High-precision aluminum alloy stamping process and die design method

Abstract

The embodiment of the invention relates to the technical field of precision machining, and discloses a high-precision aluminum alloy stamping process and a die design method.The process comprises the steps that material data of a to-be-machined aluminum alloy are predicted according to experimental data of the to-be-machined aluminum alloy; the material data comprises material flow, springback and stress distribution data; according to the material data of the to-be-processed aluminum alloy, simulating and predicting the mold profile for compensating the springback so as to manufacture a stamping mold; according to the components of the lubricant, the inner surface structure of the stamping die and the material data of the aluminum alloy to be machined, the spraying thickness of the lubricant is determined; spraying on the surface of the to-be-processed aluminum alloy according to the spraying thickness; stamping the aluminum alloy to be processed step by step; the stamped aluminum alloy is subjected to springback calibration, and the calibrated aluminum alloy is obtained; and after the calibrated aluminum alloy is subjected to blanking and trimming, the formed aluminum alloy is obtained. According to the embodiment of the invention, the springback problem in the machining process is effectively reduced, and the trial and error cost is reduced.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of precision machining, and particularly to a high-precision aluminum alloy stamping process and a die design method. Background Art

[0002] At present, aluminum alloy is one of the most widely used metal materials in industries such as aviation. For example, it has extensive applications in aircraft airframe structural components (skins, stringers, frames, etc.), engine components, landing gears, and housings of airborne equipment. Aluminum alloy parts have the characteristics of light weight, good specific stiffness, and corrosion resistance, but at the same time, they have poor ductility and resilience. Therefore, in order to improve the production efficiency and product quality of aluminum alloy parts, it is necessary to study the aluminum alloy stamping forming process, especially the high-precision aluminum alloy stamping forming process.

[0003] The prior art discloses a method for preparing high-density aluminum-lithium alloy sheets based on a full mixing and extrusion process and its application. This solution uses a multi-roll mill to cold-roll the slab, reducing the energy consumption of subsequent processes. However, this solution does not consider the material characteristics of aluminum alloy and cannot guarantee the forming accuracy of aluminum alloy.

[0004] The inventors of the present application found that the existing aluminum alloy stamping process does not fully consider the characteristics of aluminum alloy materials and the problems that may occur during the processing, resulting in large springback and unstable accuracy of aluminum alloy stamping products, which cannot meet the requirements of the aerospace field. Summary of the Invention

[0005] In view of the above problems, the embodiments of the present invention provide a high-precision aluminum alloy stamping process and a die design method to solve the problems of large springback and unstable accuracy of aluminum alloy stamping products existing in the prior art.

[0006] According to one aspect of the embodiments of the present invention, a high-precision aluminum alloy stamping process is provided, and the process includes:

[0007] Predict the material data of the aluminum alloy to be processed according to the experimental data of the aluminum alloy to be processed; the material data includes material flow, springback, and stress distribution data;

[0008] Simulate and predict the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die;

[0009] Determine the spraying thickness of the lubricant according to the components of the lubricant, the inner surface structure of the stamping die, and the material data of the aluminum alloy to be processed;

[0010] Spray according to the spraying thickness on the surface of the aluminum alloy to be processed;

[0011] The aluminum alloy to be processed is step - stamped to obtain the stamped aluminum alloy;

[0012] The stamped aluminum alloy is subjected to springback calibration to obtain the calibrated aluminum alloy;

[0013] After blanking and trimming the calibrated aluminum alloy, the formed aluminum alloy is obtained.

[0014] Optionally,

[0015] Based on the experimental data of the aluminum alloy to be processed, predict the material data of the aluminum alloy to be processed, including:

[0016] According to the basic information of the selected aluminum alloy to be processed, query the experimental data of the aluminum alloy to be processed in the pre - established aluminum alloy material database; among them, the aluminum alloy material database includes the basic information, physical and chemical property information, mechanical property information, processing property information, surface treatment property information and corrosion resistance property information of aluminum alloy materials;

[0017] Input the experimental data of the aluminum alloy to be processed into a non - linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed; among them, the non - linear elastoplastic model is obtained by training a deep learning algorithm based on training data; the training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of experimental aluminum alloy and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy according to the experimental data of the experimental aluminum alloy through finite element simulation software.

[0018] Optionally, the method of simulating and predicting the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die includes:

[0019] According to the material data of the aluminum alloy to be processed, use finite element analysis software to perform 3D modeling on the die and the aluminum alloy to be processed, and perform simulation prediction to obtain the first compensation data of the first die surface for compensating springback;

[0020] Input the material data of the aluminum alloy to be processed and the initial modeling die into a springback compensation model to obtain the second compensation data of the second die surface for compensating springback; among them, the springback compensation model is obtained by training based on the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data;

[0021] Perform weighted fusion according to the first compensation data and the second compensation data to obtain the target compensation data;

[0022] Perform springback compensation design on the initial modeling die according to the target compensation data, and manufacture a stamping die.

[0023] Optionally, before obtaining the target compensation data by performing weighted fusion on the first compensation data and the second compensation data, the process further includes:

[0024] Calculate the difference between the first compensation data and the second compensation data;

[0025] If the difference is within a preset difference threshold, perform the step of obtaining the target compensation data by performing weighted fusion on the first compensation data and the second compensation data;

[0026] If the difference is greater than the preset difference threshold, analyze the cause of the difference according to a preset analysis step, and recalculate the first compensation data and / or the second compensation data according to the cause of the difference.

[0027] Optionally, the components of the lubricant include water-based synthetic ester, nano-graphite, and isobutene sulfide.

[0028] Optionally, determining the spraying thickness of the lubricant according to the components of the lubricant, the inner surface structure of the stamping die, and the material data of the aluminum alloy to be processed includes:

[0029] Determine the friction force between the aluminum alloy to be processed and the stamping die when the lubricant is sprayed with different thicknesses on the aluminum alloy to be processed;

[0030] Determine the springback influence of different friction forces on the aluminum alloy to be processed;

[0031] Determine the target friction force and the corresponding spraying thickness of the lubricant according to the springback influence.

[0032] Optionally, after performing blanking and trimming on the calibrated aluminum alloy to obtain a formed aluminum alloy, the process further includes:

[0033] Perform quality inspection on the formed aluminum alloy to determine the dimensional accuracy, surface defects, grain size, and heat treatment effect of the formed aluminum alloy.

[0034] Another embodiment of the present invention also provides an aluminum alloy die design method, the method including:

[0035] Use simulation software to simulate and predict the material data of the aluminum alloy to be processed; the material data includes material flow, springback, and stress distribution data;

[0036] According to the material data of the aluminum alloy to be processed, simulate and predict the die surface for compensating springback to manufacture a stamping die.

[0037] Optionally, simulate using simulation software to predict the material data of the aluminum alloy to be processed, including:

[0038] Conduct a tensile test on the aluminum alloy to be processed to obtain the mechanical property parameters of the aluminum alloy to be processed; the mechanical property parameters include elastic modulus, yield strength, and hardening index;

[0039] Use a strain measurement system to obtain the deformation data of the aluminum alloy to be processed during the actual stamping process;

[0040] Take the mechanical property parameters and the deformation data as the experimental data of the aluminum alloy to be processed;

[0041] Input the experimental data of the aluminum alloy to be processed into a non-linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed; among them, the non-linear elastoplastic model is trained based on training data for a deep learning algorithm; the training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of experimental aluminum alloys and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy according to the experimental data of the experimental aluminum alloy through finite element simulation software.

[0042] Optionally, the method of simulating and predicting the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die includes:

[0043] According to the material data of the aluminum alloy to be processed, use finite element analysis software to perform three-dimensional modeling on the die and the aluminum alloy to be processed, and conduct simulation and prediction to obtain the first compensation data of the first die surface for compensating springback;

[0044] Input the material data of the aluminum alloy to be processed and the initial modeling die into a springback compensation model to obtain the second compensation data of the second die surface for compensating springback; among them, the springback compensation model is trained based on the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data;

[0045] Perform weighted fusion according to the first compensation data and the second compensation data to obtain the target compensation data;

[0046] Conduct springback compensation design on the initial modeling die according to the target compensation data and manufacture a stamping die.

[0047] In the embodiments of the present invention, when predicting the material data of the aluminum alloy to be processed, it is determined through a pre-established database and in combination with a non-linear elastoplastic model, thereby effectively improving the accuracy and efficiency of prediction. Furthermore, when performing springback compensation design, the computer-aided engineering (CAE) technology and artificial intelligence (AI) technology can be fully utilized to realize the digital design of aluminum alloy materials, reduce the trial-and-error cost, and then quickly and accurately obtain information such as the physical and mechanical properties and process properties of aluminum alloy materials, providing strong support for the development of aluminum alloy stamping products. Moreover, by designing a suitable lubricant, the springback can be further reduced. In addition, the present invention can also analyze the microstructure of aluminum alloy materials to understand the variation law of their mechanical properties, thereby optimizing the design scheme of aluminum alloy stamping products, reducing the manufacturing cost, and improving the production efficiency.

[0048] The above description is only an overview of the technical solution of the embodiments of the present invention. In order to be able to understand the technical means of the embodiments of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the embodiments of the present invention more obvious and understandable, the following specifically illustrates the embodiments of the present invention. Brief Description of the Drawings

[0049] The drawings are only used to illustrate the embodiments and are not considered as a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0050] Figure 1 A flowchart showing the high-precision aluminum alloy stamping process provided by the embodiments of the present invention is shown;

[0051] Figure 2 A flowchart showing the mold design method provided by another embodiment of the present invention is shown. Detailed Embodiments

[0052] The exemplary embodiments of the present invention will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein.

[0053] Figure 1 A flowchart showing the high-precision aluminum alloy stamping process provided by the embodiments of the present invention is shown. As Figure 1 shown, the process includes the following steps:

[0054] Step 110: Predict the material data of the aluminum alloy to be processed according to the experimental data of the aluminum alloy to be processed; the material data includes material flow, springback and stress distribution data.

[0055] Among them, in the embodiment of the present invention, the experimental data of the aluminum alloy to be processed is first obtained, which specifically includes the following steps:

[0056] Step 1101: According to the basic information of the selected aluminum alloy to be processed, query the experimental data of the aluminum alloy to be processed in the pre-established aluminum alloy material database.

[0057] In the embodiment of the present invention, the experimental data of the aluminum alloy to be processed is obtained by querying in the pre-established aluminum alloy material database according to the basic information of the selected aluminum alloy to be processed.

[0058] Specifically, in the embodiment of the present invention, an aluminum alloy material database is pre-established, which mainly includes the following parts: the basic information of aluminum alloy materials, physical and chemical property information, mechanical property information, processing property information, surface treatment property information, and corrosion resistance property information. Among them, the basic information of aluminum alloy materials includes grade, composition, state, etc.; the physical and chemical properties of aluminum alloy materials include density, melting point, conductivity, coefficient of thermal expansion, etc.; the mechanical properties of aluminum alloy materials include tensile strength, yield strength, elongation, hardness, etc.; the processing properties of aluminum alloy materials include weldability, machinability, casting performance, etc.; the surface treatment properties of aluminum alloy materials include anodic oxidation, painting, electroplating, etc.; the corrosion resistance properties of aluminum alloy materials include salt spray test, acid-base solution immersion test, etc. In the embodiment of the present invention, by establishing an aluminum alloy material database, the information of various aluminum alloy materials can be conveniently queried and managed, and support can be provided for the development and production of aluminum alloy stamping products.

[0059] Among them, the information in the aluminum alloy material database is obtained by pre-experimenting on various aluminum alloy materials. For example, a tensile test is performed on the aluminum alloy to be processed to obtain the mechanical property parameters of the aluminum alloy to be processed; the mechanical property parameters include elastic modulus, yield strength, and hardening index. In the embodiment of the present invention, the tensile test on the aluminum alloy to be processed can be performed according to the detection method of a preset standard (such as national standard, etc.) to obtain the mechanical property parameters of the aluminum alloy to be processed. A strain measurement system is used to obtain the deformation data of the aluminum alloy to be processed during the actual stamping process; among them, the strain measurement system can be an existing strain measurement device, etc., and the embodiment of the present invention does not make specific limitations.

[0060] Step 1102: Input the experimental data of the aluminum alloy to be processed into the non-linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed.

[0061] Among them, the non-linear elastoplastic model is obtained by training a deep learning algorithm based on training data. The training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of the experimental aluminum alloy and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy according to the experimental data of the experimental aluminum alloy through a finite element simulation software.

[0062] In the embodiment of the present invention, the deep learning algorithm is pre-trained to obtain the non-linear elastoplastic model.

[0063] Specifically, it includes the following steps:

[0064] 1. Obtain training data. The training data includes training sample historical aluminum alloy experimental data and corresponding historical material data, as well as sample labels: experimental data of the experimental aluminum alloy and corresponding simulated material data.

[0065] 2. Input the training data into a deep learning neural network for training to obtain predicted material data;

[0066] 3. According to the predicted material data and the sample labels, use a preset loss function to calculate the loss of the deep learning neural network.

[0067] 4. Adjust the parameters of the deep learning neural network according to the loss, and continue iterative training until a preset number of iterations is reached to obtain the non-linear elastoplastic model.

[0068] Step 001: Obtain training data: 1. Collect historical experimental data of historical aluminum alloys and historical material data of corresponding historical aluminum alloy materials, parameters such as elastic modulus, yield strength, and hardening index of historical aluminum alloy materials, as well as data such as stress-strain curves and springback amounts. 2. Obtain simulated material data: Use finite element simulation software (such as ABAQUS, LS-DYNA) to simulate the stamping process of historical aluminum alloy materials. Input aluminum alloy simulation experimental data (such as material parameters, boundary conditions, loads, etc.) to generate simulated material data (such as stress distribution, strain distribution, springback amount, etc.). Ensure the consistency of the simulated data and the experimental data, and verify the accuracy of the model through comparison. 3. Data collation: Collate the experimental data and the simulated data into a structured data set, including input features (such as strain, load, material parameters) and output features (such as stress, springback amount, material flow path). The data set includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of new experimental aluminum alloys and simulated material data.

[0069] Step 002: Nonlinear elastoplastic model construction: 1. Select a suitable nonlinear elastoplastic material model for aluminum alloy, such as Tresca criterion, Hill criterion, or a model based on strain energy density function (such as Neo-Hookean model). Define material parameters (such as elastic modulus, yield strength, hardening index, etc.), and calibrate them according to experimental data. 2. Deep learning model training: Select a suitable deep learning algorithm (such as convolutional neural network CNN, long short-term memory network LSTM, etc.) for modeling and predicting the nonlinear behavior of materials. Use the organized training data to train the deep learning model, with the input features being experimental data and simulation data, and the output features being the predicted material data (such as stress, strain, springback amount, etc.). Improve the prediction accuracy of the model through cross-validation and hyperparameter optimization techniques (such as grid search, random search).

[0070] Step 003: Model verification and optimization: Use the validation set to evaluate the prediction ability of the model, and calculate metrics such as root mean square error (RMSE) or coefficient of determination (R 2 ) etc. Adjust the model structure or parameters according to the verification results to optimize the model performance.

[0071] After training the nonlinear elastoplastic model, input the experimental data of the aluminum alloy to be processed into the nonlinear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed.

[0072] Step 120: According to the material data of the aluminum alloy to be processed, simulate and predict the die surface for compensating springback to manufacture a stamping die.

[0073] In the embodiment of the present invention, during die production, if the die parameters cannot be accurately determined, the generated experimental die does not meet the requirements, resulting in repeated modification of the experimental die to obtain the final die, thus wasting time and materials. In the embodiment of the present invention, combining model prediction with finite element software analysis improves the accuracy during die springback compensation, reduces the number of times of repeatedly modifying the experimental die, effectively reduces the trial-and-error cost, and improves work efficiency. Specifically, according to the material data of the aluminum alloy to be processed, simulating and predicting the die surface for compensating springback to manufacture a stamping die specifically includes:

[0074] Step 1201: According to the material data of the aluminum alloy to be processed, use finite element analysis software to perform three-dimensional modeling on the die and the aluminum alloy to be processed, and perform simulation prediction to obtain the first compensation data of the first die surface for compensating springback.

[0075] Among them, first, according to parameters such as elastic modulus, yield strength, and hardening index in the material data of the aluminum alloy to be processed, the stamping process of the aluminum alloy is simulated using finite element simulation software (such as ABAQUS, LS-DYNA) to generate simulated material data (such as stress distribution, strain distribution, springback amount, etc.). After that, a three-dimensional model of the aluminum alloy sheet and the initial die is created using CAD software (such as SolidWorks, CATIA). It is imported into the finite element analysis software for mesh generation to ensure that the mesh accuracy meets the requirements.

[0076] Then, the initial die surface modeling is carried out. In the finite element software, the geometric models of the initial modeling die corresponding to the aluminum alloy to be processed and the aluminum alloy sheet are set, and material parameters (such as elastic modulus, yield strength, hardening index) and process parameters (such as blank holding force, friction coefficient) are input.

[0077] Finally, the stamping and springback simulation are carried out. The surface data after stamping is recorded, the springback process is simulated after unloading, the magnitude and distribution of the springback amount are extracted, and the first compensation data of the first die surface is obtained.

[0078] Step 1202: Input the material data of the aluminum alloy to be processed and the initial modeling die into the springback compensation model to obtain the second compensation data of the second die surface for compensating springback; among them, the springback compensation model is trained according to the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data. Among them, the initial modeling die is obtained according to the software modeling in step 1201.

[0079] Among them, before using artificial intelligence technology to predict the springback behavior of aluminum alloy materials, training is carried out in the following way:

[0080] First, training data is obtained. Experimental data is collected, and a large amount of experimental data of aluminum alloy materials, including mechanical properties, chemical compositions, microstructures, etc., is collected from the laboratory. Then, the data is preprocessed: the collected data is cleaned and standardized for subsequent use in machine learning algorithms. The experimental data of the experimental aluminum alloy, the corresponding modeling die data, and the simulated material data corresponding to the experimental data are organized into a training data set. The data set should include input features (such as strain, load, material parameters) and output features (such as stress, springback amount, material flow path). By using the training data set to train the deep learning model and optimizing the model parameters, a trained springback compensation model is obtained. After obtaining the trained springback compensation model, an independent test set is used to verify the model and evaluate its prediction accuracy. The verified model is deployed to the production environment for real-time prediction of the springback behavior of aluminum alloy materials.

[0081] After obtaining the trained springback compensation model, input the material data of the aluminum alloy to be processed and the initial modeling die into the springback compensation model to obtain the second compensation data of the second die surface for compensating springback.

[0082] Step 1203: Perform weighted fusion based on the first compensation data and the second compensation data to obtain target compensation data.

[0083] Among them, weighted fusion is performed based on the first compensation data and the second compensation data to obtain target compensation data. The weighted fusion formula is:

[0084] S traget =α·S first +(1 - α)S second

[0085] Among them, α is the weighting coefficient, usually taking values between 0.5 and 1, S first is the first compensation data, and S second is the second compensation data. In the embodiments of the present invention, the relative importance of the two models is controlled by adjusting the weight coefficient, making the final target compensation data more accurate and reliable. In addition, if it is found that the prediction results of the two models differ greatly, other measures can also be taken for correction, such as introducing expert experience or manual intervention and other means. Among them, in the embodiments of the present invention, the prediction results of the two are mutually verified by setting a difference value. Therefore, before performing weighted fusion based on the first compensation data and the second compensation data to obtain target compensation data, the process further includes:

[0086] Calculate the difference between the first compensation data and the second compensation data;

[0087] If the difference is within the preset difference threshold, then perform the step of performing weighted fusion based on the first compensation data and the second compensation data to obtain target compensation data;

[0088] If the difference is greater than the preset difference threshold, analyze the cause of the difference according to the preset analysis steps, and recalculate the first compensation data and / or the second compensation data according to the cause of the difference. Among them, the reason for the deviation of the first compensation data may be problems such as incorrect selection of numerical calculation methods or insufficient mesh division accuracy. Therefore, for the first compensation data, a numerical calculation method can be reselected or the mesh can be divided for simulation calculation to update the first compensation data.

[0089] Step 1204: Perform springback compensation design on the initial modeling die according to the target compensation data and manufacture a stamping die.

[0090] Among them, the target compensation data is imported into the CAD software to adjust the initial die surface. The surface editing functions of the CAD software (such as offset, deformation, fitting, etc.) are used to adjust the geometric shape of the die surface according to the compensation data. Specifically, for the areas with large springback, the curvature of the surface can be appropriately increased; for the areas with small springback, fine-tuning can be carried out. The finite element analysis software (such as ABAQUS, Dynaform) is used to re-simulate the compensated die surface to verify the compensation effect. After completing the compensation design in the CAD software, a machining file (such as NC code) of the die surface is generated to ensure that the accuracy and format of the machining file meet the requirements of the CNC machining center. The machining file is imported into the CNC machining center to machine the die surface to obtain a stamping die.

[0091] In the embodiment of the present invention, after obtaining the stamping die, actual stamping tests and verifications can be carried out on it to verify the compensation effect.

[0092] Step 130: Determine the spraying thickness of the lubricant according to the components of the lubricant, the inner surface structure of the stamping die, and the material data of the aluminum alloy to be processed.

[0093] In the embodiments of the present invention, the components of the lubricant include water-based synthetic esters, nano-graphite, and sulfurized isobutene. Specifically, in the embodiments of the present invention, considering the springback problem of the aluminum alloy to be processed, the stability of the lubricant during the heating process, and the easy cleaning problem, the embodiments of the present invention adjust the frictional force of the aluminum alloy material by changing the composition, composition ratio, and spraying thickness of the lubricant, thereby effectively reducing the springback phenomenon during the stamping process. Among them, in the embodiments of the present invention, considering stability, nano-graphite is added. Since graphite is prone to agglomeration, surface modification (such as treatment with silane coupling agent) or addition of dispersants (such as polyether type) is required to ensure its uniform suspension in the lubricant. Furthermore, extreme pressure additives need to be compatible with the base carrier (oil-based / water-based) to avoid stratification or precipitation. The lamellar structure of graphite forms a physical lubricating film during the friction process, reducing the friction coefficient. Nano-graphite is a solid lubricant, and its layered structure forms a physical lubricating film at the friction interface, reducing the dynamic friction coefficient (which can be reduced by 30%-40%). It still maintains lubricating performance at high temperatures (above 400°C), suitable for high-speed stamping or warm stamping processes. Graphite can promote the uniform flow of materials, reduce areas with insufficient local plastic deformation (such as stamping corners), thereby reducing the springback amplitude. By reducing the friction between the die and the sheet, the residual stress inside the material is released, reducing the elastic recovery energy. Secondly, considering the combination of graphite and sulfurized isobutene, the embodiments of the present invention use water-based synthetic esters as the base carrier. On the one hand, it can provide a uniform lubricating medium to ensure that nano-graphite and sulfurized isobutene form a continuous lubricating film on the surface of the aluminum alloy. On the other hand, it has the characteristics of rapid volatilization / cleaning, avoiding residues from interfering with subsequent processes (such as welding, anodizing). By stabilizing the lubricating film thickness, the difference in local flow resistance caused by uneven lubrication is reduced, thereby reducing the asymmetry of the residual stress distribution (which is one of the main causes of springback). In order to suppress the non-uniform stress concentration caused by local adhesion and make the overall deformation of the material closer to the ideal state. To reduce the uncertainty of springback caused by friction fluctuations during stamping, the embodiments of the present invention add the sulfur-based extreme pressure additive sulfurized isobutene to the lubricant.

[0094] Among them, for the spraying thickness of the lubricant, it can be determined by the following methods including:

[0095] Determine the frictional force between the aluminum alloy to be processed and the stamping die when the lubricant is sprayed with different thicknesses on the aluminum alloy to be processed; determine the influence of different frictional forces on the springback of the aluminum alloy to be processed; according to the springback influence, determine the target frictional force and the corresponding spraying thickness of the lubricant.

[0096] Specifically, the present invention can measure the slip behavior of aluminum alloy materials under different conditions through experimental tests or numerical simulations, etc., so as to determine the spraying thickness of the lubricant. By setting the spraying thickness of the lubricant components in the embodiments of the present invention, the following effects can be achieved: Friction coefficient optimization: By setting this lubricant, the friction coefficient can be reduced to 0.05 - 0.08 (about 0.2 - 0.3 without lubrication), significantly reducing the material flow resistance. Experimental data: In the deep drawing test of 6061 - T6 aluminum alloy, using this formula can reduce the springback amount from ±0.25 mm to ±0.12 mm (a reduction of about 50%). CAE simulation shows that after the friction coefficient is reduced, the standard deviation of the springback distribution is reduced by 60%, and the consistency is significantly improved. Material flow and stress balance: Nano - graphite ensures low global friction, and isobutylene sulfide supplements lubrication in the high - pressure area. The two work together to achieve uniform plastic deformation: avoiding insufficient deformation (the source of springback) in local areas due to excessive friction. Symmetrical residual stress distribution: Reducing warping caused by stress imbalance after demolding. Enhanced process stability: Fast film - forming property of water - based synthetic ester: Reducing batch differences caused by uneven spraying of the lubricant. Persistence of the extreme - pressure film: Maintaining stable lubrication performance during continuous stamping, avoiding lubrication failure caused by mold temperature rise (reducing the risk of springback fluctuation).

[0097] Step 140: Spray on the surface of the aluminum alloy to be processed according to the spraying thickness.

[0098] Among them, different parts may correspond to different spraying thicknesses, and spray on the surface of the aluminum alloy to be processed according to the spraying thickness.

[0099] Step 150: Perform step - by - step stamping on the aluminum alloy to be processed to obtain the stamped aluminum alloy.

[0100] Among them, during stamping, to avoid cracking caused by excessive single - pass deformation, step - by - step stamping is carried out by a servo motor. Each stamping process can include drawing, shaping, and flanging. At the same time, through temperature control, local heating is carried out to improve material recovery, and thermal deformation is reduced through mold constant - temperature control.

[0101] Step 160: Perform springback calibration on the stamped aluminum alloy to obtain the calibrated aluminum alloy.

[0102] In the embodiments of the present invention, after obtaining the stamped aluminum alloy, precision straightening dies or robotic manipulators can be used for assisted shaping, or laser shock peening can be used to locally adjust the residual stress, so as to obtain the calibrated aluminum alloy.

[0103] Step 170: After blanking and trimming the calibrated aluminum alloy, the formed aluminum alloy is obtained.

[0104] Among them, negative clearance blanking (the clearance between the punch and the die is <5% of the material thickness) is adopted to improve the cross-section quality; a fine blanking die with a V-ring pressure plate is used to achieve a bright zone ratio of more than 90%. For complex contours, fiber laser cutting (accuracy ±0.05 mm) is adopted to avoid burrs. After blanking and trimming, the formed aluminum alloy is obtained.

[0105] In some possible embodiments, after blanking and trimming the calibrated aluminum alloy to obtain the formed aluminum alloy, the process further includes:

[0106] Perform a quality inspection on the formed aluminum alloy to determine the dimensional accuracy, surface defects, grain size, and heat treatment effect of the formed aluminum alloy. Specifically, the present invention can adopt a variety of detection means to ensure the quality and reliability of aluminum alloy stamping products, including non-destructive testing, metallographic inspection, mechanical property testing, etc. These detection means can timely detect and correct quality problems in the production process, thereby preventing defective products from flowing into the market and causing losses.

[0107] In the embodiment of the present invention, when predicting the material data of the aluminum alloy to be processed, it is determined through a pre-established database and combined with a non-linear elastoplastic model, thereby effectively improving the accuracy and efficiency of prediction. Furthermore, when performing springback compensation design, computer-aided engineering (CAE) technology and artificial intelligence (AI) technology can be fully utilized to realize the digital design of aluminum alloy materials, reduce the trial-and-error cost, and then quickly and accurately obtain information such as the physical and mechanical properties and process properties of aluminum alloy materials, providing strong support for the development of aluminum alloy stamping products. Furthermore, by designing a suitable lubricant, the springback can be further reduced. In addition, the present invention can also analyze the microstructure of aluminum alloy materials to understand the variation law of their mechanical properties, thereby optimizing the design scheme of aluminum alloy stamping products, reducing the manufacturing cost, and improving the production efficiency.

[0108] See Figure 2 As shown, another embodiment of the present invention also provides an aluminum alloy mold design method, including the following steps:

[0109] Step 210: Use simulation software to simulate and predict the material data of the aluminum alloy to be processed; the material data includes material flow, springback, and stress distribution data;

[0110] In some possible embodiments, predicting the material data of the aluminum alloy to be processed according to the experimental data of the aluminum alloy to be processed includes:

[0111] According to the basic information of the aluminum alloy to be processed selected, query the experimental data of the aluminum alloy to be processed in the pre-established aluminum alloy material database; wherein, the aluminum alloy material database includes the basic information, physical and chemical property information, mechanical property information, processing property information, surface treatment property information and corrosion resistance property information of the aluminum alloy material.

[0112] Input the experimental data of the aluminum alloy to be processed into a non-linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed; wherein, the non-linear elastoplastic model is obtained by training a deep learning algorithm according to training data; the training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of experimental aluminum alloy and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy according to the experimental data of the experimental aluminum alloy through finite element simulation software.

[0113] Step 220: According to the material data of the aluminum alloy to be processed, simulate and predict the die surface for compensating springback to manufacture a stamping die.

[0114] Specifically, the present invention can select different simulation software and algorithms according to different characteristics of aluminum alloy materials to achieve accurate simulation and prediction of its stamping forming process. For example, for high-strength aluminum alloy materials, CAE software based on the finite element method can be selected for simulation, while for low-strength aluminum alloy materials, CAE software based on the boundary element method can be selected for simulation.

[0115] In some possible implementation manners, the simulating and predicting the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die includes:

[0116] According to the material data of the aluminum alloy to be processed, use finite element analysis software to perform three-dimensional modeling on the die and the aluminum alloy to be processed, and perform simulation and prediction to obtain the first compensation data of the first die surface for compensating springback.

[0117] Input the material data of the aluminum alloy to be processed and the initial modeling die into a springback compensation model to obtain the second compensation data of the second die surface for compensating springback; wherein, the springback compensation model is obtained by training according to the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data.

[0118] Perform weighted fusion according to the first compensation data and the second compensation data to obtain target compensation data.

[0119] Perform springback compensation design on the initial modeling die according to the target compensation data and manufacture a stamping die.

[0120] Among them, the specific implementation steps of this aluminum alloy mold design method are generally the same as those of steps 110-step 120 of the aforementioned high-precision aluminum alloy stamping process, and will not be elaborated here.

[0121] In the embodiment of the present invention, when predicting the material data of the aluminum alloy to be processed, it is determined by a pre-established database and in combination with a non-linear elastoplastic model, so as to effectively improve the accuracy and efficiency of prediction. Furthermore, when performing springback compensation design, the computer-aided engineering (CAE) technology and artificial intelligence (AI) technology can be fully utilized to realize the digital design of aluminum alloy materials, reduce the trial-and-error cost, and then quickly and accurately obtain information such as the physical and mechanical properties and process properties of aluminum alloy materials, providing strong support for the development of aluminum alloy stamping products. Furthermore, by designing a suitable lubricant, the springback can be further reduced. In addition, the present invention can also analyze the microstructure of aluminum alloy materials to understand the change law of their mechanical properties, thereby optimizing the design scheme of aluminum alloy stamping products, reducing the manufacturing cost, and improving the production efficiency.

[0122] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings based herein. The structure required to construct such a system will be apparent from the above description. In addition, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of a particular language above is to disclose the best mode of the present invention.

[0123] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0124] Similarly, it should be understood that, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting the intention that the claimed invention requires more features than are expressly recited in each claim.

[0125] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from those of the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and they can also be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be adopted to combine all the features disclosed in this specification (including the accompanying claims, abstract and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise clearly stated, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) can be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0126] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specially stated, should not be construed as limiting the order of execution.

Claims

1. A high-precision aluminum alloy stamping process, characterized in that The process includes: Predicting the material data of the aluminum alloy to be processed according to the experimental data of the aluminum alloy to be processed; the material data includes material flow, springback, and stress distribution data; Simulating and predicting the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die; Determining the spraying thickness of the lubricant according to the components of the lubricant, the inner surface structure of the stamping die, and the material data of the aluminum alloy to be processed; Spraying according to the spraying thickness on the surface of the aluminum alloy to be processed; Performing step-by-step stamping on the aluminum alloy to be processed to obtain the stamped aluminum alloy; Performing springback calibration on the stamped aluminum alloy to obtain the calibrated aluminum alloy; After blanking and trimming the calibrated aluminum alloy, obtaining the formed aluminum alloy.

2. The process according to claim 1, characterized in that, The predicting the material data of the aluminum alloy to be processed according to the experimental data of the aluminum alloy to be processed includes: Querying the experimental data of the aluminum alloy to be processed in a pre-established aluminum alloy material database according to the basic information of the selected aluminum alloy to be processed; wherein, the aluminum alloy material database includes the basic information, physical and chemical property information, mechanical property information, processing property information, surface treatment property information, and corrosion resistance property information of the aluminum alloy material; Inputting the experimental data of the aluminum alloy to be processed into a non-linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed; wherein, the non-linear elastoplastic model is obtained by training a deep learning algorithm according to training data; the training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data of the experimental aluminum alloy and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy through finite element simulation software according to the experimental data of the experimental aluminum alloy.

3. The process according to claim 2, characterized in that, The simulating and predicting the die surface for compensating springback according to the material data of the aluminum alloy to be processed to manufacture a stamping die includes: Performing three-dimensional modeling on the die and the aluminum alloy to be processed using finite element analysis software according to the material data of the aluminum alloy to be processed, and performing simulation and prediction to obtain the first compensation data of the first die surface for compensating springback; Inputting the material data of the aluminum alloy to be processed and the initial modeling die into a springback compensation model to obtain the second compensation data of the second die surface for compensating springback; wherein, the springback compensation model is obtained by training according to the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data; Performing weighted fusion according to the first compensation data and the second compensation data to obtain target compensation data; Performing springback compensation design on the initial modeling die according to the target compensation data and manufacturing a stamping die.

4. The process according to claim 3, characterized in that, Before performing weighted fusion according to the first compensation data and the second compensation data to obtain target compensation data, the process further includes: Calculating the difference between the first compensation data and the second compensation data; If the difference is within a preset difference threshold, perform the step of weighted fusion based on the first compensation data and the second compensation data to obtain target compensation data; If the difference is greater than the preset difference threshold, analyze the cause of the difference according to a preset analysis step, and recalculate the first compensation data and / or the second compensation data according to the cause of the difference.

5. The process according to any one of claims 1 to 4, characterized in that, The components of the lubricant include water-based synthetic ester, nano-graphite, and sulfurized isobutene.

6. The process according to claim 5, characterized in that, Determining the spraying thickness of the lubricant according to the components of the lubricant, the inner surface structure of the stamping die, and the material data of the aluminum alloy to be processed includes: Determining the friction force between the aluminum alloy to be processed and the stamping die when the lubricant is sprayed with different thicknesses on the aluminum alloy to be processed; Determining the springback influence of different friction forces on the aluminum alloy to be processed; Determining the target friction force and the corresponding spraying thickness of the lubricant according to the springback influence.

7. The process according to any one of claims 1 to 4, characterized in that, After blanking and trimming the calibrated aluminum alloy to obtain a formed aluminum alloy, the process further includes: Performing a quality inspection on the formed aluminum alloy to determine the dimensional accuracy, surface defects, grain size, and heat treatment effect of the formed aluminum alloy.

8. A method for designing an aluminum alloy mold, characterized in that, The method includes: Using simulation software to simulate and predict the material data of the aluminum alloy to be processed; the material data includes material flow, springback, and stress distribution data; According to the material data of the aluminum alloy to be processed, simulating and predicting the die surface for compensating springback to manufacture a stamping die.

9. The method according to claim 8, wherein Using simulation software to simulate and predict the material data of the aluminum alloy to be processed includes: Performing a tensile test on the aluminum alloy to be processed to obtain the mechanical property parameters of the aluminum alloy to be processed; the mechanical property parameters include elastic modulus, yield strength, and hardening index; Using a strain measurement system to obtain the deformation data of the aluminum alloy to be processed during the actual stamping process; Taking the mechanical property parameters and the deformation data as the experimental data of the aluminum alloy to be processed; Inputting the experimental data of the aluminum alloy to be processed into a non-linear elastoplastic model to obtain the predicted material data of the aluminum alloy to be processed; wherein, the non-linear elastoplastic model is trained by a deep learning algorithm according to training data; the training data includes historical aluminum alloy experimental data and corresponding historical material data, as well as experimental data corresponding to experimental aluminum alloys and corresponding simulated material data; the simulated material data is obtained by simulating the stamping process of the experimental aluminum alloy through finite element simulation software according to the experimental data corresponding to the experimental aluminum alloy.

10. The method according to claim 9, wherein According to the material data of the aluminum alloy to be processed, simulating and predicting the die surface for compensating springback to manufacture a stamping die includes: According to the material data of the aluminum alloy to be processed, using finite element analysis software to perform three-dimensional modeling on the die and the aluminum alloy to be processed, and performing simulation and prediction to obtain the first compensation data of the first die surface for compensating springback; Input the material data of the aluminum alloy to be processed and the initial modeling die into the springback compensation model to obtain the second compensation data of the second die surface for compensating springback; wherein, the springback compensation model is trained based on the experimental data of the experimental aluminum alloy, the modeling die corresponding to the experimental aluminum alloy, and the simulated material data corresponding to the experimental data. Perform weighted fusion according to the first compensation data and the second compensation data to obtain the target compensation data. Perform springback compensation design on the initial modeling die according to the target compensation data and manufacture a stamping die.

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