WO2021221200A1

WO2021221200A1 - Gating design method and system

Info

Publication number: WO2021221200A1
Application number: PCT/KR2020/005639
Authority: WO
Inventors: 김성빈; 노동훈; 정재우
Original assignee: 주식회사 애니캐스팅 소프트웨어
Priority date: 2020-04-29
Filing date: 2020-04-29
Publication date: 2021-11-04

Abstract

The present invention relates to a method and a system for designing a casting or injection plan, and to a method for designing an optimal casting or injection plan applicable to a target product to be manufactured through casting or injection. According to one embodiment of the present invention, a method for designing gating may be provided, the method comprising the steps of: presenting a reference product similar to a target product through a determination core; and, in order to use a gating design applied to the reference product as a guide for a gating design of the target product, presenting an evaluation index for a gating design plan of the reference product presented as a similar reference product, from a database in which the evaluation index for the gating design plan of the reference products is stored.

Description

Gating Design Methods and Systems

The present invention relates to a method and system for designing a casting or injection method, and to a method for designing an optimal casting or injection method applicable to a target product to be manufactured through casting or injection.

The present invention relates more particularly to a gating design method and design system.

Casting and injection refer to a method or operation for obtaining a product as a solid metal or polymer material after solidifying by injecting a molten material into a mold having a dimensional space of the product shape.

Casting and injection specifically refer to the process of product design, casting/injection method setting, model preparation, melting and injection, and finishing into products, in order to finally obtain metal and polymer material products.

After a product is designed, the process of deciding how to manufacture the product through casting and injection is called the design of the casting and injection method.

The design of the casting and injection scheme is very important. That is, it is very important to design an optimal casting and injection method for a product to be manufactured (hereinafter referred to as a "target product") among various casting and injection methods.

Basically, it is necessary to design a low-cost and high-quality casting and injection method for the target product. For this, how to design and combine molds is an important issue. In particular, it is preferable to set the entire target product to be divided into two without using the core as much as possible.

In addition, it should be set so that bubbles and inclusions in the target product do not occur, shrinkage due to volume reduction during solidification, and partial defects in which the molten material cannot enter every corner of the product due to lack of fluidity, etc.

Casting light injection methods include runner, gate, overflow, vent, and press (in the case of casting), and it is desirable to optimally perform casting and injection by optimally applying these various casting and injection methods to mold design. do. However, when setting a casting and injection method for a target product, it is common to consider a casting and injection method for a conventional product. This is because, if a new casting and injection method is set without considering the conventional casting and injection method, very high cost and time are inevitably required. That is, the cost-efficiency may be low.

Therefore, it can be said that it is very effective to find a previous product similar to the target product as much as possible and to set a new casting and injection method by reflecting the casting light injection method applied to the previous product. However, this casting and injection method setting method has a problem that greatly depends on the skill level of the designer. Therefore, a method for setting an optimal casting and injection method regardless of the skill of the designer is being sought.

Here, it is very important to determine the number of gates, the position of the gates, and the size of the gates in order to inject the molten raw material into the mold among the casting and injection methods. Accordingly, this casting and injection scheme design is referred to as a gating design.

Korean Patent No. 10-0682028 (hereinafter referred to as "prior patent") proposes a method of setting a casting method by matching an existing product similar to a target product through tomography.

However, according to the prior patent, there is a problem that tomography of the target product is required as well as tomography of the previous product. That is, a very large cost and time are inevitably invested in newly building the old database.

In addition, when comparing the target product and the previous product through the BMP file (image file) formed by tomography, there is a problem in that the previous product that is similar to the maximum is searched according to the skill level of the designer.

The same problem can occur not only in casting but also in injection. That is, the same problem may occur in the gating design including the gate position, the number of gates, and the size of the gate into which the molten raw material is injected into the mold.

Therefore, when designing a gating for manufacturing a target product using a molten raw material, there is a need for an optimal solution for finding a previous product most similar to the target product.

On the other hand, even though the most similar previous product was found, it is also very difficult to apply its gating design method to the target product. Therefore, it can be said that an optimal solution for whether a gating design method that can be referenced can be optimally guided to a gating design method of a target product is also required.

An object of the present invention is to solve the problem of the conventional gating design.

An object of the present invention is to provide a gating design method and system capable of performing a gating design using an existing database.

It is an object of the present invention to provide a gating design method and system that can consider the gating design of a reference product that is similar to the external structure as well as the internal structure of the target product.

It is an object of the present invention to provide a gating design method and system capable of implementing an optimal gating design by calculating and providing a degree of similarity between a target product and a reference product through an embodiment of the present invention.

An embodiment of the present invention provides a gating design method and system that can easily determine the similarity between an object and a reference product through 3D shape data.

Through an embodiment of the present invention, a gating design method that can easily determine the similarity between a target product and a reference product through simple conversion of three-dimensional shape data without using a separate equipment such as a camera or a tomography apparatus, and We want to provide a system.

According to an embodiment of the present invention, it is an object of the present invention to provide a gating design method and system capable of presenting an accurate and rapid gating design regardless of a designer's skill level through a machine learning-based image recognition model. In particular, by applying an image recognition model including a deep neural network or a multi-layer neural network built on the basis of deep learning-based learning, the algorithm configuration is highly reliable. An easy gating design method and system are provided.

It is an object of the present invention to provide a gating design method and system that can easily apply a gating design method of a reference product to a target product through an embodiment of the present invention.

Through one embodiment of the present invention, a gating design method and system that can significantly reduce the number and time of simulation by planning acquisition and storage of evaluation papers for the gating design method of a reference product, and presenting it to the gating design of the target product would like to provide

An embodiment of the present invention provides a gating design method and system capable of easily and quickly gating design by performing a correction of an evaluation index according to the correction of an entity, and confirming in advance the appropriateness of a gating design of a target product.

In order to realize the above object, according to an embodiment of the present invention, the method comprising: presenting a reference product similar to the target product through the judgment core; And in order to guide the gating design applied to the reference product to the gating design of the target product, from the database in which the evaluation index for the gating design method of the reference products is stored, the evaluation index for the gating design method of the reference product presented as a similar reference product A gating design method comprising the step of presenting may be provided.

It is preferable that evaluation indicators for a plurality of gating design methods for each of a plurality of reference products are stored in the database.

The evaluation index preferably includes a plurality of detailed evaluation indexes for evaluating the optimality of the gating design method for the reference product.

The detailed evaluation index may include at least one of a filling time, a gas amount, an oxide, a solidification time, and a solidification bond in the casting process.

The database preferably stores a gating design method for a reference product and an evaluation index previously derived as a simulation result thereof.

The gating design of a product is performed through a plurality of entities that are basic components, and the entities are a product entity for the shape of the product and an ingate entity through which the injected molten metal flows into the mold. may include

In the database, an evaluation index previously derived as a simulation result for a gating design method in which different ingate entities are applied to a reference product may be stored.

A step of presenting the different gating design methods and their evaluation indicators to be distinguished may be included.

When any one of the different gating design methods is selected, the method may include presenting a gating design method in which the selected gating design method of the reference product is reflected to a mold entity for the shape of the target product.

The gating design method is preferably presented graphically.

In order to achieve the above object, according to an embodiment of the present invention, there is provided a method comprising: providing a projected thickness map (PTM) image of a target product with respect to a specific projection surface of the target product to a judgment core; determining a degree of similarity between the PTM image of the target product and the PTM image of the reference product through the determination core; And in order to guide the gating design applied to the reference product to the gating design of the target product, a gating design method comprising the step of presenting a similarity determination result between the target product and the reference product may be provided.

The specific projection plane may be a plane penetrating the target product and the reference product, respectively.

The specific projection plane is preferably a division plane of the mold. Since the target product and the reference product can be cast through the two-part mold surface, the target product and the reference product can be compared with each other through a PTM image of the same reference, that is, a specific projection plane that is a division plane of the mold.

Preferably, the thickness is the total thickness of each of the target product and the reference product in the mold extraction direction. Specifically, the mold subtraction direction is a direction perpendicular to a specific projection plane, ie, a dividing plane, and thus, the thickness can be referred to as a product height in a direction perpendicular to the dividing plane.

For example, when a specific projection plane is represented by an xy plane, the vertical height of the product at the (x, y) coordinate point (pixel) may be referred to as z.

It is preferable that the PTM image is a 2D image in which the deviation of the thickness with respect to the specific projection surface is expressed as a brightness deviation. That is, the difference in the z value may be expressed as a difference in brightness in the corresponding pixel.

The method may further include acquiring the PTM image through 3D shape data of the target product. Before providing the PTM image to the decision core, a PTM image may be obtained through 3D shape data, and the obtained PTM image may be provided to the decision core. Of course, it is also possible to provide 3D shape data and information about a specific projection plane to the judgment core, and use the provided information to generate a PTM image for the judgment core to find a similar reference product.

On the other hand, it is preferable that the result of the image recognition model using the PTM image of the reference product (hereinafter referred to as "learning result") is pre-stored in the database. Of course, such a PTM image is preferably generated through 3D shape data of the reference product. Therefore, when 3D shape data for the reference product is provided and the gating design for the reference product is already prepared, the learning result evolved as the number of reference products increases can be stored in the database.

Preferably, an application programming interface (API) is provided to perform the operation of the decision core. Preferably, the PTM image is provided to the decision core by a user's input through the API, and a decision result from the decision core is presented to the user through the API.

In order to achieve the above object, according to an embodiment of the present invention, a projected thickness map (PTM) image of a target product for a specific projection plane of the target product and a specific projection plane of a reference product through a judgment core determining the similarity of the PTM image of the reference product; And in order to guide the gating design applied to the reference product to the gating design of the target product, a gating design method comprising the step of presenting a similarity determination result between the target product and the reference product may be provided.

It is preferable that the PTM image of the target product and the PTM image of the reference product are generated through conversion of respective 3D shape data.

Preferably, the PTM image of the target product and the PTM image of the reference product are generated by setting each mold division plane as the specific projection plane.

As a result of determining the similarity, a plurality of reference products may be sequentially presented according to the degree of similarity. That is, a plurality of reference products having a high degree of similarity may be sequentially presented. Of course, one reference product with the greatest similarity may be presented. In either case, the degree of similarity may be presented together with a similar reference product. For example, when the target product and the reference product are the same product, if the similarity is 1, the closer to 1, the greater the similarity.

In order to implement the above object, according to an embodiment of the present invention, in a gating design system including an API (application programming interface) and a decision core, the API is generated through 3D shape data for a target product. Provide projected thickness map (PTM) image information of a target product for a specific projection surface of the target product to the judgment core, wherein the judgment core is configured to perform a PTM image of a reference product through a machine learning-based image recognition model A gating design system may be provided, characterized in that it determines the similarity with the provided image information among the information, and provides the determination result to the API.

The image recognition model may include a deep neural network or a multi-layer neural network constructed based on deep learning-based learning.

The image recognition model may include a convolutional neural network (CNN) constructed based on deep learning-based learning.

The API may provide one piece of basic PTM image information (raw PTM image information) for the target product to the decision core.

The gating design system may include a database for storing PTM image information on the reference product, and the database may store PTM image information by classifying the reference product by type.

The database may store one piece of basic PTM image information (unprocessed PTM image information) and PTM image information obtained by processing the basic PTM image information for reference products classified by type.

For example, when there are 10 reference products, a plurality of PTM image information may be stored in each of 10 categories. In one category, basic PTM image information of a specific reference product and PTM image information obtained by processing basic PTM image information may be stored.

The processed PTM image information may be PTM image information in which any one of processing such as rotation, enlargement, reduction, and blurring is performed on the basic PTM image information. Accordingly, basic PTM image information of a reference product and a plurality of processed PTM image information may be pre-stored in one category.

Thus, the database can be expanded as the number of reference products increases. A database with information of reference products may be provided for learning. That is, learning through the decision core may be performed through data stored in a database. Therefore, this database can be called a learning DB by distinguishing it from the database of the judgment core.

Preferably, the determination core compares the basic PTM image information of the target product with the basic PTM image information and processed PTM image information of the reference product classified by type to determine the degree of similarity.

More specifically, the learning result learned through the PTM image information of the reference product may be stored in the judgment core DB. Input to the judgment core is basic PTM image information of the target product. The basic PTM information of the target product input from the judgment core is analyzed through the image recognition model. A similarity determination may be performed by comparing the analysis result with the pre-stored learning result.

Therefore, although the time required for learning may be relatively long, the time for deriving an output value with respect to an input value input to the decision core after a learning result is derived is relatively small.

The decision core outputs a similar reference product using PTM image information through a machine learning-based image recognition model.

The gating design method and system in the above-described embodiment may be equally applied to injection.

According to one embodiment of the present invention, there is provided a method comprising: providing a projected thickness map (PTM) image of a target product for a specific projected surface of the target product to a judgment core; determining a degree of similarity between the PTM image of the target product and the PTM image of the reference product through the determination core; And in order to guide the casting and injection method applied to the reference product to the casting and injection method of the target product, a casting and injection method setting method comprising the step of presenting a similarity determination result between the target product and the reference product may be provided. have.

According to an embodiment of the present invention, a degree of similarity between a projected thickness map (PTM) image of the target product for a specific projection surface of the target product and a PTM image of the reference product with respect to a specific projection plane of the reference product through a judgment core determining; And in order to guide the casting and injection method applied to the reference product to the casting and injection method of the target product, a casting and injection method setting method comprising the step of presenting a similarity determination result between the target product and the reference product may be provided. have.

According to an embodiment of the present invention, in an injection method setting system including an API (application programming interface) and a decision core, the API is, Provides projected thickness map (PTM) image information of a target product to the decision core, and the decision core uses the machine learning-based image recognition model to receive the provided image information from PTM image information for a reference product An injection method setting system, characterized in that by determining the degree of similarity to and providing the determination result to the API, may be provided.

Accordingly, according to an embodiment of the present invention, it can be said that it relates to a design method and system for manufacturing a product from a molten raw material.

Through an embodiment of the present invention, it is possible to provide a gating design method and system capable of performing a gating design using an existing database.

Through one embodiment of the present invention, it is possible to provide a gating design method and system that can consider the gating design of a reference product similar to the external structure as well as the internal structure of the target product.

Through an embodiment of the present invention, it is possible to provide a gating design method and system capable of implementing an optimal gating design by calculating and providing a degree of similarity between a target product and a reference product.

Through an embodiment of the present invention, it is possible to provide a gating design method and system that can easily determine the similarity between the object and the reference product through 3D shape data.

Through an embodiment of the present invention, a gating design method that can easily determine the similarity between a target product and a reference product through simple conversion of three-dimensional shape data without using a separate equipment such as a camera or a tomography apparatus, and system can be provided.

Through an embodiment of the present invention, it is possible to provide a gating design method and system capable of presenting an accurate and rapid gating design regardless of a designer's skill level through a machine learning-based image recognition model. In particular, by applying an image recognition model including a deep neural network or a multi-layer neural network built on the basis of deep learning-based learning, the algorithm configuration is highly reliable. An easy gating design method and system can be provided.

Through one embodiment of the present invention, it is possible to provide a gating design method and system that can easily apply the gating design method of a reference product to a target product.

Through one embodiment of the present invention, a gating design method and system that can significantly reduce the number and time of simulation by planning acquisition and storage of evaluation papers for the gating design method of a reference product, and presenting it to the gating design of the target product can provide

Through an embodiment of the present invention, it is possible to provide a gating design method and system capable of easily and quickly gating design by performing a correction of an evaluation index according to the correction of an entity, and confirming in advance the appropriateness of a gating design of a target product. .

Figure 1 (a) shows a isometric image of the product for which the gating design is to be designed,

Figure 1 (b) shows a projected thickness map (PTM) image for the product shown in Figure 1 (a),

2 shows a conceptual diagram for explaining PTM,

3 shows the basic PTM image and the processed PTM image for each of the reference products;

Figure 4 shows an example of the gating design system configuration according to an embodiment of the present invention,

5 shows an example of a gating design flow applicable to an embodiment of the present invention,

6 is an example in which an image recognition model using a multi-layer neural network is applied to a decision core, and shows an example in which the decision core performs the similarity determination and determination result output shown in FIG. 5;

7 is an example in which an image recognition model using CNN is applied to a decision core, and shows an example in which the decision core performs the similarity determination and determination result output shown in FIG. 5 .

8 shows an example of a gating design flow according to another embodiment of the present invention;

9 shows an example of a gating design scheme of a product.

In general, there are various products that can be manufactured through casting or injection molding, and various gating designs exist for each product.

When designing the gating of a new product, it is desirable to find the most similar conventional product. In the conventional product, the optimal gating design has already been performed, and the gating design is already verified as the product is manufactured by casting or injection with the designed gating design.

Accordingly, by finding a conventional product that is as similar as possible to a new product and considering a gating design applied to the conventional product, a very effective, fast and accurate gating design for a target product is possible. That is, since the conventional gating design can be guided to the gating design for the target product, the final gating design can be quickly and accurately performed.

Here, the new product may be referred to as a target product, and the conventional product may be referred to as a reference product for reference for gating design.

The question is how to find a reference product that is as similar as possible to the target product.

As described above, in the prior art, it has been common to depend on the designer's sense based on the designer's skill level. In addition, the prior patent suggests finding a similar conventional product using an image generated through tomography of a target product. That is, a skilled designer has no choice but to judge the similarity by looking at the conventional product and the target product, or judge the similarity by looking at the tomography image of the conventional product and the target product.

Recently, many attempts have been made to apply machine learning-based image recognition models to various fields.

For example, in the case of home appliances that process an object, many attempts have been made to optimally process the object by grasping the current state of the object through an image taken of the object. That is, when an image of the current object is input to a machine learning-based image recognition model and the processing state of the current object is output, an attempt is made to optimally correct the processing algorithm of the object according to the output result.

In other words, many attempts are being made to improve the accuracy of judgment by allowing a machine (artificial intelligence), not a human, to judge the similarity of an image or what kind of image it is through a machine learning-based image recognition model.

In this background, the present inventors have conducted various studies on which image can be used to easily determine the similarity between the target product and the conventional product. Of course, the study was conducted in consideration of the ease of image acquisition or generation. The study was conducted in consideration of the fact that the product image should optimally represent the product characteristics, especially the product characteristics related to the gating design.

In addition, it was found that the present inventor is basically designing a product based on 3D (3-dimensions) at a site requiring a gating design system or program. As an example, it was found that the product was designed through 3D CAD, and 3D shape data for the product was basically created and retained.

Even without additional equipment, such as a camera or a tomography device, a wide variety of images can be acquired through 3D shape data. That is, a 2D image may be acquired. These images may be in the form of images viewed from various directions of the appearance of the product or may be in the form of cross-sectional images in various planes.

However, even for different products, product images in a specific direction may be identical to each other. Therefore, in order to properly grasp the characteristics of a product, images of various types must be secured. Therefore, judging the similarity between products through a general perspective view, left and right side views, front view, back view, plan view, or cross-sectional view is inevitably a very difficult task, even if a designer directly performs it or uses a computer program.

Accordingly, the present inventor was able to derive a specific image that can be easily obtained through 3D shape data, can fully reflect gating design characteristics, and can easily determine product similarity.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. For convenience, an embodiment of the present invention will be described as an embodiment for casting.

1 shows an example of an image of a target product (FIG. 1(a)) and an example of a thickness map image of the target product with respect to a specific projection surface of the target product (FIG. 1(b)). 2 illustrates a process of converting a thickness map image as an example.

The image of the target product shown in FIG. 1( a ) is an example of a perspective image that can be obtained through 3D shape data. That is, it can be said that, for example, it is a perspective image of a target product to be newly manufactured through casting.

A target product formed in 3D can be projected onto any 2D plane. The projected image may resemble a general cross-sectional view. However, a general cross-sectional view only provides information on the shape and cross-section of the target product with respect to the projection plane, but does not provide the distance between the projection plane and the outer (profile) of the target product.

For example, the projection surface may be a 2D plane penetrating the target product. In this case, the cross-sectional view does not provide thickness information of the target product in a direction perpendicular to the projection plane.

Accordingly, the present inventors have found that the projection plane is not set outside the target product but passes through the inside of the target product, and thickness information of the target product with respect to the projection plane can be displayed in one image.

In particular, it was found that it was possible to obtain a thickness map image of the target product for a specific projection plane that can be considered as the dividing plane of the mold among countless projection planes penetrating the inside of the target product.

As shown in FIG. 2( a ), it can be assumed that the 3D diamond-shaped product is the target product (O).

It is common to use a two-part mold for casting. Therefore, two divided mold surfaces or divided surfaces (B) exist, and it is common that the molds are vertically separated from the top and bottom or left and right of the division surface (B). Such a mold surface is generally determined in consideration of the mold extraction direction so that an undercut does not occur.

Accordingly, when a specific projection plane is set as the dividing plane B, the gating design feature can be effectively reflected through the image obtained.

Here, assuming that the division plane, ie, the projection plane, is an xy plane, the thickness of the target product projected on the xy plane (thickness of the target product in the same direction as the mold subtraction direction) may be defined as z. Therefore, when the profile of the target product is changed according to the xy coordinates, the z value is changed.

As shown in Fig. 2(a), any coordinate A on the projection surface B has corresponding x and y values, and has a thickness value z as shown in Fig. 2(a).

Here, with respect to the projection plane, the target product may have a profile in the positive z-direction and the negative z-direction, respectively. Therefore, the thickness z of the target product projected on the xy plane is 0 or more, and it can be said that it is the sum of the thickness in the positive z direction and the thickness in the negative z direction. For this reason, the thickness of the product projected onto the projection plane may also be referred to as the projected thickness.

Here, how to represent the difference in thickness z on the projection surface becomes a problem. Accordingly, in an embodiment of the present invention, the difference in thickness z could be expressed as a difference in brightness.

The brightness may be set by dividing 0 to 255, for example. That is, as an example, the difference in brightness or contrast ratio may be displayed on the image in 256 steps. For example, the greater the thickness of the target product on the projection surface, the brighter it may be displayed. Of course, the reverse is also possible.

Since the thickness of the target product at the position (pixel) where the target product is not projected on the projection plane is 0, it can have the brightness of 0, the darkest level, and the brightest, 255 levels of brightness at the position (pixel) with the largest thickness. Positions (pixels) having any thickness between zero thickness and the maximum thickness of the target product may be proportionally luminous.

In addition, the preset maximum thickness may be set to 255 levels of brightness, and if the thickness is higher than that, it may be displayed as 255 levels of brightness by default. In addition, a position having an arbitrary thickness between the thickness 0 and the maximum thickness may be able to adjust the brightness proportionally.

In either case, it is possible to acquire an image in which the brightness of pixels is different depending on the thickness of the target product on the projection surface. That is, through the 3D shape of the target product, a projected thickness map (PTM) in which the thickness of the target product is reflected on a specific projected plane (x, y coordinate) of the target product can be obtained, and the PTM image can be obtained through the PTM. can be obtained

Accordingly, in FIG. 2 , the point A of the projection surface is converted to the point C and has a brightness corresponding to the corresponding z value. In other words, although not shown, the triangle of FIG. 2(b) has a black color at the outer angle and gradually brightens toward the center to appear brightest at the center. This is because the thickness of the target product is greatest at the center of the triangle.

Here, the PTM image is not an image formed by a camera or a tomography apparatus, but an image that can be acquired through data processing using 3D shape data. Therefore, it becomes possible to acquire a PTM image very easily and quickly.

In addition, since the PTM image is acquired in consideration of the mold division plane and the mold extraction direction, it can be said to be an image representing optimal target product information in relation to the gating design.

In addition, the PTM image can be easily obtained not only for the target product but also for the reference product. That is, as long as 3D shape data for a reference product for which a gating design has been specified and manufactured is provided, PTM images can be easily obtained for reference products through data processing as well. Accordingly, database construction and expansion for reference products can also be easily performed.

On the other hand, one product may be represented by a plurality of pixels on the projection surface. The higher the number of pixels, the more detailed the product can be expressed. The PTM image expresses the difference in the protrusion thickness of the product projected on the projection surface by varying the brightness in each pixel.

Therefore, the higher the number of pixels, the more accurately the characteristics of the product itself can be expressed. However, as the number of pixels increases, the amount of data to be processed inevitably increases. Therefore, a PTM image for a product can have approximately 200x200 pixels.

For example, when a specific projection plane penetrating a product and including the entire product is expressed as an image of 200x200 pixels, each pixel has an xy coordinate as well as a brightness value corresponding to the protruding thickness of the product.

Looking at the target product image shown in FIG. 1( a ), it can be seen that the portion protruding upward in the shape of a cylinder at the center of the body has the largest protrusion thickness in the entire target product. Assuming that the projection surface is a plane perpendicular to the cylindrical projection direction, it can be seen that the projection thickness at the cylindrical portion with respect to the projection surface is the same.

Therefore, it can be seen that when the target product shown in Fig. 1(a) is converted into a PTM image as in Fig. 1(b), the cylindrical portion may appear the brightest.

When the PTM image of the target product is acquired, the PTM image of the reference product to be compared with the PTM image should be prepared. That is, a database must be prepared.

The database may be prepared separately for each reference product. Here, referring to each reference product, it may be prepared separately for each item (category).

The more categories you compare against, the more likely it is that a reference product that is most similar to the target product actually exists. Therefore, the more categories, the more reliable gating design can be performed. This means that as the gating design experience according to an embodiment of the present invention is accumulated, the database is expanded, and learning is performed as described below, so that an evolving gating design can be preferably performed.

3 shows examples in which a PTM image is stored in a database according to an embodiment of the present invention.

Here, the database is a database storing information about the reference product. That is, it is a database in which PTM images of reference products are stored, and therefore it can be called a reference database, and it can also be called a learning database because information for performing learning is stored.

As described above, in the reference or learning database, the PTM images may be stored in each reference product or category. 3 shows an example in which three conventional products are distinguished from each other, and a plurality of PTM images are stored in each conventional product. That is, the conventional products shown in Figs. 3(a), 3(b) and 3(c) are divided into different products, and therefore, they can be said to have different gating designs. Of course, for an effective gating design, the number of conventional products should be greater.

One category may contain a basic PTM image for one reference product. Here, the basic PTM image may be an original PTM image generated through 3D data. In addition, the basic PTM image may be converted into sampling images that can be sampled in various ways. The sampling image may include images rotated, reduced, enlarged, and blurred with respect to the basic PTM image. That is, the processed images may be included. Here, the sampling image may be obtained through simple data processing on the basic PTM image. Of course, as described above, the basic PTM image may also be obtained through simple data processing using 3D shape data.

For example, one category may include a basic PTM image and a plurality of sampling images. As shown in FIG. 3A , a basic PTM image and a plurality of sampling images (processed PTM images) may be pre-stored in one conventional product category.

Basically, an embodiment of the present invention compares the basic PTM image of a target product with a plurality of images in a plurality of categories, derives a similar image, and finds a category (conventional product) of a similar image.

Of course, it may be possible to find a similar reference product by comparing the basic PTM image of the target product with the basic PTM image of each of the reference products. However, due to the scale difference between the target product and the reference product, the difference in protrusion thickness, the difference in the rotation angle of the projection surface in the protrusion thickness direction, etc. It would be more preferable to compare with

Hereinafter, a gating design system according to an embodiment of the present invention will be described in detail with reference to FIG. 4 .

The gating design system may include a user interface or an application programming interface (API) 10 and a decision core 30 .

The API 10 may be referred to as an interface for providing information to the decision core 30 and requesting the decision core 30 to operate. In addition, the API 10 may be referred to as an interface for receiving the result of the operation of the decision core 30 , that is, the decision result and providing it to the user.

Here, the API 10 may provide the PTM information of the target product to the determination core 30 , and may provide a PTM image obtained by imagining the PTM information to the determination core 30 . That is, it can be said that the API is provided to provide information on the target product to the determination core 30 .

For example, the API 10 may provide PTM image information for a target product to the decision core 30 and instruct the decision core to recommend a similar conventional product. Specifically, the API may instruct the decision core to output the decision result in what form.

This embodiment may further include a decision core database (DB, 40). That is, it may further include a judgment core database 40 for storing the learning results learned through the PTM images for the conventional product.

Details of the learning database 50 have been described above.

The determination core 30 may output a similar reference product among reference products stored in the database 50 with respect to the target product input through the image recognition model. Details of the image recognition model will be described later.

The decision core 30 may perform learning through the PTM image of the reference product stored in the learning database 50 . Such a learning result, that is, a learning result may be stored in the decision core database 40 .

When the PTM image of the target product is input to the decision core 20 through the API 10, the decision core 20 analyzes the input PTM image and compares it with the learning results stored in the decision core database 40, similar to The judgment result of what the reference product is is output. That is, it can be said that learning is performed before input and output are performed, and output on input is performed by reflecting the previously performed learning result.

The gating design for the target product is completed, and PTM information for the target product may be stored in the learning database 50 . That is, the learning database 50 may be expanded. As the learning database 50 is continuously expanded, re-learning may be performed. As such re-learning is performed, learning outcomes can evolve. Therefore, as the learning result by re-learning evolves, more accurate and similar judgment results can be derived.

Meanwhile, the image recognition model may be provided in various forms. Therefore, the gating design method and system according to the present embodiment can be said to use the provided image recognition model.

Accordingly, the decision core 30 and the decision core database 40 may be separately provided. Of course, the decision core 30 and the database 40 may be constructed as one system, and this may be referred to as the decision core system 20 . In this case, an embodiment of the present invention may include an API and a decision core system. In addition, apart from the decision core database 40 , the present embodiment may further include a learning database 50 .

Like the decision core 30 , the API may be modified in various forms, and the provider of the API and the decision core or the decision core system may be different from each other. Accordingly, one embodiment of the present invention can also be said to relate to a gating design method and system including a decision core 30 for providing a decision result to the API through information input through the API.

In addition, the provider of the decision core 30 may provide the database format together. Accordingly, an embodiment of the present invention includes a determination core 30 as an image recognition model for providing a determination result to the API through information input through the API and a database 40 of the determination core 30 It can also be said to be related to the gating design method and system. In other words, the judgment core 20 as an image recognition model includes a judgment core 30 (a narrow judgment core) and a judgment core database 40 that stores a learning result through the judgment core 30, and the judgment core 20 in a broad sense is distinguished from each other. can be provided.

The gating design method and system according to the present embodiment can be provided by using the judgment core 30 in a narrow sense provided by other providers, and in this embodiment using the judgment core 20 in the broad sense provided by other providers. A gating design method and system according to may be provided.

Meanwhile, in the present embodiment, the design DB 60 storing specific information on the gating design may be included. The design DB may store gating design information applied to each of the reference products.

A reference product for the target product is derived through the determination core 20 , and the user can know what the reference product is. If the user knows what the reference product is, the user can know the specific gating design information to which the reference product is applied. That is, it is possible to know specific gating design information applied to the reference product through the design DB.

Information of the reference product may be input through the API 10 , and gating design information for the reference product may be output from the design DB.

FIG. 4 illustrates requesting and outputting information on the reference product to the design DB 60 through the API 10 . However, it is possible to request information on a similar reference product from the determination core 20 through the API 10 and to output information about the reference product after determining what a similar reference product is.

Hereinafter, a gating design flow according to an embodiment of the present invention will be described in detail with reference to FIG. 5 .

The similarity determination ( S20 ) is performed through the PTM image or PTM image information of the target product. Such similarity determination may be performed by the determination core 30 .

Meanwhile, PTM information of the target product may be input to the determination core 30 to determine the degree of similarity ( S10 ). The input of such information may be performed through the API 10 .

The start of the overall gating design flow can be called 3D modeling (S1) of the target product. That is, after designing the shape of the target product in 3D, gating design can be started.

After the target product is 3D modeled, the division plane may be set (S2) through angle analysis or the like. That is, the division surface of the mold can be set. When such a division plane is set, a PTM image of the target product may be set. That is, PTM image information of the target product can be easily generated through the 3D data and the split surface data of the target product.

Here, the 3D modeling of the target product, the setting of the division plane, and the PTM image generation may be performed through separate processing, and the final result of the processing may be input to the decision core 30 through the API 10 .

The determination core 30 determines the similarity and outputs the determination result based on the input target product information and pre-stored reference product information ( S30 ).

The output format of the determination result may be designated through the API 10 . For example, whether to output only the most similar reference product, to output the degree of similarity together, or to output a plurality of reference products sequentially according to the degree of similarity can be specified through the API.

The API receives the judgment result of the judgment core and presents a similar reference product (S40).

If a reference product that is as similar as possible is presented, a gating design applied to the reference product may be presented ( S50 ). That is, the gating design stored in the design DB 50 may be presented. The designer may consider, reflect, or modify the proposed gating design so that the target product gating design is finally performed.

Hereinafter, an image recognition model applied to the decision core will be described in detail with reference to FIGS. 6 and 7 . That is, the detailed contents of the similarity determination ( S20 ) described with reference to FIG. 5 will be described. Here, the image recognition model is an example of artificial intelligence, and deep learning will be described in detail.

By comparing the PTM image of the target product and the PTM image of the reference product, it is necessary to find a reference product similar to the target product. This can be done through deep learning without the designer doing it manually.

Artificial intelligence is a field of computer science and information technology that studies how computers can do the thinking, learning, and self-development that human intelligence can do. Machine learning, one of the research fields of artificial intelligence, can mean a system that makes predictions based on empirical data and improves its own performance through learning.

Deep learning technology, which is a type of machine learning, learns by going down to a deep level in multiple stages based on data.

Deep learning can represent a set of machine learning algorithms that extract core data from multiple pieces of data as the level goes up.

The deep learning structure may include an artificial neural network (ANN), for example, the deep learning structure is a deep neural network such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a deep belief network (DBN). (deep neural network) can be configured.

Referring to FIG. 6 , the artificial neural network may include an input layer, a hidden layer, and an output layer. Each layer includes a plurality of nodes, and each layer is connected to the next layer. Nodes between adjacent layers may be connected to each other with weights.

Here, the hidden layer may have a single layer or a plurality of layers. The former may be referred to as a single-layer neural network, and the latter may be referred to as a multi-layer neural network. 4 shows an example in which a multi-layer neural network is applied to a decision core.

The decision core may form a feature-map by discovering a certain pattern from the input data. The data input to the decision core may be PTM image information of the target product.

For example, as shown in FIG. 6 , the decision core extracts a low-level feature (layer 1), an intermediate-level feature (layer 2), and a high-level feature, recognizes an object, and outputs the result (label) have.

The artificial neural network can be abstracted into higher-level features as it goes to the next layer. And, it can be said that the number of nodes decreases as the next layer progresses.

Each node may operate based on an activation model, and an output value corresponding to an input value may be determined according to the activation model.

An output value of an arbitrary node, for example, a low-level feature (layer 1) may be input to a node of a next layer connected to the node, for example, a node of a middle-level feature (layer 2). A node of a next layer, for example, a node of an intermediate-level feature (layer 2) may receive values output from a plurality of nodes of a lower-level feature (layer 1).

In this case, the input value of each node may be a value in which a weight is applied to the output value of the node of the previous layer. The weight may mean the strength of a connection between nodes.

In addition, the deep learning process can be viewed as a process of finding an appropriate weight.

Meanwhile, an output value of an arbitrary node, for example, the intermediate-level feature (lalyer 2) may be input to a next layer connected to the corresponding node, for example, a node of the upper-level feature (label). A node of a next layer, for example, a node of a higher-level feature (label) may receive values output from a plurality of nodes of an intermediate-level feature (layer 2).

The artificial neural network may extract feature information corresponding to each level by using a learned layer corresponding to each level. The artificial neural network may sequentially abstract and output the highest-level feature information (label).

The determination core may determine the degree of similarity by comparing the characteristic information of the highest level with the characteristic information of pre-learned conventional products.

Here, the similarity can be defined as 1 in the case of the same product, and the closer to 1, the more similar.

As shown in FIG. 6 , with respect to the input target product, the judgment core may present the most similar conventional product according to the determined degree of similarity. The degree of similarity as well as the conventional product can be presented.

In addition, the judgment core may sequentially present a plurality of conventional products according to the degree of similarity as well as the most similar conventional products. Of course, the degree of similarity to the conventional product may also be presented.

Here, the determination core may provide the determination result as an API, and the determination result may be presented to the user through the API.

The deep learning structure according to the present invention may use various well-known structures as well as the aforementioned multi-layer neural network network. For example, the deep learning structure according to the present invention may be a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a Deep Belief Network (DBN).

RNN (Recurrent Neural Network) is widely used in natural language processing, etc., and is an effective structure for processing time-series data that changes with time. .

DBN (Deep Belief Network) is a deep learning structure composed of multi-layered Restricted Boltzman Machine (RBM), a deep learning technique. By repeating Restricted Boltzman Machine (RBM) learning, when a certain number of layers is reached, a Deep Belief Network (DBN) having the corresponding number of layers can be configured.

7 shows an example in which similarity determination is performed through a decision core using CNN. That is, it may be the same as that shown in FIG. 4 except for the deep learning structure. Therefore, a detailed description of the CNN and a flow using the same will be omitted.

Similarly, the PTM image of the input target product may be analyzed by the judgment core, and reference products may be output according to the degree of similarity by comparing with the learning result.

As shown in FIG. 7 , a class 1 reference product having a similarity value of 0.6 closest to 1 may be output.

On the other hand, casting and injection are very similar to each other as processes for manufacturing a product by melting a raw material and injecting it into a mold, and then solidifying the raw material. Casting uses metal raw materials, while injection uses plastic raw materials.

Therefore, it will be possible to apply the gating design method and system described above to not only casting but also injection. In this case, the casting mold (mold) can be said to correspond to the injection mold. Therefore, according to an embodiment of the present invention, not only casting but also injection design can be performed very quickly and accurately.

In the above, when preparing a gating design method for a target product, a method for finding a reference product that is as similar as possible in order to refer to the gating design method of the reference product has been mainly described. Specifically, it has been described that PTM can be used to easily and accurately find a reference product that is as similar as possible.

The gating design method of the reference product may not be one but may be provided in plurality. For example, a plurality of ingate entities such as the number, position, cross-sectional shape, and cross-sectional area of ingates through which molten metal flows into the mold may be provided.

In addition, when there are a plurality of gating design schemes to which different ingate entities are applied to one reference product, the evaluation index for each gating design scheme may be different.

Here, the evaluation index may be expressed in plurality as an index for evaluating the gating design method. That is, the evaluation index can be said to be an index that can intuitively and numerically indicate which aspect is relatively superior in gating design.

An example of a representative evaluation index will be described as follows. For convenience of description, casting is assumed.

The total time required for pouring the molten metal into the mold may be referred to as the filling time.

The amount of gas generated or mixed in the product during the casting process may also be one of the evaluation indicators. The gas amount evaluation index may indicate the total amount of gas and distribution by location.

An oxide amount generated during the casting process may also be one of the evaluation indicators. The oxide evaluation index may indicate the total amount of oxide and distribution by location.

The total time required for the molten metal to solidify may be referred to as a solidification time.

Also, a porosity amount generated during the casting process may be one of the evaluation indicators. The coagulation defect evaluation index may indicate the total amount of coagulation defects and the distribution by location.

The filling time, gas amount, oxide, solidification time and solidification defect are examples of evaluation indices, and each of these may be referred to as detailed evaluation indices. Assuming the same product manufacturing performance, it can be seen that the lower the overall evaluation index or the lower the sum of the detailed evaluation indexes, the better the gating design is.

Of course, these plurality of detailed evaluation indicators may conflict with each other. That is, if one detailed evaluation index is excellent, the other detailed evaluation index may be low. Therefore, it is possible to select an optimal gating design method through the sum of detailed evaluation indices, or to select an optimal gating design method by giving importance to specific detailed evaluation indices. For example, in a casting environment in which an oxide evaluation index is very important, a gating design method having the best oxide evaluation index may be selected even if other detailed evaluation indexes are relatively low.

However, it takes a very long time to obtain an evaluation index for one gating design method. A simulation of the gating design method is performed, and as a result, an evaluation index is derived. Here, in general, it takes about 5 hours or more to perform the simulation.

Therefore, it can be said that it is a very time-consuming task to prepare an optimal gating design method by repeatedly performing simulation while correcting the gating design method. Of course, it is possible to reduce the number of simulations by preparing a gating design method for the target product based on the gating design method for the reference product. Nevertheless, it is inevitably necessary to repeatedly perform simulations.

In order to solve this problem, in the present embodiment, evaluation indicators for a reference product are presented, and a gating design method capable of selecting an optimal gating design method through the presented evaluation indicators can be presented.

The gating design method according to the present embodiment may be premised on presenting a reference product similar to the target product through the determination core. That is, the designer can use the judgment core to find a reference product through a computer, without manually finding a reference product similar to the target product.

As shown in FIG. 4 , when the designer provides information on the target product to the decision core 20 through the API 10 , the decision core outputs a similar reference product through the API 10 .

The DB 20 of the decision core may store identification numbers or identification IDs for a plurality of reference products, and when outputting a specific reference product as a similar reference product, the ID or identification number of the specific reference product may be output.

When the designer inputs the ID of the reference product into the design DB 60 through the API 10 , the gating design method of the reference product may be output through the design DB 60 . Of course, the designer may refer to the gating design method by accessing the reference product storage area in the design DB 60 .

Accordingly, a plurality of reference product IDs are stored separately in the design DB 60 , and a gating design method may be stored in each reference product ID. In addition, a plurality of gating design methods may be stored in the reference product ID.

Here, each gating design method may be simulated in advance. In addition, the evaluation index, which is a result of the simulation in advance, may be stored in the design DB 60 .

Accordingly, a plurality of gating design methods may be pre-stored for one reference product, and an evaluation index that is a simulation result of the plurality of gating design methods may be pre-stored.

The evaluation index is an index indicating the excellence of the gating design method, and as described above as a detailed index, may include at least one of charging time, gas amount, oxide, solidification time, and solidification defect.

For example, the reference product A may have gating design methods a, b, c, and d stored in advance. In each gating design method, each evaluation index may be stored. The evaluation index may include numerical values of the detailed evaluation index.

Therefore, when the reference product A is presented to the target product, the designer can select one optimal gating design method by looking at the evaluation indicators presented in the a, b, c, and d gating design methods.

Here, the design DB 60 may be provided by a provider that provides a gating design method through software. That is, the design DB 60 may be built in advance and provided to the designer. Of course, the provided design DB 60 may be updated as reference products are added.

Since the design DB 60 has pre-simulated results for a plurality of gating design methods as evaluation indexes, there is no need to perform simulations for them at the design site. That is, since the software provider performs the simulation that takes a lot of time in advance, and the result is stored and provided, it is possible to very effectively and significantly reduce the number and time of performing the simulation at the design site.

In the above-described embodiment, as shown in FIG. 5 , a similar reference product is presented (S40) and a gating design method of a similar reference product is presented (S50), and a gating design method of the target product is determined through the presented gating design method The step of (S60) has been described.

In this embodiment, after a similar reference product is presented, specific steps for finally performing the gating design of the target product will be described in detail.

A method of selecting an optimal gating design method from among a plurality of gating design methods will be described in detail with reference to FIG. 8 .

When a reference product similar to the target product is determined, a gating design method applied to the reference product may be presented ( S51 ). For example, the importance of detailed evaluation indicators may vary depending on the casting environment. Therefore, it is preferable to present a plurality of gating design methods instead of one gating design method for the reference product.

Each gating design method has a different evaluation index, and an optimal gating design method can be selected from among a plurality of gating design methods according to the casting environment.

To this end, the gating design method is preferably presented together with the evaluation index (S52).

The designer can review the evaluation index of each of the gating design methods a, b, c and d for the reference product A. For example, the gating design method a may have the best oxide evaluation index than other design methods. In addition, the gating design method a may have moderately excellent evaluation indicators such as charging time. If the oxide evaluation index is the most important design factor over other detailed evaluation indexes in the casting environment, the designer can select the gating design method a.

The gating design scheme may be implemented through an entity. That is, a gating design method may be prepared through a combination of a plurality of entities.

Entities may include product entities that are basic building blocks for the shape of products and/or molds. Furthermore, the entity may include a core entity and a chill entity. The product entity, core entity, and cooling entity are entities designed in 3D in relation to the shape of the mold, which may be referred to as the mold entity. Accordingly, the template entity can be created as 3D data and presented graphically through software such as CAD.

The present inventor has proposed an entity-based gating design through Korean Patent Application No. 10-2017-0031858. Accordingly, detailed description of the entity will be omitted.

Entities may include casting entities related to how to feed and pour molten metal into the mold.

The casting entity may include an ingate entity. In more detail, the ingate entity may include information on the number of ingates, positions of ingates, and the shape and size of ingates.

Therefore, it can be said that the gating design method is determined by combining the mold entity and the casting entity.

When different casting entities are combined for the same casting entity to determine a gating design method, different evaluation indicators are obtained according to the characteristics of each casting entity. Accordingly, the designer can view the evaluation index and select a gating design method having the optimal evaluation index.

Of course, casting entities may include pouring path entities, path-line entities, joint entities, branch entities, vent entities and riser entities. have. The riser entity may specifically include a side riser entity, a top riser entity, and a riser neck entity.

As a result, the gating design method may be generated through 3D data in which a plurality of entities are combined. That is, the gating design method according to the present embodiment can be implemented through software, and it can be said that the gating design method is formed of 3D data through the software.

Of course, the gating design method according to the present embodiment may include performing a simulation on the finally determined gating design method. That is, it may be provided in the form of software that forms a gating design scheme and simulates it. However, since the simulation itself is very complex and takes a lot of time, software for forming a gating design scheme and software for performing the simulation can be distinguished.

That is, it is possible to finally verify the gating design method by forming a gating design method and inputting it into simulation software to derive simulation results.

Meanwhile, in this embodiment, in order to minimize simulation performance, each gating design method may be visually presented to a designer in a 3D form or a 2D form having a 3D shape. A plurality of the aforementioned entities may appear in the presented graphic.

In addition, each gating design method can be represented by including an evaluation index that is a result of a simulation. Of course, since the evaluation index is expressed as a numerical value, the designer can intuitively judge the superiority of the gating design method by looking at the numerical value.

When the designer selects the design method a of the reference product A, a design method in which the mold entity of the target product and the casting entity of the selected design method a are combined may be presented ( S53 ). In this case, the evaluation index of the design method a may also be presented. Of course, the same can be said for a case where another design method is selected.

A designer may intuitively and/or empirically determine a correlation between a plurality of casting entities and an evaluation index that appear visually as a design method. For example, when the number of ingates is large, it can be seen that the charging time evaluation index is relatively excellent.

Accordingly, it is possible for the designer to select any one of a plurality of design methods, and to easily correct the selected design method in consideration of entities applied to the other design method and an evaluation index thereof.

As mentioned above, there are many types of casting entities. However, it can be said that the ingate entity has the greatest influence on the evaluation index. Accordingly, the designer can expect that the evaluation index is obtained in a desired direction by correcting the in-gate entity in the selected gating design method.

Of course, it is possible to calibrate the ingate entity as well as other casting entities. This correction is not performed simply and vaguely. This is because the entity used in the correction is the entity applied to the gating design method in which the evaluation index is already presented. Accordingly, the designer can very easily grasp in what form the evaluation index can be corrected while calibrating the casting entity.

On the other hand, when one gating design method has a unique evaluation index, the influence of each casting entity constituting the gating design method on the evaluation index may be considered. That is, in the gating design method a, the influence of a specific in-gate entity on the charging time evaluation index may be considered. Similarly, in the gating design scheme b, the influence of other specific in-gate entities on the charging time evaluation index may be considered.

Accordingly, when the ingate entity applied to the gating design method b is combined with the gating design method a, the charging time evaluation index in the gating design method a may be corrected.

As a result, a new evaluation index can be suggested by combining a plurality of gating design methods for a reference product. Of course, the combined gating design scheme can be presented visually.

Here, the presentation of the new evaluation index can be said to schematically suggest the optimal degree for the combined gating design method. Accordingly, the designer can expect the evaluation index in the desired direction through the correction of the casting entity.

Finally, when a gating design method is selected, it is possible to perform an optimal gating design (S60) by simulating it (S55).

9 shows an example of the finally determined gating design. Fig. 9 (a) is an example shown in a 2D image, and Fig. 9 (b) is an example shown in a 3D image with respect to the gating design.

Of course, FIG. 9 may show that the gating design method of the reference product is applied to the target product.

Although not shown, when the gating design is presented, the evaluation index may also be presented. And, when entities are changed in the gating design, the evaluation index may also be changed to reflect this.

It becomes possible for the designer to tentatively determine the gating design through the graphical gating design and evaluation index.

Described in the detailed description of the invention.

Claims

providing to the judgment core a projected thickness map (PTM) image of the target product for a specific projected surface of the target product;

determining a degree of similarity between the PTM image of the target product and the PTM image of the reference product through the determination core; and

In order to guide the gating design applied to the reference product to the gating design of the target product, a gating design method comprising the step of presenting a similarity determination result between the target product and the reference product.
The method of claim 1,

The specific projection surface is a gating design method, characterized in that the plane passing through the target product and the reference product, respectively.
3. The method of claim 2,

A gating design method, characterized in that the specific projection surface of the target product and the specific projection surface of the reference product are preset based on the same reference.
The method of claim 1,

The specific projection surface is a gating design method, characterized in that the divided surface of the mold
5. The method of claim 4,

The thickness is a gating design method, characterized in that the total length of each of the target product and the reference product in the mold extraction direction.
6. The method according to any one of claims 1 to 5,

The PTM image is a gating design method, characterized in that it is a 2D image in which the deviation of the thickness with respect to the specific projection surface is expressed as a brightness deviation.
The method of claim 1,

The gating design method according to claim 1, further comprising: acquiring the PTM image through 3D shape data of the target product.
The method of claim 1,

The PTM image is provided to the decision core by a user's input through an application programming interface (API), and a decision result from the decision core is presented to the user through the API.
determining a similarity between a projected thickness map (PTM) image of the target product with respect to a specific projection surface of the target product and a PTM image of the reference product with respect to a specific projection surface of the reference product through a judging core; and

In order to guide the gating design applied to the reference product to the gating design of the target product, a gating design method comprising the step of presenting a similarity determination result between the target product and the reference product.
10. The method of claim 9,

The Gating design method, characterized in that the PTM image of the target product and the PTM image of the reference product are generated through conversion of each 3D shape data.
The method of claim 1,

A gating design method comprising the step of presenting an evaluation index for a gating design method of a reference product presented as a similar reference product from a database in which an evaluation index for a gating design method of reference products is stored.
12. The method of claim 11,

The database, gating design method, characterized in that the evaluation index for a plurality of gating design method for each of a plurality of reference products is stored.
13. The method of claim 12,

The evaluation index is a gating design method, characterized in that it includes a plurality of detailed evaluation indices for evaluating the optimal degree of the gating design method for the reference product.
14. The method of claim 13,

The detailed evaluation index is a gating design method, characterized in that it includes at least one of a filling time, a gas amount, an oxide, a solidification time, and a solidification bond in the casting process.
15. The method of claim 14,

In the database, a gating design method for a reference product and a gating design method, characterized in that the evaluation index derived as a simulation result thereof is stored.
16. The method according to any one of claims 11 to 15,

The gating design of a product is performed through a plurality of entities that are basic components,

The gating design method according to claim 1, wherein the entity includes a product entity for the shape of the product and an ingate entity through which the injected molten metal flows into the mold.
17. The method of claim 16,

The database, gating design method, characterized in that the gating design method, characterized in that stored in the reference product as a result of the simulation of the gating design method for applying different in-gate entities to the evaluation index.
18. The method of claim 17,

The gating design method comprising the step of presenting the different gating design methods and their evaluation indicators to be distinguished.
19. The method of claim 18,

When any one of the different gating design methods is selected, a gating design method comprising the step of presenting a gating design method in which the selected gating design method of the reference product is reflected to a mold entity for the shape of the target product; .
presenting a reference product similar to the target product through the judgment core; and

In order to guide the gating design applied to the reference product to the gating design of the target product, the evaluation index for the gating design method of the reference product presented as a similar reference product is obtained from the database in which the evaluation index for the gating design method of the reference product is stored. A gating design method comprising the step of presenting.