Online quenching regulation and control method and system for industrial aluminum profile production line

Abstract

The invention discloses an online quenching regulation and control method and system for an industrial aluminum profile production line, particularly relates to the technical field of aluminum alloy profile machining control, and aims to solve the problem of performance fluctuation caused by the fact that static parameter setting cannot adapt to material quenching sensitivity dynamic change in an existing quenching process. The method comprises the following steps: acquiring spectral components, a surface temperature field and a vibration spectrum of an aluminum profile at an extrusion outlet in real time to generate quenching sensitivity evaluation parameters and micro strain energy density distribution; analyzing an offset characteristic value of the critical cooling rate interval in combination with the temperature field data; obtaining a phase boundary movement rate based on the offset characteristic value, fusing a heat flow vector field rotation distortion rate, and dynamically adjusting an air cooling and water cooling medium strength distribution ratio through a hyperbolic tangent mapping function; and finally, synchronously adjusting the fan rotating speed and the nozzle flow according to the generated quenching medium regulation and control instruction set. The quenching strength is precisely matched with the real-time state of the material, and the consistency of the mechanical property of the high-strength aluminum alloy profile is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of aluminum alloy profile processing control technology, and more specifically, to an online quenching control method and system for industrial aluminum profile production lines. Background Technology

[0002] In industrial aluminum profile production lines, online quenching is a core process that determines the properties of aluminum alloys. Especially in the production of high-strength aluminum alloy profiles, rapid cooling of the high-temperature profiles at the extrusion exit is necessary to suppress excessive precipitation of alloying elements and ensure the potential for subsequent age hardening. Existing technologies generally adopt a fixed-parameter quenching control strategy, that is, preset the cooling medium intensity and action time for a specific alloy grade, and fine-tune the flow rate or air velocity through feedback from temperature sensors.

[0003] However, the quenching process window of high-strength aluminum alloys is inherently sensitive, and its critical cooling rate range is extremely narrow. In actual production, uncontrollable factors such as slight fluctuations in the composition of alloy raw materials and changes in extrusion temperature will cause deviations in the ideal cooling rate range. Since existing technologies rely on preset static parameters and lack the ability to perceive the real-time quenching sensitivity of materials, they cannot dynamically adapt to the dynamic changes in the critical cooling rate, resulting in frequent occurrences of insufficient or over-quenching, making it difficult to guarantee the consistency of the mechanical properties of profiles. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an online quenching control method and system for industrial aluminum profile production lines to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Online quenching control methods for industrial aluminum profile production lines include: S1. Real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of aluminum profiles at the extrusion exit; S2. Generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data; S3. Based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile section, the micro-strain energy density distribution induced by thermal stress is generated. S4. Combining quenching sensitivity evaluation parameters, micro-strain energy density distribution, and surface temperature field distribution data, analyze the offset characteristic values ​​of the critical cooling rate range. S5. Based on the offset eigenvalue, the phase boundary migration rate is obtained. Combined with the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differential of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. S6. Adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

[0006] Furthermore, real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of the aluminum profile at the extrusion exit is performed, including: Spectral composition data are generated by scanning the surface of the aluminum profile at the end of the extrusion exit using a laser-induced breakdown spectral probe. Infrared thermal imagers were used in line scanning mode to capture the temperature gradient of the cross section of aluminum profiles to generate surface temperature field distribution data. A triaxial accelerometer is installed at the flange of the main hydraulic cylinder of the extruder to collect vibration time-domain signals. The signals are then converted into frequency domain power spectral density distributions through fast Fourier transform, and the main peak frequency and amplitude are extracted as the vibration spectrum of the extruder.

[0007] Furthermore, based on the spectral composition data, quench sensitivity assessment parameters for the current alloy are generated, including: Extract the mass percentages of magnesium, silicon, and copper from the spectral composition data; Calculate the magnesium-silicon equivalent ratio based on the mass percentage of magnesium and the mass percentage of silicon. Input the magnesium-silicon equivalent ratio and the mass percentage of copper into the quenching sensitivity factor calculation formula: The quenching sensitivity factor is equal to the magnesium-silicon equivalent ratio multiplied by the logarithm of the copper mass percentage. The quenching sensitivity factor is used as a parameter for evaluating quenching sensitivity.

[0008] Furthermore, based on the extruder vibration spectrum and the geometric characteristics of the aluminum profile cross-section, a micro-strain energy density distribution induced by thermal stress is generated, including: Extract the main vibration frequency and corresponding amplitude value from the vibration spectrum of the extruder; A two-dimensional finite element mesh model was established based on the cross-sectional geometric characteristics of the aluminum profile. The principal vibration frequency and its corresponding amplitude value are applied as boundary conditions to the two-dimensional finite element mesh model. The equivalent stress of mesh nodes under thermal stress is calculated by harmonic response analysis. The strain energy density of each grid node is calculated based on the equivalent stress and the elastic modulus of the material. The strain energy density of all grid nodes is integrated to generate a microscopic strain energy density distribution.

[0009] Furthermore, by combining quenching sensitivity assessment parameters, micro-strain energy density distribution, and surface temperature field distribution data, the shift characteristic values ​​of the critical cooling rate range are analyzed, including: Calculate the rate of change of temperature gradient in the thickness direction of the aluminum profile based on the surface temperature field distribution data; Convert the quenching sensitivity assessment parameters into critical cooling rate benchmark values; Retrieve the thermal diffusivity of the current alloy from the aluminum alloy material database; The thermal conduction acceleration coefficient is obtained by multiplying the rate of change of the temperature gradient with the thermal diffusivity. Extract the maximum strain energy density value from the micro-strain energy density distribution; The maximum strain energy density value is divided by the thermal conduction acceleration coefficient as the dynamic compensation factor. The critical cooling rate baseline value is multiplied by the dynamic compensation factor to obtain the corrected critical cooling rate. The relative deviation between the corrected critical cooling rate and the preset critical cooling rate is calculated as the offset characteristic value.

[0010] Furthermore, based on the phase boundary migration rate obtained from the offset eigenvalues, and combined with the curl distortion rate of the heat flux vector field obtained from the spatiotemporal differentiation of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set, including: The phase boundary migration rate is obtained by looking up the time-temperature transition curve database based on the offset characteristic value. When looking up the time-temperature transition curve database, the alloy grade and quenching sensitivity evaluation parameters of the current aluminum profile are matched. Spatiotemporal differentiation is performed on the surface temperature field distribution data to generate a heat flux vector field and the curl modulus is calculated. The standard deviation of the statistical curl modulus on the aluminum profile surface is used as the curl distortion rate of the heat flux vector field; The phase boundary migration rate and the curl distortion rate of the heat flux vector field are input into the hyperbolic tangent mapping function to calculate the water cooling medium proportion coefficient; The fan speed command and water-cooled nozzle flow command are generated based on the water-cooling medium ratio coefficient as a set of quenching medium control commands.

[0011] Furthermore, the spatiotemporal differentiation of the surface temperature field distribution data to generate a heat flux vector field and the calculation of the curl modulus include: The spatial three-dimensional partial derivative and the time first-order partial derivative are performed on the surface temperature field distribution data, and the heat flux vector field is generated according to Fourier's law of heat conduction. Calculate the curl of the heat flux vector field and obtain the curl modulus value.

[0012] Furthermore, the phase boundary migration rate and the curl distortion rate of the heat flux vector field are input into the hyperbolic tangent mapping function to calculate the water-cooling medium proportion coefficient, including: The input variable is obtained by multiplying the phase boundary migration rate by the curl distortion rate of the heat flux vector field; Perform hyperbolic tangent operation on the input variable and linearly scale it to the [0,1] interval, and output the water cooling medium ratio coefficient.

[0013] Furthermore, the air-cooling fan speed and water-cooling nozzle flow rate are adjusted according to the quenching medium control instruction set, including: Extract the fan speed command from the quenching medium control command set; The speed of the air-cooled fan is controlled by adjusting the output frequency of the frequency converter according to the fan speed command. Extract water-cooled nozzle flow instructions from the quenching medium control instruction set; The water-cooled nozzle flow rate is controlled by adjusting the opening of the proportional valve according to the water-cooled nozzle flow rate command. Simultaneously perform the operation of adjusting the speed of the air-cooled fan and the flow rate of the water-cooled nozzle.

[0014] On the other hand, the present invention provides an online quenching control system for an industrial aluminum profile production line, comprising: The data acquisition module is used to collect in real time the spectral composition data, surface temperature field distribution data and extruder vibration spectrum of the aluminum profile at the extrusion exit; The sensitivity assessment module is used to generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data. The strain generation module is used to generate a microscopic strain energy density distribution induced by thermal stress based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile cross section. The offset analysis module is used to analyze the offset characteristic values ​​of the critical cooling rate range by combining quenching sensitivity assessment parameters, micro-strain energy density distribution and surface temperature field distribution data. The instruction generation module is used to obtain the phase boundary migration rate based on the offset eigenvalue, combine the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differentiation of the surface temperature field, and adjust the intensity distribution ratio of the air-cooled medium and the water-cooled medium through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. The medium control module is used to adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. It realizes the dynamic adaptive control capability of quenching process. By acquiring spectral composition data in real time, it accurately captures the instantaneous fluctuation of alloy elements. Combined with vibration spectrum and profile geometric characteristics, it quantifies the distribution of micro-strain energy and constructs a multi-parameter coupled real-time evaluation system for quenching sensitivity. It can keenly sense the dynamic shift trend of the critical cooling rate range and fundamentally solve the problem of quenching window mismatch caused by raw material composition fluctuation. Compared with static preset strategy, it makes the cooling intensity adapt to the actual state of the material in real time, avoids under-quenching or over-quenching, and ensures the consistency of profile mechanical properties. 2. Based on the spatiotemporal differential operation of the surface temperature field, a heat flow vector field is generated. The cooling uniformity defect is quantified by the curl distortion rate. Combined with the phase boundary migration rate, the medium distribution ratio is dynamically corrected. The hyperbolic tangent mapping function intelligently correlates the material phase transformation dynamics characteristics with the heat conduction state, so that the strength ratio of the air-water dual medium always tracks the optimal quenching path. This breaks through the limitations of traditional single temperature feedback control, eliminates residual stress while suppressing grain boundary precipitation, and achieves a synergistic improvement in the strength and toughness of high-strength aluminum alloy profiles. Attached Figure Description

[0016] Figure 1 This is a flowchart of the online quenching control method for industrial aluminum profile production lines according to the present invention; Figure 2 This is a schematic diagram of the online quenching control system for industrial aluminum profile production lines according to the present invention. Detailed Implementation

[0017] 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. 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.

[0018] Example 1: Figure 1 The present invention provides an online quenching control method for industrial aluminum profile production lines, comprising: S1. Real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of aluminum profiles at the extrusion exit; S2. Generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data; S3. Based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile section, the micro-strain energy density distribution induced by thermal stress is generated. S4. Combining quenching sensitivity evaluation parameters, micro-strain energy density distribution, and surface temperature field distribution data, analyze the offset characteristic values ​​of the critical cooling rate range. S5. Based on the offset eigenvalue, the phase boundary migration rate is obtained. Combined with the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differential of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. S6. Adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

[0019] S1. Real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of the aluminum profile at the extrusion exit. Specifically, this is implemented as follows: Spectral composition data is generated by scanning the surface of the aluminum profile at the extrusion exit using a laser-induced breakdown spectroscopy probe. The probe reciprocates perpendicular to the extrusion direction, covering 100% of the profile's surface width. The laser pulse frequency is dynamically adjusted based on the extrusion speed, specifically by increasing the pulse frequency as the speed increases, ensuring a constant number of laser breakdown points of at least 5 per square centimeter. The plasma radiation signal generated at each breakdown point is received by a fiber optic spectrometer with a resolution of 0.1 nanometers and a wavelength range from 200 to 800 nanometers. After wavelength-intensity calibration of the radiation spectrum at each breakdown point, characteristic peak matching is performed using a pre-constructed standard alloy spectral database. This database is established by preparing standard aluminum alloy samples with different composition gradients through melting and collecting their spectra. Characteristic spectral line intensity values ​​of aluminum, magnesium, silicon, and copper are extracted, and the mass percentage of each element is calculated using a pre-calibrated intensity-concentration conversion curve. This curve is obtained by fitting a quadratic polynomial to the element concentration and spectral line intensity of a series of standard samples. Finally, spectral composition data containing elemental composition at all detection points is output.

[0020] Infrared thermal imagers are used in line-scan mode to capture the temperature gradient across the cross-section of aluminum profiles, generating surface temperature field distribution data. The infrared thermal imager is mounted directly above the cross-section perpendicular to the extrusion direction, with its scanning direction parallel to the width direction of the aluminum profile. The scanning frequency of the infrared thermal imager is set to, for example, 100 Hz, and the infrared radiation intensity of, for example, 1024 pixels is collected in each scan cycle. The radiation intensity of each pixel is converted into a temperature value using a pre-stored temperature-radiation intensity mapping table in a blackbody radiation calibration furnace. The calibration furnace temperature covers a range of 400°C to 600°C, with a calibration interval of 5°C temperature steps. The 1024 temperature values ​​obtained in a single scan are mapped to the width coordinates of the aluminum profile surface according to pixel position, forming a cross-sectional temperature distribution curve. The curves from 100 consecutive scans are stitched together in a time sequence to form a two-dimensional temperature matrix. The rows of the matrix correspond to the time dimension, the columns correspond to the width dimension, and the matrix elements are temperature values. This matrix represents the surface temperature field distribution data.

[0021] A triaxial accelerometer is installed at the flange of the main hydraulic cylinder of the extruder to collect vibration time-domain signals. The three sensing axes of the triaxial accelerometer are aligned with the axial, radial, and tangential directions of the extruder, respectively. The sampling frequency is set to the 10 kHz level, and the range covers ±50 m / s² acceleration values. The output signal of the accelerometer is processed by an anti-aliasing filter with a cutoff frequency of 5 kHz. Raw vibration acceleration data is continuously collected for at least 5 seconds and stored as a three-channel time-domain signal, with 50,000 data points in each channel.

[0022] The vibration time-domain signal was converted into a frequency-domain power spectral density distribution using Fast Fourier Transform (FFT). A Hanning window weighting was applied to 50,000 time-domain data points for each channel, with the window width equal to the data length. After windowing, an FFT was performed on the data, with 65,536 transform points and 15,536 zero-padding points. The FFT output a complex spectrum. The square of the modulus was divided by the sampling frequency and the window function energy coefficient (0.5, the energy normalization factor of the Hanning window function) to obtain a one-sided power spectral density estimate. The arithmetic mean of the power spectral densities of the three channels at the same frequency points was used to generate the final power spectral density distribution curve, with a frequency resolution of 0.1526 Hz and a frequency range covering 0 Hz to 5000 Hz.

[0023] The dominant peak frequency and amplitude are extracted from the power spectral density distribution curve to form the extruder vibration spectrum. All local maxima are identified in the power spectral density distribution curve; a local maxima is defined as a point whose power spectral density value is greater than the power spectral density values ​​of the two adjacent frequency points. Points with power spectral density values ​​greater than three times the overall root mean square (RMS) value are selected as candidate peaks. Candidate peaks are sorted in descending order of their power spectral density values, and the highest-valued candidate peak is selected as the dominant candidate peak. The dominant peak frequency is defined as the weighted average frequency of all frequency points within a 10 Hz frequency width extending to the left and right of the frequency point corresponding to the dominant candidate peak, with the weight being the power spectral density value of each frequency point. The dominant peak amplitude is defined as the square root of the power spectral density value corresponding to the dominant peak frequency multiplied by a coefficient of 1.414, which is the peak factor used to convert the RMS value to the peak value. Finally, the dominant peak frequency and amplitude values ​​constitute the extruder vibration spectrum.

[0024] S2. Generate quenching sensitivity evaluation parameters for the current alloy based on spectral composition data, specifically as follows: The mass percentages of magnesium, silicon, and copper were extracted from the spectral composition data. The spectral composition data was a two-dimensional data matrix containing elemental compositions from multiple detection points. Rows corresponded to the detection point numbers, and columns corresponded to the element types. For each detection point, the mass percentage values ​​of aluminum, magnesium, silicon, and copper were read. When the number of detection points was greater than one, the arithmetic mean of the magnesium mass percentage from all detection points was taken as the final magnesium mass percentage; the arithmetic mean of the silicon mass percentage from all detection points was taken as the final silicon mass percentage; and the arithmetic mean of the copper mass percentage from all detection points was taken as the final copper mass percentage. The elemental mass percentage values ​​were retained to three decimal places, the precision of which was determined by the spectrometer's measurement resolution. If the silicon mass percentage detection value was zero, the minimum silicon mass percentage was set to 0.01 to avoid division by zero errors.

[0025] The magnesium-silicon equivalent ratio is calculated based on the mass percentages of magnesium and silicon. The magnesium-silicon equivalent ratio is defined as the product of the mass percentage of magnesium and the mass percentage of silicon, multiplied by a coefficient of 1.73. The coefficient 1.73 is an equivalent conversion factor for silicon's sensitivity to quenching. This coefficient was determined by fitting the critical cooling rate variation trend of aluminum alloys with different silicon contents through experimental measurements. Specifically, a series of standard samples with silicon content gradients from 0.3% to 1.0% were prepared, and the hardness loss rate of each sample was measured at a fixed cooling rate. A linear regression was performed with silicon content as the abscissa and hardness loss rate as the ordinate. The reciprocal of the slope of the regression line is the equivalent conversion factor. The calculation process is as follows: first, multiply the mass percentage of silicon by the coefficient 1.73 to obtain the silicon equivalent value; then divide the mass percentage of magnesium by this silicon equivalent value to obtain the magnesium-silicon equivalent ratio. The calculation result is retained to two decimal places. For example, when the mass percentage of magnesium is 0.8 and the mass percentage of silicon is 0.5, the silicon equivalent is 0.5 × 1.73 = 0.865, and the magnesium-silicon equivalent ratio is 0.8 / 0.865 ≈ 0.925.

[0026] Input the magnesium-silicon equivalent ratio and the copper mass percentage into the quenching sensitivity factor calculation formula. The quenching sensitivity factor calculation formula is: the quenching sensitivity factor equals the magnesium-silicon equivalent ratio multiplied by the logarithm of the copper mass percentage. The logarithm of the copper mass percentage refers to the base-10 logarithmic function applied to the copper mass percentage value. When the copper mass percentage is less than or equal to 0.001 mass percentage, the logarithm of the copper mass percentage is fixed at -3, based on the limiting characteristics of the logarithmic function when the copper content approaches zero; when the copper mass percentage is greater than 0.001 mass percentage, the base-10 logarithm is directly calculated. Before the multiplication operation, ensure that both input parameters are dimensionless scalar values. For example, when the magnesium-silicon equivalent ratio is 0.925 and the copper mass percentage is 0.12 mass percentage, the logarithm of the copper mass percentage is log10(0.12)≈-0.921, and the quenching sensitivity factor is 0.925×(-0.921)≈-0.852.

[0027] The quenching sensitivity factor is equal to the magnesium-silicon equivalent ratio multiplied by the logarithm of the copper mass percentage. The physical meaning of this formula is that the magnesium-silicon equivalent ratio reflects the basic level of quenching sensitivity of the base alloy, while the logarithm of the copper mass percentage characterizes the nonlinear influence of trace elements on quenching sensitivity. The logarithmic function in the formula is used to mitigate the abrupt change in sensitivity in high copper content regions, making the calculation results more consistent with actual process performance. The calculation process must follow the rules of arithmetic operation priority: calculate the logarithmic function value first, then perform the multiplication operation. The calculation result is retained to three decimal places; negative values ​​indicate decreased quenching sensitivity, and positive values ​​indicate increased quenching sensitivity. The numerical range of the quenching sensitivity factor has been experimentally verified to be, for example, between -2.5 and 1.8.

[0028] The quenching sensitivity factor is used as a parameter for evaluating quenching sensitivity. This parameter is a single-valued scalar that directly characterizes the tendency of aluminum alloys to experience grain boundary precipitation and strength loss during quenching. A negative quenching sensitivity parameter indicates that the alloy can achieve complete quenching at a lower cooling rate; a positive quenching sensitivity parameter indicates that the alloy requires a higher cooling rate to suppress precipitate formation. This parameter will serve as the baseline input for subsequent critical cooling rate correction.

[0029] S3. Based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile cross-section, a micro-strain energy density distribution induced by thermal stress is generated, specifically as follows: The dominant vibration frequency and corresponding amplitude are extracted from the extruder vibration spectrum. The extruder vibration spectrum contains two scalar parameters: the dominant peak frequency and the dominant peak amplitude. The dominant vibration frequency is defined as the weighted average frequency corresponding to the highest peak value in the power spectral density distribution curve. The specific extraction rule is as follows: search for the maximum power spectral density point within the frequency range of 0 Hz to 5000 Hz, extend a bandwidth of, for example, 10 Hz to the left and right of this point, and calculate the weighted average power spectral density of all frequency points within this bandwidth, with the weight being the power spectral density value of each point. The dominant vibration amplitude is defined as the square root of the power spectral density value corresponding to the dominant peak frequency multiplied by a coefficient of 1.414. If there are multiple candidate peaks with a power spectral density peak difference of less than, for example, 5%, the candidate peak with the lowest frequency is selected as the dominant vibration frequency. For example, when two candidate peaks with power spectral density values ​​of 1000 m² / s³ and 980 m² / s³ are detected, their difference is 2%, which is less than 5%, so the candidate peak with the lower frequency is preferentially selected as the dominant vibration frequency.

[0030] A two-dimensional finite element mesh model was established based on the geometric characteristics of the aluminum profile cross-section. The geometric characteristics of the aluminum profile cross-section were obtained from computer-aided design drawings, including the external outline dimensions and the dimensions of the internal hollow structure. The cross-sectional outline was discretized into linear triangular elements, with the element size adaptively adjusted according to the profile wall thickness: when the wall thickness was less than, for example, 5 mm, the element size was set to 0.8 times the wall thickness; when the wall thickness was greater than, for example, 5 mm, the element size was set to 1.2 times the wall thickness. During mesh generation, nodes were forcibly placed at the corners of the cross-section. When the corner curvature radius was less than, for example, 2 mm, the node density was increased to half the curvature radius. The material properties were defined as isotropic linear elastic material, with the density set to, for example, 2700 kg / m³ and the Poisson's ratio set to, for example, a constant of 0.33. The final generated two-dimensional finite element mesh model includes a node coordinate matrix and an element topology matrix. The node coordinate matrix records the two-dimensional coordinate values ​​of each node, and the element topology matrix records the node connections that make up each element.

[0031] The dominant vibration frequency and its corresponding amplitude are applied as boundary conditions to the two-dimensional finite element mesh model. The boundary condition application rule is as follows: a simple harmonic displacement constraint is applied to the contour edge where the aluminum profile section contacts the extrusion die. The displacement amplitude is calculated from the dominant vibration amplitude using the formula: the displacement amplitude equals the dominant vibration amplitude divided by the square of the angular frequency, where the angular frequency equals 2 multiplied by pi and then multiplied by the dominant vibration frequency. The displacement direction is perpendicular to the contact contour edge, and the phase angle is set to zero degrees. The contact contour edge is identified through geometric features as all boundary segments with a theoretical contact length with the die greater than, for example, 3 mm. For example, when the dominant vibration frequency is 120 Hz and the dominant vibration amplitude is 0.5 m / s², the displacement amplitude is 0.5 / (753.6)²≈8.8 × 10⁻⁶. -7 rice.

[0032] The equivalent stress of mesh nodes under thermal stress was calculated using harmonic response analysis. The direct frequency response method was employed for harmonic response analysis, with the solution frequency set as a single frequency point, i.e., the principal vibration frequency. The structural damping coefficient was set to 0.02, a value determined based on experimental measurements of the damping characteristics of similar aluminum alloys at high temperatures. The governing equations were a system of linear equations where the stiffness matrix multiplied by the displacement vector equals the load vector. The stiffness matrix was generated by assembling the element stiffness matrices, and the load vector consisted of equivalent thermal expansion loads. The equivalent thermal expansion load was calculated as follows: a uniform temperature field was applied to each element, with the temperature change set to a 50°C calibration value, corresponding to the typical temperature difference at the extrusion exit; the coefficient of thermal expansion was set to 23.6 × 10⁻⁶. -6 / degrees Celsius. After obtaining the displacement vectors of each node, the element stress tensor is calculated, and then converted into Mises equivalent stress values ​​according to the fourth strength theory. The equivalent stress values ​​are scalars, and the unit is uniformly set to megapascals (MPa).

[0033] The strain energy density of each grid node is calculated based on the equivalent stress and the elastic modulus of the material. The elastic modulus is obtained by querying the material database for the current aluminum alloy grade. The database stores elastic modulus values ​​at different temperatures according to the alloy grade. The formula for calculating strain energy density is: strain energy density equals the square of the equivalent stress divided by twice the elastic modulus. The calculation process is as follows: first, read the equivalent stress value of a node, calculate its square, and then divide it by twice the elastic modulus value. The unit of the calculation result is joules per cubic meter. This calculation process is repeated for all nodes. When a node is shared by multiple elements, the arithmetic mean of the strain energy densities of the associated elements is taken as the final value of that node. For example, when the equivalent stress of a node is 80 MPa and the elastic modulus is 65 GPa, the strain energy density is (80 × 10⁻⁶) / 2π × 10⁻⁶. 6 )² / (2×65×10 9 ) = 6400 × 10¹² / (130 × 10 9 ) = 4.923 × 10 4 Joules per cubic meter.

[0034] A microscopic strain energy density distribution is generated by integrating the strain energy densities of all mesh nodes. The strain energy density value of each node is mapped back to the node location of the 2D finite element mesh model, forming a spatial distribution dataset. The dataset structure is an N x 3 matrix, where N is the number of nodes, the first column is the node's X-coordinate, the second column is the node's Y-coordinate, and the third column is the strain energy density value. Linear interpolation is used for data at non-node locations: the 2D cross-sectional region is divided into 0.1 mm × 0.1 mm grids, and the strain energy density value of each grid point is calculated by taking the inverse distance weighted average of the data from the three nearest nodes. The inverse distance weighted average is calculated as follows: the weight of each node is equal to the inverse of the distance from that node to the grid point; the weights of the three nodes are normalized, multiplied by the corresponding strain energy density value, and then summed. The final output microscopic strain energy density distribution is a 2D scalar field data that perfectly matches the geometric characteristics of the aluminum profile cross-section.

[0035] S4. Combining quenching sensitivity evaluation parameters, micro-strain energy density distribution, and surface temperature field distribution data, analyze the offset characteristic values ​​of the critical cooling rate range. Specifically, the implementation is as follows: The rate of change of temperature gradient along the thickness direction of the aluminum profile is calculated based on surface temperature field distribution data. The surface temperature field distribution data is a two-dimensional temperature matrix in time and space, where rows correspond to the time dimension and columns correspond to the position dimension along the width direction of the aluminum profile. The method for calculating the rate of change of temperature gradient along the thickness direction is as follows: Select the centerline position along the width direction of the aluminum profile, extract the temperature values ​​of all time points in that column to form a time series; calculate the first derivative of the time series using the central difference formula: Rate of change of temperature gradient = (Temperature value at time point w+1 - Temperature value at time point w-1) / (2 × Time interval), where w is the time point number, and the time interval is determined by the scanning frequency of the infrared thermal imager. For example, when the scanning frequency is 100 Hz, the time interval is 0.01 seconds. This calculation is repeated for other positions along the width direction, and the final output is the average rate of change of temperature gradient at all positions, with the unit uniformly set to Kelvin per second.

[0036] The quenching sensitivity assessment parameters are converted into critical cooling rate benchmark values. The quenching sensitivity assessment parameters are dimensionless scalars, mapped to critical cooling rate benchmark values ​​through a pre-established conversion model. The conversion model is a piecewise linear function: when the quenching sensitivity assessment parameter < 0, the critical cooling rate benchmark value = 20 + 10 × |quenching sensitivity assessment parameter|; when the quenching sensitivity assessment parameter ≥ 0, the critical cooling rate benchmark value = 20 + 30 × quenching sensitivity assessment parameter. This model is based on experimental data. The experimental method involves preparing standard samples with different quenching sensitivity factors, measuring the minimum cooling rate at which grain boundary precipitation does not occur, and fitting the piecewise function parameters. For example, when the quenching sensitivity assessment parameter is -0.85, the critical cooling rate benchmark value = 20 + 10 × 0.85 = 28.5 Kelvin per second.

[0037] The thermal diffusivity of the current alloy is retrieved from the aluminum alloy material database. This database stores the physical property parameters of different grades of aluminum alloys. The thermal diffusivity is retrieved based on the alloy grade identified from the spectral composition data and the current average temperature. The current average temperature is calculated using the arithmetic mean of all elements in the surface temperature field distribution data matrix. The rule for retrieving the thermal diffusivity is: if an exact match of grade and temperature exists in the database, the corresponding value is returned directly; otherwise, the two data points closest to the temperature point are selected and linearly interpolated for calculation. For example, the thermal diffusivity of 6061 aluminum alloy at 450 degrees Celsius is 5.8 × 10⁻⁶. -5 Square meters per second.

[0038] The thermal conduction acceleration coefficient is obtained by multiplying the rate of change of the temperature gradient by the thermal diffusivity. The thermal conduction acceleration coefficient is obtained by directly multiplying the temperature gradient rate of change by the thermal diffusivity: Thermal conduction acceleration coefficient = Temperature gradient rate of change × Thermal diffusivity. To maintain unit consistency: Multiply the temperature gradient rate of change (Kelvin / s) by the thermal diffusivity (square meters / s) to obtain the thermal conduction acceleration coefficient (Kelvin / square meters / second²). Before calculation, it is necessary to confirm that both parameters are instantaneous values ​​at the same time point. If the deviation between the temperature gradient rate calculation time point and the thermal diffusivity lookup temperature point is >5 Kelvin, the thermal diffusivity should be recalculated by interpolation. For example, if the temperature gradient rate of change is 15 Kelvin / s and the thermal diffusivity is 5.8 × 10⁻⁶ Kelvin / s, the thermal conduction acceleration coefficient should be calculated by interpolation. -5 The thermal conduction acceleration coefficient is 15 × 5.8 × 10⁻⁶ square meters per second. -5 =8.7×10 -4 Kelvin square meters per second squared.

[0039] Extract the maximum strain energy density value from the micro-strain energy density distribution. The micro-strain energy density distribution is two-dimensional scalar field data. The maximum value is found by traversing all data points. The search algorithm is as follows: Initialize the maximum value to the strain energy density value of the first data point; sequentially compare subsequent data points, updating the maximum value if the current value is greater than the maximum value; output the maximum value after traversal. When multiple identical maximum values ​​exist, the value at the first occurrence is taken. For example, in a distribution containing 5000 data points, the maximum strain energy density value is 62000 joules per cubic meter.

[0040] The dynamic compensation factor is obtained by dividing the maximum strain energy density by the thermal conduction acceleration coefficient. The division operation is performed as follows: Dynamic compensation factor = Maximum strain energy density / Thermal conduction acceleration coefficient. A zero-value check is performed before calculation: if the thermal conduction acceleration coefficient < 10... -6 Kelvin square meters per second squared, therefore, is forcibly set to 10. -6 Avoid division by zero errors. For example, the maximum strain energy density of 62,000 joules per cubic meter is divided by 8.7 × 10⁻⁶. -6 Kelvin square meters per second squared, dynamic compensation factor = 62000 / 8.7 × 10 -4 ≈7.126×10 7 Joules per cubic meter squared Kelvin.

[0041] The modified critical cooling rate is obtained by multiplying the critical cooling rate reference value by the dynamic compensation factor. The multiplication operation is performed as follows: Modified critical cooling rate = Critical cooling rate reference value × Dynamic compensation factor. Before multiplication, dimensional normalization is required: the critical cooling rate reference value is converted to a double-precision floating-point number, and the dynamic compensation factor is converted to scientific notation before scalar multiplication. For example, the critical cooling rate reference value of 28.5 Kelvin per second is multiplied by 7.126 × 10⁻⁶. 7Joules per cubic meter squared (Kelvin), corrected critical cooling rate = 28.5 × 7.126 × 10⁻⁶ 7 =2.031×10 9 Joules per cubic meter squared.

[0042] The relative deviation between the corrected critical cooling rate and the preset critical cooling rate is calculated as the offset characteristic value. The preset critical cooling rate is obtained from the process database and determined based on the aluminum alloy grade and profile wall thickness. The formula for calculating the relative deviation is: Relative deviation = [(Corrected critical cooling rate - Preset critical cooling rate) / Preset critical cooling rate] × 100%. The calculation result is rounded to two decimal places; a negative value indicates a decrease in the required critical cooling rate, and a positive value indicates an increase in the required rate. For example, when the preset critical cooling rate is 25 Kelvin per second and the corrected critical cooling rate is 28.5 Kelvin per second, the relative deviation = [(28.5 - 25) / 25] × 100% = 14.00%.

[0043] S5. Based on the offset eigenvalues, the phase boundary migration rate is obtained. Combined with the curl distortion rate of the heat flux vector field obtained from the spatiotemporal differential of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. The specific implementation is as follows: The phase boundary migration rate is obtained by reverse lookup from the time-temperature transition curve database based on the offset characteristic value. The offset characteristic value is a relative deviation value, and the time-temperature transition curve database stores phase transformation kinetic data for different alloy grades under specific quenching sensitivities. The reverse lookup operation is as follows: using the alloy grade identified by spectral composition data as the primary index and the quenching sensitivity evaluation parameter as the secondary index, the corresponding data table is located in the database; in the data table, the column value closest to the offset characteristic value is found, and the corresponding phase boundary migration rate value is read. The phase boundary migration rate is defined as the migration speed at the interface between the solid solution and the precipitated phase, and the unit is micrometers per second. For example, for 6061 aluminum alloy, when the quenching sensitivity evaluation parameter is -0.85 and the offset characteristic value is 14.00%, the phase boundary migration rate is 0.8 micrometers per second.

[0044] A heat flux vector field is generated by performing spatiotemporal differentiation on the surface temperature field distribution data. The surface temperature field distribution data is a three-dimensional temperature matrix in time and space, with dimensions corresponding to time t, length position x, and width position y, respectively. Spatiotemporal differentiation includes three-dimensional spatial partial derivatives and first-order temporal partial derivatives: First, the spatial gradient is calculated, and the partial derivative of temperature T with respect to x in the length direction, ∂T / ∂x, is calculated, and the partial derivative of temperature T with respect to y in the width direction, ∂T / ∂y, is calculated to obtain the spatial components of the heat flux vector; then, the first-order temporal partial derivative, ∂T / ∂t, is calculated. According to Fourier's law of heat conduction: the heat flux vector Q = k × (-∇T), where k is the thermal conductivity, ∇T is the temperature gradient vector, and the thermal conductivity k is obtained from the material database using the current alloy grade. The final generated heat flux vector field is a three-dimensional vector matrix, with each element containing three components: Qx (heat flux density in the length direction, in watts per square meter), Qy (heat flux density in the width direction, in watts per square meter), and ∂T / ∂t (temperature change rate over time, in Kelvin per second).

[0045] Calculate the curl modulus of the heat flux vector field. Curl calculation focuses on the spatial components of the heat flux vector: curl in a two-dimensional plane is defined as the partial derivative of the heat flux density in the width direction with respect to length minus the partial derivative of the heat flux density in the length direction with respect to width. Specifically, the central difference method is used: at grid point (i,j), the curl value = [(Qy(i+1,j)-Qy(i-1,j)) / (2×Δx)]-[(Qx(i,j+1)-Qx(i,j-1)) / (2×Δy)], where Qx(i,j) represents the heat flux density component in the length direction at grid point (i,j), Qy(i,j) represents the heat flux density component in the width direction at grid point (i,j), Δx is the distance between adjacent grid points in the length direction (in meters), and Δy is the distance between adjacent grid points in the width direction (in meters). The absolute value of this curl value is taken as the curl modulus, with the unit uniformly set to watts per cubic meter.

[0046] Where i is the grid index number in the length direction and j is the grid index number in the width direction.

[0047] The standard deviation of the curl modulus values ​​on the aluminum profile surface is used as the curl distortion rate of the heat flux vector field. Curl modulus values ​​of all grid points on the aluminum profile surface are collected to form a dataset. The arithmetic mean of this dataset is calculated. Then, the sum of squared deviations of each point's curl modulus value from the mean is calculated, divided by the total number of data points minus one to obtain the variance. The square root of the variance is the standard deviation. This standard deviation is defined as the curl distortion rate of the heat flux vector field, with the same unit as the curl modulus value. For example, when there are 5000 points distributed on the surface, the average curl modulus is 120 watts per cubic meter, and the standard deviation is 25 watts per cubic meter, the curl distortion rate is 25 watts per cubic meter.

[0048] The phase boundary migration rate and the curl distortion rate of the heat flux vector field are input into the hyperbolic tangent mapping function to calculate the water-cooling medium proportion coefficient. First, the input variables are calculated: Input variable = Phase boundary migration rate × Curve distortion rate of the heat flux vector field. The phase boundary migration rate is converted from micrometers per second to meters per second (multiplied by 10). -6 The curl distortion rate unit, watts per cubic meter, remains unchanged, while the product unit is uniformly watts per square meter per second. The hyperbolic tangent mapping function calculation process is as follows: First, calculate the hyperbolic tangent function value = tanh(scaling factor × input variable), with the empirical value of the scaling factor being 1.5; then linearly map the result to the interval [0,1]: water-cooling medium proportion coefficient = (hyperbolic tangent function value + 1) / 2. For example, when the phase boundary migration rate is 0.8 micrometers per second (i.e., 8 × 10⁻⁶), the calculation is performed as follows: -7 When the curl distortion rate is 25 watts per cubic meter (meters per second), the input variable = 8 × 10 -7 ×25=2×10 -5 Watts per square meter per second, tanh(1.5×2×10) -5 )≈0.00003, water cooling medium proportion coefficient≈(0.00003+1) / 2=0.500015.

[0049] The fan speed and water-cooled nozzle flow rate commands are generated based on the water-cooling medium proportion coefficient as a quenching medium control command set. The fan speed command conversion formula is: Fan speed = Maximum speed × (1 - Water-cooling medium proportion coefficient), where the maximum speed is set to 3000 rpm based on the fan model. The water-cooled nozzle flow rate command conversion formula is: Water flow rate = Maximum flow rate × Water-cooling medium proportion coefficient, where the maximum flow rate is set to 50 liters per minute based on the nozzle specification. The command set includes specific control parameter values ​​and execution timestamps, synchronized with the quenching process stage. For example, when the water-cooling medium proportion coefficient is 0.75, the fan speed = 3000 × (1 - 0.75) = 750 rpm, and the water flow rate = 50 × 0.75 = 37.5 liters per minute.

[0050] S6. Adjust the air-cooled fan speed and water-cooled nozzle flow rate according to the quenching medium control instruction set. Specifically: The fan speed command is extracted from the quenching medium control command set. The quenching medium control command set is a structured data set containing fan speed commands and water-cooled nozzle flow commands. This command set is generated through step S5 and stored in the registers of the process control system. The extraction operation specifically involves parsing the key-value pair format of the command set data structure, locating the data field with the key name "fan speed," and reading its stored numerical parameter value. The unit for the fan speed command is revolutions per minute (rpm), and the numerical range is limited by the equipment performance to between 0 and 3000 rpm. For example, when the quenching medium control command set contains the key-value pair "fan speed: 750," the extracted fan speed command value is 750 rpm. During the extraction process, data validity verification is performed: if the read value is not numerical or exceeds the 0-3000 rpm range, a safe default value of 1000 rpm is enabled, and an abnormal alarm signal is triggered.

[0051] The speed of the air-cooled fan is controlled by adjusting the inverter's output frequency according to the fan speed command. A linear conversion relationship is established between the fan speed and the inverter frequency: the inverter output frequency equals the fan speed command divided by the maximum speed multiplied by the maximum frequency. The maximum speed is fixed at 3000 rpm according to the fan nameplate parameters, and the maximum frequency is fixed at 50 Hz according to the inverter specifications. After conversion, the frequency command is sent to the inverter via the Modbus-RTU industrial bus, and the inverter adjusts the power supply frequency of the three-phase asynchronous motor accordingly. For example, when the fan speed command is 750 rpm, the inverter output frequency = (750 / 3000) × 50 = 12.5 Hz. The frequency adjustment response time is set to less than 0.5 seconds, and the speed control accuracy is required to be within ±5 rpm. The actual speed is verified in real time by an encoder installed on the fan shaft.

[0052] Extract the water-cooled nozzle flow command from the quenching medium control command set. Parse the key-value pair format of the command set data structure, locate the data field with the key name "water-cooled flow," and read its stored numerical parameter value. The unit of the water-cooled nozzle flow command is liters per minute (L / min), and the numerical range is limited to 0 to 50 L / min by the equipment performance. For example, when the quenching medium control command set contains the key-value pair "water-cooled flow: 37.5," the extracted water-cooled nozzle flow command value is 37.5 L / min. The extraction process performs a data integrity check: if the target field is missing, it inherits the valid command value stored in the previous process cycle; if the system is running for the first time and there is no historical data, it uses the safe default value of 25 L / min.

[0053] The flow rate of the water-cooled nozzle is controlled by adjusting the opening of a proportional valve according to the flow command. A flow-opening conversion relationship is established: Valve opening percentage = (Water-cooled nozzle flow command / Maximum flow rate) × 100%. The maximum flow rate is fixed at 50 liters per minute based on the nozzle specification. A proportional-integral-derivative (PID) controller outputs a 4-20 mA current signal to drive the proportional valve, achieving an opening control resolution of 0.5%. For example, when the water-cooled nozzle flow command is 37.5 liters per minute, the valve opening = (37.5 / 50) × 100% = 75%. Flow monitoring uses an electromagnetic flowmeter to detect the actual flow rate in real time, forming a closed-loop control to ensure that the deviation between the actual flow rate and the command value is controlled within ±0.5 liters per minute. When a sustained deviation exceeding 1 liter per minute is detected, the backup valve is automatically switched on.

[0054] The operation of adjusting the speed of the air-cooled fan and the flow rate of the water-cooled nozzle is performed synchronously. The synchronization mechanism uses a timestamp matching method: a unified timestamp parameter is read from the quenching medium control instruction set. This timestamp is synchronized with the extrusion production line cycle time and is uniformly timed by the central controller. When the system real-time clock reaches the specified timestamp, two independent execution threads are triggered in parallel: the first execution thread sends a speed control command to the frequency converter, and the second execution thread sends an opening control command to the proportional valve. Both threads are set to the same execution timeout threshold of 500 milliseconds. If either operation is not completed within the 500-millisecond time limit, the entire adjustment process is aborted, and the equipment is restored to the previous stable state. After the synchronization adjustment is completed, an execution confirmation signal is sent to the central controller, updating the current value of the equipment status register.

[0055] During fan speed adjustment, when the inverter output frequency exceeds 45 Hz, the overload protection mechanism is automatically activated: the output frequency is limited to below 45 Hz and overload event code E101 is recorded. During water cooling flow adjustment, when the actual flow rate deviates from the commanded value by more than 1 liter per minute for more than 2 seconds, flow abnormality alarm code W201 is triggered and the system switches to the backup flow control loop. All abnormal events are recorded in the system event log, and the log entries include fields such as timestamp, device identifier, abnormal code, and recovery measures.

[0056] The fan speed control employs a closed-loop regulation method: the actual speed measurement value is acquired through a 1024-line incremental encoder. The encoder pulse signal is converted into a speed value by a high-speed counter. This feedback value is compared with the command value and then input to the proportional-integral-derivative (PID) controller. The controller outputs a command to dynamically adjust the frequency of the frequency converter. Water cooling flow control also employs closed-loop regulation: an electromagnetic flowmeter detects the flow velocity of the medium in the pipeline in real time. The flow velocity value is multiplied by the pipeline cross-sectional area to obtain the actual flow rate. This feedback value is compared with the command value and then outputs a current signal through the PID controller to drive the proportional valve. The proportional coefficient of the PID controller is set to 0.8, the integral time to 2 seconds, and the derivative time to 0.5 seconds. These parameters were optimized and determined through pre-calibration tests.

[0057] Example 2: Figure 2 A schematic diagram of the online quenching control system for an industrial aluminum profile production line according to the present invention is provided. The online quenching control system for an industrial aluminum profile production line includes: The data acquisition module is used to collect in real time the spectral composition data, surface temperature field distribution data and extruder vibration spectrum of the aluminum profile at the extrusion exit; The sensitivity assessment module is used to generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data. The strain generation module is used to generate a microscopic strain energy density distribution induced by thermal stress based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile cross section. The offset analysis module is used to analyze the offset characteristic values ​​of the critical cooling rate range by combining quenching sensitivity assessment parameters, micro-strain energy density distribution and surface temperature field distribution data. The instruction generation module is used to obtain the phase boundary migration rate based on the offset eigenvalue, combine the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differentiation of the surface temperature field, and adjust the intensity distribution ratio of the air-cooled medium and the water-cooled medium through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. The medium control module is used to adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

[0058] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0059] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0060] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0061] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0063] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0065] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0067] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An online quenching control method for industrial aluminum profile production lines, characterized in that, include: S1. Real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of aluminum profiles at the extrusion exit; S2. Generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data; S3. Based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile section, the micro-strain energy density distribution induced by thermal stress is generated. S4. Combining quenching sensitivity evaluation parameters, micro-strain energy density distribution, and surface temperature field distribution data, analyze the offset characteristic values ​​of the critical cooling rate range. S5. Based on the offset eigenvalue, the phase boundary migration rate is obtained. Combined with the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differential of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. S6. Adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

2. The online quenching control method for industrial aluminum profile production lines according to claim 1, characterized in that, Real-time acquisition of spectral composition data, surface temperature field distribution data, and extruder vibration spectrum of aluminum profiles at the extrusion exit, including: Spectral composition data are generated by scanning the surface of the aluminum profile at the end of the extrusion exit using a laser-induced breakdown spectral probe. Infrared thermal imagers were used in line scanning mode to capture the temperature gradient of the cross section of aluminum profiles to generate surface temperature field distribution data. A triaxial accelerometer is installed at the flange of the main hydraulic cylinder of the extruder to collect vibration time-domain signals. The signals are then converted into frequency domain power spectral density distributions through fast Fourier transform, and the main peak frequency and amplitude are extracted as the vibration spectrum of the extruder.

3. The online quenching control method for industrial aluminum profile production lines according to claim 2, characterized in that, Based on spectral composition data, quench sensitivity assessment parameters for the current alloy are generated, including: Extract the mass percentages of magnesium, silicon, and copper from the spectral composition data; Calculate the magnesium-silicon equivalent ratio based on the mass percentage of magnesium and the mass percentage of silicon. Input the magnesium-silicon equivalent ratio and the mass percentage of copper into the quenching sensitivity factor calculation formula: The quenching sensitivity factor is equal to the magnesium-silicon equivalent ratio multiplied by the logarithm of the copper mass percentage. The quenching sensitivity factor is used as a parameter for evaluating quenching sensitivity.

4. The online quenching control method for industrial aluminum profile production lines according to claim 3, characterized in that, Based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile cross-section, a micro-strain energy density distribution induced by thermal stress is generated, including: Extract the main vibration frequency and corresponding amplitude value from the vibration spectrum of the extruder; A two-dimensional finite element mesh model was established based on the cross-sectional geometric characteristics of the aluminum profile. The principal vibration frequency and its corresponding amplitude value are applied as boundary conditions to the two-dimensional finite element mesh model. The equivalent stress of mesh nodes under thermal stress is calculated by harmonic response analysis. The strain energy density of each grid node is calculated based on the equivalent stress and the elastic modulus of the material. The strain energy density of all grid nodes is integrated to generate a microscopic strain energy density distribution.

5. The online quenching control method for industrial aluminum profile production lines according to claim 4, characterized in that, Combining quenching sensitivity assessment parameters, micro-strain energy density distribution, and surface temperature field distribution data, the shift characteristic values ​​of the critical cooling rate range are analyzed, including: Calculate the rate of change of temperature gradient in the thickness direction of the aluminum profile based on the surface temperature field distribution data; Convert the quenching sensitivity assessment parameters into critical cooling rate benchmark values; Retrieve the thermal diffusivity of the current alloy from the aluminum alloy material database; The thermal conduction acceleration coefficient is obtained by multiplying the rate of change of the temperature gradient with the thermal diffusivity. Extract the maximum strain energy density value from the micro-strain energy density distribution; The maximum strain energy density value is divided by the thermal conduction acceleration coefficient as the dynamic compensation factor. The critical cooling rate baseline value is multiplied by the dynamic compensation factor to obtain the corrected critical cooling rate. The relative deviation between the corrected critical cooling rate and the preset critical cooling rate is calculated as the offset characteristic value.

6. The online quenching control method for industrial aluminum profile production lines according to claim 5, characterized in that, Based on the phase boundary migration rate obtained from the offset eigenvalues, and combined with the curl distortion rate of the heat flux vector field obtained from the spatiotemporal differentiation of the surface temperature field, the intensity distribution ratio of the air-cooled medium and the water-cooled medium is adjusted through the hyperbolic tangent mapping function to determine the quenching medium control instruction set, including: The phase boundary migration rate is obtained by looking up the time-temperature transition curve database based on the offset characteristic value. When looking up the time-temperature transition curve database, the alloy grade and quenching sensitivity evaluation parameters of the current aluminum profile are matched. Spatiotemporal differentiation is performed on the surface temperature field distribution data to generate a heat flux vector field and the curl modulus is calculated. The standard deviation of the statistical curl modulus on the aluminum profile surface is used as the curl distortion rate of the heat flux vector field; The phase boundary migration rate and the curl distortion rate of the heat flux vector field are input into the hyperbolic tangent mapping function to calculate the water cooling medium proportion coefficient; The fan speed command and water-cooled nozzle flow command are generated based on the water-cooling medium ratio coefficient as a set of quenching medium control commands.

7. The online quenching control method for industrial aluminum profile production lines according to claim 6, characterized in that, The process of generating a heat flux vector field and calculating the curl modulus by performing spatiotemporal differentiation on surface temperature field distribution data includes: The spatial three-dimensional partial derivative and the time first-order partial derivative are performed on the surface temperature field distribution data, and the heat flux vector field is generated according to Fourier's law of heat conduction. Calculate the curl of the heat flux vector field and obtain the curl modulus value.

8. The online quenching control method for an industrial aluminum profile production line according to claim 6, characterized in that, The phase boundary migration rate and the curl distortion rate of the heat flux vector field are input into the hyperbolic tangent mapping function to calculate the water-cooling medium proportion coefficient, including: The input variable is obtained by multiplying the phase boundary migration rate by the curl distortion rate of the heat flux vector field; Perform hyperbolic tangent operation on the input variable and linearly scale it to the [0,1] interval, and output the water cooling medium ratio coefficient.

9. The online quenching control method for an industrial aluminum profile production line according to claim 6, characterized in that, Adjust the air-cooled fan speed and water-cooled nozzle flow rate according to the quenching medium control instruction set, including: Extract the fan speed command from the quenching medium control command set; The speed of the air-cooled fan is controlled by adjusting the output frequency of the frequency converter according to the fan speed command. Extract water-cooled nozzle flow instructions from the quenching medium control instruction set; The water-cooled nozzle flow rate is controlled by adjusting the opening of the proportional valve according to the water-cooled nozzle flow rate command. Simultaneously perform the operation of adjusting the speed of the air-cooled fan and the flow rate of the water-cooled nozzle.

10. An online quenching control system for an industrial aluminum profile production line, used to implement the online quenching control method for an industrial aluminum profile production line as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect in real time the spectral composition data, surface temperature field distribution data and extruder vibration spectrum of the aluminum profile at the extrusion exit; The sensitivity assessment module is used to generate quenching sensitivity assessment parameters for the current alloy based on spectral composition data. The strain generation module is used to generate a microscopic strain energy density distribution induced by thermal stress based on the vibration spectrum of the extruder and the geometric characteristics of the aluminum profile cross section. The offset analysis module is used to analyze the offset characteristic values ​​of the critical cooling rate range by combining quenching sensitivity assessment parameters, micro-strain energy density distribution and surface temperature field distribution data. The instruction generation module is used to obtain the phase boundary migration rate based on the offset eigenvalue, combine the curl distortion rate of the heat flux vector field obtained by the spatiotemporal differentiation of the surface temperature field, and adjust the intensity distribution ratio of the air-cooled medium and the water-cooled medium through the hyperbolic tangent mapping function to determine the quenching medium control instruction set. The medium control module is used to adjust the speed of the air-cooled fan and the flow rate of the water-cooled nozzle according to the quenching medium control instruction set.

Images