Metal stamping die rapid forming method for self-adaptive compensation difference value

Abstract

The invention provides a metal stamping die rapid forming method with a self-adaptive compensation difference value. The metal stamping die rapid forming method with the self-adaptive compensation difference value comprises the steps that a, a three-dimensional geometric model of a die to be compensated is obtained, and the actual geometric shape of the die is obtained through scanning equipment, b, meshing processing is conducted on the obtained three-dimensional geometric model, a grid data model is generated, and c, the compensation difference value is calculated based on the grid data model, and d, a compensation algorithm is optimized through multiple iterations, a self-adaptive compensation strategy is formed, and the calculation burden is reduced. According to the metal stamping die rapid forming method for the self-adaptive compensation difference value, the self-adaptive compensation difference value algorithm is adopted, the three-dimensional geometric model is processed through gridding, the compensation difference value is calculated based on the algorithm, and the complex shape of the die can be efficiently processed.

Description

Technical Field

[0001] The invention relates to the technical field of metal stamping die forming, in particular to a metal stamping die rapid forming method for adaptively compensating for differences. Background Art

[0002] The metal stamping die rapid prototyping method with adaptive compensation difference combines modern rapid prototyping technology with adaptive compensation control, and is mainly used to improve the precision and production efficiency of metal stamping dies. First, the basic structure of the stamping die is designed and analyzed through computer-aided design and computer-aided engineering technology. Then, real-time monitoring technology is used to dynamically detect the deformation and deviation that may occur in the die during the stamping process, and fine-tune it through an adaptive compensation mechanism. The compensation system automatically calculates the compensation parameters and adjusts the die shape based on the deformation difference of the die to ensure that the accuracy of the final product meets the design requirements. In actual operation, rapid prototyping technology is used to manufacture the die prototype, supplemented by a compensation control mechanism to quickly achieve the molding of high-precision dies.

[0003] Although this method has certain advantages in improving production efficiency and mold accuracy, it also has several drawbacks. The compensation algorithm needs to process a large amount of complex data and calculations. In the case of complex molds and multiple stampings, the algorithm has a heavy computational burden, resulting in low efficiency. The stability of the system is affected by factors such as material properties, heat treatment process, and equipment accuracy. In practical applications, inconsistent or unstable compensation effects are prone to occur. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a rapid prototyping method for metal stamping dies with adaptive compensation differences, which solves the problem that the compensation algorithm needs to process a large amount of complex data and calculations, and the algorithm has a heavy computational burden leading to low efficiency when faced with complex dies and multiple stampings; the stability of the system is affected by factors such as material properties, heat treatment process and equipment accuracy, and inconsistent or unstable compensation effects are prone to occur in practical applications.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A rapid prototyping method for a metal stamping die with adaptive compensation difference, comprising: a. obtaining a three-dimensional geometric model of a die to be compensated, and obtaining the actual geometric shape of the die through a scanning device;

[0007] b. Meshing the obtained three-dimensional geometric model to generate a mesh data model;

[0008] c. Calculate the compensation difference based on the grid data model and determine the difference compensation amount through an algorithm;

[0009] d. Optimize the compensation algorithm through multiple iterations to form an adaptive compensation strategy and reduce the computational burden;

[0010] e. Adjust the compensation difference according to the characteristics of different materials, heat treatment processes and equipment accuracy;

[0011] f. Use finite element analysis method to simulate compensation effect, and conduct verification and correction;

[0012] g. Generate optimized stamping die design data as input for rapid prototyping;

[0013] h. Use 3D printing technology or CNC machining technology to quickly manufacture compensated molds;

[0014] i. Heat treatment and surface treatment of the manufactured mold to improve its stability in use;

[0015] j. During the use of the mold, the compensation effect is monitored in real time and the compensation strategy is adjusted through the feedback mechanism;

[0016] k. Predict and pre-compensate errors in future stamping processes based on compensation results to improve the long-term stability of the mold;

[0017] l. During multiple stamping processes, the stamping efficiency and product quality are optimized by dynamically adjusting the compensation difference.

[0018] Preferably, the compensation difference calculation formula is:

[0019]

[0020] Among them, ΔC(x,y) is the compensation difference, G i (x, y) is the actual geometry of the mold, T i (x, y) is the target geometry, W i is the weight factor and N is the number of nodes.

[0021] Preferably, the adaptive optimization algorithm used in the multiple iterative optimization compensation algorithm is a method based on particle swarm optimization or genetic algorithm, and the optimization process is implemented by the following mathematical model:

[0022]

[0023] Among them, C i (x) is the mold compensation difference, C target is the expected compensation effect, f(x) is the objective function, and M is the number of iterations.

[0024] Preferably, the compensation strategy evaluates different compensation effects and makes dynamic adjustments based on the mold material characteristics to ensure the long-term stability of the compensation effect. The adjustment formula of the compensation strategy is:

[0025] ΔC adjusted =ΔC(x,y)·(1+α·(M factor -E factor ))

[0026] Where, ΔC adjusted is the adjusted compensation difference, α is the adjustment coefficient, M factor is the material characteristic factor, E factor is the device precision factor.

[0027] Preferably, the finite element analysis method is used to simulate the influence of the compensation difference on the mold shape and performance, analyze and optimize the compensation effect, and the finite element model is:

[0028] σ=E·ε

[0029] Among them, σ is stress, E is the elastic modulus of the mold material, and ε is strain.

[0030] Preferably, the 3D printing technology adopts laser melting deposition or selective laser sintering process, and the adaptive adjustment model of compensation difference is combined to optimize the mold precision during the printing process. The CNC machining technology adopts high-precision equipment such as CNC milling machines and CNC lathes. The compensation error in the CNC machining process is monitored in real time by sensors and fed back to the system for dynamic compensation.

[0031] Preferably, the heat treatment process includes annealing, hardening or surface spraying and other treatment processes. The temperature and time of the heat treatment process are dynamically adjusted according to the compensation effect. The feedback mechanism collects real-time data during the stamping process through sensors and feeds it back to the compensation algorithm for dynamic adjustment. The feedback data includes mold temperature, stamping pressure and deformation.

[0032] Preferably, the process of dynamically adjusting the compensation difference is based on a machine learning algorithm, which can self-learn and improve compensation accuracy. The machine learning model is:

[0033]

[0034] in, To predict the compensation effect, x is the input feature and θ is the learning parameter. The prediction and pre-compensation process is optimized through historical data analysis and regression model. The regression model is:

[0035] ΔC predicted =β0+β1·x1+β2·x2+…+β n ·x n

[0036] Where, ΔC predicted is the predicted compensation difference, β0,β1,…,β n are regression coefficients, x1, x2,…, x n For input characteristics, the compensation strategy is adaptively adjusted in combination with process parameters according to different process requirements to improve system stability and accuracy.

[0037] The present invention provides a metal stamping die rapid prototyping method with adaptive compensation difference, which has the following beneficial effects:

[0038] The adaptive compensation difference metal stamping die rapid prototyping method adopts an adaptive compensation difference algorithm, processes the three-dimensional geometric model by meshing, and calculates the compensation difference based on the algorithm, which can efficiently process the complex shape of the die. Through multiple iterations of optimizing the compensation algorithm, especially the adaptive compensation strategy based on particle swarm optimization or genetic algorithm, the calculation burden is greatly reduced and the compensation accuracy is improved. In addition, the calculation formula and optimization process of the compensation difference are precisely controlled by mathematical models to ensure that the compensation effect is highly consistent with the target geometric shape, thereby achieving accuracy improvement in the rapid prototyping process.

[0039] The present invention dynamically adjusts the compensation strategy by combining factors such as the characteristics of different materials, heat treatment processes and equipment accuracy, thereby effectively improving the stability of the system. The finite element analysis method is used to simulate and correct the compensation effect to ensure that the compensation difference optimizes the shape and performance of the mold. At the same time, the feedback mechanism uses sensors to collect data during the stamping process in real time, and feeds it back to the compensation algorithm for dynamic adjustment, further optimizing the long-term stability of the mold. The machine learning algorithm can continuously optimize the compensation strategy based on historical data and real-time data, so that the system can continue to learn and adaptively adjust, thereby effectively improving stamping efficiency and product quality during multiple stamping processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] like Figure 1As shown, an embodiment of the present invention provides a rapid prototyping method for metal stamping dies with adaptive compensation differences, including: a. obtaining a three-dimensional geometric model of the die to be compensated, and obtaining the actual geometric shape of the die through a scanning device, wherein the scanning device is a three-dimensional laser scanner, a CT scanner or other high-precision measuring equipment to ensure high-precision restoration of the die shape, and the obtained three-dimensional data should be subjected to noise removal and data smoothing processing to improve the accuracy of subsequent meshing processing.

[0043] b. Mesh the obtained three-dimensional geometric model to generate a mesh data model. The mesh processing can use tetrahedral or hexahedral meshes. The generated mesh data model must meet certain density and accuracy requirements to ensure the accuracy and optimization effect of subsequent calculations. The density of the mesh can be dynamically adjusted according to the complexity of the mold and the required compensation accuracy.

[0044] c. Calculate the compensation difference based on the grid data model, and determine the difference compensation amount through the algorithm. The compensation difference calculation formula is:

[0045]

[0046] Among them, ΔC(x,y) is the compensation difference, G i (x, y) is the actual geometry of the mold, T i (x, y) is the target geometry, W i is the weight factor and N is the number of nodes.

[0047] d. Through multiple iterations of optimization compensation algorithm, an adaptive compensation strategy is formed to reduce the computational burden. The adaptive optimization algorithm used in the multiple iterations of optimization compensation algorithm is a method based on particle swarm optimization or genetic algorithm. The optimization process is realized through the following mathematical model:

[0048]

[0049] Among them, C i (x) is the mold compensation difference, C target is the desired compensation effect, f(x) is the objective function, M is the number of iterations, and the compensation algorithm is optimized through multiple iterations to form an adaptive compensation strategy to reduce the computational burden. The adaptive optimization algorithm used in the multiple iteration optimization compensation algorithm is a method based on particle swarm optimization or genetic algorithm. The optimization process is implemented through the following mathematical model:

[0050]

[0051] Among them, F is the objective function, ΔX expected is the desired compensation effect, ΔX actual is the actual calculated compensation difference, and N is the number of iterations.

[0052] e. Adjust the compensation difference according to the characteristics of different materials, heat treatment processes and equipment accuracy.

[0053] f. The finite element analysis method is used to simulate the compensation effect, verify and correct it. The finite element analysis method is used to simulate the influence of the compensation difference on the mold shape and performance, analyze and optimize the compensation effect. The finite element model is:

[0054] σ=E·ε

[0055] Among them, σ is stress, E is the elastic modulus of the mold material, and ε is strain.

[0056] g. Generate optimized stamping die design data as input for rapid prototyping.

[0057] h. Use 3D printing technology or CNC machining technology to quickly manufacture compensated molds. 3D printing technology uses laser melting deposition or selective laser sintering technology. During the printing process, the adaptive adjustment model of the compensation difference is combined to optimize the mold accuracy. CNC machining technology uses high-precision equipment such as CNC milling machines and CNC lathes. The compensation error in the CNC machining process is monitored in real time by sensors and fed back to the system for dynamic compensation. During the 3D printing process, the adaptive adjustment of the compensation difference can correct the printing error in real time and improve the molding accuracy. During the CNC machining process, the sensor monitoring data not only provides real-time feedback, but also automatically adjusts the cutting path to reduce the impact of equipment accuracy errors.

[0058] i. Perform heat treatment and surface treatment on the manufactured mold to improve its stability in use. The heat treatment process includes annealing, hardening or surface spraying. The temperature and time during the heat treatment process are dynamically adjusted according to the compensation effect. The feedback mechanism collects real-time data during the stamping process through sensors and feeds it back to the compensation algorithm for dynamic adjustment. The feedback data includes mold temperature, stamping pressure and deformation. The dynamic adjustment during the heat treatment process can be achieved through a closed-loop control system. The mold hardness, toughness and other physical properties are optimized in real time according to the feedback data to improve the long-term stability and durability of the mold.

[0059] j. During the use of the mold, the compensation effect is monitored in real time, and the compensation strategy is adjusted through the feedback mechanism. The compensation strategy evaluates different compensation effects and makes dynamic adjustments based on the characteristics of the mold material to ensure the long-term stability of the compensation effect. The adjustment formula of the compensation strategy is:

[0060] ΔC adjusted =ΔC(x,y)·(1+α·(M factor -E factor ))

[0061] Where, ΔC adjustedis the adjusted compensation difference, α is the adjustment coefficient, M factor is the material characteristic factor, E factor is the device precision factor.

[0062] k. Based on the compensation results, the errors in the future stamping process are predicted and pre-compensated to improve the long-term stability of the mold. The process of dynamically adjusting the compensation difference is based on the machine learning algorithm, which can self-learn and improve the compensation accuracy. The machine learning model is:

[0063]

[0064] in, To predict the compensation effect, x is the input feature, θ is the learning parameter, and the prediction and pre-compensation process is optimized through historical data analysis and regression model. The regression model is:

[0065] ΔC predicted =β0+β1·x1+β2·x2+…+β n ·x n

[0066] Where, ΔC predicted is the predicted compensation difference, β0,β1,…,β n are regression coefficients, x1, x2,…, x n For input features, the compensation strategy is adaptively adjusted in combination with process parameters according to different process requirements to improve system stability and accuracy.

[0067] l. During multiple stamping processes, the stamping efficiency and product quality are optimized by dynamically adjusting the compensation difference.

[0068] Table 1: Data showing the mold compensation difference calculation and optimization process. This table may be used to track the mold compensation difference, optimization strategy, material characteristics and feedback data during use.

[0069]

[0070] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A metal stamping die rapid prototyping method with adaptive compensation difference, characterized in that: include: a. Obtain the three-dimensional geometric model of the mold to be compensated, and obtain the actual geometric shape of the mold through a scanning device; b. Meshing the obtained three-dimensional geometric model to generate a mesh data model; c. Calculate the compensation difference based on the grid data model and determine the difference compensation amount through an algorithm; d. Optimize the compensation algorithm through multiple iterations to form an adaptive compensation strategy and reduce the computational burden; e. Adjust the compensation difference according to the characteristics of different materials, heat treatment processes and equipment accuracy; f. Use finite element analysis method to simulate compensation effect, and conduct verification and correction; g. Generate optimized stamping die design data as input for rapid prototyping; h. Use 3D printing technology or CNC machining technology to quickly manufacture compensated molds; i. Heat treatment and surface treatment of the manufactured mold to improve its stability in use; j. During the use of the mold, the compensation effect is monitored in real time and the compensation strategy is adjusted through the feedback mechanism; k. Predict and pre-compensate errors in future stamping processes based on compensation results to improve the long-term stability of the mold; l. During multiple stamping processes, the stamping efficiency and product quality are optimized by dynamically adjusting the compensation difference.

2. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The compensation difference calculation formula is: Among them, ΔC(x,y) is the compensation difference, G i (x, y) is the actual geometry of the mold, T i (x, y) is the target geometry, W i is the weight factor and N is the number of nodes.

3. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The adaptive optimization algorithm used in the multiple iterative optimization compensation algorithm is a method based on particle swarm optimization or genetic algorithm, and the optimization process is implemented through the following mathematical model: Among them, C i (x) is the mold compensation difference, C target is the expected compensation effect, f(x) is the objective function, and M is the number of iterations.

4. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The compensation strategy evaluates different compensation effects and makes dynamic adjustments based on the mold material characteristics to ensure the long-term stability of the compensation effect. The adjustment formula of the compensation strategy is: ΔC adjusted =ΔC(x,y)·(1+α·(M factor -E factor )) Where, ΔC adjusted is the adjusted compensation difference, α is the adjustment coefficient, M factor is the material characteristic factor, E factor is the device precision factor.

5. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The finite element analysis method is used to simulate the influence of the compensation difference on the mold shape and performance, analyze and optimize the compensation effect. The finite element model is: σ=E·ε Among them, σ is stress, E is the elastic modulus of the mold material, and ε is strain.

6. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The 3D printing technology adopts laser melting deposition or selective laser sintering process, and the adaptive adjustment model of compensation difference is combined with the printing process to optimize the mold accuracy. The CNC machining technology adopts high-precision equipment such as CNC milling machines and CNC lathes. The compensation error in the CNC machining process is monitored in real time by sensors and fed back to the system for dynamic compensation.

7. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The heat treatment process includes annealing, hardening or surface spraying and other treatment processes. The temperature and time of the heat treatment process are dynamically adjusted according to the compensation effect. The feedback mechanism collects real-time data during the stamping process through sensors and feeds it back to the compensation algorithm for dynamic adjustment. The feedback data includes mold temperature, stamping pressure and deformation.

8. The metal stamping die rapid prototyping method with adaptive compensation difference according to claim 1, characterized in that: The process of dynamically adjusting the compensation difference is based on a machine learning algorithm, which can self-learn and improve compensation accuracy. The machine learning model is: in, To predict the compensation effect, x is the input feature and θ is the learning parameter. The prediction and pre-compensation process is optimized through historical data analysis and regression model. The regression model is: ΔC predicted =β0+β1·x1+β2·x2+…+β n ·x n Where, ΔC predicted is the predicted compensation difference, β0,β1,…,β n are regression coefficients, x1, x2,…, x n For input characteristics, the compensation strategy is adaptively adjusted in combination with process parameters according to different process requirements to improve system stability and accuracy.

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