WO2022196208A1 - 特異材料検出装置、制御方法、及び非一時的なコンピュータ可読媒体 - Google Patents

特異材料検出装置、制御方法、及び非一時的なコンピュータ可読媒体 Download PDF

Info

Publication number
WO2022196208A1
WO2022196208A1 PCT/JP2022/005625 JP2022005625W WO2022196208A1 WO 2022196208 A1 WO2022196208 A1 WO 2022196208A1 JP 2022005625 W JP2022005625 W JP 2022005625W WO 2022196208 A1 WO2022196208 A1 WO 2022196208A1
Authority
WO
WIPO (PCT)
Prior art keywords
node
specification information
physical property
assigned
nodes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2022/005625
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
直樹 鍬守
昭裕 撫佐
陽平 瀧川
悠加 風間
佳彦 佐藤
広明 小林
豪太 菊川
朋永 岡部
一彦 小松
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tohoku University NUC
NEC Corp
Original Assignee
Tohoku University NUC
NEC Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tohoku University NUC, NEC Corp filed Critical Tohoku University NUC
Priority to EP22770967.2A priority Critical patent/EP4310718A4/en
Priority to US18/282,145 priority patent/US20240152664A1/en
Priority to JP2023506875A priority patent/JP7572664B2/ja
Priority to CN202280022384.XA priority patent/CN117043781A/zh
Publication of WO2022196208A1 publication Critical patent/WO2022196208A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—Two-dimensional [2D] image generation
    • G06T11/20—Drawing from basic elements
    • G06T11/26—Drawing of charts or graphs
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30176—Document
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics

Definitions

  • This disclosure relates to technology for providing information related to product development.
  • Patent Literature 1 discloses a system that uses a self-organizing map to support understanding of causal relationships between design values and physical property values of tires.
  • Patent Document 1 a self-organizing map is used to identify which of a plurality of tire design variables is an important factor. Therefore, it is not assumed that the self-organizing map will be used for any other purpose.
  • the present disclosure has been made in view of the above problems, and an object of the present disclosure is to provide a new technology that provides useful information for product development.
  • the specific material detection apparatus of the present disclosure includes, for each of a plurality of patterns of materials that can be used in a target process, material specification information that represents the material specifications of the material, and a product that can be generated in the process using the material.
  • an acquisition unit that acquires physical property information indicating physical property amounts for each of a plurality of physical properties of the product; and a self-organizing map generation unit that generates a self-organizing map in which each node is assigned a physical property vector to indicate each of the material specification information, based on the physical property information corresponding to the material specification information.
  • a peculiar node detection unit that detects a peculiar node located at a peculiar position in the map space from among the nodes to which the material specification information is assigned.
  • the control method of the present disclosure is executed by a computer.
  • the control method includes, for each of a plurality of patterns of materials that can be used in a target process, material specification information representing the material specifications of the material, and a plurality of physical properties of products that can be produced in the process using the material. an acquisition step of acquiring physical property information indicating physical property amounts for each; and a physical property vector indicating a position on a map space and a value related to physical property amounts of each of the plurality of types of physical properties of the product using the physical property information.
  • the non-transitory computer-readable medium of the present disclosure stores a program that causes a computer to execute the control method of the present disclosure.
  • FIG. 4 is a diagram illustrating an overview of the operation of the peculiar material detection device of Embodiment 1;
  • FIG. 2 is a block diagram illustrating the functional configuration of the specific material detection device of Embodiment 1;
  • FIG. It is a block diagram which illustrates the hardware constitutions of the computer which implement
  • 4 is a flowchart illustrating the flow of processing executed by the unique material detection device of Embodiment 1; It is a figure which illustrates material specification information in a table form. It is a figure which illustrates physical-property information in a table form.
  • FIG. 4 is a diagram illustrating, in a table format, a configuration of a self-organizing map after material specification information is assigned;
  • FIG. 4 is a diagram illustrating a visualized map space
  • FIG. 10 is a diagram illustrating a case of detecting singular nodes from each of a plurality of clusters
  • FIG. 4 illustrates a map image with singular nodes highlighted
  • FIG. 4 is a diagram illustrating a map image including information on material specifications and physical properties corresponding to singular nodes;
  • predetermined information such as predetermined values and threshold values is stored in advance in a storage device or the like that can be accessed from a device that uses the information.
  • FIG. 1 is a diagram illustrating an overview of the operation of the specific material detection device 2000 of Embodiment 1.
  • FIG. 1 is a diagram for facilitating understanding of the outline of the peculiar material detection device 2000, and the operation of the peculiar material detection device 2000 is not limited to that shown in FIG.
  • the peculiar material detection device 2000 detects materials 60 capable of producing a product 70 having peculiar physical properties from various types of materials 60 that can be used in a specific process of product development (hereinafter referred to as a target process).
  • used for Artifact 70 is what is predicted or actually produced by processing material 60 in the production process of the subject process.
  • Materials 60 are materials that are used to generate artifacts 70 .
  • Various patterns of material 60 can be utilized in the process of interest.
  • the physical properties of the product 70 may vary depending on the material 60 utilized.
  • One pattern of the material 60 is specified by material specifications. In other words, materials 60 with different material specifications are treated as materials 60 with different patterns. On the other hand, materials 60 having the same material specifications are treated as materials 60 having the same pattern.
  • Material specifications are represented, for example, by the type of material, the type of substances that make up the material, the mixing ratio of each substance, and the type of processing performed to create the material.
  • Types of materials include, for example, carbon fiber reinforced plastics and stainless steel.
  • the material specifications of the material 60 include the types of one or more carbon fibers that make up the material 60 (such as polyacrylonitrile fibers and carbonized cellulose fibers), and one or more resins that make up the material 60. type (epoxy, polyether terephthalate, etc.) and the blending ratio of those substances.
  • the material specifications may further include the type of fiber directional polymerization method, the type of compression method, resin composition, and the like.
  • the peculiar material detection device 2000 operates as follows. That is, the peculiar material detection device 2000 detects the material specification information 10 representing the material specification of the material 60 for each of the plurality of patterns of the material 60 (in other words, for the material 60 specified by each of the plurality of patterns of material specification). Then, the physical property information 20 indicating the physical properties of the product 70 that can be generated in the target process using the material 60 is obtained.
  • the physical property information 20 indicates physical property quantities of each of a plurality of types of physical properties of the product 70 . Types of physical properties are, for example, flame retardancy, heat resistance, elastic modulus, toughness, and the like.
  • the peculiar material detection device 2000 uses the physical property information 20 to generate a self-organizing map 30 representing the physical property distribution of the product 70 .
  • the self-organizing map 30 has a plurality of nodes arranged on an m-dimensional space.
  • this m-dimensional space will be referred to as a map space.
  • m is set to 2 or 3 when the peculiar material detection apparatus 2000 generates an image in which the arrangement of nodes in the map space is visualized.
  • a node can be expressed as a square of squares or a grid on a grid.
  • Each node of the self-organizing map 30 is assigned multi-dimensional data (hereinafter referred to as a physical property vector) representing the magnitude of each physical property of multiple types of physical properties.
  • a physical property vector representing the magnitude of each physical property of multiple types of physical properties.
  • the physical property vector is four-dimensional data representing the magnitude of each of these four types of physical properties.
  • the number of dimensions of the physical property vector is assumed to be n. Note that n>m. That is, in the self-organizing map 30, the physical property vector space is a high-dimensional space, and the map space is a low-dimensional space.
  • the physical property information 20 indicates physical property amounts for n or more types of physical properties.
  • the peculiar material detection apparatus 2000 learns the self-organizing map 30 using the physical property amounts of the n types of physical properties indicated by the physical property information 20, thereby determining the physical property vector to be assigned to each node. , generates a self-organizing map 30 .
  • the peculiar material detection device 2000 assigns each piece of material specification information 10 to any node of the self-organizing map 30 .
  • the singular node detection unit 2060 identifies a node having a physical property vector most similar to the physical property vector obtained from the physical property information 20 corresponding to the material specification information 10 . Then, the singular node detection unit 2060 assigns the material specification information 10 to the specified node.
  • the peculiar material detection device 2000 detects, as peculiar nodes, nodes at peculiar positions in the distribution of those nodes in the map space among the nodes associated with the material specification information 10 . This means that the position of the peculiar node is a statistically peculiar position (outlier) in the map space distribution of the nodes associated with the material specification information 10 .
  • the peculiar material detection device 2000 can detect materials 60 that can produce peculiar artifacts. Specifically, the peculiar material detection device 2000 uses the physical property information 20 to generate the self-organizing map 30 . As a result, a self-organizing map 30 is obtained that represents the distribution of physical properties according to the arrangement of nodes in the map space. Furthermore, the peculiar material detection device 2000 assigns each piece of material specification information 10 to a node of the self-organizing map 30, and detects peculiar nodes among the nodes to which the material specification information 10 is assigned. A singular node is a node located far away from other nodes among the nodes to which the material specification information 10 is assigned.
  • the material 60 represented by the material specification information 10 assigned to the peculiar node is the material 60 capable of producing the product 70 having peculiar physical properties. Therefore, by using the peculiar material detection device 2000, the user of the peculiar material detection device 2000 can grasp the material 60 that can produce a peculiar product.
  • the specific material detection device 2000 of this embodiment will be described in more detail below.
  • FIG. 2 is a block diagram illustrating the functional configuration of the specific material detection device 2000 of Embodiment 1.
  • the peculiar material detection device 2000 has an acquisition section 2020 , a self-organizing map generation section 2040 and a peculiar node detection section 2060 .
  • the acquisition unit 2020 acquires the material specification information 10 and the physical property information 20 for each of the multiple patterns of the material 60 .
  • the self-organizing map generator 2040 uses the physical property information 20 to generate the self-organizing map 30 .
  • the singular node detection unit assigns each piece of material specification information 10 to each node of the self-organizing map 30 .
  • the peculiar node detection unit 2060 detects, as peculiar nodes, nodes at peculiar positions in their distribution in the map space among the nodes to which the material specification information 10 is assigned.
  • Each functional component of the peculiar material detection device 2000 may be realized by hardware (eg, hardwired electronic circuit, etc.) that implements each functional component, or may be realized by a combination of hardware and software (eg, : a combination of an electronic circuit and a program that controls it, etc.).
  • hardware e.g, hardwired electronic circuit, etc.
  • software e.g, : a combination of an electronic circuit and a program that controls it, etc.
  • FIG. 3 is a block diagram illustrating the hardware configuration of the computer 500 that implements the peculiar material detection device 2000.
  • Computer 500 is any computer.
  • the computer 500 is a stationary computer such as a server machine or a PC (Personal Computer).
  • the computer 500 is a portable computer such as a smart phone or a tablet terminal.
  • Computer 500 may be a dedicated computer designed to implement specific material detection apparatus 2000, or may be a general-purpose computer.
  • the computer 500 realizes each function of the specific material detection device 2000.
  • the application is composed of a program for realizing each functional component of the peculiar material detection device 2000 .
  • the acquisition method of the above program is arbitrary.
  • the program can be acquired from a storage medium (DVD disc, USB memory, etc.) in which the program is stored.
  • the program can be obtained by downloading the program from a server device that manages the storage device in which the program is stored.
  • Computer 500 has bus 502 , processor 504 , memory 506 , storage device 508 , input/output interface 510 and network interface 512 .
  • the bus 502 is a data transmission path through which the processor 504, memory 506, storage device 508, input/output interface 510, and network interface 512 exchange data with each other.
  • the method of connecting the processors 504 and the like to each other is not limited to bus connection.
  • the processor 504 is various processors such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), or FPGA (Field-Programmable Gate Array).
  • the memory 506 is a main memory implemented using a RAM (Random Access Memory) or the like.
  • the storage device 508 is an auxiliary storage device implemented using a hard disk, SSD (Solid State Drive), memory card, ROM (Read Only Memory), or the like.
  • the input/output interface 510 is an interface for connecting the computer 500 and input/output devices.
  • the input/output interface 510 is connected to an input device such as a keyboard and an output device such as a display device.
  • a network interface 512 is an interface for connecting the computer 500 to a network.
  • This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).
  • the storage device 508 stores a program for realizing each functional component of the peculiar material detection device 2000 (a program for realizing the application described above).
  • the processor 504 reads this program into the memory 506 and executes it, thereby realizing each functional component of the peculiar material detection apparatus 2000 .
  • the peculiar material detection device 2000 may be realized by one computer 500 or may be realized by a plurality of computers 500. In the latter case, the configuration of each computer 500 need not be the same, and can be different.
  • FIG. 4 is a flow chart illustrating the flow of processing executed by the specific material detection device 2000 of the first embodiment.
  • the acquiring unit 2020 acquires the material specification information 10 and the physical property information 20 for each of the plurality of materials 60 that can be used in the target process (S102).
  • the self-organizing map generator 2040 uses the physical property information 20 to generate the self-organizing map 30 (S104).
  • the peculiar node detection unit 2060 assigns each piece of material specification information 10 to any node of the self-organizing map 30 (S106).
  • the peculiar node detection unit 2060 detects, as peculiar nodes, nodes at peculiar positions in their distribution in the map space among the nodes to which the material specification information 10 is assigned (S108).
  • FIG. 5 is a diagram illustrating the material specification information 10 in a table format.
  • the table 100 of FIG. 5 has columns of material identification information 102 and material specifications 104 .
  • Material identification information 102 indicates identification information assigned to material 60 .
  • a material specification 104 indicates the specification of the material 60 .
  • the material specification information 10 is represented by one record in the table 100. That is, the material specification information 10 associates the identification information of the material 60 with the material specification of the material 60 having the identification information.
  • FIG. 6 is a diagram exemplifying the physical property information 20 in a table format.
  • the table 200 in FIG. 6 has columns of product identification information 202 and physical properties 204 .
  • the product identification information 202 indicates identification information of the product 70 .
  • a physical property 204 indicates a physical property of the deliverable 70 .
  • the physical properties of the product 70 are represented by showing a correspondence of "label representing type of physical property: physical amount of the physical property" for each physical property.
  • the physical property information 20 is represented by one record in the table 200. That is, the physical property information 20 associates the identification information of the product 70 with the physical properties of the product 70 having the identification information.
  • the acquisition unit 2020 acquires multiple pairs of material specification information 10 and physical property information 20 .
  • a pair of material specification information 10 and physical property information 20 is stored in advance in any storage device accessible from the specific material detection device 2000 .
  • the acquisition unit 2020 acquires a pair of material specification information 10 and physical property information 20 by accessing this storage device.
  • the acquisition unit 2020 may acquire a pair of the material specification information 10 and the physical property information 20 by accepting user input for inputting the pair of the material specification information 10 and the physical property information 20 .
  • the acquisition unit 2020 may acquire a pair of material specification information 10 and physical property information 20 by receiving a pair of material specification information 10 and physical property information 20 transmitted from another device. good.
  • the pair of material specification information 10 and physical property information 20 is generated by simulating the generation of the product 70 . Specifically, by giving specific material specifications as input and executing a simulation, the physical property information 20 indicating the predicted value of the physical quantity of each physical property is generated for the product 70 . Then, a pair of the generated physical property information 20 and the material specification information 10 indicating the material specification given as an input is obtained.
  • a technology that realizes a simulation that acquires material specifications as input and outputs predictive data on the physical properties of deliverables that are produced in specific processes using the materials specified by the material specifications. can use existing technology.
  • the pair of material specification information 10 and physical property information 20 may be generated by actually generating the deliverable 70.
  • the product 70 is experimentally generated by using the material 60 represented by specific material specifications in the target process.
  • the physical property information 20 is generated by measuring the physical quantity of each physical property of the generated product 70 . As a result, a pair of generated physical property information 20 and material specification information 10 representing the material 60 used is obtained.
  • the physical property information 20 acquired by the acquisition unit 2020 may include information with different data representation methods. For example, different labels may be used for essentially identical physical properties. In addition, it is conceivable that the physical quantity of the same physical property is expressed in units different from each other. In such a case, the acquisition unit 2020 preferably unifies the data expression method by unifying labels, converting units, and the like.
  • a situation in which the physical property information 20 has different data representation methods is, for example, the physical property information 20 generated using a simulation and the physical property information 20 generated by experimentally generating a product 70. It is thought that this may occur when both are acquired. In addition, it is preferable that such unification of data expression methods is also performed for the material specification information 10 in the same manner.
  • the self-organizing map generator 2040 uses the physical property information 20 to generate the self-organizing map 30 (S104).
  • the self-organizing map 30 has a plurality of nodes arranged on an m-dimensional map space. The number of dimensions of the map space may be predetermined or specified by the user.
  • Each node of the self-organizing map 30 is assigned an n-dimensional physical property vector. Assignment of physical property vectors to each node is performed by learning the self-organizing map 30 . Learning of the self-organizing map 30 can be performed by inputting n-dimensional training data used for learning into the self-organizing map 30 . An existing method can be used as a specific method for learning a self-organizing map using training data.
  • the self-organizing map generator 2040 initializes the self-organizing map 30 by any method.
  • an initialization method for example, a method of initializing the physical property vector of each node to a random value can be adopted.
  • the self-organizing map generation unit 2040 generates a plurality of n-dimensional physical property vectors by extracting physical property amounts of n types of physical properties from each of the acquired plurality of physical property information 20 .
  • the self-organizing map generation unit 2040 generates the self-organizing map 30 by learning the self-organizing map 30 by treating each of the plurality of physical property vectors as training data.
  • the physical property vector of each node of the self-organizing map 30 becomes n-dimensional data indicating the value of each physical property quantity of n types of physical properties.
  • the physical property vector obtained from the physical property information 20 may indicate the physical property amount of each of the n types of physical properties indicated by the physical property information 20 as it is, or each physical property amount may be converted by a predetermined method (for example, normalization, standardization, etc.). You may indicate the value obtained by
  • the number of physical properties indicated by the physical property information 20 may be greater than n.
  • part of the data indicated by the physical property information 20 is used for generating the self-organizing map 30.
  • FIG. which type of physical properties among the physical properties indicated by the physical property information 20 is used to generate the self-organizing map 30 may be determined in advance or may be designated by the user.
  • the peculiar node detection unit 2060 assigns each piece of material specification information 10 acquired by the acquisition unit 2020 to any node of the self-organizing map 30 (S106). Specifically, the singular node detection unit 2060 identifies a node having a physical property vector most similar to the physical property vector obtained from the physical property information 20 corresponding to the material specification information 10 . Then, the singular node detection unit 2060 assigns the material specification information 10 to the specified node.
  • FIG. 7 is a diagram exemplifying the configuration of the self-organizing map 30 after the material specification information 10 has been assigned, in a table format.
  • Table 300 of FIG. 7 has three columns: position 302 , property vector 304 , and material identification information 306 .
  • Table 300 has one record per node.
  • the material identification information 306 indicates, for a node to which the material specification information 10 is assigned, identification information of the material 60 indicated by the material specification information 10 assigned to the node. In the record of the node to which the material specification information 10 is not assigned, the material identification information 306 indicates "-".
  • the peculiar node detection unit 2060 detects peculiar nodes among the nodes to which the material specification information 10 is assigned (S108).
  • a peculiar node is a node at a peculiar position in the distribution of those nodes in the map space among the nodes to which the material specification information 10 is assigned.
  • the peculiar node detection unit 2060 determines a reference position and a peculiar threshold value based on the distribution of positions on the map space of nodes to which the material specification information 10 is assigned, and uses these to detect peculiar nodes.
  • the reference position and the peculiar threshold are determined so that the position of the node whose distance from the reference position is greater than the peculiar threshold, among the nodes to which the material specification information 10 is assigned, is a statistically peculiar position in the above distribution. be done. Then, among the nodes to which the material specification information 10 is assigned, a node whose distance from the reference position is greater than the peculiar threshold is detected as a peculiar node.
  • the peculiar node detection unit 2060 calculates the barycenter position or geometric center of each node based on the position on the map space to which the material specification information 10 is assigned, and uses the calculated position as a reference position. use.
  • the singular node detection unit 2060 calculates a statistical value (standard deviation, etc.) representing the size of the distribution of those positions based on the position of each node to which the material specification information 10 is assigned on the map space. Then, the statistic value or a value obtained by multiplying the statistic value by a predetermined positive real number is calculated as a singular threshold.
  • existing methods are used for the method of calculating their centroid positions and geometric centers, and the method of calculating statistical values representing the size of their distribution. can.
  • the peculiar node detection unit 2060 calculates the distance between each node to which the material specification information 10 is assigned and the reference position, and determines whether the calculated distance is greater than the peculiar threshold. Then, the peculiar node detection unit 2060 detects, as a peculiar node, a node whose distance from the reference position is determined to be greater than the peculiar threshold.
  • FIG. 8 is a diagram illustrating a visualized map space.
  • the dimensionality of the map space is two.
  • Each node is represented as a square of squares.
  • the display 42 shows information that can identify the material specification information 10 associated with the node. Specifically, in the example of FIG. 8, display 42 indicates material identification information.
  • a reference position 44 represents a reference position.
  • a region 46 represents a range in which the distance from the reference position 44 is equal to or less than the singular threshold.
  • the node corresponding to the display 42 showing P109 is located outside the area 46. That is, the position of this node is the position where the distance from the reference position 44 is greater than the singularity threshold. Therefore, this node is detected as a singular node.
  • the peculiar node detection unit 2060 may divide the nodes to which the material specification information 10 is assigned into a plurality of clusters and detect peculiar nodes for each cluster. For example, the peculiar node detection unit 2060 detects, for each node to which the material specification information 10 is assigned, multidimensional data (hereinafter referred to as a specification vector ). Then, the peculiar node detection unit 2060 divides the plurality of nodes to which the material specification information 10 is assigned into a plurality of clusters by clustering them based on the corresponding specification vectors. Any clustering algorithm such as the k-means method can be used as a specific clustering method.
  • the specification vector may indicate values for all the parameters indicated by the material specification information 10, or may indicate values for some of the parameters. That is, if the number of dimensions of the specification vector is k, the value of k may be the same as the number of parameters indicated by the material specification information 10, or may be smaller than the number of parameters indicated by the material specification information 10.
  • the material specification information 10 indicates both parameters that take continuous values (for example, the compounding ratio of substances) and parameters that do not take continuous values (for example, the type of processing).
  • the specification vector is generated using only parameters that take continuous values.
  • the specification vector may indicate the value of the parameter indicated by the material specification information 10 as it is, or the value of each parameter may be converted by a predetermined method (for example, normalization or standardization). You may indicate the value obtained by
  • the clustering method is not limited to the method using the specification vector.
  • the singular node detection unit 2060 may cluster nodes based on the type of material indicated by the material specification information 10 .
  • different types of materials 60 such as a material 60 classified as carbon fiber reinforced plastic and a material 60 classified as stainless steel are included. may include those shown. Therefore, the peculiar node detection unit 2060 classifies the nodes into clusters for each type of material 60 indicated by the assigned material specification information 10 . Thereby, a singular node can be detected for each type of material 60 .
  • the peculiar node detection unit 2060 may further apply clustering based on the specification vector to each cluster.
  • the peculiar node detection unit 2060 calculates the reference position i and the peculiar threshold i based on the positions of each of the plurality of nodes included in the cluster i among the nodes to which the material specification information 10 is assigned.
  • the calculation method the same method as in the case where clustering is not performed can be adopted. That is, for example, the peculiar node detection unit 2060 calculates the centroid position and the geometric center of the positions of a plurality of nodes included in the cluster i among the nodes to which the material specification information 10 is assigned, and calculates This position is treated as the reference position i.
  • the singular node detection unit 2060 calculates a statistic value representing the size of the distribution of the positions of a plurality of nodes included in the cluster i among the nodes to which the material specification information 10 is assigned, The statistic value or a value obtained by multiplying the statistic value by a predetermined positive real number is treated as the singular threshold i.
  • the peculiar node detection unit 2060 determines whether the distance between the position of each node included in the cluster i and the reference position i is greater than the peculiar threshold i, among the nodes to which the material specification information 10 is assigned. determine whether Then, the peculiar node detection unit 2060 detects a node whose distance from the reference position i is greater than the peculiar threshold i as a peculiar node in the cluster i.
  • FIG. 9 is a diagram illustrating a case of detecting singular nodes from each of a plurality of clusters.
  • the nodes assigned with the material specification information 10 are divided into two clusters.
  • Nodes classified into cluster 1 are indicated by solid lines in the display 42 .
  • nodes classified into cluster 2 are indicated by dotted lines in the display 42 .
  • a reference position 44-1 represents the reference position calculated for cluster 1.
  • a region 46-1 represents a range in which the distance from the reference position 44-1 is equal to or less than the peculiar threshold value calculated for cluster 1.
  • the reference position 44-2 represents the reference position calculated for cluster 2.
  • a region 46-2 represents a range in which the distance from the reference position 44-2 is equal to or less than the peculiar threshold value calculated for cluster 2.
  • FIG. Here, all nodes classified into cluster 2 are located in region 46-2. Therefore, no peculiar node is detected from cluster 2 .
  • the peculiar material detection device 2000 performs clustering based on material specifications, and detects peculiar nodes for each cluster. By doing so, it is possible to detect the materials 60 that can produce a unique product 70 from among the materials 60 that are similar to each other.
  • the peculiar material detection device 2000 outputs output information representing the processing result.
  • the output information includes information that enables understanding of the detected singular node.
  • Information from which the detected peculiar nodes can be grasped includes, for example, an image representing the visualized map space (hereinafter referred to as a map image) and screen data including the image.
  • a map image generation unit the functional configuration unit that generates the map image.
  • FIG. 10 is a diagram illustrating a map image in which singular nodes are highlighted. Note that the map image 40 of FIG. 10 is the same as that of FIG. 8 except that the display 42 corresponding to the singular node is highlighted.
  • the singular node is a node with material identification information of P109. Therefore, the display 42 attached to this node is larger in size and has a thicker border than the other displays 42 .
  • the map image 40 may include information on material specifications and physical properties associated with the singular node.
  • FIG. 11 is a diagram illustrating a map image 40 containing information on material specifications and physical properties corresponding to singular nodes.
  • the content of FIG. 11 is the same as that of FIG. 10, except that the content of display 42 is different from that shown in FIG.
  • the display 42 of the peculiar node shows the material identification information "P109" and the material specifications of the compounding ratio of substances A to C as the contents of the material specification information 10 corresponding to the peculiar node.
  • the display 42 of the singular node indicates the physical quantity of each of the physical properties U, V, and W as the content of the physical property corresponding to the singular node.
  • the screen data including the map image 40 is included in the output information
  • an input from the user may be received on the screen represented by the screen data, and information regarding the node may be output according to the input.
  • the map image 40 illustrated in FIG. 10 is shown on the screen that is first output to the display device of the terminal used by the user. That is, the content of the display 42 of each node is made material identification information.
  • the display 42 for that node is displayed as illustrated in FIG. As shown in 42, the display is changed to display 42 showing the details of material specifications and physical properties.
  • the value of each element of the physical property vector assigned to the node may not represent the physical property amount as it is, but may be converted by normalization or the like.
  • the peculiar material detection apparatus 2000 converts the value of the element of the physical property vector into a physical property amount for the node designated by the user, and includes the physical property amount obtained by the conversion in the display 42.
  • the output information is not limited to the map image 40 or a screen containing the map image.
  • the output information is text information indicating information such as the position on the map space, the contents of the assigned material specification information 10, and the physical properties of the product 70 represented by the assigned physical property vector for the singular node.
  • the output information may further indicate the degree of peculiarity based on the position on the map space for the peculiar node.
  • the degree of peculiarity can be represented by a value obtained by dividing the distance between the peculiar node and the reference position by the peculiarity threshold. This degree of singularity may be indicated on the map image 40 .
  • the output mode of the output information is arbitrary.
  • the peculiar material detection device 2000 stores output information in any storage device accessible from the peculiar material detection device 2000 .
  • the peculiar material detection device 2000 displays output information on any display device that can be controlled from the peculiar material detection device 2000 .
  • the peculiar material detection device 2000 transmits output information to any device communicably connected to the peculiar material detection device 2000 .
  • Non-transitory computer readable media include various types of tangible storage media.
  • Examples of non-transitory computer-readable media include magnetic recording media (e.g., floppy disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical discs), CD-ROMs, CD-Rs, CD-Rs /W, including semiconductor memory (e.g. mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM);
  • the program may also be provided to the computer on various types of transitory computer readable medium. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. Transitory computer-readable media can deliver the program to the computer via wired channels, such as wires and optical fibers, or wireless channels.
  • Each of the material specification information is assigned to one of the nodes based on the physical property information corresponding to the material specification information, and among the nodes to which the material specification information is assigned, in the map space and a singular node detection means for detecting a singular node at a singular position.
  • the singular node detection means assigns each of the plurality of material specification information to the node to which the physical property vector most similar to the physical property vector obtained from the physical property information corresponding to the material specification information is assigned.
  • Supplementary note 1 the specific material detection device.
  • the singular node detection means is dividing the plurality of nodes to which the material specification information is assigned into a plurality of clusters based on the material specification information; any one of Appendices 1 to 4, wherein for each of the plurality of clusters, the singular node is detected from among the nodes belonging to the cluster based on the distribution of the positions of the nodes included in the cluster on the map space;
  • the specific material detection device according to item 1.
  • the peculiar material detection device according to any one of Appendices 1 to 5, wherein the map image includes a display indicating the peculiar node.
  • the display indicating the singular node is either one of material specifications represented by the material specification information assigned to the singular node and physical properties represented by the physical property vector assigned to the singular node. or both.
  • a control method implemented by a computer comprising: For each of a plurality of patterns of materials that can be used in the target process, material specification information that represents the material specifications of the material, and physical property amounts for each of the multiple physical properties of the deliverable that can be generated in the process using the material an acquisition step of acquiring physical property information indicating Using the physical property information, a self-organizing map is generated in which each node is assigned a position on the map space and a physical property vector indicating a physical property amount of each of a plurality of types of physical properties of the product.
  • Each of the material specification information is assigned to one of the nodes based on the physical property information corresponding to the material specification information, and among the nodes to which the material specification information is assigned, in the map space a singular node detection step of detecting a singular node at a singular position.
  • a singular node detection step of detecting a singular node at a singular position.
  • the display indicating the singular node is either one of material specifications represented by the material specification information assigned to the singular node and physical properties represented by the physical property vector assigned to the singular node. or both.
  • (Appendix 15) to the computer For each of a plurality of patterns of materials that can be used in the target process, material specification information that represents the material specifications of the material, and physical property amounts for each of the multiple physical properties of the deliverable that can be generated in the process using the material an acquisition step of acquiring physical property information indicating Using the physical property information, a self-organizing map is generated in which each node is assigned a position on the map space and a physical property vector indicating a physical property amount of each of a plurality of types of physical properties of the product.
  • Each of the material specification information is assigned to one of the nodes based on the physical property information corresponding to the material specification information, and among the nodes to which the material specification information is assigned, in the map space and a singular node detection step of detecting singular nodes at singular locations.
  • each of the plurality of material specification information is assigned to the node to which the physical property vector most similar to the physical property vector obtained from the physical property information corresponding to the material specification information is assigned.
  • Material specification information 20 Physical property information 30 Self-organizing map 40 Map image 42 Display 44 Reference position 46 Area 60 Material 70 Product 100 Table 102 Material identification information 104 Material specification 200 Table 202 Product identification information 204 Physical property 300 Table 302 Position 304 Physical property vector 306 Material identification information 500 Computer 502 Bus 504 Processor 506 Memory 508 Storage device 510 Input/output interface 512 Network interface 2000 Unique material detection device 2020 Acquisition unit 2040 Self-organizing map generation unit 2060 Unique node detection unit

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
PCT/JP2022/005625 2021-03-18 2022-02-14 特異材料検出装置、制御方法、及び非一時的なコンピュータ可読媒体 Ceased WO2022196208A1 (ja)

Priority Applications (4)

Application Number Priority Date Filing Date Title
EP22770967.2A EP4310718A4 (en) 2021-03-18 2022-02-14 SINGLE MATERIAL DETECTION DEVICE, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
US18/282,145 US20240152664A1 (en) 2021-03-18 2022-02-14 Singular material detection apparatus, control method, and non-transitory computer readable medium
JP2023506875A JP7572664B2 (ja) 2021-03-18 2022-02-14 特異材料検出装置、制御方法、及びプログラム
CN202280022384.XA CN117043781A (zh) 2021-03-18 2022-02-14 奇异材料检测设备、控制方法和非暂时性计算机可读介质

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2021-044487 2021-03-18
JP2021044487 2021-03-18

Publications (1)

Publication Number Publication Date
WO2022196208A1 true WO2022196208A1 (ja) 2022-09-22

Family

ID=83322234

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/005625 Ceased WO2022196208A1 (ja) 2021-03-18 2022-02-14 特異材料検出装置、制御方法、及び非一時的なコンピュータ可読媒体

Country Status (5)

Country Link
US (1) US20240152664A1 (https=)
EP (1) EP4310718A4 (https=)
JP (1) JP7572664B2 (https=)
CN (1) CN117043781A (https=)
WO (1) WO2022196208A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024135553A1 (ja) * 2022-12-23 2024-06-27 株式会社レゾナック 設計評価装置、設計評価方法及びプログラム

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007087098A (ja) * 2005-09-22 2007-04-05 Nissan Motor Co Ltd 最適化システム、最適化方法、最適化プログラム、及びプログラム媒体
JP2008293315A (ja) * 2007-05-25 2008-12-04 Yokohama Rubber Co Ltd:The データ解析プログラム、データ解析装置、構造体の設計プログラム、および構造体の設計装置
JP2016099737A (ja) * 2014-11-19 2016-05-30 横浜ゴム株式会社 データの分析方法およびデータの表示方法
JP2016148988A (ja) 2015-02-12 2016-08-18 横浜ゴム株式会社 データの分析方法およびデータの表示方法
JP2021044487A (ja) 2019-09-13 2021-03-18 シーアンドディー セミコンダクター サービシーズ，インコーポレイテッド スピンコーティング装置におけるクリーニングのための、基板材料の底部表面および／またはその縁部の最適露出

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007087098A (ja) * 2005-09-22 2007-04-05 Nissan Motor Co Ltd 最適化システム、最適化方法、最適化プログラム、及びプログラム媒体
JP2008293315A (ja) * 2007-05-25 2008-12-04 Yokohama Rubber Co Ltd:The データ解析プログラム、データ解析装置、構造体の設計プログラム、および構造体の設計装置
JP2016099737A (ja) * 2014-11-19 2016-05-30 横浜ゴム株式会社 データの分析方法およびデータの表示方法
JP2016148988A (ja) 2015-02-12 2016-08-18 横浜ゴム株式会社 データの分析方法およびデータの表示方法
JP2021044487A (ja) 2019-09-13 2021-03-18 シーアンドディー セミコンダクター サービシーズ，インコーポレイテッド スピンコーティング装置におけるクリーニングのための、基板材料の底部表面および／またはその縁部の最適露出

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4310718A4

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024135553A1 (ja) * 2022-12-23 2024-06-27 株式会社レゾナック 設計評価装置、設計評価方法及びプログラム

Also Published As

Publication number Publication date
US20240152664A1 (en) 2024-05-09
CN117043781A (zh) 2023-11-10
EP4310718A4 (en) 2025-03-19
EP4310718A1 (en) 2024-01-24
JPWO2022196208A1 (https=) 2022-09-22
JP7572664B2 (ja) 2024-10-24

Similar Documents

Publication Publication Date Title
JP6654119B2 (ja) プラントデータ表示処理装置及びプラント制御システム
JP6863926B2 (ja) データ分析システム及びデータ分析方法
CN113853614A (zh) 在量子特征空间中使用量子相似性矩阵的无监督聚类
JP6586764B2 (ja) データ分析装置及びデータ分析方法
US20200012269A1 (en) Plant data classification device, plant data display processing device, and plant control system
WO2022196208A1 (ja) 特異材料検出装置、制御方法、及び非一時的なコンピュータ可読媒体
KR20150112832A (ko) 산출 프로그램, 산출 장치 및 산출 방법
CN116702005A (zh) 一种基于神经网络的数据异常诊断方法和电子设备
CN114066261B (zh) 一种电表的篡改检测方法、装置、计算机设备和存储介质
JP7598107B2 (ja) マップ画像生成装置、制御方法、及びプログラム
CN115810419B (zh) 智慧手术室的运营管理方法、装置、设备及存储介质
JP7621598B2 (ja) 推奨データ生成装置、制御方法、及びプログラム
Yin et al. Integration of wavelet packet transform, residual convolution neural network, and support vector machine for series arc fault detection
JP7701681B2 (ja) マップ生成装置、マップ生成方法、及びプログラム
JP7164060B1 (ja) 情報処理装置、情報処理方法及び情報処理プログラム
KR20240028822A (ko) 사용자 맞춤형 솔루션을 제공하기 위한 다중 센서 기반의 예측 모델 생성 방법 및 그러한 방법을 수행하는 전자 장치
US20250232083A1 (en) Recommendation data generation apparatus, recommendation data generation method, and non-transitory computer-readable medium
Cupek et al. Improving KPI based performance analysis in discrete, multi-variant production
US20250022191A1 (en) Method for generating training image used to train image-based artificial intelligence model for analyzing images obtained from multi-channel one-dimensional signals, and device performing same
US20240161354A1 (en) Physical property map image generation apparatus, control method, and non-transitory computer readable medium
Zhang et al. Image segmentation by selecting eigenvectors based on extended information entropy
KR100925162B1 (ko) 객체위치 변화 이력 모델 생성 방법과 장치 및 이를 이용한객체위치 변화 이력 유형 인식 방법과 시스템
CN120010846A (zh) 一种基于特征分析的交互界面动态设计方法及系统
CN119293322A (zh) 一种数值类型标签的画像分布构建方法及其相关设备
CN112529205A (zh) 一种基于Web的机器学习可视化方法及系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22770967

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023506875

Country of ref document: JP

WWE Wipo information: entry into national phase

Ref document number: 18282145

Country of ref document: US

WWE Wipo information: entry into national phase

Ref document number: 202280022384.X

Country of ref document: CN

WWE Wipo information: entry into national phase

Ref document number: 2022770967

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2022770967

Country of ref document: EP

Effective date: 20231018