JPH0641936B2 - Online Prediction Method of Ferrite Grain Size of Steel - Google Patents

Description

Detailed Description of the Invention [Industrial applications]

The present invention relates to an online prediction method of a ferrite grain size of a steel material, and in particular, a ferrite grain size of a rolled steel material manufactured by a hot strip mill, a plate mill or the like (including the concept of grain size, the same applies hereinafter), It relates to a method for online prediction with high accuracy.

[Prior art]

Since the ferrite grain size of steel has a great influence on the strength and low temperature toughness of the steel, ductility, yield ratio, cold formability, etc., it is an important metallurgical property requiring control in the production of steel products. It is one of the factors. It is well known that many techniques for refining crystal grains have been developed as one means for simultaneously achieving high toughness and high strength, such as hot-rolled steel sheets for high-strength line pipes. Also, the formability of low carbon steel is closely related to the crystal grain size. If the crystal grain size is too large, roughening called orendi peel occurs after forming, and conversely the crystal grain size is too small. It is also known that in this case, the moldability itself deteriorates, so that in order to obtain good quality and moldability, it is necessary to adjust to the optimum crystal grain size. Thus, while the crystal grain size is an important factor that affects the mechanical properties of steel products, the conventional measurement method used to collect specimens from the manufactured products and then mechanically or chemically perform mirror polishing. The crystal grain boundary was revealed by an appropriate corrosive liquid, and it was finally possible to know it by observing it with a microscope.

[Problems to be Solved by the Invention]

However, these measuring operations are extremely complicated,
Moreover, since it is a destructive inspection, the measurement points are limited, and there is a problem that it is not possible to grasp the variation of the crystal grain size of the entire steel product. On the other hand, X-ray diffraction method (Hoshino et al .; Iron and Steel Vol. 64, No. 5 (1978) P62)
An online non-contact measurement method according to 1) is also known, but this method is a measurement method that is intended for the crystal grain size on the surface of the material to be measured in principle. For example, in order to perform controlled rolling, the plate thickness surface layer part In the case of high-strength line pipe material in which the crystal grain size is significantly different between the center part and the central part, the measurement results do not necessarily correspond to the mechanical properties of the product and are not useful,
And that the measurement accuracy is easily affected by the variation in the distance between the measured material and the detector, and further, the measuring device becomes large,
Moreover, in a measurement environment in which cooling water, water vapor, or the like exists, sufficient measurement accuracy cannot be obtained, so that there is a problem that the measurement position is restricted.

[Object of the Invention]

The present invention has been made in view of the problems of the conventional art as described above, and it is possible to continuously measure the crystal grain size of the steel material in the hot rolling step or the heat treatment step, which was difficult to achieve conventionally, without contact and online. An object of the present invention is to provide an online prediction method of a ferrite grain size of a steel material, which is capable of highly accurately controlling and predicting the material quality of a steel material being manufactured from this measurement information.

[Means for solving problems]

In the present invention, when cooling from the austenite state, γ /
In a method of predicting a ferrite grain size of a steel material that undergoes α transformation, as shown in the outline of FIG. 1, the γ / α transformation rate is 0 to 0 during transportation of the hot rolling line or heat treatment line of the steel material.
A procedure for obtaining the γ / α transformation progress rate of the steel material in a predetermined γ / α transformation rate range in the range of 80% by measuring the transport speed and the transformation rate at a plurality of positions in the longitudinal direction of the steel material, and the transformation progress The above object is achieved by including a procedure in which the speed is made to correspond to the relationship between the ferrite grain size and the transformation progress speed which are obtained in advance. A preferred embodiment in the above configuration is that the predetermined γ / α transformation rate range is set to 0 to 50% when the C equivalent of the steel material is less than 0.6%. This enables more accurate grain size prediction of the steel material.

[Action]

The present invention relates to an "on-line detection device for the amount of transformation and flatness of steel" proposed by the present applicant (Japanese Patent Laid-Open No. 59-1885).
08) was used to measure various hot rolled steel products, and there was an extremely high correlation between the progress and behavior of the γ / α transformation of the steel product in a specific range and the ferrite grain size in the final structure. It was found that the above was found, and it was constructed based on this finding. Below, an example of the results of the investigation by the present inventors regarding the above findings will be described. Fig. 2 shows C / 0.21%, Si / 0.12%, and Mn /
When a steel sheet with a composition of 0.69% is rolled into a hot-rolled steel strip with a thickness of 3.2 mm in a hot strip mill, the final ferrite grain size after hot rolling is significantly increased in the longitudinal direction within the same material. Γ → α transformation progress rate (hereinafter abbreviated as ₂₀ V ₃₀ ) in a range of γ / α transformation rate of 20 to 30% when different finishing rolling temperatures are changed and rolling is performed. It shows the relationship with the ferrite grain size Dα in the final structure of the steel strip. The transformation rate was measured by an online transformation rate detection device installed on the runout table. As can be seen from Figure 1, between the transformation moving speed ₂₀ V ₃₀ and ferrite grain size Dα there is a clear response, ₂₀ V ₃₀
It is recognized that Dα tends to decrease with an increase of This indicates that it is possible to predict the final ferrite crystal grain size by grasping the transformation progress rate in the initial stage of the γ / α transformation. The above phenomenon is considered as follows. Considering the progressing behavior of the γ / α transformation in subco-folded steel, first the transformation starts with the austenite grain boundaries or the nuclei of the first-fold ferrite grains from the deformation zones introduced into the austenite grains by rolling. However, even in the initial stage of the subsequent transformation, the transformation proceeds due to this nucleation for a while. Therefore, the degree of transformation progress during this period should have a magnitude commensurate with the nucleation rate. If the rate of nucleation of ferritic grains increases,
The number of ferrite transformation nuclei per unit volume increases, which acts to reduce the grain size of the ferrite grains in the final structure. That is, the rate of transformation progress in the initial stage of the γ / α transformation reflects the number of nuclei of the formed ferrite grains, and it is considered that this has a close relationship with the grain size of the ferrite in the final structure. Here, the reason for limiting the numerical values in the present invention is as follows. That is, limiting the γ / α transformation rate to a value within the range of 0 to 80% as the transformation progress rate used for predicting the ferrite grain size is the most important factor affecting the final ferrite grain size as described above. This is because a large factor is the number of ferrite transformation nuclei generated in the γ / α transformation process, and therefore it is necessary to select a transformation rate region in which the ferrite transformation nuclei are frequently generated in order to improve the prediction accuracy. According to the knowledge obtained by the present inventors, in a region where the γ / α transformation ratio exceeds 80%, the frequency of generation of the ferrite transformation nuclei decreases, and therefore, when the transformation progress speed in this region is used, the prediction accuracy is high. It is not preferable because the decrease of The most suitable range of the transformation rate in the range of 0 to 80% varies slightly depending on the chemical composition of the steel to be measured. Qualitatively C
As the equivalent weight (C (%) + Mn (%) / 6) becomes higher, the optimum range tends to move to the low transformation rate side.
If the C equivalent is less than 0.6%, it is most preferable to select the transformation rate within the range of 0 to 50%. When predicting the ferrite grain size from the transformation progress rate, is it necessary to individually obtain the relation between the transformation progress rate and the variation and the grain size for each steel type in advance as shown in FIG. Alternatively, for example, as shown in the equation (2) described later, it can be performed by obtaining a functional expression expressing the ferrite grain size Dα by the transformation progress rate iVj and the influence term C _{EQ of the} chemical component.

【Example】

Embodiments of the present invention will be described in detail below with reference to the drawings. First, a manufacturing process for carrying out the method of the present invention will be described. Third
In the drawing, reference numeral 10 is a finish rolling mill in the hot rolling process, 12 is a hot rolled steel sheet, 14 is cooling water for cooling the hot rolled steel sheet 12, for example, mist, jet, pipe laminar or slit laminar state is applied to the steel sheet 12. Pour water. The water injection device arranged on the runout table is shown. The cooling water is supplied from the water supply device 16 and the amount of water is adjusted by a water amount adjusting valve 20 which is driven according to an instruction from the valve controller 18, and then the cooling water is injected into the hot-rolled steel plate 12 by the water injection device 14. A1 to A8 represent transformation rate detecting devices, and the devices A1 to A8
The γ / α transformation rate of the hot-rolled steel sheet 12 passing above is quantitatively detected, and the measurement signal is transmitted to the arithmetic unit 22. The valve controller 18 is connected to the arithmetic unit 22 and operates according to a control signal from the arithmetic unit 22 to adjust the opening degree of the valve 20. In addition, 24 is a speedometer for measuring the transport speed of the hot-rolled steel sheet 12 on the runout table, B1 is a thermometer for measuring the finishing rolling temperature, B2 is a thermometer for measuring the intermediate temperature on the runout table, and B3 is a take-up coil. Thermometer for measuring temperature, 26
Indicates a winder. The transformation rate detection devices A1 to A8 are used for γ of the hot rolled steel sheet 12 during cooling.
Any measuring means can be adopted as long as it is possible to measure the / α transformation rate online and quickly and quantitatively. In this embodiment, the applicant has already proposed in Japanese Patent Application No. 58-064147. An on-line detector for the amount of transformation and flatness of steel materials was used. As shown in FIG. 4, the transformation amount online detection devices A1 to A8 are arranged on one side of the hot-rolled steel plate 12 as the material to be measured, and an exciting coil which allows alternating magnetic flux to be generated by the AC exciting device 52. 53 and two detections arranged on the same side as the exciting coil 53 and at positions different from the exciting coil 53 by l ₁ and l _{2 so} that they are mutually induced by the exciting coil 53. Coils 55 ₁ and 55 ₂
If, comprising an arithmetic unit 57 for obtaining the transformation rate of the steel plate position 2 the difference of the detection signal generated Te cowpea the differences each detection coil 55 _1, 55 flux linkage amount of _2. Reference numeral ₅₄₁ in the figure is generated by the exciting coil 53, the steel plate 12
Through the detection coil 55 ₁ ,
₂ is a magnetic flux interlinking with the detection coil 55 ₂ . When the steel sheet 12 has not started transformation, that is, when it is in the γ single phase, it is in the paramagnetic state, so that the detection coils 55 ₁ , 55 ₂
The magnetic flux 54 _1, 54 ₂ interlinked in a state in which the induced voltage proportional located in these respective constant intensity corresponding to the distance l _1, l ₂ from the excitation coil 53 is generated (hereinafter initial state) is there. Occurs gamma → alpha transformation in the steel plate 12, the alpha phase of the ferromagnetic are fold-out, alpha phase is magnetized, occur variations in the field strength of the steel plate 12, the strength of the magnetic flux 54 _1, 54 ₂ from the initial state Since they are deviated, they are detected as changes in the induced voltages of the detection coils 55 ₁ and 55 ₂ , respectively. The detection signals 56 ₁ and 56 ₂ from the detection coils 55 ₁ and 55 ₂ are transmitted to the arithmetic unit 57, and the detection coil 5 5
5 ₁ and by relatively comparing the magnitude of the measured signal with 55 _2, and requests transformation rate of the steel plate 12 by the arithmetic unit 57. Next, a prediction method will be described. First, the transition of the transformation rate on the run-out table obtained by the transformation rate detection devices A1 to A8 and the steel plate transport speed meter 2
A predetermined transformation rate range (i% to j%) according to the signal from 4
The transformation progress speed iVj of is calculated. This transformation progress speed iV
In the calculation of j, it goes without saying that the greater the number of transformation rate detection devices installed on the run-out table, the more accurate the measurement can be made. The calculation can be performed with relatively high accuracy. That is, according to the knowledge of the present inventors, the progress of the transformation rate on the run-out table is as follows: γ / α transformation rate is Y (%),
When the elapsed time after finish rolling is t (sec), the constants determined by the chemical composition of the steel sheet are k and a, and the value dependent on the transformation progress rate is n, it can be approximated by the following formula (1). . Y = exp [− {(k−t) / a} ⁿ ] × 100 (1) Therefore, the measured value of the γ / α transformation rate Y by the transformation rate detection devices A1 to A8 closest to the desired transformation rate range. , And the elapsed time t calculated from the transport speed are substituted into the equation (1) to obtain n
Then, by using this value of n, the elapsed time ti, tj in a desired transformation rate range, for example, Yi, Yj, is obtained. The transformation progress rate is iVj = (Yj-Yi) / (t
j-ti). Next, the ferrite crystal grain meter is predicted from the transformation progress rate obtained by the above procedure. As described above, the method is to obtain the relationship between the transformation progress rate and the ferrite grain size as shown in FIG. 1 for each steel type in advance, or
Alternatively, for example, as shown in the following formula (2), the ferrite crystal grain size Dα is defined as the influence term C of the transformation progress rate iVj and the chemical composition.
_This can be done by obtaining and placing a functional expression expressed by _EQ and. Dα = f (iVj, C _EQ ) ... (2) The above arithmetic means is performed by the arithmetic device 22, and the measurement signals from the transformation rate detection devices A1 to A8, the signal from the transport speed meter 24, and the arithmetic device 22 are separately provided. It is intended to predict the ferrite grain size from the input chemical composition information of the steel to be measured. Next, the results of the investigation for confirming the effects of the method of the present invention will be described. Regarding the steel material having the chemical composition shown in FIG. 5, the hot strip mill (A to I steel) and the plate mill (J to L steel) were subjected to accelerated cooling after rolling under predetermined conditions, and the hot strip mill material was a coil. And the plate mill material was air-cooled. For each of these, the transformation rate detection device installed in the cooling device was used to measure the γ / α transformation progress rate of the steel sheet during cooling, and prediction of the ferrite grain size in the final structure after cooling was performed based on the method of the present invention. Then, a sample for microscopic observation was cut out from the product after cooling from the position corresponding to each predicted position, and the actual ferrite crystal grain size was measured and compared with the above predicted value. FIG. 6 shows rolling conditions, cooling conditions, the transformation progress rate used for prediction, the predicted value of the ferrite crystal grain size, and the measured value of the ferrite crystal grain size by microscopic observation. From FIG. 6, the predicted value of the ferrite grain size by the method of the present invention shows a very good correspondence with the measured ferrite grain size in any steel material, and the transformation rate range of the transformation progress rate used for the prediction is It can be confirmed that the prediction accuracy is deteriorated for those outside the limited range of the present invention.

As described above, according to the method of the present invention, it is possible to predict the ferrite grain size of a steel material produced in a rolling line online, in a non-contact manner, continuously, and with high accuracy. The excellent effect of being possible is obtained. As a result, the predicted value of the ferrite grain size obtained by the method of the present invention is used as online information, various hot rolling lines,
Or manufacturing conditions of heat treatment line (for example, cooling conditions)
If it is applied to the material control of the product after rolling, the quality control can be made highly precise. The method of use included is extremely diverse.

[Brief description of drawings]

FIG. 1 is a flow chart showing the outline of the method for online prediction of the ferrite grain crystal diameter of a steel material according to the present invention, and FIG. 2 is the transformation progress rate between the γ / α transformation rates of 20 to 30% and the ferrite structure in the final structure. Fig. 3 is a diagram showing an example of the relation of grain sizes, Fig. 3 is a block diagram showing the constitution of an embodiment of a hot strip mill adopting the method of predicting the ferrite grain size according to the method of the present invention, Fig. 4 FIG. 5 is a block diagram showing the configuration of a γ / α transformation rate detection device used in the hot strip mill, and FIG. 5 is a steel material to be inspected in a survey conducted to confirm the effects of the present invention. FIG. 6 is a diagram showing the chemical composition of FIG. 10 ... Finishing rolling mill, 12 ... Hot rolled steel plate, 14 ... Water injection device, 16 ... Water supply device, 18 ... Valve controller, 20 ... Water amount adjusting valve, 22 ... Computing device, 24 ... Speed Total: 26 ... Winding machine, A1 to A8 ... Transformation rate detection device, B1 to B3 ... Thermometer, iVj ... γ → α transformation progress rate in the range of i% to j%, Dα ... Ferrite crystal grains Diameter (granularity), C _EQ …… Influence term of chemical composition.

Claims (2)

[Claims] 1. When cooling from an austenitic state, γ
In the method for predicting the ferrite grain size of a steel material that undergoes α / α transformation, γ /
Predetermined γ / α in the range of α transformation rate of 0 to 80%
A procedure for obtaining the γ / α transformation progress rate of the steel material in the transformation rate range by measuring the transport speed and the transformation rate at a plurality of positions in the longitudinal direction of the steel material, and the transformation progress speed with the previously determined ferrite grain size. An online prediction method of a ferrite grain size of a steel material, including a procedure corresponding to a relationship with a transformation progress rate. 2. The steel material ferrite according to claim 1, wherein the predetermined γ / α transformation rate range is set to 0 to 50% when the C equivalent of the steel material is less than 0.6%. Online method of grain size prediction.