Shape data output program, shape data output method, and information processing device

Abstract

To properly classify multiple pieces of shape data to derive a relationship in size between sections.SOLUTION: An information processing apparatus 101 normalizes each of multiple pieces f shape data for each component in coordinate axis directions to generate unit shape data. The information processing apparatus 101 classifies the multiple pieces of shape data on the basis of the unit shape data for each shape data. The information processing apparatus 101 specifies a relationship in size between different sections of the shape data in a group on the basis of the sizes of the sections of the shape data in the classified group. The information processing apparatus 101 outputs information indicating the specified relationship in size between the different sections of the shape data, in association with the unit shape data of the shape data in the group.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a shape data output program, a shape data output method, and an information processing device. [Background technology]

[0002] Traditionally, designing and drawing 3D (Dimensions) shapes requires a lot of man-hours, so designs using past 3D shape data are sometimes carried out. However, past 3D shape data is often insufficiently classified, and there are a large number of similar shapes, so when designing a new product with a similar shape to an existing product, it is desirable to properly classify the past 3D shape data and standardize the design.

[0003] Prior art techniques include, for example, searching for standard parts having a range of attribute values ​​and shape feature data that match the attribute values ​​and shape feature data of the part to be designed, searching for standard parts having a range of attribute values ​​similar to the attribute values ​​of the part to be designed and shape feature data similar to the shape feature data of the part to be designed, and searching for example parts having attribute values ​​similar to the attribute values ​​of the part to be designed and shape feature data similar to the shape feature data of the part to be designed.

[0004] There is also a technique for searching for similar part model numbers associated with the extracted part number by referring to similar part data, transmitting part data of the extracted model number and part data of all searched similar part model numbers to a user terminal, and displaying a single-item search result details screen showing the model numbers of each part. There is also a technique for detecting a design process that directly corresponds to the extracted relevance information and a design process that corresponds to other relevance information to which the design parameters included in the relevance information are further related, and changing design parameters that specify the part shape based on the relevance information and a plurality of design processes that correspond to the other relevance information to generate the shape of the part.

[0005] There is also a technology that uses labels attached to the faces and edges of a recognition model to determine the topology of the recognition model, such as the unevenness of edges and the unevenness of vertices within a surface, to generate an objective function and constraint conditions, and to regulate the shape recognition of the recognition model, thereby executing shape recognition. There is also a technology that inputs a two-dimensional or three-dimensional shape into a computer or the like to create the shape and dimensions, and then allows an operator to change (delete, add, modify, etc.) the shape or dimensions. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 9-179892 [Patent Document 2] International Publication No. 2017 / 217032 [Patent Document 3] JP 2006-139486 A [Patent Document 4] JP 2004-21773 A [Patent Document 5] Japanese Patent Application Publication No. 3-206564 Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the conventional technology, when standardizing a design, it is difficult to appropriately classify design data of past designs and derive the dimensional relationships between parts such as edges and holes.

[0008] In one aspect, the present invention aims to appropriately classify a plurality of shape data and derive the dimensional relationship between parts. [Means for solving the problem]

[0009] In one embodiment, a shape data output program is provided which normalizes each of a plurality of shape data for each component in each coordinate axis direction to create unit shape data, classifies the plurality of shape data based on the unit shape data of each of the created shape data, identifies a dimensional relationship between different parts of the shape data within the classified group based on dimensions of parts of each shape data within the group, and outputs information indicating the identified dimensional relationship by correlating it with the unit shape data of the shape data within the group. Effect of the Invention

[0010] Advantageous Effects of Invention According to one aspect of the present invention, it is possible to obtain an effect that a plurality of shape data can be appropriately classified to derive a dimensional relationship between parts. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is an explanatory diagram illustrating an example of a shape data output method according to an embodiment. [Diagram 2] FIG. 2 is an explanatory diagram showing an example of a system configuration of an information processing system 200. As shown in FIG. [Diagram 3] FIG. 3 is a block diagram showing an example of the hardware configuration of the information processing device 101. As shown in FIG. [Figure 4] FIG. 4 is an explanatory diagram showing an example of the contents stored in the 3D shape DB 220. As shown in FIG. [Diagram 5] FIG. 5 is an explanatory diagram (part 1) showing an example of the contents stored in the standard shape DB 230. As shown in FIG. [Figure 6] FIG. 6 is a block diagram showing an example of the functional configuration of the information processing device 101. As shown in FIG. [Figure 7] FIG. 7 is an explanatory diagram showing an example of creating unit 3D shape data. [Figure 8] FIG. 8 is an explanatory diagram showing an example of classification of 3D shape data. [Figure 9A] FIG. 9A is an explanatory diagram showing an example of 3D shape data. [Figure 9B]FIG. 9B is an explanatory diagram showing an example of constructing a relational equation showing the dimensional relationship between parts. [Figure 10] FIG. 10 is an explanatory diagram (part 2) showing an example of the contents stored in the standard shape DB 230. In FIG. [Figure 11] FIG. 11 is a flowchart showing an example of a preparation process procedure of the information processing device 101. [Figure 12] FIG. 12 is a flowchart illustrating an example of a specific processing procedure of the unit shape generating process. [Figure 13] FIG. 13 is a flowchart showing an example of a standard shape registration processing procedure of the information processing device 101. [Figure 14] FIG. 14 is a flowchart illustrating an example of a specific processing procedure of the shape classification process. [Figure 15] FIG. 15 is a flowchart illustrating an example of a specific processing procedure of the relational equation construction process. [Figure 16] FIG. 16 is a flowchart showing an example of a first design processing procedure of the information processing device 101. [Figure 17] FIG. 17 is a flowchart showing an example of the second design processing procedure of the information processing device 101. [Figure 18] FIG. 18 is an explanatory diagram showing an example of generating design data. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, with reference to the drawings, embodiments of a shape data output program, a shape data output method, and an information processing device according to the present invention will be described in detail.

[0013] (Embodiment) Fig. 1 is an explanatory diagram showing an example of a shape data output method according to an embodiment. In Fig. 1, an information processing device 101 is a computer that supports the design of an object. The object is an object to be designed, and is, for example, a three-dimensional object such as a part, a product, a construction member, or a building.

[0014] Here, because designing 3D shapes requires a lot of man-hours, it would be extremely useful to be able to reuse past 3D shape data for design.However, past 3D shape data is often insufficiently classified, and there are a large number of similar shapes, so manually searching for 3D shape data that matches the design intent takes time and effort.

[0015] For this reason, when designing a new product with a similar shape to an existing product, it is desirable to properly classify past 3D shape data and standardize the design. For example, if past 3D shape data can be properly classified and the dimensional relationship between parts of the 3D shape (parametric model) can be derived, then a design that specifies the 3D shape (so-called parametric design) can be performed by setting various dimensions for the model.

[0016] One method for classifying past 3D shape data is to compare 3D shape data to find similarity and classify the 3D shape data based on the similarity.Specifically, for example, it is possible to specify target 3D shape data and search for 3D shape data to be classified into the same group based on the similarity with the target 3D shape data.

[0017] The target 3D shape data is, for example, 3D shape data for specifying the design intent for an object. The design intent is, for example, the rough shape of the object (a shape without detailed dimensions), the dimensional ratio between some parts, the relative positional relationship between some parts, etc.

[0018] The target 3D shape data is specified, for example, by selecting any 3D shape data from 3D shape data designed in the past. As the target, for example, from among the past 3D shape data, 3D shape data that is different in detailed dimensions from the target object but was created with the same design intent as the target object is specified.

[0019] This classification method searches for 3D shape data that has the same shape as the target or a geometrically similar shape. However, even in 3D shape data with similar shapes, there may be cases where there is a relationship between the dimensions of parts depending on the design intent, and there may be cases where there is no relationship between the dimensions of parts depending on the design intent. Therefore, 3D shape data with the same shape or similar shapes are not necessarily 3D shape data that you want to classify into the same group.

[0020] Another option is to manually check past 3D shape data one by one and classify them while considering the design intent. However, manually classifying a huge number of 3D shape data takes time and effort, which leads to an increase in the number of steps required for designing the target object. Furthermore, it is difficult for people other than the designer to determine the design intent from the 3D shape data.

[0021] In this embodiment, a shape data output method is described in which a plurality of shape data are classified based on unit shape data obtained by normalizing each shape data, and a dimensional relationship between parts in the shape data classified by the unit shape data is specified to construct a parametric model. The parametric model is information that corresponds to the unit shape data and indicates the dimensional relationship between parts in the shape classified by the unit shape data. Here, an example of processing by the information processing device 101 is described.

[0022] (1) The information processing device 101 normalizes each of the plurality of shape data for each component in each coordinate axis direction to create unit shape data. Here, the shape data is information that represents the shape of the design, and includes, for example, position information, surface information, hole information, etc. of each feature point of the design. The feature points are, for example, the vertices of the design, the center points of holes, etc.

[0023] The position information of a feature point indicates, for example, the coordinates of the feature point in a Cartesian coordinate system. The surface information of a feature point is, for example, information that specifies the surface to which the feature point belongs. The hole information of a feature point is, for example, information that specifies the shape, size, etc. of the hole to which the feature point belongs. The shape data may include, for example, color information, material information, etc. of each surface.

[0024] The shape data is, for example, three-dimensional shape data. More specifically, the shape data is, for example, design data previously designed using three-dimensional CAD (Computer Aided Design). However, the shape data may be two-dimensional shape data.

[0025] Normalizing shape data for each component in each coordinate axis direction means transforming the shape data while maintaining the dimensional relationship between parts in each coordinate axis direction (e.g., x-axis, y-axis, z-axis). Taking the x-axis direction as an example, for example, the shape data is transformed while maintaining the dimensional relationship between parts in the x-axis direction by moving the shape data to the origin and then dividing the x-coordinate value of each vertex of the shape data by the maximum coordinate in the x-axis direction of the shape data.

[0026] In the example of Fig. 1, 3D shape data 11 to 18 designed in the past are displayed as multiple shape data. Each of the 3D shape data 11 to 18 represents the shape of each design object in a three-dimensional space. In this case, the information processing device 101 creates unit shape data by normalizing each of the 3D shape data 11 to 18 for each component in each coordinate axis direction, for example.

[0027] (2) The information processing device 101 classifies a plurality of shape data based on the unit shape data of each of the created shape data. Specifically, for example, the information processing device 101 calculates the similarity between the unit shape data of each of the shape data. Any existing technology may be used to calculate the similarity between the unit shape data.

[0028] For example, the similarity between unit shape data may be calculated from a result of comparing images of unit shape data captured from multiple directions between the unit shape data. Then, the information processing device 101 classifies the multiple shape data such that shape data corresponding to a combination of unit shape data whose calculated similarity is equal to or greater than a threshold value are grouped together.

[0029] That is, the information processing device 101 compares the unit shape data of each piece of shape data and classifies shape data having similar unit shape data into the same group. By classifying based on normalized unit shape data, not only the same or similar shapes but also shapes having partially different dimensions (for example, the vertical and horizontal dimensions are the same but the depth dimensions are different) are classified into the same group.

[0030] 1, it is assumed that target 3D shape data 20 is specified, and 3D shape data 16-18 are classified into the same group 30 from among 3D shape data 11-18 as figures similar to the target 3D shape data 20. The 3D shape data 16-18 are a collection of 3D shape data in which the similarity between the unit 3D shape data and the target 3D shape data 20 is equal to or greater than a threshold value.

[0031] Note that, although the case where the target 3D shape data 20 is specified has been described as an example here, the present invention is not limited to this. For example, the information processing device 101 may specify a unit 3D shape data group in which the similarity between each unit 3D shape data is equal to or greater than a threshold, and classify 3D shape data corresponding to each unit 3D shape data in the unit 3D shape data group into the same group.

[0032] (3) The information processing device 101 identifies the dimensional relationship between different parts of the shape data in the group based on the dimensions of the parts of each shape data in the classified group. Here, the part of the shape data is a part of the shape represented by the shape data. The part is, for example, a side. The dimension of the part is, for example, the length of the side.

[0033] The shape data classified into the same group are similar to each other in terms of unit shape data (the normalized shapes are substantially the same) and have corresponding parts. For example, each vertex and each side of the 3D shape data 16 in the group 30 corresponds to each vertex and each side of the other 3D shape data 17 and 18 in the group 30.

[0034] The information processing device 101 derives the relationship between a certain part and other parts by a method such as regression analysis based on the dimensions of the parts of each shape data in the group. Specifically, for example, the information processing device 101 creates a relational expression indicating the dimensional relationship between different parts by using one of the multiple parts in each shape data as a response variable and the other parts as explanatory variables.

[0035] In the example of FIG. 1, one of the relational expressions created based on the dimensions of each side of the 3D shape data 16 to 18 classified into the same group is the side l 1 and edge l 2 The equation "l" shows the relationship between the dimensions 1 =a·l 2 +b" is shown (a and b are coefficients). This relational expression "l 1 =a·l 2 "+b" is one of the parametric models of 3D shapes classified by unit 3D shape data of 3D shape data 18.

[0036] Each side of the 3D shape data corresponds to each side of the unit 3D shape data of the 3D shape data. For example, side l in the 3D shape data 18 1 and edge l 2 corresponds to any one of the sides in the unit 3D shape data of the 3D shape data 18. It is possible to specify which side in the unit shape data each variable (parameter) included in the relational expression corresponds to.

[0037] (4) The information processing device 101 outputs information indicating the dimensional relationship between different parts of the identified shape data in association with the unit shape data of the shape data in the group. Specifically, for example, the information processing device 101 may output unit shape data to which a relational equation of dimensions between parts is added as shape data to be used for new design of the target object.

[0038] In the example of FIG. 1, for example, the 3D shape data 18 is used as an example of a relational expression between dimensions of different parts in the 3D shape data 18 (for example, the relational expression “l 1 =a·l 2+b") is added to the unit 3D shape data 18'', which is output. The unit 3D shape data 18'' is the unit 3D shape data of the 3D shape data 18.

[0039] In this way, the information processing device 101 can construct a parametric model (relationship of dimensions between parts) of shapes classified by unit shape data. This enables parametric design and reduces the number of steps required for design. For example, when creating a new design, design data can be easily generated by setting the dimensions of each part according to the parametric model based on the unit shape data (parametric design).

[0040] In the example of Figure 1, the relationship between the dimensions of the parts (for example, the relationship "l 1 =a·l 2 Based on the unit 3D shape data 18'' to which the relational expression "+b") has been added, the design of the target object can be performed. For example, when creating a new design, a designer can specify design requirements (e.g., detailed dimensions), and by converting the dimensions of each part based on the unit 3D shape data 18'' so as to satisfy a relational expression, design data according to the design intent can be generated.

[0041] (System configuration example of information processing system 200) Next, a system configuration example of an information processing system 200 including the information processing device 101 will be described. In the following description, "3D (three-dimensional) shape data" will be taken as an example of shape data. The information processing system 200 is applied to a computer system that supports the design of 3D shapes related to products and buildings, for example.

[0042] Fig. 2 is an explanatory diagram showing an example of a system configuration of an information processing system 200. In Fig. 2, the information processing system 200 includes an information processing device 101 and a client device 201. In the information processing system 200, the information processing device 101 and the client device 201 are connected via a wired or wireless network 210. The network 210 is, for example, the Internet, a LAN, a WAN (Wide Area Network), or the like.

[0043] Here, the information processing device 101 has a 3D shape DB (Database) 220 and a standard shape DB 230. The information processing device 101 is, for example, a server. The 3D shape DB 220 is a database that stores 3D shape data designed in the past. The standard shape DB 230 is a database that stores standard shape data. The contents stored in the 3D shape DB 220 and the standard shape DB 230 will be described later with reference to FIG. 4 and FIG. 5.

[0044] The client device 201 is a computer used by a user. The client device 201 is, for example, a PC (Personal Computer), a tablet PC, etc. The user is, for example, a designer who designs an object.

[0045] 2, only one client device 201 is shown, but the present invention is not limited to this. For example, the information processing system 200 may include multiple client devices 201. In addition, the information processing device 101 is provided separately from the client device 201, but the present invention is not limited to this. For example, the information processing device 101 may be realized by the client device 201.

[0046] (Example of hardware configuration of information processing device 101) Fig. 3 is a block diagram showing an example of a hardware configuration of the information processing device 101. In Fig. 3, the information processing device 101 has a CPU (Central Processing Unit) 301, a memory 302, a disk drive 303, a disk 304, a communication I / F (Interface) 305, a portable recording medium I / F 306, and a portable recording medium 307. In addition, each component is connected to each other by a bus 300.

[0047] Here, the CPU 301 is responsible for the overall control of the information processing device 101. The CPU 301 may have multiple cores. The memory 302 has, for example, a read only memory (ROM), a random access memory (RAM), and a flash ROM. Specifically, for example, the flash ROM stores an operating system (OS) program, the ROM stores application programs, and the RAM is used as a work area for the CPU 301. The programs stored in the memory 302 are loaded into the CPU 301 to cause the CPU 301 to execute the coded processes.

[0048] The disk drive 303 controls reading / writing of data from / to the disk 304 under the control of the CPU 301. The disk 304 stores data written under the control of the disk drive 303. Examples of the disk 304 include a magnetic disk and an optical disk.

[0049] The communication I / F 305 is connected to the network 210 through a communication line, and is connected to an external computer (for example, the client device 201 shown in FIG. 2) through the network 210. The communication I / F 305 manages the interface between the network 210 and the inside of the device, and controls input and output of data from the external computer. For example, a modem or a LAN adapter can be used as the communication I / F 305.

[0050] The portable recording medium I / F 306 controls reading / writing of data from / to the portable recording medium 307 under the control of the CPU 301. The portable recording medium 307 stores data written under the control of the portable recording medium I / F 306. Examples of the portable recording medium 307 include a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disk), and a USB (Universal Serial Bus) memory.

[0051] Note that the information processing device 101 may not have, for example, the disk drive 303, the disk 304, the portable recording medium I / F 306, and the portable recording medium 307 among the above-mentioned components. Also, the information processing device 101 may have, for example, a display, an input device, etc., in addition to the above-mentioned components. Also, the client device 201 shown in FIG. 2 can be realized by the same hardware configuration as the information processing device 101.

[0052] (Storage contents of various DB220, 230) 4 and 5, the contents stored in the various DBs 220, 230 of the information processing device 101 will be described. The various DBs 220, 230 are realized by storage devices such as the memory 302 and disk 304 shown in FIG.

[0053] Fig. 4 is an explanatory diagram showing an example of the contents stored in the 3D shape DB 220. In Fig. 4, the 3D shape DB 220 has fields for ID and 3D shape data, and stores 3D shape management information (for example, 3D shape management information 400-1 to 400-3) as records by setting information in each field.

[0054] Here, id is an identifier that uniquely identifies the 3D shape data. The 3D shape data is 3D shape data that was designed in the past. For convenience of explanation, each 3D shape data is represented as "D1, D2, D3, ...". For example, the 3D shape management information 400-1 indicates the 3D shape data D1 with id "1".

[0055] Fig. 5 is an explanatory diagram (part 1) showing an example of the contents stored in the standard shape DB 230. In Fig. 5, the standard shape DB 230 has fields for ID, standard shape data, relational expressions, and similar shape list, and stores standard shape management information (for example, standard shape management information 500-1, 500-2) as records by setting information in each field.

[0056] Here, id is the id of the 3D shape data from which the standard shape data was created. The standard shape data is unit 3D shape data registered as standard shape data. The relational expression is a mathematical expression that indicates the dimensional relationship between different parts (e.g., sides) in the 3D shape data from which the standard shape data was created.

[0057] Here, Re## indicates different mathematical expressions (## is a number). Each mathematical expression indicates, for example, the relationship of dimensions between different sides in 3D shape data. The similar shape list is a list of IDs of 3D shape data classified into the same group based on standard shape data (unit 3D shape data).

[0058] For example, standard shape management information 500-1 indicates standard shape data D1'' corresponding to 3D shape data D1 with id "1", relational expressions {Re11, Re12, ...}, and similar shape list {1, 7, 18, 21, 33}. Note that variables representing each part in the standard shape data (unit shape data) correspond to variables in the relational expressions. In other words, it is possible to identify which part in the standard shape data (unit shape data) corresponds to which variable in the relational expressions.

[0059] (Example of functional configuration of information processing device 101) FIG. 6 is a block diagram showing an example of a functional configuration of the information processing device 101. In FIG. 6, the information processing device 101 includes a receiving unit 601, a creating unit 602, a classifying unit 603, a specifying unit 604, a searching unit 605, a generating unit 606, an output unit 607, and a storage unit 610. The receiving unit 601 to the output unit 607 are functions that become a control unit, and specifically, the functions are realized by making the CPU 301 execute a program stored in a storage device such as the memory 302, the disk 304, or the portable recording medium 307 shown in FIG. 3, or by the communication I / F 305. The processing results of each functional unit are stored in a storage device such as the memory 302 or the disk 304. In addition, the storage unit 610 is realized by a storage device such as the memory 302 or the disk 304. Specifically, for example, the storage unit 610 stores the 3D shape DB 220 shown in FIG. 4 and the standard shape DB 230 shown in FIG. 5.

[0060] The receiving unit 601 receives the designation of the 3D shape data of the target. The 3D shape data of the target is, for example, 3D shape data for designating the design intent of the object, and is designated from among the 3D shape data stored in the 3D shape DB 220. The 3D shape data of the target may be 3D shape data in a state where the basic design (designed with rough dimensions) of the object has been completed.

[0061] Specifically, for example, the reception unit 601 receives the designation of the target 3D shape data by receiving, from the client device 201, the designation of an ID of any of the 3D shape data stored in the 3D shape DB 220. The reception unit 601 may also receive the target 3D shape data itself from the client device 201.

[0062] The creation unit 602 normalizes each of the 3D shape data for each component in each coordinate axis direction to create unit 3D shape data. Specifically, for example, the creation unit 602 extracts the minimum value in each coordinate axis direction from the coordinates of each feature point of each 3D shape data. The feature point is, for example, a vertex of a design object, a center point of a hole, etc.

[0063] The creation unit 602 then subtracts the extracted minimum value in each coordinate axis direction from each value of the coordinate of each feature point.The creation unit 602 then extracts the maximum value in each coordinate axis direction from the coordinate of each feature point after subtraction.The creation unit 602 then divides each value of the coordinate of each feature point after subtraction by the extracted maximum value in each coordinate axis direction to create unit 3D shape data for each 3D shape data.

[0064] Here, an example of creating unit 3D shape data will be described with reference to Fig. 7. In the following description, any 3D shape data among a plurality of 3D shape data may be referred to as "3D shape data Di" (where i is a natural number equal to or greater than 1).

[0065] FIG. 7 is an explanatory diagram showing an example of creating unit 3D shape data. In FIG. 7, the coordinates of each vertex j of the 3D shape data Di (indicated by a solid line in FIG. 7) are expressed as P ij (x ij ,y ij ,z ij ) (j=1, 2, ..., J). Here, the case where the 3D shape data Di is a "rectangular parallelepiped" will be described as an example. Also, the feature points of the 3D shape data Di are "vertices."

[0066] First, the creation unit 602 extracts the minimum value in each coordinate axis direction from the coordinates of each vertex j of the 3D shape data Di. Then, the creation unit 602 extracts the minimum value in each coordinate axis direction from the coordinates P ij The 3D shape data Di is translated by subtracting the minimum value of each coordinate axis direction from each value of . The new 3D shape data obtained here (dashed line in FIG. 7) is called "Di'", and the coordinates of each vertex are P ij '(x ij ',y ij ',z ij ').

[0067] x ij ',y ij ',z ij ' is expressed by the following formulas (1) to (3). Here, "i, j, J ∈ natural numbers" and "x ij,y ij ,z ij >0".

[0068] x ij '=x ij -Min([x i1 ,x i2 ,…,x iJ ]) ···(1) y ij '=y ij -Min([y i1 ,y i2 ,…,y iJ ]) ···(2) z ij '=z ij -Min([z i1 ,z i2 ,…,z iJ ]) ···(3)

[0069] Next, the creation unit 602 calculates the coordinates P ij Then, the creation unit 602 extracts the maximum value in each coordinate axis direction from the coordinates P ij Each value of ' is divided by the maximum value of each coordinate axis direction extracted to create normalized unit 3D shape data. The unit D shape data obtained here (dotted line in Figure 7) is called "Di''" and the coordinates of each vertex are called P ij ''(x ij ",y ij ",z ij '').

[0070] x ij ",y ij ",z ij '' is expressed by the following formulas (4) to (6).

[0071] x ij ''=x ij ' / Max([x i1 ',x i2 ',…,x iJ ']) ···(4) y ij ''=y ij ' / Max([y i1 ',y i2 ',…,y iJ']) ···(5) z ij ''=z ij ' / Max([z i1 ',z i2 ',…,z iJ ']) ···(6)

[0072] Returning to the description of Fig. 6, the classification unit 603 classifies the multiple 3D shape data based on the unit 3D shape data of each created 3D shape data. Specifically, for example, the classification unit 603 calculates the similarity between the unit 3D shape data of each 3D shape data. Next, the classification unit 603 identifies a combination of unit 3D shape data in which the calculated similarity is equal to or greater than a threshold value.

[0073] Then, the classification unit 603 classifies the plurality of 3D shape data such that the 3D shape data corresponding to each of the unit 3D shape data included in the identified combination of unit 3D shape data are grouped together. The threshold value can be set arbitrarily. For example, the threshold value is set to a value that allows the unit 3D shape data to be determined to match each other if the similarity is equal to or greater than the threshold value.

[0074] More specifically, for example, the classification unit 603 calculates the similarity between the unit 3D shape data by comparing the unit 3D shape data of the specified target 3D shape data with the unit 3D shape data of each 3D shape data in the 3D shape DB 220. For example, the classification unit 603 calculates the similarity between the images by comparing images of each unit 3D shape data captured from multiple directions. Then, the classification unit 603 calculates the similarity between the images by accumulating the calculated similarities between the images.

[0075] Then, the classification unit 603 classifies into the same group the 3D shape data corresponding to the combination of unit 3D shape data whose calculated similarity is equal to or greater than a threshold value. This makes it possible to extract 3D shape data whose unit 3D shape data is similar to the target 3D shape data from the 3D shape DB 220.

[0076] Here, an example of classification of 3D shape data will be described with reference to FIG.

[0077] Fig. 8 is an explanatory diagram showing an example of classification of 3D shape data. In Fig. 8, 3D shape data D11 to D15 are displayed. The 3D shape data D11 is 3D shape data of a target. The 3D shape data D12 is 3D shape data having a different orientation from the 3D shape data D11 of the target.

[0078] The 3D shape data D13 is 3D shape data that is similar to the 3D shape data D11 of the target. The 3D shape data D14 and D15 are 3D shape data that are partially different in length from the 3D shape data D11 of the target. 1 ~l 18 indicates a side in the 3D shape data D11. Also, the numerical values ​​in each of the 3D shape data D11 to D15 indicate the size of each side.

[0079] Here, when each of the 3D shape data D11 to D15 is normalized for each component in the coordinate axis direction as described in Fig. 7, unit 3D shape data 800 (corresponding to 3D shape data D11'' to D15'') having the same shape is created. In this case, the 3D shape data D11 to D15 are classified into the same group.

[0080] In this way, by determining similarity based on unit 3D shape data, it is possible to classify objects according to the ratio of sides in each coordinate axis direction. This makes it possible to classify not only objects with the same shape or similar shapes as the target, but also objects with some differences in length into the same group.

[0081] Returning to the explanation of Fig. 6, the identification unit 604 identifies the dimensional relationship between different parts of the 3D shape data in the group based on the dimensions of the parts of each 3D shape data in the classified group. Here, the part of the 3D shape data is the part of the design represented by the 3D shape data, for example, a part that represents a feature of the design such as an edge or a hole. The dimension of the part is, for example, the length of the edge or the diameter of the hole.

[0082] Specifically, for example, the identification unit 604 creates a vector for each part in each 3D shape data in the group, with the dimensions of the part in each 3D shape data as elements. Then, the identification unit 604 creates a relational equation showing the dimensional relationship between different parts based on the created vector for each part, with one of the multiple parts in each 3D shape data as the objective variable and the other parts as the explanatory variables. The dimensional relationship between different parts can be constructed by, for example, a method such as linear regression analysis, nonlinear regression analysis, or machine learning using a neural network.

[0083] The specifying unit 604 may also determine, as the objective variable, a part in which the variance of the elements of the created vector is relatively high among the multiple parts in each 3D shape data. This allows the relational equation to be constructed by excluding parts whose dimensions do not change between 3D shape data in a group from the objective variable. Note that the correlation coefficient between variables may be calculated, and variables with relatively low correlation may be excluded.

[0084] Furthermore, the identifying unit 604 may select, as an explanatory variable, a part having a relatively high contribution rate to the objective variable among a plurality of parts in each 3D shape data. The contribution rate (coefficient of determination) is a value indicating how much of the objective variable the explanatory variable can explain. To explain in more detail, for example, when constructing the relational expression, the identifying unit 604 selects, as an explanatory variable, a variable having a high contribution rate to the objective variable by a stepwise method.

[0085] Here, an example of constructing a relational equation that indicates the dimensional relationship between different parts in 3D shape data will be described with reference to FIGS. 9A and 9B.

[0086] FIG. 9A is an explanatory diagram showing an example of 3D shape data. FIG. 9B is an explanatory diagram showing an example of constructing a relational expression showing the relationship between dimensions between parts. Here, 3D shape data classified into the same group Ga based on unit 3D shape data is referred to as "3D shape data DG 1 ~D.G. M" M is the total number of 3D shape data in the group Ga.

[0087] In FIG. 9A, the 3D shape data DG i is 3D shape data DG 1 ~D.G. M (i=1, 2, ..., M). i In l i1 ~l i18 is 3D shape data DG i It shows the inner edge (part).

[0088] First, the identification unit 604 identifies each 3D shape data DG 1 ~D.G. M The direction of the x-axis, y-axis, and z-axis of each unit 3D shape data DG in the group Ga is aligned based on the unit 3D shape data whose direction is aligned. 1 ~D.G. M By aligning the directions and ensuring consistency of vertices and dimensions that are the same part, a dimension table 900 for each part (sides in this case) as shown in FIG. 9B is created.

[0089] In FIG. 9B, the dimension table 900 includes each 3D shape data DG 1 ~D.G. M Each edge l in i1 ~l iN The dimensions are shown below. ij is 3D shape data DG i N indicates the j-th edge of the 3D shape data DG i The number of sides of the 3D shape data DG shown in FIG. i In this example, "N=18".

[0090] Next, the specification unit 604 derives a relational expression by performing regression analysis or the like on the relationship between the dimensions of each side with reference to the created dimension table 900. For example, the specification unit 604 derives a relational expression on the relationship between the dimensions of each side with reference to the created dimension table 900. i For each side in the 1 ~D.G. MCreate a column vector v (e.g., v1, v2, …) whose elements are the dimensions of

[0091] Then, the identification unit 604 uses the variable v [v1, v2, ..., vN] to find a relational expression that indicates the relationship between the dimensions of each side. For example, the identification unit 604 divides the variable v into a response variable y and an explanatory variable x to construct a regression model. For example, if a regression model is constructed with vN as the response variable y and the remaining variables as explanatory variables xk, it can be expressed as in the following formula (7). However, β 0 ,β 1 ,β 2 ,… are the regression parameters.

[0092]

number

[0093] The 3D shape data DG shown in FIG. i In this case, the following relational expressions (regression models) (8) to (12) are obtained. Note that, since the upper and lower surfaces have the same shape, the relations between v7 to v12 are omitted here.

[0094]

number

[0095] This allows the 3D shape data DG i Unit 3D shape data DG i Shapes classified by '' (3D shape data DG 1 ~D.G. M ) (the above formulas (8) to (12)) can be generated.

[0096] Returning to the explanation of Fig. 6, the output unit 607 outputs information indicating the dimensional relationship between the identified parts in association with the unit shape data of the shape data in the classified group. The output format of the output unit 607 may be, for example, storage in a storage device such as the memory 302 or the disk 304, transmission to another computer (for example, the client device 201 shown in Fig. 2) via the communication I / F 305, or display on a display (not shown).

[0097] Specifically, for example, the output unit 607 may store information indicating the dimensional relationship between the identified parts in association with the unit 3D shape data of the 3D shape data in the group in the storage unit 610. The information indicating the dimensional relationship between the parts is, for example, a relational expression such as the above-mentioned expressions (8) to (12).

[0098] To explain in more detail, for example, the output unit 607 may store the unit 3D shape data of the 3D shape data in the group as standard shape data in the standard shape DB 230 shown in Fig. 5 in association with a relational expression indicating the dimensional relationship between the 3D shape data in the identified group. At this time, the output unit 607 may store information capable of identifying 3D shape data classified into the same group based on the standard shape data (unit 3D shape data), such as a similar shape list, together with the standard shape DB 230.

[0099] This makes it possible to accumulate knowledge, including the unit 3D shape data as well as the parametric models (relational expressions) of 3D shapes classified by the unit 3D shape data.

[0100] Furthermore, the creation unit 602 normalizes the 3D shape data of the specified target for each component in the direction of each coordinate axis, to create unit 3D shape data of the target.

[0101] The search unit 605 searches for first unit 3D shape data similar to the created unit 3D shape data of the target with reference to the storage unit 610. Specifically, for example, the search unit 605 calculates the similarity between the unit 3D shape data of the target and the standard shape data with reference to the standard shape DB 230. Then, the search unit 605 searches for standard shape data for which the calculated similarity is equal to or greater than a threshold value.

[0102] The output unit 607 outputs the searched first unit 3D shape data and information indicating the dimensional relationship between different parts associated with the first unit 3D shape data and stored in the storage unit 610. Specifically, for example, the output unit 607 outputs the searched standard shape data and a relational expression associated with the standard shape data and stored in the standard shape DB 230.

[0103] The destination of the standard shape data and the relational expressions is, for example, the client device 201. This enables a designer to design an object based on the standard shape data and the relational expressions (parametric relationships) when creating a new design.

[0104] The receiving unit 601 also receives designation of design requirements for an object. Here, the design requirements for an object indicate conditions that must be met when designing the object, such as the dimensions of a specific portion.

[0105] The generating unit 606 generates design data for the object based on the searched first unit 3D shape data and the specified design requirements, in accordance with information indicating the dimensional relationship between different portions associated with the first unit 3D shape data and stored in the storage unit 610. In this case, the output unit 607 outputs the generated design data for the object.

[0106] Specifically, for example, the generation unit 606 generates design data for the object based on the searched standard shape data and the specified design requirements, in accordance with a relational equation stored in the standard shape DB 230 in correspondence with the standard shape data.

[0107] This allows, for example, when a designer specifies the dimensions of a specific side of standard shape data, the dimensions of other sides that have a dimensional relationship with that side are automatically changed according to the relational expression, and design data for the target object can be automatically generated. Note that, if a design requirement that violates the relational expression is specified, for example, an error will occur as a requirement violation.

[0108] Furthermore, the 3D shape data in the group may be stored in association with unit 3D shape data of the 3D shape data in the group in the storage unit 610. In this case, the output unit 607 may output the 3D shape data in the group stored in the storage unit 610 in association with the searched first unit shape data.

[0109] Specifically, for example, first, the output unit 607 identifies a similar shape list stored in the standard shape DB 230 in association with the searched standard shape data. Next, the output unit 607 extracts 3D shape data of the id included in the identified similar shape list from the 3D shape DB 220. Then, the output unit 607 outputs the extracted 3D shape data.

[0110] This allows designers, when creating a new design, to design the object by reusing previously designed 3D shape data that was classified into the same group as the target based on the unit 3D shape data, for example.

[0111] The output unit 607 may output the 3D shape data in the group and information indicating the dimensional relationship between different parts, which are associated with the searched first unit shape data and stored in the storage unit 610. Specifically, for example, the output unit 607 first identifies a similar shape list stored in the standard shape DB 230 in association with the searched standard shape data.

[0112] Next, the output unit 607 extracts 3D shape data of the id included in the identified similar shape list from the 3D shape DB 220. Then, the output unit 607 outputs the extracted 3D shape data and the relational expression stored in the standard shape DB 230 in association with the searched standard shape data.

[0113] This allows designers to design objects according to relational expressions (parametric relationships) based on previously designed 3D shape data when creating new designs. For example, when a designer specifies the dimensions of a side of previously designed 3D shape data, the dimensions of other sides that have a dimensional relationship with that side are automatically changed according to the relational expressions, and design data for the object can be automatically generated.

[0114] In the above description, the unit 3D shape data for each of the 3D shape data classified into the same group is stored in the standard shape DB 230. However, this is not limited to this. For example, the standard shape DB 230 may store only the unit 3D shape data of any of the 3D shape data classified into the same group as standard shape data.

[0115] Here, the contents stored in the standard shape DB 230 in the case where only unit 3D shape data of any one of the 3D shape data classified into the same group is registered as standard shape data will be described with reference to FIG.

[0116] Fig. 10 is an explanatory diagram (part 2) showing an example of the contents stored in the standard shape DB 230. In Fig. 10, the standard shape DB 230 has fields for sid, standard shape data, relational expressions, and similar shape list, and stores standard shape management information (e.g., standard shape management information 1000-1, 1000-2) as records by setting information in each field.

[0117] Here, sid is an identifier that uniquely identifies the standard shape data. The standard shape data is unit 3D shape data registered as standard shape data. The standard shape data is unit 3D shape data of any of the 3D shape data classified into the same group based on the unit 3D shape data.

[0118] The relational expression is a mathematical formula that indicates the relationship between dimensions between different parts in the 3D shape data from which the standard shape data (unit 3D shape data) was created. The similar shape list is a list of IDs of 3D shape data that are classified into the same group based on the standard shape data (unit 3D shape data).

[0119] For example, the standard shape management information 1000-1 indicates the standard shape data SD1 of sid "1", the relational expressions {Re11, Re12, ...}, and the similar shape list {1, 7, 18, 21, 33}. In this way, by registering only the unit 3D shape data of any of the 3D shape data classified into the same group as the standard shape data, the storage capacity of the standard shape DB 230 can be reduced compared to the case shown in FIG.

[0120] (Various processing procedures of the information processing device 101) Next, various processing procedures of the information processing device 101 will be described with reference to Fig. 11 to Fig. 17. First, a preparation processing procedure of the information processing device 101 will be described with reference to Fig. 11.

[0121] Fig. 11 is a flowchart showing an example of a procedure of advance preparation processing of the information processing device 101. In the flowchart of Fig. 11, first, the information processing device 101 refers to the 3D shape DB 220 and selects unselected 3D shape data (step S1101).

[0122] Next, the information processing device 101 executes a unit shape creation process for the selected 3D shape data (step S1102). A specific processing procedure of the unit shape creation process will be described later with reference to Fig. 12. Then, the information processing device 101 refers to the 3D shape DB 220 and determines whether or not there is any unselected 3D shape data (step S1103).

[0123] If there is unselected 3D shape data (step S1103: Yes), the information processing device 101 returns to step S1101. On the other hand, if there is no unselected 3D shape data (step S1103: No), the information processing device 101 ends a series of processes according to this flowchart.

[0124] This allows for the creation of unit 3D shape data for each piece of 3D shape data registered in the 3D shape DB 220 as advance preparation. Note that the created unit 3D shape data may be stored in the 3D shape DB 220, for example, in association with the original 3D shape data from which it was created.

[0125] Next, a specific procedure of the unit shape creation process in step S1102 shown in FIG. 11 will be described with reference to FIG.

[0126] Fig. 12 is a flowchart showing an example of a specific processing procedure of the unit shape creation process. In the flowchart of Fig. 12, first, the information processing device 101 extracts the minimum value in each coordinate axis direction from the coordinates of each vertex of the 3D shape data (step S1201). Then, the information processing device 101 subtracts the extracted minimum value in each coordinate axis direction from each value of the coordinate of each vertex, and translates the 3D shape data (step S1202).

[0127] Next, the information processing device 101 extracts the maximum value in each coordinate axis direction from the coordinates of each vertex of the 3D shape data after the translation (step S1203).Then, the information processing device 101 divides each value of the coordinate of each vertex of the 3D shape data after the translation by the extracted maximum value in each coordinate axis direction to create unit 3D shape data (step S1204), and returns to the step that called the unit shape creation process.

[0128] This makes it possible to normalize the 3D shape data while maintaining the dimensional relationships between parts in each coordinate axis direction.

[0129] Next, a procedure of a standard shape registration process of the information processing device 101 will be described with reference to Fig. 13. The standard shape registration process is a process for pre-registering standard shape data in the standard shape DB 230 before designing an object.

[0130] Fig. 13 is a flowchart showing an example of a standard shape registration processing procedure of the information processing device 101. In the flowchart of Fig. 13, first, the information processing device 101 determines whether or not a designation of a target shape has been accepted (step S1301). The target shape designated here is 3D shape data designated to classify 3D shape data registered in the 3D shape DB 220, and is designated from the 3D shape DB 220, for example.

[0131] Here, the information processing device 101 waits for acceptance of the designation of the target shape (step S1301: No). Then, when the information processing device 101 accepts the designation of the target shape (step S1301: Yes), it executes a unit shape creation process for the target shape (step S1302).

[0132] Note that the specific processing procedure of the unit shape creation process for the target shape is omitted from illustration and description because it is the same as the processing procedure shown in Fig. 12. If the unit 3D shape data of the target shape has already been created, the information processing device 101 may skip step S1302.

[0133] Next, the information processing device 101 executes a shape classification process for classifying a plurality of pieces of 3D shape data based on the created unit 3D shape data (step S1303). A specific processing procedure of the shape classification process will be described later with reference to FIG.

[0134] Next, the information processing device 101 executes a relational equation construction process for constructing a relational equation of dimensions between different parts of the 3D shape data in the classified group (step S1304). A specific processing procedure of the relational equation construction process will be described later with reference to FIG. 15.

[0135] Then, the information processing device 101 treats the unit 3D shape data of each 3D shape data in the group as standard shape data, associates the standard shape data with the constructed relational equation and the similar shape list, and registers the standard shape data in the standard shape DB 230 (step S1305), thereby completing the series of processes according to this flowchart.

[0136] This makes it possible to store in a database the standard shape data (unit 3D shape data) as well as the parametric models (relational equations) of 3D shapes classified by the standard shape data.

[0137] Next, a specific processing procedure of the shape classification process in step S1303 shown in Fig. 13 will be described with reference to Fig. 14. Here, the unit 3D shape data of the target shape created in step S1302 shown in Fig. 13 is referred to as a "unit target shape."

[0138] Fig. 14 is a flowchart showing an example of a specific processing procedure of the shape classification process. In the flowchart of Fig. 14, first, the information processing device 101 selects unselected unit 3D shape data from among the unit 3D shape data of each 3D shape data in the 3D shape DB 220 (step S1401).

[0139] Then, the information processing device 101 calculates the similarity between the selected unit 3D shape data and the created unit target shape (step S1402). Next, the information processing device 101 determines whether or not there is any unselected unit 3D shape data among the unit 3D shape data of each 3D shape data in the 3D shape DB 220 (step S1403).

[0140] If there is unselected unit 3D shape data (step S1403: Yes), the information processing device 101 returns to step S1401. On the other hand, if there is no unselected unit 3D shape data (step S1403: No), the information processing device 101 identifies unit 3D shape data whose calculated similarity is equal to or greater than a threshold (step S1404).

[0141] Next, the information processing device 101 extracts 3D shape data corresponding to the identified unit 3D shape data from the 3D shape DB 220 (step S1405). Then, the information processing device 101 classifies the extracted 3D shape data into the same group (step S1406) and returns to the step that called the shape classification process.

[0142] This makes it possible to classify 3D shape data whose target 3D shape data and unit 3D shape data are similar into the same group.

[0143] Next, a specific procedure of the relational equation construction process in step S1304 shown in FIG. 13 will be described with reference to FIG.

[0144] Fig. 15 is a flowchart showing an example of a specific processing procedure of the relational equation construction process. In the flowchart of Fig. 15, first, the information processing device 101 aligns the orientation of the 3D shape data in the group classified in step S1303 by matching the directions of the x-axis, y-axis, and z-axis (step S1501).

[0145] Next, the information processing device 101 creates a dimension table for each part of the 3D shape data in the group (step S1502). Then, the information processing device 101 refers to the created dimension table for each part and creates a column vector for each part, with dimensions in each 3D shape data as elements (step S1503).

[0146] Next, the information processing device 101 selects the variable with the highest variance from the created column vectors of each part as variables (step S1504). Then, the information processing device 101 determines whether the variance of the selected variable is equal to or less than a predetermined threshold (step S1505). The predetermined threshold can be set arbitrarily.

[0147] Here, if the variance is equal to or smaller than the predetermined threshold (step S1505: Yes), the information processing device 101 returns to the step that called the relational equation construction process.

[0148] On the other hand, if the variance is greater than the predetermined threshold (step S1505: No), the information processing device 101 sets the selected variable as the objective variable (step S1506). Next, the information processing device 101 sets variables having a high contribution rate to the objective variable as explanatory variables by a stepwise method (step S1507).

[0149] Then, the information processing device 101 creates a relational equation of dimensions between different parts of the 3D shape data by regression analysis based on the created column vector (step S1508). Next, the information processing device 101 determines whether there are any unselected variables (step S1509).

[0150] If there are unselected variables (step S1509: Yes), the information processing device 101 returns to step S1503. On the other hand, if there are no unselected variables (step S1509: No), the information processing device 101 returns to the step that called the relational equation construction process.

[0151] This makes it possible to construct a relational expression that indicates the dimensional relationship between different parts of 3D shape data classified into the same group.

[0152] Next, a first design processing procedure of the information processing device 101 will be described with reference to Fig. 16. The first design processing is a process for generating design data of an object by specifying 3D shape data of a target having a shape similar to that of the object.

[0153] Fig. 16 is a flowchart showing an example of a first design processing procedure of the information processing device 101. In the flowchart of Fig. 16, first, the information processing device 101 determines whether or not a designation of a target shape has been accepted (step S1601). The target shape designated here is 3D shape data having a shape similar to that of the target object, and is designated from the 3D shape DB 220, for example.

[0154] Here, the information processing device 101 waits for acceptance of the designation of the target shape (step S1601: No). Then, when the information processing device 101 accepts the designation of the target shape (step S1601: Yes), it executes a unit shape creation process for the target shape (step S1602).

[0155] Note that the specific processing procedure of the unit shape creation process for the target shape is omitted from illustration and description because it is the same as the processing procedure shown in Fig. 12. If the unit 3D shape data of the target shape has already been created, the information processing device 101 may skip step S1602.

[0156] Next, the information processing device 101 searches the standard shape DB 230 for standard shape data similar to the unit 3D shape data of the target shape (step S1603). Then, the information processing device 101 determines whether similar standard shape data has been searched for (step S1604).

[0157] If the standard shape data is not found (step S1604: No), the information processing device 101 executes a shape classification process to classify multiple 3D shape data (step S1605). Next, the information processing device 101 executes a relational equation construction process to construct a relational equation of dimensions between different parts of the 3D shape data in the classified group (step S1606), and proceeds to step S1607.

[0158] Note that the specific processing procedure of the shape classification processing in step S1605 is similar to the processing procedure shown in Fig. 14, and therefore illustration and description are omitted. Also, the specific processing procedure of the relational equation construction processing in step S1606 is similar to the processing procedure shown in Fig. 15, and therefore illustration and description are omitted.

[0159] Furthermore, if standard shape data is found in step S1604 (step S1604: Yes), the information processing device 101 outputs the found standard shape data and a relational equation corresponding to the standard shape data (step S1607). Note that if a relational equation is constructed in step S1606, the information processing device 101 outputs unit 3D shape data of any one of the 3D shape data in the group as standard shape data.

[0160] Next, the information processing device 101 judges whether or not input of dimensions of a specific part in the outputted standard shape data has been received (step S1608). The specific part in the standard shape data corresponds to a specific part of the 3D shape data from which the standard shape data was created. The dimensions of the specific part correspond to the design requirements for the target object.

[0161] Here, the information processing device 101 waits for the input of the dimensions of the specific part to be accepted (step S1608: No). When the information processing device 101 accepts the input of the dimensions of the specific part (step S1608: Yes), the information processing device 101 converts the standard shape data according to the relational expressions based on the input dimensions of the specific part to generate design data related to the target object (step S1609).

[0162] Then, the information processing device 101 outputs the generated design data (step S1610) and ends a series of processes according to this flowchart. Note that, in step S1607, the information processing device 101 may output the 3D shape data classified by the standard shape data.

[0163] This allows designers to specify the target shape and dimensions of specific parts when creating a new design, and automatically generate design data for the object according to relational equations (parametric relationships) based on the standard shape data.

[0164] Next, a second design processing procedure of the information processing device 101 will be described with reference to Fig. 17. The second design processing is a process for generating design data of an object by using an assembly configured of standard parts (standard shape data).

[0165] Fig. 17 is a flowchart showing an example of a second design processing procedure of the information processing device 101. In the flowchart of Fig. 17, first, the information processing device 101 judges whether or not input of design requirements for a structure has been accepted (step S1701). The design requirements for a structure include, for example, information specifying dimensions of the structure to be designed (for example, dimensions of a specific portion of a standard part constituting the structure) and constraint conditions between parts. The constraint conditions between parts indicate, for example, portions whose dimensions change in conjunction with each other between parts.

[0166] Here, the information processing device 101 waits for the input of the design requirements of the structure to be accepted (step S1701: No). When the information processing device 101 accepts the input of the design requirements of the structure (step S1701: Yes), the information processing device 101 extracts standard shape data corresponding to standard parts that constitute the structure from the standard shape DB 230 (step S1702).

[0167] Next, the information processing device 101 converts each standard shape data according to the relational expressions corresponding to each standard shape data extracted based on the design requirements of the structure, and generates design data related to the structure (step S1703). Then, the information processing device 101 outputs the generated design data (step S1704), and ends a series of processes according to this flowchart.

[0168] This makes it possible to automatically generate design data for an object using an assembly made up of standard parts (standard shape data).

[0169] Here, an example of generating design data for an object using an assembly made up of standard parts will be described with reference to FIG.

[0170] Fig. 18 is an explanatory diagram showing an example of generating design data. In Fig. 18, structure A is an assembly composed of standard parts a-std, b-std, c-std, and d-std. In this case, when the design requirements of structure A are input, the information processing device 101 extracts standard shape data corresponding to the standard parts a-std, b-std, c-std, and d-std that constitute structure A from the standard shape DB 230.

[0171] Next, the information processing device 101 converts each standard shape data according to a relational expression (parametric model) corresponding to each extracted standard shape data based on the design requirements of the structure A, and generates 3D shape data of each of the parts a, b, c, and d. Then, the information processing device 101 generates design data for the structure A based on the generated 3D shape data of each of the parts a, b, c, and d.

[0172] As described above, the information processing device 101 according to the embodiment can normalize each of a plurality of 3D shape data for each component in each coordinate axis direction to generate unit 3D shape data, and classify the plurality of 3D shape data based on the unit 3D shape data of each of the generated 3D shape data. Also, the information processing device 101 can specify a dimensional relationship between different parts of the 3D shape data in the group based on the dimensions of the parts of each 3D shape data in the classified group, and output information indicating the dimensional relationship between the specified parts in association with the unit shape data of the shape data in the group.

[0173] This makes it possible to construct a parametric model (relationship of dimensions between parts) of 3D shapes classified by unit 3D shape data. Therefore, for example, design data according to the design intent can be easily generated based on the unit 3D shape data and in accordance with the parametric model.

[0174] Moreover, the information processing device 101 can extract the minimum value in each coordinate axis direction from the coordinates of each feature point of each 3D shape data, and subtract the extracted minimum value in each coordinate axis direction from each value of the coordinates of each feature point. Then, the information processing device 101 can extract the maximum value in each coordinate axis direction from the coordinates of each feature point after subtraction, and divide each value of the coordinates of each feature point after subtraction by the extracted maximum value in each coordinate axis direction, thereby creating unit 3D shape data of each 3D shape data.

[0175] This allows the 3D shape data to be translated and normalized while maintaining the dimensional relationships between parts along each coordinate axis. This makes it possible to classify 3D shape data based on the same ratio along each coordinate axis and build a parametric model. For example, not only shapes that are the same or similar, but also shapes that have some differences in dimensions can be classified into the same group, allowing for grouping that is closer to human sensibility and improving versatility when designing new models.

[0176] In addition, according to the information processing device 101, the similarity between unit 3D shape data of each 3D shape data is calculated, and multiple 3D shape data can be classified so that 3D shape data corresponding to combinations of unit 3D shape data for which the calculated similarity is equal to or greater than a threshold value are grouped together.

[0177] This makes it possible to classify previously designed 3D shape data with similar normalized unit shape data into the same group.

[0178] In addition, according to the information processing device 101, for each part in each 3D shape data, a vector is created whose elements are the dimensions of the part in each 3D shape data, and one of the multiple parts in each 3D shape data is used as the objective variable and the other parts are used as explanatory variables, and a relational equation showing the dimensional relationship between the parts can be created based on the vector created for each part.

[0179] This makes it possible to derive a relational equation that expresses the dimensions of a certain part of the 3D shape data using parameters that represent the dimensions of other parts, using statistical methods such as regression analysis.

[0180] Moreover, according to the information processing device 101, among a plurality of parts in each 3D shape data, a part in which the variance of the elements of the created vector is relatively high can be set as the objective variable.

[0181] This makes it possible to construct a relational equation by excluding from the objective variable any part whose dimensions do not change between 3D shape data within a group.

[0182] Furthermore, according to the information processing device 101, information indicating the dimensional relationship between the identified parts can be stored in the storage unit 610 (for example, the standard shape DB 230) in association with the unit 3D shape data of the 3D shape data in the group.

[0183] This makes it possible to store in a database not only the unit 3D shape data but also the parametric models (relational equations) of 3D shapes classified by the unit 3D shape data.

[0184] In addition, the information processing device 101 can accept specification of 3D shape data of a target, normalize the specified 3D shape data of the target for each component in each coordinate axis direction to create unit 3D shape data of the target, refer to the memory unit 610 to search for first unit 3D shape data similar to the created unit 3D shape data of the target, and output information indicating the dimensional relationship between the searched first unit 3D shape data and parts stored in the memory unit 610 in correspondence with the first unit 3D shape data.

[0185] This makes it possible to provide unit shape data with a parametric model built based on the dimensions of each part of 3D shape data that is similar to the target 3D shape data as standard shape data.For example, when creating a new design, a designer can specify design requirements (e.g., dimensions of a specific part) and automatically generate design data for the target object (parametric design) based on the standard shape data and in accordance with the parametric model.

[0186] In addition, according to the information processing device 101, the 3D shape data can be further stored in the memory unit 610 in association with unit 3D shape data of the 3D shape data in the group, and the shape data stored in the memory unit 610 can be output in association with the searched first unit 3D shape data.

[0187] This makes it possible to provide past 3D shape data in which the target 3D shape data and unit 3D shape data are similar. For example, when creating a new design, a designer can use past 3D shape data in which the target 3D shape data and unit 3D shape data are similar to each other when designing an object.

[0188] In addition, according to the information processing device 101, it is possible to output information indicating the relationship between the shape data stored in the memory unit 610 (3D shape DB 220, standard shape DB 230) in correspondence with the searched first unit 3D shape data and the dimensions between parts.

[0189] This makes it possible to provide past 3D shape data that is similar to the target 3D shape data and unit 3D shape data together with the parametric model.For example, when creating a new design, a designer can specify design requirements (e.g., dimensions of a specific part) and automatically generate design data for the target object according to the parametric model based on the past 3D shape data.

[0190] For these reasons, the information processing device 101 can use standard shape data to which a parametric model (relationship equations between dimensions of parts) is added to perform new design of an object. This can reduce the number of steps required for designing a 3D shape. Furthermore, when creating a new design, design errors can be reduced by changing the dimensions of each part according to the parametric model. Furthermore, by searching for standard shape data to be used when creating a new design based on unit 3D shape data, standard shape data that matches the design intent can be easily searched for.

[0191] The shape data output method described in the present embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. The shape data output program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, a DVD, or a USB memory, and is executed by being read from the recording medium by the computer. The shape data output program may also be distributed via a network such as the Internet.

[0192] Moreover, the information processing device 101 described in this embodiment can also be realized by an application-specific IC such as a standard cell or a structured ASIC (Application Specific Integrated Circuit) or a PLD (Programmable Logic Device) such as an FPGA.

[0193] The following supplementary notes are further disclosed regarding the above-described embodiment.

[0194] (Appendix 1) Each of the plurality of shape data is normalized for each component in each coordinate axis direction to create unit shape data, classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relationship between dimensions of different portions of the shape data in the group based on dimensions of portions of each of the classified shape data; outputting information indicating the relationship between the specified dimensions and the unit shape data of the shape data in the group; A shape data output program that causes a computer to execute a process.

[0195] (Appendix 2) The process for creating the above is as follows: extracting a minimum value in each coordinate axis direction from the coordinates of each feature point of each of the shape data; subtracting the extracted minimum value in each of the coordinate axis directions from each value of the coordinates of each of the feature points; extracting a maximum value in each coordinate axis direction from the coordinates of each of the feature points after subtraction; and dividing each value of the coordinates of each of the feature points after the subtraction by the extracted maximum value in each of the coordinate axis directions, thereby generating unit shape data of each of the shape data. 2. A shape data output program according to claim 1.

[0196] (Appendix 3) The classification process is Calculating a similarity between unit shape data of each of the shape data; classifying the plurality of shape data such that shape data corresponding to a combination of unit shape data whose calculated similarity is equal to or greater than a threshold value are grouped together; 3. A shape data output program according to claim 1 or 2.

[0197] (Appendix 4) The process to be specified is: For each part in each of the shape data, a vector is created whose elements are the dimensions of the part in each of the shape data; a relational expression showing the relationship between the dimensions is created based on the created vector for each of the plurality of parts in each of the shape data, with one of the parts being a response variable and the other parts being explanatory variables; 4. The shape data output program according to claim 1,

[0198] (Appendix 5) The shape data output program according to appendix 4, characterized in that, among the multiple parts in each of the shape data, a part having a relatively high variance of the elements of the created vector is used as a target variable.

[0199] (Appendix 6) The shape data output program according to appendix 4 or 5, characterized in that among the multiple parts in each of the shape data, a part having a relatively high contribution rate to the objective variable is used as an explanatory variable.

[0200] (Appendix 7) The output process is 7. The shape data output program according to claim 1, further comprising: storing information indicating the relationship between the identified dimensions in a storage unit in association with unit shape data of the shape data in the group.

[0201] (Appendix 8) Accepts the target shape data, The specified shape data of the target is normalized for each component in each coordinate axis direction to generate unit shape data of the target; referring to the storage unit to search for first unit shape data similar to the created unit shape data of the target; outputting information indicating a relationship between the retrieved first unit shape data and dimensions stored in the storage unit in association with the first unit shape data; 8. A shape data output program according to claim 7, characterized in that the program causes the computer to execute a process.

[0202] (Additional Note 9) The storage unit further stores the shape data in association with unit shape data of the shape data in the group, The output process includes: 9. The shape data output program according to claim 8, further comprising: outputting shape data stored in the storage unit in association with the searched first unit shape data.

[0203] (Appendix 10) The output process is 10. The shape data output program according to claim 9, further comprising: outputting information indicating a relationship between the shape data stored in the memory unit and dimensions in association with the searched first unit shape data.

[0204] (Appendix 11) Each of the plurality of shape data is normalized for each component in each coordinate axis direction to create unit shape data; classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relationship between dimensions of different portions of the shape data in the group based on dimensions of portions of each of the classified shape data; outputting information indicating the relationship between the specified dimensions and the unit shape data of the shape data in the group; A shape data output method, comprising the steps of: executing processing by a computer.

[0205] (Appendix 12) Each of the plurality of shape data is normalized for each component in each coordinate axis direction to create unit shape data; classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relationship between dimensions of different portions of the shape data in the group based on dimensions of portions of each of the classified shape data; outputting information indicating the relationship between the specified dimensions and the unit shape data of the shape data in the group; 1. An information processing device comprising: a control unit that executes processing. [Explanation of symbols]

[0206] 101 Information processing device 200 Information Processing Systems 201 Client device 210 Network 220 3D shape DB 230 Standard shape DB 300 Bus 301 CPU 302 Memory 303 Disk Drive 304 Disk 305 Communication I / F 306 Portable Recording Media I / F 307 Portable recording media 601 Reception 602 Creation Department 603 Classification Department 604 Specific part 605 Search Department 606 Generation part 607 Output section 610 Storage section 900 Dimensions

Claims

1. A method for creating unit shape data by normalizing each of a plurality of shape data representing a part for each component in each coordinate axis direction, classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relational expression that indicates a relationship between the lengths of different sides of a part represented by any of the shape data in the group, based on the length of the side of the part represented by each of the shape data in the classified group; outputting the specified relational expression in association with unit shape data of any one of the shape data in the group; The process is executed by a computer, The creating process includes: extracting a minimum value in each coordinate axis direction from the coordinates of each feature point of each of the shape data; subtracting the extracted minimum value in each of the coordinate axis directions from each value of the coordinates of each of the feature points; extracting a maximum value in each coordinate axis direction from the coordinates of each of the feature points after subtraction; and dividing each value of the coordinates of each of the feature points after the subtraction by the extracted maximum value in each of the coordinate axis directions, thereby generating unit shape data of each of the shape data. A shape data output program comprising:

2. The classification process comprises: Calculating a similarity between unit shape data of each of the shape data; classifying the plurality of shape data such that shape data corresponding to a combination of unit shape data whose calculated similarity is equal to or greater than a threshold value are grouped together; 2. The shape data output program according to claim 1,

3. The process of identifying creating a vector whose elements are the lengths of the edges in each of the shape data for each of the components represented by the shape data; a relational expression indicating a relationship between lengths of different sides in a part represented by any of the shape data in the group is created based on the created vectors for each of the sides, with one of the sides in the part represented by each of the shape data being a target variable and the other sides being explanatory variables; 3. The shape data output program according to claim 1, wherein the shape data output program is a program for outputting a shape data.

4. A shape data output program as described in claim 3, characterized in that the target variable is an edge among the multiple edges within the parts represented by each shape data, the edge having a relatively high variance of the elements of the created vector.

5. The output process comprises:

5. The shape data output program according to claim 1, further comprising: storing the specified relational expression in a storage unit in association with unit shape data of any one of the shape data in the group.

6. Accepting designation of target shape data; The specified shape data of the target is normalized for each component in each coordinate axis direction to generate unit shape data of the target; referring to the storage unit to search for first unit shape data similar to the created unit shape data of the target; outputting a relational expression indicating a relationship between the retrieved first unit shape data and lengths of different sides in a part represented by shape data from which the first unit shape data was created, the relational expression being stored in the storage unit in association with the first unit shape data; causing the computer to execute a process; The process of creating unit shape data of the target includes: extracting a minimum value in each coordinate axis direction from the coordinates of each feature point of the shape data of the target; subtracting the extracted minimum value in each of the coordinate axis directions from each value of the coordinates of each of the feature points; extracting a maximum value in each coordinate axis direction from the coordinates of each of the feature points after subtraction; creating unit shape data of the target by dividing each value of the coordinates of each of the feature points after the subtraction by the extracted maximum value in each of the coordinate axis directions; 6. The shape data output program according to claim 5,

7. A shape data output program as described in any one of claims 1 to 6, characterized in that each feature point of the respective shape data includes a vertex of a part represented by each of the respective shape data.

8. A method for generating unit shape data by normalizing each of a plurality of shape data representing a part for each component in each coordinate axis direction; classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relational expression that indicates a relationship between the lengths of different sides of a part represented by any of the shape data in the group, based on the length of the side of the part represented by each of the shape data in the classified group; outputting the specified relational expression in association with unit shape data of any one of the shape data in the group; The computer executes the process, The creating process includes: extracting a minimum value in each coordinate axis direction from the coordinates of each feature point of each of the shape data; subtracting the extracted minimum value in each of the coordinate axis directions from each value of the coordinates of each of the feature points; extracting a maximum value in each coordinate axis direction from the coordinates of each of the feature points after subtraction; and dividing each value of the coordinates of each of the feature points after the subtraction by the extracted maximum value in each of the coordinate axis directions, thereby generating unit shape data of each of the shape data. A shape data output method comprising:

9. A method for generating unit shape data by normalizing each of a plurality of shape data representing a part for each component in each coordinate axis direction; classifying the plurality of shape data based on the unit shape data of each of the created shape data; Identifying a relational expression that indicates a relationship between the lengths of different sides of a part represented by any of the shape data in the group, based on the length of the side of the part represented by each of the shape data in the classified group; outputting the specified relational expression in association with unit shape data of any one of the shape data in the group; A control unit for executing a process, The control unit is extracting a minimum value in each coordinate axis direction from the coordinates of each feature point of each of the shape data; subtracting the extracted minimum value in each of the coordinate axis directions from each value of the coordinates of each of the feature points; extracting a maximum value in each coordinate axis direction from the coordinates of each of the feature points after subtraction; and dividing each value of the coordinates of each of the feature points after the subtraction by the extracted maximum value in each of the coordinate axis directions, thereby generating unit shape data of each of the shape data.

23. An information processing apparatus comprising:

Images