Online detection method for average temperature of rolled piece in plate and strip rolling process

Abstract

The invention discloses an on-line detection method for the average temperature of a rolled piece in the plate and strip rolling process. The method comprises the steps that firstly, a deformation resistance model of metal to be rolled is determined; secondly, the deformation resistance of a rolled piece under each pass is inversely calculated; and thirdly, the average rolling temperature of the rolled piece in each pass is inversely calculated. All data involved in the on-line detection method for the average temperature of the rolled piece are from actual measurement data of hot rolling production or soft measurement values obtained through back calculation according to the actual measurement data, and the rolling temperature of the rolled piece obtained from an overall model is true and reliable; the defects that in an existing method, a thermodetector can only detect the surface temperature, the temperature measurement precision is greatly influenced by complex working conditions on site, and a theoretical model based on a heat conduction differential equation has high dependence on thermophysical parameters of rolled pieces and other rolling parameters are overcome.

Description

Technical Field

[0001] The invention belongs to the technical field of hot rolling production, and in particular relates to an online detection method for the average temperature of a rolled piece in a plate and strip rolling process. Background Art

[0002] Rolling temperature plays a crucial role in the plate and strip rolling process, especially in hot rolling of medium and thick plates. This is primarily due to the fact that temperature, as a key factor in the plastic deformation of metal materials, affects deformation resistance, rolling force, thickness accuracy, and plate shape quality during the rolling process. Furthermore, as a key factor in the heat treatment process of metal materials, the start and finish rolling temperatures directly affect the plate's microstructure and ultimate mechanical properties. Therefore, a fast, simple, and highly accurate method for measuring the temperature of the rolled piece during rolling is crucial for producing high-quality plate. Currently, several thermometers are installed on the hot rolling line to measure and track the slab temperature at specific points. However, these instruments can only measure surface temperatures, making it difficult to determine the average temperature of the rolled piece. Furthermore, due to complex on-site working conditions, the surface temperature can sometimes be inaccurately determined. Another common approach is to theoretically calculate the temperature using empirical formulas and finite difference methods. However, this method is highly dependent on the thermophysical properties of the rolled piece and other rolling parameters, and the accuracy of the temperature calculation during hot rolling, in particular, needs to be further improved.

[0003] Chinese invention patent application number 201911032415.6 discloses a "Method for Calculating the Temperature Field of Bloom Rolling Considering Uneven Deformation Heat." This patent considers the impact of uneven deformation on the rolling temperature field and accurately predicts the temperature field during the rolling process by establishing a three-dimensional finite element model of a single-pass bloom rolling process. Chinese invention patent application number 202210697574.3 discloses a "Method, Device, and Equipment for Predicting the Processing Temperature of Bars during Rough Rolling." This patent also predicts the temperature distribution during rough rolling of bars by establishing a finite element model that matches the actual rolling process. Chinese invention patent application number 202210671987.4 discloses a "Method for Predicting the Cross-Sectional Temperature of a Strip Rolling Process." This patent uses finite element numerical simulation to determine the influence of various factors on the cross-section temperature parameters of the rolled piece. It then performs nonlinear fitting of the cross-section temperature function to effectively predict the cross-section temperature distribution of the rolled piece. Chinese invention patent application number 201210006018.3 discloses a "computational method for predicting the temperature field of cold-rolled strip." This patent uses numerical integration to calculate frictional heat and deformation heat during the cold rolling process, thereby achieving a high-precision prediction of the strip temperature distribution. Chinese invention patent application number 201710243890.2 discloses a "two-dimensional alternating difference method for predicting the temperature field of strip during hot rolling." This patent comprehensively considers the on-site working conditions and boundary conditions of the hot rolling process and establishes a strip temperature field calculation method based on heat conduction differential equations and explicit and implicit alternating difference schemes. The prediction results are detailed and accurate. Chinese invention patent application number 202210120108.9 discloses a "high-precision prediction method for the average temperature of the intermediate billet of wide and heavy plate." This patent establishes a mechanism model for wide and heavy plate temperature prediction based on temperature partial differential equations and implicit finite difference methods, enabling rapid calculation of billet surface temperature.

[0004] From the above patents, it can be seen that they are basically based on finite difference models established based on heat conduction differential equations and finite element models established based on finite element simulation. The temperature prediction accuracy obtained by the above methods is highly dependent on the thermophysical properties of the rolled piece and other rolling parameters, and the model itself will inevitably introduce multiple assumptions at the beginning of its establishment, which will affect the temperature prediction accuracy of the hot rolling process. In addition, the time cost of the finite element model is also an important factor hindering its actual application on site. In order to further improve the accuracy of the temperature prediction model while taking into account its computational efficiency, versatility and robustness, a more effective prediction method needs to be proposed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide a method for online detection of the average temperature of rolled pieces during the plate and strip rolling process. All data involved in the online detection method of the average temperature of rolled pieces are derived from actual measured data of hot rolling production, or soft measurement values ​​obtained by reverse calculation based on the actual measured data. From the perspective of the overall model, the rolling temperature of the rolled pieces obtained is true and reliable. This method makes up for the deficiencies in the prior art, such as the fact that the thermometer can only detect the surface temperature, the temperature measurement accuracy is greatly affected by the complex working conditions on site, and the theoretical model based on the heat conduction differential equation is highly dependent on the thermophysical properties of the rolled pieces and other rolling parameters.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for online detection of the average temperature of the rolled piece during the plate and strip rolling process, characterized in that the method comprises the following steps:

[0007] Step 1: Determine the deformation resistance model of the metal to be rolled: Use hot compression experiments or machine learning to establish the deformation resistance model of the metal to be rolled. The expression of the model is

[0008] and

[0009]

[0010] Among them, ε is the degree of deformation; is the deformation rate; r is the pass reduction rate; H and h are the thickness of the rolled piece at the entrance and exit, respectively; v R is the roller linear speed; R' is the roller flattening radius; T is the deformation temperature of the rolled piece; K is the deformation resistance of the rolled piece; f is the expression of any deformation resistance model;

[0011] Step 2: Back-calculate the deformation resistance of the rolled piece at each pass: When the metal to be rolled is hot rolled at each pass, the measured data of the metal to be rolled during the hot rolling process can be obtained, and the soft measurement value K of the deformation resistance of the metal to be rolled at each pass can be back-calculated. act , the soft measurement value K of the deformation resistance of the metal to be rolled in each pass act The calculation formula is

[0012]

[0013] in,

[0014] h m =α·H+(1-α)·h,

[0015]

[0016] Where K act is the soft measurement value of the deformation resistance of the rolled piece; F actW is the measured value of the rolling force of the rolled piece; act is the actual measured value of the rolled piece width; L act is the arc length of the deformation zone; Q p is the stress state influence coefficient; and are all model parameters; h m is the average thickness of the rolled piece; α is the average thickness influence coefficient; υ is the Poisson's ratio of the roll; E is the elastic modulus of the roll; and R is the original radius of the roll.

[0017] Step 3: Back-calculate the average rolling temperature of the rolled piece in each pass: Use the deformation resistance model of the rolled metal determined in step 1, combined with the soft measurement value K of the deformation resistance of the rolled metal in each pass in step 2 act , as well as the actual deformation degree ε and actual deformation rate of the metal to be rolled during each rolling pass Get the soft measurement value T of the average rolling temperature of each pass act .

[0018] The above-mentioned method for online detection of the average temperature of the rolled piece during the plate and strip rolling process is characterized in that: in step 2, the various measured data of the metal to be rolled during the hot rolling process include the width of the metal to be rolled, the average thickness of the metal to be rolled, the arc length of the deformation zone of the metal to be rolled, the original radius of the rolling roller and the measured value of the rolling force of the rolled piece.

[0019] The beneficial effects of the present invention are that all data involved in the online detection method of the average temperature of the rolled piece are derived from the actual measured data of hot rolling production, or the soft measurement values ​​obtained by reverse calculation based on the actual measured data. The rolling temperature of the rolled piece obtained from the overall model is real and reliable. This makes up for the shortcomings of the existing method, in which the thermometer can only detect the surface temperature, the temperature measurement accuracy is greatly affected by the complex working conditions on site, and the theoretical model based on the heat conduction differential equation is highly dependent on the thermophysical properties and other rolling parameters of the rolled piece. The temperature measurement method proposed in the present invention has been used as an important temperature detection means, and is used in conjunction with the on-site temperature measuring instrument to effectively monitor the surface temperature and average temperature of the rolled piece during the rolling process. It is an important basis for controlling the final rolling temperature of the rolled piece and when the furnace needs to be returned to the furnace for temperature supplementation, thereby ensuring the smooth and effective progress of the rolling process.

[0020] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a comparison chart of the average temperature of the rolled piece obtained in Example 1 of the present invention and the surface temperature measured by a pyrometer.

[0022] Figure 2 This is a comparison chart of the average temperature of the rolled piece obtained in Example 2 of the present invention and the surface temperature measured by a pyrometer.

[0023] Figure 3 This is a comparison chart of the average temperature of the rolled piece obtained in Example 3 of the present invention and the surface temperature measured by a pyrometer. DETAILED DESCRIPTION

[0024] Example 1

[0025] Taking a seven-stand hot rolling mill as an example, Q235B steel with a raw material thickness of 40.52 mm and a width of 1551.3 mm is heated to 1040°C. After seven hot rolling passes, the resulting finished plate has a thickness of 5.56 mm and a width of 1554.2 mm. The relevant rolling parameters during the hot rolling process are shown in Table 1.

[0026] Table 1 Hot rolling process related rolling parameters

[0027]

[0028] This embodiment includes the following steps:

[0029] In step 1, the Guan Kezhi-Zhou Jihua model structure can be used, and the deformation resistance model expression is

[0030]

[0031] The deformation resistance model parameters are σ0=139.8Mpa, a1、a2、a3、 a5 and a6 are -2.861, 3.642, 0.254, -0.1993, 0.4349, and 1.51 respectively.

[0032] Step 2: When the metal to be rolled is hot rolled in each pass, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass is calculated based on the measured data of the metal to be rolled in the hot rolling process in Table 1. act , that is 139.2MPa, 153.3MPa, 163.0MPa, 167,7MPa, 181.4MPa, 186.3MPa, 193.9MPa.

[0033] Step 3: Based on the deformation resistance model of Q235B, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass in step 2 is used. act , as well as the actual deformation degree ε and actual deformation rate of the metal to be rolled during each rolling pass Get the soft measurement value T of the average rolling temperature of each pass actThe average temperatures of the rolling passes were 1018.4°C, 1004.1°C, 993.1°C, 978.8°C, 943.8°C, 899.8°C, and 873.6°C, respectively, while the actual surface temperatures of the rolled pieces obtained using a pyrometer were 956°C, 943°C, 931°C, 918°C, 905°C, 891°C, and 870°C. Figure 1 As shown in the figure, as the rolling process progresses, the thickness of the rolled piece gradually decreases, the heat conduction speed between the core and the surface of the rolled piece accelerates, the temperature drop in the core increases, and the temperature difference between the surface and the core of the rolled piece becomes smaller and smaller. At the end of rolling, the overall temperature of the rolled piece is about 870℃.

[0034] Example 2

[0035] Taking single-pass rolling on a single-stand reversing hot rolling mill as an example, a TC4 titanium alloy raw material with a thickness of 211 mm and a width of 1275 mm was heated to 960°C. After seven passes of hot rolling, the resulting finished plate had a thickness of 77 mm and a width of 1280 mm. The relevant rolling parameters during the hot rolling process are shown in Table 2.

[0036] Table 2 Hot rolling process related rolling parameters

[0037]

[0038] This embodiment includes the following steps:

[0039] Step 1: Obtain the deformation resistance model of TC4 titanium alloy in the form of an XGBoost machine learning model. The model file is named "xgb_model.pkl" and the deformation resistance is obtained by calling it.

[0040] Step 2: When the metal to be rolled is hot rolled in each pass, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass is calculated based on the measured data of the metal to be rolled in the hot rolling process in Table 1. act , that is 112.7MPa, 106.5MPa, 111.2MPa, 123.5MPa, 126.7MPa, 127.6MPa, 128MPa.

[0041] Step 3: Based on the XGBoost deformation resistance model of TC4 titanium alloy, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass in step 2 is used. act , as well as the actual deformation degree ε and actual deformation rate of the metal to be rolled during each rolling pass Get the soft measurement value T of the average rolling temperature of each pass actThe average temperatures of the rolling passes were 943.2°C, 947.3°C, 944.6°C, 935.6°C, 928.2°C, 915.5°C, and 910.8°C, respectively, while the actual surface temperatures of the rolled pieces obtained using a pyrometer were 880°C, 863°C, 852°C, 846°C, 843°C, 842°C, and 840°C. Figure 2 As shown in the figure, after the slab is rolled for 7 times in the first heat, the surface temperature drops from about 880℃ to about 840℃. The average temperature remains basically consistent in the first few passes due to the heat generated by plastic deformation in the core. After the 4th pass, as the thickness of the rolled piece decreases, the heat conduction speed between the core and the surface of the rolled piece accelerates, and the temperature drop in the core increases. By the end of rolling, the average temperature of the rolled piece is about 910℃.

[0042] Example 3

[0043] Taking the second-heat rolling process of a single-stand reversing hot rolling mill as an example, a TC4 titanium alloy raw material with a thickness of 34 mm and a width of 1507.9 mm was heated to 940°C. After seven hot rolling passes, the finished plate had a thickness of 3.7 mm and a width of 1532.4 mm. The relevant rolling parameters during the hot rolling process are shown in Table 3.

[0044] Table 3 Hot rolling process related rolling parameters

[0045]

[0046] This embodiment includes the following steps:

[0047] Step 1: Obtain the deformation resistance model of TC4 titanium alloy in the form of an XGBoost machine learning model. The model file is named "xgb_model.pkl" and the deformation resistance is obtained by calling it.

[0048] Step 2: When the metal to be rolled is hot rolled in each pass, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass is calculated based on the measured data of the metal to be rolled in the hot rolling process in Table 1. act , that is 110.9MPa, 107.0MPa, 114.6MPa, 124.3MPa, 123.9MPa, 159.5MPa, 191.3MPa.

[0049] Step 3: Based on the XGBoost deformation resistance model of TC4 titanium alloy, the soft measurement value K of the deformation resistance of the metal to be rolled in each pass in step 2 is used. act , as well as the actual deformation degree ε and actual deformation rate of the metal to be rolled during each rolling pass Get the soft measurement value T of the average rolling temperature of each pass actThe average temperatures of the rolling passes were 911.7°C, 910.3°C, 893.9°C, 872.5°C, 832.1°C, 783.5°C, and 707.5°C, respectively. The actual surface temperatures of the rolled pieces obtained using a pyrometer were 853°C, 854°C, 850°C, 833°C, 803°C, 749°C, and 696°C. Figure 3 As shown in the figure, as the rolling process progresses, the thickness of the rolled piece gradually decreases, the heat conduction speed between the core and the surface of the rolled piece accelerates, the temperature drop in the core increases, and the temperature difference between the surface and the core of the rolled piece becomes smaller and smaller. At the end of rolling, the overall temperature of the rolled piece is about 700℃.

[0050] All data involved in the online detection method for the average temperature of rolled pieces provided by the present invention are derived from measured data of hot rolling production, or soft measurement values ​​obtained by reverse calculation based on the measured data. From the perspective of the overall model, the result depends only on the intrinsic property of the high-temperature rheological behavior of the material, so the obtained rolling temperature of the rolled piece is real and reliable. This method makes up for the shortcomings of the existing method, in which the thermometer can only detect the surface temperature, the temperature measurement accuracy is greatly affected by the complex working conditions on site, and the theoretical model based on the heat conduction differential equation is highly dependent on the thermophysical properties of the rolled piece and other rolling parameters. In addition, the temperature measurement method proposed in the present invention has become an important temperature detection means, and is used in conjunction with on-site temperature measuring instruments to effectively monitor the surface temperature and average temperature of the rolled piece during the rolling process. It is an important basis for controlling the final rolling temperature of the rolled piece and when the furnace needs to be reheated, thereby ensuring the smooth and effective progress of the rolling process.

[0051] A rolling mill pass refers to a continuous rolling process that the steel billet undergoes during the rolling process, including a series of rolling deformations of the rollers, the purpose of which is to control and adjust the length and cross-section of the steel billet to obtain semi-finished products and finished products that meet specific quality requirements.

[0052] It should be noted that in step 1, when machine learning is used to establish the deformation resistance model of the metal to be rolled, a theoretical model, a machine learning model, and a neural network model can be selected for establishment.

[0053] In step 3, the soft measurement value T of the rolling temperature of each pass can be realized by bisection method, Newton iteration method or other related algorithms. act Calculation.

[0054] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for online detection of the average temperature of a rolled piece during a strip rolling process, characterized in that: The method comprises the following steps: Step 1: Determine the deformation resistance model of the metal to be rolled: Use hot compression experiments or machine learning to establish the deformation resistance model of the metal to be rolled. The expression of the model is and Among them, ε is the degree of deformation; is the deformation rate; r is the pass reduction rate; H and h are the thickness of the rolled piece at the entrance and exit, respectively; v R is the roller linear speed; R' is the roller flattening radius; T is the deformation temperature of the rolled piece; K is the deformation resistance of the rolled piece; f is the expression of any deformation resistance model; Step 2: Back-calculate the deformation resistance of the rolled piece at each pass: When the rolled metal is hot-rolled at each pass, the measured data of the rolled metal during the hot rolling process can be obtained, and the soft measurement value K of the deformation resistance of the rolled metal at each pass can be back-calculated. act , the soft measurement value K of the deformation resistance of the metal to be rolled in each pass act The calculation formula is in, h m =α·H+(1-α)·h, In the formula, K act is the soft measurement value of the deformation resistance of the rolled piece; F act W is the measured value of rolling force of rolled piece; act is the measured value of the rolled piece width; L act is the arc length of the deformation zone; Q p is the stress state influence coefficient; and are all model parameters; h m is the average thickness of the rolled piece; α is the average thickness influence coefficient; υ is the Poisson's ratio of the roll; E is the elastic modulus of the roll; and R is the original radius of the roll. Step 3: Back-calculate the average rolling temperature of the rolled piece in each pass: Use the deformation resistance model of the rolled metal determined in step 1, combined with the soft measurement value K of the deformation resistance of the rolled metal in each pass in step 2 act , as well as the actual deformation degree ε and actual deformation rate of the metal to be rolled during each rolling pass Get the soft measurement value T of the average rolling temperature of each rolling pass act .

2. The method for online detection of average temperature of rolled product during strip rolling process according to claim 1, characterized in that: In step 2, various measured data of the metal to be rolled during the hot rolling process include the width of the metal to be rolled, the average thickness of the metal to be rolled, the arc length of the deformation zone of the metal to be rolled, the original radius of the roll and the measured value of the rolling force of the rolled piece.

Images