Design support device and design support method
The design support device and method provide specific improvement proposals by calculating contribution and similarity scores, addressing the challenge of abstract solution concepts in existing methods, enabling efficient design consideration and prioritization of proposals that resolve trade-off relationships.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- HITACHI LTD
- Filing Date
- 2022-10-11
- Publication Date
- 2026-06-09
AI Technical Summary
Existing design support methods, such as TRIZ and Patent Document 1, present abstract solution concepts that require additional user skills, experience, and knowledge to concretize design proposals, leading to confusion in applying them to specific design targets like heat dissipation fins or fan blades.
A design support device and method that includes an input unit, search unit, priority calculation unit, and database to provide specific improvement proposals by calculating contribution and similarity scores based on evaluation indicators, allowing for efficient prioritization of design proposals that resolve trade-off relationships.
The method efficiently presents specific improvement proposals that resolve trade-off relationships between evaluation indicators, enabling efficient design consideration without rework by prioritizing proposals based on their contribution and similarity to the design target.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to a design support device and a design support method for assisting in the consideration of product design proposals.
Background Art
[0002] In product design, it is necessary to consider design proposals that satisfy a plurality of evaluation indicators such as product performance, cost, reliability, etc. based on customer requirements. However, some of the evaluation indicators are in a trade-off relationship with each other. For example, if the plate thickness is increased to increase the strength of a part, the cost and weight that are originally desired to be as small as possible will increase. Thus, in order to satisfy all the evaluation indicators, it is necessary to solve the trade-off relationship as described above.
[0003] A method for assisting in generating ideas for structures, functions, etc. that solve the trade-off relationship of evaluation indicators is a method called TRIZ (Russian: Teoriya Resheniya Izobretatelskikh Zadatch: Theory of Inventive Problem Solving), which is generally known. TRIZ is a theory that systematizes the viewpoints of ideas and thinking processes for innovative problem solving based on a statistical analysis of a vast amount of patent data and technical literature, numbering approximately two million. In TRIZ, for each combination of evaluation indicators in a trade-off relationship, an invention principle, which is an idea for solving that trade-off relationship, is presented.
[0004] Further, according to Patent Document 1, for the purpose of assisting in the creation of innovation by effectively presenting a solution concept to a user, "a basic information acquisition means for acquiring basic information input from the user, and using the three-stage or more association degrees between each previously acquired reference string and each solution concept classified into two or more types, based on the three-stage or more association degrees between the reference string and the solution concept corresponding to the string of the basic information acquired by the basic information acquisition means, a search means for searching for one or more solution concepts." is proposed.
Prior Art Documents
[0005] [Patent Document 1] Japanese Patent Publication No. 2019-125389 [Overview of the Initiative] [Problems that the invention aims to solve]
[0006] Patent Document 1 describes an invention that effectively presents problem-solving concepts to users, thereby leading to innovation. However, in TRIZ and the prior art described in Patent Document 1, the presented solution concepts and inventive principles are abstract, requiring additional user skills, experience, and knowledge to concretize them according to the design target.
[0007] For example, typical examples of inventive principles include "imitation and substitution" and "low cost and low durability." These have meanings such as "replacing with something cheaper, copying it" and "using disposable, free materials," respectively. However, if these are applied to the design of new heat dissipation fins or fan blades for cooling a device, users may be confused about what specific design proposal they should come up with.
[0008] Based on the above, the present invention aims to provide a design support device and a design support method that can present specific improvement proposals to resolve trade-off relationships between evaluation indicators. [Means for solving the problem]
[0009] Based on the above, the present invention comprises an input unit for inputting attribute information and multiple evaluation indicators, a search unit for obtaining information on improvement case examples using the input attribute information and the multiple evaluation indicators, a priority calculation unit for prioritizing the cases, an output unit for prioritizing and presenting to the outside improvement case examples that improve evaluation indicators in a trade-off relationship, a database having multiple cases which are sets of multiple evaluation indicators in a trade-off relationship in design, improvement cases that can improve the trade-off relationship, evaluation indicator sensitivity which qualitatively or quantitatively expresses the degree of improvement of the multiple evaluation indicators by the improvement case in a comparable manner, and attribute information relating to the design target, wherein the search unit inputs The attribute information and the multiple evaluation indicators Using the above, the priority calculation unit obtains information on the relevant improvement case from the database, and the priority calculation unit calculates the contribution according to the degree of improvement of the evaluation indicator, and inputs The attribute information and the multiple evaluation indicators, Information to be stored in the aforementioned database and It includes a similarity calculation unit that calculates similarity from the similarity of the components, and calculates an index that becomes a priority score by multiplying the contribution and the similarity. 、 This is a design support device characterized by the following features.
[0010] Furthermore, the present invention relates to a design support device comprising an input unit, a search unit, a priority calculation unit, an output unit, and a database, wherein the database has multiple cases, each case being a set of multiple evaluation indicators that are in a trade-off relationship in design, improvement examples that can improve the trade-off relationship, an evaluation indicator sensitivity that qualitatively or quantitatively expresses the degree of improvement of the multiple evaluation indicators by the improvement examples in a comparable manner, and attribute information relating to the design target, the input unit inputs attribute information and multiple evaluation indicators, the search unit uses the input attribute information and multiple evaluation indicators to obtain information on the corresponding improvement example cases from the database, the priority calculation unit prioritizes the cases, the output unit prioritizes and presents to the outside the cases of improvement examples that improve evaluation indicators in a trade-off relationship, and the priority calculation unit includes a contribution calculation unit that calculates a contribution according to the degree of improvement of the evaluation indicators, and input The attribute information and the multiple evaluation indicators, Information to be stored in the aforementioned database and The design support method is characterized by comprising a similarity calculation unit that calculates a similarity score from the similarity of the elements, and by calculating an index that becomes a priority score by multiplying the contribution score and the similarity score. [Effects of the Invention]
[0011] According to this invention, by presenting specific improvement proposals that resolve trade-off relationships between evaluation metrics, design proposals that satisfy the evaluation metrics can be efficiently considered. Furthermore, since the proposed improvement proposals are presented in order of priority, design can be supported efficiently without rework. [Brief explanation of the drawing]
[0012] [Figure 1] A diagram showing an example configuration of a design support device according to an embodiment of the present invention. [Figure 2] A diagram showing an example of a table structure in a case study database. [Figure 3] A flowchart illustrating the processing procedure for a design support method according to an embodiment of the present invention. [Figure 4] A diagram showing an example of an input screen. [Figure 5] A diagram showing an example of the results of calculating contribution, similarity, and priority scores. [Figure 6] This diagram shows an example of the results when items are sorted in descending order from the highest priority score. [Figure 7] This figure shows an example of the output screen using this device. [Modes for carrying out the invention]
[0013] Hereinafter, the following example will be described as a specific example of the design support device of the present invention with reference to the drawings. First, representative examples for presenting improvements to solve the trade-off relationship are shown as Example 1 and Example 2. In this example, taking the CAD model of the cooling fin as an example, it will be described by presenting a design plan of the "cooling fin" that satisfies the evaluation index while achieving the trade-off relationship between the evaluation indexes of "improvement of cooling performance" and "reduction of manufacturing cost".
Example
[0014] FIG. 1 is a diagram showing a configuration example of a design support device according to an embodiment of the present invention, which supports the user's idea by presenting improvement plans to the user.
[0015] The design support device in FIG. 1 is composed of an input unit 100, a case database 106, a search unit 101, a priority calculation unit 102, and an output unit 105. Further, the priority calculation unit 102 is composed of a contribution calculation unit 103 and a similarity calculation unit 104.
[0016] Among these, the input unit 100 accepts, as search conditions, the input of attribute information such as evaluation indexes in the trade-off relationship to be improved, product / part names to be designed, product functions, types such as environmental characteristics / quality / cost / delivery date related to evaluation indexes, etc. from the user. In the illustrated example, it is assumed that evaluation index 1 and evaluation index 2 as evaluation indexes in the trade-off relationship to be improved, and information on the target product, etc. are input as input information (search conditions).
[0017] The search unit 101 refers to the case database 106 based on the input information from the input unit 100, extracts improvement plans for the trade-off relationship by search, and outputs them to the priority calculation unit 102. Here, the essential search condition for searching is the evaluation index in the trade-off relationship. It is assumed that in FIG. 2, cases where evaluation index 1 is "improvement of cooling performance" and evaluation index 2 is "reduction of manufacturing cost" for the cooling fin are stored.
[0018] The case database 106, which pre-stores multiple sets of improvement proposals for trade-off relationships, is composed of data in the table structure shown in Figure 2. The case database 106 includes information such as ID (D0), improvement example D1, evaluation index 1 name D2, evaluation index 2 name D3, invention principle D4, evaluation index 1 sensitivity D5, evaluation index 2 sensitivity D6, evaluation index 1 type D7, evaluation index 2 type D8, target product / part name D9, and product function D10.
[0019] Of these, ID(D0) is an identifier assigned to each design example. Improvement example D1 is described as a specific improvement proposal. Evaluation indicator 1 name D2 and evaluation indicator 2 name D3 are evaluation indicators that need improvement and are in a trade-off relationship, and are evaluation indicators that have been improved by improvement proposal D1.
[0020] A distinctive feature of the data held by the case database 106 according to the present invention is that it includes evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6. Evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6 store the improvement effect of improvement plan D1 in a format that allows for qualitative or quantitative comparison.
[0021] In the case where the ID (D0) of the example in Figure 2 is #0001, the improvement example D1 is described as "Changing to a cheaper material with equivalent or better cooling performance to cover the machining costs during cutting." The columns for Evaluation Index 1 Name D2 and Evaluation Index 2 Name D3 are described as "Cooling Performance" and "Machining Cost," respectively, and the invention principle D4 is described as "Imitation Substitution." In the Evaluation Index 1 Sensitivity D5 column, the value indicating the degree of improvement in cooling performance due to improvement example D1 is approximately 0, and the term "Maintain, Slight Increase" is described to mean this. Subsequently, in the Evaluation Index 2 Sensitivity column, the value indicating the degree of improvement in machining costs due to improvement example D1 is described as "-2% to -3% improvement."
[0022] In evaluation index 1, type D7, the cooling performance of evaluation index 1, name D2, is defined as "quality," and in evaluation index 2, type D8, the processing cost of evaluation index 2, name D3, is defined as "cost." This type can be defined, for example, from the perspective of quality assurance, such as environmental friendliness, quality and performance, cost, and delivery time, as part of EQCD. The target part D9 is "fins," and the product function D10 is "cooling." In addition, evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6 may be visualized using arrows to show the degree of improvement as a slope, allowing for intuitive recognition.
[0023] In the example in Figure 2, for case #0002 with ID (D0), improvement example D1 states that "costs can be reduced by changing the processing method from cutting to a different method to create a structure that can be processed from sheet metal." In case #0003, improvement example states that "by using a configuration that copies small modules, the processing cost of individual parts can be reduced while maintaining performance." In these cases as well, the improvement effect of improvement proposal D1 is stored qualitatively or quantitatively in a comparable format in evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6. Quantitative effects are described by numerical values such as "+2% to -3% improvement" and "-5% to -15% improvement," while the degree of improvement is described qualitatively by the slope of the arrow or by notation indicating the magnitude of the effect.
[0024] As described above, a format that allows for qualitative or quantitative comparison of the improvement effects of improvement proposals is a format that qualitatively or quantitatively expresses the extent to which a certain issue is improved or worsened in each improvement. In this example, each improvement proposal is expressed using "%" or "good / bad," but it is not limited to these.
[0025] Returning to Figure 1, the contribution calculation unit 103 obtains the degree of improvement of evaluation indicators that have a trade-off relationship as the contribution level for design examples retrieved from the case example database 106, and calculates the contribution level so that those with a greater degree of improvement have a higher contribution level.
[0026] Here, the information handled by the contribution calculation unit 103 is basically information on cases extracted from cases where all input items match through a matching search of the data content in the case database 106. In this case, it is expected that the number of extracted cases will be limited and small. For this reason, in the search for the contribution calculation unit 103, it is preferable to generate related terms for the input terms and perform a search that includes these terms.
[0027] Furthermore, the similarity calculation unit 104, similar to the contribution calculation unit 103, obtains the similarity of attribute information of evaluation indicators that are in a trade-off relationship from design examples retrieved from the example database 106, and calculates the similarity so that similar items have a higher similarity score.
[0028] Here, the information handled by the similarity calculation unit 104 is basically information on cases extracted from cases where some input items match through a matching search of the case database 106. For example, if there are 5 input items, cases where 3 items match are extracted along with information on the matching ratio. Alternatively, the similarity may be determined from the perspective of similar technologies.
[0029] The priority calculation unit 102 uses the results of the contribution calculation unit 103 and the similarity calculation unit 104, as described above, to calculate the priority of improvement proposals for the evaluation indicators, such that those with high contribution and similarity also have a higher priority. The output unit 105 outputs the results of sorting the improvement proposals in descending order of priority.
[0030] Figure 3 is a flowchart illustrating an example of the processing procedure excluding the input unit 100 and output unit 105. According to the flow in Figure 3, the search unit 101 first retrieves keywords such as the evaluation indicators to be achieved (D2, D3) that are in a trade-off relationship, received from the input unit 100 (processing step S300). At this time, the elements other than the evaluation indicator keywords (D4-D10) include attribute information, etc.
[0031] Next, the system searches the case database 106 for relevant improvement proposals that match or are similar to the acquired keywords (processing step S301). When using keyword search, natural language processing techniques such as morphological analysis and Word2Vec may also be utilized.
[0032] In processing step S302, the system checks if there are any improvement suggestions related to the case database 106. If there are improvement suggestions, the system proceeds to the next processing step S303. On the other hand, if there are no improvement suggestions, in processing step S310, the system is instructed to re-enter the keywords entered in the input unit 100 or to enter attribute information.
[0033] From processing step S303 onward, the process is handled by the priority calculation unit 102. First, the priority calculation unit 102 obtains improvement suggestions related to the keyword in processing step S310 as a result of the search unit 101.
[0034] Next, two processes are performed. Specifically, the process of the contribution calculation unit 103 (processing step S304) and the process of the similarity calculation unit 104 (processing step S305) are executed. Details of the calculation formulas and processing contents for each will be described later.
[0035] Returning to Figure 1, the contribution calculation unit 103 calculates the contribution of the improvement proposal obtained in processing step S304 by utilizing the data of evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6, which are values that indicate the degree of improvement of evaluation indicators that have a trade-off relationship. The contribution is obtained by the following equation (1). Hereinafter, u1 is the sensitivity of evaluation index 1, u2 is the sensitivity of evaluation index 2, and K is the contribution of the improvement proposal.
[0036]
number
[0037] (1) As information for formula (1), evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6 use data stored in past case database 106. Specifically, the percentage of improvement from past cases and correlation coefficients showing the tendency to improve from past performance are stored as numerical values, and the range of values is defined as ±1.0 to 0.0. The closer the value is to 1.0, the higher the contribution. Based on the sensitivity of each evaluation index, the contribution of each improvement proposal is calculated. This evaluation is applicable when the degree of improvement can be quantitatively understood numerically.
[0038] On the other hand, in evaluation index 1 sensitivity D5 and evaluation index 2 sensitivity D6, the degree of improvement may be qualitatively understood as the slope of the arrow or as large, medium, or small, as indicated by the arrows. In such cases, it is possible to similarly address this by converting the values numerically according to the slope, or by converting large, medium, and small to numerical values such as 0.8, 0.5, and 0.2, respectively.
[0039] Next, the similarity calculation unit 104 calculates the similarity of the improvement proposal obtained as shown in processing step S305, using the data of the target product / part name D9 and product function D10, which are input as attribute information. The similarity is calculated by the following equation (2). Here, tp is the similarity of the target product / part name, f is the similarity of the product function, n is the number of items for the target product / part name and product function entered in the input unit, and R is the similarity of the improvement proposal.
[0040]
number
[0041] (2) As information related to equation (2), the similarity between the keywords received by the input unit 100 and the keywords in the data stored in the case database 106 is calculated as a quantitative numerical value. When using keyword search, natural language processing techniques such as morphological analysis and Word2Vec may be used. The similarity of each keyword is defined within the range of 0.0 to 1.0. The closer the value is to 1.0, the higher the similarity. Based on the similarity of each attribute information, the similarity of each improvement proposal to the name and function of the design target is calculated.
[0042] The final priority is determined by equation (3) below, based on the results of equations (1) and (2). Here, K is the contribution of the improvement proposal, R is the similarity of the improvement proposals, and Y is the priority score index.
[0043]
number
[0044] In summary, the priority score is calculated by multiplying contribution and similarity. Therefore, improvement proposals with high contribution and similarity scores will receive higher scores. Users can also assign weights to contribution if they wish to prioritize it.
[0045] Based on the priority scores obtained, the priority calculation unit 102 assigns a ranking to the improvement proposals and displays them in descending order from the highest-scoring proposal (processing step S306).
[0046] Figure 4 illustrates an example of an input screen. The user enters evaluation indicators that represent the trade-off relationship they wish to achieve, separated by spaces, as search keywords in the evaluation indicator input field 400. In this example, "cooling performance" and "processing cost" are entered. Next, "fin" is entered in the target product / part name input field 401 for attribute information. Subsequently, "cooling" is entered in the product function input field 402, and finally, "quality" is entered for cooling performance and "cost" for processing cost in the evaluation indicator 1 type input field 403 and evaluation indicator 2 type input field 404. After entering keywords and attribute information, the user presses the search button 405 to execute the search.
[0047] In the search, the data in input field 400 references D3 and D4 in the database structure of Figure 2, the data in input field 401 references D9, the data in input field 402 references D10, the data in input field 403 references D7, and the data in input field 404 references D8. The case numbers of cases that match these are then extracted. The contribution calculation unit 103 searches for all items to match, but the similarity calculation unit 104 searches for partial matches and extracts the case numbers of cases that match along with information on the similarity ratio.
[0048] Figure 5 shows an example of the results obtained by calculating contribution, similarity, and priority scores. Here, the results are shown based on the data in the case database shown in Figure 2, using the search keywords entered in Figure 4. Figure 5 is assumed to show the screen displaying the search and calculation results.
[0049] The calculation results include ID (D0), evaluation metric 1 sensitivity D5, evaluation metric 2 sensitivity D6, similarity of target product / part name D11, similarity of product function D12, contribution D13, similarity D14, and priority score D15, all of which are calculated and displayed.
[0050] For example, in #0001, the sensitivity D5 for evaluation index 1 is displayed as "0.01", and the sensitivity D6 for evaluation index 2 is displayed as "0.03". Substituting these values into equation (1), the contribution D13 of the improvement proposal is calculated and displayed as "0.04". Next, for similarity D14, if the search keyword and the keyword of each item are an exact match, it will be "1.0". The similarity D11 for the target product / part name is displayed as "1.00", and the similarity D11 for the product function is displayed as "1.00".
[0051] Substituting these values into equation (2), a similarity score D14 of the improvement proposal is calculated to be "1.00". Substituting the results of equations (1) and (2) into equation (3), a priority score D15 is calculated to be "0.04". From the above, the priority scores for each ID can be calculated as follows: #0001 is "0.04", #0002 is "0.18", #0003 is "0.05", and #0004 is "0.04".
[0052] Figure 6 shows an example of the results when sorted in descending order from the highest priority score. Based on the calculation results in Figure 5, the ID (D0), Rank (D16), and improvement example (D1) are linked. Assigning ranking numbers from the highest score, the results can be sorted in the order of ID#0002, ID#0003, ID#0004, and ID#0001 from top to bottom.
[0053] Figure 7 illustrates an example of the output screen. It presents the user with the results sorted in Figure 6. At this time, the user can refer to specific improvement examples that improve the trade-off relationship evaluation indicators entered in the input screen of Figure 4, starting with improvement suggestion 700, which has the highest priority score: "By replacing the processing method from cutting to a processing method, it is possible to reduce costs by creating a structure that can process sheet metal while maintaining the surface area." Information such as the invention principle and attributes stored in the example database can also be accessed from the detailed link 701 for each improvement example. Alternatively, by setting it to open related documents from the detailed link, it is also possible to refer to design standards and technical documents related to the improvement example. [Examples]
[0054] In Example 2, the basic configuration is the same as in Example 1, but in addition to the similarity of the target product / part name and product function as described above, the similarity of shape is calculated for each element used to design the CAD model and geometric information of the shape.
[0055] One method for determining similarity is to evaluate the similarity of the geometric information of the shapes and add it to the similarity item in equation (2). In this case, the similarity of the geometric information may be evaluated by, for example, using a similar partial shape search technique to recognize the similarity of partial shapes.
[0056] For example, when comparing the product / part names "fin" and "fan," all but the second letter are the same, and natural language processing technology might judge them as highly similar. However, by using the geometric information of these two shapes, we believe we can correctly evaluate the similarity between these two shapes, which are completely different, and present improvement proposals that are more suitable for the user's design target.
[0057] Furthermore, when visualizing groups with similar improvement proposals, it is desirable to be able to filter or switch the display format, such as grouping similar types of data together.
[0058] Furthermore, if the calculation results for contribution and similarity do not indicate a clear superiority or inferiority, it is best to instruct users to re-enter information or add attribute information to the input method, thereby adding the necessary items for calculation and determining the priority. [Explanation of symbols]
[0059] 100: Input section 101: Search Department 102: Priority calculation unit 103: Contribution Calculation Unit 104: Similarity calculation unit 105: Output section 106: Case Database
Claims
1. An input section for inputting attribute information and multiple evaluation metrics, A search unit that obtains information on improvement case examples using the input attribute information and the multiple evaluation indicators, A priority calculation unit that assigns priority to the aforementioned cases, An output unit that prioritizes and presents externally case studies of improvement that enhance evaluation metrics with trade-off relationships, A database having multiple cases in which multiple evaluation metrics that have a trade-off relationship in the design, improvement examples that can improve the trade-off relationship, evaluation metric sensitivity that qualitatively or quantitatively expresses the degree of improvement of the multiple evaluation metrics by the improvement examples in a comparable manner, and attribute information related to the design target are paired together. Equipped with, The search unit uses the input attribute information and the multiple evaluation indicators to obtain information on the corresponding improvement case from the database. The priority calculation unit includes a contribution calculation unit that calculates a contribution according to the degree of improvement of the evaluation indicator, and a similarity calculation unit that calculates a similarity from the similarity between the input attribute information, the multiple evaluation indicators, and the information stored in the database, and calculates an indicator that becomes the priority score by multiplying the contribution and the similarity. A design support device characterized by the following features.
2. A design support device according to claim 1, A design support device characterized by prioritizing based on the weighted contribution and similarity, wherein the weights are variable.
3. A design support device according to claim 1, The design support device is characterized in that the similarity calculation unit determines the similarity including the similarity of CAD models and geometric information of shapes.
4. A design support device according to claim 1, A design support device characterized by displaying a message prompting users to change their search criteria if no superiority or inferiority can be determined from the calculation results of the aforementioned contribution or similarity.
5. A design support method in a design support device comprising an input unit, a search unit, a priority calculation unit, an output unit, and a database, The aforementioned database has multiple cases in which it combines several evaluation metrics that have a trade-off relationship in the design, improvement examples that can improve the trade-off relationship, evaluation metric sensitivity that qualitatively or quantitatively expresses the degree of improvement of the several evaluation metrics by the improvement examples in a comparable manner, and attribute information related to the design target. The aforementioned input unit receives attribute information and multiple evaluation indicators, The search unit uses the input attribute information and the multiple evaluation indicators to obtain information on relevant improvement cases from the database. The priority calculation unit assigns a priority to the cases, The output unit prioritizes and presents to the outside improvement cases that improve evaluation indicators that have a trade-off relationship. The design support method is characterized in that the priority calculation unit comprises a contribution calculation unit that calculates a contribution according to the degree of improvement of the evaluation indicator, and a similarity calculation unit that calculates a similarity from the similarity between the input attribute information, the plurality of evaluation indicators, and the information stored in the database, and calculates an indicator that becomes a priority score by multiplying the contribution and the similarity.