Microstructure simulation during hot rolling

Abstract

The present invention relates to a method for defining the microstructure of steel during hot rolling comprising at least three passes, comprising the steps of: a) defining a representative grain size value for said steel before a first rolling pass; b) At the end of the first interpass, - the ratio of recrystallized to non-recrystallized grains, - defining representative grain sizes and dislocation densities of the recrystallized grains and the non-recrystallized grains; c) At the end of the second interpass, - the ratio of different types of particles, - defining a representative grain size and dislocation density of said grains; d) before the third rolling pass, - the ratio of representative recrystallized and non-recrystallized grains, - defining a defined representative recrystallized grain and a defined representative non-recrystallized grain.

Description

[Technical Field]

[0001] The present invention relates to a method for defining the microstructure of a semi-finished steel product during hot rolling, comprising at least three rolling passes. [Background technology]

[0002] Hot rolling allows the thickness of the slab to be reduced to obtain the desired geometry. This challenges the skilled artisan to determine the optimal rolling pattern (i.e., number of rolling passes, reduction in rolling amount) while taking into account metallurgical constraints (i.e., temperature) and equipment constraints (i.e., bonding, speed, force). Each of these parameters needs to be established for each rolling pass to determine a preset value.

[0003] For this purpose, it is important to predict the steel microstructure during multi-pass hot rolling. Indeed, it allows for better determination of rolling loads and product dimensional feasibility when coupled with process models. Furthermore, modeling the steel microstructure at the end of multi-pass hot rolling is key to modeling subsequent phase transformations and precipitation.

[0004] Several models have been proposed to predict the recrystallized fraction, grain size and dislocation density throughout multipass hot rolling.

[0005] For example, models have been developed to predict the flow behavior of steels during hot rolling passes. Such models can be based on a physical description of the dislocation density evolution law, in which the accumulation and recovery of dislocations introduced by deformation determines the flow stress and the driving force for recrystallization.

[0006] Other models have been developed to model the different recrystallization processes, recovery and precipitation of the two hot rolling passes. Such models can be based on the critical strain values ​​and temperatures proposed by Senuma.

[0007] Typically, the output of a model simulating the effects of a first hot rolling pass is used as input for modeling the microstructural changes during the first interpass, e.g., the time between the first and second hot rolling passes. The output of the first interpass modeling is then used as input for a second model simulating the effects of the second hot rolling pass for modeling the next interpass and the next rolling pass, etc.

[0008] Unfortunately, such modeling can be performed offline, but cannot be performed online due to the large number of parameters. Furthermore, if such a model is combined with a processing model, it cannot be used online because the computation time does not allow the results of the modeling to be used in the processing model. This is especially true when the number of paths is at least three.

[0009] As a result, there is a need for improved microstructural modeling of multipass hot rolling. Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention aims to enable the online use of microstructural modelling with multi-pass hot rolling comprising at least three passes. [Means for solving the problem]

[0011] The invention relates in particular to a method for determining the microstructural characteristics of a semi-finished steel product during hot rolling, as defined in claim 1. The method may comprise one or more of the additional features defined in claims 2 to 19, considered alone or in combination.

[0012] The invention also relates to an electronic device according to claim 20 and to a hot rolling mill according to claim 21.

[0013] The invention also relates to a computer program comprising instructions which, when executed on a computer, cause the computer to carry out the above-mentioned method (the computer being possibly connected to sensors, actuators and / or controllers of the hot rolling mill, depending on the detailed characteristics of the method in question). [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 illustrates a conventional model for predicting steel microstructure during multi-pass hot rolling. [Figure 2] Figure 2 shows the first three rolling stands (F1, F2, F3) and the first two interpasses (I1, I2) of a hot rolling mill. Interpass I1 extends from the first hot rolling stand to the second hot rolling stand. Interpass I2 extends from the second hot rolling stand to the third hot rolling stand. [Figure 3] In Figure 3, the average grain size determined according to the present method is plotted against the corresponding measured grain size for different hot rolling tests. DETAILED DESCRIPTION OF THE INVENTION

[0015] Preferably, the semi-finished steel product is a slab, billet or bloom at the input of the hot rolling mill. Preferably, the hot rolling comprises 10 to 15 hot rolling passes and produces a steel strip (in other words, the steel product is transformed into a steel strip during hot rolling). Preferably, the hot rolling comprises 15 to 35 hot rolling passes and produces a steel plate.

[0016] In the first step of the method, step a), a representative grain size o0 of said steel is defined, more particularly obtained, before the first hot rolling pass, such that the steel is considered to have recrystallized austenite grains, since this is done before the first hot rolling pass.

[0017] The representative grain size o0 can be any value considered representative by those skilled in the art. For example, the representative grain size o0 can be the mean or median grain size. The representative grain size o0 can be entered by an operator using a human-machine interface, or the grain size o0 can be read from a database associated with a standard (e.g., a reference number) that identifies the steel product. In practice, the exact value of the representative grain size o0 does not significantly affect the microstructural characteristics of the semi-finished steel product determined for this steel product at the end of, or during, hot rolling. In fact, typically after 5 to 10 passes, this initial grain size is almost completely lost and replaced by the grain size of newly formed grains (or the grain size of significantly deformed grains). Therefore, for rolling involving more than 10 passes, or even more than 5 passes, the exact value of o0 has no or little effect on the microstructure determined according to the present method.

[0018] As shown in Figure 2, the steel is then hot rolled in hot rolling stand F1, which plastically deforms the steel and flattens and elongates the grains. Rolling parameters such as strain rate and rolling temperature affect recrystallization. For example, dynamic recrystallization and post-dynamic recrystallization can occur during hot rolling.

[0019] Furthermore, at Interpass I1, the strain energy in the work-hardened matrix is ​​the driving force that allows the steel to recover. This strain energy, if high enough, can also induce static recrystallization, resulting in new grains at the original grain boundaries.

[0020] As a result, at the end of the first interpass I1, the microstructure can be predicted by any model deemed appropriate by a person skilled in the art (this is done in step b). The model outputs data including the ratio of non-recrystallized to recrystallized grains, as well as the grain size and dislocation density of said grains. In the following detailed description, by ratio, volume ratio (in other words, volume fraction) is meant.

[0021] Preferably, plastic deformation may be modeled using the teachings of Sinclair et al., "A model for the grain size dependent work hardening of copper," Scripta Materialia, 55, 739-742, 2006. Preferably, dynamic recrystallization may be modeled according to the teachings of Senuma et al., "Microstructural evolution of plain carbon steels in multiple hot working," 7th Riso Int. Symp., N. Hansen, D.-J. Jensen, T. Leffers, B. Half, Riso, Roskilde, Denmark, pp. 547-52, 1986; static recrystallization, recovery, and restoration may be modeled according to the teachings of Zurob et al., "Modeling recrystallization of microalloyed austenite, effect of coupling recovery, precipitation, and recrystallization," Acta Materialia, 50, 3075-3092, 2002, and "Rationalization of the softening and recrystallization behavior of microalloyed austenite using mechanism maps," Materials Science and Engineering, A 382, ​​64-81, 2004. The teachings of Perlade et al., published in "A model to predict the austenite evolution during hot strip rolling of conventional and Nb microalloyed steels," La Revue de Metallurgie - September 2008, can also be used to model plastic deformation, dynamic recrystallization, static recrystallization, recovery, and restoration.The grain size and proportion of recrystallized grains resulting from rolling can also be determined based on the teachings of the following article by Lissel et al. (particularly based on Equations 3 to 5 of Lissel et al.): "Prediction of the Microstructural Evolution during Hot Strip Rolling of Nb Microalloyed Steels," Materials Science Forum Vols 558-559 (2007) pp 1127-1132 (doi:10.4028 / www.scientific.net / MSF.558-559.1127).

[0022] The model used may in particular be a mean-field model (as in the case of, for example, Perlade et al.), in which each microstructural phase is described by one volume (one representative volume) with uniform material properties within said volume (in other words, without a detailed description of the geometry and arrangement of a large number of individual particles in the material).

[0023] In the second step of the method, step b), at the end of interpass I1, the ratio of recrystallized to non-recrystallized grains, as well as the representative grain size and representative dislocation density of both recrystallized and non-recrystallized grains, are determined (i.e., defined by calculating them) using the model described above. This determination is performed taking into account process input parameters measured during or before the first rolling pass. These measured input parameters are: - the entrance temperature of the steel product measured at the time of entry into the hot rolling mill; - the entrance thickness of the steel product when entering the rolling mill; - the exit thickness of the steel product after the first rolling stand, -Speed ​​of steel products, - the interpass time of the first interpass, - the force exerted on the steel product by the first rolling stand F1, the gap between the two workings of the first rolling stand F1, - Exit tension (after the first rolling stand).

[0024] The above-mentioned microstructural characteristics may also be determined by considering the chemical composition of the steel product, the diameter of the work rolls in the stand, and the Young's modulus of the material from which it is made. The above-mentioned microstructural characteristics are determined by considering the initial representative grain size o0.

[0025] In step b), recrystallized grains g R Regarding the ratio G in the microstructure R , typical particle size o R , and the representative dislocation density ρ R are defined (in other words, determined by calculating them). In step b), the non-recrystallized grains g N Regarding the ratio G in the microstructure N , typical particle size o N , and the representative dislocation density ρ N is specified (determined).

[0026] The representative value may be any value that is considered representative by a person skilled in the art, for example, the representative value may be the mean or median.

[0027] Preferably, this representative value of the representative recrystallized grains takes into account grains that have been recrystallized by dynamic recrystallization, post-dynamic recrystallization, and static recrystallization, as well as the proportion of each of said recrystallized grains.

[0028] The steel is then hot rolled in hot rolling stand F2, where similar phenomena occur as in the first hot rolling pass, as shown in Figure 2. The steel then travels through interpass I2, where similar phenomena occur as in interpass I1.

[0029] As a result, at the end of the second interpass I2, the microstructure can be predicted by any model deemed appropriate by a person skilled in the art (this is done in step c). The model outputs data including the ratio of grains that were not recrystallized during the second hot rolling pass to those that were recrystallized, as well as the grain size and dislocation density of said grains.

[0030] Preferably, plastic deformation may be modeled using the teachings of Sinclair. Preferably, dynamic recrystallization may be modeled according to the teachings of Senuma. Preferably, static recrystallization, recovery, and restoration may be modeled according to the teachings of Zurob. Preferably, recrystallized grain growth may be modeled using the teachings of Zenner.

[0031] However, due to the different microstructures that are hot rolled in the first pass (where all grains are considered recrystallized and are now of size o0) and the second pass (where some of the grains are recrystallized during the first rolling pass or some of the grains are not recrystallized during the first rolling pass), the microstructures at the end of the first interpass and the end of the second interpass are different.

[0032] In fact, at least four types of particles can be distinguished at the end of the second interpass: differentiation is based on the recrystallization or acrystallization of the particles at the end of the first and second interpasses.

[0033] As a result, at the end of the interpass, the microstructure - particles g that are recrystallized at the end of the first interpass and recrystallized at the end of the second interpass RR and, - particles g that are recrystallized at the end of the first interpass and non-recrystallized at the end of the second interpass RN and, - particles g that are non-recrystallized at the end of the first interpass and recrystallized at the end of the second interpass NR and, - particles g that are non-recrystallized at the end of the first interpass and non-recrystallized at the end of the second interpass NN and may be written to include:

[0034] For each type of particle, g RR , g RN , gNR and g NN is the representative dislocation density ρ RR , ρ RN , ρ NR and ρ NN , as well as typical particle size o RR , o RN , o NR and o NN The ratio of the representative particles is defined (characterized) by G RR , G RN , G NR and G NN It is shown as follows.

[0035] Preferably, the representative value g of the grains to be recrystallized at the end of the second interpass NR and g RR takes into account the grains that are recrystallized by dynamic recrystallization during the second rolling and second interpass, post-dynamic recrystallization and static recrystallization.

[0036] Step c) is performed on the numerical value G RR , G RN , G NR , G NN , o RR , o RN , o NR , o NN , ρ RR , ρ RN , ρ NR , ρ NN The determination of is again based on the numerical value G R , G N , o R , o N , ρ R , ρ N This can be achieved by taking into account the following:

[0037] Step c) is performed on the numerical value G RR , G RN , G NR , G NN , o RR , o RN , o NR , o NN , ρ RR , ρRN , ρ NR , ρ NN Determining Λ can again be accomplished by considering process input parameters for the second rolling pass measured during or prior to the second rolling pass. These measured input parameters can be - the entrance temperature of the steel product measured at the time of entry into the hot rolling mill; - the temperature of the steel product measured between the first and second rolling passes; - the thickness of the steel product between the first and second rolling passes, - the outlet thickness of the steel product after the second rolling, -Speed ​​of steel products, - Interpass time for the second interpass, - the force exerted on the steel product by the second rolling stand F2, the gap between the two work rolls of the second rolling stand F2, -Exit tension.

[0038] In step c), the above-mentioned microstructural characteristics may be determined taking into account the chemical composition of the steel product, the diameter of the work rolls of the second stand and also the Young's modulus of the material of which it is made.

[0039] If the same modeling is performed by the inventors in the next pass, the number of particles, i.e., phase types, at the end of the inter-pass n will be 2 n It was observed that each type of grain is characterized by a (representative) grain size, a (representative) dislocation density, and its proportion in the microstructure. However, such complexity appeared problematic for process product models used online, for example, to control hot rolling mills. Furthermore, apart from the problem of computational time, such models are hyperparameterized, where the number of phases grows exponentially with the number of passes. Therefore, their calibration is difficult to achieve and in some cases is not accurate or reliable.

[0040] To solve these problems, models have been developed that average the phase characteristics at the end of each interpass. Unfortunately, they provide poor predictions of the final microstructural inhomogeneity.

[0041] To this end, the invention comprises an optimized averaging step, step d), which makes it possible to reduce the number of parameters describing the microstructure.

[0042] This is because, in step d), a typical recrystallized grain g R2 and a representative non-recrystallized grain g N2 and their G R2 and G N2 This is done by determining the ratio of

[0043] Representative recrystallized grains g R2 The characteristics of the R2 and dislocation density ρ R2 is the grain size recrystallized in the second interpass g RR , g NR The ratio G RR、 G NR , typical particle size o RR , o NR and the representative dislocation density ρ RR , ρ NR (determined by calculating them).

[0044] o R2 is a set of sizes {o RR , o NR}, and this averaging is performed by averaging the respective ratios G RR、 G NR or particle g RR , g NR In particular, o R2 ,o RR , o NR may be determined by calculating a weighted arithmetic mean of G RR and G NR and o R2 =(GRR .o RR +G NR .o NR ) / (G RR +G NR )

[0045] ρ R2 can be determined similarly, but ρ RR , ρ NR , G RR and G NR It may be determined based on the following.

[0046] G R2 For example, G RR and G NR is determined by summing G R2 =G RR +G NR is.

[0047] Representative non-recrystallized particles g N2 The characteristics of the N2 and dislocation density ρ N2 is the grain size that has not been recrystallized in the second interpass. NN , g RN The ratio G NN , G RN , typical particle size o NN , o RN and the representative dislocation density ρ NN , ρ RN This determination is based on (and determined by) calculating the grain size of a representative recrystallized grain g R2 This is achieved in the same manner as described for feature (1).

[0048] As a result, in step d), the ratio G R2 , typical particle size o R2 , and the representative dislocation density ρ R2 is defined for the grains recrystallized in the second rolling and the second interpass (determined by calculating them). Also, in step d), the ratio G N2 , typical particle size oN2 , and the representative dislocation density ρ N2 are defined for grains that are not recrystallized in the second rolling and second interpass (determined by calculating them).

[0049] Thanks to this averaging procedure, the situation just before the third pass (third rolling and third interpass) is the initial value G R , G N , o R , o N , ρ R , ρ N (initial value of the second pass) is G for the third pass R2 , G N2 , o R2 , o N2 , ρ R2 , ρ N2 Note that this is similar to the situation just before the second pass (second rolling and second interpass), except that is replaced by .

[0050] The evolution of the microstructure during the third pass is determined as described above for the second pass, but with an initial value G R , G N , o R , o N , ρ R , ρ N (initial value for the second pass) is G R2 , G N2 , o R2 , o N2 , ρ R2 , ρ N2 At the end of the third pass, the microstructure thus determined comprises four types of grains.

[0051] The properties of these four types of particles may then be averaged pairwise as described above to obtain two representative types of particles: recrystallized and non-recrystallized.

[0052] More generally, the hot rolling operation may comprise a total number of passes N (N is typically 10 or more), each pass comprising a rolling pass and a subsequent inter-pass (or subsequent post-pass of the last pass N), each pass being referenced by its number n, n=1..N. Steps d) and c) may then be carried out iteratively, once for each pass n, for n=3..N, where for each pass n, n=3...N, - Step d) is performed, and the averaging procedure described above is used to calculate G Rn-1 , G Nn-1 , o Rn-1 , o Nn-1 , ρ Rn-1 , ρ Nn-1 For pass n, these averaged values ​​are calculated using the following values ​​determined during the previous execution of step c) (i.e., during the execution of step c for pass n-1): G NNn-1 , o RRn-1 , o RNn-1 , o NRn-1 , o NNn-1 , ρ RRn-1 , ρ RNn-1 , ρ NRn-1 , ρ NNn-1 determined based on 、 Next, - Step c) is based on path n and has an initial value of number G Rn-1 , G Nn-1 , o Rn-1 , o Nn-1 , ρ Rn-1 , ρ Nn-1 (G R , G N , o R , o N , ρ R , ρ N ) and the process parameters considered may be different from those of Pass 2, and during this execution of step c), RR , G RN , G NR , G NN , o RR , o RN , o NR , o NN , ρRR , ρ RN , ρ NR , ρ NN Similarly, the following number G RRn , G RNn , G NRn , G NNn , o RRn , o RNn , o NRn , o NNn , ρ RRn , ρ RNn , ρ NRn , ρ NNn is determined.

[0053] Preferably, the hot rolling is carried out in a reversing mill with one reversing roll stand.

[0054] Even more preferably, in step a), hot rolling stand 1 is the reversing rolling stand, and in step b), interpass I1 extends between the first hot rolling and the second hot rolling in the reversing rolling stand, and in steps c) and d), interpass I2 extends between the second hot rolling and the third hot rolling in the reversing rolling stand.

[0055] Preferably, the hot rolling is carried out in a tandem mill comprising at least three hot rolling stands.

[0056] Even more preferably, in step a), the hot rolling stand F1 is the first rolling stand, and in step b), the interpass I1 extends between the first hot rolling in the first stand and the second hot rolling in the second stand, and in steps c) and d), the interpass I2 extends between the second hot rolling in the second rolling stand and the third hot rolling in the third rolling stand.

[0057] Preferably, the recrystallized particles in step b) may result from dynamic recrystallization and / or post-dynamic recrystallization and / or static recrystallization.

[0058] Alternatively, the recrystallized grains of step b) may result solely from static recrystallization.

[0059] Preferably, the recrystallized particles in step c) may result from dynamic recrystallization and / or post-dynamic recrystallization and / or static recrystallization.

[0060] Alternatively, the recrystallized grains of step c) may result solely from static recrystallization.

[0061] Exemplary test results are presented in Figure 3, where the average grain size at the end of hot rolling determined according to the present method is plotted against the corresponding measured average grain size (measured directly on samples taken from the steel product at the end of hot rolling). The calculated average grain size is the average of the estimated grain size after the last pass, i.e., o RRn=N , o RNn=N , o NRn=N and o NNn=N (The average is a weighted average, and the weighting coefficients are G RRn=N , G RNn=N , G NRn=N , G NNn=N ) are averaged over the process input parameters, i.e., hot rolling conditions, which vary from one point to another in FIG. 3. FIG. 3 shows that the approximation based on the present method, which corresponds to the averaging achieved in step d), is a good approximation. In fact, this makes it possible to simplify many calculations while still providing accurate predictions, as shown in FIG. 3.

[0062] In an exemplary embodiment of the above-described method, the microstructural properties of a steel product at the end of hot rolling (i.e., after pass N) or at an intermediate stage of hot rolling (after pass n, where n=3...N-1) are determined based on process parameters, so-called process input parameters, that are actually applied when performing said hot rolling. These process parameters are measured by sensors attached to the hot rolling mill or are derived from control signals sent to the hot rolling mill's actuators. Thus, in this exemplary embodiment of the method, the method is a non-direct measurement method that allows using a model to determine the properties of the manufactured steel product (i.e., its microstructural properties) from measurements (measurements of process parameters) or actually applied control signals.

[0063] This indirect measurement method is -Controlling the operation of a hot rolling mill (in real time), or - may further be applied to determine the mechanical properties of hot rolled semi-finished products based on their microstructure.

[0064] Hot rolling mill control Knowing the microstructural characteristics in terms of grain ratio, size, and dislocation density is very useful for controlling the rolling stands of a rolling mill. In fact, these values ​​strongly influence the plasticity and formability of the semi-finished product. Therefore, for each rolling pass, the thickness or reduction rate after rolling usually depends on both the rolling parameters (such as the gap between the rolls) and the microstructure just before the rolling operation.

[0065] In practice, if the microstructure is not taken into account, it is rather common that when a new type of product is rolled in a hot rolling mill, the target thickness is not achieved (either because the material contains a small amount of recrystallized grains and is therefore stiffer than expected, or conversely because the material is softer than expected). By new type of product we mean a product made of a type of steel that has not been rolled in a hot rolling mill before and for which no experience has been gained regarding its behavior when hot rolled (for example, a steel with residual contents different from those usual for the steelmaking line in question or when the process parameters used upstream of the hot rolling are different from those usually used in this steelmaking line).

[0066] It is therefore very useful to control the rolling mill in one or more of the N passes, taking into account the microstructural characteristics determined as explained above, since this makes it possible to reliably estimate the formability of the product, whether it is a new type of product or a conventional product.

[0067] More specifically, according to a first application of the method, the rolling stand performing pass n is G Rn-1 , G Nn-1 , o Rn-1 , o Nn-1 , ρ Rn-1 , ρ Nn-1 Considering, or alternatively, G NNn-1 , o RRn-1 , o RNn-1 , o NRn-1 , o NNn-1 , ρ RRn-1 , ρ RNn-1 , ρ NRn-1 , ρ NNn-1 are controlled, or in other words, adjusted, taking into account

[0068] For example, the third rolling pass is G R2 , o R2 , ρ R2 , G N2 , o N2 , ρ N2 may be adjusted using

[0069] Adjustment of pass n is typically accomplished by also considering process input parameters for pass n, such as the current speed of the product, the temperature upstream and / or downstream of the work rolls of the rolling stand achieving pass n, the current force exerted on the steel product by the stand, the current gap between the work rolls, the current rotational speed of the work rolls, and the thickness upstream of the stand achieving pass n. This may also take into account the chemical composition of the product and rolling mill properties such as the diameter and associated Young's modulus of the work rolls. These different parameters and properties may be measured (as is the case for the product speed or its temperature), or may be pre-recorded or obtained (as is the case for the diameter of the work rolls, for example).

[0070] The adjustment of the rolling stand for pass n is as follows: - Microstructural properties (e.g., G Rn-1 , G Nn-1 , o Rn-1 , o Nn-1 , ρ Rn-1 , ρ Nn-1 ), - one or more process input parameters and other parameters or characteristics mentioned in the paragraph above, - the target exit thickness or reduction ratio to be obtained after pass n The method may include determining one or more process set points based on the

[0071] The one or more process set points may comprise one or more of the following: a set point force to be exerted by the stand on the product, a set point gap between the work rolls, and a set point rotational speed of the work rolls.

[0072] One or more process set points are sent to one or more controllers or actuators of the stands that accomplish pass n, so that pass n is accomplished according to the set points.

[0073] Adjusting the rolling stand to perform pass n can include determining the average flow stress applied during pass n, where the average flow stress is G Rn-1 , G Nn-1 , oRn-1 , o Nn-1 , ρ Rn-1 , ρ Nn-1 For example, the average flow stress applied during the third rolling pass is calculated based on the value of G R2 , o R2 , ρ R2 , G N2 , o N2 , ρ N2 and may be determined (by calculation) using process input parameters.

[0074] The determination of the mean flow stress can be achieved, for example, according to equations (6) and (7) of Lissel et al. The so-called friction Hill rolling force model can also be used to determine the applied mean flow stress. The friction Hill rolling force model used can be the Orowan model. However, it can also be any other model known to those skilled in the art, such as the Sims or Bland & Ford models. A general discussion of each of these three models can be found, for example, in "The calculation of roll pressure in hot and cold flat rolling," E. Orowan, Proceedings of the Institute of Mechanical Engineers, June 1943, Vol. 150, No. 1, pp. 140-167 for the Orowan model; "The calculation of roll force and torque in hot rolling mills," RBSims, Proceedings of the Institute of Mechanical Engineers, June 1954, Vol. 168, No. 1, pp. 191-200 for the Sims model; and "The Calculation of Roll Force and Torque in Cold Strip Rolling with Tensions," DRBland H. Ford, Proceedings of the Institute of Mechanical Engineers, June 1948, Vol. 149, p. 144 for the Bland & Ford model. The friction hill rolling force model may also be used to determine process set points to be used, such as applied force and / or rotational speed.

[0075] In the exemplary embodiment described herein, each rolling pass n (n from 1 to N) is conditioned using the conditioning techniques described above. Additionally, alternatively, based on estimated microstructural properties, only some rolling passes, or even only one rolling pass (e.g., one or more rolling passes where n≧3), may be conditioned in this manner.

[0076] Determination of mechanical properties of hot-rolled semi-finished products According to a second application of the method, the microstructural characteristics of the steel product at the end of hot rolling, i.e. after the last rolling pass (i.e. after pass N), i.e. G RRn=N , G RNn=N , G NRn=N , G NNn=N , o RRn=N , o RNn=N , o NRn=N , o NNn=N , ρ RRn=N , ρ RNn=N , ρ NRn=N , ρ NNn=N (possibly further averaged two by two as explained above) are taken into account to determine the subsequent evolution of the microstructure of the steel product during subsequent cooling on the run-out table and possibly during subsequent hot coiling.

[0077] To determine the subsequent evolution of the microstructure of a steel product, it is naturally useful to know the microstructural characteristics immediately after hot rolling as a starting point for further transformation. The determination of the subsequent evolution may be carried out according to any suitable process product model for run-out table cooling (and possibly hot coiling), for example, according to paragraph 101 of EP 3645182. One or more mechanical properties of the hot coiled coil thus obtained may then be determined based on the final microstructure. Mechanical properties refer to one of the following: yield strength (YS), ultimate tensile strength (UTS), elongation, hole expandability, and formability. To determine one or more mechanical properties from the final microstructure and chemical composition, different existing models known to those skilled in the art may be used depending on the steel grade in question. In particular, this determination can be based on the models described in the following paper by S. Allain et al., "Microstructure based modeling for the mechanical behavior of ferrite-pearlite steels suitable to capture isotropic and kinematic hardening," Materials Science and Engineering: A, Volume 496, Issues 1-2, November 25, 2008, pp. 329-336, ISSN 0921-5093, for example, based on Equations 1 to 4 of this paper, or on more elaborate forms of these equations (Equations 5 to 15) presented in Section 3 of this paper.

[0078] It should be noted that it is important in practice to determine the mechanical properties of steel products based on the process parameters used during production and on metallurgical models. In this regard, Euronorm (EN 10373) further clarifies under what conditions the mechanical properties of coils can be specified to customers based on such model calculations rather than on direct mechanical tests, which demonstrates the practical industrial usefulness of such model-based mechanical property determinations.

[0079] The method for determining microstructural properties of a semi-finished steel product during hot rolling is performed by an electronic device having a computer structure, the electronic device comprising at least a processor and a memory, and also comprising a non-transitory computer readable medium such as a hard drive or flash memory comprising instructions (or more precisely, a computer program comprising these instructions), the execution of which by the electronic device causes the electronic device to perform said method for determining microstructural properties of a steel product.

[0080] The electronic device further comprises a communication interface for receiving data, more particularly for receiving the above-mentioned process input parameters.

[0081] In embodiments of interest herein, the electronic device also includes a human-machine interface, such as a computer display, and the electronic device is configured to output one or more of the properties of the microstructure determined by the electronic device using the human-machine interface.

[0082] When the electronic device is used to control a hot rolling mill, its communication interface is connected to a controller or actuator of the hot rolling mill to receive data representing sensors (e.g., temperature, speed, or thickness sensors) and / or process parameters (e.g., force exerted on the product, or rotational speed, etc.) of the hot rolling mill.

[0083] This connection may be wired or wireless, and may be achieved using a local network or bus, for example of the CAN (Controller Area Network), CAN+ or Fieldbus type, or may be realized using a public network such as the Internet.

[0084] If the electronic device is used to control a hot rolling mill, its communication interface will also be connected to the mill controller and / or mill actuators to which the above-mentioned set points are sent in order to control the hot rolling passes.

[0085] If the electronic device is used to determine one or more mechanical properties of the steel product, its communication interface may be connected to sensors and / or actuators of the rolling mill to obtain process input parameters.

[0086] Alternatively, such parameters may be connected to an industrial production database where they are recorded during production. In this last case, the process input parameters are acquired by an electronic device after the hot rolling of the steel product.

[0087] When the electronic device is used to determine one or more mechanical properties of a steel product, the electronic device may be configured to output said mechanical properties using its communication interface such that the one or more mechanical properties are stored in the above-mentioned industrial database. Alternatively or additionally, the electronic device may be configured to output one or more mechanical properties using a human-machine interface of the electronic device.

[0088] In the examples presented above, the electronic device has the structure of a standalone computer or electronic calculator. Furthermore, in alternative embodiments, the electronic device may take the form of a distributed computer system, for example comprising two or more computers operatively connected to each other, or comprising remote computing resources, such as cloud computing resources, and possibly distributed computing resources.

Claims

1. 1. A method for determining microstructural characteristics of a semi-finished steel product during hot rolling comprising at least three passes, comprising the steps of: a) obtaining a representative grain size value of said steel product before a first rolling pass; b) First Interpass I １ At the end of Recrystallized particles g Ｒ The ratio G Ｒ , and non-recrystallized grains g Ｎ The ratio G Ｎ , The recrystallized particles g Ｒ and the non-recrystallized particles g Ｎ Typical particle size of Ｒ , o Ｎ and the representative dislocation density ρ Ｒ , ρ Ｎ determining a c) Second Interpass I ２ At the end of The first interpath I １ At the end of the second interpass I ２ Particles g recrystallized at the end of ＲＲ The ratio G ＲＲ and, The first interpath I １ At the end of the second interpass I ２ The particles g that are not recrystallized at the end of ＲＮ The ratio G ＲＮ and, The first interpath I １ At the end of the second interpass I ２ Particles g recrystallized at the end of ＮＲ The ratio G ＮＲ and, The first interpath I １ At the end of the second interpass I ２ The particles g that are not recrystallized at the end of ＮＮ The ratio G ＮＮ and, The particles g ＲＲ , g ＲＮ , g ＮＲ , g ＮＮ Typical particle size of ＲＲ , o ＲＮ , o ＮＲ , o ＮＮ and the representative dislocation density ρ ＲＲ , ρ ＲＮ , ρ ＮＲ , ρ ＮＮ and determining d) Before the third rolling pass, the following characteristics of the microstructure of the semi-finished steel product: Representative recrystallized grains g Ｒ２ The ratio G Ｒ２ , and a representative non-recrystallized grain g Ｎ２ The ratio G Ｎ２ and, Representative recrystallized grains g Ｒ２ For particle g ＲＲ and g ＮＲ Particle ratio G for particles ＲＲ , G ＮＲ , typical particle size o ＲＲ , o ＮＲ and dislocation density ρ ＲＲ , ρ ＮＲ Particle size o based on Ｒ２ and dislocation density ρ Ｒ２ and, Representative non-recrystallized grains g Ｎ２ For particle g ＲＮ and g ＮＮ Particle ratio G ＮＮ , G ＲＮ、 Typical particle size o ＲＮ , o ＮＮ and dislocation density ρ ＮＮ , ρ ＲＮ Particle size o based on Ｎ２ and dislocation density ρ Ｎ２ and determining.

2. The following process input parameters measured during or prior to said hot rolling: an inlet temperature of the steel product measured upon entry into a hot rolling mill used to accomplish said hot rolling; the entrance thickness of the steel product when entering the rolling mill; The speed of steel products and an interpass time for the first or second interpass; the force exerted by the rolls of the rolling mill; a gap between two work rolls of a rolling mill; obtaining one or more of the outlet tensions; In steps b) and c), the following values ​​are used: G Ｒ , G Ｎ , o Ｒ , o Ｎ , ρ Ｒ , ρ Ｎ、 G ＲＲ , G ＲＮ , G ＮＲ , G ＮＮ , o ＲＲ , o ＲＮ , o ＮＲ , o ＮＮ , ρ ＲＲ , ρ ＲＮ , ρ ＮＲ , ρ ＮＮ The method of claim 1 , wherein one or more of:

3. e) Ratio G Ｒ２ , G Ｎ２ and a typical particle size o Ｒ２ , o Ｎ２ and dislocation density ρ Ｒ２ , ρ Ｎ２ 3. The method of claim 1, further comprising the step of outputting to an operator on a computer display.

4. e') The industrial production database includes the ratio G Ｒ２ , G Ｎ２ and a typical particle size o Ｒ２ , o Ｎ２ and dislocation density ρ Ｒ２ , ρ Ｎ２ The method of claim 1 , further comprising the step of recording one or more of:

5. 5. The method according to any one of claims 1 to 4, wherein the hot rolling is carried out in a reversing mill with one reversing roll stand.

6. 6. The method according to any one of claims 1 to 5, wherein the hot rolling is carried out in a tandem mill comprising at least three hot rolling stands.

7. 7. The method according to any one of claims 1 to 6, wherein the recrystallized grains in step b) may result from dynamic recrystallization and / or post-dynamic recrystallization and / or static recrystallization.

8. 8. The method of claim 7, wherein the recrystallized grains in step b) can result only from static recrystallization.

9. 9. The method according to any one of claims 1 to 8, wherein the recrystallized grains in step c) may result from dynamic recrystallization and / or post dynamic recrystallization and / or static recrystallization.

10. 10. The method of claim 9, wherein the recrystallized grains in step c) can result only from static recrystallization.

11. The hot rolling stand performing the third pass is the G determined in step d). Ｒ２ , o Ｒ２ , ρ Ｒ２ , G Ｎ２ , o Ｎ２ , ρ Ｎ２ 11. The method of claim 2, or any one of claims 3 to 10 depending on claim 2, wherein the method is adjusted using

12. The average flow stress to be applied during the third rolling pass is determined in step d) as G Ｒ２ , o Ｒ２ , ρ Ｒ２ , G Ｎ２ , o Ｎ２ , ρ Ｎ２ The method of claim 11 wherein the determination is made using

13. The method of claim 12 , wherein the average flow stress applied during the third rolling pass is determined using one or more process input parameters for the third pass.

14. 14. The method of any one of claims 1 to 13, wherein the hot rolling comprises 10 to 15 hot rolling passes to produce a steel strip.

15. 15. The method of any one of claims 1 to 14, wherein the hot rolling comprises 15 to 35 hot rolling passes to produce a steel strip.

16. said hot rolling comprises a total number of passes N, each pass comprising a rolling pass and a subsequent inter-pass or post-pass, each pass being referenced by its number n, n=1...N, and steps d) and c) being carried out iteratively once for each pass n, n=3...N; During the execution of step d) for pass n, a representative particle g Ｒｎ－１ , g Ｎｎ－１ The ratio G Ｒｎ－１ , G Ｎｎ－１ , size o Ｒｎ－１ , o Ｎｎ－１ , and dislocation density ρ Ｒｎ－１ , ρ Ｎｎ－１ is the following value determined during the previous execution of step c) performed for pass n-1: G ＮＮｎ－１ , o ＲＲｎ－１ , o ＲＮｎ－１ , o ＮＲｎ－１ , o ＮＮｎ－１ , ρ ＲＲｎ－１ , ρ ＲＮｎ－１ , ρ ＮＲｎ－１ , ρ ＮＮｎ－１ is determined by averaging based on During the execution of step c) of path n, G Ｒｎ－１ , G Ｎｎ－１ , o Ｒｎ－１ , o Ｎｎ－１ , ρ Ｒｎ－１ , ρ Ｎｎ－１ Based on the following values, G ＲＲｎ , G ＲＮｎ , G ＮＲｎ , G ＮＮｎ , o ＲＲｎ , o ＲＮｎ , o ＮＲｎ , o ＮＮｎ , ρ ＲＲｎ , ρ ＲＮｎ , ρ ＮＲｎ , ρ ＮＮｎ 16. The method of claim 1, wherein:

17. For passes n having n at least 3 or more, the hot rolling stand performing pass n is The following values ​​were determined during step d) of pass n: G Ｒｎ－１ , G Ｎｎ－１ , o Ｒｎ－１ , o Ｎｎ－１ , ρ Ｒｎ－１ , ρ Ｎｎ－１ , or The following values ​​were determined during step c) of pass n-1: G ＮＮｎ－１ , o ＲＲｎ－１ , o ＲＮｎ－１ , o ＮＲｎ－１ , o ＮＮｎ－１ , ρ ＲＲｎ－１ , ρ ＲＮｎ－１ , ρ ＮＲｎ－１ , ρ ＮＮｎ－１ The method of claim 16 and claim 2, wherein the value is adjusted based on:

18. 18. The method of claim 17, wherein pass n is also adjusted taking into account one or more process input parameters for pass n, and optionally a target exit thickness or reduction ratio to be obtained after pass n.

19. The method further comprises determining one or more mechanical properties of the steel product after said hot rolling, optionally after cooling and coiling, wherein the one or more mechanical properties are determined based on the following microstructural properties of the steel product after a final pass N: G ＲＲｎ＝Ｎ , G ＲＮｎ＝Ｎ , G ＮＲｎ＝Ｎ , G ＮＮｎ＝Ｎ , o ＲＲｎ＝Ｎ , o ＲＮｎ＝Ｎ , o ＮＲｎ＝Ｎ , o ＮＮｎ＝Ｎ , ρ ＲＲｎ＝Ｎ , ρ ＲＮｎ＝Ｎ , ρ ＮＲｎ＝Ｎ , ρ ＮＮｎ＝Ｎ 16. The method of claim 2, wherein the temperature is determined from

20. 20. An electronic device comprising at least a processor and a memory configured to perform the method of any one of claims 1 to 19.

21. 21. A hot rolling mill comprising one or more rolling stands and comprising an electronic device according to claim 20, the electronic device being configured to carry out the method according to any one of claims 11 to 13 or claim 15 or 16.

22. A computer program comprising instructions for causing a computer to carry out the method of any one of claims 1 to 19.

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