Design support device

Abstract

To provide a design support device capable of effectively utilizing failure knowledge in design work and reducing man-hours needed for a detailed check of a proposed analysis result and variation due to a user personal.SOLUTION: A design support device for evaluating failures and their factors possibly occurred on a design model created by a design tool, includes: a causal model database for storing a causal model showing failures possibly occurred on a design target by use of causal relation where a phenomenon as a factor causes other phenomenon, an attribute information extraction part for extracting attribute information on the design target from the design model, and an association degree evaluation part for extracting causal relation from the causal model as a causal relation having a high relevance degree to the design model, the causal relation including contents matching with the extracted attribute information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a design support device that utilizes defect information. [Background technology]

[0002] In the manufacturing industry, efforts are being made to improve reliability by identifying possible failure modes and taking measures at the design stage. One of the methods for this is Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA). FTA is an analysis technique that takes a product defect, sequentially identifies the failure factors and develops them in a hierarchical structure to systematically search for the source of the defect.

[0003] The result of this analysis is a tree structure with the product's fault event at the top and its failure causes at the lower levels. This tree-structured analysis result is called a fault tree. The fault event of the product being analyzed is called the top event because it is located at the top of the fault tree. In the fault tree, the causes at the lower levels than the top event are called intermediate events.

[0004] In FMEA, during the design stage, possible malfunctions that may occur in the components being designed are predicted, the impact that these malfunctions will have on the unit and further on higher-level products are evaluated, and malfunctions with the greatest impact can be prevented by taking pre-emptive measures.

[0005] Conventionally, an example of this type of invention is described in Patent Document 1 (JP 2017-111657 A). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2017-111657 A Summary of the Invention [Problem to be solved by the invention]

[0007] Patent Document 1 describes a device that extracts the causal relationships of defects from past defect knowledge and, based on this, presents the defect factors to support FTA and FMEA. However, if FTA and FMEA are not performed in the design work, or if they are performed only a few times, it is not possible to effectively utilize the defect knowledge accumulated from the past.

[0008] Furthermore, in Patent Document 1, when a large number of defect causes are presented, there is a risk that it will take a long time to examine them closely, and that the results will vary depending on the individual user who performs the examination.

[0009] Therefore, the present invention provides a design support device that can utilize defect knowledge in design work and reduces the number of steps required to closely examine analysis results and the variability due to individual users. [Means for solving the problem]

[0010] In order to solve the above-mentioned problems, the present invention provides a design support device for evaluating defects that may occur in a design model generated by a design tool and their causes, the device comprising: a causal model database storing causal models that express defects that may occur in a design object using causal relationships in which a certain phenomenon is a factor causing another phenomenon; an attribute information extraction unit that extracts attribute information about the design object from the design model; and an association evaluation unit that extracts causal relationships that include content that matches the extracted attribute information from the causal model as causal relationships that are highly associated with the design model. Effect of the Invention

[0011] The present invention provides a method for designing a system that can effectively utilize defect knowledge in design work, thereby reducing the number of steps required to examine analysis results and the variation between individual users. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiment of the present invention. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a design support device according to a first embodiment. [Diagram 2] FIG. 1 shows a diagram of a causal model. [Diagram 3] FIG. 1 is a diagram showing a specific example of a causal model. [Figure 4] 13 is a diagram illustrating a process of presenting a causal relationship that is highly relevant to a target product. [Figure 5A] FIG. 1 is a diagram showing a specific example of a causal model. [Figure 5B] 13 is a diagram illustrating a process of presenting a causal relationship that is highly relevant to a target product. [Figure 6A] FIG. 1 is a diagram showing a specific example of a causal model. [Figure 6B] 13 is an example of a table of an importance evaluation unit. [Figure 7A] 13 is an example of a screen display of evaluation results. [Figure 7B] 13 is an example of a screen display of evaluation results. [Figure 8] FIG. 11 is a diagram for explaining the configuration of a design support device according to a second embodiment. [Figure 9] FIG. 13 is a diagram showing an example of an FMEA table. [Figure 10] FIG. 1 is a diagram showing a specific example of a causal model. [Figure 11] FIG. 11 is a diagram for explaining the configuration of a design support device according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The examples are illustrative for explaining the present invention, and are omitted and simplified as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0014] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0015] As an example of various information, the various information may be described using expressions such as "tables", but the various information may be expressed using other data structures. For example, various information such as "XX table" may be expressed as "XX information". When describing identification information, expressions such as "identification information", "identifier", "name", "ID", and "number" are used, but these are interchangeable.

[0016] When there are multiple components having the same or similar functions, they may be described by using the same reference numerals with different subscripts, or when there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0017] In the embodiments, there may be cases where a process is performed by executing a program. Here, a computer executes the program using a processor (e.g., a CPU or a GPU), and performs the process defined by the program while using storage resources (e.g., a memory) and interface devices (e.g., a communication port), etc.

[0018] Therefore, the subject of processing performed by executing a program may be a processor. Similarly, the subject of processing performed by executing a program may be a controller, device, system, computer, or node having a processor.

[0019] The subject of the processing performed by executing the program may be a calculation unit, and may include a dedicated circuit for performing specific processing. Here, the dedicated circuit is, for example, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or a Complex Programmable Logic Device (CPLD).

[0020] The program may be installed in the computer from a program source, which may be, for example, a program distribution server or a computer-readable storage medium.

[0021] When the program source is a program distribution server, the program distribution server may include a processor and a storage resource for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers.

[0022] Furthermore, in the embodiments, two or more programs may be realized as one program, and one program may be realized as two or more programs. (overview) In hardware design, product structures are often designed using a three-dimensional CAD system (3DCAD). In this embodiment of the present invention, the target is a scene where design is performed using 3DCAD, and attribute information related to the product to be designed, such as characteristic shapes and attribute information, is extracted from a model designed using 3DCAD (3DCAD model), and compared with predefined causal relationships of defects, their relevance and importance are evaluated, and defect factors with high relevance and importance are narrowed down and presented.

[0023] Here, if designs are being made using other models besides 3D CAD, such as DSM (Dependency Structure Matrix), QFD (quality function deployment), MBSE (Model-based Systems Engineering) models, attribute information can be extracted from those models and the display can be narrowed down to defect factors with high relevance or importance. EXAMPLES

[0024] (Equipment configuration) The configuration of the design support device of this embodiment will be described with reference to FIG.

[0025] The design support device 101 is a general computer, and includes a central control device 104, an input device 105, an output device 106, a main memory device 107, and an auxiliary memory device 108, which are interconnected by a bus or the like.

[0026] Furthermore, the design support device 101 is connected to one or more information terminals 102 via a network 103, and one or more users can use the design support device 101 via the information terminals 102 that they operate. A general computer can also be used for the information terminals 102, and the information terminals 102 have a central control device, an input device, an output device, a main storage device, and an auxiliary storage device (not shown). Note that the user can also operate and use the design support device 101 directly without using the information terminals 102.

[0027] In the auxiliary storage device 108, a causal model database 114 stores a causal model that models the causal relationships of malfunctions. In the causal model, the causes of malfunction phenomena are defined in the form of a fault tree (the detailed form of the causal model will be described later). In addition, the malfunction information database 115 stores malfunction information (which device (which part) and what kind of malfunction occurred) in, for example, a sentence format.

[0028] In the main memory device 107, the 3D CAD model input unit 109, the attribute information extraction unit 110, the relevance evaluation unit 111, and the importance evaluation unit 112 are programs. Hereinafter, when an entity is described as "XX unit", it means that the central control device 104 reads each program from the main memory device 107 and realizes the function of each program (described in detail later) on the main memory device.

[0029] The 3D CAD model input unit 109 accepts input of a 3D CAD model by a user. That is, when a design model (3D CAD model) obtained by designing separately using a design tool such as 3D CAD is input (specified) using the input device 105 (keyboard, mouse, etc.), the model is taken into a temporary storage area (not shown) of the main storage device 107.

[0030] The attribute information extraction unit 110 extracts attribute information such as product name, part name, attribute name, characteristic shape, etc. from the 3D CAD model received by the 3D CAD model input unit 109.

[0031] Based on the attribute information extracted by the attribute information extraction unit 110, the relevance evaluation unit 111 evaluates the relevance of each factor of the causal model stored in the causal model database 114 with the product to be designed.

[0032] The importance evaluation unit 112 determines what kind of defect will occur in the product due to each factor of the causal model by searching the causal models stored in the causal model database 114 from the cause side to the effect side, and evaluates the importance of the defect that will occur in the product.

[0033] The relevance evaluation unit 111 and the importance evaluation unit 112 will be described in detail later.

[0034] (Causal model) A causal model is a model of the causal relationship between the components that make up a product (part) and the phenomenon that occurs there, for a phenomenon that occurs in that product (part), and allows the causal relationship of each phenomenon to be visually grasped. Figure 2 shows an example of a causal model. Each element 201 that makes up the causal relationship is composed of a part 202 and a phenomenon 203.

[0035] The part 202 of the causal model indicates the target where the phenomenon 203 occurs, and may be not only a part but also a process such as "heat treatment" or a characteristic shape or partial shape such as an "edge part." The phenomenon 203 is the name of a phenomenon related to a defect, such as "defect," "quench crack," or "stress concentration."

[0036] In the causal model, these elements are expressed in the order of causation, as a chain. In the example of Figure 2, the element on the right side causes the element on the left side. In addition, there are AND conditions 204 and OR conditions 205 as relations connecting these elements.

[0037] An AND condition 204 indicates that an element connected to its left side is triggered if all elements connected to its right side occur, while an OR condition 205 indicates that an element connected to its left side is triggered if any one of the elements connected to its right side occurs.

[0038] 2 shows that Phenomenon D (209) occurs in part D (208), or Phenomenon E (211) occurs in part E (210), causing Phenomenon B (207) to occur in part B (206). It also shows that Phenomenon B (207) occurs in part B (206) and Phenomenon C (213) occurs in part C (212), causing Phenomenon A (203) to occur in part A (202).

[0039] Fig. 3 is a further specific example, which shows the causal relationships of defects related to heat treatment. For example, the causes of "heat treatment: defect" 301 include "heat treatment: cracking" 302 and "heat treatment: deformation" 303, and the causes of "heat treatment: cracking" 302 include "edge portion: stress concentration" 304, etc. (As in Fig. 2, these are related by AND or OR conditions, but are not shown).

[0040] In this way, the factors in the causal model are expressed in the form of "part (or processing content, manufacturing method, characteristic shape, partial shape): phenomenon". (Attribute information extraction processing) The attribute information extraction unit 110 extracts attribute information expressing the features and components of a product, such as the product name, part name, attribute name, and characteristic shape, from the 3D CAD model received by the 3D CAD model input unit 109 .

[0041] The 3D CAD model in this embodiment is, for example, a 3DA model (3D Annotated Models: three-dimensional product information added model) in which information such as product names, part names, dimensions, tolerances, attribute names such as material names, etc. are embedded in a three-dimensional shape model created by 3D CAD. The embedded product names, part names, attribute names, etc. can be extracted using an additional information checking function included in the 3D CAD software.

[0042] In addition, extraction of characteristic shapes from a 3D CAD model can be performed, for example, by recognizing characteristic shapes using the connection relationships between faces in the 3D CAD data, or by extracting image data from the 3D CAD data and using automatic image recognition technology to check whether there are any parts that match pre-defined characteristic shapes.

[0043] In addition, if information about characteristic shapes is embedded as additional information in a 3DA model, etc., the information may be extracted using the functions of 3D CAD software in the same manner as described above. Examples of characteristic shapes include "edges," "bosses," and "ribs." (Relevance evaluation process) The relevance evaluation unit 111 evaluates the relevance of each factor of the causal model stored in the causal model database 114 with the product to be designed, based on the attribute information extracted by the attribute information extraction unit 110. This makes it possible to present to the user only those factors (causal relationships) of the causal model that are related to the product to be designed (those with high relevance). There are two possible methods for evaluating the relevance.

[0044] The first method is to compare the attribute information extracted by the attribute information extraction unit 110 with the information included in each factor of the causal model, and if there is a matching factor, evaluate it as having a high degree of association.

[0045] For example, consider a case where the causal model shown in Fig. 3 is stored in the causal model database 114. The parts in this causal model include characteristic shapes and part-related factors such as "edge portion," "part B," "part C," "part D," "part E," and "part F."

[0046] FIG. 4 is a diagram for explaining the flow of a process for presenting causal relationships that are highly relevant to a target product. Assume that an “edge portion” 403 is extracted as a characteristic shape by an attribute information extraction unit 402 from a 3D CAD model such as that shown in 401.

[0047] The relevance evaluation unit 404 evaluates the factors including the characteristic shape "edge portion" as a causal relationship with a high relevance, in comparison with each factor in the causal model shown in Fig. 3. That is, in the causal model shown in Fig. 3, the causal relationship part of <"heat treatment: defective" 301 ← "heat treatment: quench crack" 302 ← "edge portion: stress concentration" 304> is presented to the designer as a causal relationship 405 with a high relevance.

[0048] This makes it possible to notice that in products having edges, stress concentration can cause quenching cracks during heat treatment, resulting in defective heat treatment.

[0049] The second method is a method of evaluating the relevance of the attribute information extracted by the attribute information extraction unit 110 and the information included in each factor of the causal model based on the co-occurrence frequency in defect information stored in the defect information database. Here, the co-occurrence frequency refers to the frequency with which two keywords appear in one document. This makes it possible to evaluate the relevance of two keywords.

[0050] For example, consider a case where a causal model as shown in Fig. 5A is stored in the causal model database 114. The components of this causal model include factor processes and shapes such as "solvent painting," "solvent paint," and "shape where paint easily accumulates."

[0051] FIG. 5B is a diagram explaining the flow of a process for presenting causal relationships that are highly relevant to a target product based on co-occurrence frequency. Assume that from a 3D CAD model such as that shown in 501, an attribute information extraction unit 502 extracts “solvent paint” as a paint type and “rib” 503 as a characteristic shape.

[0052] Also, suppose that the defect information stored in the defect information database 505 includes a statement such as "In part XX, solvent paint accumulated near the rib, causing a popping."

[0053] In the first method described above, the attribute information extracted by the attribute information extraction unit 110 is compared with the information included in each factor of the causal model, and if there is a matching factor, it is evaluated as having a high degree of association. However, with this method, even if there is actually an association, if the expressions do not match completely, it is not evaluated as having a degree of association.

[0054] On the other hand, in the second method using co-occurrence frequency, even if there is no perfect match, if there is defect information that describes both the attribute information extracted by the attribute information extraction unit 110 (502 in FIG. 5B) and the information contained in each factor of the causal model, it can be evaluated that there is a correlation between the attribute information and each factor of the causal model.

[0055] In the causal model of FIG. 5A, the cause related to the defect in solvent painting (5A1) is “Solvent painting: armpits (foaming)” 5A2 → “Shape in which paint easily accumulates: paint accumulation” 5A3, but the attribute information “rib” extracted by the attribute information extraction unit 110 (502) is not listed here.

[0056] However, when referring to the defect information description “In part XX, solvent-based paint accumulated near the rib, causing pimples” in the relevance evaluation section 504 in FIG. 5B, it is found that the attribute information 503 contains the words “solvent-based paint” and “rib,” as well as the words “pimples” in factor “5A2” and “accumulation” in factor “5A3” in the causal model, and it is possible to evaluate that these are related (the area surrounded by the dashed line in 506).

[0057] For ease of explanation, the defect information and the highly relevant causal relationships have been described in the above example in terms of a single case. However, there are also cases where there are multiple pieces of defect information and multiple candidates for related causal relationships. In such cases, the strength of each causal relationship is evaluated based on the number of pieces of defect information (co-occurrence frequency), which includes both attribute information and information on each factor in the causal model.

[0058] That is, while the first method uses a two-level evaluation of whether or not there is a match, the second method uses the number of relevant defect information (co-occurrence frequency) to evaluate, making it possible to evaluate the relevance in multiple levels in the form of the degree of relevance. For example, the relevance can be evaluated in three levels, such as "high," "medium," and "low," or expressed numerically, such as "0 (no relevance) to 10 (extremely high relevance)." In other words, when there are multiple related items, it is possible to evaluate and compare the strength of the relevance among these, and present the items in order of the strongest relevance. (Importance evaluation process) The importance evaluation unit 112 determines what kind of defect will occur in the product due to each factor of the causal model by searching the causal models stored in the causal model database 114 from the cause side to the effect side, and evaluates the importance of the defect that will occur in the product.

[0059] For example, assume that a causal model having multiple causal relationships including "screw: breakage" is stored in the causal model database 114. Since the causal relationships of defects are described in the causal model, it is possible to investigate what results are caused by "screw: breakage" by searching from "screw: breakage" to the result side.

[0060] 6A is an example of such a causal model, and if "screw: breakage" 610 is included in the causal factors of "product A: stopped" 601, it can be seen that "screw: breakage" 610 may lead to "product A: stopped" 601. Similarly, it can be seen that "screw: breakage" 610 may lead to results such as "product B: vibration" 602 and "product C: abnormal noise" 603.

[0061] In this way, even if the cause is the same "screw: breakage" 610, the resulting phenomenon may differ depending on the product. Therefore, by identifying the product name from the attribute information extracted by the attribute information extraction unit 110 and comparing it with the product name, it is possible to narrow down what kind of phenomenon may result in the product being designed.

[0062] In this example, if the attribute information extracted by the attribute information extraction unit 110 is “Product A”, the resulting phenomenon that may occur is “Product A: Stop” 601, and if the extracted attribute information is “Product C”, the resulting phenomenon that may occur is “Product C: Abnormal noise” 603.

[0063] The importance of these phenomena is then determined in advance. For example, as shown in Figure 6B, since the impact of a product stopping is significant, "stop" is determined to be of high importance, "vibration" is of medium importance, and "abnormal noise" is of low importance. The importance is evaluated based on the relationship between the resulting phenomenon and the importance. For example, if the resulting phenomenon is "Product A: Stop," the importance is determined to be "high."

[0064] This makes it possible to clearly indicate to the user the importance (seriousness) of a possible phenomenon (fault), enabling the user to adopt an action that takes into account the priority and urgency of the countermeasure.

[0065] Moreover, instead of evaluating the importance as "high," "medium," or "low," the importance may be expressed as a numerical value. For example, by defining "high" importance as "10," "medium" importance as "5," and "low" importance as "1," and predefining the importance of other events between them as "2-4, 6-9," the importance can be presented to the user in more detail.

[0066] Furthermore, it is also possible to use an index obtained by multiplying the quantified relevance and the quantified importance described above, and to present to the user causal relationships with a high index.

[0067] In addition, the malfunction causes and countermeasures are linked and saved, and countermeasures when the malfunction cause occurs are also presented in addition to the malfunction cause, thereby supporting the action to be taken against the malfunction cause. Figures 7A and 7B are examples of screen displays of malfunction causes and countermeasures on the output device 106 (e.g., a display device). In the screen display example 700 of Figure 7A, the part of the causal relationship 701 that is evaluated as being related or highly related in the causal model shown in Figure 3 is surrounded by a thick line, so that it is possible to confirm which causal relationships in the causal model are particularly related.

[0068] Moreover, when the cause "edge portion: stress concentration" 702 is clicked on with the mouse or the like of the input device 105, a details screen 710 shown in FIG. 7B is displayed.

[0069] The details screen 710 displays “Edge: stress concentration” 711 as the cause of the clicked defect, and also displays a 3D CAD model 712 and its corresponding location 713 surrounded by a dashed line or the like, allowing the user to visually confirm where in the 3D CAD model the defect may occur.

[0070] In addition, a countermeasure 714 for the cause of the defect is also displayed, which can improve the accuracy and convenience of the user's design work.

[0071] As described above, according to this embodiment, attribute information on the product to be designed, such as its characteristic shape and attribute information, is extracted from the design model generated by the design tool, and compared with predefined causal relationships of defects to evaluate the relevance and importance thereof, so that defect factors with high relevance and importance can be narrowed down and presented. EXAMPLES

[0072] The embodiment is shown in Fig. 8. The basic configuration of a design support device 801 is similar to that of the embodiment 1 shown in Fig. 1, and further includes an FMEA table generator 816, which supports FMEA in the user's design activities.

[0073] Here, FMEA (Failure Mode and Effects Analysis) is a method to predict possible failures that may occur in the parts of the design based on the FMEA table, and evaluate the impact that the failures will have on the unit and on the higher-level products during the design stage. It is then used to prevent failures by taking preventive measures for design objects that are evaluated as having a large impact.

[0074] An example of an FMEA table is shown in Figure 9. The parts column 901 is the name of the part being analyzed, the failure mode 902 is the name of the phenomenon related to the defect that may occur in that part, and the estimated cause (stress) column 903 is the cause. Furthermore, the impact of the failure is shown for each unit and for each product, and the failure impact column 904 is the impact that the failure has on the unit and the product. Also, the detection method (difficulty of detection) 905 is a numerical value indicating the difficulty of detecting the failure, and the failure grade (Risk Priority Number; RPN) 906 is a numerical value indicating the impact of the failure multiplied by the numerical value of the detection method, indicating the degree of necessity for advance measures.

[0075] Such FMEA tables are effective in verifying the accuracy and validity of design work in advance, but when an individual tries to create an FMEA table, it is not necessarily easy to identify the names of phenomena and presumed causes related to defects in the parts to be analyzed, or to evaluate the impact of the failure.

[0076] That is, depending on the individual user, there may be omissions in identifying the phenomenon name and the estimated cause related to the malfunction of the part to be analyzed, or there may be variation among individuals, and it may take a lot of man-hours to create the FMEA table, which may actually decrease efficiency. Similarly, it is also considered that the evaluation of the impact of failure may vary among individuals, and may require a lot of man-hours.

[0077] The design support device 801 of this embodiment makes it possible to create such an FMEA table. For example, consider the causal model shown in Fig. 10. This shows the causal relationships related to the thermal deformation of the plug. In this causal model, for example, the causal relationships in which a short circuit 1004 of an electrode causes heat generation 1002 in the electrode, resulting in thermal deformation 1001 of the plug are described.

[0078] By using this causal model, when an "electrode" is selected as the component to be analyzed, the FMEA table generation unit 816 extracts "heat generation" as a malfunction phenomenon occurring in the "electrode," and further extracts "electrode: short circuit" as the presumed cause of this.

[0079] Furthermore, by using the importance evaluation unit 812, it is possible to extract, by a method similar to that described in the first embodiment, what type of defect will occur in a product or unit due to each factor in the causal model.

[0080] 9. Moreover, the difficulty of detecting various types of malfunction phenomena is specified in advance in a correspondence table or the like (not shown), and the detection method 905 can be input by referring to the table. Furthermore, the failure effect 904 can also be quantified by preparing a table or the like in which the impact of various types of malfunctions is quantified in advance, so that the failure grade 906 can also be input by multiplying it by the value of the detection method 905. Moreover, the FMEA table into which each value has been input can be displayed on the output device 106 or the like.

[0081] This embodiment is not limited to the creation of an FMEA table, but can also be applied to the creation of a table for consideration when implementing DRBFM (Design Review Based on Failure Mode).

[0082] As described above, according to this embodiment, in creating an FMEA table, it is possible to reduce omissions and variations due to individual users, and to reduce the number of steps. EXAMPLES

[0083] The embodiment is shown in Fig. 11. The basic configuration of a design support device 1101 is similar to that of the embodiment 1 shown in Fig. 1, but in the embodiment 1, attribute information is extracted from a 3D CAD model received by a "3D CAD model input unit" 109.

[0084] In contrast to this, in this embodiment, the "3D CAD model input unit" 109 is replaced with an expanded "model input unit" 1109, which allows input of not only 3D CAD models but also other models, and further allows the attribute information extraction unit 1110 to extract attribute information from other models. Examples of other models include DSM (Dependency Structure Matrix), QFD (Quality Function Deployment), MBSE (Model-Based Systems Engineering), etc.

[0085] DSM expresses the dependencies between product components in a matrix. QFD expresses the relationships between requirements and functions, and functions and parts in a matrix. MBSE defines structure diagrams, requirement diagrams, behavior diagrams, and parametric diagrams using the SysML language.

[0086] Here, a structure diagram is used to represent the structure of a system. Using a structure diagram, a system can be represented as a tree structure of its components, and the relationships between the components can be represented.

[0087] A requirements diagram defines the detailed functional and non-functional requirements of a system. A requirements diagram consists of requirement classes and relationships that describe functional and non-functional requirements.

[0088] Here, functional requirements are requirements for a system that relate to its functional aspects, while non-functional requirements are requirements for a system that relate to aspects other than its functional aspects.

[0089] A behavior diagram defines the dynamic behavior of a system. It consists of states and transitions. A parametric diagram defines the system's constraints, associated values ​​and equations.

[0090] Parametric diagrams are composed of constraint blocks and connectors. A constraint block is a block that describes the system constraints and related values ​​and formulas. Based on the block structure created in the block definition diagram, related constraint blocks are connected with connectors.

[0091] The above-mentioned DSM, QFD, and MBSE models contain information such as product names, part names, and attributes, just like 3DCAD models (3DA models). Therefore, the attribute information extraction unit 1110 of this embodiment extracts attribute information from these models as well. This makes it possible to support the utilization of defect knowledge by providing defect knowledge related to the design object to users who design using models other than 3DCAD models such as DSM, QFD, and MBSE. [Explanation of symbols]

[0092] 101:Design support equipment 102: Information terminal 103: Network 104: Central control unit 105: Input device 106: Output device 107: Main memory 108:Auxiliary storage device 109: 3D CAD model input section 110: Attribute information extraction section 111: Relevance evaluation section 112: Importance evaluation section 114:Causal model database 115: Defect information database

Claims

1. A design support device for evaluating defects that may occur in a design model generated by a design tool and their causes, comprising: a causal model database storing a causal model that expresses a defect that may occur in a design object using a causal relationship in which a certain phenomenon is a factor causing another phenomenon; and an attribute information extraction unit that extracts attribute information about a design object from the design model; a relevance evaluation unit that extracts, from the causal model, a causal relationship including a content that matches the extracted attribute information as a causal relationship having a high relevance to the design model; A design support device comprising:

2. The design support device according to claim 1 , The causal model is a model in which the causal relationship of a defect is expressed in a chain format in the order of causality, with the object and the name of the phenomenon as one element, A design support device, wherein the object includes any one of a product name, a part name or a portion name, and a characteristic shape.

3. The design support device according to claim 2, The design support device, wherein the design model is a 3DA model.

4. The design support device according to claim 2, 11. A design support device, wherein the design model is any one of DSM, QFD, and MBSE.

5. The design support device according to claim 3, a defect information database storing defect information that records details of defects that have occurred in the design object; The design support device is characterized in that the relevance evaluation unit evaluates the relevance between each causal relationship element included in the causal model and the extracted attribute information based on the co-occurrence frequency in the defect information.

6. The design support device according to claim 3, a significance evaluation unit that searches for defects occurring in a product including a specific part by searching for a causal relationship from a cause side to a result side for factors of the specific part in the causal model, and evaluates the significance of the causal relationship on the product based on a predetermined significance for each defect.

7. The design support device according to claim 6, A design support device comprising an FMEA table generating unit for inputting values ​​in a failure mode column, a presumed cause column, and a failure effect column in an FMEA table.

8. The design support device according to claim 6, A design support device comprising: a display unit that distinguishes and presents causal relationships that have been evaluated by the relevance evaluation unit as being related or having a high degree of relevance.

9. The design support device according to claim 8, The design support device, wherein the display unit displays a defect cause in the causal relationship and a corresponding portion of the design model.

10. The design support device according to claim 9, The design support device, wherein the display unit displays a countermeasure against the cause of the defect.

Images