JP2017146888A - Design support device and method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically generate a design plan satisfying customer satisfaction when satisfying customer request specifications.SOLUTION: A design support device comprises: a customer request specification input part 201 for receiving customer request specifications; a simulation part 202 for generating a plurality of analysis results related to a product; a design plan candidate generation part 203 for extracting the analysis result satisfying the customer request specifications to generate a design plan candidate; a customer satisfaction input part 204 for inputting the satisfaction of a customer; a product parameter significance estimation part 205 for estimating the significance of a product parameter from the customer satisfaction; an optimal design plan search part 206 for searching the optimal design plan; and an optimal design plan determination part 206 for determining the optimal design plan from a plurality of Pareto solutions.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a design support apparatus, method, and program, and more particularly, among a plurality of design proposals made based on customer's required specifications in a build-to-order manufacturing system in which an order is received from a customer and a product is designed. The present invention relates to a design support apparatus, method, and program that automatically generate a design plan that satisfies customer satisfaction (for example, an optimal design plan that maximizes customer satisfaction).

Several support devices or support systems have been proposed for companies to provide products according to customer needs. The system described in Japanese Patent Application Laid-Open No. 2003-242329 (Patent Document 1) stores factors associated with customer needs for vehicles (products), and it has been determined that the vehicle does not meet customer needs. In this case, the customer needs are extracted from the customer database, and the specifications optimized to meet the extracted customer needs are calculated for the vehicle.
In the system described in Japanese Patent Laid-Open No. 2001-14021 (Patent Document 2), a company searches for a recommended specification while receiving a required specification from a customer while referring to a production management database, and a distance between the recommended specification and the customer specification. To calculate customer satisfaction. Recommended specification candidates whose customer satisfaction is equal to or higher than a predetermined reference value are extracted.

JP 2003-242329 A JP 2001-14021 A

  In general, when designing a build-to-order product, a designer must determine the values of a number of product parameters in order to keep the customer's required specifications as essential conditions. In recent years, a method of performing an optimal design using an optimization method such as a genetic algorithm has been used. However, since there may be a trade-off relationship between many product parameters, a large number of Pareto solutions can be obtained from the optimization results. Cannot be selected. The Pareto solution is a set of “solutions that cannot be given superiority or inferiority”, and can be said to be “a state in which other objective function values are altered when trying to improve any of the objective function values”.

FIG. 1 is an explanatory diagram for an image of a Pareto solution. In this example, the number of objective functions is two, and the optimization direction is a case where the objective function is minimized. Black dots in the figure are Pareto solutions, and white dots are inferior solutions that are inferior to Pareto solutions. In order to select an optimal design plan from a plurality of parade solutions, it is necessary to determine the importance of each product parameter, but no such technique has been proposed.
In the technique described in Patent Document 1, the specification of the vehicle is optimized so as to meet the needs of customers. However, sufficient consideration has not been given to the generation of an optimal design plan that satisfies the specifications.
In Patent Document 2, customer satisfaction is calculated based on the distance between a recommended specification created for the convenience of the factory and the actual customer specification. The calculated satisfaction actually reflects the customer's intention. Whether or not it has been fully studied.

  In view of the above points, the present invention automatically generates a design plan that satisfies customer satisfaction (e.g., an optimal design plan that maximizes customer satisfaction) in satisfying the customer requirement specifications. It is an object to provide a design support apparatus, method, and program.

According to the first solution of the present invention,
A design support device,
A customer requirement specification input unit for inputting a plurality of customer requirement specifications for the target product;
By executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance, a plurality of analysis results including a plurality of customer request specifications for a plurality of product parameters can be obtained. A simulation unit to generate,
Design plan candidates for generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or more predetermined customer requirement specifications from the plurality of analysis results generated by the simulation unit A generator,
A customer satisfaction input unit that inputs customer satisfaction for each of a plurality of design plan candidates generated by the design plan candidate generation unit and stores the customer satisfaction together with a plurality of product parameters in the customer satisfaction storage unit When,
Importance analysis of the data stored in the customer satisfaction storage unit estimates the importance of each product parameter from customer satisfaction, and stores the importance for each product parameter in the product parameter importance storage unit A degree estimation unit;
Any of a plurality of product parameters that function as a plurality of constraint functions and independent variables according to any of a plurality of objective functions and a plurality of customer requirement specifications that are set in advance for a target product. Optimal design proposal search unit that obtains a plurality of Pareto solutions including a plurality of product parameters that are objective functions and stores a plurality of Pareto solutions in a Pareto solution storage unit by performing optimization calculation according to the design variable by
The priority of the Pareto solution is calculated from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, and the Pareto solution having a high priority Is determined as a design plan, stored in the storage unit and / or output to the output unit,
Is provided.

According to the second solution of the present invention,
A design support method,
Entering multiple customer requirement specifications for the target product;
By executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance, a plurality of analysis results including a plurality of customer request specifications for a plurality of product parameters can be obtained. Generating step;
Generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or more predetermined customer requirement specifications from the plurality of generated analysis results;
For each of the generated plurality of design plan candidates, inputting customer satisfaction, and storing the customer satisfaction together with a plurality of product parameters in the customer satisfaction storage unit;
Estimating the importance of each product parameter from customer satisfaction by variance analysis for the data stored in the customer satisfaction storage unit, and storing the importance for each product parameter in the product parameter importance storage unit;
Any of a plurality of product parameters that function as a plurality of constraint functions and independent variables according to any of a plurality of objective functions and a plurality of customer requirement specifications that are set in advance for a target product. Obtaining a plurality of Pareto solutions including a plurality of product parameters, which are objective functions, and storing a plurality of Pareto solutions in a Pareto solution storage unit by performing optimization calculation according to the design variables of
The priority of the Pareto solution is calculated from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, and the Pareto solution having a high priority Determining as a design plan, storing in the storage unit and / or outputting to the output unit;
A design support method is provided.

According to the third solution of the present invention,
A design support program,
A processing unit inputting a plurality of customer request specifications related to the target product;
The processing unit includes a plurality of customer requirement specifications for a plurality of product parameters by executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance. Generating multiple analysis results;
A step of generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or a plurality of predetermined customer request specifications from the generated plurality of analysis results; ,
The processing unit inputs customer satisfaction for each of the generated plurality of design proposal candidates, and stores the customer satisfaction in a customer satisfaction storage unit together with a plurality of product parameters;
The processing unit estimates the importance of each product parameter from the customer satisfaction by variance analysis for the data stored in the customer satisfaction storage unit, and the importance for each product parameter is stored in the product parameter importance storage unit. Storing, and
A plurality of products in which the processing unit functions as a plurality of objective functions according to any one of a plurality of product parameters and a plurality of constraint conditions and independent variables according to any of a plurality of customer requirement specifications related to the target product. Obtaining a plurality of Pareto solutions including a plurality of product parameters as objective functions by performing an optimization calculation according to the design variable of any one or more of the parameters, and storing the plurality of Pareto solutions in the Pareto solution storage unit; ,
The processing unit calculates the priority of the Pareto solution from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, Determining a Pareto solution with a higher rank as a design proposal, storing it in the storage unit and / or outputting it to the output unit;
A design support program for causing a computer to execute is provided.

  According to the present invention, a design support apparatus and method for automatically generating a design plan that satisfies customer satisfaction (for example, an optimal design plan that maximizes customer satisfaction) upon satisfying customer requirement specifications. And programs can be provided.

It is explanatory drawing about the image of a Pareto solution. It is a block diagram of the design support apparatus which concerns on a present Example. It is an example of the block diagram of the simulation part which concerns on a present Example, and a design plan candidate production | generation part. It is an example of explanatory drawing of the customer satisfaction storage part which concerns on a present Example. It is an example of the block diagram of the product parameter importance estimation part which concerns on a present Example. It is an example of explanatory drawing of the product parameter importance storage part which concerns on a present Example. It is an example of the block diagram of the optimal design plan search part which concerns on a present Example. It is an example of explanatory drawing of the objective function setting part which concerns on a present Example. It is an example of an explanatory diagram of a constraint condition setting unit according to the present embodiment. It is an example of explanatory drawing of the design variable setting part which concerns on a present Example. It is an example of explanatory drawing of the Pareto solution storage part which concerns on a present Example. It is an example of the flowchart which shows the process sequence which concerns on a present Example. It is an example of explanatory drawing of the customer requirement specification storage part which concerns on a present Example. It is an example of explanatory drawing of the analysis result storage part which concerns on a present Example. An example of the explanatory view of the design plan candidate storage part concerning this example is shown.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

1. Design support device

Embodiments of the present invention will be described below with reference to the drawings.
First, FIG. 2 is a configuration diagram of the design support apparatus according to the present embodiment. 2 includes a computer 200, a processing unit 21, and a storage unit 22. The processing unit 21 includes a customer requirement specification input unit 201, a simulation unit 202, a design plan candidate generation unit 203, a customer satisfaction level input unit 204, a product parameter importance level estimation unit 205, an optimal design plan search unit 206, and an optimal design plan determination unit. 207, a customer requirement specification storage unit D201. The storage unit 22 includes an analysis result storage unit D202, a design plan candidate storage unit D203, a customer satisfaction storage unit D204, a product parameter importance storage unit D205, and a Pareto solution storage unit D206.

The customer requirement specification input unit 201 inputs customer requirement specifications for the product.
FIG. 13 is an example of an explanatory diagram of a customer requirement specification storage unit according to the present embodiment. As shown in FIG. 13, the customer requirement specification is composed of two elements. That is, the name of the required specification and the range of the required specification. For example, when there is a request that the customer wants the weight of the product to be 900 kg or less, the name of the required specification is “weight”, and the range of the required specification is “900 kg or less”. Further, for example, in the case of a compressor, there are customer requirement specifications such as efficiency being 90% or more and rotation speed being 10,000 rpm. The customer requirement specification input unit 201 stores the input customer requirement specification in the customer requirement specification storage unit D201.

  The simulation unit 202 generates an analysis model for simulation, and executes the simulation using the analysis model using the computer 200. The simulation unit 202 stores the obtained analysis result in the analysis result storage unit D202.

FIG. 3 shows an example of a configuration diagram of the simulation unit and the design plan candidate generation unit.
The simulation unit 202 includes an analysis model creation unit 301, an analysis parameter range automatic generation unit 302, an analysis execution unit 303, and a product information storage unit D301.
The analysis model creation unit 301 sets the product using an analysis model stored in advance, or based on product information such as product shape and material characteristics stored in the product information storage unit D301, an analysis model related to the product An analysis model is created by creating In the analysis parameter range automatic generation unit 302, an experiment design method and a Latin method are selected from the maximum value and the minimum value of each analysis parameter (product parameter) determined in advance for executing the analysis of the analysis model according to the required number of analyses. The level (range, value, etc.) of analysis parameters (product parameters) is automatically generated using a sampling method such as the hypersquare method. Each analysis parameter (product parameter) can be determined in advance by an operator or the like using appropriate input means, for example. The analysis execution unit 303 executes analysis of the analysis model created by the analysis model creation unit 301 using the analysis parameter (product parameter) generated by the analysis parameter range automatic generation unit 302. The analysis execution unit 303 generates an analysis result 304 and stores the analysis result 304 in the analysis result storage unit D202.

The product parameter is, for example, attribute data such as product dimensions and materials. The analysis model is, for example, a mathematical model constructed on a personal computer for a product. Data such as product shape and material attributes are reflected in the analysis model. Analysis models with various levels of detail can be constructed as required. The analysis parameter of the analysis model with a high degree of detail is the same as the product parameter, but in the analysis model with a low degree of detail, the analysis parameter is an approximation or a part of the product parameter. Therefore, it can be said that the analysis parameter is substantially the same as the product parameter. There are three inputs to the analysis model, for example, as product parameters, product shape parameters, product material parameters, and boundary conditions (for example, ambient temperature and pressure). In addition, there are many known and well-known methods for creating an analysis model, such as FEM (Finite Element Method), and refer to the respective textbooks. There are many known and well-known methods such as FEM (Finite Element Method) for details of obtaining analysis results from an analysis model, and refer to each textbook (for example, Kikuchi: Outline of Finite Element Method ( New edition) ”, Science, 1999.). In short, the analysis result can be obtained by solving the governing equation of the product.
Furthermore, a plurality of analysis models may be created for one product. Some analysis models are simple, others are complex analysis models. As described above, the product parameter is, for example, a material or a dimension of the product, and the analysis model is, for example, a function. One of the simplest analysis models is a mathematical model, for example, y = f (x). Here, x is a product parameter, and y is a customer requirement specification. Therefore, if x is given, y can also be calculated. In the case of a complex analysis model, it cannot be expressed by a simple mathematical formula, but the principle can be similar. In addition, there are a plurality of combinations of product parameters for one product. This is because the product parameters are not determined at the design stage and may take various values. There are various ways of determining the product parameters, for example, there are cases where several values are set by the experience of the designer. There are also cases where values are randomly assigned and values are evenly distributed between the minimum value and the maximum value.

  FIG. 14 shows an example of an explanatory diagram of the analysis result storage unit. Each analysis result includes a set of product parameters and a customer requirement specification (for example, weight, cost,..., Efficiency, etc.) analyzed thereby. The analysis result storage unit D102 stores all analysis results.

The design plan candidate generation unit 203 includes a customer requirement specification satisfaction determination unit 305. The customer requirement specification satisfaction determination unit 305 includes a customer requirement specification (for example, weight, cost,...) Stored in the customer requirement specification storage unit D201 among the plurality of analysis results 304 generated by the simulation unit 202. (1) Those that satisfy the efficiency are selected as design proposal candidates 306. The customer requirement specification satisfaction determination unit 305 stores the selected design plan candidate 306 in the design plan candidate storage unit D203. When selecting a product that satisfies customer requirement specifications, for example, one that satisfies all customer requirement specifications may be selected, or one that satisfies one or more predetermined customer requirement specifications is selected. You may do it.
FIG. 15 shows an example of an explanatory diagram of the design plan candidate storage unit. In the design plan candidate storage unit D203, for example, as shown in FIG. 13, if the weight is 900 kg or less in the customer requirement specification, the analysis result having a weight of 900 kg or less is set as a design plan candidate. The above are not candidates for design proposals.

In the customer satisfaction input unit 204, a customer or a designer or the like inputs a numerical value of each satisfaction with an appropriate input means for each design plan candidate stored in the design plan candidate storage unit D203. For example, when the customer is 100% satisfied with the design plan candidate, the customer inputs 100 as the satisfaction level. The customer satisfaction information input by the customer satisfaction input unit 204 is stored in the customer satisfaction storage unit D204 together with the product parameters of the corresponding design plan candidate.
FIG. 4 shows an example of an explanatory diagram of the customer satisfaction storage unit. The column of customer requirement specifications (for example, weight, cost,..., Efficiency, etc.) in FIG. 4 is information written for reference, and is not an essential item. The satisfaction level input by the customer is stored in the customer satisfaction level storage unit D204 together with the product parameters of the corresponding product.

FIG. 5 shows an example of a configuration diagram of the product parameter importance degree estimation unit. In the product parameter importance estimation unit 205, the ANOVA execution unit 505 acquires information from the customer satisfaction storage unit D204, and performs a variance analysis that is a statistical method, thereby contributing each product parameter to the customer satisfaction. The rate can be calculated. Analysis of variance is a method of statistical hypothesis testing that determines the effects of factors and interactions by decomposing changes in the observed data into error variations and variations due to each factor and their interaction. By using analysis of variance, the contribution ratio of each factor can be calculated. Here, product parameters are a factor. The detailed algorithm of analysis of variance is publicly known and well known, so please refer to the textbook of statistics. Since the contribution rate represents the importance level of the product parameter itself, the calculated contribution rate can be set as the importance level of each product parameter. The product parameter importance estimation unit 205 stores the importance of each product parameter estimated by the analysis of variance execution unit 505 in the product parameter importance storage unit D205.
FIG. 6 shows an example of an explanatory diagram of the product parameter importance storage unit.

In the optimum design search unit 206, the designer sets an optimization problem related to the target product, and performs the optimization calculation using the computer 200. The Pareto solution obtained by the optimization calculation is stored in the Pareto solution storage unit D206.
FIG. 7 shows an example of a configuration diagram of the optimum design search unit. The optimum design plan search unit 206 includes an objective function determination unit 701, a constraint condition determination unit 702, a design variable determination unit 703, and an optimization execution unit 704. The optimization problem setting unit includes an objective function determination unit 701, a constraint condition determination unit 702, and a design variable determination unit 703.
FIG. 8 is an example of an explanatory diagram of an objective function setting unit according to the present embodiment. In the objective function setting unit 701, a designer or the like presets, as an objective function, a parameter to be maximized or minimized among product parameters by an appropriate input means. The specific setting contents of the objective function setting unit will be described. There are two items to be set as the objective function, that is, the name of the objective function and designation of maximization or minimization. For example, when it is desired to maximize efficiency and simultaneously minimize cost, maximize efficiency and minimize cost are set as objective functions.
FIG. 9 is an example of an explanatory diagram of a constraint condition setting unit according to the present embodiment. In the constraint condition setting unit 702, a designer or the like presets parameters of customer request specifications as constraint conditions using appropriate input means. The specific setting contents of the constraint condition setting unit will be described. There are two items to be set as the constraint condition, that is, the name of the constraint condition and the constraint range. For example, when there is a customer requirement specification that the weight is desired to be between 10 kg and 100 kg, the name of the constraint is “weight” and the constraint range is “10 kg to 100 kg”.
FIG. 10 is an example of an explanatory diagram of the design variable setting unit according to the present embodiment. In a design variable setting unit 703, a designer or the like presets a parameter that functions as an independent variable among product parameters as a design variable by an appropriate input unit. The specific setting contents of the design variable setting unit will be described. There are two items to be set as the design variable, that is, the name of the design variable and the range of the design variable. For example, when designing the value of the rotational speed between 5000 rpm and 6000 rpm, the name of the design variable is set to “rotational speed” and the range of the design variable is set to “5000 rpm to 6000 rpm”. The variation range of the value of the design variable is set by the database of the product or the experience of the designer.

The optimization execution unit 704 executes optimization calculation for the optimization problem set by the optimization problem setting unit using the computer 200 using an optimization algorithm such as a genetic algorithm. In the optimization calculation, for example, in a design space composed of design variables as shown in FIG. 10, a simulation is executed within the range of the design variables, and the constraint condition and the objective function are calculated. Then, the optimization execution unit 704 updates the simulation so that the objective function evolves as shown in FIG. 8 while satisfying the constraint conditions as shown in FIG. The update algorithm is determined by an optimization algorithm. In the case of multi-objective optimization, the Pareto solution that has a trade-off relationship between the objective functions is obtained as the result (objective function: product parameter) obtained by the optimization calculation as shown in FIG. The optimization execution unit 704 stores the Pareto solution in the Pareto solution storage unit D206.
FIG. 11 is an example of an explanatory diagram of the Pareto solution storage unit according to the present embodiment. The Pareto solution storage unit D206 stores the values of the first to Nth objective functions (product parameters) for the Pareto solution numbers.

  The optimum design plan determining unit 208 acquires the Pareto solution stored in the Pareto solution storage unit D206, and uses the product parameter importance stored in the product parameter importance storage unit D205 as shown in the following equation (1). The priority of each Pareto solution is calculated and the priority of the Pareto solution is determined.

式（１）に各符号の意味は以下の通りである。
Ａ_ｋ： ｋ番目のパレート解の優先度
ＮＯＦ：目的関数の数。これは目的関数設定部７０１で設定した目的関数の数である。
ｙ_ｋ，ｉ： ｋ番目のパレート解におけるｉ番目の目的関数の値。これはパレート解格納部Ｄ２０６より取得する。
ｙ_{ｉ，ｍｉｎ}： ｉ番目の目的関数の最小値。これはすべての目的関数の値を比較し、最小とする値を計算することで取得することができる。
ｙ_{ｉ，ｍａｘ}： ｉ番目の目的関数の最大値。これはすべての目的関数の値を比較し、最大とする値を計算することで取得することができる。
ｗ_ｉ： ｉ番目の目的関数の重要度。目的関数は製品パラメータの中から選出されるのでｗ_ｉは製品パラメータ重要度格納部Ｄ２０５から取得することができる。

The meaning of each symbol in the formula (1) is as follows.
A _k : k-th Pareto solution priority NOF: number of objective functions. This is the number of objective functions set by the objective function setting unit 701.
y _{k, i} : The value of the i th objective function in the k th Pareto solution. This is acquired from the Pareto solution storage unit D206.
y _{i, min} : The minimum value of the i-th objective function. This can be obtained by comparing the values of all objective functions and calculating the minimum value.
y _{i, max} : Maximum value of the i-th objective function. This can be obtained by comparing the values of all objective functions and calculating the maximum value.
w _i : importance of the i-th objective function. Since the objective function is selected from the product parameters, w _i can be acquired from the product parameter importance storage unit D205.

  After calculating the priority Ak of each Pareto solution, the design plan determining unit 208 determines the priority order of the Pareto solutions in the order of higher priority, and stores them in an appropriate storage unit and / or an output unit (display unit) Can be output.

2. Design support processing

A series of processing procedures of the embodiment configured as described above will be described with reference to FIG.
FIG. 12 is an example of a flowchart showing a processing procedure in the design support apparatus shown in FIGS. The operation based on this flowchart is shown below.
Step S1201: The processing unit 21 causes the customer requirement specification input unit 201 to input the requirement specification for the product as described above, and stores the customer requirement specification in the customer requirement specification storage unit D201 (FIG. 13).
Step S1202: The processing unit 21 causes the simulation unit 202 to execute a plurality of simulations on the product by the simulation unit 202 as described above, and store a plurality of analysis results in the analysis result storage unit D202 (FIG. 14).
Step S1203: The processing unit 21 causes the design plan candidate generation unit 203 to select, as described above, the designer who satisfies the customer requirement specification from the analysis results stored in the analysis result storage unit D202. The design plan candidate is stored in the design plan candidate storage unit D203 (FIG. 15).
Step S1204: The processing unit 21 uses the customer satisfaction input unit 204 to set the customer requirement specification value for each design plan candidate stored in the design plan candidate storage unit D203 as described above. The customer satisfaction is input by examining whether or not the request is satisfied, and the customer satisfaction is stored in the customer satisfaction storage unit D204 together with the product parameters (FIG. 4).
Step S1205: The processing unit 21 performs a variance analysis on the data stored in the customer satisfaction storage unit D204 by the product parameter importance estimation unit 205 as described above, and contributes to the customer satisfaction of each product parameter. The rate is calculated, and the contribution rate is stored in the product parameter importance storage unit D205 as the importance (FIG. 6).
Step S1206: The processing unit 21 causes the optimum design plan searching unit 206 to set an optimization problem related to the target product by the designer and to perform optimization calculation using an optimization algorithm as described above. As shown in FIG. 7, the optimization problem setting procedure can include at least three parts: an objective function setting, a constraint condition setting, and a design variable setting. Each setting is shown in FIGS. 8, 9, and 10 as described above. The processing unit 21 stores the Pareto solution obtained by the optimization calculation by the optimum design plan searching unit 206 in the Pareto solution storage unit D206 (FIG. 11).
Step S1207: The processing unit 21 stores the product parameter importance degree for each Pareto solution stored in the Pareto solution storage unit D206 using the above-described equation (1) by the optimum design plan determining unit 207 as described above. The priority of the Pareto solution is calculated using the product parameter importance stored in the part D205, and the priority order of the Pareto solution is determined. Then, the processing unit 21 selects the Pareto solution with the highest priority as the optimal design plan by the optimal design plan determination unit 207. The processing unit 21 stores the selected design plan in a memory in an appropriate storage unit 22 or stores a mark indicating that it has been selected in a record of the Pareto solution storage unit D206.

3. Modified example

[Modification 1]
In the present embodiment, it is described that the construction and execution of the design support apparatus are performed by the same computer. However, the design support apparatus can be performed by a different computer by using the network environment.

[Modification 2]
In the present embodiment, the customer request specification is acquired from the apparatus in the first embodiment through wired or wireless network communication.

[Modification 3]
In the present embodiment, the customer satisfaction is acquired through wired or wireless network communication among the devices in the first or second embodiment.

[Modification 4]
In this embodiment, among the devices in Embodiments 1 to 3, customer satisfaction is not a numerical value but is qualitatively input using a questionnaire format. For example, one of several satisfaction levels such as “very satisfied”, “somewhat satisfied”, “cannot say anything”, “somewhat dissatisfied”, and “very dissatisfied” is selected for the design plan candidate. Then, the satisfaction level is converted into a numerical value. For example, the following conversion formula is used.
Very satisfied: 90 points Somewhat satisfied: 70 points Cannot say anything: 50 points Somewhat dissatisfied: 30 points Very dissatisfied: 10 points

[Modification 5]
In the present embodiment, among the devices in the first to third embodiments, the customer does not directly input the satisfaction level, but the person on the designer side responds to a certain design plan candidate according to the conversation with the customer. Estimate customer satisfaction.

4). Effects of the embodiment

According to the present embodiment, it is possible to quantitatively determine the importance of a large number of product parameters based on customer satisfaction when satisfying the customer's required specifications. Further, according to the present embodiment, it is possible to automatically generate an optimal design plan so as to maximize customer satisfaction.

5. Appendix

In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.
Each of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files for realizing each function can be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.
Further, the control lines and information lines indicate what is considered necessary for the explanation, and not all the control lines and information lines on the product are necessarily shown. Actually, it may be considered that almost all the components are connected to each other.

  A design support method or a design support apparatus / system of the present invention includes a design support program for causing a computer to execute each procedure, a computer-readable recording medium recording the design support program, and an internal memory of the computer including the design support program Can be provided by a program product that can be loaded on the computer, a computer such as a server including the program, and the like.

200 Computer 201 Customer Requirement Specification Input Unit 202 Simulation Unit 203 Design Plan Candidate Generation Unit 204 Customer Satisfaction Input Unit 205 Product Parameter Importance Estimation Unit 206 Optimal Design Plan Search Unit 207 Optimal Design Plan Determination Unit D201 Customer Specification Storage Unit D202 Analysis Results Storage unit D203 Design plan candidate storage unit D204 Customer satisfaction storage unit D205 Product parameter importance storage unit D206 Pareto solution storage unit

Claims (15)

A design support device,
A customer requirement specification input unit for inputting a plurality of customer requirement specifications related to the target product;
By executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance, a plurality of analysis results including a plurality of customer request specifications for a plurality of product parameters can be obtained. A simulation unit to generate,
Design plan candidates for generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or more predetermined customer requirement specifications from the plurality of analysis results generated by the simulation unit A generator,
A customer satisfaction input unit that inputs customer satisfaction for each of a plurality of design plan candidates generated by the design plan candidate generation unit and stores the customer satisfaction together with a plurality of product parameters in the customer satisfaction storage unit When,
Importance analysis of the data stored in the customer satisfaction storage unit estimates the importance of each product parameter from customer satisfaction, and stores the importance for each product parameter in the product parameter importance storage unit A degree estimation unit;
Any of a plurality of product parameters that function as a plurality of constraint functions and independent variables according to any of a plurality of objective functions and a plurality of customer requirement specifications that are set in advance for a target product. Optimal design proposal search unit that obtains a plurality of Pareto solutions including a plurality of product parameters that are objective functions and stores a plurality of Pareto solutions in a Pareto solution storage unit by performing optimization calculation according to the design variable by
The priority of the Pareto solution is calculated from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, and the Pareto solution having a high priority Is determined as a design plan, stored in the storage unit and / or output to the output unit,
Design support device with
The design support apparatus according to claim 1,
The customer requirement specification input unit is:
A design support apparatus for storing a plurality of customer requirement specifications including a range or value of a requirement specification for a requirement specification name in a customer requirement specification storage unit.
The design support apparatus according to claim 1,
The simulation unit
Refer to the product information storage unit that stores product information including product shape and material in advance for the target product, create an analysis model for the target product,
For the created analysis model, a plurality of product parameters are generated according to the predetermined minimum and maximum values of each product parameter,
Analyze the analysis model using each of multiple product parameters, generate multiple customer requirements specifications as analysis results,
For each analysis result, store an analysis result including a plurality of customer request specifications for a plurality of product parameters in the analysis result storage unit.
A design support apparatus characterized by that.
The design support apparatus according to claim 3,
The design plan candidate generation unit
By extracting a plurality of analysis results satisfying one or a plurality of customer requirement specifications from a plurality of analysis results stored in the analysis result storage unit, a plurality of design plan candidates are generated, and each design plan candidate is generated. A design support apparatus for storing a plurality of customer requirement specifications for a plurality of product parameters in a design plan candidate storage unit.
The design support apparatus according to claim 4,
The customer satisfaction input part is:
For each of a plurality of design plan candidates stored in the design plan candidate storage unit, customer satisfaction is input, and customer satisfaction is stored in the customer satisfaction storage unit together with a plurality of product parameters. Design support device.
The design support apparatus according to claim 5,
The product parameter importance estimation unit includes:
Based on the information stored in the customer satisfaction storage unit, by performing an analysis of variance that is a statistical method, the contribution rate of each product parameter to the customer satisfaction is calculated, the importance is estimated by the contribution rate, A design support apparatus, wherein the estimated importance of each product parameter is stored in the product parameter importance storage unit.
The design support apparatus according to claim 1,
The optimum design search unit includes:
Set multiple objective functions in multiple product parameters and each objective function to specify maximization or minimization, and set any multiple constraints in each customer requirement specification and the range of each constraint. An optimization problem setting unit that sets an optimization problem by setting and setting any one of a plurality of design variables that function as independent variables among a plurality of product parameters and a range of each design variable,
A Pareto solution that has a trade-off relationship between each objective function is obtained by executing optimization calculation using a predetermined optimization algorithm for the optimization problem set by the optimization problem setting unit. An optimization execution unit that stores a plurality of Pareto solutions including each objective function in the Pareto solution storage unit,
A design support apparatus characterized by comprising:
In the design support device according to claim 7,
The optimization execution unit
The optimization problem set in the optimization problem setting unit is subjected to optimization calculation using a genetic algorithm or other optimization algorithm,
In the optimization calculation, in a design space including a plurality of set design variables, a simulation is executed within the range of each design variable, the constraint condition and the objective function are calculated, and the plurality of set constraint conditions are determined. A Pareto solution including a plurality of objective functions is obtained by updating the simulation so that the objective function evolves to be the maximum value or the minimum value of the set objective function while being satisfied.
A design support apparatus characterized by that.
The design support apparatus according to claim 1,
The optimum design plan determining unit
According to the following equation (1), the design support apparatus calculates the priority A _k for each Pareto solutions, and determines the priority of the Pareto solutions.

The meaning of each symbol in the formula (1) is as follows.
A _k : k-th Pareto solution priority NOF: the number of objective functions set by the optimum design search unit.
y _{k, i} : The value of the i-th objective function in the k-th Pareto solution obtained from the Pareto solution storage unit.
y _{i, min} : The minimum value of the i-th objective function.
y _{i, max} : Maximum value of the i-th objective function.
w _i : Importance of the product parameter, which is the i-th objective function, obtained from the product parameter importance storage unit.
The design support apparatus according to claim 1,
The simulation unit performs a simulation on the analysis model using a computer;
And / or
The optimal design search unit performs an optimization calculation using a computer for an optimization problem related to a target product set by a designer,
Design support device characterized by
The design support apparatus according to claim 1,
The customer requirement specification input unit is:
A design support apparatus for receiving a plurality of customer request specifications by means of wired or wireless network communication means.
The design support apparatus according to claim 1,
The customer satisfaction input part is:
A design support apparatus that acquires customer satisfaction through wired or wireless network communication.
The design support apparatus according to claim 1,
The customer satisfaction input part is:
A design support apparatus for inputting customer satisfaction by selecting one from a plurality of qualitative satisfaction levels and converting the satisfaction level into a numerical value.
A design support method,
Entering multiple customer requirement specifications for the target product;
By executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance, a plurality of analysis results including a plurality of customer request specifications for a plurality of product parameters can be obtained. Generating step;
Generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or more predetermined customer requirement specifications from the plurality of generated analysis results;
For each of the generated plurality of design plan candidates, inputting customer satisfaction, and storing the customer satisfaction together with a plurality of product parameters in the customer satisfaction storage unit;
Estimating the importance of each product parameter from customer satisfaction by variance analysis for the data stored in the customer satisfaction storage unit, and storing the importance for each product parameter in the product parameter importance storage unit;
Any of a plurality of product parameters that function as a plurality of constraint functions and independent variables according to any of a plurality of objective functions and a plurality of customer requirement specifications that are set in advance for a target product. Obtaining a plurality of Pareto solutions including a plurality of product parameters, which are objective functions, and storing a plurality of Pareto solutions in a Pareto solution storage unit by performing optimization calculation according to the design variables of
The priority of the Pareto solution is calculated from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, and the Pareto solution having a high priority Determining as a design plan, storing in the storage unit and / or outputting to the output unit;
Design support method including
A design support program,
A processing unit inputting a plurality of customer request specifications related to the target product;
The processing unit includes a plurality of customer requirement specifications for a plurality of product parameters by executing a simulation according to each of a plurality of predetermined product parameters using an analysis model of a target product set or created in advance. Generating multiple analysis results;
A step of generating a plurality of design plan candidates by extracting a plurality of analysis results satisfying one or a plurality of predetermined customer request specifications from the generated plurality of analysis results; ,
The processing unit inputs customer satisfaction for each of the generated plurality of design proposal candidates, and stores the customer satisfaction in a customer satisfaction storage unit together with a plurality of product parameters;
The processing unit estimates the importance of each product parameter from the customer satisfaction by variance analysis for the data stored in the customer satisfaction storage unit, and the importance for each product parameter is stored in the product parameter importance storage unit. Storing, and
A plurality of products in which the processing unit functions as a plurality of objective functions according to any one of a plurality of product parameters and a plurality of constraint conditions and independent variables according to any of a plurality of customer requirement specifications related to the target product. Obtaining a plurality of Pareto solutions including a plurality of product parameters as objective functions by performing an optimization calculation according to the design variable of any one or more of the parameters, and storing the plurality of Pareto solutions in the Pareto solution storage unit; ,
The processing unit calculates the priority of the Pareto solution from the plurality of Pareto solutions stored in the Pareto solution storage unit using the product parameter importance stored in the product parameter importance storage unit, Determining a Pareto solution with a higher rank as a design proposal, storing it in the storage unit and / or outputting it to the output unit;
Design support program to make computer execute.

Images