Segmented bundling stretch forming apparatus for double-curved aluminum plate coated by large-curvature roller

Abstract

The present application relates to the technical field of machining, and discloses a segmented type bundling and stretching forming equipment for large-curvature roller coating hyperbolic aluminum plate, which comprises the following steps: establishing a distributed material gene library, designing a material parameter verification and correction mechanism, calibrating parameters, and triggering model retraining when continuous abnormalities occur; establishing an environmental influence coefficient matrix, calculating actual yield strength, strain threshold and pre-stretching force; collecting aluminum plate surface images in real time; dynamically adjusting stretching parameters, optimizing stretching speed to control stretching force loading; reconstructing a strain field in real time, and designing a self-adaptive termination mechanism; inputting material and strain parameters to predict critical curvature, calculating square tube structure parameters, calculating initial pressure and optimizing pressure; designing a multi-stage stretching and pressure maintaining process, and reducing pressure of a corresponding area if local strain exceeds a limit; controlling progressive unloading by using an exponential decay curve, detecting residual stress, performing full-size detection, analyzing deformation deviation, solving optimal parameters, and updating a parameter library.

Description

Technical Field

[0001] This invention relates to the field of machining technology, specifically to a segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates. Background Technology

[0002] The core technology path of existing segmented bundling and stretching forming equipment includes: segmented fixing of aluminum plates through multiple sets of rollers or clamps, and adjustment of clamping distance using sliding supports and lead screws; curved surface forming by hydraulic cylinder lifting or horizontal stretching, and lifting of the worktable by hydraulic cylinder driving the hydraulic system; processing of different curvatures by changing molds, and proposing a structure in which the mold base and the upper hydraulic cylinder cooperate; introducing contact measurement or manual visual inspection, and indirectly judging the forming state by the displacement of the clamping block; In existing technologies, current equipment relies on rigid mechanical connections to achieve segmented coordination. Displacement deviations occur during high-curvature stretching, leading to abrupt curvature changes at the junctions of adjacent segments. Traditional hydraulic drive systems suffer from pressure fluctuations, making it difficult to synchronously control precision in areas of abrupt curvature changes. Existing patents often employ fixed mold compensation, which cannot adapt to the springback deformation after stretching high-curvature aluminum plates. The roll coating process and the stretching process are independent; the pre-roll coating process does not consider the coating's elongation characteristics during stretching, resulting in large coating thickness deviations. Existing equipment lacks a real-time surface detection system, making it unable to identify orange peel textures generated during stretching. And micro-crack defects; traditional aluminum sheet stretching process adopts continuous integral stretching. For roll-coated hyperbolic aluminum sheets with extremely large transverse cross-sectional curvature, due to insufficient material fluidity, wrinkles, cracks or springback failures are easily generated in the curvature change area during forming; material flow stress concentration leads to a large wrinkle rate and high springback in the curvature change area; relying on repeated manual mold repair, the average processing time per piece is high; traditional molds cannot solve the problem of local strain mismatch. When the yield strength of the aluminum sheet is greater than or equal to the preset yield strength threshold and the elongation is less than the preset elongation threshold, the existing process is difficult to meet the forming requirements of curvature below R150; Therefore, there is a need to provide segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum sheets. Summary of the Invention

[0003] The purpose of this invention is to provide a segmented bundling and stretching forming device for high-curvature roll-coated hyperbolic aluminum sheets. To solve the above-mentioned problems in the prior art, this invention achieves this through the following technical solution: In a first aspect, the segmented bundling and stretching forming equipment for large-curvature roll-coated hyperbolic aluminum plates provided in the embodiments of the present invention specifically includes the following units: Parameter calibration unit: Establish a distributed material gene library to store data on the entire life cycle of aluminum plates, design a material parameter verification and correction mechanism and calibrate the parameters, trigger model retraining when there are continuous anomalies; establish an environmental impact coefficient matrix to calculate the actual yield strength, strain threshold and pre-tension force; Monitoring and control unit: Based on calibration reports, it acquires images of the aluminum plate surface in real time; dynamically adjusts tensile parameters, optimizes tensile speed, and uses an S-curve to control tensile force loading; it reconstructs the strain field in real time, generates a color thermogram, and designs an adaptive termination mechanism. Predictive matching unit: Based on the strain field, a hybrid neural network model is constructed. Material and strain parameters are input, critical curvature is predicted, and square tube structure parameters are calculated; a square tube model database is established, and the best model is automatically matched. Bundling and stretching unit: Based on the square tube structure parameters, a four-parameter coupled dynamic bundling system is designed, and the initial pressure and optimized pressure of the hydraulic tensioner are calculated; a multi-stage stretching and pressure holding process is designed, combined with DIC real-time monitoring, and if the local strain exceeds the limit, the pressure in the corresponding area is reduced; Verification and optimization unit: Based on the obtained optimized pressure, the progressive unloading is controlled by the exponential decay curve. The binding is removed in the order from the middle to both ends, residual stress is detected, full-size inspection is carried out, deformation deviation is analyzed, a response surface model of deviation and parameters is established, the optimal parameters are solved and the parameter library is updated.

[0004] Secondly, the segmented bundling and stretching forming method for high-curvature roll-coated hyperbolic aluminum plates provided in the embodiments of the present invention specifically includes the following steps: Step 1: Establish a distributed material gene library to store data on the entire life cycle of aluminum plates; design a material parameter verification and correction mechanism and calibrate the parameters; trigger model retraining when continuous anomalies occur; establish an environmental impact coefficient matrix and calculate the actual yield strength, strain threshold, and pre-tension force; generate a calibration report containing multiple parameters. Step 2: Based on the calibration report, acquire images of the aluminum plate surface in real time; dynamically adjust the tensile parameters, optimize the tensile speed, and use an S-curve to control the tensile force loading; reconstruct the strain field in real time, generate a color thermogram, and design an adaptive termination mechanism to homogenize the stress after termination by holding pressure. Step 3: Based on the strain field, construct a hybrid neural network model, input material and strain parameters, predict the critical curvature, and calculate the square tube structure parameters; establish a square tube model database and automatically match the optimal model; achieve precise placement of the square tube through a 6-axis collaborative robot and vision guidance system; Step 4: Based on the square tube structure parameters, design a four-parameter coupled dynamic binding system, calculate the initial pressure and optimized pressure of the hydraulic tensioner using formulas; design a multi-stage tensioning and pressure holding process, combined with DIC real-time monitoring, if the local strain exceeds the limit, reduce the pressure in the corresponding area; Step 5: Based on the obtained optimized pressure, use the exponential decay curve to control the gradual unloading, remove the binding in the order from the middle to both ends, detect the residual stress, perform full-size inspection, analyze the deformation deviation, establish the response surface model of deviation and parameters, solve for the optimal parameters and update the parameter library.

[0005] The beneficial effects of this invention are: 1. Integrating blockchain, DIC technology, deep learning, and digital twin technologies into the aluminum plate forming process, a fully intelligent system is formed, encompassing pretreatment, stretching, matching, bundling, and verification, breaking through the experience-dependent nature of traditional processes. Through a multi-dimensional coupled model of environmental perception, material properties, and time effects, real-time dynamic adjustment of pre-stretching force, bundling pressure, and stretching rate parameters is achieved, solving the accuracy problem caused by parameter fixation in large curvature forming. A closed loop of detection, deviation analysis, parameter optimization, and model updating is constructed, and combined with genetic algorithms and parameter library updates, the self-evolution of process parameters is achieved, overcoming the limitations of one-time calibration in traditional processes. For the critical zone of large curvature aluminum plates, a combination strategy of real-time marking of strain thermograms, adaptive matching of square tubes, and dynamic adjustment of local pressure is used to achieve refined control of the critical zone, filling the gap in existing technology for controlling complex curvature areas. 2. By precisely calibrating material parameters, controlling the strain field in real time, and implementing multi-stage tensile control, the strain error in the critical zone is reduced, minimizing the risk of springback and cracking, and improving the dimensional accuracy and shape stability of the hyperbolic aluminum plate. From material parameter retrieval and tensile process control to square tube deployment and quality verification, the entire process requires no manual intervention. Automation is achieved through machine vision, robotics, and algorithms, reducing operational errors. Through S-shaped force loading, progressive unloading, and local pressure adjustment strategies, material stress concentration and coating damage are reduced, improving product qualification rate and service life. With a material gene library and parameter self-learning system, it can adapt to different batches and specifications of aluminum plates without large-scale process adjustments, making it suitable for multiple scenarios. Blockchain-based material data storage and full-process parameter recording enable full lifecycle traceability of the product, facilitating quality problem investigation and process optimization. Attached Figure Description

[0006] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0007] Figure 1 This is a schematic diagram of the segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of the steps of the segmented bundling and stretching forming method for high-curvature roll-coated hyperbolic aluminum plates provided in Embodiment 2 of the present invention. Detailed Implementation

[0008] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0009] Example 1: As Figure 1 As shown in the embodiment of the present invention, the segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates specifically includes the following units: Parameter calibration unit: Establish a distributed material gene library to store data on the entire life cycle of aluminum plates, design a material parameter verification and correction mechanism and calibrate the parameters, trigger model retraining when there are continuous anomalies; establish an environmental impact coefficient matrix, calculate the actual yield strength, strain threshold and pre-tension force; generate a calibration report containing multiple parameters; In a specific embodiment, a three-in-one preprocessing system integrating a material gene library, environmental perception, and parameter self-learning is constructed. By dynamically coupling the material parameters stored on the blockchain with the real-time environmental compensation model, accurate prediction of pre-stretching parameters is achieved, thereby improving parameter matching accuracy. Establish a distributed material gene bank to store the full life cycle data of each batch of aluminum plates: by scanning the unique label of the aluminum plate, retrieve the material performance parameters stored on the blockchain, including: yield strength, elongation, elastic modulus, coating thickness and coating adhesion strength. Material parameter verification: Randomly select 3% of aluminum plates from any batch for sampling and testing. Use a universal testing machine to verify the yield strength and elongation. If the error is less than or equal to 3%, the verification is qualified; otherwise, the parameter correction mechanism is activated. Specifically, the parameter correction mechanism is implemented as follows: for abnormal batches of aluminum plates, the sampling ratio is increased to 10%, and high-precision testing equipment is used to retest the yield strength and elongation to obtain the retested values; a linear correction model between the test values ​​and the gene pool values ​​is established. The yield strength and elongation of the gene bank are obtained. The ratio of the retested yield strength to the yield strength obtained from the sampling test is processed to obtain the yield strength correction ratio. The yield strength correction ratio is multiplied by the yield strength of the gene bank to obtain the corrected yield strength. The elongation rate is calculated by comparing the retested elongation rate with the elongation rate obtained from the sampling test to obtain the elongation rate correction ratio; the elongation rate correction ratio is then multiplied by the gene pool elongation rate to obtain the corrected elongation rate. Simultaneously correct the elastic modulus and coating adhesion strength, using the elastic modulus and coating adhesion strength from historical data of the same batch; If the parameter correction mechanism is triggered in two consecutive batches, it is determined to be a deviation of the material gene library model. The machine learning parameter prediction model is then retrained, the latest detection data is added, the gene library data acquisition process is checked, and real-time correlation storage of coating thickness and ambient temperature and humidity is added. Deploy integrated temperature and humidity sensors to collect ambient temperature and relative humidity in real time, establish an environmental impact coefficient matrix, and then use formulas... The actual yield strength at the actual temperature is calculated. ,in, The yield strength at a preset standard temperature. For ambient temperature, This is the temperature influence coefficient, with a preset value of 0.002. Based on the obtained actual yield strength, a dynamic calculation model for the strain threshold is established, using the formula:

[0010] The strain threshold was calculated. ,in, For the initial strain, This is the temperature influence coefficient, with a preset value of 0.02. For ambient temperature, This is the humidity influence coefficient, with a preset value of 0.005. For ambient humidity, This is the time influence coefficient, with a preset value of 0.01. It is a time constant; Based on the calculated actual yield strength, the pretension force is adaptively set using the formula:

[0011] Calculate the pretension force ,in, This is the pretension force influence coefficient, with a preset value of 0.5. This represents the actual yield strength. For time steps, The width of the aluminum plate. For the target deformable variable, This is the curvature correction factor; A calibration report containing material parameters, environmental parameters, strain thresholds, and pre-tension force is generated and transmitted to the equipment control system as reference parameters for the tensioning process. Monitoring and control unit: Based on the calibration report, it acquires images of the aluminum plate surface in real time; dynamically adjusts the tensile parameters, optimizes the tensile speed, and uses an S-curve to control the tensile force loading; it reconstructs the strain field in real time, generates a color thermogram, and designs an adaptive termination mechanism, which homogenizes the stress by holding pressure after termination. Develop a pre-stretch control system with global strain monitoring, local strain early warning and intelligent termination decision, and combine DIC technology with deep learning strain prediction model to achieve precise control of strain field and reduce strain control error in critical zone. The DIC system is deployed and calibrated to spray random speckle patterns onto the surface of the aluminum plate to ensure that the image contrast reaches the preset contrast range. High-speed industrial cameras are deployed to simultaneously acquire images of the aluminum plate surface from different angles, and the system is calibrated using the Zhang Zhengyou calibration method. The parameters of the pre-stretching process are dynamically adjusted using the following formula for dynamic adjustment of the stretching speed:

[0012] Calculate the stretching speed ,in, This is a preset correction factor, with a preset value of 0.2. For elongation, For time steps, This is the intensity correction factor, with a default value of 0.01. To adjust the actual yield strength, a new actual yield strength correction term is added. When the actual yield strength increases, the tensile speed is reduced to avoid brittle fracture of the material. The tensile force loading is controlled using an S-curve to avoid initial impact, as shown by the formula:

[0013] Instantaneous tensile force was calculated ,in, For pre-tension force, For time steps, Loading time; Real-time reconstruction and analysis of the strain field, processing of DIC images, calculation of the full-field displacement u in the x-direction and the full-field displacement v in the y-direction, and optimization of strain calculation are as follows:

[0014] The normal strain in the x-direction was calculated. Normal strain in the y direction Engineering shear strain in the xy plane ,in, Let x be the rate of change of displacement in the x-direction. Let be the rate of change of displacement in the y-direction with respect to the x-direction. Let be the rate of change of displacement in the y-direction. Let x be the rate of change of displacement in the x-direction with respect to the y-direction. These are the original coordinates; The strain field data is updated once at a preset period, and a color strain thermogram is generated. If the measured strain is greater than or equal to... The area marked in red represents the high strain zone. If the measured strain is less than or equal to... The blue area indicates a low-strain region. If the measured strain is greater than... and less than This indicates the normal strain zone; An adaptive termination mechanism is set up to determine the termination condition. If the maximum principal strain in the curvature abrupt change region is greater than or equal to... If the area of ​​the region is greater than or equal to a preset area threshold, a pre-stretching termination signal is triggered. Based on the obtained pre-stretch termination signal, pre-stretch holding is performed, and the current tensile force is maintained after pre-stretch termination to homogenize the material stress. Predictive matching unit: Based on the strain field, a hybrid neural network model is constructed. Material and strain parameters are input, critical curvature is predicted, and square tube structure parameters are calculated. A square tube model database is established to automatically match the best model. The square tube is accurately placed through a 6-axis collaborative robot and a vision guidance system. A deep learning-based critical curvature prediction model and gradient functional square tube design system are constructed, and a machine vision-guided precision deployment system is combined to achieve adaptive matching between the square tube and the critical region. Construct a CNN-LSTM hybrid neural network model with input parameters including: yield strength, maximum principal strain, strain gradient, coating thickness, and curvature correction coefficient; Training is performed using historical data, using the formula:

[0015] The critical curvature was calculated. ,in, This is the curvature correction factor. For time steps, This represents the actual yield strength. For elastic modulus, The strain threshold; Based on the obtained critical curvature, the formula is: The side length of the structure was calculated. With the wall thickness of the tubular structure ; Establish a square tube model database and automatically match the optimal square tube model based on the calculated critical curvature, structural side length and tubular structure wall thickness. A robotic arm deployment system is established using a 6-axis collaborative robot, equipped with a vision guidance system; The vision system identifies critical zone markers, calculates the placement coordinates of the square tube, the robotic arm picks up the square tube, moves it to a preset height above the target position, and descends at a preset speed. The force sensor detects the contact force, and if the contact force is greater than or equal to a preset contact force threshold, the descent stops. The bonding gap between the square tube and the aluminum plate is scanned by a laser profilometer. If the bonding gap is less than or equal to the preset gap threshold, it is marked as qualified; otherwise, a secondary adjustment is initiated. Bundling and stretching unit: Based on the square tube structure parameters, a four-parameter coupled dynamic bundling system is designed. The initial pressure and optimized pressure of the hydraulic tensioner are calculated by formula. A multi-stage stretching and pressure holding process is designed, combined with DIC real-time monitoring. If the local strain exceeds the limit, the pressure in the corresponding area is reduced. In a specific embodiment, a dynamic strapping system with four coupled parameters of pressure, displacement, gap, and air film is developed. Combined with an adaptive PID control algorithm, it achieves precise control of strapping force and synergistic optimization of coating protection. Specifically, configure and initialize the hydraulic tensioner, obtain the critical zone length, and arrange the hydraulic tensioner within a preset unit length within the critical zone length, using the formula:

[0016] The initial pressure was calculated. ,in, This is the yield strength correction factor, with a preset value of 0.02. This represents the actual yield strength. This is the curvature correction factor, with a default value of 0.01. The critical curvature; Based on the obtained initial pressure, the optimization formula is derived according to the pressure servo control model:

[0017] The optimized pressure is calculated, where, This is the influence coefficient of tensile displacement. This represents the tensile displacement increment in the i-th stage. The strain influence factor is preset to a value of 0.05. For the current strain in stage i, The strain threshold; A multi-stage stretching and holding system based on the material time effect was constructed, and combined with intelligent creep compensation algorithm and strain rate sensitive control, to achieve precise forming of large curvature regions; Target deformation allocation: 40% pre-stretching completed, 60% remaining in the final stretch, with the final stretch performed in two stages: Initial stage: 30% complete, strain rate is 0.001 / s; Final stage: 30% complete, strain rate is 0.0003 / s, reduce the rate to reduce springback; Determination of the material strain rate sensitivity index m: Through tensile tests at different rates, m = 0.015-0.025 was obtained by fitting. The strain distribution of the final tensile section is monitored in real time using the DIC system. When the strain in any region exceeds 0.8... When this happens, the pressure of the corresponding tensioner in that area is automatically reduced to prevent excessive stretching that could cause cracking. Verification and optimization unit: Based on the obtained optimized pressure, the progressive unloading is controlled by the exponential decay curve. The binding is removed in the order from the middle to both ends, residual stress is detected, full-size inspection is carried out, deformation deviation is analyzed, a response surface model of deviation and parameters is established, the optimal parameters are solved and the parameter library is updated. In a specific embodiment, a closed-loop verification system with progressive unloading, multi-physics detection, and digital twin iteration is developed. Combined with residual stress imaging and AI deviation analysis, the system achieves self-optimization of process parameters and improves detection efficiency. Unloading rate planning is performed, and the tensile force unloading is controlled using an exponential decay curve, through the formula:

[0018] Calculated unloading force ,in, To unload the initial holding force, For time steps, It is a time constant; The tensioning and untying sequence should proceed from the middle to both ends, with the tensioner pressure decreasing sequentially. The pressure of each tensioner should decrease from its current value to 0 within ≥5 seconds to avoid localized stress concentration. Residual stress was detected using time-of-flight ultrasonic diffraction, with the probe covering the entire surface of the aluminum plate. Danger zone determination: Areas with residual stress greater than or equal to 0.6σs are marked as red warning zones, and the location and stress value of the area are recorded; areas with residual stress less than 0.6σs are marked as normal zones. A laser tracker was used for full-size inspection, with a measurement point density of one measurement point every 5 mm in the critical zone and one measurement point every 10 mm in the non-critical zone. Obtain the measured three-dimensional coordinates and the design coordinates, calculate the deformation deviation of the hyperbolic aluminum plate, and process the ratio of the obtained deformation deviation to the preset deviation threshold and take the absolute value to obtain the curvature deviation. If the curvature deviation is less than the preset curvature deviation threshold, it is marked as normal accuracy; otherwise, it is marked as abnormal accuracy. Establish a bias-parameter response surface model using the formula: Calculation of molding deviation ,in, This is the curvature correction factor. The strain threshold, To initialize pressure, For holding time; An improved genetic algorithm is used to solve for the optimal parameter combination. The optimized parameters are then fed back to the material parameter calibration system to achieve self-learning iteration of process parameters. The parameter library is updated every 10 products produced.

[0019] Example 2: Figure 2 As shown in the embodiment of the present invention, the segmented bundling and stretching forming method for high-curvature roll-coated hyperbolic aluminum plates specifically includes the following steps: Step 1: Establish a distributed material gene library to store data on the entire life cycle of aluminum plates; design a material parameter verification and correction mechanism and calibrate the parameters; trigger model retraining when continuous anomalies occur; establish an environmental impact coefficient matrix and calculate the actual yield strength, strain threshold, and pre-tension force; generate a calibration report containing multiple parameters. Step 2: Based on the calibration report, acquire images of the aluminum plate surface in real time; dynamically adjust the tensile parameters, optimize the tensile speed, and use an S-curve to control the tensile force loading; reconstruct the strain field in real time, generate a color thermogram, and design an adaptive termination mechanism to homogenize the stress after termination by holding pressure. Step 3: Based on the strain field, construct a hybrid neural network model, input material and strain parameters, predict the critical curvature, and calculate the square tube structure parameters; establish a square tube model database and automatically match the optimal model; achieve precise placement of the square tube through a 6-axis collaborative robot and vision guidance system; Step 4: Based on the square tube structure parameters, design a four-parameter coupled dynamic binding system, calculate the initial pressure and optimized pressure of the hydraulic tensioner using formulas; design a multi-stage tensioning and pressure holding process, combined with DIC real-time monitoring, if the local strain exceeds the limit, reduce the pressure in the corresponding area; Step 5: Based on the obtained optimized pressure, use the exponential decay curve to control the gradual unloading, remove the binding in the order from the middle to both ends, detect the residual stress, perform full-size inspection, analyze the deformation deviation, establish the response surface model of deviation and parameters, solve for the optimal parameters and update the parameter library.

[0020] The above provides a detailed description of one embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. The above formulas are all dimensionless numerical calculations, and the formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world situation. The preset parameters in the formulas are set by those skilled in the art based on actual conditions and historical experience, and can be adjusted according to actual conditions. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. All equivalent changes and improvements made in accordance with the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum sheets, characterized in that, Includes the following units: Establish a distributed materials gene library to store data on the entire life cycle of aluminum plates, design a material parameter verification and correction mechanism and calibrate the parameters, triggering model retraining when continuous anomalies occur; establish an environmental impact coefficient matrix to calculate the actual yield strength, strain threshold and pre-tension force; Based on the calibration report, images of the aluminum plate surface are acquired in real time; the tensile parameters are dynamically adjusted and the tensile speed is optimized; the tensile force loading is controlled by an S-curve; the strain field is reconstructed in real time, a color thermogram is generated, and an adaptive termination mechanism is designed. Based on the strain field, a hybrid neural network model is constructed. The material and strain parameters are input, the critical curvature is predicted, and the parameters of the square tube structure are calculated. Establish a square tube model database and automatically match the best model; Based on the structural parameters of the square tube, a four-parameter coupled dynamic binding system was designed, and the initial pressure and optimized pressure of the hydraulic tensioner were calculated. A multi-stage tensioning and pressure holding process was designed, and combined with real-time monitoring by DIC, if the local strain exceeds the limit, the pressure in the corresponding area is reduced. Based on the obtained optimized pressure, an exponential decay curve is used to control the gradual unloading. The binding is removed sequentially from the middle to both ends, residual stress is detected, full-size inspection is carried out, deformation deviation is analyzed, a response surface model of deviation and parameters is established, the optimal parameters are solved, and the parameter library is updated.

2. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for specifying the calibration parameters is as follows: The yield strength and elongation of the gene bank are obtained. The ratio of the retested yield strength to the yield strength obtained from the sampling test is processed to obtain the yield strength correction ratio. The yield strength correction ratio is multiplied by the yield strength of the gene bank to obtain the corrected yield strength. The elongation rate is calculated by comparing the retested elongation rate with the elongation rate obtained from the sampling test to obtain the elongation rate correction ratio; the elongation rate correction ratio is then multiplied by the gene pool elongation rate to obtain the corrected elongation rate. The elastic modulus and coating adhesion strength were adjusted simultaneously using historical data from the same batch.

3. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for calculating the actual yield strength, strain threshold, and pretension force is as follows: Real-time collection of ambient temperature and relative humidity, establishment of an environmental impact coefficient matrix, and application of formulas The actual yield strength at the actual temperature is calculated. ,in, The yield strength at a preset standard temperature. For ambient temperature, This is the temperature influence coefficient; Based on the obtained actual yield strength, a dynamic calculation model for the strain threshold is established, using the formula: ； The strain threshold was calculated. ,in, For the initial strain, This is the temperature influence coefficient. For ambient temperature, Humidity influence coefficient For ambient humidity, The time influence coefficient. It is a time constant; Based on the calculated actual yield strength, the pretension force is adaptively set using the formula: ； Calculate the pretension force ,in, This is the pretension force influence coefficient. This represents the actual yield strength. For time steps, The width of the aluminum plate. For the target deformable variable, This is the curvature correction factor.

4. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for dynamically adjusting the stretching parameters is as follows: The parameters of the pre-stretching process are dynamically adjusted using the following formula for dynamic adjustment of the stretching speed: ； Calculate the stretching speed ,in, This is a preset correction factor. For elongation, For time steps, This is the intensity correction factor. This represents the actual yield strength. The tensile force loading is controlled using an S-curve to avoid initial impact, as shown by the formula: ； Instantaneous tensile force was calculated ,in, For pre-tension force, For time steps, This refers to the loading time.

5. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for real-time reconstruction of the strain field is as follows: Real-time reconstruction and analysis of the strain field, processing of DIC images, calculation of the full-field displacement u in the x-direction and the full-field displacement v in the y-direction, and optimization of strain calculation are as follows: ； The normal strain in the x-direction was calculated. Normal strain in the y direction Engineering shear strain in the xy plane ,in, Let x be the rate of change of displacement in the x-direction. Let be the rate of change of displacement in the y-direction with respect to the x-direction. Let be the rate of change of displacement in the y-direction. Let x be the rate of change of displacement in the x-direction with respect to the y-direction. These are the original coordinates; Set a preset cycle to update the strain field data once, and generate a color strain thermogram.

6. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for designing the adaptive termination mechanism is as follows: An adaptive termination mechanism is set up to determine the termination condition. If the maximum principal strain in the curvature abrupt change region is greater than or equal to... If the area of ​​the region is greater than or equal to a preset area threshold, a pre-stretching termination signal is triggered. Based on the obtained pre-stretch termination signal, pre-stretch holding is performed, and the current tensile force is maintained after pre-stretch termination to homogenize the material stress.

7. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for calculating the parameters of the square tube structure is as follows: Construct a CNN-LSTM hybrid neural network model with input parameters including: yield strength, maximum principal strain, strain gradient, coating thickness, and curvature correction coefficient; Training is performed using historical data, using the formula: ； The critical curvature was calculated. ,in, This is the curvature correction factor. For time steps, This represents the actual yield strength. For elastic modulus, The strain threshold; Based on the obtained critical curvature, the formula is: The side length of the structure was calculated. With the wall thickness of the tubular structure ; Establish a square tube model database and automatically match the optimal square tube model based on the calculated critical curvature, structural side length, and tubular structure wall thickness.

8. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for obtaining the optimized pressure is as follows: Configure and initialize the hydraulic tensioner, obtain the critical zone length, and arrange the hydraulic tensioner within a preset unit length within the critical zone length, using the formula: ； The initial pressure was calculated. ,in, This is the yield strength correction factor. This represents the actual yield strength. This is the curvature correction factor. The critical curvature; Based on the obtained initial pressure, the optimization formula is derived according to the pressure servo control model: ； The optimized pressure is calculated, where, This is the influence coefficient of tensile displacement. This represents the tensile displacement increment in the i-th stage. As the strain influencing factor, For the current strain in stage i, This is the strain threshold.

9. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for designing a multi-stage stretching and pressure holding process is as follows: A multi-stage stretching and holding system based on the material time effect was constructed, and combined with intelligent creep compensation algorithm and strain rate sensitive control, to achieve precise forming of large curvature regions; Target deformation allocation: 40% pre-stretching completed, 60% remaining in the final stretch, with the final stretch performed in two stages: Initial stage: 30% complete, strain rate is 0.001 / s; Final stage: 30% complete, strain rate is 0.0003 / s; Determination of the material strain rate sensitivity index m: Through tensile tests at different rates, m = 0.015-0.025 was obtained by fitting. The strain distribution of the final tensile section is monitored in real time using the DIC system. When the strain in any region exceeds 0.8... When this happens, the pressure of the corresponding tightener in that area is automatically reduced.

10. The segmented bundling and stretching forming equipment for high-curvature roll-coated hyperbolic aluminum plates according to claim 1, characterized in that, The method for analyzing deformation deviation is as follows: Obtain the measured three-dimensional coordinates and the design coordinates, calculate the deformation deviation of the hyperbolic aluminum plate, and process the ratio of the obtained deformation deviation to the preset deviation threshold and take the absolute value to obtain the curvature deviation. If the curvature deviation is less than the preset curvature deviation threshold, it is marked as normal accuracy; otherwise, it is marked as abnormal accuracy. Establish a bias-parameter response surface model using the formula: Calculation of molding deviation ,in, This is the curvature correction factor. The strain threshold, To initialize pressure, For holding time; An improved genetic algorithm is used to solve for the optimal parameter combination, and the optimized parameters are fed back to the material parameter calibration system.

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