TWM604243U - System for optimizing metal stamping process parameters - Google Patents

Abstract

Embodiments of the present disclosure provide a system for optimizing metal stamping process parameters, thereby performing mold parameters optimization and stamping forming curve optimization to achieve various design goals. Embodiments of the present disclosure automatically model the mold parameters and stamping forming curves, and import them into an optimization process. Embodiments of the present disclosure use a response surface method to fit a linear polynomial function, and then perform optimization on a response surface to obtain a best mold parameters combination and a best stamping forming curve.

Description

System for optimizing metal stamping process parameters

The present invention relates to a system for optimizing metal stamping process parameters, and in particular to a system for optimizing mold parameters and optimizing stamping forming curves.

In the stamping and drawing forming process, the geometry of the die and the stamping forming curve will affect the quality of the formed workpiece. The conventional technology department relies on the experience and intuition of the technicians, or uses the trial and error method (Trial and Error) to design the mold parameters and the stamping forming curve. The conventional technology is often time-consuming, and increases the cost of many mold designs.

With the continuous improvement of computer efficiency and the development of computer-aided design and manufacturing, the use of computers to solve the problems encountered in engineering has become the current industry development trend. Another conventional technology is to use the Finite Element Method (FEM) in computer-aided engineering to analyze the behavior of the formed workpiece during the stamping process, so as to predict the thickness change, dimensional change and change of the formed workpiece during the forming process. The amount of springback, etc., is used as a reference for the initial design. However, this kind of conventional technology still needs to use human judgment to adjust the model, so it will still consume a lot of labor costs.

Therefore, it is necessary to propose a method and system for optimizing metal stamping process parameters, so as to shorten the number of mold trials, reduce the cost and development time of trial molds, and overcome the shortcomings of the conventional technology.

The purpose of the present invention is to provide a method and system for optimizing metal stamping process parameters, so as to obtain the best mold parameter combination and stamping forming curve, thereby reducing blind spots in human experience judgment and reducing the number of mold trials and costs.

According to one aspect of the present invention, a method for optimizing metal stamping process parameters is provided. In this method, a mold model and a workpiece model are first established. The workpiece model is placed in the mold model. The workpiece model has at least one quality item, and each quality item has a design goal. Then, according to a stamping curve and using the mold model and the workpiece model, the simulation operation is performed. Use this simulation operation in conjunction with the full factorial experimental design method to determine multiple mold parameters that affect the quality of the mold model, and the numerical range of mold parameters. Then, the aforementioned simulation operation is repeated in the numerical range of the mold parameters to obtain a complex set of sample data, wherein each set of sample data includes the numerical value of the mold parameter and the numerical value of the corresponding quality item. Then, perform a fitting response surface operation on these sets of sample data to obtain a response surface. Then, for the design goal, an optimization algorithm is used to optimize the response surface to obtain an optimal set of mold parameters.

According to another aspect of the present invention, a system for optimizing metal stamping process parameters is provided. This system runs on a host computer and includes: model building module, pre-processing module, simulation module, parameter determination module, sample generation module, response surface fitting module and optimization module. The model building module is used to build a mold model and a workpiece model, wherein the workpiece model is arranged in the mold model, the workpiece model has at least one quality item, and each quality item has a design goal. The pre-processing module is used to define a stamping curve. The simulation module is used to perform a simulation operation using the mold model and the workpiece model according to the stamping curve. The parameter determination module is used to use this simulation operation in conjunction with a full factorial experimental design method to determine a plurality of mold parameters of the mold model that affects the quality item, and the numerical range of the mold parameters. The sample generation module is used to repeat the aforementioned simulation operation in the numerical range of the mold parameters to obtain a complex array of sample data, each set of sample data includes the value of the mold parameter and the value of the corresponding quality item. The response surface fitting module is used to perform a fitting response surface operation on these sets of sample data to obtain a response surface. The optimization module is used to perform an optimization operation on the response surface according to the aforementioned design objective to obtain a set of optimal values of mold parameters.

In some embodiments, the aforementioned mold parameters include the upper mold angle, the lower mold angle, and the upper mold extension depth, the aforementioned quality items include the thickness of the formed workpiece, and the aforementioned design goal includes the uniformity of the thickness of the formed workpiece or the maximum thickness of the minimum thickness.化.

In some embodiments, the aforementioned step of repeating the simulation operation in the numerical range of the mold parameters is performed in an automated manner.

In some embodiments, the aforementioned fitting response surface operation uses the Sequential Response Surface Method, and the aforementioned optimization algorithm includes genetic algorithm, annealing method, hybrid algorithm or leapfrog method.

According to another aspect of the present invention, a method for optimizing metal stamping process parameters is provided. In this method, a mold model and a workpiece model are first established. The workpiece model is placed in the mold model. The workpiece model has at least one quality item, and each quality item has a design goal. Then, define a plurality of stamping curves. Then, according to each stamping curve and using the mold model and the workpiece model, a simulation operation is performed to obtain a complex set of sample data, wherein each set of sample data includes the aforementioned stamping curve and the corresponding quality item value. Then, perform a fitting response surface operation on these sets of sample data to obtain a response surface. Next, for the aforementioned design goal, an optimization algorithm is used to perform an optimization operation on the response surface to obtain an optimal stamping curve.

According to another aspect of the present invention, a system for optimizing metal stamping process parameters is provided. This system runs on a computer host and includes: model building module, pre-processing module, simulation module, sample generation module, response surface fitting module and optimization module. The model building module is used to build a mold model and a workpiece model, wherein the workpiece model is arranged in the mold model, the workpiece model has at least one quality item, and each quality item has a design goal. The pre-processing module is used to define a plurality of punching curves. The simulation module is used to perform a simulation operation based on one of the stamping curves and using the mold model and the workpiece model. The sample generation module is used to repeat the aforementioned simulation operation using the mold model and the workpiece model according to each stamping curve to obtain a complex set of sample data, where each set of sample data includes the stamping curve and its corresponding quality items The numerical value. The response surface fitting module is used to perform a fitting response surface operation on these sets of sample data to obtain a response surface. The optimization module is used to use an optimization algorithm to perform an optimization operation on the response surface according to the aforementioned design goal to obtain an optimal stamping curve.

In some embodiments, the aforementioned stamping curve includes: a blanking curve, a holding pressure curve, a multiple back pressure curve and/or an oscillation curve, the at least one quality item includes a springback of a formed workpiece or a thickness reduction rate, the The design target includes a minimum value of the springback of the formed workpiece or a minimum range of the thickness reduction rate.

In some embodiments, the aforementioned steps of defining a stamping curve and the steps of using a mold model and a workpiece model to simulate operations according to each stamping curve are performed in an automated manner.

In some embodiments, the aforementioned fitting response surface operation uses a Sequential Response Surface Method, and the aforementioned optimization algorithm includes genetic algorithm, annealing method, hybrid algorithm, or leapfrog method.

Therefore, by applying the embodiment of the present invention, the best mold parameter combination and stamping forming curve can be obtained through optimization and optimization, thereby reducing the blind spots of human experience judgment, and reducing the number of mold trials and the cost.

Regarding the “first”, “second”, etc. used in this text, it does not specifically refer to the meaning of order or sequence, but only to distinguish elements or operations described in similar technical terms.

The embodiments of the present invention provide a method and system for optimizing metal stamping process parameters, so as to optimize mold parameters and stamping forming curves to achieve various design goals. The embodiment of the present invention models mold parameters and stamping forming curves in an automated manner, and imports them into the optimization process. The optimization method of the embodiment of the present invention adopts the surface response method to fit the linear polynomial function, and then optimizes the response surface to obtain the best mold parameter combination and stamping forming curve.

The methods and systems for optimizing mold parameters in some embodiments of the present invention are described below.

Please refer to Figure 1, Figure 2A and Figure 2B. Figure 1 is a schematic flow diagram illustrating a method for optimizing metal stamping process parameters according to some embodiments of the present invention, where the metal stamping process parameters are mold parameters; Figures 2A and 2B are drawings A schematic diagram illustrating the mold model 20 and the workpiece model 10 according to some embodiments of the present invention is shown. In this example, the mold model 20 and the workpiece model 10 are used to form a bearing holder. It is worth mentioning that the mold model 20 and the workpiece model 10 are only used as examples. The embodiment of the present invention is applicable to molds and workpieces in the metal stamping process to form any product, so the embodiment of the present invention is not limited thereto.

First, step 100 is performed to establish a mold model 20 and a workpiece model 10, wherein the workpiece model 10 is placed in the mold model 20 to form a combined model 30. The mold model 20 includes an upper mold (punch) 22 and a lower mold 24, and the workpiece model 10 includes a guide punch 12 and a sheet workpiece 14. The upper mold (punch) 22 can move along the Y-axis direction, but not Rotation; The sheet material 14 can move and rotate freely; the lower die 24 and the guide punch 12 are fixed and cannot move and rotate. The embodiment of the present invention can use, for example, LS-DYNA finite element software or other similar software to create the workpiece model 10 and the mold model 20. The workpiece model 10 has at least one quality item, such as the thickness of the formed workpiece. Each quality item has a design goal, such as the uniformity of the thickness of the formed workpiece or the maximization of the minimum thickness.

Then, step 110 is performed to perform a simulation operation based on a stamping curve and using the mold model and the workpiece model. Please refer to FIG. 3A. FIG. 3A is a schematic diagram illustrating a stamping forming curve according to some embodiments of the present invention. In the embodiment of the present invention, the BOUNDARY_PRESCRIBED_MOTION_RIGID card in LS-DYNA software can be used to control the movement of the punch, and the movement mode of speed or displacement can be arbitrarily selected to control various active/passive parts of the mold. The action curve (ie, the punching curve) of the upper die (punch) 22 can be controlled by, for example, a DEFINE_CURVE card, as shown in FIG. 3A. In the process of simulation, the set stamping curve is to avoid the travel time from the top dead center of the mold height to the empty travel of the contact sheet, which can be started directly from when the upper mold (punch) 22 touches the sheet workpiece 14. Simulate operation to improve analysis efficiency.

Then, step 120 is performed to use the simulation operation and the full factorial experimental design method to determine a plurality of mold parameters of the mold model that affects the quality item, and the numerical range of the mold parameters. The full factorial experimental design method used in the embodiment of the present invention is known to those who are accustomed to this technique, so it will not be repeated here. Please refer to FIG. 2C. FIG. 2C is a schematic diagram illustrating mold parameters according to some embodiments of the present invention. The plural mold parameters of the mold model that affect the quality items determined by the embodiment of the present invention include: upper mold angle α1, lower mold angle α2, and upper mold extension depth D1. Of course, according to actual needs, the embodiment of the present invention may also include other mold parameters that affect quality items.

請參照圖3B和圖3C，圖3B和圖3C為繪示根據本新型一些實施例用以例示說明上模角度和下模角度的示意圖，其中上模(沖頭)22具有長度L1、

和寬度W1，下模24具有長度L2、

和寬度W2，A1和A2為模具角度輸入之模型縮放量。本新型實施例可在自動建模的過程中，將面與面之間進行比例縮放功能(即調整模型縮放量A1和A2，以達到不同的模具模型角度。上模引伸深度D1的變化不影響模具建模程序故可直接輸入。此技術最大優點是不用針對所需要的模具參數模型去一一手動輸入建模，故可大幅地節省時間。 Then, step 130 is performed to repeat the aforementioned simulation operation in the numerical range of the mold parameters to obtain a complex set of sample data, wherein each set of sample data includes the numerical value of the mold parameter and the numerical value of the corresponding quality item. The numerical range of the upper die angle α1 defined by the embodiment of the present invention is 5 degrees to 10 degrees; the numerical range of the lower die angle α2 is 0 degrees to 5 degrees; the numerical range of the upper die extension depth D1 is 1.6mm to 2.5mm . The embodiment of the present invention can automatically compile the established basic model and the above-defined mold parameters into the LSPREPOST command in an automated manner. The formulas of the upper mold angle α1 and the lower mold angle α2 are as follows.

Please refer to FIGS. 3B and 3C. FIGS. 3B and 3C are schematic diagrams illustrating the upper die angle and the lower die angle according to some embodiments of the present invention, wherein the upper die (punch) 22 has a length L1,

And width W1, lower mold 24 has length L2,

And width W2, A1 and A2 are the model zooming amount of mold angle input. The embodiment of the present invention can perform the scaling function between the surface and the surface during the automatic modeling process (that is, adjust the model scaling amount A1 and A2 to achieve different mold model angles. The change of the upper mold extension depth D1 does not affect Therefore, the mold modeling program can be directly input. The biggest advantage of this technology is that it does not need to manually input the modeling for the required mold parameter model, so it can save time greatly.

Then, step 140 is performed to perform a fitting response surface operation on the aforementioned multiple sets of sample data to obtain a response surface. The embodiment of the present invention uses, for example, the Sequential Response Surface Method (Sequential Response Surface Method) to construct the meta-model, and the region translation and zooming functions of this method can quickly find the optimal region, and iteratively converge to the expected result. Subsequently, an optimized algorithm is imported for the response surface generated by each iteration. Step 140 is mainly to define a suitable parameter combination in the design space according to the response, and sprinkle points in the full factor state, and generate a response surface meta-model from the simulation analysis of the points; the number of sprinkled points will determine the calculation times, if the model fits well If it is less than 75%, the reliability is low, and the experimental factors must be readjusted. The continuous surface response method used in the embodiment of the present invention is known to those skilled in the art, so it will not be repeated here.

Next, proceed to step 150 to use an optimization algorithm to optimize the response surface obtained in step 140 according to the design goals (such as the uniformity and minimum thickness of the formed workpiece) to obtain an optimal set of mold parameters Numerical value. In the embodiment of the present invention, the continuous surface response method optimization strategy is used to optimize the region. Each time the region is generated, an approximate response surface element model is generated, and then algorithm optimization, iteration, and domain reduction are used. The optimization algorithm used in the embodiment of the present invention may include genetic algorithm, annealing method, hybrid algorithm or leapfrog method. Genetic algorithm, annealing method, hybrid algorithm and leapfrog method are known to those who are accustomed to this technique, so I won't repeat them here.

In summary, the method adopted by the embodiment of the present invention is mainly to import the mold model to the simulation and optimization step in an automated manner. First, select a combination of specific design variables in the design space, and the point distribution is performed using experimental design. Then, the response surface meta-model is constructed by simulating the above-mentioned experimental design combination points in the space. Then, the SRSM meta-model strategy is used to reduce the domain of the experimental design space. In each iteration of the reduced domain, it will be Generate a new response surface. Then, for this response surface, for example, a hybrid algorithm is executed to find its optimal value. For each iteration, the optimal value produced by the previous iteration is used as the benchmark. Repeat this step until the result converges and the program stops. In this way, the accuracy of the meta-model can be made more accurate and the known parameter combinations have more reference value.

Please refer to FIG. 4. FIG. 4 shows the optimized result of the thickness of the formed workpiece according to some embodiments of the present invention. Curve 40 represents the thickness of the formed workpiece obtained after the optimization of the upper die angle α1, the lower die angle α2, and the upper die extension depth D1 in the embodiment of the present invention, where α1 is 4.99 degrees, D1 is 1.6 mm, and α2 is 5.05 degrees. The curve 42 represents the thickness of the formed workpiece obtained by the initial design upper die angle α1, lower die angle α2, and upper die extension depth D1, where α1 is 5 degrees, D1 is 2.27 mm, and α2 is 9 degrees. It can be seen from Fig. 4 that R1 and R2 at the cross section of the R angle of the sheet workpiece are the places where the thickness is the smallest. The initial thickness shown by R2 at the cross section of curve 42 is about 0.206mm, and the initial thickness shown by R1 at the cross section of curve 40 It is 0.302mm, so the embodiment of the present invention can greatly increase the thickness of the R angle. At the same time, it can be seen from the thickness changes of the curve 42 and the curve 40 that the thickness of the formed workpiece obtained by the embodiment of the present invention has a better uniformity.

The embodiment of the present invention further provides a system for optimizing metal stamping process parameters to implement the above-mentioned steps. Please refer to FIG. 5. FIG. 5 is a block diagram illustrating a system for optimizing metal stamping process parameters according to some embodiments of the present invention. The system operates on a computer host 200, which includes a processor and memory, and is installed with software such as LS-DYNA. This system includes: a model building module 210, a pre-processing module 220, a simulation module 230, a parameter determination module 240, a sample generation module 250, a response surface fitting module 260, and an optimization module 270. The model building module 210 is used to build a mold model and a workpiece model (step 100), wherein the workpiece model is placed in the mold model, the workpiece model has at least one quality item, and each quality item has a design goal. The pre-processing module 220 is used to define a punching curve. The simulation module is used to perform a simulation operation based on the stamping curve and using the mold model and the workpiece model (step 110). The parameter determination module 230 is used to use this simulation operation and a full factorial experimental design method to determine a plurality of mold parameters of the mold model that affects the quality item and the numerical range of the mold parameters (step 120). The sample generation module 250 is used to repeat the aforementioned simulation operation (step 130) in the numerical range of the mold parameters to obtain a complex set of sample data. Each set of sample data includes the numerical value of the mold parameter and its corresponding quality item Numerical value. The response surface fitting module 260 is used to perform a fitting response surface operation on these sets of sample data (step 140) to obtain a response surface. The optimization module 270 is used to perform an optimization operation (step 150) on the response surface according to the aforementioned design objective to obtain an optimal set of mold parameters.

The following describes the method and system for optimizing the stamping forming curve in other embodiments of the present invention. Please refer to FIGS. 6A to 6F, and FIGS. 7A to 7H. FIG. 6A is a schematic flow diagram illustrating a method for optimizing metal stamping process parameters according to still other embodiments of the present invention; Some embodiments are used to illustrate schematic diagrams of the stamping forming curve; FIG. 6F is a schematic diagram illustrating still other embodiments of the present invention used to illustrate the amount of springback of a formed workpiece; FIGS. 7A to 7H are diagrams illustrating some other embodiments according to the present The embodiment is used to illustrate a schematic diagram of defining a stamping forming curve.

In the method for optimizing the parameters of the metal stamping process, first, step 300 is performed to establish the mold model 20 and the workpiece model 10 as shown in FIG. 2A, wherein the workpiece model 20 is placed in the mold model 10. In this example, the mold model 20 and the workpiece model 10 are used for performance testing of high-strength steel plates. The workpiece model 20 has at least one quality item, such as the springback of the formed workpiece (ie, the rebound angle RA of the high-strength steel plate as shown in FIG. 6F) or the thickness reduction rate. Each quality item has a design goal, such as the minimum value of the springback (rebound angle RA) of the formed workpiece or the minimum range of the thickness reduction rate. Then, step 310 is performed to define a plurality of punching curves. These pressing curves include the blanking curve shown in FIG. 6D, the holding pressure curve shown in FIG. 6B, the multiple back pressure curve shown in FIG. 6C, and/or the oscillation curve shown in FIG. 6E. In some embodiments of the present invention, the punch positions and slopes (speeds) of various punching curves are adjusted in an automated manner. For example: adjust the positions of points Q1, Q2, Q3, and Q4 on the pressure holding curve shown in Figure 7A to define the pressure holding curves shown in Figures 7B to 7D; adjust the point P1 on the pressure holding curve shown in Figure 7A The positions of, P2 and P3 define the pressure holding curve shown in Figures 7F to 7G. In addition, Figure 7H is a blanking curve.

Next, proceed to step 320 to perform simulation operations based on each punching curve defined in step 310 and using the mold model and the workpiece model to obtain a complex set of sample data, where each set of sample data includes the aforementioned punching curve and its The value of the corresponding quality item. Then, step 330 is performed to perform a fitting response surface operation on these sets of sample data to obtain a response surface. Then, step 340 is performed to use an optimization algorithm to perform an optimization operation on the response surface according to the aforementioned design goal to obtain an optimal stamping curve. The algorithm of step 330 and step 340 is the same as that of step 140 and step 150, so it will not be repeated here.

The embodiment of the present invention also provides a system for optimizing metal stamping process parameters to implement the above-mentioned steps. Please refer to FIG. 8. FIG. 8 is a block diagram illustrating a system for optimizing metal stamping process parameters according to other embodiments of the present invention. The system operates on a computer host 400, which includes a processor and memory, and is installed with software such as LS-DYNA. This system includes: a model building module 410, a pre-processing module 420, a simulation module 430, a sample generation module 440, a response surface fitting module 450, and an optimization module 460. The model building module 410 is used to build a mold model and a workpiece model (step 300), wherein the workpiece model is placed in the mold model, the workpiece model has at least one quality item, and each quality item has a design goal. The pre-processing module 410 is used to define a plurality of punching curves (step 310). The simulation module 420 is used to perform a simulation operation based on one of the stamping curves and using the mold model and the workpiece model. The sample generation module 430 is used to repeat the aforementioned simulation operation using the mold model and the workpiece model according to each stamping curve to obtain a complex array of sample data (step 320), wherein each set of sample data includes the stamping curve and The value of its corresponding quality item. The response surface fitting module 450 is used to perform a fitting response surface operation on these sets of sample data (step 330) to obtain a response surface. The optimization module 460 is used to use an optimization algorithm to perform an optimization operation on the response surface (step 340) to obtain an optimal stamping curve according to the aforementioned design goal.

In summary, the embodiment of the present invention can obtain the best mold parameter combination and stamping forming curve, and can reduce the blind spots of human experience judgment, and reduce the number of mold trials and the cost.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of this new model shall be subject to those defined by the attached patent scope.

10: Workpiece model 12: Guiding punch 14: Sheet workpiece 20: Mold model 22: upper die 24: Lower die 30: Combination model 40: Curve 42: Curve 100: Build mold model and workpiece model 110: According to a stamping curve and use the mold model and the workpiece model to simulate operation 120: Use the simulation operation and the full factorial experimental design method to determine the mold parameters and their numerical ranges 130: Repeat the simulation operation in the numerical range of mold parameters to obtain complex array sample data 140: Perform fitting response surface operations 150: Perform optimization operations 200: computer host 210: Model building module 220: pre-processing module 230: analog module 240: Parameter determination module 250: sample generation module 260: Response surface fitting module 270: Optimization module 300: Build mold model and workpiece model 310: Define multiple stamping curves 320: According to each stamping curve and use the mold model and the workpiece model to perform simulation operations to obtain a complex array of sample data 330: Perform a fitting response surface operation 340: Perform an optimization operation 400: Computer host 410: Model Building Module 420: Pre-processing module 430: Analog Module 440: sample generation module 450: Response Surface Fitting Module 460: Optimization module A1: Model scaling A2: Model scaling α1: Upper die angle α2: Die angle D1: Depth of upper die extension L1: length L2: length P1: point P2: point P3: point Q1: point Q2: point Q3: point Q4: point R1: at the section R2: at the section RA: rebound angle W1: width W2: width

For a more complete understanding of the embodiments and their advantages, reference is now made to the following description in conjunction with the accompanying drawings, in which [Fig. 1] is a schematic flow diagram showing a method for optimizing metal stamping process parameters according to some embodiments of the present invention; [FIG. 2A] and [FIG. 2B] are schematic diagrams illustrating the mold model and the workpiece model according to some embodiments of the present invention; [FIG. 2C] is a schematic diagram illustrating mold parameters according to some embodiments of the present invention; [FIG. 3A] is a schematic diagram illustrating a stamping forming curve according to some embodiments of the present invention; [FIG. 3B] and [FIG. 3C] are schematic diagrams illustrating the upper mold angle and the lower mold angle according to some embodiments of the present invention; [Figure 4] shows the optimized results of the thickness of the formed workpiece according to some embodiments of the present invention; [FIG. 5] is a block diagram showing a system for optimizing metal stamping process parameters according to other embodiments of the present invention; [FIG. 6A] is a schematic flow diagram illustrating a method for optimizing metal stamping process parameters according to other embodiments of the present invention; [FIG. 6B] to [FIG. 6E] are schematic diagrams illustrating still other embodiments of the present invention for illustrating the stamping forming curve; [FIG. 6F] is a schematic diagram illustrating the springback of a formed workpiece according to still other embodiments of the present invention; [FIG. 7A] to [FIG. 7H] are schematic diagrams illustrating the definition of a stamping forming curve according to still other embodiments of the present invention; and [Fig. 8] is a schematic flow diagram showing a system for optimizing metal stamping process parameters according to other embodiments of the present invention.

200: computer host

210: Model building module

220: pre-processing module

230: analog module

240: Parameter determination module

250: sample generation module

260: Response Surface Fitting Module

270: Optimization module

Claims (8)

A system for optimizing metal stamping process parameters, which runs on a computer host and includes: A model building module for building a mold model and a workpiece model, wherein the workpiece model is placed in the mold model, the workpiece model has at least one quality item, and each of the at least one quality item has a design goal; A pre-processing module to define a stamping curve; A simulation module for performing a simulation operation based on the stamping curve and using the mold model and the workpiece model; A parameter determination module for using the simulation operation and a full factorial experimental design method to determine a plurality of mold parameters of the mold model that affect the at least one quality item, and the numerical range of the mold parameters; The sample generation module is used to repeat the simulation operation in the numerical range of the mold parameters to obtain a complex set of sample data, wherein each of the sets of sample data includes the numerical values of the mold parameters and their corresponding The value of at least one quality item; A response surface fitting module for performing a fitting response surface operation on the set of sample data to obtain a response surface; and An optimization module is used to perform an optimization operation on the response surface according to the design goal to obtain a set of optimal values of the mold parameters. The system for optimizing metal stamping process parameters according to claim 1, wherein the mold parameters include an upper mold angle, a lower mold angle, and an upper mold extension depth, the at least one quality item includes a thickness of a formed workpiece, and the design goal Including a uniformity of the thickness of the shaped workpiece or maximization of a minimum thickness. The system for optimizing metal stamping process parameters as described in claim 1, wherein the step of repeating the simulation operation in the numerical range of the mold parameters is performed in an automated manner. The system for optimizing metal stamping process parameters as described in claim 1, wherein the fitting response surface operation uses a sequential response surface method (Sequential Response Surface Method), and the optimization algorithm includes genetic algorithm, annealing method, and hybrid algorithm Method or Leapfrog Method. A system for optimizing metal stamping process parameters, operating on a host computer, including: A model building module for building a mold model and a workpiece model, wherein the workpiece model is placed in the mold model, the workpiece model has at least one quality item, and each of the at least one quality item has a design goal; A pre-processing module to define a plurality of stamping curves; A simulation module for performing a simulation operation based on one of the stamping curves and using the mold model and the workpiece model; The sample generation module is used to repeat the simulation operation according to each of the stamping curves and using the mold model and the workpiece model to obtain a complex set of sample data, wherein each of the sets of sample data includes the The stamping curve and the corresponding value of the at least one quality item; A response surface fitting module for performing a fitting response surface operation on the set of sample data to obtain a response surface; and An optimization module is used to perform an optimization operation on the response surface by using an optimization algorithm for the design goal to obtain an optimal stamping curve. The system for optimizing metal stamping process parameters according to claim 5, wherein the stamping curves include: a blanking curve, a holding pressure curve, a multiple back pressure curve and/or an oscillation curve, and the at least one quality item includes a The springback of the formed workpiece or a thickness reduction rate, the design target includes a minimum value of the springback of the formed workpiece or a minimum range of the thickness reduction rate. The system for optimizing metal stamping process parameters according to claim 5, wherein the steps of defining the stamping curves and the steps of using the mold model and the workpiece model to perform the simulation operation according to each of the stamping curves are It is done in an automated way. The system for optimizing metal stamping process parameters as described in claim 5, wherein the fitting response surface operation uses a sequential response surface method (Sequential Response Surface Method), and the optimization algorithm includes genetic algorithm, annealing method, and hybrid algorithm Method or Leapfrog Method.

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