Method for manufacturing analysis by using ontology and semantic analysis

Abstract

The invention discloses a method for manufacturing analysis by utilizing ontology and semantic analysis, and provides manufacturing analysis. The method includes identifying features of a product model defined by geometric, physical, and system elements having semantic tag, name, or title descriptions. In accordance with the machine-readable body knowledge model, machine-readable semantic links connecting the features to elements in the enterprise knowledge base are created. And identifying the producibility risk or opportunity of the product model according to the semantic relationship of the elements linked to the features in the enterprise knowledge base. A user is alerted of producibility risks or opportunities, and annotations are added to manufacturing design specifications of the product model. The annotations provide several solutions for producibility risks or opportunities, where the solutions are identified in an enterprise knowledge base according to semantic relationships.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 508,188, filed on June 14, 2023, and entitled “Method of Manufacturing Anaysis with Ontologies and Semantic Analysis,” which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates generally to manufacturing and, more particularly, to computer-aided modeling systems for manufacturing. Background Art

[0004] Manufacturing has a primary set of requirements that are derived from the product to be manufactured. Requirements can be derived from three-dimensional (3D) specifications used in computer-aided modeling. Computer-aided modeling or computer-aided design (CAD) creates a virtual model of an object or system that allows engineers and designers to visualize and simulate structures or processes. Manufacturing can obtain requirements from other system domains (e.g., facility requirements / constraints, equipment and tooling constraints, or human factors requests).

[0005] Such manufacturing requirements may have an impact on product design. However, manufacturing requirements derived from 3D specifications typically only flow downward to manufacturing operations. Traditionally, feedback from manufacturing can be late and can only be done through engineering and stakeholder design review meetings, isolated spreadsheets or paper design review checklists, or manufacturing (or other areas such as human factors) change requests or risk project reports. All methods require humans to identify risks or opportunities by reviewing the specifications themselves and using their expertise or reference materials as disconnected data. Summary of the invention

[0006] An illustrative embodiment provides a computer-implemented method of manufacturing analysis. The method includes identifying features of a product model defined by geometric, physical, and system elements having semantic tags, names, or title descriptions. Machine-readable semantic links are created that connect the features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model. Producibility risks or opportunities for the product model are identified based on semantic relationships of elements in the enterprise knowledge base that are linked to the feature. A user is alerted to the producibility risk or opportunity, and an annotation is added to the manufacturing design specification of the product model. The annotation provides several solutions for the producibility risk or opportunity, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationships.

[0007] Another illustrative embodiment provides a system for manufacturing analysis. The system includes a storage device storing program instructions and one or more processors, the one or more processors being operably connected to the storage device and configured to execute the program instructions to cause the system to: identify features of a product model defined by geometric, physical, and system elements having semantic tags, names, or title descriptions; create machine-readable semantic links connecting the features to elements in an enterprise knowledge base according to a machine-readable ontology knowledge model; identify manufacturability risks or opportunities of the product model according to semantic relationships of elements linked to the features in the enterprise knowledge base; alert a user to the manufacturability risks or opportunities; and add annotations to the manufacturing design specifications of the product model, wherein the annotations provide a number of solutions for the manufacturability risks or opportunities, wherein the solutions are identified in the enterprise knowledge base according to the semantic relationships.

[0008] Another illustrative embodiment provides a computer program product for manufacturing analysis. The computer program product includes a computer-readable storage medium having program instructions embodied thereon to perform the following operations: identifying features of a product model defined by geometric, physical, and system elements having semantic tags, names, or title descriptions; creating machine-readable semantic links connecting the features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model; identifying manufacturability risks or opportunities for the product model based on semantic relationships of elements in the enterprise knowledge base linked to the features; alerting a user to the manufacturability risks or opportunities; and adding annotations to a manufacturing design specification for the product model, wherein the annotations provide a number of solutions for the manufacturability risks or opportunities, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationships.

[0009] The features and functions can be achieved independently in various embodiments of the present disclosure or may be combined in yet other embodiments in which additional details can be seen with reference to the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The novel features which are believed to be unique to the illustrative embodiments are set forth in the appended claims. However, the illustrative embodiments together with the preferred mode of use, further objects and features thereof will be best understood by reference to the following detailed description of illustrative embodiments of the disclosure when read in conjunction with the accompanying drawings, in which:

[0011] Figure 1 is a pictorial representation of a network of data processing systems in which the illustrative embodiments may be implemented;

[0012] Figure 2 is an illustration of a block diagram of a manufacturing analysis system according to an illustrative embodiment;

[0013] Figure 3shows the relationship between a product model and manufacturing process steps according to an illustrative embodiment;

[0014] Figure 4 depicts a production system model according to an illustrative embodiment;

[0015] Figure 5 depicts a semantic knowledge graph according to an illustrative embodiment;

[0016] Figure 6 depicts a knowledge table according to an illustrative embodiment;

[0017] Figure 7 depicts an alarm and resolution table overlaid on a production system model according to an illustrative embodiment;

[0018] Figure 8 depicts an alert and resolution diagram overlaid on a production system model in accordance with an illustrative embodiment;

[0019] Fig. 9 shows the use of a data graph in product development specifications according to an illustrative embodiment;

[0020] Fig.10 depicts a flow chart of a manufacturing analysis process according to an illustrative embodiment; and

[0021] Fig.11 is an illustration of a block diagram of a data processing system in accordance with an illustrative embodiment. DETAILED DESCRIPTION

[0022] The illustrative embodiments recognize and take into account that manufacturing requirements are often derived from 3D specifications used in computer-aided modeling, and that such manufacturing requirements may have an impact on product design.

[0023] The illustrative embodiments also recognize and take into account that manufacturing requirements derived from 3D modeling specifications and annotations typically only flow downward to manufacturing operations.

[0024] The illustrative embodiments provide a method to flow requirements from downstream areas such as manufacturing, human factors, equipment engineering, and tool engineering back to the product design stage. This flow-back process allows design engineers to take remedial actions on product standards or product designs. The illustrative embodiments also integrate design and product data to identify production risks and opportunities and provide solutions.

[0025] refer to Figure 1, depicts a pictorial representation of a network of data processing systems in which the illustrative embodiments may be implemented. Network data processing system 100 is a network of computers in which the illustrative embodiments may be implemented. Network data processing system 100 contains network 102, which is the medium used to provide communications links between various devices and computers connected together within network data processing system 100. Network 102 may include connections such as wires, wireless communication links, or fiber optic cables.

[0026] In the depicted example, server computer 104 and server computer 106 are connected to network 102 along with storage unit 108. In addition, client device 110 is connected to network 102. In the depicted example, server computer 104 provides information, such as boot files, operating system images, and applications, to client device 110. Client device 110 can be, for example, a computer, a workstation, or a network computer. As depicted, client device 110 includes client computers 112, 114, and 116. Client device 110 can also include other types of client devices (such as mobile phone 118, tablet computer 120, and smart glasses 122).

[0027] In this illustrative example, server computer 104, server computer 106, storage unit 108, and client device 110 are network devices connected to network 102, where network 102 is the communication medium for these network devices. Some or all of client devices 110 may form an Internet of Things (IoT), where these physical devices may be connected to network 102 and exchange information with each other through network 102.

[0028] In this example, client device 110 is a client to server computer 104. Network data processing system 100 may include additional server computers, client computers, and other devices not shown. Client device 110 connects to network 102 using at least one of a wired, fiber optic, or wireless connection.

[0029] Program code located in network data processing system 100 may be stored on a computer recordable storage medium and downloaded to the data processing system or other device for use. For example, program code may be stored on a computer recordable storage medium on server computer 104 and downloaded to client device 110 via network 102 for use on client device 110.

[0030] In the depicted example, network data processing system 100 is the Internet with network 102, which represents a global collection of networks and gateways that use the Transmission Control Protocol / Internet Protocol (TCP / IP) protocol suite to communicate with each other. The core of the Internet is a backbone of high-speed data communication lines between major nodes or host computers consisting of thousands of commercial computer systems, government computer systems, educational computer systems, and other computer systems that route data and messages. Of course, network data processing system 100 may also be implemented using several different types of networks. For example, network 102 may include at least one of the Internet, an intranet, a local area network (LAN), a metropolitan area network (MAN), or a wide area network (WAN). Figure 1 This is intended as an example, not as an architectural limitation for the different illustrative embodiments.

[0031] Reference now Figure 2 , depicts an illustration of a block diagram of a manufacturing analysis system, according to an illustrative embodiment. In this illustrative example, the manufacturing analysis system 200 includes a plurality of components that can be implemented in hardware (e.g., Figure 1 Components implemented in the hardware shown in the network data processing system 100 in .

[0032] The manufacturing analysis system 200 includes a machine-readable knowledge model 202 that provides an ontological framework for establishing semantic connections between manufactured products and accumulated, dispersed enterprise knowledge related to production methods and available manufacturing resources. The machine-readable knowledge model 202 can be presented as a semantic knowledge graph 204, which can be displayed by a display device 274. The machine-readable knowledge model 202 includes a number of semantic concepts 206 related to manufactured products and methods and resources used to manufacture the manufactured products, and models semantic relationships 208 between the semantic concepts.

[0033] The manufacturing analysis system 200 also includes a manufacturing resource object 212, which can represent a production system model associated with a manufacturing resource for producing a manufactured product. The manufacturing resource object 212 can include a number of sub-resource objects 214. Each sub-resource object 214 includes a number of attributes 218 and a feature semantic name 216. The manufacturing resource object 212 can represent a specific configuration 220 and revision level 222 of the manufactured product in question. As the configuration 220 and revision level 222 change, the (one or more) sub-resource objects 214 comprising the manufacturing resource object 212 can also change.

[0034] The manufacturing analysis system 200 includes an enterprise knowledge base 240, which includes several databases 242 related to designing and producing manufactured products. The enterprise knowledge base 240 brings together the accumulated and dispersed institutional knowledge related to manufacturing methods, resources, manufactured products, etc., and brings together the risks and opportunities for improvement related to these categories based on past experience. The enterprise knowledge base 240 can be continuously updated.

[0035] The enterprise knowledge base 240 pulls together several databases 242 including elements 244. For example, the database 242 may include a database for manufacturing processes and process standards related to features, parts, objects, or object assemblies. The database 242 may also include a database of design guides, manuals, recommendations, checklists, processes, and practices. Another example is a database of manufacturing assets related to the manufacturing process of a manufactured product. Another example is a database of previously identified non-conformities, defects, failure mode effect analyses, or risks related to the manufacturing process of a manufactured product and means to resolve or improve the process. The database 242 may also include a database of formally modeled machine-readable ontologies unique to the company's intellectual property, research, interests, and business, as well as industry standards.

[0036] The article of manufacture in question is represented by a product model 254, which includes a number of defining features 256. Each feature 258 includes a number of functional or physical, structural, geometric elements 260. The features 256 are determined by the design specifications 262 and configuration 236 of the article of manufacture, which may change depending on the revision level 268 of the design of the item.

[0037] The product model 254 is converted into a manufacturing design specification 246, which includes a number of design specification objects 248, such as would be used for computer-aided design (CAD) and requirements modeling. The manufacturing design specification 246 can be used to define a number of manufacturing states 224 (see Figure 3 ). Each manufacturing state 226 represents a different step in the manufacturing process of product model 254 that is subject to change according to configuration 236 and revision level 238 , which correspond to configuration 264 and revision level 268 of product model 254 .

[0038] Manufacturing status 226 involves number of sub-objects 228 that may be added, removed, modified, etc. (with respect to features 230 that correspond to features 258 of product model 254 ). Features 230 include attributes 232 and semantic names 234 .

[0039] The machine-readable knowledge model 202 provides an ontology framework for creating machine-readable semantic links 210 that connect and relate manufacturing resource objects 212, enterprise knowledge base 240, and product models 254 (via manufacturing states 224 and manufacturing design specifications 246). The semantic links 210 add context to the model represented by the manufacturing design specifications 246 based on the classifications in the enterprise knowledge base 240.

[0040] Through this context, semantic links 210 facilitate identifying potential manufacturability risks or opportunities to improve production processes / models based on past experience and institutional knowledge, however recorded in enterprise knowledge base 240. Manufacturability risks or opportunities may be brought to the user's attention via user interface 276 in display device 274.

[0041] Identification of these producibility risks or opportunities results in the creation of annotations 250 including requirements 252, which can be added to the manufacturing design specification 246 as new design specification objects 248. Annotations 250 come from manufacturing engineering to make the product model 254 more producible. This annotation 250 can constrain and cause changes to features 256, design specifications 262, configurations 264, and therefore revision levels 268. Annotations 250 from manufacturing / production engineering can go either way. It can flow requirements to the product model 254, and then into the manufacturing state 224 of the product model.

[0042] Program code 280 and processor unit 278 may use generative artificial intelligence (AI) to make suggestions, which are translated into design specification objects 248. A user may read the suggestions from the generative AI code and then write the design specification object / suggestions into product model 254. Alternatively, a generative AI script may provide design specification object 248 from database 242.

[0043] As information and generative AI code become more precise and accurate, production systems and manufactured goods (e.g., aircraft, cars, etc.) can be automatically modeled and designed through semantic search via semantic links. AI algorithms can be trained to provide better and better suggestions, allowing design and modeling to move from focusing on single elements to larger, more complex solution models.

[0044] The manufacturing analysis system 200 generates a digital thread 270 that collects producibility risks or opportunities 272 and measurement criteria related to producibility risks or opportunities and links them to the final product for quality assurance.

[0045] Display device 274 may include at least one of the following: a light emitting diode (LED) display, a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a computer monitor, a projector, a flat panel display, a head-up display (HUD), a head-mounted display (HMD), or some other suitable device that can output information for visual presentation of the information.

[0046] The manufacturing analysis system 200 can be implemented in software, hardware, firmware, or a combination thereof. When software is used, the operations performed by the manufacturing analysis system 200 can be implemented in program code configured to run on hardware such as a processor unit. When firmware is used, the operations performed by the manufacturing analysis system 200 can be implemented in program code and data and stored in persistent memory to run on a processor unit. When hardware is employed, the hardware can include circuits that operate to perform the operations in the manufacturing analysis system 200.

[0047] In an illustrative example, the hardware may be in the form of at least one selected from a circuit system, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware configured to perform several operations. Through a programmable logic device, the device may be configured to perform several operations. The device may be reconfigured at a later time, or may be permanently configured to perform several operations. Programmable logic devices include, for example, programmable logic arrays, programmable array logic, field programmable logic arrays, field programmable gate arrays, and other suitable hardware devices. Additionally, the process may be implemented in organic components integrated with inorganic components, and may be composed entirely of organic components without humans. For example, the process may be implemented as a circuit in an organic semiconductor.

[0048] Computer system 290 is a physical hardware system and includes one or more data processing systems. When there is more than one data processing system in computer system 290, these data processing systems communicate with each other using a communication medium. The communication medium may be a network. The data processing system may be selected from at least one of a computer, a server computer, a tablet computer, or some other suitable data processing system.

[0049] As depicted, computer system 290 includes several processor units 278, which are capable of executing program code 280, thereby implementing the process in the illustrative example. As used herein, the processor unit in several processor units 278 is a hardware device, and includes a hardware circuit, such as a hardware circuit on an integrated circuit, which responds to and processes the instructions and program code of the operating computer. When several processor units 278 execute the program code 280 for the process, several processor units 278 are one or more processor units that can be on the same computer or on different computers. In other words, the process can be distributed between processor units on the same or different computers in the computer system. In addition, several processor units 278 can be processor units of the same type or different types. For example, several processor units can be selected from at least one of a single-core processor, a dual-core processor, a multi-processor core, a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP) or some other type of processor unit.

[0050] The manufacturing analysis system 200 helps digitally thread and make manufacturing quality challenges detected, resolved, and reported in isolated databases more visible and useful. The following example helps illustrate the application of the manufacturing analysis system 200.

[0051] The first example is applying a non-destructive inspection (NDI) ultrasonic, water-coupled pulse-echo system to a composite panel for defect detection. Features on the composite panel may become curved, wavy, or have bumps during manufacturing. This knowledge is captured in the machine-readable knowledge model 202 and the enterprise knowledge base 240, and is not specified or known in the manufacturing state of the product model 254 and product model 224. The risk of inspectability here is that if the tip of the probe is on the near side surface, the tip of the probe may be tripped by a bump, or the signal is lost when the signal echoes from the far side surface that fluctuates too much.

[0052] The semantic knowledge graph 204 may relate the problem to the causes of bumps and fluctuations, which may be manufacturing resource objects 212. The semantics of the manufacturing resource objects 212 may be highlighted in a variety of ways (e.g., Figure 7 ), and one can click on the semantic phrase or alert 703 to view a solution table 702 showing potential manufacturability risks or opportunities 704 (see Figure 7). Risk 704 may indicate that the NDI system cannot scan over certain types of undulations and bumps, and a solution 706 will be recommended. One may implement the suggested solution by converting it from solution 706 into annotation 250. Annotation 250 may be a natural language text description with images, process instructions, standard structured text specifications, programming code, or parameter values, arrays, or equations. Annotation 250 may be referenced as an example within the specification of design specification 248 of manufacturing design specification 246 or design specification 262 of product model 254.

[0053] Another similar example is the application of a non-destructive inspection (NDI) ultrasonic, water-coupled, pulse-echo inspection system where the back surface of a composite panel is wet. In this case, water may interfere with the return of the ultrasonic signal, resulting in a corrupted or distorted return signal. Such corrupted signal data may affect the accuracy and reliability of defect detection in the composite panel. Again, this knowledge is captured in the machine-readable knowledge model 202 and the enterprise knowledge base 240 and is not necessarily specified in the manufacturing state of the product model 224. The inspectability risk here is that the back surface needs to be dry during the inspection scan. The semantic knowledge graph 204 can associate this problem by suggesting a solution 706, which can be an annotation 250 of the manufacturing design specification 246, which will specify a requirement for the manufacturing state of the product model 224 that the back surface will be dry during the NDI scan. The manufacturing operation can define a requirement for the manufacturing resource object 212 to keep the back surface dry. Therefore, the manufacturing resource object 212 can be a water barrier or a tool for blowing the back surface dry.

[0054] The above examples illustrate the ability of the illustrative embodiments to integrate information from historically isolated information sources into actionable information that is unaccounted for in a product model and would otherwise be omitted from a product's manufacturing design specifications and manufacturing procedures.

[0055] Figure 3 The relationship between the product model and the manufacturing process steps according to the illustrative embodiment is shown. The product model 254 is converted into a manufacturing design specification 246 (i.e., a CAD model). A number of manufacturing states 224a-224c are derived from the manufacturing design specification 246, which correspond to respective manufacturing processes 302-306 at different production stages of the manufactured product represented by the product model 254.

[0056] Several feature or attribute annotations 250a-250b may be added to constrain the manufacturing design specification 246 based on the identification of producibility risks or opportunities identified from the enterprise knowledge base. Any number of feature or attribute annotations may exist at different levels of granularity of the product model.

[0057] Figure 4 400 depicts a production system model according to an illustrative embodiment. The production system model 400 may be composed of Figure 2 The manufacturing status 224 and manufacturing resource object 212 of the product model in are represented.

[0058] Figure 4 The example production system model 400 shown in is presented using the System Modeling Language (SysML). In this example, the production system model 400 involves an aircraft part 402 to be manufactured. The features of the part have been identified as feature components 404. The feature components 404 need to be integrated with an F-type probe 406. These feature integration risks may not be known in advance. However, pre-existing information in the manufacturing database can mark this problem through the knowledge graph.

[0059] Figure 5 Depicts a semantic knowledge graph according to an illustrative embodiment. The knowledge graph 500 may be Figure 2 An example of a semantic knowledge graph 202 in .

[0060] The semantic knowledge graph 500 is represented as a graph having nodes and edges, which can be viewed in a graphical user interface (e.g. Figure 2 The data is displayed to the user in the user interface 276 in FIG.

[0061] Figure 5 An example of how a user can navigate from a production system model is shown. The production system model 400 has < <block>406, whose semantic name is FTypeProbe. (In Figure 2 , this block will constitute the feature semantic name 216. ) The semantic link 502 will be < <block>>406 Link to the semantic knowledge graph 500 < <concept>>FTypeProbe504.

[0062] From <concept>>FTypeProbe 504 to isNotCapableOfScanning semantics for semantic concept< <conception>>Feature 506。< <concept>>Feature 506 is then semantically linked to the semantic concept <concept>>SupplementalToolX 508. If the semantic concept is linked to other semantics in the model, the user can navigate to the enterprise knowledge base and database, which may contain risk elements and solution elements ( Figure 2 244 in the example). They can have the semantics SupplementalToolX.

[0063] The illustrative embodiments may analyze semantic features that are known to have problems that have been discovered in the past through testing of features that do appear in the model, and therefore need to be controlled through annotations. While CAD does specify parametric features such as points, lines, planes, and solids, the semantics of these features can provide hints for manufacturing quality features that are also problematic and can be addressed through additional annotations (not necessarily direct parametric design changes).

[0064] For example, the manufacturing quality of the co-cured vents results in resin bulging around the caul plate, which is not explicitly known or specified in the CADGD&T (geometry dimensioning and tolerancing). This effect is known through testing and controlled through design guidance, annotations, which can ultimately lead to optimization of the caul plate geometry design to reduce the thickness along the edges that cause resin buildup. Alternatively, it can specify a sanding operation after debag to remove all bumps before NDI so that the probe does not get stuck. This knowledge is buried in an isolated R&D document on a server. The illustrative embodiments find ways to link these pieces of information to relevant terms that the product modeler can place on the new product model.

[0065] As another example, the manufacturing quality of the composite wing panel may leave the back surface wet for various reasons, which is not advantageous when using the NDI pulse echo scanning method on the front. For example, a washing situation before NDI may leave the back wet if not dried in advance. Or water used as a coupling agent during NDI is splashed and drips down onto the back surface. The semantic knowledge graph can pick up this and alert the production system designer to design features to stop water from entering the back, or use various tools and methods to wipe and dry the back before NDI scanning.

[0066] In some cases, an enterprise knowledge base may have billions of elements related to the same single semantic phrase. Therefore, model context and specific semantics and definitions / meanings can be used to narrow down the relevant elements. Advanced scripts can be written to filter relationships.

[0067] There are multiple variations of navigating semantic links to view risks or opportunities and the resulting solutions. Users can manually point, click, and find meaningful, related information. Scripts can filter for a portion of the relationship. Program code can automatically look through the model context and the semantic knowledge of all models, enterprise knowledge bases, databases, and find relevant suggestions for the user on risk and solution elements. This process can be performed by a background manufacturing analysis engine that continuously flags or suggests ideas in the model.

[0068] The program code may be smart enough to see that the manufacturing resource object 212 (eg, FTypeProbe) cannot scan the manufacturing state 226 of the product model 254 (eg, Feature) and search the enterprise knowledge base 240 and database 242 to find the SupplementalToolX and design specification object elements.

[0069] Figure 6 600 is a knowledge table according to an illustrative embodiment. Figure 2 An alternative presentation of the machine-readable knowledge model 202 in FIG.

[0070] Also shown is a description window 604, which can be invoked by a user clicking on or hovering over an active semantic link associated with a description of a concept / phrase in the knowledge table 600. In the example shown, the semantic link 602 (indicated by the underline) and the description window 604 are associated with an F-type probe. It should be noted that a similar active semantic link pop-up description window can be used with the semantic knowledge graph 500.

[0071] The vocabulary profile allows users to link to a semantic knowledge graph (such as semantic knowledge graph 500) across models (which can be any type of model, such as a logical block model or a CAD model with semantics / phrases).

[0072] In addition to showing the definition of the concept / phrase in question, description window 604 may also associate a manufacturability risk or opportunity with the term.

[0073] Figure 7 Depicted is an alert and resolution table overlaid on a production system model according to an illustrative embodiment. The example shows the use of semantic links in a production system model 400 associated with an F-type probe 406. By clicking on or hovering over the link, an alert 703 and resolution table 702 appear, which includes several potential risks and opportunities 704 associated with the F-type probe 406. Alternatively, the alert 703 may have been displayed in the model across different concepts / phrases on the object / model, and hovering over the alert invokes the resolution table 702. Also shown in the description of the alert and resolution table 702 are corresponding resolution processes 706 to resolve each of the risks or opportunities 704.

[0074] Figure 8 Depicted is an alert and resolution diagram overlaid on a production system model in accordance with an illustrative embodiment. Figure 8 The example shown in is essentially similar to Figure 7 In this example, activating the semantic link associated with the F-type probe 406 in the production system model 400 invokes the alert 703 and the resolution diagram 802, which presents the Figure 7 The same information as shown in the Alerts and Solutions table 702. Similar to Figure 7 , alerts 703 may also have been displayed in the model across different concepts / phrases on the object / model, and hovering over the alert to invoke the resolution table 802. Generative AI may also be activated to present high priority alerts, as well as color code them according to the sensitivity or criticality level of the risk that needs to be presented.

[0075] Fig. 9 The application of a data graph in a product development specification is shown in accordance with an illustrative embodiment.Once the associated risks or opportunities of a feature are identified, an annotation object 904 (annotation 250) may be created within the product design model 902.

[0076] Identifying and reusing risk or problem objects from the alert and solution diagram 802 can speed up the development process and improve the quality of risk analysis by finding relevant solutions if it already exists in the knowledge base. Users can use the solutions from the alert 703 and solution diagram 802 to create design requirement objects 906, 908, which are then imported into the product development engineering model 902 to specify design solutions and retire risks or gain opportunities. In this case, the solution comes from manufacturing to make the product design more manufacturable.

[0077] Thus, the solutions 910 to the risks from the alerts 703 and solution diagrams 802 can become part of the design requirements and can be reused from one development program to another.

[0078] Fig.10 A flow diagram of a manufacturing analysis process is depicted in accordance with an illustrative embodiment. Fig.10 The processes in may be implemented in hardware, software, or both. When implemented in software, the processes may take the form of program code that is executed by one or more processor units in one or more hardware devices in one or more computer systems. For example, the processes may be implemented in Figure 2 The manufacturing analysis system 200 is implemented in the computer system 290 in the manufacturing analysis system 200.

[0079] Process 1000 begins by identifying features of a product model defined by geometric, physical, and system elements with semantic labels, names, or title descriptions (operation 1002). Features of a producer object may include entities within a computer-aided design (CAD) environment that define the geometry or functional portion of a part. Examples of features may include a package folder, a collector, a system block, a behavior, an object, an object attribute, an object method, a physical product, a part, a 3D shape specification, an annotation set, a note, a point, a line, a plane, a curve, a surface, or a solid shape.

[0080] The geometric, physical, and system elements of features can include material properties, ply stack thickness, radius of curvature, manufacturing characteristics (e.g., required overage), manufacturing quality; manufacturing tools, machining specifications, painting specifications, inspection specifications, washing and drying process index tool coordination features, tool coordination, geometric dimensions and tolerances, shipping, handling and logistics characteristics.

[0081] Process 1000 creates machine-readable semantic links that connect features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model (operation 1004). The manufacturing process library may include several databases. These databases may include, for example, a database of manufacturing processes and process standards related to features, parts, objects, or object assemblies; a database of design guides, manuals, recommendations, checklists, processes, and practices; a database of manufacturing assets related to the manufacturing process of the object; a database of previously identified non-conformities, defects, failure mode effect analyses, or risks related to the manufacturing process of the object and means to resolve or improve the process; and a database of formally modeled machine-readable ontologies specific to the company's intellectual property, research, interests, and business, as well as industry standards.

[0082] Process 1000 identifies the producibility risks or opportunities of the product model based on the semantic relationship of the elements linked to the feature in the enterprise knowledge base (operation 1006). Identifying the producibility risks or opportunities of the product model can be performed by semantic analysis of the enterprise knowledge base. Identifying the producibility risks or opportunities can be performed via natural language processing (NLP). The digital thread can be used to collect the producibility risks or opportunities and the measurement criteria related to the producibility risks or opportunities and link them to the final product for quality assurance.

[0083] Process 1000 alerts a user to the producibility risk or opportunity (operation 1008 ).

[0084] Process 1000 adds annotations to a manufacturing design specification for a product model, wherein the annotations provide several solutions to a producibility risk or opportunity, wherein the solutions are identified in an enterprise knowledge base based on semantic relationships, thereby integrating information from the enterprise knowledge base into actionable information that is not described in the product model and would otherwise be omitted from the manufacturing design specification (operation 1010).

[0085] The solution to the manufacturability risk or opportunity includes at least one of a change in the design specification of the object or a change in the configuration of the object. Examples of design specifications or configurations that may be changed include material properties, ply stack thickness, radius of curvature, manufacturing features (e.g., required overage), manufacturing quality; manufacturing tools, machining specifications, painting specifications, inspection specifications, washing and drying process indexing tool coordination features, tool coordination, geometric dimensions and tolerances, shipping, handling, and logistics features.

[0086] Process 1000 then ends.

[0087] The flow charts and block diagrams in the different depicted embodiments illustrate the architecture, function and operation of some possible implementations of the apparatus and method in the illustrative embodiments. In this regard, each frame in the flow chart or block diagram can represent at least one of a module, a segment, a function or a portion of an operation or a step. For example, one or more of the frames can be implemented as program code, hardware, or a combination of program code and hardware. When implemented in hardware, the hardware can, for example, take the form of an integrated circuit, which is manufactured or configured to perform one or more operations in the flow chart or block diagram. When implemented as a combination of program code and hardware, the implementation can take the form of firmware. Each frame in the flow chart or block diagram can be implemented using a dedicated hardware system, which performs different operations or combinations of the program code run by dedicated hardware and dedicated hardware.

[0088] In some alternative implementations of the illustrative embodiments, one or more functions annotated in the blocks may not appear in the order annotated in the drawings. For example, in some cases, two blocks shown in succession may be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order, depending on the functions involved. Furthermore, other blocks may be added in addition to the blocks shown in the flowchart or block diagram.

[0089] Now go to Fig.11 , depicts an illustration of a block diagram of a data processing system according to an illustrative embodiment. Data processing system 1100 may be used to implement Figure 1 The server computers 104 and 106 and the client device 110 in Figure 2 1104, memory 1106, persistent storage 1108, communications unit 1110, input / output (I / O) unit 1112, and display 1114. In this example, communications framework 1102 takes the form of a bus system.

[0090] Processor unit 1104 is used to execute instructions of software that may be loaded into memory 1106. Processor unit 1104 may be a number of processors, a multi-processor core, or some other type of processor, depending on the particular implementation. In an embodiment, processor unit 1104 includes one or more conventional general-purpose central processing units (CPUs). In an alternative embodiment, processor unit 1104 includes one or more graphics processing units (GPUs).

[0091] Memory 1106 and persistent storage 1108 are examples of storage devices 1116. A storage device is any piece of hardware capable of storing information, such as, for example, but not limited to, at least one of data, program code in functional form, or other suitable information, on a temporary basis, a permanent basis, or both a temporary basis and a permanent basis. In these illustrative examples, storage devices 1116 may also be referred to as computer-readable storage devices. In these examples, memory 1106 may be, for example, a random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 1108 may take various forms, depending on the particular implementation.

[0092] For example, persistent storage 1108 may include one or more components or devices. For example, persistent storage 1108 may be a hard drive, a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination thereof. The media used by persistent storage 1108 may also be removable. For example, a removable hard drive may be used for persistent storage 1108. In these illustrative examples, communication unit 1110 provides for communication with other data processing systems or devices. In these illustrative examples, communication unit 1110 is a network interface card.

[0093] Input / output unit 1112 allows for input and output of data with other devices that may be connected to data processing system 1100. For example, input / output unit 1112 may provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Additionally, input / output unit 1112 may send output to a printer. Display 1114 provides a mechanism for displaying information to a user.

[0094] Instructions for at least one of the operating system, applications, or programs may be located in storage devices 1116, which are in communication with processor unit 1104 through communications framework 1102. The processes of the different embodiments may be performed by processor unit 1104 using computer-implemented instructions, which may be located in a memory, such as memory 1106.

[0095] These instructions are referred to as program code, computer usable program code, or computer readable program code, which can be read and executed by a processor in processor unit 1104. The program code in different embodiments may be embodied on different physical or computer readable storage media, such as memory 1106 or persistent storage 1108.

[0096] Program code 1118 is located in a functional form on computer readable medium 1120, which is selectively removable, and program code 1118 can be loaded onto data processing system 1100 or transferred to data processing device 1100 for execution by processor unit 1104. In these illustrative examples, program code 1118 and computer readable medium 1120 form computer program product 1122. In one example, computer readable medium 1120 may be computer readable storage medium 1124 or computer readable signal medium 1126.

[0097] In these illustrative examples, computer-readable storage media 1124 is a physical or tangible storage device for storing program code 1118, rather than a medium that propagates or transmits program code 1114. Computer-readable storage media 1124, as used herein, should not be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires, as used herein.

[0098] Alternatively, program code 1118 may be transferred to data processing system 1100 using computer readable signal media 1126. Computer readable signal media 1126 may be, for example, a propagated data signal containing program code 1118. For example, computer readable signal media 1126 may be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals may be transmitted over at least one of a communication link, such as a wireless communication link, a fiber optic cable, a coaxial cable, an electrical wire, or any other suitable type of communication link.

[0099] The different components illustrated for data processing system 1100 are not meant to provide architectural limitations to the manner in which the different embodiments may be implemented. The different illustrative embodiments may be implemented in a data processing system that includes components in addition to or in place of those illustrated for data processing system 1100. Fig.11 Other components shown in the can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program code 1118.

[0100] As used herein, when used with a list of items, the phrase "at least one of" means that different combinations of one or more of the listed items may be used, and only one of each item in the list may be required. In other words, "at least one of" means that any combination of items and numbers of items in the list may be used, but not all items in the list are required. An item may be a specific object, thing, or category.

[0101] For example, but not limited to, "at least one of item A, item B, or item C" may include item A, item A and item B, or item B. This example may also include item A, item B, and item C, or item B and item C. Of course, any combination of these items may exist. In some illustrative examples, "at least one of" may be, for example, but not limited to, two of item A; one of item B; and ten of item C; four of item B and seven of item C; or other suitable combination.

[0102] As used herein, when used with reference to items, "a number" means one or more items. For example, "a number of different types of networks" is one or more different types of networks. In the illustrative examples, but when used with reference to items, "a set" means one or more items. For example, a set of metrics is one or more metrics.

[0103] The descriptions of different illustrative embodiments have been presented for the purpose of illustration and description and are not intended to be exhaustive or limited to the embodiments of the disclosed forms. Different illustrative examples describe components that perform actions or operations. In the illustrative embodiments, the components can be configured to perform the described actions or operations. For example, the component can have a configuration or design of a structure that provides the component with the ability to perform the actions or operations described as being performed by the component in the illustrative examples. In addition, to the extent that the terms "include", "comprise", "have", "contain" and variations thereof are used herein, such terms are intended to be inclusive in a manner similar to the term "comprises" as an open transition word, without excluding any additional or other elements.

[0104] Many modifications and variations will be apparent to those of ordinary skill in the art. In addition, different illustrative embodiments may provide different features than other desired embodiments. The selected one or more embodiments are chosen and described in order to best explain the principles of the embodiments, the practical application, and to enable others of ordinary skill in the art to understand the disclosure of various embodiments with various modifications as are suitable for the particular use contemplated.

[0105] In addition, this application includes the following examples:

[0106] Example 1. A computer-implemented method for manufacturing analysis, the method comprising: identifying features of a product model defined by geometric, physical, and system elements having semantic tags, names, or title descriptions; creating machine-readable semantic links connecting the features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model; identifying producibility risks or opportunities for the product model based on semantic relationships of the elements in the enterprise knowledge base linked to the features; alerting a user to the producibility risks or opportunities; and adding annotations to manufacturing design specifications for the product model, wherein the annotations provide several solutions to the producibility risks or opportunities, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationships.

[0107] Example 2. The method according to Example 1 also includes using a digital thread to collect the producibility risks or opportunities and measurement criteria related to the producibility risks or opportunities and linking the producibility risks or opportunities and the measurement criteria related to the producibility risks or opportunities to the final product for quality assurance.

[0108] Example 3. The method of Example 1, wherein identifying the producibility risk or opportunity is performed via natural language processing.

[0109] Example 4. A method according to Example 1, wherein the features of the product model include at least one of the following items: a package folder; a collector; a system block; a behavior; an object; an object property; an object method; a physical product; a part; a 3D shape specification; an annotation set; a note; a point; a line; a plane; a curve; a surface; or a solid shape.

[0110] Example 5. The method of Example 1, wherein the features of the product model include entities within a computer-aided design (CAD) environment that define a geometric or functional portion of a part.

[0111] Example 6. A method according to Example 1, wherein the geometric, physical, and system elements include at least one of the following: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or transportation, handling and logistics features.

[0112] Example 7. A method according to Example 1, wherein the enterprise knowledge base includes at least one of the following items: a database of manufacturing processes and process standards associated with features, parts, objects, or object assemblies; a database of design guides, manuals, recommendations, checklists, processes, and practices; a database of manufacturing assets associated with the manufacturing process of the objects; a database of previously identified non-conformities, defects, failure mode effect analyses, or risks associated with the manufacturing process of the objects and means of addressing or improving the processes; and a database of formally modeled machine-readable ontologies unique to the company's intellectual property, research, interests, and business, as well as industry standards.

[0113] Example 8. The method of Example 1, wherein identifying the producibility risk or opportunity of the product model is performed by semantic analysis of the enterprise knowledge base.

[0114] Example 9. The method of Example 1, wherein the solution to the producibility risk or opportunity comprises at least one of: a change in a design specification of the product model; or a change in a configuration of the product model.

[0115] Example 10. A method according to Example 9, wherein the design specifications include at least one of the following: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or transportation, handling and logistics features.

[0116] Example 11. A method according to Example 9, wherein the configuration includes at least one of the following items: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or shipping, handling and logistics features.

[0117] Example 12. The method of Example 1, wherein identifying features of a product model further comprises identifying variants of the product model.

[0118] Example 13. The method of Example 12, wherein the variant of the product model comprises a manufacturing state of the product model.

[0119] Example 14. A system for manufacturing analysis, the system comprising: a storage device storing program instructions; one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to: identify features of a product model defined by geometric, physical, and system elements having semantic labels, names, or title descriptions; create machine-readable semantic links connecting the features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model; identify producibility risks or opportunities for the product model based on semantic relationships of the elements linked to the features in the enterprise knowledge base; alert a user to the producibility risks or opportunities; and add annotations to the manufacturing design specifications of the product model, wherein the annotations provide several solutions to the producibility risks or opportunities, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationships.

[0120] Example 15. A system according to Example 14, wherein the processor also executes program instructions to cause the system to use a digital thread to collect the producibility risks or opportunities and measurement criteria related to the producibility risks or opportunities and link the producibility risks or opportunities and the measurement criteria related to the producibility risks or opportunities to the final product for quality assurance.

[0121] Example 16. The system of Example 14, wherein identifying the producibility risk or opportunity is performed via natural language processing.

[0122] Example 17. A system according to Example 14, wherein the features of the product model include at least one of the following: a package folder; a collector; a system block; a behavior; an object; an object property; an object method; a physical product; a part; a 3D shape specification; an annotation set; a note; a point; a line; a plane; a curve; a surface; or a solid shape.

[0123] Example 18. A system according to Example 14, wherein the features of the product model include entities within a computer-aided design (CAD) environment that define geometric or functional portions of a part.

[0124] Example 19. A system according to Example 14, wherein the geometric, physical, and system elements include at least one of the following: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or transportation, handling, and logistics features.

[0125] Example 20. A system according to Example 14, wherein the enterprise knowledge base includes at least one of the following: a database of manufacturing processes and process standards associated with features, parts, objects, or assemblies of objects; a database of design guides, manuals, recommendations, checklists, processes, and practices; a database of manufacturing assets associated with the manufacturing process of the objects; a database of previously identified non-conformities, defects, failure mode effect analyses, or risks associated with the manufacturing process of the objects and means of addressing or improving the processes; and a database of formally modeled machine-readable ontologies unique to the company's intellectual property, research, interests, and business, as well as industry standards.

[0126] Example 21. The system of Example 14, wherein identifying the producibility risk or opportunity of the product model is performed by semantic analysis of the enterprise knowledge base.

[0127] Example 22. The system of Example 14, wherein the solution to the producibility risk or opportunity comprises at least one of: a change in a design specification of the product model; or a change in a configuration of the product model.

[0128] Example 23. A system according to Example 22, wherein the design specifications include at least one of: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or shipping, handling and logistics features.

[0129] Example 24. A system according to Example 22, wherein the configuration includes at least one of the following: material properties; ply stack thickness; radius of curvature; manufacturing features; manufacturing quality; manufacturing tools; machining specifications; painting specifications; inspection specifications; washing and drying processes; index tool coordination features; tool coordination; geometric dimensions and tolerances; or shipping, handling and logistics features.

[0130] Example 25. The system of Example 14, wherein identifying features of a product model further comprises identifying variants of the product model.

[0131] Example 26. The system of Example 25, wherein the variant of the product model comprises a manufacturing state of the product model.

[0132] Example 27. A computer program product for manufacturing analysis, the computer program product comprising: a computer-readable storage medium having program instructions embodied thereon to perform the following operations: identifying features of a product model defined by geometric, physical, and system elements having semantic tags, names, or title descriptions; creating machine-readable semantic links connecting the features to elements in an enterprise knowledge base based on a machine-readable ontology knowledge model; identifying producibility risks or opportunities for the product model based on semantic relationships of the elements in the enterprise knowledge base linked to the features; alerting a user to the producibility risks or opportunities; and adding annotations to the manufacturing design specifications for the product model, wherein the annotations provide several solutions to the producibility risks or opportunities, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationships.< / concept> < / concept> < / conception> < / concept> < / concept> < / block> < / block>

Claims

1. A computer-implemented method of manufacturing analysis, the method include: identifying (1002) features (258) of a product model (254) defined by geometric, physical, and system elements described by semantic labels, names, or titles; Creating (1004) a machine-readable semantic link (210) connecting the feature to an element (244) in an enterprise knowledge base (240) based on the machine-readable ontology knowledge model (202); identifying (1006) a manufacturability risk or opportunity (272) of the product model based on a semantic relationship (208) of the element linked to the feature in the enterprise knowledge base; alerting (1008) a user of the producibility risk or opportunity; as well as An annotation (250) is added (1010) to a manufacturing design specification (246) for the product model, wherein the annotation provides a plurality of solutions to the producibility risk or opportunity, wherein the solutions are identified in the enterprise knowledge base based on the semantic relationship.

2. The method according to claim 1 also includes using a digital thread (270) to collect the manufacturability risks or opportunities and measurement criteria related to the manufacturability risks or opportunities and linking the manufacturability risks or opportunities and the measurement criteria related to the manufacturability risks or opportunities to the final product for quality assurance. 3 . The method of claim 1 , wherein identifying the producibility risk or opportunity is performed via natural language processing.

4. The method according to claim 1, wherein the characteristics of the product model include at least one of the following: Package folder; Collector; System blocks; Behavior; Object; Object attributes; Object methods; Physical products; Component; 3D shape specifications; Annotated collections; Notes; point; Wire; flat; curve; surface; or Three-dimensional shape.

5. The method of claim 1, wherein the features of the product model include entities within a computer-aided design (CAD) environment that define geometric or functional portions of a part.

6. The method of claim 1, wherein the geometric, physical, and system elements include at least one of: Material properties; ply stack thickness; Radius of curvature; Manufacturing features; Manufacturing quality; Manufacturing tools; Machining specifications; Painting specifications; Check specifications; washing and drying process; Indexing tools coordinate features; Tool coordination; Geometric dimensioning and tolerances; or Transport, handling and logistics characteristics.

7. The method of claim 1, wherein the enterprise knowledge base comprises at least one of the following: A database of manufacturing processes and process standards associated with features, parts, objects, or assemblies of objects; designing databases of guidelines, manuals, recommendations, checklists, processes, and practices; a database of manufacturing assets related to the manufacturing process of the object; a database of previously identified nonconformities, defects, failure mode effects analyses, or risks associated with the manufacturing process of said object and means to resolve or improve said process; as well as A database of formally modeled, machine-readable ontologies unique to a company's intellectual property, research, interests, and operations, as well as industry standards.

8. The method of claim 1, wherein identifying the producibility risks or opportunities of the product model is performed by semantic analysis of the enterprise knowledge base.

9. The method of claim 1, wherein the solution to the manufacturability risk or opportunity comprises at least one of: a change in the design specifications (262) of the product model; or A change in the configuration (264) of the product model.

10. The method of claim 9, wherein the design specification comprises at least one of the following: Material properties; ply stack thickness; Radius of curvature; Manufacturing features; Manufacturing quality; Manufacturing tools; Machining specifications; Painting specifications; Check specifications; washing and drying process; Indexing tools coordinate features; Tool coordination; Geometric dimensioning and tolerances; or Transport, handling and logistics characteristics.

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