Recommended material search system, recommended material search method, and program

Abstract

To make it possible to select more appropriate recommended materials taking molding quality into consideration.SOLUTION: In a recommended material search system having one or more processors and one or more memory resources, the memory resource stores a material search program, and the processor executes the material search program to search for recommended materials using predetermined property evaluation items and property values as search conditions, determines a recommendation order of the recommended materials based on a comparison of similarity between molding quality of the searched recommended materials and the molding quality of a reference material, and outputs a search result in which the recommended materials are associated with the recommendation order.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a recommended material search system, a recommended material search method, and a program.

Background Art

[0002] In recent years, while the demand for plastics has been on the rise, it is expected that in the future, the production volume of new plastics derived from oil, which are depleted resources, will decrease, and the proportion of recycled plastics will increase significantly.

[0003] Also, recently, the release of products applying recycled materials to exterior parts and the like has been increasing, and the utilization of recycled materials is becoming a new competitive axis.

[0004] However, regarding the rapidly increasing demand for recycled materials, the current situation is that the methods and data for selecting candidate materials are insufficient. Therefore, manufacturers and the like need to repeat prototyping until appropriate candidate materials are adopted, which poses a problem of taking a great deal of time and cost.

[0005] In addition, Patent Document 1 discloses a molding condition determination support device for estimating the molten state of resin in a cavity. Specifically, Patent Document 1 describes that "it includes a feature quantity generation unit that generates a group of feature quantities regarding detection data based on detection data detected during molding by a sensor attached to an injection molding machine, an identification parameter value calculation unit that calculates a resin state identification parameter representing the molten state of resin corresponding to each feature quantity based on the group of feature quantities and a group of control parameter values, and a group acquisition unit that acquires a group of the molten states of resin by applying multivariate analysis using the resin state identification as an explanatory variable based on the resin state identification parameter value."

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

[0007] With the increased use of recycled materials, it is expected that the proportion of recycled materials used in the exterior components of products will increase in the future. On the other hand, when using recycled materials in exterior components, the required specifications for dimensions and molding quality accuracy are higher than when using them in internal components. Therefore, if appropriate recycled materials are not selected to meet the required specifications, adjustments during the injection molding stage may not be sufficient.

[0008] Furthermore, the molding condition determination support device described in Patent Document 1 groups the molten state of the resin in the cavity and determines the amount of modification to the injection molding conditions according to the group. However, the technology in the same document concerns the determination of injection molding conditions and does not consider the selection of candidate materials, which is an earlier stage. Therefore, it is difficult to solve the above problem with the technology in Patent Document 1.

[0009] This invention has been made in view of the above problems, and aims to enable the selection of more appropriate candidate materials while taking molding quality into consideration. [Means for solving the problem]

[0010] The present invention includes several means for solving at least part of the above problems, and an example thereof is as follows. A recommended material search system according to one aspect of the present invention for solving the above problems is a recommended material search system having one or more processors and one or more memory resources, wherein the memory resources store a material search program, and by executing the material search program, the processor searches for recommended materials using predetermined physical property evaluation items and physical property values ​​as search conditions, determines the recommendation rank of the recommended materials based on a comparison of the similarity between the molding quality of the searched recommended materials and the molding quality of a reference material, and outputs search results that associate the recommendation rank with the recommended materials. [Effects of the Invention]

[0011] According to the present invention, it is possible to select a more appropriate recommended material that takes molding quality into consideration.

[0012] Other issues, configurations, and effects will be clarified in the following description of the embodiments. [Brief explanation of the drawing]

[0013] [Figure 1] This diagram shows an example of the overall configuration of the recommended materials search system. [Figure 2] This diagram shows an example of the schematic configuration of a recommended material search device. [Figure 3] This is a diagram showing an example of material information. [Figure 4] This figure shows an example of physical property information. [Figure 5] This figure shows an example of a formula and graph used to calculate liquidity. [Figure 6] This is a flowchart illustrating an example of the recommended material search process. [Figure 7] This is a flowchart illustrating an example of a similarity comparison process. [Figure 8] This figure shows an example of a data plot for standard material and candidate material. [Figure 9] This figure shows an example of the distribution of molding quality for standardized reference materials and candidate materials. [Figure 10] This figure shows an example of the relationship between the Euclidean distance between a reference material and a candidate material, and the similarity of their molding quality. [Modes for carrying out the invention]

[0014] The following embodiments are illustrative examples for illustrating the present invention, and have been omitted and simplified as appropriate for clarity of explanation. The present invention can also be implemented in various other forms. Furthermore, unless otherwise specified, each component may be singular or plural.

[0015] In addition, the positions, sizes, shapes, ranges, etc. of the components shown in the drawings may not represent the actual positions, sizes, shapes, ranges, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, ranges, etc. disclosed in the drawings.

[0016] In addition, as examples of various types of information, expressions such as "table" and "list" may be used for explanation, but the various types of information may be represented by data structures other than these. For example, various types of information such as "**table" may be "**information". Also, when explaining identification information, expressions such as "identification information", "identifier", "name", "ID", "number", etc. are used, but these can be mutually replaced.

[0017] In addition, when there are a plurality of components having the same or similar functions, they may be described by attaching different subscripts to the same reference numeral. Also, when it is not necessary to distinguish these plurality of components, the subscripts may be omitted in the description.

[0018] In addition, in the embodiments, the processing performed by executing a program may be described. Here, the computer executes the program by a processor (for example, CPU: Central Processing Unit, GPU: Graphics Processing Unit), and performs the processing defined by the program while using storage resources (for example, memory resources) and interface devices (for example, communication ports). Therefore, the subject of the processing performed by executing the program may be the processor.

[0019] Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be an arithmetic unit (processing unit), and may include dedicated circuits that perform specific processing. Here, dedicated circuits include, for example, FPGAs (Field Programmable Gate Arrays), ASICs (Application Specific Integrated Circuits), CPLDs (Complex Programmable Logic Devices), etc.

[0020] Furthermore, the program may be installed on the computer from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer. If the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. Also, in the embodiment, two or more programs may be implemented as one program, or one program may be implemented as two or more programs.

[0021] Embodiments of the present invention will be described below with reference to the drawings.

[0022] The recommended material search system according to this embodiment comprises a recommended material search device and an external device, and is a system that searches for materials with a high degree of similarity to the molding quality of a reference material as recommended materials. Although the recommended material search system can search for various types of materials, in this embodiment, materials used in injection molding (for example, recycled materials such as resin materials and plastic materials) will be described as an example.

[0023] The recommended material search system is used, for example, when a manufacturer is trying to change from the materials currently being used to other materials (e.g., recycled materials), to select suitable candidate materials (hereinafter sometimes referred to as "candidate materials," "recommended materials," or "recommended materials") that can be used in the manufacture of the target product.

[0024] Specifically, the recommended material search device accepts input such as the current materials used in the manufacture of the product or materials intended for use as reference materials (hereinafter sometimes referred to as "reference materials"). The recommended material search device then narrows down the candidate materials based on the physical properties that the search user considers important, and then determines the recommended order of the candidate materials in order of their molding quality being closest to that of the reference materials, and outputs this as a search result.

[0025] This recommended material search system allows for the selection of candidate materials with similar molding quality to the materials currently used in existing products, and the search results can be presented to manufacturers. In other words, the system can enable the selection of more appropriate recommended materials that take molding quality into consideration.

[0026] <Overall configuration of the recommended material search system 1000> Figure 1 shows an example of the overall configuration of the recommended material search system 1000. As shown in the figure, the recommended material search device 100 acquires material data (e.g., material information and physical property information) from external devices 10, such as a computer of a material manufacturer or recycler, and stores it in a database. The recommended material search device 100 also receives a search request for candidate materials from an external device 10, such as a computer of a manufacturer. The recommended material search device 100 then searches the database for suitable candidate materials using predetermined information (e.g., physical property information and molding quality information) and outputs search results with a recommendation ranking based on similarity to a reference material.

[0027] <Outline configuration of the recommended material search device 100> Figure 2 shows an example of the schematic configuration of the recommended material search device 100. As shown in the figure, the recommended material search device 100 (processor system) is connected to an external device 10 so as to be able to communicate with it via, for example, a communication cable or a predetermined communication network (e.g., the Internet, LAN (Local Area Network), or WAN (Wide Area Network)) N.

[0028] <<External device 10>> External device 10 is a device that transmits various types of information to the recommended material search device 100. In this case, external device 10 is, for example, a computer belonging to a material manufacturer or recycler, and transmits material data (for example, material information and physical property information) to the recommended material search device 100.

[0029] Furthermore, the external device 10 is a device that makes search requests to the recommended material search device 100 and displays the search results. In this case, the external device 10 is, for example, a computer of a business that uses the search service provided by this system, such as a manufacturing company.

[0030] <<Details of Recommended Material Search Device 100>> The recommended material search device 100 is a processor system that performs various processes by having the processor 20 read programs and various information stored in the memory resource 30. Specifically, the recommended material search device 100 performs a recommended material search process to search for recommended materials (candidate materials) that are similar in molding quality to the reference material. Details of this process will be described later.

[0031] The recommended material search device 100 is, for example, a server computer, a cloud server, or a personal computer, and is a system that includes at least one of these computers.

[0032] Specifically, the recommended material search device 100 includes a processor 20, memory resources 30, a Network Interface Device (NI) 40, and a User Interface Device (UI) 50.

[0033] The processor 20 is an arithmetic unit that reads the program 210 stored in the memory resource 30 and executes the processing corresponding to the program 210. Examples of the processor 20 include a microprocessor 20, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), or other arithmetic semiconductor devices.

[0034] The memory resource 30 is a storage device that stores various types of information. Specifically, the memory resource 30 is a non-volatile or volatile storage medium such as RAM (Random Access Memory) or ROM (Read Only Memory). The memory resource 30 may also be a rewritable storage medium such as flash memory, a hard disk, or an SSD (Solid State Drive), or a USB (Universal Serial Bus) memory, memory card, or hard disk.

[0035] NI40 is a communication device that communicates information with the external device 10. NI40 communicates information with the external device 10 via a predetermined communication network N, such as a LAN or the Internet. Unless otherwise specified below, it is assumed that information communication between the recommended material search device 100 and the external device 10 is performed via NI40.

[0036] UI50 is an input device that inputs user (operator) instructions to the recommended material search device 100, and an output device that outputs information generated by the recommended material search device 100. Input devices include, for example, pointing devices such as keyboards, touch panels, and mice, and voice input devices such as microphones.

[0037] Output devices include, for example, displays, printers, and speech synthesizers. Unless otherwise specified below, user operations on the recommended material search device 100 (e.g., inputting information, outputting information, and issuing instructions to execute processing) are assumed to be performed via the UI 50.

[0038] Furthermore, some or all of the configurations, functions, and processing means of the recommended material search device 100 may be implemented in hardware, for example, by designing them as integrated circuits. Also, some or all of the functions of the recommended material search device 100 may be implemented in software, or through the collaboration of software and hardware. Furthermore, the recommended material search device 100 may use hardware with fixed circuits, or it may use hardware in which at least some of the circuits can be modified.

[0039] Furthermore, the recommended material search device 100 can also be implemented by having the user (operator) perform some or all of the functions and processes realized by each program.

[0040] The program executed by the recommended material search device 100 may be stored on a non-volatile storage medium that the device can read. The program stored on the non-volatile storage medium may be read directly by the recommended material search device 100, or a program distribution processor system may read the program from the medium and then transmit (distribute) the program to the recommended material search device 100. An example of a non-volatile storage medium is the non-volatile memory described as memory resource 30, but other optical disc media may also be used.

[0041] <<<Material information DB110>>> The Material Information DB110 is a database that stores material information. The material information contains information about various materials (including virgin materials and recycled materials) used in the manufacture of products. Specifically, the material information is registered with the following associated information: identification information 110a, material name and type 110b, model number 110c, material manufacturer 110d, lot number 110e, and mixing ratio 110f.

[0042] Figure 3 shows an example of material information. Here, identification information 110a is information for uniquely identifying the material registered in each record of material information. Name / Type 110b is information indicating the name and type of material (for example, the type of resin material or plastic material). Model number 110c, material manufacturer 110d, and lot number 110e are information indicating the material model number, the material manufacturer, and the lot number, respectively. Mixing ratio 110f is information indicating, for example, the mixing ratio of virgin material and recycled material.

[0043] Furthermore, even within the same material category, different model numbers and grades are assigned depending on the material manufacturer, and their physical properties also differ from one another. Therefore, material information registers materials of the same category but with different model numbers and grades as separate material categories.

[0044] <<<Physical Properties Information DB120>>> The physical property information DB120 is a database that stores physical property information. The physical property values ​​for each material are registered in the physical property information. Specifically, the physical property information is registered with the following associated information: identification information 120a, material name and type 120b, material manufacturer 120c, and physical property value 120d.

[0045] Figure 4 shows an example of physical property information. Here, identification information 120a is information that uniquely identifies the material and corresponds to the identification information of the material. Name / Type 120b and material manufacturer 120c are information indicating the name and type of the material and the manufacturer of the material, respectively.

[0046] Furthermore, the physical property value 120d is information about the physical properties of each material, and includes registered physical properties and their values, such as mechanical properties and thermal properties. Here, mechanical properties include, for example, elastic modulus, tensile strength, and impact properties, while thermal properties include, for example, crystallization temperature, melting temperature, and thermal distortion temperature. Note that these physical properties are just examples, and the physical property information includes various other types of physical properties and their values ​​in addition to the examples above.

[0047] <<<Molding quality information DB130>>> The molding quality information DB130 is a database that stores molding quality information. This information includes, for example, material fluidity (viscosity characteristics) and shrinkage. In this embodiment, fluidity and shrinkage are used as examples of molding quality in the following explanation.

[0048] Fluidity is information indicating the material's fluidity, and is calculated, for example, based on the output value from a pressure sensor during the prototyping of a test specimen using an injection molding machine. Shrinkage, on the other hand, is information indicating the material's shrinkage properties, and is obtained, for example, based on the dimensional measurement of a test specimen.

[0049] Such prototypes are performed multiple times (for example, 10 times each) for each combination of material type, model number, material manufacturer, and lot number, and each time, values ​​related to fluidity and shrinkage are registered in the molding quality information.

[0050] Figure 5 shows an example of the formula and graph used to calculate fluidity (viscosity feature). The graph shows the relationship between sensor pressure P and time t when the velocity is controlled at a constant speed from the start time tint to the end time tvend, and then pressure control is performed with variable pressure after tvend. The viscosity feature (μindex) is a feature of the velocity-controlled section (from tint to tvend). The value of fluidity (viscosity feature) is calculated based on the following equation (1).

[0051]

number

[0052] Here, P(t) represents the sum of the sensor pressures over the changing time t.

[0053] Furthermore, the values ​​for fluidity and shrinkage are registered in the molding quality information, associated with the material identification information (corresponding to the material identification information) and the number of prototypes (e.g., nth time).

[0054] <<<Environmental information DB140>>> The Environmental Information DB140 is a database that stores environmental information. This environmental information includes information about environmental impact, such as carbon dioxide emissions. Specifically, the environmental information is registered with a correspondence between information about environmental impact and material identification information (corresponding to the material identification information).

[0055] <<<Material Search Program 211>>> The material search program 211 is a program that searches for recommended materials (candidate materials). Specifically, the material search program 211 performs a recommended material search process. More specifically, the material search program 211 searches for candidate materials from the database and outputs search results accompanied by a recommendation ranking based on the similarity to the molding quality of the reference material.

[0056] The details of the recommended material search device 100 have been explained above.

[0057] <Explanation of the process> Figure 6 is a flowchart showing an example of the recommended material search process. This process is executed by the processor 20, which reads the material search program 211, when the recommended material search device 100 receives a request to search for recommended materials from the external device 10.

[0058] When processing begins, the processor 20 receives input for the reference material from the external device 10 (step S10). Specifically, the processor 20 receives input for the name and type of the reference material from the search user of the external device 10.

[0059] Next, the processor 20 acquires material information of the reference material that has been received as input (step S20). Specifically, the processor 20 retrieves the corresponding material information from the material information DB 110 based on the name and type of the input reference material. The processor 20 also retrieves the corresponding physical property information and molding quality information from the physical property information DB 120 and molding quality information DB 130, respectively, based on the identification information 110a of the retrieved material information.

[0060] Next, the processor 20 accepts the selection of physical property evaluation items and the input of evaluation property values ​​(step S30). Specifically, the processor 20 accepts the selection of physical property evaluation items that the search user considers important from among multiple physical property evaluation items corresponding to the physical property value 120d in the physical property information of the reference material, such as the modulus of elasticity in mechanical properties and the crystallization temperature in thermal properties. For example, if the modulus of elasticity, tensile strength, and melting temperature are registered as physical property value 120d in the physical property information of the reference material, the processor 20 accepts the selection of physical property evaluation items that the search user considers important from among these (for example, tensile strength and melting temperature).

[0061] Furthermore, the processor 20 accepts input of evaluation property values ​​for the selected physical property evaluation items. For example, if elastic modulus and crystallization temperature are selected as physical property evaluation items, the processor 20 accepts the numerical range of elastic modulus and crystallization temperature desired (accepted) by the search user as evaluation property values.

[0062] Next, the processor 20 determines the search criteria for candidate materials (step S40). Specifically, the processor 20 determines the received physical property evaluation items and the evaluated physical property values ​​as search criteria for candidate materials. The processor 20 may also include the type of reference material in the search criteria. By including the type of reference material as a search criterion, the processor 20 can narrow the search range and reduce the processing load.

[0063] Next, the processor 20 performs a search for candidate materials (step S50). Specifically, the processor 20 searches the physical property information DB120 and material information DB110 based on the search conditions and identifies candidate materials that satisfy the search conditions. Typically, multiple candidate materials that satisfy the search conditions are identified.

[0064] Next, the processor 20 performs a similarity comparison process (step S60). Specifically, the processor 20 compares the similarity between the reference material and the candidate material based on the Euclidean distance between them.

[0065] Figure 7 is a flowchart showing an example of the similarity comparison process. First, the processor 20 generates data plots of the reference material and the candidate material (step S061). Specifically, the processor 20 generates data plots of the reference material and the candidate material based on predetermined quality evaluation items. More specifically, the processor 20 generates data plots of the reference material and the candidate material with shrinkage as quality evaluation item 1 (horizontal axis) and fluidity as quality evaluation item 2 (vertical axis).

[0066] Figure 8 shows an example of data plots for reference material and candidate material. As shown in the figure, even within the same lot number, there is some variation in molding quality for each measurement for both the reference material and the candidate material. Therefore, the data plot is represented by a point cloud showing the variation (distribution) of molding quality for each of the reference material and candidate material.

[0067] Next, the processor 20 normalizes the reference material and candidate material based on statistical values ​​(step S062). The data plot represents the values ​​of quality evaluation items for different types of materials as a point cloud, so the absolute values ​​and other parameters are not consistent across the data. Therefore, the processor 20 uses the data plot to normalize the reference material and candidate material based on statistical values ​​such as the mean and standard deviation of the reference material.

[0068] Standardization is carried out using the following equations (2) and (3).

[0069]

number

[0070] "X'Ai" indicates the horizontal axis position of candidate material A after normalization. "XAi" indicates the horizontal axis plot position of candidate material A before normalization. "Xo" indicates the horizontal mean value of the standard material before normalization. "σxo" indicates the horizontal standard deviation of the standard material before normalization.

[0071]

number

[0072] Similarly, equations (2) and (3) are used for standardizing other candidate materials.

[0073] Figure 9 shows an example of the distribution of molding quality for standardized reference material and candidate material. Standardization quantifies the variation in molding quality of the reference material and each candidate material, making them comparable to one another.

[0074] Next, the processor 20 calculates the Euclidean distance used for comparing similarities (step S063). Specifically, the processor 20 calculates the Euclidean distance between the point cloud (cluster) of the reference material and the point cloud (cluster) of the candidate material using a known method such as the group average method. The calculation of the Euclidean distance d is performed using the following equation (4).

[0075]

number

[0076] Next, the processor 20 compares the similarity between the reference material and the candidate material (step S64). Specifically, the processor 20 compares the similarity between the reference material and each candidate material based on the magnitude of the Euclidean distance. More specifically, the processor 20 evaluates the similarity as higher the closer the Euclidean distance. This is because the similarity is proportional to the Euclidean distance between the reference material and the recommended material, which become comparable by standardizing the molding quality.

[0077] Furthermore, the processor 20 determines the recommended ranking of the candidate materials based on the comparison of similarity (step S65). Specifically, the processor 20 determines the recommended ranking of the candidate materials by assigning a higher recommendation ranking to each material in order of its similarity.

[0078] Figure 10 shows an example of the relationship between the Euclidean distance between the reference material and candidate materials and the similarity of their molding quality. In the example shown, candidate materials B, C, and A are closest in Euclidean distance to the reference material, in that order. In this case, the processor 20 evaluates candidate materials B, C, and A to be the most similar in terms of molding quality to the reference material, in that order, and determines the recommended order of each candidate material according to that order.

[0079] Furthermore, once the processor 20 has determined the recommended order, it proceeds to step S70 (Figure 6).

[0080] Next, the processor 20 calculates the environmental impact based on the environmental information (step S70). Specifically, the processor 20 identifies the corresponding environmental information based on the identification information of the candidate material. The processor 20 then uses the identified environmental information to calculate the environmental impact related to a predetermined environmental load, such as carbon dioxide emissions. The environmental impact may be the numerical value related to the environmental load registered in the environmental information.

[0081] Next, the processor 20 outputs the search results (step S80). Specifically, the processor 20 generates search results that associate the material information of each candidate material with the recommended ranking, and outputs (sends) them to the search user's computer. The processor 20 may also include the physical properties information and molding quality information of the candidate materials in the search results.

[0082] Furthermore, once the processor 20 outputs the search results, it terminates processing this flow.

[0083] The recommended material search process has now been explained.

[0084] Such a recommended material search system makes it possible to select more appropriate recommended materials while taking molding quality into consideration.

[0085] In particular, the recommended material search system quantifies variations in molding quality, such as fluidity and shrinkage, and determines the recommendation ranking by comparing the similarity between the standardized reference material and the candidate material. Therefore, the recommended material search system can find recommended materials with appropriate molding quality similar to that of the reference material.

[0086] Furthermore, since the search results include information on environmental impact associated with the recommended materials, users can select suitable materials by also considering the environmental impact.

[0087] Furthermore, the recommended material search device 100 may include the cost of the recommended material with the search results. Cost information can be managed, for example, by linking it to various materials in the material information. With such a recommended material search system, the search user can select a suitable material by referring to the cost of the recommended material in addition to the similarity to the reference material and the environmental impact.

[0088] Furthermore, this embodiment is not limited to the above, and various modifications are possible. For example, in the above-described embodiment, it was assumed that the material information DB110, physical property information DB120, molding quality DB, and environmental information DB140 are pre-stored in the memory resource 30, but these databases may be stored on another computer (for example, a cloud server). In other words, in this case, the recommended material search system 1000 includes a computer (cloud server) that stores the databases.

[0089] In this case, the recommended material search device 100 retrieves target material information, physical property information, molding quality information, and environmental information from the database of the computer (cloud server) when executing the recommended material search process.

[0090] A recommended material search system with this configuration can also enable the selection of more appropriate recommended materials, taking molding quality into consideration.

[0091] Furthermore, the present invention is not limited to the embodiments and modifications described above, but includes a variety of other embodiments and modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Also, it is possible to replace parts of the configuration of one embodiment with the configurations of other embodiments or modifications, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0092] Furthermore, the control lines and information lines shown above are those deemed necessary for the explanation, and do not necessarily represent all control lines and information lines present in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of symbols]

[0093] 1000...Recommended material search system, 100...Recommended material search device, 110...Material information DB, 120...Physical property information DB, 130...Molding quality information DB, 140...Environmental information DB, 210...Program, 211...Material search program, 20...Processor, 30...Memory resources, 40...NI (Network Interface Device), 50...UI (User Interface Device), 10...External devices, N...Network

Claims

1. A recommended material search system having one or more processors and one or more memory resources, The aforementioned memory resource stores the material search program, By executing the material search program, the processor Search for recommended materials using the specified physical property evaluation items and physical property values ​​as search criteria. Based on a comparison of the similarity between the molding quality of the searched recommended material and the molding quality of the reference material, the recommendation ranking of the recommended material is determined. Output search results that associate the recommended materials with the recommended ranking. A recommended material search system characterized by the following features.

2. A recommended material search system according to claim 1, The molding quality is information indicating the fluidity and shrinkage of the recommended material and the standard material. A recommended material search system characterized by the following features.

3. A recommended material search system according to claim 1, The aforementioned processor, Based on the aforementioned molding quality statistics, the recommended material and the standard material are standardized, and the similarity between the standardized recommended material and the standard material is compared. A recommended material search system characterized by the following features.

4. A recommended material search system according to claim 3, The aforementioned processor, The similarity is compared based on the Euclidean distance between the cluster of the standardized recommended material and the cluster of the standard material. The closer the Euclidean distance, the higher the similarity is considered to be. A higher recommendation rank is assigned to the recommended material that has a high degree of similarity to the aforementioned reference material. A recommended material search system characterized by the following features.

5. A recommended material search system according to claim 1, The aforementioned processor, The environmental impact of the recommended material is calculated and included in the search results. A recommended material search system characterized by the following features.

6. A recommended material search system according to claim 1, The aforementioned processor, The system accepts the selection of corresponding physical property evaluation items from the physical property information of the aforementioned reference material, and the input of physical property values, and then searches for the recommended material. A recommended material search system characterized by the following features.

7. A recommended material search system according to claim 1, The aforementioned reference material is the material currently used in the product or the material intended for use. The previously recommended material is recycled material. A recommended material search system characterized by the following features.

8. A recommended material search method performed by a recommended material search system having one or more processors and one or more memory resources, The aforementioned memory resource stores the material search program, By executing the material search program, the processor A step of searching for recommended materials using predetermined physical property evaluation items and physical property values ​​as search conditions, A step of determining the recommendation ranking of the recommended materials based on a comparison of the similarity between the molding quality of the searched recommended materials and the molding quality of the reference material, The process involves outputting search results that associate the recommended materials with the recommended ranking. A recommended material search method characterized by the following:

9. A method for searching for recommended materials according to claim 8, The aforementioned processor, In the step of determining the recommended order of the recommended materials, A step of standardizing the recommended material and the standard material based on the aforementioned molding quality statistics, The steps include comparing the degree of similarity between the standardized recommended material and the standard material. A recommended material search method characterized by the following:

10. A method for searching for recommended materials according to claim 9, The aforementioned processor, In the step of determining the recommended order of the recommended materials, The step of comparing similarity based on the Euclidean distance between the cluster of the standardized recommended material and the cluster of the standard material is performed. The closer the Euclidean distance, the higher the similarity is considered to be. A higher recommendation rank is assigned to the recommended material that has a high degree of similarity to the aforementioned reference material. A recommended material search method characterized by the following:

11. A program for a recommended material search system that is executed on an information processing device having one or more processors and one or more memory resources, The aforementioned memory resource stores the material search program, The material search program is provided to the processor, The system searches for recommended materials using specified physical property evaluation items and physical property values ​​as search criteria. Based on a comparison of the similarity between the molding quality of the searched recommended material and the molding quality of the reference material, the recommendation ranking of the recommended material is determined. Output search results that associate the recommended materials with the recommended ranking. A program characterized by the following features.

12. The program according to claim 11, The material search program is provided to the processor, Based on the aforementioned molding quality statistics, the recommended material and the standard material are standardized, and the similarity between the standardized recommended material and the standard material is compared. A program characterized by the following features.

13. The program according to claim 12, The material search program is provided to the processor, The similarity is compared based on the Euclidean distance between the cluster of the standardized recommended material and the cluster of the standard material. The closer the Euclidean distance, the higher the similarity is evaluated. The recommended material with a high degree of similarity to the aforementioned reference material is assigned a higher recommendation ranking. A program characterized by the following features.

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