Method, system and device for evaluating KPI in production process based on geometric model

Abstract

The present invention provides a method, system, and apparatus for evaluating KPIs in a production process based on a geometric model. The method comprises the following steps: S1, extracting geometric features and their characteristic attributes from a product model; S2, converting the geometric features into process features to obtain the process steps associated with the entire production process of the product and the equipment corresponding to each process step; S3, simulating the entire industrial manufacturing process of the product based on the process steps associated with the production process and the equipment corresponding to each process step, and outputting industrial manufacturing key performance indicators. The present invention can evaluate the KPIs of the entire industrial manufacturing process of a product based on the product model.

Description

Technical Field

[0001] The present invention relates to the field of simulation, and in particular to a method, system and device for evaluating KPIs in a production process based on a geometric model. Background Art

[0002] Driven by the trend of consumption upgrading, industrial manufacturing is facing increasing pressure in terms of mass deep customization and fast product iteration. As a result, industrial manufacturers often need to make decisions on changing product features to attract potential business.

[0003] However, for most industrial manufacturers, such as small and medium enterprises (SMEs), they lack a suitable tool to help them evaluate the impact of changing product geometry throughout the manufacturing process.

[0004] Existing manufacturing simulation tools present two problems. First, they are numerous and expensive, and small and medium-sized enterprises often lack experience, making them unsuitable for purchase. Second, there is a disconnect between product design and its manufacturing process. For example, it's unclear how to derive the manufacturing process from changing the product design. To coordinate product design and its manufacturing process, existing technologies often rely on human resources, such as experts familiar with the manufacturing processes of multiple products and possessing specialized knowledge.

[0005] Most existing manufacturing software simulation tools support product design and manufacturing simulation, respectively. For example, tools for product design include NX, Pro / Engineering, and Solidworks, while tools for manufacturing simulation include Tecnomatix (including Process Simulate and Plant Simulation) and AnyLogic. These tools are designed for use at specific stages in the product lifecycle, and their users encompass diverse roles, such as product designers, production designers, and plant engineers, requiring extensive expertise and technical expertise to model, execute logic, and perform simulations. These software simulation tools can output a wealth of detail regarding products and production, but they are not suitable for rapid evaluation from design to manufacturing.

[0006] Therefore, there aren't many existing software tools specifically designed to link data from different stages of the product lifecycle. Some existing software tools serve as a large database for all product modeling and production data, which is also linked to trigger the execution of independent software such as NX and Tecnomatix. However, these software tools serve as collaborative tools for engineers in product lifecycle engineering, and are not specifically designed to evaluate the impact of changing product geometry throughout the entire manufacturing process.

[0007] Furthermore, these existing industrial manufacturing software tools cannot quickly evaluate the entire manufacturing process, including stages such as material preparation, processing, and assembly. The industry needs an industrial manufacturing evaluation mechanism that can automatically start from a product geometry model and calculate key performance indicators related to manufacturing. Summary of the Invention

[0008] The first aspect of the present invention provides a method for evaluating KPIs in a production process based on a geometric model, which includes the following steps: S1, extracting geometric features and their characteristic attributes from a product model; S2, converting the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; S3, simulating the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputting key performance indicators of industrial manufacturing.

[0009] Furthermore, the step S2 also includes the following steps: S21, instantiating the geometric features into a knowledge base; S22, converting the geometric features into process features through the rules in the knowledge base; S23, analyzing the process features, obtaining multiple process steps in a specific order related to the production process of the process features, and allocating equipment corresponding to each process step.

[0010] Furthermore, the knowledge base includes an ontology base and a rule base.

[0011] Furthermore, the ontology library includes classes, attributes, and instances.

[0012] Furthermore, the rules of the rule base include: a first relationship rule between the geometric features and the process features; a plurality of process steps in a specific order related to the process features and the production process; and a second relationship rule between each process step and its corresponding equipment.

[0013] Furthermore, the step S22 also includes the following steps: calling the first relationship rule in the knowledge base, and matching the geometric feature to the corresponding process feature based on the first relationship rule, and the step S23 also includes the following steps: calling the second relationship rule in the knowledge base, and mapping the process feature to multiple process steps of specific steps related to the production process of the process feature based on the second relationship rule; calling the third relationship rule in the knowledge base, and assigning corresponding equipment to each process step based on the third relationship rule.

[0014] A second aspect of the present invention provides a system for evaluating KPIs in a production process based on a geometric model, which includes: a processor; and a memory coupled to the processor, the memory having instructions stored therein, and when the instructions are executed by the processor, the system for evaluating KPIs in a production process based on a geometric model performs actions, the actions including: S1, extracting geometric features and their feature attributes from a product model; S2, converting the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; S3, simulating the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputting key performance indicators of industrial manufacturing.

[0015] Furthermore, the action S2 also includes: S21, instantiating the geometric feature into the knowledge base; S22, converting the geometric feature into a process feature through the rules in the knowledge base; S23, analyzing the process feature, obtaining multiple process steps in a specific order related to the production process of the process feature, and allocating equipment corresponding to each process step.

[0016] Furthermore, the knowledge base includes an ontology base and a rule base.

[0017] Furthermore, the ontology library includes classes, attributes, and instances.

[0018] Furthermore, the rules of the rule base include: a first relationship rule between the geometric features and the process features; a plurality of process steps in a specific order related to the process features and the production process; and a second relationship rule between each process step and its corresponding equipment.

[0019] Furthermore, the action S22 also includes: calling the first relationship rule in the knowledge base, and matching the geometric feature to the corresponding process feature based on the first relationship rule, and the action S23 also includes: calling the second relationship rule in the knowledge base, and mapping the process feature to multiple process steps of specific steps related to the production process of the process feature based on the second relationship rule; calling the third relationship rule in the knowledge base, and assigning corresponding equipment to each process step based on the third relationship rule.

[0020] The third aspect of the present invention provides a device for evaluating KPIs in a production process based on a geometric model, which includes: an extraction module that extracts geometric features and their characteristic attributes from a product model; a conversion module that converts the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; a simulation module that simulates the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputs key performance indicators of industrial manufacturing.

[0021] Furthermore, the conversion module is also used to: instantiate the geometric features into a knowledge base; convert the geometric features into process features through the rules in the knowledge base; analyze the process features, obtain multiple process steps in a specific order related to the production process of the process features, and assign equipment corresponding to each process step.

[0022] Furthermore, the rules of the rule base include: a first relationship rule between the geometric features and the process features; a plurality of process steps in a specific order related to the process features and the production process; and a second relationship rule between each process step and its corresponding equipment.

[0023] Furthermore, the conversion module is also used to: call the first relationship rule in the knowledge base, and match the geometric feature to the corresponding process feature based on the first relationship rule, and the simulation module is also used to: call the second relationship rule in the knowledge base, and map the process feature to multiple process steps of specific steps related to the production process of the process feature based on the second relationship rule; call the third relationship rule in the knowledge base, and assign corresponding equipment to each process step based on the third relationship rule.

[0024] A fourth aspect of the present invention provides a computer program product tangibly stored on a computer readable medium and comprising computer executable instructions which, when executed, cause at least one processor to perform the method according to the first aspect of the present invention.

[0025] A fifth aspect of the present invention provides a computer-readable medium having computer-executable instructions stored thereon, which, when executed, cause at least one processor to perform the method according to the first aspect of the present invention.

[0026] The present invention maps the design features of a product model to the production process and equipment information for that product through a knowledge structure—an ontology—that automatically generates possible process paths and the required equipment from the product design. A product can be described by certain design features, such as geometric features and their characteristic attributes determined from its design model. These features can then be mapped to the production process that could potentially produce those design features. This knowledge can be continuously built and extended over time and with experience. Therefore, the present invention has broad application prospects in many industrial fields, such as production process identification of assembly steps in robotic systems.

[0027] Furthermore, the present invention uses simulation to obtain production information to automatically assess KPIs within the manufacturing process. After obtaining the production routes and equipment types for a specific product, a factory model can be generated within the simulation tool. The present invention then simulates and automatically calculates factory KPIs, including the types and quantities of products planned for production. This allows for the rapid and automated assessment of production gaps and capacity, as well as performance metrics from design to production. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 2 is a schematic diagram of an architecture of an evaluation mechanism for evaluating KPIs in a production process based on a geometric model according to a specific embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of a CAD model of a cup product according to a specific embodiment of the present invention;

[0030] Figure 3 is a schematic diagram of an instantiation of an evaluation mechanism for evaluating KPIs in a production process based on a geometric model according to a specific embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the conversion reasoning of the evaluation mechanism of the KPI in the production process based on the geometric model according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0032] The specific embodiments of the present invention are described below with reference to the accompanying drawings.

[0033] The present invention provides a method for evaluating KPIs in the production process based on geometric models. It can evaluate multiple key performance indicators of the product in the entire production process starting from the product's geometric design model, and can analyze the impact of changing the product's geometric features on the key performance indicators in the entire product production process.

[0034] Figure 1 Figure 2 is a schematic diagram of the architecture of a system for evaluating KPIs in production processes based on geometric models, according to a specific embodiment of the present invention. The system comprises an extraction module 100, a conversion module 200, a simulation module 300, and a generation module 400. These modules are linked by a database 500, which stores data and information generated by these modules.

[0035] A first aspect of the present invention provides a method for evaluating KPIs in a production process based on a geometric model.

[0036] First, S1 is executed, and the extraction module 100 extracts geometric features and their characteristic attributes from the product model, wherein, in particular, the product model is a CAD model. Specifically, Figure 1 As shown, the input of the extraction module 100 is a CAD model. The geometric features extracted include the surfaces of the product model, such as flat, convex, and concave surfaces, which can all be identified. Surface parameters can be automatically generated based on the different shapes of the CAD model. Relationships between multiple surfaces are also generated simultaneously. Therefore, the characteristic attributes of all surfaces can be extracted and output to the conversion module 200. These characteristic attributes also include material and other design-related information based on specific implementation needs.

[0037] Specifically, the extraction module 100 is used to extract geometric features from a geometric model obtained after the product design is completed, and to generate a list of geometric feature information. In particular, the geometric model is a CAD model, and the geometric features exemplarily include faces, their materials, and the relationship between multiple faces. The format of the list of geometric feature information can be XML (other formats such as JSON can also be used). Specifically, according to a specific embodiment of the present invention, the extraction module 100 is used to obtain the attribute parameters of basic elements in a CAD model, wherein the basic elements are geometric faces in most cases. Each face has many attribute parameters, such as shape, size, dimensions, and radius. The face also includes the material information of the product and the relationship between the face and other faces. The geometric feature information of the geometric models of these product designs can be directly extracted and classified from the geometric models. Therefore, customers can directly download a new CAD model of a product design to the extraction module 100 to detect its geometric features for further analysis.

[0038] In this embodiment, the product to be produced is a cup. The product model of the cup 600 is as follows: Figure 2 Therefore, the geometric features of the cup 600 extracted in step S1 include: the cup body 610, the bottom surface 620, the cup cover 630, and the sealing ring 640 between the cup body 610 and the cup cover 630. The cup cover 630 includes an upper surface 631 and a cover body 632.

[0039] Among them, the characteristic attributes of the above-mentioned geometric features include: radius, height, material, etc. Each geometric feature corresponds to multiple characteristic attributes, and the characteristic attributes can be extracted and identified together when extracting the geometric features. Figure 3 As shown, the cup 600 includes a cup body 610 with geometric features, a bottom surface 620, a cup lid 630, and a sealing ring 640 between the cup body 610 and the cup lid 630. Specifically, the first surface F1 included in the cup body 610 is a cylindrical surface, and the characteristic attributes of the geometric characteristic cup body 610 include radius, height, shape, and material. Among them, the radius is R1, the height is H1, the shape is cylindrical, and the material is stainless steel. The cup lid 630 includes a second surface F2 and a third surface F3, and the second surface F2 and the third surface F3 are respectively a plane and a cylinder. This is because the cup lid 630 includes an upper surface 631 and a lid body 632, the upper surface 630 is a plane, and the lid body 632 is a cylinder. Among them, the characteristic attributes of the second surface F2 and the third surface F3 include radius, height, shape, and material, respectively. Among them, the radius of the second surface F2 is R2, the height of the second surface F2 is H2, the shape of the second surface F2 is a plane, and the material of the second surface F2 is stainless steel. The third surface F3 has a radius of R3, a height of H3, a cylindrical shape, and is made of stainless steel. Cup 600 also includes a geometrically characterized sealing ring 640, which includes a fourth surface F4 that is a ring. The characteristic attributes of fourth surface F4 include radius, height, shape, and material. The radius is R4, the height is H4, the shape is a ring, and the material is rubber. Furthermore, cup 600 also includes a geometrically characterized bottom surface 620, which includes a fifth surface F5 that is a plane. The characteristic attributes of a plane include radius, height, shape, and material. The radius is R5, the height is H5, the shape is a plane, and the material is stainless steel.

[0040] Optionally, the above geometric features and their related feature attributes may be written in an XML file.

[0041] Geometric features include points, lines, and surfaces, but geometric features are not objects that can be recognized by machines. Therefore, the present invention needs to infer geometric features into processing features, and then map the processing features to process steps and their corresponding devices.

[0042] Then, step S2 is executed, where the conversion module 200 converts the geometric features into processing features to obtain the process steps associated with the entire production process of the product and the equipment corresponding to each process step. The conversion module 200 includes a knowledge base 210, an instantiation module 220, an identification module 230, and a planning module 240.

[0043] Among them, the knowledge base 210 is pre-set based on domain knowledge and can also be continuously updated and enriched during the implementation of the present invention. The knowledge base 210 pre-stores general knowledge about products, production processes and equipment. Among them, the product clearly belongs to a specific field, all geometric features can be used to describe the product, and all production processes are used to manufacture the product. The knowledge base includes an ontology library and a rule library. The ontology library includes classes, attributes, and instances. The ontology library stores ontologies, which are composed of abstract classes such as different types of faces and their logical relationships. Specifically, the knowledge base 210 also includes domain abstract knowledge of products, process and devices. Among them, all geometric features can be used to describe products, and all process are required to produce products. The ontology library identifies the feature through the geometric features of the instance and matches the category through the geometric features. The categories of devices, product geometric features and process are used to describe the attribute features of the above types, which are determined by the ontology.

[0044] Step S2 includes sub-steps S21, S22 and S23.

[0045] First, step S21 is executed to instantiate the geometric features into the knowledge base 210. Specifically, the instantiation module 220 obtains the geometric features extracted from the product model from the extraction module 100 and classifies the geometric features so that the technical features correspond to different categories in the knowledge base 210, so as to prepare for subsequent analysis and reasoning. Figure 3 As shown, knowledge base 210 includes an ontology library, which contains an ontology-based product framework. In this embodiment, the ontology library includes a framework for a cup, which contains the basic information that defines a cup. Specifically, a cup has a body, which has faces, and faces have radius, height, shape, and material. Therefore, instantiating cup 600 into the ontology library involves filling the geometric features and characteristic attributes of cup 600 into the aforementioned framework in the ontology library, making it an instance with content.

[0046] like Figure 3As shown, the instantiated ontology file contains an instance of a cup 600. Cup 600 has multiple main bodies, including a cup body 610, a bottom surface 620, a cup lid 630, and a sealing ring 640. These multiple main bodies have multiple faces, including a first face F1, a second face F2, a third face F3, a fourth face F4, and a fifth face F5. First face F1 has a radius R1, a height H1, a cylindrical shape, and is made of stainless steel. Second face F2 has a radius R2, a height H2, a flat shape, and is made of stainless steel. Third face F3 has a radius R3, a height H3, a cylindrical shape, and is made of stainless steel. Fourth face F4 has a radius R4, a height H4, a ring shape, and is made of rubber. The fifth surface F5 has a radius R5, a height H5, a shape of a plane, and a material of stainless steel.

[0047] Then, step S22 is executed to convert the geometric features into process features using the rules in the knowledge base 210. The rules in the rule base include a first relationship rule between the geometric features and the process features. Specifically, step S22 further includes the following steps: invoking the first relationship rule in the knowledge base and matching the geometric features to corresponding process features based on the first relationship rule.

[0048] Specifically, the conversion module 200 uses knowledge-based technology to implement automatic geometric feature recognition and computer-aided process planning, and ultimately generates a process list file for subsequent applications. The conversion module 200 mainly consists of a pre-constructed knowledge base, process instances derived from input geometric feature data, an automatic feature recognition module (Auto Feature Recognition), and a computer-aided process planning module (Computer Aided Process Planning). Among them, the instantiation module 220 is used to extract individuals from the geometric features and assign them to corresponding categories in the knowledge base 210 to prepare for subsequent reasoning and analysis. Therefore, the computer-aided process planning module also uses reasoning analysis to identify, and its essence is to find the processing characteristics of the entire production process.

[0049] Among them, the recognition module 230 is an automatic feature recognition module (Auto Feature Recognition), which is used to infer the above geometric features to processing features. Its essence is to identify processing features through reasoning analysis and map the extracted geometric features to corresponding processing features. Figure 4As shown, the processing features in the ontology library are also frames without content. For example, the processing feature K of a cup has radius, height, material and shape. The processing feature K of the cup also includes several sub-processing features, including a first processing feature K1 corresponding to the cup body, a second processing feature K2 corresponding to the bottom surface, a third processing feature K3 corresponding to the cup lid, and a fourth processing feature K4 corresponding to the sealing ring. The process steps that form the entire production process in the ontology library are a set, which has multiple specific process steps, wherein the multiple process steps have a specific order and have the time of the process steps. In addition, the processing step K also corresponds to multiple equipment, and the equipment has cost, model number and equipment type. Among them, the equipment is a set.

[0050] Next, step S23 is executed to analyze the process feature, obtain multiple process steps in a specific sequence related to the production process of the process feature, and assign corresponding equipment to each process step. The rules of the rule base include a second relationship rule between the process feature and the process steps related to the production process, and the equipment corresponding to each process step. Therefore, step S23 also includes the following steps: calling the second relationship rule in the knowledge base, and based on the second relationship rule, mapping the process feature to multiple process steps of specific steps related to the production process of the process feature; calling the third relationship rule in the knowledge base, and based on the third relationship rule, assigning corresponding equipment to each process step.

[0051] The planning module 240 is a computer-aided process planning module (Computer Aided Process Planning), which is used to plan the entire production process based on processing features. That is, after each geometric feature is extracted from the CAD model, these geometric features can be a geometric feature, such as a bottom surface 620 or a cup body 610, but they can only be identified as a plane or a cylindrical surface. The plane cannot be immediately identified as the bottom surface 620, or the cylindrical surface cannot be immediately identified as the first cylindrical surface 610. Therefore, at this time, there is no way to know how to manufacture these geometric features and products with the above geometric features. The recognition module 230 is used to classify each geometric feature and attribute it to different process characteristics, so that each geometric feature corresponds to a specific production process in the knowledge base 210.

[0052] Specifically, the instantiated ontology file for cup 600 is read and automatically matched to the corresponding cup's processing feature K. Taking cup body 610 as an example, the inference process automatically identifies the radius, height, material, and shape corresponding to cup body 610. Therefore, the entire production process of cup body 610 is mapped to include cutting, bulging, and cleaning and drying. Cutting has the next process step of bulging, and bulging has the next process step of cleaning and drying, thus obtaining multiple process steps in the above specific order. Furthermore, cutting, bulging, and cleaning and drying each have a process step time.

[0053] The process feature K also includes multiple devices, and the device set includes multiple devices, such as cutting equipment, bulging equipment, and cleaning and drying equipment. In this embodiment, after the entire production process of the cup body 610 is planned, each process step in the production process is mapped to a device. The cutting step corresponds to the cutting device, the bulging step corresponds to the bulging device, and the cleaning and drying device corresponds to the cleaning and drying device.

[0054] In summary, after the above analysis and reasoning is completed, the process steps related to the entire production process of a specific product and the relevant equipment information for each process step can be obtained, and a list file containing this information is generated. The content and format of this list file can be customized according to needs.

[0055] Finally, step S3 is executed, where the manufacturing simulation verification module 300 simulates the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputs the key performance indicators of industrial manufacturing.

[0056] The manufacturing simulation and verification module 300 generates a factory model based on the production process and executes factory simulations to calculate key performance parameters corresponding to the product design, including productivity, daily OEE, operating costs, and potential profit. The manufacturing simulation and verification module 300 receives the processing characteristics output by the conversion module 200 and constructs a corresponding factory model. The calculation module then automatically performs simulations based on the comparison of these parameter characteristics to accurately calculate production capacity and costs.

[0057] Specifically, the production and manufacturing simulation verification module 300 is used to generate a factory model from the production process and perform factory simulation to calculate the KPI value. Specifically, the output of the conversion module 200 is first parsed to obtain information on each step of the entire production process, including process type, process step time, process step sequence, equipment type, and cost, etc. Then, a factory model is generated according to each step, and the simulation parameters input in the database are obtained. Among them, the simulation parameters include the number of products to be manufactured, product type, and other simulation-related information. Then, the information is loaded into the factory model and the factory model is simulated. Finally, the KPI value is calculated after the simulation is completed, and the KPI value result is sent to the database.

[0058] User interface 400 includes multiple web pages that receive input from client pages and display intermediate results from feature extraction, process reasoning, and simulation validation, while also generating a final evaluation via a key performance parameter page. User interface 400 and database 500 communicate with backend components and are used for data storage and information exchange.

[0059] Among them, the user interface 400 is a knowledge-based tool that interacts with the other modules mentioned above. The user interface 400 includes five modules, namely product selection, feature extraction, process analysis and simulation confirmation, and KPI dashboard. The product selection module provides a product list and can be added, updated and deleted. Once a product is selected in the user interface 400, the background modules will communicate with each other and transmit information related to the product. The feature extraction module will display the surface feature analysis results of the extraction module 100. In the process analysis module, an ontology map including production routes and required equipment will be displayed, and the production routes and required equipment of two different products can be compared in the map. In the simulation confirmation module, the product quantity is entered, and based on the communication between the factory simulation and the background module, the product information will be explained as input for the factory simulation and product production simulation, and the start will be automatically triggered. Factory KPIs such as daily expenses, daily profits, machine OEE, etc. are displayed in the KPI dashboard.

[0060] For example, to produce a new cup, the production line must be modified. Using this invention, only a 3D model of the new cup is needed to compare the differences and gaps between the new and traditional cups. By inferring processing features based on geometric features, the processing technology and equipment are mapped. All of the factory's processes and equipment, and therefore the processing capabilities, are instantiated. If the equipment available is not available, it must be purchased. Therefore, customers can evaluate the entire production process based solely on the product model.

[0061] The present invention maps the design features of a product model to the production process and equipment information for that product through a knowledge structure—an ontology—that automatically generates possible process paths and the required equipment from the product design. A product can be described by certain design features, such as geometric features and their characteristic attributes determined from its design model. These features can then be mapped to the production process that could potentially produce those design features. This knowledge can be continuously built and extended over time and with experience. Therefore, the present invention has broad application prospects in many industrial fields, such as production process identification of assembly steps in robotic systems.

[0062] Furthermore, the present invention uses simulation to obtain production information to automatically assess KPIs within the manufacturing process. After obtaining the production routes and equipment types for a specific product, a factory model can be generated within the simulation tool. The present invention then simulates and automatically calculates factory KPIs, including the types and quantities of products planned for production. This allows for the rapid and automated assessment of production gaps and capacity, as well as performance metrics from design to production.

[0063] A second aspect of the present invention provides a system for evaluating KPIs in a production process based on a geometric model, which includes: a processor; and a memory coupled to the processor, the memory having instructions stored therein, and when the instructions are executed by the processor, the system for evaluating KPIs in a production process based on a geometric model performs actions, the actions including: S1, extracting geometric features and their feature attributes from a product model; S2, converting the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; S3, simulating the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputting key performance indicators of industrial manufacturing.

[0064] Furthermore, the action S2 also includes: S21, instantiating the geometric feature into the knowledge base; S22, converting the geometric feature into a process feature through the rules in the knowledge base; S23, analyzing the process feature, obtaining multiple process steps in a specific order related to the production process of the process feature, and allocating equipment corresponding to each process step.

[0065] Furthermore, the knowledge base includes an ontology base and a rule base.

[0066] Furthermore, the ontology library includes classes, attributes, and instances.

[0067] Furthermore, the rules of the rule base include: a first relationship rule between the geometric features and the process features; a plurality of process steps in a specific order related to the process features and the production process; and a second relationship rule between each process step and its corresponding equipment.

[0068] Furthermore, the action S22 also includes: calling the first relationship rule in the knowledge base, and matching the geometric feature to the corresponding process feature based on the first relationship rule, and the action S23 also includes: calling the second relationship rule in the knowledge base, and mapping the process feature to multiple process steps of specific steps related to the production process of the process feature based on the second relationship rule; calling the third relationship rule in the knowledge base, and assigning corresponding equipment to each process step based on the third relationship rule.

[0069] The third aspect of the present invention provides a device for evaluating KPIs in a production process based on a geometric model, which includes: an extraction module that extracts geometric features and their characteristic attributes from a product model; a conversion module that converts the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; a simulation module that simulates the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputs key performance indicators of industrial manufacturing.

[0070] Furthermore, the conversion module is also used to: instantiate the geometric features into a knowledge base; convert the geometric features into process features through the rules in the knowledge base; analyze the process features, obtain multiple process steps in a specific order related to the production process of the process features, and assign equipment corresponding to each process step.

[0071] Furthermore, the rules of the rule base include: a first relationship rule between the geometric features and the process features; a plurality of process steps in a specific order related to the process features and the production process; and a second relationship rule between each process step and its corresponding equipment.

[0072] Furthermore, the conversion module is also used to: call the first relationship rule in the knowledge base, and match the geometric feature to the corresponding process feature based on the first relationship rule, and the simulation module is also used to: call the second relationship rule in the knowledge base, and map the process feature to multiple process steps of specific steps related to the production process of the process feature based on the second relationship rule; call the third relationship rule in the knowledge base, and assign corresponding equipment to each process step based on the third relationship rule.

[0073] A fourth aspect of the present invention provides a computer program product tangibly stored on a computer readable medium and comprising computer executable instructions which, when executed, cause at least one processor to perform the method according to the first aspect of the present invention.

[0074] A fifth aspect of the present invention provides a computer-readable medium having computer-executable instructions stored thereon, which, when executed, cause at least one processor to perform the method according to the first aspect of the present invention.

[0075] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as limiting the present invention. After reading the above content, various modifications and substitutions of the present invention will be obvious to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims. In addition, any figure marks in the claims should not be regarded as limiting the claims involved; the word "comprising" does not exclude devices or steps not listed in other claims or the specification; words such as "first" and "second" are used only to indicate names and do not indicate any particular order.

Claims

1. Method for evaluating KPIs in production processes based on geometric models, where: The steps include: S1, extracting geometric features and their feature attributes from the product model; S2, converting the geometric features into process features to obtain process steps related to the entire production process of the product and equipment corresponding to each process step; S3, based on the process steps related to the production process and the equipment corresponding to each process step, simulate the entire industrial manufacturing process of the product and output the key performance indicators of industrial manufacturing; Wherein, the step S2 further includes the following steps: S21, instantiating the geometric features into a knowledge base; S22, converting the geometric features into process features according to the rules in the knowledge base; S23, analyzing the process characteristics, obtaining a plurality of process steps arranged in sequence related to the production process of the process characteristics, and allocating equipment corresponding to each process step; The knowledge base includes an ontology base and a rule base; The ontology library includes classes, attributes, and instances; The rules of the rule base include: - a first relationship rule between the geometric feature and the process feature; - the process characteristics and the multiple process steps arranged in sequence related to the production process; - A second relationship rule for each process step and its corresponding equipment.

2. The method according to claim 1, characterized in that The step S22 further includes the following steps: calling the first relationship rule in the knowledge base, and matching the geometric feature to a corresponding process feature based on the first relationship rule, The step S23 further includes the following steps: calling the second relationship rule in the knowledge base, and mapping the process feature into a plurality of process steps arranged in sequence related to a production process of the process feature based on the second relationship rule; The third relationship rule in the knowledge base is called, and corresponding equipment is allocated to each process step based on the third relationship rule.

3. A system for evaluating KPIs in the production process based on geometric models, where: include: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, wherein when the instructions are executed by the processor, the system for evaluating KPIs in a production process based on a geometric model performs actions, the actions comprising: S1, extracting geometric features and their feature attributes from the product model; S2, converting the geometric features into process features to obtain process steps related to the entire production process of the product and equipment corresponding to each process step; S3, based on the process steps related to the production process and the equipment corresponding to each process step, simulate the entire industrial manufacturing process of the product and output the key performance indicators of industrial manufacturing; The action S2 further includes: S21, instantiating the geometric features into a knowledge base; S22, converting the geometric features into process features according to the rules in the knowledge base; S23, analyzing the process characteristics, obtaining a plurality of process steps arranged in sequence related to the production process of the process characteristics, and allocating equipment corresponding to each process step; The knowledge base includes an ontology base and a rule base; The ontology library includes classes, attributes, and instances; The rules of the rule base include: - a first relationship rule between the geometric feature and the process feature; - the process characteristics and the multiple process steps arranged in sequence related to the production process; - A second relationship rule for each process step and its corresponding equipment.

4. The system according to claim 3, characterized in that The action S22 further includes: calling the first relationship rule in the knowledge base, and matching the geometric feature to a corresponding process feature based on the first relationship rule, The action S23 further includes: calling the second relationship rule in the knowledge base, and mapping the process feature into a plurality of process steps arranged in sequence related to a production process of the process feature based on the second relationship rule; The third relationship rule in the knowledge base is called, and corresponding equipment is allocated to each process step based on the third relationship rule.

5. A device for evaluating KPIs in the production process based on geometric models, wherein: include: An extraction module, which extracts geometric features and their feature attributes from the product model; a conversion module, which converts the geometric features into process features to obtain process steps related to the entire production process of the product and the equipment corresponding to each process step; The simulation module simulates the entire industrial manufacturing process of the product based on the process steps related to the production process and the equipment corresponding to each process step, and outputs the key performance indicators of industrial manufacturing; Wherein, the conversion module is further used for: Instantiating the geometric features into a knowledge base; Converting the geometric features into process features through rules in a knowledge base; Analyze the process characteristics, obtain a plurality of process steps arranged in sequence related to the production process of the process characteristics, and allocate equipment corresponding to each process step; The knowledge base includes an ontology base and a rule base; The ontology library includes classes, attributes, and instances; The rules of the rule base include: - a first relationship rule between the geometric feature and the process feature; - the process characteristics and the multiple process steps arranged in sequence related to the production process; - A second relationship rule for each process step and its corresponding equipment.

6. The device according to claim 5, characterized in that The conversion module is also used for: calling the first relationship rule in the knowledge base, and matching the geometric feature to a corresponding process feature based on the first relationship rule, The simulation module is also used for: calling the second relationship rule in the knowledge base, and mapping the process feature into a plurality of process steps arranged in sequence related to a production process of the process feature based on the second relationship rule; The third relationship rule in the knowledge base is called, and corresponding equipment is allocated to each process step based on the third relationship rule.

7. A computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the method according to claim 1 or 2.

8. A computer-readable medium having stored thereon computer-executable instructions which, when executed, cause at least one processor to perform the method according to claim 1 or 2.

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