 (23) d _f0 = exp [f ₁ + f ₂ · lnX _f + f ₃ · k ₃ + f ₄ · k ₄ + f ₅ · ln (1 + B ₃ · Δε) + f ₆ · ln (% C) + f ₇・ Ln (% Mn)] (T ≦ 723k) k ₃ = ln {[f ₈ + f ₉・ (% Si) + f ₁₀・ (% Si) ² ] ³ × [(2.24 / 40) / (2.24 / 40 + B ₂ )]} d _f = d _f0 + d ₁₁ · exp (f ₁₂ / T) (24) (T> 723k) f _{1 to} f ₁₂ are obtained by experiments. TS = g ₁ + g ₂ · X _f · d _f ^−1/2 + g ₃ · X _b · d _γ ^−1/2 + g ₄ · Xm ^1/2 + g ₅ (H _f · X _f + Hp · X _p + H _b · _{X b) (25) YS =} g 6 + g 7 · X f · d f -1/2 + g 8 · X b · d γ -1/2 + g 9 · Xm 1/2 + g 10 (H f · X f + Hp・ X _p + H _b・ X _b ) (26) T.El ＝ g ₁₁ + g ₁₂・ X _f・ d _f ^−1/2 + g ₁₃・ X _b・ d _γ ^−1/2 + g ₁₄・ Xm ^1/2 + g _{15 (H f · X f +} Hp · X p + H b · X b) (27) g 1 ~g 15 is determined by experiment.