A Method for Online Monitoring and Control of Rail Bending and Torsion Based on Thermal 3D Full-Section Profile Scanner

Abstract

This invention relates to the field of steel rolling production quality inspection technology, specifically a method for online monitoring and control of rail bending and torsion based on a thermal 3D full-section profile scanner. Applied to the final rolling mill, the method includes: acquiring rail data, including bending angle, torsion angle, and temperature data; formulating corresponding control strategies based on the rail data; dynamically optimizing the control strategies using a PID control algorithm, combining monitoring data and the state of the final rolling mill; establishing a mapping relationship between rail deformation and parameters, and setting up an anti-over-adjustment mechanism to achieve online monitoring and control of rail bending and torsion. This invention acquires real-time three-dimensional point cloud and temperature data of multiple rail layers, calculates bending and torsion parameters using an intelligent algorithm, and transmits the data in real-time to the final rolling mill control system via industrial Ethernet. Based on a preset control model, it automatically adjusts parameters such as roll pressure and roll gap, forming a real-time closed loop of "detection-calculation-adjustment" to ensure that the rail geometry meets standards during the steel tapping stage.

Description

Technical Field

[0001] This invention relates to the field of steel rolling production quality inspection technology, and in particular to a method for online monitoring and control of rail bending and torsion based on a thermal 3D full-section profile scanner. Background Technology

[0002] In the hot rolling process of steel rails, bending (lateral and vertical deviations > 1 mm / m) and torsion (> 0.3° / m) defects at the final mill tapping stage mainly originate from factors such as uneven roll pressure and roll gap deviation. Traditional monitoring methods have the following drawbacks:

[0003] Control lag: Offline detection delay > 10 min, final mill parameter adjustment relies on manual experience, resulting in batches of defective rails; Lack of linkage: Offline detection and final mill control operate independently, making it impossible to dynamically adjust steel output parameters based on real-time deformation data; Insufficient accuracy: Offline detection cannot cover the entire cross-section, thermal deformation compensation is inaccurate, and bending and torsion control error > 1 mm / m.

[0004] Existing technologies lack deep integration of hot full-section monitoring and mill control, resulting in lag in the adjustment of final mill output parameters (such as roll pressure and roll gap), making it difficult to meet the real-time forming requirements of high-speed rail tracks (curvature ≤1mm / m). Therefore, there is an urgent need for an intelligent monitoring system with real-time linkage control capabilities. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an online monitoring and control method for rail bending and torsion based on a thermal 3D full-section profile scanner. This invention acquires real-time three-dimensional point cloud and temperature data of multiple rail layers. After calculating bending and torsion parameters using an intelligent algorithm, the data is transmitted in real-time to the final rolling mill control system (PLC) via industrial Ethernet. Based on a preset control model, the system automatically adjusts parameters such as roll pressure and roll gap, forming a real-time closed loop of "detection-calculation-adjustment" to ensure that the rail geometry meets standards during the steel tapping stage.

[0006] The technical means employed in this invention are as follows:

[0007] A method for online monitoring and control of rail bending and torsion based on a hot 3D full-section profile scanner, applied to a final rolling mill, includes:

[0008] Obtain data on the rails, including bending angle, torsion angle, and temperature data;

[0009] Develop corresponding control strategies based on the data from the steel rails;

[0010] By combining monitoring data and the status of the final mill, the control strategy is dynamically optimized using a PID control algorithm.

[0011] Establish a mapping relationship between rail deformation and parameters, and set up an anti-over-adjustment mechanism to realize online monitoring and control of rail bending and torsion.

[0012] Furthermore, the data calculation method for the rail is as follows:

[0013] The formula for calculating the bending angle θ is:

[0014]

[0015] θ2=arctan(Δw / L)

[0016] θ = θ1 / θ2

[0017] Where θ1 represents the vertical bending angle, θ2 represents the lateral bending angle, Δh is the height difference of the rail along the vertical direction, reflecting the degree of vertical bending deformation; L is the length of the bent part of the rail, used as the reference length when calculating the bending angle; and Δw is the width difference of the rail along the horizontal direction, reflecting the width change caused by lateral bending.

[0018] The formula for calculating the torsion angle Δθ is:

[0019] Δθ=∑arccos(n i ·n i+1 )

[0020] Where, n i This represents the normal vector of the i-th cross-section. The normal vector is perpendicular to the cross-section and is used to describe the spatial orientation of the cross-section; n i+1 This represents the normal vector of the (i+1)th cross section, and n i Adjacent sections are used to determine the torsion angle of the rail by calculating the relationship between the normal vectors of two adjacent sections.

[0021] Furthermore, the corresponding control strategies are formulated based on the rail data, including bending control and torsion control;

[0022] The bending control means that when the vertical bending angle θ1 > 3°, the vertical roller pressure will be increased by 0.5-1.0 kN / mm; when the lateral bending angle θ2 > 2°, the horizontal roller gap will be adjusted by ±0.3 mm.

[0023] The torsion control means that when the torsion angle Δθ > 1.5° / m, the roll speed compensation is triggered to correct the torque unevenness during the rolling process.

[0024] Furthermore, the dynamic optimization of the control strategy through the PID control algorithm specifically includes:

[0025] By incorporating the hot deformation compensation model into the final rolling temperature influence factor, the modified formula is as follows:

[0026] (X′,Y′,Z′)=(X,Y,Z)*(1+a*(T-20℃)+β*ΔF / F0)

[0027] Where (X',Y',Z') represents the coordinate values ​​of the corrected rail cross-sectional profile in three-dimensional space, used to characterize the rail morphology after considering the effects of thermal deformation and pressure; (X,Y,Z) represents the three-dimensional coordinates of the uncorrected rail cross-sectional profile; 'a' is the temperature influence coefficient, reflecting the degree of influence of temperature changes on rail deformation correction, used to quantify the role of temperature factors; 'T' is the final rolling temperature, i.e. the temperature when the rail final rolling process is completed; 'β' is the pressure influence coefficient, a parameter used to quantify the degree of correction of rail hot deformation by changes in roll pressure; 'ΔF' is the roll pressure adjustment amount, which is the value of adjusting the roll pressure in actual production; and 'F0' is the initial roll pressure, which is the initial pressure reference value set for the roll.

[0028] Furthermore, the establishment of the mapping relationship between rail deformation and parameters means obtaining the roll pressure adjustment amount and roll gap adjustment amount by fitting historical data;

[0029] The formula for calculating the roll pressure adjustment ΔF is as follows:

[0030] ΔF=k1·θ1

[0031] The formula for calculating the roll gap adjustment ΔS is as follows:

[0032] ΔS=k2·θ2

[0033] Where k1 and k2 are specification coefficients.

[0034] The over-adjustment prevention mechanism adjusts the gradient by setting parameters and uses feedback verification to ensure control stability.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] The present invention provides an online monitoring and control method for rail bending and torsion based on a thermal 3D full-section profile scanner. This method acquires rail data by installing cameras and integrating infrared thermal imagers on the exit roller conveyor of the final rolling mill, and formulates corresponding control strategies based on the rail data. Combining monitoring data and the state of the final rolling mill, the control strategy is dynamically optimized using a PID control algorithm. A mapping relationship between rail deformation and parameters is established, and an anti-over-adjustment mechanism is set up to achieve online monitoring and control of rail bending and torsion.

[0037] The present invention provides an online monitoring and control method for rail bending and torsion based on a hot-state 3D full-section profile scanner. This method achieves deep linkage between hot-state monitoring and final mill steel output control, with a detection and adjustment delay of <500ms, solving the lag problem of traditional methods and reducing the defect rate by 75%. The invention enables precise forming control, achieving bending control accuracy of ±0.5mm / m and torsion accuracy of ±0.5° / m through full-section scanning and intelligent algorithms, meeting the stringent requirements of high-speed rail (speed ≥350km / h).

[0038] The present invention provides an online monitoring and control method for rail bending and torsion based on a thermal 3D full-section profile scanner. This method possesses adaptive adjustment capabilities, optimizes the control model based on historical data and machine learning, and automatically adapts to rails of different specifications (43kg / m-75kg / m), with parameter adjustment error <5%. This invention can support intelligent production, and its data traceability and effect verification functions provide quantitative basis for process optimization.

[0039] Based on the above reasons, this invention can be widely promoted in fields such as steel rolling production quality inspection technology. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of the online monitoring and control method for rail bending and torsion based on a thermal 3D full-section profile scanner in this invention.

[0042] Figure 2 This invention describes the arrangement structure of the camera on the exit roller table of the final rolling mill.

[0043] Figure 3 This is a schematic diagram of the rail cross-section in this invention. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0048] like Figure 1 As shown, this invention provides an online monitoring and control method for rail bending and torsion based on a thermal 3D full-section profile scanner. Applied to a final rolling mill, eight industrial-grade cameras (Baslerac A2040-10gm, 1000fps, IP68) are installed at a 120° angle above and on both sides of the final rolling mill exit roller conveyor, covering the entire rail cross-section. Figure 2 As shown; it integrates an infrared thermal imager (±2℃) and an encoder (0.01mm resolution) to synchronously acquire temperature field and rolling speed (5-30m / s). It communicates with the final mill PLC in real time via the PROFINET protocol, with a communication delay of <50ms.

[0049] Obtain data on the rails, including bending angle, torsion angle, and temperature; rail cross-section as shown. Figure 3 As shown, Figure 3 The components include single cross-sections and multiple cross-sections, encompassing movement, combination, arrangement of profile cross-sections, and reconstruction of three-dimensional objects on the roller conveyor, including defects. In a preferred embodiment of this invention, the data calculation method for the steel rail is as follows:

[0050] The formula for calculating the bending angle θ is:

[0051]

[0052] θ2=arctan(Δw / L)

[0053] θ = θ1 / θ2

[0054] Where θ1 represents the vertical bending angle, θ2 represents the lateral bending angle, Δh is the height difference of the rail along the vertical direction, reflecting the degree of vertical bending deformation, such as the height difference between the two ends of a rail due to bending; L is the length of the bent part of the rail, used as the reference length when calculating the bending angle, which can be understood as the distance between two measurement points along the length of the rail; Δw is the width difference of the rail along the horizontal direction, reflecting the width change caused by lateral bending, that is, the width difference between the two ends of the rail in the horizontal direction when bending laterally, with an accuracy of ±0.5°.

[0055] The formula for calculating the torsion angle Δθ is:

[0056] Δθ=∑arccos(n i ·n i+1 )

[0057] Where, n i This represents the normal vector of the i-th cross-section. The normal vector is perpendicular to the cross-section and is used to describe the spatial orientation of the cross-section; n i+1 This represents the normal vector of the (i+1)th cross section, and n i Adjacent sections are used to determine the rail's torsion angle by calculating the relationship between the normal vectors of two adjacent sections, with a resolution of 0.05° / m. Thresholds are automatically matched based on rail specifications (e.g., 60kg / m, 75kg / m), and manual calibration is supported (error ±5%).

[0058] Based on the data of the rails, a corresponding control strategy is formulated. In a preferred embodiment of the present invention, the formulation of the corresponding control strategy based on the data of the rails includes bending control and torsion control.

[0059] The bending control means that when the vertical bending angle θ1 > 3°, the vertical roller pressure will be increased by 0.5-1.0 kN / mm, and dynamically adjusted according to the deviation; when the lateral bending angle θ2 > 2°, the horizontal roller gap will be adjusted by ±0.3mm.

[0060] The torsion control means that when the torsion angle Δθ > 1.5° / m, the roll speed compensation (±0.5%) is triggered to correct the torque unevenness during the rolling process.

[0061] By combining monitoring data and the status of the final mill, the control strategy is dynamically optimized using a PID control algorithm.

[0062] In a specific implementation, as a preferred embodiment of the present invention, the dynamic optimization of the control strategy through the PID control algorithm specifically includes:

[0063] By incorporating the hot deformation compensation model into the final rolling temperature influence factor, the modified formula is as follows:

[0064] (X′,Y′,Z′)=(X,Y,Z)*(1+a*(T-20℃)+β*ΔF / F0)

[0065] Where (X',Y',Z') represents the coordinate values ​​of the corrected rail cross-sectional profile in three-dimensional space (which can be understood as a coordinate system), used to characterize the rail shape after considering the effects of thermal deformation and pressure; (X,Y,Z) represents the three-dimensional coordinates of the uncorrected (original) rail cross-sectional profile; 'a' is the temperature influence coefficient, which reflects the degree of influence of temperature changes on rail deformation correction and is used to quantify the role of temperature factors; 'T' is the final rolling temperature, i.e., the temperature when the rail final rolling process is completed; 'β' is the pressure influence coefficient, a parameter used to quantify the degree of correction of rail hot deformation by changes in roll pressure; 'ΔF' is the roll pressure adjustment amount, which is the value of adjusting the roll pressure in actual production; and 'F0' is the initial roll pressure, which is the initial pressure reference value set for the roll.

[0066] Establish a mapping relationship between rail deformation and parameters, and set up an anti-over-adjustment mechanism to realize online monitoring and control of rail bending and torsion.

[0067] In a specific implementation, as a preferred embodiment of the present invention, the establishment of the mapping relationship between rail deformation and parameters means obtaining the roll pressure adjustment amount and roll gap adjustment amount by fitting historical data;

[0068] The formula for calculating the roll pressure adjustment ΔF is as follows:

[0069] ΔF=k1·θ1

[0070] The formula for calculating the roll gap adjustment ΔS is as follows:

[0071] ΔS=k2·θ2

[0072] Where k1 and k2 are specification coefficients, which are optimized through machine learning.

[0073] The anti-over-adjustment mechanism adjusts the gradient by setting parameters, such as adjusting the amount by ≤10% of the maximum range each time, and with feedback verification, re-measures the deformation within 1 second after adjustment to ensure control stability.

[0074] The system architecture has also been optimized in this invention. The optimized system includes a perception layer, an algorithm layer, an application layer, and a data traceability layer.

[0075] Add a final mill status sensor (roll pressure sensor and roll gap displacement sensor) to the sensing layer, with accuracies of ±0.5%FS and ±0.01mm respectively, to collect equipment operating parameters in real time.

[0076] The algorithm integrates monitoring data with the final rolling mill status, and dynamically optimizes the adjustment strategy through PID control algorithm with a response time of <100ms; the hot deformation compensation model is introduced into the influence factor of final rolling temperature (800-1200℃).

[0077] A linkage control interface is developed at the application layer to display the final mill parameters (pressure / roll gap / speed) and rail deformation curve in real time, supporting manual intervention and automatic control mode switching;

[0078] The data traceability layer stores linkage adjustment records (including timestamps, adjustment parameters, and retest results), achieving a traceability accuracy of 0.1 seconds.

[0079] Key steps in the system architecture and final mill linkage include:

[0080] (1) Real-time monitoring: The scanner collects point cloud data of the entire cross section at a frequency of 150Hz and completes deformation calculation within 100ms;

[0081] (2) Deviation determination: θ 1 θ 2 The system compares Δθ with a preset threshold and generates adjustment instructions (such as "increase vertical roller pressure by 0.8 kN / mm").

[0082] (3) Parameter adjustment: Send commands to the final mill PLC via PROFINET, and the PLC will complete the adjustment of roll pressure / roll gap within 500ms;

[0083] (4) Effect verification: After adjustment, monitor continuously for 3 scan cycles (300ms). If the deviation does not converge, trigger a second-level adjustment (increase of 10%).

[0084] Example

[0085] In this embodiment, taking the hot rolling of 60kg / m rail as an example, the online monitoring and control method for rail bending and torsion based on a hot 3D full-section profile scanner of the present invention is used to control the final rolling mill.

[0086] (1) Equipment and system linkage configuration

[0087] Final mill parameters: Initial pressure F of vertical rolls v =500kN / mm, horizontal roll gap S h =150mm, rolling speed v=10m / s;

[0088] Scanner deployment: 8 cameras cover the entire cross-section of the rail, and an infrared thermal imager monitors the rail head temperature in real time (1050±20℃).

[0089] (2) Example of linkage control

[0090] Bending exceeding the limit handling: When a vertical bending of θ1 = 3.5° (threshold 3°) is detected, the system calculates ΔF. v =0.5×(3.5-3.0)=0.25kN / mm, send the instruction to the PLC; the PLC will send F within 150ms. v The value was adjusted to 500.25 kN / mm, and θ1 was remeasured after 3 seconds, which is 2.8°, meeting the standard.

[0091] Handling excessive torsion: If Δθ = 2.0° / m (threshold 1.5° / m), the system triggers roll speed compensation of +0.5%, and at the same time finely adjusts the horizontal roll gap by ±0.3mm. After 2s, the torsion angle drops to 1.2° / m.

[0092] (3) Verification of control effect

[0093] After 100 batches of testing, the rate of excessive bending within 5m of the rail end after the final rolling mill linkage decreased from 18% to 4.5%, and the rate of qualified torsion increased from 82% to 97%.

[0094] The control delay is stabilized at 180ms, and the deformation convergence time after parameter adjustment is less than 5s, which is 10 times more efficient than traditional manual adjustment.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online monitoring and control of rail bending and torsion based on a hot-state 3D full-section profile scanner, applied to a final rolling mill, characterized in that, include: Data on the steel rails is acquired, including bending angle, torsion angle, and temperature data; the calculation method for the steel rail data is as follows: The bending angle The calculation formula is: in, Indicates the vertical bending angle. Indicates the lateral bending angle. The vertical height difference of the rail reflects the degree of vertical bending deformation; L is the length of the bent portion of the rail, used as a reference length when calculating the bending angle. This represents the width difference of the rail along the horizontal direction, reflecting the width change caused by lateral bending. The torsion angle The calculation formula is: in, This represents the normal vector of the i-th cross section. The normal vector is perpendicular to the cross section and is used to describe the spatial orientation of the cross section. Let the normal vector of the (i+1)th section be denoted as , and Adjacent sections are used to determine the torsion angle of the rail by calculating the relationship between the normal vectors of two adjacent sections. Develop corresponding control strategies based on rail data; these strategies include bending control and torsional control. The bending control refers to the bending direction when bending vertically. At this time, increase the vertical roller pressure by 0.5-1.0 kN / mm; lateral bending angle At this time, adjust the horizontal roller gap to ±0.3mm; The torsion control refers to when the torsion angle... At this time, the roll speed compensation is triggered to correct the uneven torque during the rolling process; By combining monitoring data and the status of the final mill, the control strategy is dynamically optimized using a PID control algorithm. Establish a mapping relationship between rail deformation and parameters, and set up an anti-over-adjustment mechanism to realize online monitoring and control of rail bending and torsion.

2. The method for online monitoring and control of rail bending and torsion based on a thermal 3D full-section profile scanner according to claim 1, characterized in that, The dynamic optimization of the control strategy through the PID control algorithm specifically includes: By incorporating the hot deformation compensation model into the final rolling temperature influence factor, the modified formula is as follows: Where (X',Y',Z') represent the coordinate values ​​of the corrected rail cross-sectional profile in three-dimensional space, used to characterize the rail morphology after considering the effects of thermal deformation and pressure; (X,Y,Z) represent the three-dimensional coordinates of the uncorrected rail cross-sectional profile; 'a' is the temperature influence coefficient, reflecting the degree of influence of temperature changes on rail deformation correction, used to quantify the role of temperature factors; and 'T' is the final rolling temperature, i.e., the temperature at which the final rolling process of the rail is completed. This is the pressure influence coefficient, a parameter used to quantify the degree to which changes in roll pressure correct for the hot deformation of the rail. This refers to the adjustment amount of the roll pressure, which is the value used to adjust the roll pressure during actual production. The initial roll pressure is the initial pressure reference value set for the roll.

3. The method for online monitoring and control of rail bending and torsion based on a thermal 3D full-section profile scanner according to claim 1, characterized in that, The establishment of the mapping relationship between rail deformation and parameters means that the roll pressure adjustment amount and roll gap adjustment amount are obtained by fitting historical data. The roll pressure adjustment amount The calculation formula is: The roll gap adjustment amount The calculation formula is: in, , Specification coefficient; The over-adjustment prevention mechanism adjusts the gradient by setting parameters and uses feedback verification to ensure control stability.

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