JP2023023411A - Method for automatically optimizing shape of die and apparatus for the same - Google Patents

Abstract

To solve the problem that knowledge of a skilled operator taking account of a spring-back phenomenon and time are required in order to obtain a target shape with good accuracy, in press-molding.SOLUTION: Points constituting a cross section are subjected to clustering for a shape of a product, and representative points and angles of clusters of the points subjected to clustering are calculated. A comparison-source cluster whose angle becomes perpendicular at most to a press direction during press processing is selected from the clusters subjected to clustering. A predicted shape is calculated from data on a specific die and points constituting a cross section are subjected to clustering for the calculated predicted shape. A comparison-destination cluster which is most similar to the comparison-source cluster is selected from the clusters in which the points constituting the cross section are subjected to clustering for the calculated predicted shape. Representative points and angles of the comparison-destination cluster are calculated, and the representative points of the angles of the comparison-source cluster are compared with the representative points of the angles of the comparison-destination cluster to evaluate a spring-back amount, so that a shape of a die is optimized using information about the evaluated spring-back amount.SELECTED DRAWING: Figure 1

Description

The present invention relates to a technology related to die shape design in press working, and relates to an automatic die shape optimization method and apparatus for automatically generating a die shape in anticipation of a springback amount from drawing information of a product.

Many automobile parts are manufactured by press molding of sheet materials. Press molding is a method in which the shape created by the upper and lower dies is transferred by pressing it onto the material to be processed. Most press-formed products are formed by pressing a three-dimensional mold against a plate-shaped material. When the mold is removed from the molded plate, the springback phenomenon occurs and the shape changes. Therefore, in order to obtain the target shape with high accuracy, it is necessary to consider the amount of springback in the die design. In the past, the shape of the mold was determined by repeated experiments or numerical simulations based on the wisdom of experts.

As a background art, there is Japanese Patent Laying-Open No. 2015-072634 (Patent Document 1). In this patent document 1, "a cross-section setting step of setting a cross-section to be evaluated, a cross-sectional shape acquiring step of acquiring cross-sectional shapes before and after releasing the set cross-section, and based on the acquired cross-sectional shapes before and after releasing the mold. (a) the absolute value of the maximum or minimum geometrical moment of inertia of the cross-sectional shape, (b) the ratio of the maximum to the minimum geometrical moment of inertia of the cross-sectional shape, (c) the cross-sectional shape (d) the absolute value of the length of the long side or short side of the minimum rectangle enclosing the cross-sectional shape, (e) the length of the minimum rectangle enclosing the cross-sectional shape The ratio of the long side to the short side or (f) the difference between the long side and the short side of the minimum rectangle surrounding the cross-sectional shape is calculated, and the amount of change before and after the release is calculated. Evaluate the amount of mouth opening or mouth closing in a specific cross section of the press-formed product 1 based on." (see abstract).

JP 2015-072634 A

In press forming, when the die is removed from the formed plate material, the springback phenomenon of the return of the curvature radius and angle occurs, and the shape changes. Therefore, there is a problem that obtaining the target shape with high accuracy requires the wisdom of an expert and takes time.

In Patent Document 1, a cross-section to be evaluated is set, and the maximum and minimum values of the geometrical moment of inertia or the minimum rectangle surrounding the cross-sectional shape are calculated based on the cross-sectional shape before and after releasing the mold in the press process of this set cross-section. The amount of mouth opening or mouth closing due to springback is evaluated based on the amount of change in .

However, in press molding, when evaluating the shape error immediately after molding and after unloading and automatically correcting the mold shape, the appropriate correction amount considering the amount of springback is the vertical component to the pressing direction. and the parallel component must be determined separately, but Patent Document 1 does not disclose such an idea.

The present invention solves the above-described problems of the prior art and provides an automatic mold shape optimization method and apparatus that enable creation of optimum mold shape data based on the product shape.

In order to solve the above-described problems of the prior art, in the present invention, a product shape, which is a shape that is actually desired to be processed, and a predicted shape, which is a shape that will be obtained when press working is performed using a specific mold. In the automatic optimization method of the mold shape for optimizing the shape of the mold for press molding by evaluating the amount of springback during press working by comparing the Calculate the representative points and angles of each clustered cluster, select the comparison source cluster that has the most perpendicular angle to the pressing direction during stamping from each clustered cluster, and predict the shape from the data of a specific mold. Calculate, cluster the constituent points of the cross section for the calculated predicted shape, select the comparison target cluster that is most similar to the comparison source cluster from each cluster obtained by clustering the cross section constituent points for the calculated predicted shape, and select the comparison destination cluster Calculate the representative point and angle of , compare the representative point and angle of the comparison source cluster and the comparison target cluster to evaluate the amount of springback, and optimize the shape of the mold using the information on the amount of springback that has been evaluated I made it

In addition, in order to solve the above-described problems of the conventional technology, the present invention includes a molding shape prediction unit, a cross-sectional shape geometric characteristic calculation unit, a shape error and correction value calculation unit, a mold shape correction unit, A mold shape for optimizing the shape of a mold for press molding using an automatic mold shape optimization device having an output information creation unit, a geometric characteristic judgment reference storage unit, and a correction value generation reference storage unit. In the automatic optimization method, the forming shape prediction unit reads the analysis model information, predicts the forming shape considering springback, and the geometric characteristic calculation unit of the cross-sectional shape stores it in the geometric characteristic judgment reference storage unit. Using the geometric characteristic judgment criteria, calculate the cross-sectional geometric characteristics of the product shape, which is the shape that you actually want to process, and the molded shape predicted by the molding shape prediction section, and correct it in the shape error and correction value calculation section. A mold for press molding by calculating the shape error between the geometric characteristics of the product shape and the cross-sectional geometric characteristics calculated by the cross-sectional geometric characteristics calculation unit using the criteria for generating correction values stored in the value generation criteria storage unit In the mold shape correction unit, the shape of the mold for press molding is corrected using the shape error and the correction value calculated in the correction value calculation unit, and the output information creation unit , Determines whether or not the shape of the press-molded mold corrected by the mold shape correction unit needs to be re-corrected, and outputs the shape of the press-molded mold for which re-correction is not required as optimum mold shape data. I made it
A method for automatically optimizing a mold shape, characterized by:

In addition, in order to solve the above-described problems of the prior art, in the present invention, the product shape, which is the shape that is actually desired to be processed, and the predicted shape, which is the shape that will be obtained when using a specific mold, are combined. In comparison, an automatic mold shape optimization device that evaluates the amount of springback during press working and optimizes the shape of the mold for press molding reads the analysis model information and creates a molding shape that takes springback into account. Prediction of product shape and molding shape using a molding shape prediction unit that predicts a geometric characteristic criterion storage unit that stores geometric characteristic criterion storage unit and the geometric characteristic criterion stored in this geometric characteristic criterion storage unit a cross-sectional shape geometric characteristic calculation unit that calculates the cross-sectional shape geometric characteristic of the molded shape predicted by the section; a correction value generation reference storage unit that stores a reference for generating a correction value for the shape of the mold to be press-molded; Using the reference for generating correction values stored in the correction value generation reference storage unit, the shape error between the geometric characteristics of the product shape and the geometric characteristics of the cross-sectional shape calculated by the geometric characteristic calculation unit of the cross-sectional shape is calculated, and press molding is performed. A shape error and correction value calculation unit that calculates correction values for the mold shape, and a mold shape correction unit that corrects the shape of the mold for press molding using the correction values calculated by the shape error and correction value calculation unit. Then, it is determined whether it is necessary to re-modify the shape of the press-molded mold corrected by the mold shape correction unit, and the shape of the press-molded mold determined as not requiring re-modification is output as the optimum mold shape data. and an output information creating unit.

According to the present invention, compared with the conventional method, if the z-coordinate is corrected from the vertical plane and the y-coordinate from the parallel plane, it can be corrected easily, quickly and accurately. can be optimized with

1 is a block diagram showing a schematic configuration of an automatic mold shape optimization apparatus according to an embodiment of the present invention; FIG. FIG. 2 is a flowchart showing the flow of processing using the automatic optimization device for mold shape according to the embodiment of the present invention; FIG. 3 is a flow diagram showing a detailed processing flow of step S320 in the flow diagram of FIG. 2; FIG. 3 is a flow diagram showing a detailed processing flow of step S440 in the flow diagram of FIG. 2; It is an example of an analysis model in press molding and a product shape to be molded, (a) is a perspective view showing an example of an analysis model image diagram, and (b) is a perspective view showing an example of an image diagram of the product shape and its cross-sectional shape. It is an image diagram for calculating the geometric characteristics of the cross-sectional shape according to the embodiment of the present invention, (a) is an image diagram of the cross-sectional shape of the product shape, (b) is an image diagram of classifying the cross-sectional shape of the product shape, and (c). is an image of the cross-sectional shape of the predicted shape, (d) is an image of the cross-sectional geometric characteristics of the product shape, (e) is an image of the cross-sectional geometric characteristics of the predicted shape, and (f) is the cross-sectional geometric characteristics of the product shape and predicted FIG. 3 is a comparative diagram of cross-sectional geometrical properties of shapes;

The present invention automatically evaluates the shape error between the predicted shape of the numerical molding analysis and the product shape considering the influence of springback using the geometric characteristics of the cross-sectional shape, and calculates the correction value of the mold shape from the evaluated shape error. It provides a mold shape optimization system that can

This system consists of the following six parts. (1) Molded shape prediction unit by numerical molding analysis; (2) Reference DB for storing geometric characteristic criteria and correction value generation criteria; (3) Geometric characteristics of product shape and predicted shape using geometric characteristic criteria ( vector angle, coordinate value) calculation unit; (4) calculation unit for shape error of product shape and predicted shape using cross-section geometric characteristics and correction value generation criteria and correction value for mold shape; (5) mold shape correction (6) Output of optimized mold shape data.

BEST MODE FOR CARRYING OUT THE INVENTION Below, embodiments of the present invention will be described in detail based on the drawings. However, the present invention should not be construed as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed without departing from the idea or gist of the present invention.

An embodiment will be described below with reference to the drawings.

FIG. 1 shows a functional configuration diagram of a die shape optimization apparatus 100 according to the present embodiment. The mold shape optimization apparatus 100 is composed of a computer system having a mold shape automatic correction program execution unit 160 and a reference DB 170 .

The mold shape automatic correction program execution unit 160 includes a molding shape prediction unit 110, a cross-sectional shape geometric characteristic calculation unit 120, a shape error and correction value calculation unit 130, a mold shape correction unit 140, and an output information creation unit. It has 150.

A molding shape prediction unit 110 reads the analysis model 101 including material information, product shape, processing conditions, mold shape, tolerance of the analysis model, etc., and predicts a molding shape in consideration of springback. The cross-sectional geometric characteristic calculation unit 120 calculates the product shape and the predicted shape predicted in 110 using the geometric characteristic criterion stored in the geometric characteristic criterion storage unit 171 that stores the geometric characteristic criterion. Calculate the cross-sectional geometrical properties of The shape error and correction value calculation unit 130 uses the correction value generation reference stored in the correction value generation reference storage unit 172 that stores the reference for generating the correction value of the mold shape. In 120, the shape error between the calculated geometrical characteristics of the product shape and the geometrical characteristics of the predicted shape is calculated, and the corrected value of the mold shape is calculated. The mold shape correction unit 140 corrects the mold shape using the shape error and the correction value calculated by the correction value calculation unit 130 . The output information creation unit 150 determines whether or not the mold shape corrected by the mold shape correction unit 140 needs to be re-corrected, and outputs the optimized mold shape as the optimum mold shape data 102 .

In the mold shape automatic correction program execution unit 160 having such a configuration, the shape of the product to be molded of the mold shape is predicted using the analysis model 101 first read by the molding shape prediction unit 110, and the cross-sectional shape is predicted. The geometric characteristic calculator 120 generates cross-sectional shapes of the predicted shape and the target product shape, and calculates the geometric characteristic of each cross section. Next, the shape error and correction value calculation unit 130 calculates the shape error of the geometric characteristics in each cross section of the predicted shape and the product shape. Calculate the shape correction value. Next, in the mold shape correction unit 140, the mold shape is corrected using the shape error and the correction value calculated by the correction value calculation unit 130, and the output information creation unit 150 performs molding with the corrected mold shape. Predict the shape and evaluate the shape error between the predicted shape and the product shape. This flow is initiated when a user requests via the input device 180 to optimize the mold geometry.

Details of the die shape optimization device 100 in press molding will be described below with reference to FIGS. 2 to 6. FIG.

An example of optimizing the analysis model shown in FIG. 5A will be described below as a procedure for generating a target shape in die forging using the optimization device 100. FIG.

FIG. 5 shows an analysis model in press forming and an example of a product shape to be formed. FIG. 5(a) shows the press forming analysis model. In FIG. 5(a), blank 511, which is the material to be processed, is placed on die 512 of the lower die, and while pressing with pad 513, punch 514 of the upper die is pressed in the direction of arrow B to form the material to be processed. When transferring the shape of the punch: 514 of the upper mold to the blank: 511, after the punch: 514 of the upper mold is raised in the direction opposite to the arrow B after the transfer, the molded blank: 511 is transferred to the lower die. : This is an analytical model that takes into consideration the springback phenomenon that occurs by removing from 512.

An image diagram of a molded product shape 520 is shown in FIG. 5(b). The information of the cross section 531 on the surface 530 of the product shape 520 is composed of the number No of the constituent points indicated by the point sequence forming the cross section 531 and the coordinate values P(x, y, z) thereof.

An example of optimizing the analysis model shown in FIG. 5(a) will be described along the flow chart of FIG.

In the process flow shown in FIG. 2, first, in step S100, the molding shape prediction unit 110 reads the analysis model including the product shape, material information, product shape, processing conditions, and mold shape from the analysis model 101. .

Next, in step S<b>200 , the forming shape prediction unit 110 reads the tolerance Et{At, Zt, Yt} of the analysis model from the analysis model 101 .

Next, in step S300, the molding shape prediction unit 110 reads from the analysis model 101 cross-section generation conditions such as the position and direction of the cross-section for evaluating the product shape.

Next, in step S310, the cross-sectional shape geometrical characteristic calculator 120 generates cross-sectional shape data for the product shape read in step S100 under the cross-sectional generation conditions read in step S300. An example of the generated cross-sectional shape data 631 is shown in FIG. 6(a).

Next, in step S320, the cross-sectional geometric characteristic calculator 120 calculates the geometric characteristic of the cross-sectional shape data of the product shape generated in step S310.

A method of calculating the geometrical characteristics of the product shape cross-sectional shape data 631, which is executed in the cross-sectional geometrical characteristics calculation unit 120 in step S320, will be described with reference to the flowchart of FIG.

First, at step S321 in FIG. 3, the cross-sectional shape data 631 generated at step S310 in the flowchart of FIG. 2 is read.

Next, in step S322, the vectors A _i of two adjacent points of the cross-sectional shape data 631 read in step S321 are calculated, and the points with the vector values A _i greater than the designated condition A ₀ are classified. FIG. 6(b) shows an example of the vectors of adjacent two points in the cross-sectional shape data shown in FIG. 6(a) and class 632. FIG.

Next, in step S323, in each class 632 divided in step S322, the average value of the vector A _i of two adjacent points is calculated, and as shown in _FIG . Let B be the pressing direction of the punch 514 in the analysis model shown in 5(a), and calculate the angle AB formed with the class vector A _Gi .

Next, in step S324, the class in which the angle AB calculated in step S323 is the most vertical is selected as the direction class Ga. Then, the class vector A _Gi is set as the directional characteristic A of the cross-sectional shape data: 634.

Next, in step S325, the class in which the angle AB calculated in step S323 is the most vertical is selected as a vertical coordinate class Gz. 635.

In step S326, the class in which the angle AB calculated in step S323 is the most parallel is selected and set as the left-right coordinate class Gy. and Here, at least one Py is required, and at most two Pz are required. If there are two objects Py:636, the average value (Py1/2+Py2/2) is taken as the left-right coordinate characteristic Py:636.

In step S327, the geometric characteristics A: 634, Pz; save. FIG. 6(c) shows an image of the cross-sectional shape data of the product shape and its geometric characteristics.

Next, returning to the flowchart of FIG. 2, a series of processes from step S400 to step S440 are executed by the shape error and correction value calculation unit 130. FIG.

First, in step S400, press analysis is performed using the analysis model read in step S100.

Next, in step S410, springback analysis is performed using the analysis result of step S400, and the shape to be formed by the die shape read in step S400 is predicted.

Next, in step S420, using the analysis result predicted in step S410, predicted shape data of the die shape read in step S400 is generated.

Next, at step S430, cross-sectional shape data is generated from the predicted shape data generated at step S420 under the cross-sectional generating conditions read at step S300. An example of the generated cross-sectional shape data 640 is shown in FIG. 6(d).

Next, in step S440, geometric characteristics of the cross-sectional shape data of the predicted shape generated in step S430 are calculated.

The details of the method of calculating the geometrical characteristics of the cross-sectional shape data of the product shape in step S440 will be described with reference to the flowchart of FIG.

First, in step S441, the cross-sectional shape data of the predicted shape generated in step S430 are read.

Next, in step S442, vectors A _si : 641 of adjacent two points of the shape data read in step S410 are calculated, and the points with vector values A _si : 641 greater than the specified condition A ₀ are classified. FIG. 6(e) shows an example of vector A _si : 641 and classification: 642 of adjacent two points in the cross-sectional shape data shown in FIG. 6(d).

Next, in step S443, in each class divided in step S442, the average value of the vectors A _i of two adjacent points is calculated and set as the class vector A _Gi .

Next, in step S444, based on the class number of the product cross section calculated in step S443, the class vector _AGi of the IDa-th class is set as the directional characteristic As of the cross-sectional shape data of the predicted shape.

Next, in step S445, based on the class number of the product cross section calculated in step S444, the average value of the coordinate values in the punch pressing direction of the IDz-th class is set as the vertical coordinate characteristic Psz of the cross-sectional shape data.

Next, in step S446, based on the class number of the product cross section calculated in step S445, the average value of the coordinate values perpendicular to the punch pressing direction of the IDy-th class is set as the left-right coordinate characteristic Psy of the cross-sectional shape data. . If there are two target groups, the average value (Psy1/2+Psy2/2) is taken as the left-right coordinate characteristic Psy.

Next, in step S447, the geometric characteristics As, Psz, and Psy obtained in steps S444 to S446 are output together with their class numbers, and stored as the geometric characteristics of cross-sectional shape data of the product shape. FIG. 6(e) shows the cross-sectional shape data of the predicted shape and an image of its geometric characteristics.

Next, referring back to the flowchart of FIG. 2, the processing executed by the mold shape correction section 140 will be described.

In step S500, the geometric characteristics of the cross-sectional shape data of the product shape calculated by the geometric characteristic calculation unit 120 of the cross-sectional shape in step S320, and the cross-sectional shape of the predicted shape calculated by the shape error and correction value calculation unit 130 in step S440 Calculate the difference of the geometrical characteristics of the data and set it as the shape error En{An, Zn, Yn}.

here,
An＝A-As ・・・ (Number 1)
Zn=Pz-Psz (Equation 2)
Yn=Py-Psy (Equation 3)
is.

Next, in step S600, the shape error En{An, Zn, Yn} calculated in step S500 and the tolerance En{At, Zt, Yt} read in step S200 are compared. If En>Et, proceed to step S610. If En<=Et, the process proceeds to step S700.

If the process proceeds to step S610, the mold shape correction unit 140 uses the shape error En{An, Zn, Yn} calculated in step S500 to correct the mold shape under the cross-section generation conditions read in step S300. Generate DE{Ad,Zd,Yd}.

Ad, Zd and Yd are represented by the following formulas.
Ad=An*Ka (Equation 4)
Zd=Zn*Kz (Equation 5)
Yd=Yn*Ky (Equation 6)
Here, Ka, Kz, and Ky are coefficients that can be specified according to the material of the material, and the default value is 1.

Next, in step S620, the mold shape correction unit 140 moves the cross section of the correction box (corresponding to 530 in FIG. 5B) including the mold shape using the correction value calculated in step S610. Then, correct the shape of the mold contained in the correction box so that it becomes smooth. The mold shape in step S400 is updated with the corrected mold shape, and steps S400 to S500, steps S610, and steps S620 are repeated until step S600 becomes YES.

Finally, in step S700, if the shape error En{An, Zn, Yn} calculated in step S500 is smaller than or equal to the tolerance En{At, Zt, Yt} read in step S200, in step S620 The shape data of the modified mold shape is output as an optimized mold (FIG. 6(f)).

According to this embodiment, by correcting the z-coordinate from the vertical plane and the y-coordinate from the parallel plane, it is possible to correct more easily, quickly and accurately than the conventional method. This has made it possible to automatically optimize the mold shape considering the effect of springback, unlike the conventional manual trial-and-error method.

Although the invention made by the present inventor has been specifically described above based on the embodiments, it goes without saying that the invention is not limited to the above embodiments, and various modifications can be made without departing from the gist of the invention. stomach. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the described configurations. Moreover, it is possible to add, delete, or replace a part of the configuration of the embodiment with another configuration.

REFERENCE SIGNS LIST 100 optimization device 110 molding shape prediction unit 120 cross-sectional shape geometric characteristic calculation unit 130 shape error and correction value calculation unit 140 mold shape correction unit 150 output information creation unit 170 reference DB

Claims (7)

The amount of springback during press working is evaluated by comparing the product shape, which is the shape that is actually desired to be processed, and the predicted shape, which is the shape that will be obtained when press working is performed using a specific mold. A mold shape automatic optimization method for optimizing the shape of a mold for press molding by
clustering the configuration points of the cross section for the product shape;
Calculate the representative point and angle of each cluster clustered,
Selecting a comparison source cluster having the most perpendicular angle to the pressing direction during the pressing from among the clustered clusters,
calculating the predicted shape from the data of the specific mold;
Clustering constituent points of the cross section for the calculated predicted shape,
Selecting a comparison target cluster most similar to the comparison source cluster from among the clusters obtained by clustering the constituent points of the cross section for the calculated predicted shape,
Calculate the representative point and angle of the comparison target cluster,
evaluating a springback amount by comparing the representative points and angles of the comparison source cluster and the comparison target cluster;
A method for automatically optimizing the shape of a mold, comprising optimizing the shape of the mold using the information on the evaluated amount of springback. 2. The method for automatically optimizing the mold shape according to claim 1, wherein a comparison source cluster that is not only perpendicular to the pressing direction during press working but also has the most parallel angle is selected, and for the predicted shape, the comparison A comparison target cluster that is most similar to the original cluster is selected, and the representative points (Pz, Pz') of the vertical planes of the selected comparison source cluster and the comparison target cluster are used to correct the z coordinates, and the representative points of the parallel plane Points (Py, Py') are used to correct the y coordinate, select Py or Py' one by one (total two) on the left and right sides of Pz or Pz', and calculate the average value of Py and the average value of Py' is used to correct the y-coordinate. A molding shape prediction unit, a cross-sectional geometric characteristic calculation unit, a shape error and correction value calculation unit, a mold shape correction unit, an output information creation unit, a geometric characteristic criterion storage unit, and a correction value generation unit An automatic mold shape optimization method for optimizing the shape of a mold for press molding using an automatic mold shape optimization device equipped with a reference storage unit,
The forming shape prediction unit reads the analysis model information and predicts the forming shape considering springback,
In the cross-sectional shape geometric characteristic calculation unit, using the geometric characteristic criterion stored in the geometric characteristic criterion storage unit, the product shape, which is the shape that is actually desired to be processed, and the molding shape predicted by the molding shape prediction unit Calculate the cross-sectional geometrical properties of
The cross-sectional shape calculated by the geometrical characteristics of the product shape and the geometrical characteristics of the cross-sectional shape using the reference for generating the correction value stored in the correction value generation reference storage unit in the shape error and correction value calculation unit Calculate the correction value of the shape of the mold for press molding by calculating the shape error with the geometric characteristics,
In the mold shape correction unit, the shape of the mold for press molding is corrected using the correction values calculated by the shape error and the correction value calculation unit,
The output information creation unit determines whether or not the shape of the press-molding mold corrected by the mold shape correction unit needs to be re-corrected, and the press-molding mold determined not to need re-correction. A method for automatically optimizing mold shape, characterized by outputting the shape of as optimum mold shape data. The automatic optimization method of the mold shape according to claim 3,
When the output information creation unit determines whether or not the shape of the mold for press molding corrected by the mold shape correction unit needs to be re-corrected, and determines that the re-correction is necessary, Calculating a correction value for the shape of the mold for press molding in the shape error and correction value calculation unit; and correcting the shape of the mold for press molding in the mold shape correction unit. A method for automatically optimizing a mold shape, wherein the output information creation unit repeats the re-correction until it is determined that the re-correction is unnecessary. The product shape, which is the shape you actually want to process, is compared with the predicted shape, which is the shape that will be obtained when using a specific mold, and the amount of springback during press processing is evaluated before press molding. An automatic mold shape optimization device for optimizing the mold shape,
A molding shape prediction unit that reads analysis model information and predicts a molding shape that takes springback into consideration;
a geometric characteristic criterion storage unit for storing geometric characteristic criterion;
Cross-sectional geometric characteristic calculation for calculating cross-sectional geometric characteristics of the product shape and the molded shape predicted by the molded shape prediction unit, using the geometric characteristic criterion stored in the geometric characteristic criterion storage unit Department and
a correction value generation reference storage unit that stores a reference for generating a correction value for the shape of the mold for press molding;
calculating a shape error between the geometrical characteristics of the product shape and the cross-sectional geometrical characteristics calculated by the geometrical characteristics of the cross-sectional shape using the reference for generating the correction value stored in the correction value generation reference storage unit; a shape error and correction value calculation unit for calculating a correction value for the shape of the mold for press molding;
a mold shape correction unit that corrects the shape of the mold for press molding using the correction values calculated by the shape error and correction value calculation unit;
It is determined whether or not the shape of the mold for press molding corrected by the mold shape correction unit needs to be re-corrected, and the shape of the mold for press molding that has been determined not to need re-correction is stored as optimum mold shape data. A mold shape automatic optimization device characterized by comprising a creation unit for output information output as. The automatic optimization device for mold shape according to claim 5,
When the output information creation unit determines whether or not the shape of the mold for press molding corrected by the mold shape correction unit needs to be re-corrected, and determines that the re-correction is necessary, Calculating the correction value of the shape of the mold for press molding in the shape error and correction value calculation unit, and correcting the shape of the mold for press molding in the mold shape correction unit, The apparatus for automatically optimizing a mold shape, wherein the output information creating unit repeats the re-correction until it is determined that the re-correction is unnecessary. The automatic optimization device for mold shape according to claim 5,
An automatic optimization device for a mold shape, wherein the automatic optimization device for the mold shape comprises a computer system.

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