WO2022218706A1 - Procédé pour analyser la fabricabilité de produits - Google Patents

Procédé pour analyser la fabricabilité de produits Download PDF

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Publication number
WO2022218706A1
WO2022218706A1 PCT/EP2022/058412 EP2022058412W WO2022218706A1 WO 2022218706 A1 WO2022218706 A1 WO 2022218706A1 EP 2022058412 W EP2022058412 W EP 2022058412W WO 2022218706 A1 WO2022218706 A1 WO 2022218706A1
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WIPO (PCT)
Prior art keywords
machine
product
manufactured
knowledge graph
machines
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PCT/EP2022/058412
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German (de)
English (en)
Inventor
Irlan Grangel Gonzalez
Felix LOESCH
Original Assignee
Robert Bosch Gmbh
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Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Publication of WO2022218706A1 publication Critical patent/WO2022218706A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence

Definitions

  • the present invention relates to techniques for analyzing manufacturability of products. Related aspects relate to a computer program and a computer-implemented system.
  • a typical problem when developing new products or product variants is to carry out a manufacturability analysis of products or product variants on one or more machines, e.g. B. on one or more industrial machines in a specific industrial plant.
  • This task can be understood as a simulation process that takes into account the technical characteristics of a new product and the technical capabilities of one or more available machines to determine whether the one or more machines can produce the new product or product variant.
  • this task is addressed by a time-consuming and error-prone analysis that is performed partially manually. For example, production engineers perform the analysis by consulting a large number of different data sources and especially expert knowledge to determine whether the new product or product variant can be manufactured on a specific machine.
  • a first general aspect of the present disclosure relates to a computer-implemented method for analyzing a manufacturability of products.
  • the method includes providing a computer implemented machine knowledge graph containing information about a plurality of machines.
  • the machine knowledge graph comprises a conceptual model comprising a plurality of concepts, each concept mapping a corresponding one of a plurality of machine capabilities, the concepts being attributed and their respective interrelationships being defined.
  • the machine knowledge graph of the first aspect further includes a plurality of machine instances associated with the respective concepts in the conceptual model. Each machine instance comprises machine capability data points of the corresponding machine in the plurality of machines.
  • the method also includes providing one or more features of a product to be manufactured in the machine knowledge graphs.
  • the method includes determining the manufacturability of a product based on the information contained in the machine knowledge graph.
  • a second general aspect of the present disclosure relates to a computer program that is designed to carry out the computer-implemented method according to the first general aspect of the present disclosure.
  • a third general aspect of the present disclosure relates to a computer-implemented system for analyzing a manufacturability of products, configured to execute the computer program according to the second general aspect of the present disclosure.
  • the techniques of the first through third general aspects may have one or more of the following advantages.
  • the techniques of the present disclosure provide the ability to provide a method for analyzing a manufacturability of products using expert knowledge about product features and machine capabilities contained in a knowledge graph such that analyzing manufacturability is performed automatically on a computer-implemented system instead of using a lengthy process that requires analyzing and communicating between many different systems.
  • the outlay for analyzing manufacturability can be significantly reduced. In other words, a simulation can be run to determine if the technical capabilities of a particular machine are sufficient to produce a particular product or product variant.
  • the present techniques can make it possible to efficiently find out on which machine or group of machines a certain product with certain characteristics can be manufactured.
  • the present techniques can find out the reasons why it is not possible to manufacture a certain product on a given set of machines by performing the appropriate calculations. Based on these calculations, the techniques of the present disclosure may suggest how certain machines may need to be reconfigured to produce additional product variants, resulting in higher utilization of existing manufacturing assets (eg, machinery present in a particular industrial facility).
  • the present techniques can provide valuable information to engineers on how to redesign product variants at an early stage so that they can be manufactured on existing manufacturing facilities.
  • the term "product” includes any product or good that is manufactured in an industrial way (e.g. in an automated or partially automated manufacturing process) on one or more (industrial) machines.
  • the product can be an electrical or electro-mechanical component, for example.
  • the product may be a finished device (designed to perform a specific function).
  • the product may be an intermediate stage of a manufacturing process designed for further processing.
  • the product can be a computer, a tablet computer, a smartphone or an electronic device (e.g. a tool, a household appliance or a gardening device).
  • the product may be a vehicle component (the term "vehicle” is understood to mean any device designed to transport passengers and/or cargo, for example the vehicle may be a motor vehicle, in particular an at least partially autonomously operating / assisted motor vehicle) .
  • a product may be an apparatus for use in an industrial environment (e.g., an assembly line).
  • the term "product” also includes all internal (e.g. mechanical or electrical) components or parts of the above devices or devices (e.g. a motor, a control device, a gearbox, a camera-based system, a motor control, an assistance system, a sensor , a lamp, a battery, a circuit board, a housing).
  • An internal component (or part) can also be part of the systems described above or a combination of several of the systems described above (or parts of them).
  • product variant includes a modification of a product with regard to one or more technical or other characteristics (e.g. dimensions, materials used, etc.).
  • product features includes all features that define a product, its components or parts (e.g., their dimensions, shape, weight, or material, etc.) and which should be taken into account when manufacturing the product.
  • machine means any industrial machine that is suitable for manufacturing a product.
  • the machine of the present disclosure may be a fully automated or semi-automated industrial machine controlled, for example, by a system such as a computer.
  • the term “machine” also includes a group of industrial machines (e.g. an industrial plant or production line with linked industrial machines), which may be connected in a single manufacturing process.
  • the industrial machine or group of industrial machines can be designed to manufacture different components of a device.
  • machine capability includes any property of a machine, for example an industrial machine as explained above, which plays a role in the manufacturability of a product (e.g. workspace, existing tools and/or capabilities of the existing tools or more complex information that is necessary for the manufacturability of a product or a product component play a role).
  • a product e.g. workspace, existing tools and/or capabilities of the existing tools or more complex information that is necessary for the manufacturability of a product or a product component play a role.
  • Manufacturing in the present disclosure includes both processes in which material is formed or otherwise modified (e.g., an injection molding process) and processes in which components are connected or assembled.
  • knowledge graph refers to all approaches that describe data points (e.g. data that depict the respective product features of a specific product or machine capabilities of a specific machine or machine type) on a semantic level, i.e. approaches in which the data points even be transferred to conceptual models in order to make them usable.
  • the data points can be interpreted as data instances of concepts, which are defined in a conceptual model with appropriate relationships to each other.
  • attributes can be assigned to the concepts.
  • a knowledge graph consists of its conceptual model (or ontology in others terminology) and the data instances of the concepts defined in the conceptual model. Further explanations can be found below.
  • 1a is a flow chart illustrating an example of a computer-implemented method for analyzing a manufacturability of products according to the first aspect.
  • 1b and 1c are flow charts showing further possible method steps according to the first aspect.
  • Fig. 2 shows schematically a structure of a machine knowledge graph 1 with a conceptual model 10 and a plurality of machine instances 20.
  • a structure of a knowledge graph of product features 2 with a conceptual model of product features 30 and a plurality of product instances 40 can also be seen in this figure .
  • FIG. 3 schematically shows 20 possible configurations and further aspects of a computer-implemented method for analyzing the manufacturability of products.
  • FIGS. 1a to 1c the techniques for analyzing the manufacturability of products are described with reference to FIGS. 1a to 1c. Then, exemplary structures of the machine knowledge graph 1 and the knowledge graph of product features 2 are discussed with reference to FIG. 2 . Finally, possible configurations and further aspects of the present disclosure are presented with reference to FIG. 3 .
  • a first general aspect relates to a computer-implemented method for analyzing a manufacturability of products.
  • Manufacturability within the meaning of the present disclosure means a possibility to manufacture a product, for example with respective product components, through a manufacturing process (eg on a fully automatic or semi-automatic industrial machine or a group of such industrial machines).
  • the method steps of the corresponding independent claim are shown in the solid line boxes in Fig. La to lc, while the method steps of some dependent claims are shown in the boxes represented by dashed lines.
  • the method initially includes the provision 100 of a machine knowledge graph 1 implemented on a computer, which contains information about a plurality of machines.
  • machine knowledge graph means that this knowledge graph refers to a machine or a type of machine, and is only used to distinguish it from other knowledge graphs in cases where multiple knowledge graphs are used.
  • the machines can be, for example, machine tools (eg milling machines or lathes, mechanical presses and machine hammers for forging, and eroding machines) or power machines (eg electric power machines).
  • the machine knowledge graph may include a conceptual model 10 having a plurality of concepts 11,12. In one example, each concept may map a corresponding one of a plurality of machine capabilities.
  • a machine capability may refer to a machine capability of a machine type.
  • the machine capability can, for example, relate to a size and/or a material of the product which the machine can accommodate or process (eg in a manufacturing process).
  • the machine capability may be a working temperature interval that the machine can provide during a manufacturing process (eg, when a product or product components need to be manufactured below a certain temperature interval, eg, refrigerated).
  • machine capability may refer to functional product features that the machine can provide during the manufacturing process.
  • the product components need to be assembled in a certain way in order to fulfill the required product functions (e.g., a case top must be tightened with the bottom with screws to prevent the two parts at high temperatures fall apart).
  • the machine capabilities of a machine can vary from a system, such as a computer-implemented system, may be queried, and the machine may provide information regarding the machine capabilities to the system as a result of that query (eg, if the machine is operational).
  • a plurality of machine capabilities of a machine or machine type relevant to the manufacture of a product may be defined 50 by manufacturing engineers.
  • a machine capability of a machine may be given by a workspace for manufacture of a product (in Figure 2 the corresponding concept is denoted as "workspace” 11 ).
  • the concept of "workspace” can be further provided with the attribute that can include a maximum workspace (for example, a minimum or maximum length, width or height of products or product components, or a combination of these characteristics) that a machine can accommodate and/ or can edit (in Fig. 2 the corresponding attribute is denoted as "value ability" 14).
  • a machine capability of a machine can be a housing material that it can machine: This concept is denoted as “housing material” 12 in FIG. 2 .
  • housing material can in turn be provided with an attribute that, for example, provides information about a number of possible materials (e.g. metal, plastic and/or materials manufactured with a certain process, such as die casting) that a machine can process (referred to as “enumeration capability” 15 in Figure 2).
  • the relationship between the concepts may be such that, in some examples, selected features of one concept dictate the selection of another concept (e.g., a machine may have different working areas of the "working area” concept that depend on the selected material of the "housing material” concept) .
  • the conceptual model can include all possible machine capabilities 51 offered by a machine type or machine. In some examples, machine capabilities may be associated with machine types.
  • Mapping machine capabilities to machine types can be used when all machines of a given machine type offer the same machine capabilities. If this is not the case, machine abilities e.g. B. can be assigned individually to specific machines.
  • the conceptual model may contain the concepts related to the machines belonging to the same group of machines belong.
  • the conceptual model may include the concepts related to machines belonging to different groups of machines. For example, the groups of machines can be located in the same industrial plant or in different industrial plants.
  • the machine knowledge graph may include a plurality of machine instances 20 associated with the respective concepts in the conceptual model.
  • each machine instance 21 may include machine capability data points 22-24 of the corresponding one of the plurality of machines.
  • a conceptual model representing general characteristics of the plurality of machine capabilities can be instantiated 52 by defining concrete machine instances of the concepts contained in the conceptual model that relate to a particular machine or machine type.
  • such an association between a particular machine and the corresponding concepts may be made through machine types if the machine capabilities have been mapped to the machine types.
  • this connection can be provided directly to a machine under consideration if the machine capabilities are directly associated with the machines. For example, as shown in Fig.
  • the machine instance of a particular machine may be given by the data points containing the machine capabilities "WorkArea1" 22, "Housing Materiall” 23 and “Housing Material2" 24, each associated with the Concepts "working area” 11 and "housing material” 12 of the conceptual model are connected.
  • the data points "Working area” comprise the definition of the maximum values of the working area of a particular machine, ie length of 120 mm, width of 250 mm and height of 50 mm. In other examples, the working area can be defined by other sizes.
  • the data points "Housing Material1" and “Housing Material2" define the housing material processed by a specific machine, in this example metal or plastic. In other examples, other materials can be used.
  • the next step of the computer-implemented method includes providing 200 one or more features of a product to be manufactured in the machine knowledge graphs.
  • These product features can e.g. B. the dimensions or shape of a product or product components (e.g., length, width, and height), the product material of a product or product component (e.g., metal, plastic, or die-cast), the weight of a product, or other product characteristics that describe physical (e.g. mechanical or electrical) or chemical properties of the product (or its components) that are relevant to the manufacture of the product.
  • a plurality of product features of a product may be defined 60 by product engineers.
  • the manufacturability of a product can be determined 300 using the information contained in the machine knowledge graph.
  • the machine knowledge graph 1 of the present disclosure can be configured to receive the one or more characteristics of the product.
  • the machine knowledge graph 1 can be designed to transfer received features of the product into the conceptual model and/or into the data instances.
  • providing 200 the one or more features of the product to be manufactured in the machine knowledge graphs may further comprise providing 210 a knowledge graph of product features 2 .
  • This can contain information about a number of products.
  • the knowledge graph of product features may contain a conceptual model of product features 30 comprising a different plurality of concepts 31 , 32 .
  • each concept from the other plurality of concepts may map a corresponding one of a plurality of product features.
  • the concepts from the other plurality of concepts can be provided with attributes and their respective relationships with one another are defined.
  • a concept from the other plurality of concepts can be viewed as a node 31, 32 of the knowledge graph of product features 2 and a relationship between the Concepts as edges that connect the concepts (or nodes) together.
  • typical product characteristics can be defined based on the requirements for the plurality of products (e.g., a product cluster).
  • a product feature of a product may be given by a product size (in Figure 2 the corresponding concept is denoted as "product size" 31).
  • a product feature of a product can include a product material (see label “product material” 32 in FIG. 2 for the corresponding concept) that is required to manufacture the product.
  • one or more concepts of the conceptual model of product features may contain information about requirements related to product components of a product that should be assembled in a certain way to fulfill the required product functions (e.g. the housing top must be secured with screws attached to the base to prevent the two parts from falling apart at high temperatures).
  • the conceptual model of product features can be defined 61 on the basis of the product features formulated as general design options or design alternatives.
  • the housing of a product can be made of different materials, such as plastic, metal or die-cast.
  • the knowledge graph of product features may include a plurality of product instances 40 associated with the respective concepts from the other plurality of concepts in the conceptual model of product features.
  • each product instance (41) may include data points (42, 43) of product attributes of the corresponding product in the plurality of products.
  • a conceptual product feature model that represents common product features of the plurality of products (e.g., as general design options or design alternatives) can be instantiated by defining concrete product instances of the concepts contained in the conceptual product feature model that relate to a specific product (or a specific product cluster).
  • the product instances of specific products may be provided based on input from a product engineer (eg, using a user interface of a computer-implemented system) and stored in the knowledge graph of product features.
  • the knowledge graph of product features can be implemented 62, for example, in a computer-implemented system together with the conceptual model of product features (eg, in a computer program running on the computer-implemented system).
  • the product feature knowledge graph may be implemented on the same computer-implemented system as the machine knowledge graph.
  • the product feature knowledge graph may be implemented on a computer-implemented system different than that on which the machine knowledge graph is implemented.
  • the step of “determining” 200 the one or more characteristics of the product to be manufactured in the machine knowledge graphs can include selecting 220 the one or more characteristics of the product to be manufactured based on the information contained in the knowledge graph of product characteristics. For example, based on the requirements specification for a new product or product variant, the product engineer may select certain options from the design alternatives or
  • Product features are provided (e.g. through one or more concepts of the conceptual model of product features).
  • this selection can define the search criteria 63, 64 for the analysis of the manufacturability for the new product or the product variant.
  • this approach of the present techniques can be applied to pre-existing products or product variants.
  • the selection can be used for new products or product variants.
  • product engineers can verify manufacturability of new products early in the design phase (e.g., right after customer requirements are specified) using the techniques of the present disclosure.
  • the present techniques may calculate changes made to product features and/or Machine skills need to be made to manufacture the new product.
  • the knowledge graph of product features can be configured to translate the selected one or more features of the product to be manufactured into the machine knowledge graph 31A, 32A.
  • the product engineer need not provide the full specification of all design alternatives for a new product.
  • the product engineer B. does not select a specific design alternative for a specific product feature, this means that during the manufacturability analysis all design alternatives provided for this feature of the new product can be considered, which are stored in the knowledge graph of product features. That is, a set of product variants (e.g., given by the plurality of product instances) can be selected for the manufacturability analysis from the knowledge graph of product features, rather than a fully specified product where design alternatives have been specified for each feature of the product.
  • the machine knowledge graph of the present techniques can be configured to receive transmitted characteristics of the product to be manufactured.
  • determining 300 the manufacturability of the product may further comprise, for each feature of the one or more selected features of the product to be manufactured, searching 310 for one or more machine instances from the plurality of machine instances whose data points of machine capabilities of the corresponding Machine from the plurality of machines provide manufacturability of the respective selected feature.
  • the machines can belong to the same group of machines or to different groups of machines, which can be located in the same industrial plant or in different industrial plants.
  • the "search" step 310 of the method is performed using the machine knowledge graph.
  • the "determine" step 300 may include searching, for each feature from the one or more selected features of the product to be manufactured, after all machine instances from the plurality of machine instances whose data points of machine capabilities of the corresponding machine from the plurality of machines provide the manufacturability of the respective selected feature.
  • searching may include identifying 320, using the machine knowledge graph, one or more matches (e.g., exact matches) between the selected one or more features of the product to be manufactured and the machine instances contained in the machine knowledge graph.
  • identifying the one or more matches may be performed by comparing the selected one or more characteristics of the product to be manufactured and the machine capability data points contained in the machine instances.
  • the method may include designating 330 one or more corresponding machines for each characteristic of the selected one or more characteristics of the product to be manufactured for which the match is identified.
  • the product to be manufactured e.g. a new product to be manufactured
  • one or more machines about which information is contained in the machine knowledge graph may be capable of processing plastic housings.
  • an exact match can be identified, i. H. the one or more machines or all machines possessing that machine capability may be designated (or selected) (e.g., by executing the computer program mentioned above).
  • searching may include identifying 340, using the machine knowledge graph, one or more value-based matches between the selected one or more features of the product to be manufactured and the machine instances contained in the machine knowledge graph.
  • value-based matches are those matches that relate to features of the product that are characterized by one or more values.
  • identifying the one or more value-based Matching is performed by comparing the one or more selected characteristics of the product to be manufactured and the machine capability data points contained in the machine instances.
  • identifying may further include calculating 350 the one or more value-based matches.
  • the calculating may be performed based on rules that determine when one of a plurality of machine capabilities can process a feature of the selected one or more features of the product to be manufactured.
  • the selected characteristics of the product to be manufactured can e.g. B. define a product size in terms of length, width and height in millimeters.
  • a match may be identified by calculating (e.g., by executing the computer program mentioned above) if the product size in terms of length, width, and height is less than or equal to the is the maximum work area that the machine type or machine can handle.
  • the method may include designating 360 a corresponding machine or machines for each characteristic of the selected one or more characteristics of the product to be manufactured for which the value-based match is identified.
  • the techniques of the present disclosure may further include selecting 370 an identified machine or an identified plurality of machines from the plurality of machines that are appropriate for the manufacturability of the product to be manufactured for which the match or the value-based match with each feature of the one or the plurality of selected characteristics of the product to be manufactured.
  • one or more machine capabilities can be determined from a plurality of machine capabilities that are compatible with each corresponding feature.
  • a proportion of the selected machines may be determined 380 using the machine knowledge graph that are suitable for the manufacturability of the product to be manufactured.
  • the selected machines may be selected from a total number of machines in the plurality of machines present at an industrial facility.
  • determining the proportion of selected machines for each industrial plant be carried out, which contains at least one selected machine that is suitable for the manufacturability of the product to be manufactured.
  • the next step of the present disclosure may further include determining 390, using the machine knowledge graph, one or more machines from the plurality of machines for which there is no match for one or more characteristics from the selected one or more characteristics of the product to be manufactured is identified. Additionally or alternatively, the method may further comprise determining, using the machine knowledge graph, one or more machines from the plurality of machines for which no value-based match is identified for one or more features from the selected one or more features of the product to be manufactured becomes. In some examples, the method may further include determining, using the machine knowledge graph, one or more features from the selected one or more features of the product to be manufactured for which no machine is identified from the plurality of machines with corresponding matches.
  • one or more machine capabilities can be determined from a plurality of machine capabilities that are incompatible with the one or more features of the product to be manufactured. Furthermore, this may further comprise determining one or more machines from the plurality of machines for which the match or the value-based match to each other characteristic of the selected one or more characteristics of the product to be manufactured is identified for which the determined one or the does not contain several features for which no machine is identified from the plurality of machines. For example, the information about mismatched machine capabilities can be valuable for the product or manufacturing engineers, since this information explains in detail why a certain product or a product variant cannot be manufactured on a certain machine or a certain type of machine.
  • the machine knowledge graph of the present techniques can be further configured to output information about a corresponding result of the searching step.
  • the output of the result can do that Providing information regarding the outcome to one
  • the output of the result can provide information regarding the result to a
  • Outputting the result may also include displaying information on a graphical user interface.
  • the information regarding the outcome may be provided to a remote device or entity.
  • the information regarding the result can be sent to a user.
  • Another step of the present disclosure may include selecting 400 one or more machines of the industrial facility on which to manufacture the product.
  • the selection can be made on the basis of the information provided by the machine knowledge graph.
  • the industrial facility may be classified 410 as suitable for the manufacturability of the product to be manufactured if the proportion of the selected machines that are present on the industrial facility and are suitable for the manufacturability of the product to be manufactured exceeds a predefined threshold. Otherwise, the industrial plant can be classified as unsuitable for the manufacturability of the product to be manufactured. So e.g. B. Those industrial plants are excluded from the manufacture of the product to be manufactured (e.g. by the product engineer) that have a percentage of the selected machines of less than, for example, 95% or less than 70% or less than 45%.
  • the techniques of the present disclosure may include modifying the one or more features from the selected one or more features of the product to be manufactured for which no machine is identified from the plurality of machines with the corresponding matches to enable the to be manufactured product is manufactured on at least one machine from the plurality of machines, wherein the modifying of the one or more features is based on the information provided by the machine knowledge graph. if e.g. B. If none of the machines are capable of producing the new product or product variant (e.g. if not all features of the product to be produced can be produced), the product engineer can produce them change “problematic” features 65 so that the new product can be manufactured.
  • the product engineer can use this information provided by the machine knowledge graph, which, among other things, enables the calculation of the one or more value-based matches as disclosed above.
  • the product engineer can check if the product can be redesigned so that the height of the product is less than 235mm.
  • the method may comprise modifying one or more of a plurality of machine capabilities to enable the product to be manufactured at least one machine of the plurality of machines is manufactured.
  • modifying the one or more machine capabilities can be done based on the information provided by the machine knowledge graph. For example, if none of the machines in the plurality of machines are capable of manufacturing the new product or product variant (e.g. if not all features of the product to be manufactured cannot be manufactured), the manufacturing engineer can use the information provided by the machine knowledge graph in Use reference to the machine capabilities that are not compatible with the new product. For example, the manufacturing engineer may either reconfigure or modify 55 one or more machines so that the product can be manufactured.
  • the techniques of the present disclosure may include manufacturing products using the information provided by the machine knowledge graph.
  • a second general aspect of the present disclosure relates to a computer program configured to carry out the computer-implemented method according to the first general aspect.
  • the present disclosure also relates to a computer-readable medium and signals storing or encoding the computer program of the present disclosure.
  • a third general aspect of the present disclosure relates to a computer-implemented system for analyzing a manufacturability of products, configured to execute the computer program of the second general aspect.
  • the computer-implemented system may be further configured to generate and/or use the machine knowledge graph.
  • the computer-implemented system may be further configured to generate and/or use the knowledge graph of product features.
  • the computer-implemented system can be configured to manufacture products using the information provided by the machine knowledge graph
  • the computer-implemented system may include at least one processor, at least one memory (which may contain programs that, when executed, perform the methods of the present disclosure), and at least one interface for inputs and outputs.

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Abstract

Un aspect de la présente invention concerne un procédé mis en œuvre par ordinateur pour analyser la fabricabilité de produits. Le procédé consiste à fournir un graphe de connaissances sur des machines mises en œuvre par un ordinateur, qui contient des informations sur une pluralité de machines. Le graphe de connaissances sur les machines comprend un modèle conceptuel, qui comprend une pluralité de concepts, chaque concept reproduisant une capacité de machine correspondante à partir d'une pluralité de capacités de machine, les concepts étant pourvus d'attributs et leurs relations respectives étant définies les unes par rapport aux autres. Le graphe de connaissances sur les machines du premier aspect comprend en outre une pluralité d'instances de machine qui sont associées aux concepts respectifs dans le modèle conceptuel. Chaque instance de machine présente des points de données de capacités de machine de la machine correspondante faisant partie de la pluralité de machines. En outre, le procédé consiste à fournir une ou plusieurs caractéristiques d'un produit à fabriquer dans le graphe de connaissances sur les machines. Enfin, le procédé consiste à déterminer la fabricabilité d'un produit à l'aide des informations contenues dans le graphe de connaissances sur les machines.
PCT/EP2022/058412 2021-04-13 2022-03-30 Procédé pour analyser la fabricabilité de produits WO2022218706A1 (fr)

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DE102021109170.5 2021-04-13
DE102021109170.5A DE102021109170A1 (de) 2021-04-13 2021-04-13 Verfahren zum analysieren einer herstellbarkeit von produkten

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Citations (1)

* Cited by examiner, † Cited by third party
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