A method for constructing a digital mockup model of a part machining

Abstract

The application provides a part machining digital mockup model construction method, including: based on the block definition diagram of SysML, respectively, the process design model, the manufacturing process model and the quality model, the structured modeling is carried out, and the data elements in each model and the relationship type between the data elements are defined; based on the activity diagram of SysML, the activity sequence, input and output and judgment logic in the process design flow, manufacturing execution flow and quality inspection flow of part machining are described, and the behavior modeling is carried out; based on the parameter diagram of SysML, the machining constraint model is constructed, the constraint conditions and mathematical relationships between machining parameters, tool states and quality indexes are defined, and the parameterized modeling is carried out; the structured modeling, behavior modeling and parameterized modeling are integrated to form a digital mockup model. The application uses SysML language to supplement the modeling system in three dimensions of structure, behavior and parameter, so that the description of the model to the part machining process is more systematic and more standardized.

Description

Technical Field

[0001] This application relates to the field of data description and organization related to parts processing, and more specifically, to a method for constructing a digital prototype model for parts processing. Background Technology

[0002] Against the backdrop of Industry 4.0 driving the digital and intelligent transformation of manufacturing, digital prototypes have evolved from the 1.0 stage, which focused on the design phase, to the 2.0 stage, which focuses on the manufacturing phase. Building upon the 1.0 model, the digital prototype 2.0 model further digitizes manufacturing process information, working in conjunction with the digital prototype 1.0 model to support the digital needs of complex equipment parts processing. The parts processing of complex equipment involves multiple key stages, including process design, manufacturing process, and quality inspection, involving a wealth of multi-source heterogeneous data. These include process documents from the process design stage, process data such as equipment operating parameters during manufacturing, and dimensional accuracy measurement data from quality inspection. This data is scattered across different stages and varies in format.

[0003] In the prior art, patent application CN120493560A discloses a method and system for generating a 3D CAD model from a SysML model. This method uses a meta-model to drive the integration of information and generate the 3D CAD model. The generation method includes the following steps: S1, using a meta-model to identify the target structural information to be integrated in the system modeling language model and the CAD model; S2, checking whether the system modeling language model has been identified using a meta-model, and traversing the system modeling language model tree from top to bottom for identified models; S3, creating the corresponding 3D CAD model by calling an application programming interface (API) based on the meta-model mapping relationship; S4, calling the API to set the attribute information of the 3D CAD model based on the attribute values ​​of the system modeling language model; S5, calling the API to add meta-model identifiers to the 3D CAD model. However, the current model in this technology does not systematically organize and correlate process, quality-related data, resulting in poor data reusability and consistency.

[0004] Current digital prototype 2.0 modeling methods lack semantically unified standards for these multi-source data and their relationships, and also fail to systematically organize and correlate process, quality-related data. This results in poor data reusability and coherence, making it difficult to effectively support dynamic control, quality traceability, and process optimization in the manufacturing process. Consequently, it restricts the practical application of digital prototype 2.0 models in the field of digital manufacturing of complex equipment parts. Summary of the Invention

[0005] In view of one of the defects in the prior art, the purpose of this application is to provide a method for constructing a digital prototype model for part processing.

[0006] A first aspect of this application provides a method for constructing a digital prototype model for part machining, comprising: A data model framework is built based on the SysML language, which includes a process design model, a manufacturing process model, and a quality model. Based on SysML block definition graphs, structured modeling is performed on the process design model, the manufacturing process model, and the quality model, defining the data elements within each model and the relationship types between the data elements; Based on SysML activity diagrams, we describe the process design flow, manufacturing execution flow, and quality inspection flow of part machining, including activity sequences, inputs and outputs, and decision logic, and perform behavioral modeling. A machining constraint model is constructed based on SysML parametric graphs, defining the constraints and mathematical relationships between machining parameters, tool states, and quality indicators, and performing parametric modeling. The structured modeling, behavioral modeling, and parametric modeling are linked and integrated to form a digital prototype model.

[0007] Optionally, the data elements include models, manufacturing resources, manufacturing data, manufacturing processes, and manufacturing objects; The relationship types between the data elements include at least one of the following: composition relationship, generalization relationship, referencing relationship, physical / data flow relationship, sequence relationship, and constraint relationship; The manufactured object includes digital domain attributes and physical domain attributes; the digital domain attributes are used to describe the theoretical parameters of the manufactured object, and the physical domain attributes are used to describe the measured parameters of an individual manufactured object.

[0008] Optionally, the structured modeling includes: Construct a process design model and link it with the manufacturing bill of materials, material quota model, process model, process document model, and process resource model; Define the attributes of each module, wherein the manufacturing bill of materials includes the attributes of unit validity, material, and part drawing number, and the process resource model associates tooling and machine tools and specifies the technical parameters; The hierarchical structure and associated logic of each module are described by the definition diagram in the SysML block, and the mapping relationship between the digital domain abstract class and the physical domain is established.

[0009] Optionally, the behavior modeling includes: Construct a process design activity diagram, starting with the decomposition of process planning, which includes resource allocation, data initialization, process specification preparation and review, tool selection, process content preparation and integration, etc., and clarifies the process judgment nodes and input-output relationships. Construct a manufacturing process activity diagram that covers processes such as task allocation, blank preparation, fixture clamping, component processing, process monitoring, and quality inspection, and define exception handling logic; Construct a quality inspection activity diagram that includes steps such as part coding, inspection equipment calibration, part quality testing, handling of out-of-tolerance issues, and warehousing, and clarify the flow path of inspection data.

[0010] Optionally, the parameter modeling includes: Construct a machining constraint model that covers cutting parameter constraints, tool state constraints, mass constraints, cutting force constraints, and machining deformation constraints; Define the mathematical logic relationship between each constraint. Among them, the cutting parameter constraint specifies the range of values ​​for cutting depth, feed rate, and spindle speed, while the quality constraint includes dimensional tolerance, geometric tolerance, and surface roughness requirements. By using SysML's "equal" rule, constraint parameters are bound to actual data in the process model, process resource model, and quality model to ensure parameter consistency.

[0011] Optionally, the step of associating and integrating the structured modeling, the behavioral modeling, and the parametric modeling to form a digital prototype model includes: The model elements are converted into structured XML files using SysML's XML serialization mechanism, preserving hierarchical relationships and semantic associations. The XML file enables data parsing and semantic alignment with CAD / CAM, MES, and PLM systems. A version control mechanism is established using the traceability metadata of the XML file to record the entire lifecycle changes of the model and related data, forming a digital prototype model.

[0012] Optionally, after forming the digital prototype model, the following may also be included: Dynamic monitoring and anomaly handling of the processing are carried out based on the constructed digital prototype model; The processing and inspection data are fed back to the digital prototype model to iteratively optimize the process parameters and constraints.

[0013] Optionally, the dynamic monitoring and anomaly handling of the processing based on the constructed digital prototype model includes: Real-time acquisition of physical domain status data of tooling, equipment, and cutting tools, including tool wear, machine tool operating parameters, and fixture clamping status; Verify the compliance of machining parameters based on the machining constraint model, and monitor the machining status and tool status in real time; When an abnormal machining condition or a tool condition exceeding a threshold is detected, the system automatically triggers a process to adjust cutting parameters, stop for inspection, or change the tool, and retains key process data.

[0014] Optionally, the step of feeding back the processing and inspection data to the digital prototype model to iteratively optimize the process parameters and constraints includes: The quality inspection results, manufacturing process anomaly data, and tool status data are synchronously fed back to the process design model. Optimize process parameters and adjust tool selection or quality requirement thresholds based on feedback data; The constraints and relationships in the digital prototype model are updated to form an iterative optimization process of design-production-testing-optimization.

[0015] Optionally, the process document module is associated with production task work orders, equipment lists, material lists, and process change orders. The attributes of the production task work order include task name, work order number, and associated process route number, which are used to drive the execution and tracking of specific processing tasks.

[0016] This application provides a method for constructing a digital prototype model for parts processing. It employs the SysML modeling language to model the data involved in processing from three aspects: technology, process, and quality. This method utilizes SysML to uniformly build a data model framework encompassing three core modules: process design, manufacturing process, and quality. Block definition diagrams are used to structurally organize the data elements and relationships of each model. Activity diagrams clarify the dynamic execution logic of the entire processing flow. Parametric diagrams establish quantitative constraints between processing parameters, tool states, and quality indicators. Finally, multi-dimensional modeling and integration are achieved, effectively solving the problems of inconsistent semantics, fragmented data relationships, and incomplete process descriptions in parts processing. This method realizes systematic data organization and full-process traceability, providing precise data support for dynamic control, process optimization, and quality management of the manufacturing process. It enhances the completeness, standardization, and practicality of the digital prototype model, facilitating the digital and intelligent transformation of complex equipment parts processing and improving production efficiency and product quality stability.

[0017] Other technical effects resulting from the additional features will be further illustrated in the corresponding embodiments. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a mechanical processing design model diagram illustrating an exemplary embodiment; Figure 2A process document model diagram illustrating an exemplary embodiment; Figure 3 A process design activity diagram illustrated according to an exemplary embodiment; Figure 4 This is a model diagram of a machining manufacturing process according to an exemplary embodiment; Figure 5 This is a model diagram of a machining unit according to an exemplary embodiment; Figure 6 This is an activity diagram illustrating the part machining process according to an exemplary embodiment; Figure 7 This is a model diagram of machining quality according to an exemplary embodiment; Figure 8 This is a diagram illustrating a quality inspection activity according to an exemplary embodiment; Figure 9 This is a machining constraint model illustrated according to an exemplary embodiment; Figure 10 A parameter diagram of a machining constraint model according to an exemplary embodiment; Figure 11 This is a flowchart illustrating a method for constructing a digital prototype model of a part according to an exemplary embodiment. Detailed Implementation

[0019] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all fall within the protection scope of the present application. Parts not described in detail in the following embodiments can be implemented using existing technology.

[0020] Current digital prototype modeling methods lack a unified semantic standard for these multi-source data and their relationships, and also fail to systematically organize and correlate process, quality-related data. This results in poor data reusability and coherence, making it difficult to effectively support dynamic control, quality traceability, and process optimization in the manufacturing process. Consequently, this restricts the practical application of digital prototype models in the field of digital manufacturing of complex equipment parts. To address these issues, this application provides a method for constructing a digital prototype model for parts processing, thereby resolving the aforementioned problems.

[0021] Reference Figure 11 As shown in one embodiment of this application, a method for constructing a digital prototype model for part machining includes: S100. A data model framework is built based on the SysML language. The data model framework includes a process design model, a manufacturing process model, and a quality model. S200, based on SysML block definition diagrams, performs structured modeling of process design model, manufacturing process model and quality model respectively, and defines the data elements within each model and the relationship types between data elements; S300, based on SysML activity diagrams, describes the activity sequences, inputs, outputs, and judgment logic in the process design flow, manufacturing execution flow, and quality inspection flow of part processing, and performs behavioral modeling; S400: Based on SysML parametric diagrams, construct a machining constraint model, define the constraints and mathematical relationships between machining parameters, tool states, and quality indicators, and perform parametric modeling. S500 integrates structured modeling, behavioral modeling, and parametric modeling to form a digital prototype model.

[0022] Specifically, a unified data model framework, including a process design model, a manufacturing process model, and a quality model, is built using the SysML language. Then, structured modeling of the three core models is performed using SysML block definition diagrams to clarify the categories of data elements within each model and the types of relationships between these elements. Subsequently, SysML activity diagrams are used to clearly describe the activity sequences, input and output data, and key judgment logic of process design, manufacturing execution, and quality inspection throughout the entire part machining process, completing dynamic behavioral modeling. Simultaneously, a machining constraint model is constructed based on SysML parametric diagrams, quantifying and defining the constraints and mathematical relationships between machining parameters, tool states, and quality indicators, achieving parametric modeling. Finally, the results of structured modeling, behavioral modeling, and parametric modeling are integrated in multiple dimensions to form a digital prototype model covering the entire lifecycle of part machining.

[0023] The embodiments described above in this application achieve semantic unification and system association of multi-source heterogeneous data through the SysML language, solving the problems of data dispersion and one-sided description in traditional modeling. The combination of the three core models and multi-dimensional modeling allows for a comprehensive presentation of the static data structure, dynamic process logic, and quantitative parameter constraints of part processing. The structured, behavioral, and parameterized association and integration mechanism ensures the continuity and reusability of data, providing accurate data support for the dynamic control of the manufacturing process. The construction and application of the machining constraint model realizes the quantitative control of the machining process, assisting in quality traceability and process optimization. It also enhances the ability of digital prototypes to support the digital needs of complex equipment part processing.

[0024] It should be noted that the constructed SysML model is a logically unified multi-view integration model. Its structure, behavior, and parameter views achieve dynamic linkage through shared core definitions and parameter associations, specifically reflected in: Unified data definition: The data flowing in the behavioral model (activity diagram) (such as "cutting parameters") has its data structure and attributes completely referenced from the corresponding modules already defined in the structural model (such as the attributes of the "process step" module). This ensures the consistency of data semantics throughout the entire process.

[0025] Logical rule-driven: Quantitative rules (such as "exceeding limits judgment") defined in the parametric model (parametric graph) are associated with attributes in the structural model and judgment conditions in the behavioral model. At the model level, this establishes a logical driving relationship of "if the parameter exceeds the limit, the corresponding behavior is triggered." For example, the logical output of "exceeding limits judgment" can directly correspond to the branch conditions of the "judgment node" in the behavioral model, thereby driving the process to activities such as "stop and check".

[0026] This embodies the core idea of ​​SysML multi-view modeling: using a structural model as the static data foundation, a behavioral model to describe dynamic processes, and a parametric model to define quantification rules. These three are integrated into an organic whole through sharing model elements and parameter associations.

[0027] The structural model, behavioral model, and parametric model constructed through the above steps do not exist in isolation within the SysML modeling environment. Instead, they form a logically unified whole by sharing model elements and associated parameters. The structural model serves as the data carrier: it defines the core elements and their attributes involved in the entire process.

[0028] The behavioral model references the structure: the data manipulated by the activity nodes in the behavioral model (activity diagram) and the conditions upon which the judgment nodes are based, all point to the corresponding entities and attributes defined in the structural model in terms of type and value. For example, the "tool status" data read by the "machining process monitoring" activity comes from the attributes of the "tool" module in the structural model.

[0029] The parametric model constrains both aspects: the mathematical constraints defined in the parametric model (parametric diagram) (such as the range of machining parameters) establish a relationship between their variables and the attributes in the structural model and the judgment conditions in the behavioral model. This logically defines the execution boundaries and judgment criteria of the behavioral model (e.g., "when the measured dimension exceeds the tolerance constraint, trigger the out-of-tolerance handling procedure"). This application directly outputs a standardized data model (i.e., a SysML model file) based on the SysML language and integrating structure, behavior, and parametric views. This model, as a standardized data carrier and knowledge encapsulation, possesses standardization, exchangeability, and supportability. Specifically, standardization provides a unified and unambiguous semantic description framework for the process, procedure, and quality data of part manufacturing. Exchangeability allows it to be exported as a structured file using standard formats such as XML, which can be imported into engineering platforms or systems such as MES and PLM that support SysML. Supportability provides a consistent, complete, and resolvable data foundation for advanced application functions such as simulation analysis, process monitoring, quality traceability, and process optimization in these downstream systems.

[0030] In some specific embodiments of this application, data elements include models, manufacturing resources, manufacturing data, manufacturing processes, and manufacturing objects.

[0031] The types of relationships between data elements include at least one of the following: composition relationship, generalization relationship, referencing relationship, physical / data flow relationship, sequence relationship, and constraint relationship.

[0032] The manufactured object includes digital domain attributes and physical domain attributes; digital domain attributes are used to describe the theoretical parameters of the manufactured object, while physical domain attributes are used to describe the measured parameters of an individual manufactured object.

[0033] Specifically, the element types of the data model are defined as follows: (1) Model: The meaning of model is data model, including process design data model, manufacturing process data model, and quality data model. (2) Manufacturing resources: The meaning of manufacturing resources is the processing unit, production line, workshop that performs manufacturing tasks, as well as the equipment, special tooling, general-purpose cutting tools, software system, etc. that make up the processing unit. (3) Manufacturing data: Data forms and records generated by the manufacturing system. (4) Manufacturing process: The processing process includes the process route, operation, and step in the product manufacturing process, representing the process of inputting raw materials, using processing units to complete processing tasks, and outputting product parts. (5) Manufacturing object: The processing object includes the raw material blank before processing and the parts after processing, and has physical domain attributes and digital domain attributes. The digital domain attributes are identified by the drawing number and describe the theoretical parameters of the processing object (material hardness, processing feature dimension tolerance range, etc.); the physical domain attributes are identified by the serial number and describe the measured parameters of the individual processing object (measured hardness of the blank, measured value of the feature dimension of the part, etc.).

[0034] The embodiments described above in this application, by clearly defining data elements encompassing models, manufacturing resources, manufacturing data, manufacturing processes, and manufacturing objects, cover the core information dimensions of the entire lifecycle of part processing. Simultaneously, by defining at least one relationship type such as composition, generalization, and referencing, standardized association specifications are provided for multi-source heterogeneous data, solving the problems of data semantic confusion and fragmented associations in traditional modeling. Furthermore, the manufacturing objects specifically distinguish between the digital domain attributes describing theoretical parameters and the physical domain attributes describing individual measured parameters, achieving a mapping between design intent and actual manufacturing state. This ensures the integrity of data description and provides clear data support for individual traceability and quality verification of part processing, improving the data organization standardization, association coherence, and practical application adaptability of the digital prototype model.

[0035] It should be noted that, regarding the association between digital and physical domain attributes, this model employs an efficient and clear association design for reusable resources (tools) and one-time processed objects (parts). Take manufacturing objects and manufacturing resources as an example.

[0036] For manufactured objects (such as parts): the association is achieved through "material number". The manufacturing bill of materials module defines the part drawing number and material number; the blank entity module defines the material number and serial number. Thus, by matching the material number, a specific entity instance (identified by the serial number) can be accurately associated with its design specifications (identified by the part drawing number), realizing a "one (drawing number) to many (serial number)" mapping.

[0037] For manufacturing resources (such as cutting tools): the correlation mechanism is more direct, see reference... Figure 5 As shown. Taking a cutting tool as an example, the cutting tool module is associated with both the cutting tool digital domain attribute module and the cutting tool physical domain attribute module. An instance of a cutting tool module (representing a specific cutting tool) is formed by binding its digital domain attributes and physical domain attributes through these two associations, thus constituting a complete information entity. The association method for fixtures, gauges, and machine tools is the same.

[0038] The relationship types between elements of the data model are defined as follows: (1) Composition relationship: Composition relationship is used to describe the composition relationship between physical objects, processes, and data. (2) Generalization relationship: Generalization relationship is used to describe the inheritance relationship between parent and child classes in each model element. The child class inherits all the attribute parameters and association relationships of the parent class, and the child class has unique attribute parameters and association relationships. (3) Reference relationship: Reference relationship describes the association reference between data. (4) Physical object / data flow: Input and output relationship between processing process and physical object / data, described by dependency connection lines and ports. (5) Sequence relationship: Sequential relationship between processing processes. (6) Constraint relationship: Use parametric diagrams to describe the constraints between each element in the processing system to meet the modeling requirements of the internal relationship of the production line model.

[0039] It should be noted that "manufacturing object" is a logical concept, referring to a blank or part in the manufacturing process. The allocation of its attributes in the SysML model follows clear data management principles: Numerical field attributes: Reflect design specifications and type-level identifiers. For example, the "part drawing number" is used to uniquely identify the design drawing and technical requirements of the manufactured object. Parts in the same batch share the same drawing number. It is defined in the "Manufacturing Bill of Materials" module of the management specification master data to ensure the uniqueness and consistency of the data source.

[0040] Physical domain attributes: These reflect the actual state and fluctuations of individual manufactured entities, such as blank serial number and measured dimensions, and are defined in the "Blank Entity" module that represents the specific entity.

[0041] In the SysML model, a "blank entity" (Block) can be associated with its "material number" attribute and the corresponding attribute in the "manufacturing bill of materials" to determine its "part drawing number." This demonstrates the SysML modeling method of representing complex objects through module combination and association. For example, Figure 5 The "Tool Module" also describes a tool completely by associating it with two sub-modules: "Tool Digital Domain Attributes" (specifications) and "Tool Physical Domain Attributes" (status).

[0042] In some specific embodiments of this application, structured modeling includes: constructing a process design model, associating it with a manufacturing bill of materials, a material quota model, a process model, a process document model, and a process resource model; defining the attributes of each module, wherein the manufacturing bill of materials includes unit validity, material, and part drawing number attributes, and the process resource model associates tooling and machine tools and clarifies technical parameters; describing the hierarchical structure and association logic of each module through definition diagrams in SysML blocks, and establishing a mapping relationship between digital domain abstract classes and physical domains.

[0043] Specifically, refer to Figure 1 As shown, the process design model is first constructed as the top-level module, linking the manufacturing bill of materials, material quota model, process model, process document model, and process resource model. It contains a model number, model name, description, etc., used to clarify the basic identifiers and functional specifications of the entire process design model. The relationships and attribute definitions of the various module elements in the diagram are described using a module definition diagram in SysML.

[0044] The attributes of the manufacturing bill of materials include unit validity, material, component type, material number, and part drawing number. These contents can clearly define the material identification information corresponding to the parts required for processing, which facilitates material verification and full-process traceability in subsequent processing stages.

[0045] The material quota model is linked to the blank information. The blank information attributes include cutting dimensions, number of parts, blank specifications, quota parameters, material name, material grade, material specifications, and blank quantity. This information can clarify the material consumption standards and basic parameters of the blank during the processing, helping to accurately control material costs and rationally plan material preparation.

[0046] The process model is associated with the process route, operations, and steps, and is also linked to the process specification. The process route attributes include the part drawing number, consists of operations, and is associated with the process specification; the operations attributes include the operation number and the unit of work, and are associated with the CNC program, machining process card, and inspection procedure; the steps attributes cover cutting parameters, machining content, step number, type of work, quality requirements, and are also associated with tooling and blank information.

[0047] The process document model comprises CNC programs, inspection procedures, process technical documents, and operating procedures, along with supporting documents such as bills of materials, process change orders, and equipment lists. These documents ensure the completeness of various technical documents during the processing, facilitating standardized execution and document traceability in each processing step. The process procedures include attributes such as procedure name, procedure number, machining process card corresponding to the operation number, CNC program corresponding to the program number, and inspection procedures corresponding to the version number and procedure name, thus linking the technical specifications and execution basis of the entire processing flow.

[0048] The process resource model links tooling and machine tools. Tooling includes cutting tools, fixtures, and measuring tools. Cutting tool attributes include tool number, tool geometry parameters, tool specifications, tool wear condition, tool life, tool cutting parameters, tool function, and material. Fixture attributes include fixture number, fixture type, positioning accuracy, and fixture description. Measuring tool attributes include measuring tool number, measurement range, measurement accuracy, and measuring tool type. These attributes collectively clarify the parameters and status of various resources required for machining, providing data support for the rational allocation and effective use of resources.

[0049] Reference Figure 2 As shown, the process document model consists of blocks such as process change orders, process technical documents, bill of materials, production task orders, equipment lists, and process routes. It also has attributes such as operation process, document number, responsible personnel, part number, and page number. Furthermore, it is linked to the 3D model, process cards, and part design information, thereby clarifying the core content and related basis of the entire process document model.

[0050] The production task work order attributes include task name, work order number, process route number, and part number, and are also associated with the process route. This clearly identifies the specific processing task information, facilitating subsequent task assignment, tracking, and process route matching. The equipment list is associated with cutting tools, fixtures, machine tools, and measuring instruments. This association clearly identifies the various equipment and tools required for the processing task, providing a clear basis for equipment allocation and preparation. The material list attributes include supplier information, name, quantity, and specifications. This clearly identifies the source, identification, and usage of the materials required for processing, aiding in material procurement, verification, and inventory management. The process change order attributes include document number, change content, change time, and responsible person. This information records the specific content of the process adjustment and the relevant responsible persons, facilitating subsequent tracing of the reasons and process of the process change. The process technical document attributes include technical content, document number, change time, and responsible person. This content summarizes the technical requirements and document information corresponding to the processing task, providing support for the technical execution and document management of the processing stage.

[0051] The process route includes procedural documents, whose attributes cover the process sequence and workstations, along with a process flow diagram. This clearly outlines the execution steps, sequence, and corresponding work locations of processing tasks, providing guidance for the standardized execution of the processing flow. Procedural documents include process procedures, operating procedures, and testing procedures, whose attributes cover specific content, process parameters, operating methods, equipment requirements, and responsible personnel. This content details the execution standards and supporting requirements for various procedures, providing a basis for the standardized implementation of processing and testing. Operating procedures include attributes such as reviewer, version number, effective date, compiler, procedure content, and procedure name. This information clarifies the compilation and review information and specific content of the operating procedures, ensuring the standardization and effectiveness of the operating procedures. Process procedures include attributes such as reviewer, version number, effective date, compiler, procedure content, and procedure name. This information clarifies the core content and management information of process-related specifications, ensuring the consistency of process execution. The attributes of the testing procedure include the reviewer, version number, effective date, compiler, procedure content, and procedure name. These contents clarify the execution standards and management information of the testing process, providing a standardized basis for the testing and control of processing quality.

[0052] Reference Figure 4 As shown, the module definition diagram of the machining manufacturing process includes modules such as operators, machining unit entities, blank entities, machining process records, and production planning and scheduling information, which are used to clarify the core components of the manufacturing process model and the related logic of each link.

[0053] The operator module includes attributes such as name, employee number, job type, and skill level. These attributes are used to clarify the identity and skill information of the personnel involved in the processing, providing data support for the division of labor and skill matching in the processing process.

[0054] The machining unit entity is associated with tool entities, fixture entities, and machine tool entities: the tool entity's attributes include tool sequence and usage status; the fixture entity's attributes include fixture sequence and usage status; and the machine tool entity's attributes include machine tool type, machine tool location, and machine tool number. This information clearly identifies the equipment and tools required by the machining unit and their current status, supporting the allocation of machining resources and real-time status monitoring.

[0055] The attributes of the blank entity include the blank serial number, measured dimensions, and physical and chemical test results. These are used to record the identification, actual state, and preliminary verification data of the blank to be processed, and to assist in the verification of the basic information and status tracking of the blank during the processing.

[0056] The machining process recording module's attributes cover cutting parameter adjustment, measured data recording, equipment operation records, and part sequence. It also links to machining flow cards and quality inspection sheets to retain key parameters and equipment status information during machining, providing a basis for process traceability and quality analysis. The machining flow card's attributes include the number of inspections, process number, process name, work description, operator, date, and inspector, used to standardize the recording of execution information for each machining process and clarify the responsible party and execution nodes. The quality inspection sheet's attributes include the process, measured value, verification certificate, and part sequence, used to record the quality inspection results of each process, assisting in the judgment and traceability of machining quality.

[0057] The production planning and scheduling information module includes attributes such as production units, planned start time, actual end time, production batch, production priority, production start time, and planned end time. These attributes clarify the planning and scheduling requirements of production tasks, supporting the progress control and resource coordination of processing tasks.

[0058] Reference Figure 5 As shown in the diagram, the machining unit module definition diagram includes two modules: tooling and machine tool. It contains attributes such as unit location and operator number to clarify the basic positioning of the machining unit and the identification of the operator, supporting the basic information management of the machining unit.

[0059] The tooling module comprises three sub-modules: measuring tools, fixtures, and cutting tools. These correspond to the measuring, clamping, and cutting tools required during the machining process, enabling categorized management of machining auxiliary resources. The measuring tool module associates both digital and physical domain attributes: the digital domain attributes include supplier, measurement range, measurement accuracy, measuring tool name, measuring tool model, and measuring tool type, used to record the basic identification and technical parameters of the measuring tool; the physical domain attributes include actual calibration deviation, calibration date, and calibration status, used to record the actual usage status and calibration information of the measuring tool, ensuring measurement accuracy. The fixture module associates both digital and physical domain attributes: the digital domain attributes include fixture name, supplier, fixture model, fixture type, clamping range, maximum clamping force, material information, and applicable machine tool, used to clarify the basic identification, technical parameters, and applicable scenarios of the fixture; the physical domain attributes include actual clamping deviation, calibration status, and cumulative clamping count, used to record the actual usage status and performance changes of the fixture, supporting status monitoring and maintenance. The tool module associates tool digital domain attributes and tool physical domain attributes: The tool digital domain attributes include supplier, tool geometry information, tool number, tool type, and material information, which are used to record the basic identification and technical parameters of the tool; The tool physical domain attributes cover remaining life, real-time cutting force, real-time cutting temperature, damage status, and wear amount, which are used to record the actual usage status and machining performance data of the tool, and assist in tool replacement and parameter adjustment.

[0060] The equipment / machine tool module associates machine tool digital domain attributes and machine tool physical domain attributes: The machine tool digital domain attributes cover supplier, parameter setting range, worktable size specifications, theoretical machining accuracy, equipment name, and equipment model, used to record the basic identification and technical parameters of the machine tool; The machine tool physical domain attributes include historical fault descriptions, remaining service life, real-time machining accuracy, cumulative usage time, maintenance measures records, and equipment status, used to record the actual operating status, faults, and maintenance information of the machine tool, supporting the operation monitoring and maintenance management of the machine tool.

[0061] Reference Figure 7The diagram shows the module definition for machining quality, comprising two modules: Quality Exceptions and Deviations, and Quality Characteristic Requirements. This forms the core data framework for parts machining quality control, covering key aspects such as quality standards and deviation records. The Quality Characteristic Requirements module includes two sub-modules: Quality Requirements and Specialized Measuring Tools. The attributes of specialized measuring tools include the tool serial number, accuracy, tool description, and measuring range. These clearly define the identification, technical parameters, and applicable scope of the measuring tools used for quality inspection, providing tool-level support for the accuracy of inspection data. The attributes of quality requirements include the process number, dimensional tolerance, tooling name, tooling annotation, geometric tolerance, inspection method, feature number, roughness requirements, and part drawing number. These clearly define the quality judgment standards corresponding to each machining process, serving as the core basis for quality inspection and control during machining. The Quality Exceptions and Deviations module is linked to three sub-modules: First Article Inspection Information, Out-of-Tolerance Report, and Non-Conforming Product Review Report. The First Article Inspection Information includes attributes such as inspector number, comparison with subsequent articles, inspection time, inspection result, and first article serial number. It records the inspection information after the first article is processed, enabling quality verification and benchmark establishment in the initial stages of processing. The Out-of-Tolerance Report includes attributes such as handling measures, handling result, approver number, out-of-tolerance report number, reason for out-of-tolerance, out-of-tolerance component serial number, and out-of-tolerance item. It records situations where quality parameters exceed standards during processing and the handling process, providing data support for deviation tracing and process adjustment. The Non-Conforming Product Review Report includes attributes such as non-conforming component serial number, non-conforming item, approver number, approval time, review report number, and review measures. It standardizes the review and handling process for non-conforming products, clarifying the basis for handling non-conforming products and the responsible party.

[0062] In some specific embodiments of this application, behavior modeling includes: Construct a process design activity diagram, starting with the decomposition of process planning, which includes resource allocation, data initialization, process specification preparation and review, tool selection, process content preparation and integration, and clarifies the process judgment nodes and input-output relationships. Construct a manufacturing process activity diagram that covers processes such as task allocation, blank preparation, fixture clamping, component processing, process monitoring, and quality inspection, and define exception handling logic; Construct a quality inspection activity diagram that includes steps such as part coding, inspection equipment calibration, part quality testing, handling of out-of-tolerance issues, and warehousing, and clarify the flow path of inspection data.

[0063] In the embodiments described above, behavioral modeling constructs three core activity diagrams—process design, manufacturing process, and quality inspection—to describe the dynamic logic of the entire part processing process from initial planning to mid-term execution and final inspection. The process design activity diagram starts with the decomposition of process planning, connecting resource allocation, process specification preparation and review, and other links, clarifying process judgment nodes and input-output relationships to ensure the standardization and operability of process design. The manufacturing process activity diagram covers the entire process, including processing task allocation, component processing, and process monitoring, and clarifies the abnormal handling logic, realizing the visualization and controllability of the manufacturing execution process. The quality inspection activity diagram refines the parts coding, equipment calibration, and out-of-tolerance handling links, and the inspection data flow path, providing guidance for quality traceability and solving the problems of fragmented process description and ambiguous key nodes in traditional modeling.

[0064] Among them, reference Figure 3 As shown, the activity diagram of process design differs from the structural block diagram that focuses on the association of elements. It focuses on the dynamic execution logic of the process, clearly presenting the activity flow sequence and judgment nodes of the entire process design.

[0065] The activity begins with "Process Planning and Decomposition," followed by the "Resource Allocation" and "Data Initialization" steps. After data initialization, it enters the "Process Specification" stage—this stage uses design drawings, part design information, equipment, tools, fixtures, etc., as input, and simultaneously outputs the corresponding specification document. Next, it enters the "Process Specification Review" checkpoint. If the review fails, it needs to return to the "Process Specification" stage to readjust the content; if the review passes, it enters the "Tool Selection" stage, which outputs a tool list and an equipment list. Then, it triggers a "Tool Trial Required" checkpoint: if a trial is required, it performs the "Tool Trial" operation and outputs the trial results; if no trial is required, it directly enters the "Process Content Compilation" stage. Process content compilation integrates information from previous processes to complete the content summary, then enters the "Process Content Review" checkpoint. If the review fails, it needs to return to the "Process Content Compilation" stage for adjustments; if the review passes, it executes the "Process Content Integration" step, finally outputting the process document and completing the entire process design activity.

[0066] Reference Figure 6As shown in the activity diagram of the manufacturing process, it presents the flow sequence and decision points of the entire process from task allocation to processing completion. The activity starts with "processing task allocation," which takes the process route and process specifications as input and outputs a production task order. Then, the steps of "blank preparation," "fixture clamping," "tool selection," and "tool clamping" are executed in sequence. Among them, fixture clamping calls the fixture as input, and tool selection calls the tool as input. After the tool clamping is completed, the corresponding component processing stage of the process step is entered. This stage calls the process step (including process step number, type of work, processing content), cutting parameters, blank information, quality requirements, etc. as input, and "process monitoring" is carried out in parallel. Process monitoring will trigger two decision points: one is "whether the processing status is abnormal." If abnormal, "cutting parameter adjustment" is executed, and then it is judged whether the abnormality is resolved. If not, "stop the machine for inspection and replace the tool" is executed; the second is "whether the tool status exceeds the threshold." If so, "stop the machine for inspection and replace the tool" is also executed. After component processing is completed, the process enters the "Parts Processing Quality Inspection" stage, outputting a quality inspection report and simultaneously conducting "Parts Performance Testing." This is followed by a "Pass / Fail" judgment node: if unqualified, "Rework / Repair or Scrap Processing" is executed; if qualified, this branch activity ends, and the information is fed back to the "Process Optimization Feedback" stage, providing input for subsequent process optimization. After stopping the machine for inspection and tool replacement, "Tool Wear and Damage Measurement" is executed. This stage uses the tool's physical domain attributes as input, triggering a "Need for Regrinding" judgment: if regrinding is needed, "Send for Repair" is executed, ending this branch activity; if regrinding is not needed, further judgment is made regarding scrapping. If so, "Tool Scrap" is executed, ending the activity; otherwise, "Tool Placement" is executed, ending the activity.

[0067] Reference Figure 8 As shown in the activity diagram of quality inspection, it presents the activity flow sequence and judgment nodes of the entire process from inspection initiation to result handling. The activity starts from "Activity Start", first executing the "Part Coding" step, using the part master data as input to complete the confirmation of part identification; then it enters the "Inspection Equipment Calibration" stage to ensure the accuracy and compliance of the testing equipment; next, "Part Quality Testing" is carried out, which calls performance test data, form and position accuracy, dimensional accuracy, and geometric feature data as input, and synchronously links them with the quality checklist (covering information such as whether it is qualified, metrology personnel, metrology date, and test items such as mechanical properties and machining accuracy), and outputs the corresponding test results.

[0068] After completing the part quality test, the process proceeds to the "Out of Tolerance" judgment node: if the result is "No," "Qualification Confirmation" is executed, followed by the "Warehouse Entry" operation, and this branch activity ends; if the result is "Yes," "Out of Tolerance Record" is executed, followed by the "Out of Tolerance Record Review" stage, using the out of tolerance record as input to complete the review process. After the out of tolerance record review, the "Repairable" judgment is triggered: if the result is "No," "Part Scrap" is executed, and this branch activity ends; if the result is "Yes," the "Rework Handover," "Rework and Reprocessing," and "Rework Data Record" steps are executed sequentially, with the rework data record outputting the part rework record, followed by the "Rework Qualified" judgment node—if the result is "Yes," the "Warehouse Entry" operation is executed, and the activity ends; if the result is "No," the process returns to the rework process for reprocessing. Furthermore, information from the entire quality inspection process is synchronously transferred to the "Quality Traceability Feedback Optimization" stage, ultimately outputting to the process design model to provide data support for subsequent process quality optimization.

[0069] In some specific embodiments of this application, parameter modeling includes: Construct a machining constraint model that covers cutting parameter constraints, tool state constraints, mass constraints, cutting force constraints, and machining deformation constraints; Define the mathematical logic relationship between each constraint. Among them, the cutting parameter constraint specifies the range of values ​​for cutting depth, feed rate, and spindle speed, while the quality constraint includes dimensional tolerance, geometric tolerance, and surface roughness requirements. By using SysML's "equal" rule, constraint parameters are bound to actual data in the process model, process resource model, and quality model to ensure parameter consistency.

[0070] In the embodiments described above, parametric modeling constructs a multi-dimensional machining constraint model encompassing cutting parameters, tool state, quality, cutting force, and machining deformation. This model quantifies and controls key parameters during part machining, avoiding the problems of one-sided and unsystematic parameter constraints in traditional modeling. Simultaneously, it clarifies the mathematical logic relationships of each constraint, defines the value ranges for machining parameters such as depth of cut and feed rate, and establishes clear judgment standards for quality indicators such as dimensional tolerances and surface roughness, providing clear quantitative basis for parameter constraints. Furthermore, the "equal" rule in SysML precisely binds the constraint parameters to actual data in the process, process resources, and quality model, solving the problem of disconnect between theoretical constraints and actual machining data and ensuring parameter consistency throughout the entire process.

[0071] Among them, refer to the appendix Figure 9As shown, a machining constraint model is described. Its top layer is the "Machining Constraint Model" module, which associates constraint attributes such as cutting parameter constraints, tool state constraints, quality constraints, cutting force constraints, and machining deformation constraints, thus constructing a constraint framework covering machining parameters, tool state, and quality standards.

[0072] The tool status constraint is associated with two constraints: "tool wear value" and "tool breakage state". The constraint logic for tool wear value is {VB=f(p,signals)} and {Wear_state=f(p,signals)}, with parameters including p, VB, and Wear_state. The constraint logic for tool breakage state is {B=f(signals,p)}, with parameters including p and B, which are used to define the tool's usage state threshold.

[0073] The quality constraints are related to two constraints: "dimensional tolerance and geometric tolerance" and "surface roughness constraint". The constraint logic of dimensional tolerance and geometric tolerance is {Dmin≤δ≤Dmax}, and the parameters include Dmax, Dmin, and δ. The constraint logic of surface roughness constraint is {Ra=fr² / 8r}, and the parameters include fr, r, and Ra. This clarifies the allowable range and calculation logic of the quality indicators.

[0074] Furthermore, the logic for cutting parameter constraints is {apmin≤ap≤apmax} {frmin≤fr≤frmax} {vcmin≤vc≤vcmax}, where the parameters include ap, fr, vc, and their corresponding upper and lower limits. The logic for cutting force constraints is... The parameters include ap, Kc, and Fc. The logic of the processing deformation constraint is {Y=Fc / k}, and the parameters include Fc, k, and Y. These constraints together define the relationship logic between the parameter boundaries and physical quantities in the processing process.

[0075] Reference Figure 10 As shown, the parameter relationship diagram of the machining constraint model focuses on the parameter input and output mapping relationship between each constraint and the external model, clarifying the parameter source and linkage object of the constraint logic: The cutting parameter constraints, such as ap (cutting), vc (spindle speed), and fr (feed rate), are directly associated with the corresponding parameters in the "process model (step)" through the "equal" rule, achieving synchronization between the constraint parameters and the actual machining parameters. The cutting force constraint parameter Fc is linked to the cutting parameter constraints ap and fr, and also serves as an input parameter for machining deformation constraints, forming a logical transfer of physical quantities.

[0076] The dimensional tolerances, geometric tolerances, and surface roughness requirements corresponding to the quality constraints are mapped to the corresponding parameters in the "quality model (quality requirements)" through the "equal" rule, ensuring the consistency between the constraint indicators and the quality standards. The VB parameter of the tool wear value and the B parameter of the tool breakage state are associated with the tool life, wear state, and other parameters in the "process resource model (tool)" to achieve the linkage between the constraints and the actual state of the tool.

[0077] By using parameter association rules, constraint logic is bound to the actual parameters of models such as processes, process resources, and quality, ensuring that constraint requirements can be implemented in the specific data of the processing process, thus guaranteeing the executability of constraints and data consistency.

[0078] In some specific embodiments of this application, structured modeling, behavioral modeling, and parametric modeling are integrated to form a digital prototype model, including: The model elements are converted into structured XML files using SysML's XML serialization mechanism, preserving hierarchical relationships and semantic associations. Data parsing and semantic alignment with CAD / CAM, MES, and PLM systems are achieved through XML files; A version control mechanism is established by using the traceability metadata of XML files to record the entire lifecycle changes of the model and related data, forming a digital prototype model.

[0079] Specifically, SysML's XMI serialization mechanism is first used to convert the integrated multi-dimensional model elements into standardized structured XML files, fully preserving their semantics and hierarchy. This XML file enables data parsing and mapping with heterogeneous systems such as CAD, MES, and PLM, driving collaboration in design, production, and management. Finally, the traceability metadata built into the XML file is used to establish a comprehensive control mechanism, recording every change to the model itself and related data throughout the entire lifecycle of design, production, inspection, and optimization, forming a digital prototype model that covers the entire parts processing process, is data-interoperable, and traceable.

[0080] To achieve semantic alignment, firstly, a unified semantic dictionary is defined based on the SysML model to clarify the definitions of each core concept (such as process, resource, and quality characteristics); secondly, after generating the XML file, a clear correspondence is established between the tags and attributes in the XML and the target system (such as work order fields in MES and material attributes in PLM) through preset semantic mapping rules (e.g., XSLT style sheets or mapping configuration files); finally, data transformation is performed through these mapping rules to achieve semantically lossless data exchange.

[0081] It should be noted that the model in the above example is specifically the complete model of the digital prototype of part processing built based on the SysML language, including the process design data model, manufacturing process data model, quality data model, and various sub-models under each model.

[0082] The embodiments described above in this application, through SysML's XML serialization mechanism, ensure that the hierarchical relationships and semantic associations of structured, behavioral, and parametric modeling results are not lost, providing a reliable guarantee for the effective integration of multi-dimensional modeling data; by using XML files to achieve data parsing and semantic alignment with multiple external systems, the "information silo" problem of data isolation between systems in traditional manufacturing is solved, and the cross-scenario reusability of data is improved; based on the XML traceability metadata version control mechanism, the traceability of changes to the model and related data throughout their entire lifecycle is realized.

[0083] It's important to note the unified semantic framework: Data models built on SysML for process design, manufacturing processes, and quality inherently provide a unified semantic framework and conceptual definitions for cross-domain data. Predefined semantic mapping rules: When generating XML / XML files, predefined semantic mapping rules (such as using XSLT transformation scripts or configuring mapping tables) are used to map elements in the SysML model to specific data fields or structures of the target system (CAD / CAM / MES / PLM).

[0084] In some specific embodiments of this application, after forming the digital prototype model, the following is also included: Dynamic monitoring and anomaly handling of the manufacturing process are based on the constructed digital prototype model; The processing and inspection data are fed back to the digital prototype model to iteratively optimize the process parameters and constraints.

[0085] In the embodiments described above, after the digital prototype model is formed, dynamic monitoring and anomaly handling of the processing process are carried out based on the model. Parameter deviations, equipment malfunctions, and potential quality problems in production are captured in real time, avoiding resource waste and quality defects caused by process loss of control and anomaly expansion in traditional production, thus ensuring the stability and continuity of the processing process. At the same time, processing and inspection data are fed back to the model to iteratively optimize process parameters and constraints, forming a closed-loop mechanism of "modeling-application-feedback-optimization." This enables the model to continuously adapt to changes in the actual production scenario, solving the problem of traditional digital models being static and difficult to keep up with dynamic production adjustments, and improving the model's adaptability to production guidance.

[0086] In some specific embodiments of this application, dynamic monitoring and anomaly handling of the manufacturing process are performed based on the constructed digital prototype model, including: Real-time acquisition of physical domain status data of tooling, equipment, and cutting tools, including tool wear, machine tool operating parameters, and fixture clamping status; Verify the compliance of machining parameters based on the machining constraint model, and monitor the machining status and tool status in real time; When an abnormal machining condition or a tool condition exceeding a threshold is detected, the system automatically triggers a process to adjust cutting parameters, stop for inspection, or change the tool, and retains key process data.

[0087] Specifically, after the production plan is finalized, the entire process is executed according to the manufacturing process activity diagram, completing preliminary operations such as blank preparation, fixture clamping, and tool selection, while real-time collection of physical domain status data for tooling and equipment. Upon entering the component processing stage, processing monitoring is initiated simultaneously, verifying the compliance of processing parameters based on the machining constraint model and dynamically monitoring tool usage status. If any abnormal processing status occurs, parameter adjustment or machine stoppage inspection procedures are automatically triggered, and key data is retained through the machining process recording module, achieving dynamic control and traceability of the processing process, thus addressing the shortcomings of traditional modeling which focuses on structural description and lacks process control capabilities.

[0088] In some specific embodiments of this application, processing and inspection data are fed back to the digital prototype model to iteratively optimize process parameters and constraints, including: The quality inspection results, manufacturing process anomaly data, and tool status data are synchronously fed back to the process design model. Optimize process parameters and adjust tool selection or quality requirement thresholds based on feedback data; Update the constraints and relationships in the digital prototype model to form an iterative optimization process of design-production-testing-optimization.

[0089] The embodiments described above in this application, by synchronously feeding back quality inspection results, manufacturing process anomaly data, and tool status data to the process design model, can accurately locate core issues such as deviations in process parameters, insufficient tool adaptation, or unreasonable quality thresholds; they solve the pain point of the disconnect between the model and actual production in traditional modeling, making optimization measures more in line with actual production needs; and further update the constraints and relationships in the digital prototype model, forming a complete iterative closed loop of "design-production-inspection-optimization", improving the model's adaptability to complex processing scenarios.

[0090] In some specific embodiments of this application, the process document module is associated with production task work orders, equipment lists, material lists, and process change orders. The attributes of the production task work order include task name, work order number, and associated process route number, which are used to drive the execution and tracking of specific processing tasks.

[0091] The usage process of this data model is as follows: First, a structured system for the core data model is constructed based on the module definition diagram. Using the process design model as the top-level framework, it integrates related modules such as the manufacturing bill of materials, material quotas, processes, process documents, and process resources. Through SysML block definition specifications, the attributes and relationships of each module are defined, clarifying core information such as unique part identifiers, material consumption standards, and the technical parameters and usage status of tooling and equipment. Simultaneously, by leveraging SysML relationship types such as generalization and composition, a precise mapping is established between abstract classes in the digital domain and concrete instances in the physical domain. This ensures consistent semantic descriptions of the same object across different stages such as design, process, and production, resolving the fragmentation and semantic conflicts inherent in traditional data descriptions.

[0092] Based on standardized models, specific production tasks and scheduling plans are generated by combining process document models and manufacturing process models. By associating core information such as part numbers and process route numbers with production task work orders, task identification and execution requirements are clearly defined. The process route module organizes the sequence of operations and workstations, generating corresponding process flow diagrams and machining process cards to standardize operational content and responsible parties. Simultaneously, relevant parameters are entered using the production planning and scheduling information module, combined with equipment status and operator skill level data, to complete the allocation of processing units, personnel division of labor, and resource scheduling, ensuring reasonable resource matching and resolving the problem of disconnect between traditional production plans and actual resources.

[0093] Once the production plan is finalized, the entire process is executed according to the manufacturing process activity diagram. This includes pre-processing operations such as blank preparation, fixture clamping, and tool selection, while real-time collection of physical domain status data for tooling and equipment. Upon entering the component processing stage, processing monitoring is initiated simultaneously. The compliance of processing parameters is verified based on the machining constraint model, and the tool usage status is dynamically monitored. If any abnormal processing status occurs, parameter adjustment or machine stoppage for inspection is automatically triggered. Key data is retained through the machining process recording module, achieving dynamic control and traceability of the processing process. This addresses the shortcomings of traditional modeling, which focuses on structural description and lacks process control capabilities.

[0094] After the processing steps are completed, full-process quality control is carried out according to the quality inspection activity chart. First, the testing equipment is calibrated to confirm the accuracy and compliance of the measuring tools. Then, the actual measured data of the parts are collected and compared with the standard values. If the inspection is qualified, the warehousing operation is completed. If there are deviations, relevant information is recorded through the deviation slip and handled according to the procedure. All quality inspection data are synchronously fed back to the process design model to optimize process parameters, tool selection, or quality requirement thresholds, forming a closed loop of "design-production-inspection-optimization" to improve the quality stability of subsequent processing.

[0095] This application provides a standardized and traceable data foundation and relationships through a closed loop of "design-production-testing-optimization". The specific execution logic of the closed-loop optimization is implemented at the application layer based on this model's data foundation.

[0096] Optimization trigger thresholds: Optimization trigger conditions originate from the quality constraints defined within the model itself. For example, a trigger signal is generated when the measured dimensional attributes in the "Quality Inspection Data" continuously or significantly deviate from the dimensional tolerance constraints defined by the process module in the "Process Design Model." Tool wear is determined based on the wear threshold defined by the tool module in the "Process Resource Model" (its value can be referenced from standards such as ISO). The model's role is to centrally and clearly define and maintain these thresholds and correlate them with specific data.

[0097] Feedback data filtering rules: The distinction between deviations (random / systematic) relies on statistical analysis of the feedback data, which is typically performed by the external system deploying the model (such as MES, quality analysis system). The model provides input for analyses such as SPC (Statistical Process Control) by offering historical data on the process context. For example, the system can analyze the quality data of multiple serial number entities under the same part drawing number and process number to identify trends.

[0098] The parameter update process is optimized, including: Proposal: Based on the analysis findings, suggest modifications to specific parameters in the model (such as the range of cutting parameters).

[0099] Review and Approval: This change requires manual review and approval to ensure the rationality of the optimization suggestion.

[0100] Model Updates and Synchronization: After approval, the properties or constraints of relevant modules are updated in the SysML model. Through model relationships (such as bindings in the parametric diagram), related changes are automatically associated with views such as the "Manufacturing Process Model" and "Quality Model". Finally, through XML serialization and version control mechanisms, the updated model is synchronized to downstream systems to drive production execution.

[0101] In summary, the model constructed in this application provides a structured and interconnected data environment and a clear update carrier for closed-loop optimization, enabling the above-mentioned steps to be carried out in an orderly manner based on a unified data source.

[0102] To address the issue of heterogeneous data across multiple systems, SysML's XML serialization mechanism is used to convert all model elements in the digital prototype model into structured XML files. This process fully preserves hierarchical relationships and semantic connections, enabling data parsing and semantic alignment across different systems such as CAD / CAM, MES, and PLM. Furthermore, the XML file's traceability metadata and version control mechanism records all data changes throughout the entire process, achieving full lifecycle traceability of the data.

[0103] The foregoing has described some specific embodiments of this application. It should be understood that this application is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the substantive content of this application. The above-described preferred features can be used in any combination without conflict.

Claims

1. A method for constructing a digital prototype model for part machining, characterized in that, include: A data model framework is built based on the SysML language, which includes a process design model, a manufacturing process model, and a quality model. Based on SysML block definition graphs, structured modeling is performed on the process design model, the manufacturing process model, and the quality model, defining the data elements within each model and the relationship types between the data elements; Based on SysML activity diagrams, we describe the activity sequences, inputs, outputs, and decision logic in the process design, manufacturing execution, and quality inspection processes of part machining, and perform behavioral modeling. A machining constraint model is constructed based on SysML parametric graphs, defining the constraints and mathematical relationships between machining parameters, tool states, and quality indicators, and performing parametric modeling. The structured modeling, behavioral modeling, and parametric modeling are linked and integrated to form a digital prototype model.

2. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, The data elements include models, manufacturing resources, manufacturing data, manufacturing processes, and manufacturing objects; The relationship types between the data elements include at least one of the following: composition relationship, generalization relationship, referencing relationship, physical / data flow relationship, sequence relationship, and constraint relationship; The manufactured object includes digital domain attributes and physical domain attributes; the digital domain attributes are used to describe the theoretical parameters of the manufactured object, and the physical domain attributes are used to describe the measured parameters of an individual manufactured object.

3. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, The structured modeling includes: Construct a process design model and link it with the manufacturing bill of materials, material quota model, process model, process document model, and process resource model; Define the attributes of each module, wherein the manufacturing bill of materials includes the attributes of unit validity, material, and part drawing number, and the process resource model associates tooling and machine tools and specifies the technical parameters; The hierarchical structure and associated logic of each module are described by the definition diagram in the SysML block, and the mapping relationship between the digital domain abstract class and the physical domain is established.

4. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, The behavior modeling includes: Construct a process design activity diagram, starting with the decomposition of process planning, which includes resource allocation, data initialization, process specification preparation and review, tool selection, process content preparation and integration, and determines process nodes and input-output relationships. Construct a manufacturing process activity diagram, covering processing task allocation, blank preparation, fixture clamping, component processing, processing monitoring, quality inspection process, and define the exception handling logic; Construct a quality inspection activity diagram, including part coding, inspection equipment calibration, part quality testing, out-of-tolerance handling, and warehousing, and determine the flow path of inspection data.

5. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, The parameter modeling includes: Construct a machining constraint model that covers cutting parameter constraints, tool state constraints, mass constraints, cutting force constraints, and machining deformation constraints; Define the mathematical logic relationship between each constraint. Among them, the cutting parameter constraint specifies the range of values ​​for cutting depth, feed rate, and spindle speed, while the quality constraint includes dimensional tolerance, geometric tolerance, and surface roughness requirements. By using SysML's equal rules, constraint parameters are bound to actual data in the process model, process resource model, and quality model to ensure parameter consistency.

6. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, The process of integrating the structured modeling, behavioral modeling, and parametric modeling to form a digital prototype model includes: Using SysML's XML serialization mechanism, all model elements are converted into structured XML files, preserving hierarchical relationships and semantic associations; The XML file enables data parsing and semantic alignment with CAD / CAM, MES, and PLM systems. A version control mechanism is established using the traceability metadata of the XML file to record all lifecycle changes of the model and related data, forming a digital prototype model.

7. The method for constructing a digital prototype model for part machining according to claim 1, characterized in that, After the digital prototype model is created, the following is also included: Dynamic monitoring and anomaly handling of the manufacturing process are performed based on the constructed digital prototype model. The processing and inspection data are fed back to the digital prototype model to iteratively optimize the process parameters and constraints.

8. The method for constructing a digital prototype model for part machining according to claim 7, characterized in that, The dynamic monitoring and anomaly handling of the processing based on the constructed digital prototype model includes: Real-time acquisition of physical domain status data of tooling, equipment, and cutting tools, including tool wear, machine tool operating parameters, and fixture clamping status; Verify the compliance of machining parameters based on the machining constraint model, and monitor the machining status and tool status in real time; When an abnormal machining condition or a tool condition exceeding a threshold is detected, the system automatically triggers a process to adjust cutting parameters, stop for inspection, or change the tool, and retains key process data.

9. The method for constructing a digital prototype model for part machining according to claim 7, characterized in that, The step of feeding back processing and inspection data to the digital prototype model to iteratively optimize process parameters and constraints includes: The quality inspection results, manufacturing process anomaly data, and tool status data are synchronously fed back to the process design model. Optimize process parameters and adjust tool selection or quality requirement thresholds based on feedback data; The constraints and relationships in the digital prototype model are updated to form an iterative optimization process of design-production-testing-optimization.

10. The method for constructing a digital prototype model for part machining according to claim 3, characterized in that, The process document module is associated with production task work orders, equipment lists, material lists, and process change orders. The attributes of the production task work order include task name, work order number, and associated process route number, which are used to drive the execution and tracking of specific processing tasks.

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