A manufacturing equipment virtual model fusion method for digital twin service

Abstract

This invention discloses a method for fusing virtual models of manufacturing equipment for digital twin services, comprising the following steps: Step 1: hierarchical division of the virtual model of manufacturing equipment based on the S3C2 architecture; Step 2: determining the fusion mechanism of sub-models; Step 3: determining the fusion method of sub-models; Step 4: verifying the fusion of sub-models; Step 5: updating the fusion of sub-models according to changes in digital twin services. This invention explores a method for fusing virtual models of manufacturing equipment digital twins from the perspective of digital twin services, forms a hierarchical architecture for virtual models of manufacturing equipment oriented towards digital twin services, realizes the fusion process of virtual models of manufacturing equipment, and designs a method for fusion and verification of virtual models of manufacturing equipment.

Description

Technical Field

[0001] This invention belongs to the field of digital twin modeling technology for manufacturing equipment, and particularly relates to a method for fusing digital twin virtual models of manufacturing equipment. Background Technology

[0002] A digital twin virtual model is a collection of heterogeneous sub-models that describe various aspects of a physical object and are interconnected. Its modeling methods include model assembly and model fusion.

[0003] Current research on digital twin virtual modeling methods for manufacturing equipment suffers from inadequacy and imbalance. Much research focuses on the construction and assembly of geometric models, while studies on model fusion methods are relatively limited. Model assembly can only achieve 3D appearance modeling of manufacturing equipment in virtual space, failing to provide an objective description of its functions, characteristics, behaviors, physics, and processes. Therefore, in-depth research is needed on digital twin virtual model fusion methods for manufacturing equipment.

[0004] Model fusion technology primarily aims to address the pain points of low mapping accuracy and inability to describe the complex behavior of physical objects by leveraging information interaction, data sharing, dynamic coupling, and collaborative work among heterogeneous sub-models. Only by perfecting virtual model fusion methods and combining them with existing model assembly technologies can accurate, effective, complete, and high-fidelity virtual models of manufacturing equipment be formed. Furthermore, service is the ultimate goal of digital twin technology's practical application. High-fidelity virtual models of manufacturing equipment are the foundation for digital twin services to function effectively, reliably, and dependably.

[0005] For example, for CNC machine tools, a common manufacturing equipment in the industrial field, the heterogeneous sub-models in their digital twin virtual models during the product manufacturing process include, but are not limited to: tool wear prediction models, key component status monitoring models, product quality prediction models, process parameter optimization models, machine tool thermal error models, and machining energy consumption models.

[0006] However, the heterogeneous sub-models developed to meet specific application scenarios, stages, and needs suffer from insufficient mapping accuracy, limited functional services, and difficulties in information interaction, thus failing to enable manufacturing equipment to handle complex tasks. For example, for machining quality improvement services for CNC machine tools, relying solely on pre-machining process parameter optimization models or in-process tool wear prediction models cannot guarantee improved workpiece machining accuracy; for condition-based maintenance services for critical components, relying solely on component condition monitoring models without considering optimal spare parts inventory decision-making models cannot achieve comprehensive operation and maintenance management of manufacturing equipment.

[0007] Therefore, it is necessary to study virtual model fusion methods from the perspective of digital twin services, to realize information interaction and dynamic collaboration between heterogeneous sub-models, and thus provide effective and accurate solutions for the increasingly complex digital twin services of manufacturing equipment. Summary of the Invention

[0008] To address the aforementioned shortcomings of existing technologies, the present invention aims to provide a method for fusing virtual models of manufacturing equipment for digital twin services, thereby solving problems such as the lack of model fusion methods, insufficient model mapping accuracy, information barriers between heterogeneous sub-models, insufficient interaction, and difficulty in dynamic collaboration in existing technologies.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method for fusing virtual models of manufacturing equipment for digital twin services, characterized by comprising the following steps:

[0011] Step 1: Hierarchical division of the virtual model of manufacturing equipment based on S3C2 architecture;

[0012] Step 2: Determine the fusion mechanism of the sub-models;

[0013] Step 3: Determine the sub-model fusion method;

[0014] Step 4: Perform sub-model fusion verification;

[0015] Step 5: Based on the changes in digital twin services, perform sub-model fusion and updates.

[0016] Furthermore, the hierarchical division of the virtual model of manufacturing equipment in step 1 includes: (11) dividing the heterogeneous sub-models based on application scenarios to meet the fusion application of different functional services to the sub-models; (12) dividing the heterogeneous sub-models in the same scenario into hierarchical divisions on a time scale to meet the ability of specific functional services to express the correct content and hide irrelevant information at different stages.

[0017] Furthermore, the hierarchical division of the manufacturing equipment virtual model based on the S3C2 architecture includes coarse-grained division of different application scenarios according to different modeling research objects. The virtual model after scenario division can support the functional implementation of different digital twin services.

[0018] Furthermore, the fusion mechanism for determining the sub-model includes: concurrent fusion mechanism, sequential fusion mechanism, selective fusion mechanism, interrupted fusion mechanism, embedded fusion mechanism, and hybrid fusion mechanism.

[0019] Furthermore, the method for determining the sub-model fusion in step 3 is a manufacturing equipment virtual model fusion method based on the combination of SysML and process algebra π calculus, which is used to express the specific interactive behaviors between sub-models.

[0020] Furthermore, the manufacturing equipment virtual model fusion method combining SysML and process algebra π calculus includes the π calculus semantics of sequential transformations of SysML state machine diagrams: P1 and P2 represent process 1 and process 2, respectively. in Indicates the input channel, eve out Indicates the output channel. For the conditional judgment of the guard, true and false represent true and false respectively. This represents a set of events, where e1 represents the specific event 1, and [x = e1] is the event matching construct.

[0021]

[0022]

[0023] The π-calculus semantics of selection transitions in a SysML state machine diagram: P1 to P5 represent processes 1 to 5 respectively, eve in Indicates the input channel. Represents an event set, e i Let i represent a specific event, e1 to e4 represent events 1 to 4 respectively, [x = e i ] is used for event matching construction.

[0024]

[0025] The π-calculus semantics of parallel assembly of SysML state machine diagrams: {P1, P2} represent the pre-bidding state and the post-merging state, {P 3s P 4s ,P 5s} represent the initial entry states of the three orthogonal regions, respectively, {P join} represents the state when orthogonal regions merge, eve in Indicates the input channel, eve in Indicates the input channel. Represents an event set, e i Let i represent a specific event, e1 and e2 represent events 1 and 2, respectively, [x = e i ] is constructed for event matching;

[0026]

[0027]

[0028] π-calculus semantics of asynchronous message passing in SysML sequence graphs: Q1 and Q2 represent processes 1 and 2, respectively. in Indicates the input channel, mes out Indicates the output channel. Let m1 and m2 represent message set, respectively, and [x = m1] be the message matching construction.

[0029]

[0030]

[0031] The π-calculus semantics of synchronous message passing in SysML sequence graphs: {Q 11 Q 12 Q 13} and {Q 21 Q 22 Q 23} represents a set of processes, mes in Indicates the input channel, mes out Indicates the output channel. Represents a message set, m i Let i represent specific message, m1 and m2 represent specific messages 1 and 2, respectively, [x = m i ] is used for message matching construction;

[0032]

[0033]

[0034]

[0035]

[0036] The π-calculus semantics of the selected branch transmission in SysML sequence graphs: Q i Let Q1 represent process i, Q4 represent processes 1 through 4 respectively, and mes in Indicates the input channel. Represents a message set, m i Let i represent a specific message, m1 to m4 represent messages 1 to 4 respectively, [x = m i ] is used for message matching construction;

[0037]

[0038] π-calculus semantics of concurrent message passing in SysML sequence graphs {Q 11 Q 12 Q 13} and {Q 21 Q 22Q 23} represents a set of processes, mes in Indicates the input channel, mes out Indicates the output channel. Represents a message set, m i Let i represent specific message, m1 and m2 represent specific messages 1 and 2, respectively, [x = m i ] is the message matching construction, where messages r1 and r2 are process Q. 22 The semaphore is set to true when the branch action is completed; therefore, when the semaphore for each branch is true, process Q... 22 Through the passage Send feedback message m return And trigger the conversion to enter process Q 23 ;

[0039]

[0040]

[0041]

[0042]

[0043] Furthermore, in step 4: the mutual simulation equivalence theory of π calculus and activity detection are used to provide verification for the fusion of digital twin virtual models of manufacturing equipment for digital twin services, and to realize semantic consistency checks and system activity analysis between different aspects of SysML subgraphs during the fusion of digital twin virtual models of manufacturing equipment.

[0044] Furthermore, step 5 involves selecting the appropriate sub-model for fusion and updating based on changes in the digital twin service type and service time granularity.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This invention establishes a virtual model fusion process for manufacturing equipment from the perspective of digital twin services. This process includes: hierarchical partitioning of the virtual model of manufacturing equipment based on the S3C2 architecture, a heterogeneous sub-model fusion mechanism, a heterogeneous sub-model fusion method based on Sysml and π-calculus, heterogeneous sub-model fusion verification, and heterogeneous sub-model fusion update. It meets the important characteristic requirements of virtual models in digital twins, achieves accurate digital twin services, and solves the technical problems of existing technologies such as the lack of model fusion methods, insufficient model mapping accuracy, information barriers between heterogeneous sub-models, insufficient interaction, and difficulty in dynamic collaboration. It fills the modeling gap in the field of digital twin virtual models for manufacturing equipment, namely, model fusion.

[0047] 2. This invention proposes a hierarchical partitioning method for virtual models of manufacturing equipment based on the S3C2 architecture, from the perspective of digital twin services. S3 represents a platform-independent model with a higher level of abstraction, specifically representing the digital twin service, time scale, and application scenario. C2 represents a platform-related model containing specific technical implementation details, specifically representing the heterogeneous sub-model components and implementation code. Partitioning the virtual model of manufacturing equipment using the S3C2 architecture effectively reduces the complexity of virtual model fusion, clarifies the relationships between models, between models and their context, between models and services, and between services. This allows model fusion to mask irrelevant information and express correct content at the appropriate time scale, thus providing a supporting foundation for subsequent model fusion based on digital twin services and improving model fusion efficiency.

[0048] 3. This invention proposes a fusion mechanism for digital twin virtual models of manufacturing equipment, including concurrent fusion mechanism, sequential fusion mechanism, selective fusion mechanism, interrupted fusion mechanism, embedded fusion mechanism and hybrid fusion mechanism, which provides support for the interactive structure in the fusion process of virtual models of manufacturing equipment.

[0049] 4. This invention proposes a heterogeneous sub-model fusion method and a model fusion verification method based on Sysml and π calculus. This method highlights the advantages of Sysml's high readability and ease of understanding in modeling, while π calculus establishes a rigorous mathematical foundation for the fusion process. Furthermore, it compensates for the lack of fusion model analysis methods in SysML itself, thereby improving the accuracy of model fusion. Attached Figure Description

[0050] Figure 1 This invention provides the S3C2 hierarchical architecture for a virtual manufacturing equipment model oriented towards digital twin services.

[0051] Figure 2 This invention provides a concurrent fusion mechanism for virtual models of manufacturing equipment for digital twin services.

[0052] Figure 3 This invention provides a sequential fusion mechanism for virtual models of manufacturing equipment for digital twin services.

[0053] Figure 4 The present invention selects a fusion mechanism for virtual models of manufacturing equipment for digital twin services;

[0054] Figure 5 This invention provides a mechanism for interrupting and fusing virtual models of manufacturing equipment for digital twin services.

[0055] Figure 6This invention provides a virtual model embedding and fusion mechanism for manufacturing equipment oriented towards digital twin services.

[0056] Figure 7 This invention provides a hybrid fusion mechanism for virtual models of manufacturing equipment oriented towards digital twin services.

[0057] Figure 8 The π-calculus semantics of the sequential transformation of the Sysml state machine diagram in this invention;

[0058] Figure 9 This refers to the π-calculus semantics of the sequential transitions with guard conditions in the SysML state machine diagram of this invention.

[0059] Figure 10 The π-calculus semantics of the selection transition of the SysML state machine diagram in this invention;

[0060] Figure 11 The π-calculus semantics of the parallel assembly of SysML state machine diagrams in this invention;

[0061] Figure 12 This refers to the π-calculus semantics of asynchronous message transmission in SysML sequence graphs in this invention (input only);

[0062] Figure 13 The π-calculus semantics of asynchronous message transmission in SysML sequence graphs in this invention (which has both input and output);

[0063] Figure 14 The π-calculus semantics of synchronous message transmission of SysML sequence graph in this invention (object 1);

[0064] Figure 15 The π-calculus semantics of synchronous message transmission of SysML sequence graph in this invention (object 2);

[0065] Figure 16 The π-calculus semantics of the selected branch transmission of the SysML sequence graph in this invention;

[0066] Figure 17 The π-calculus semantics of concurrent message transmission of SysML sequence graphs in this invention (object 1);

[0067] Figure 18 The π-calculus semantics of concurrent message transmission of SysML sequence graphs in this invention (object 2);

[0068] Figure 19 Sysml block definition diagram for the fusion model of the processing quality improvement service stage considering thermal deformation error in the example;

[0069] Figure 20Sysml sequence diagram of the fusion model for the processing quality improvement service stage considering thermal deformation error in the example;

[0070] Figure 21 The Sysml state machine diagram of the pre-optimization model for gear hobbing process parameters, which considers accuracy, cost, and efficiency, in the fusion model of the processing quality improvement service stage considering thermal deformation error, is shown in the example.

[0071] Figure 22 The Sysml state machine diagram of the hobbing tool optimization model in the fusion model of the machining quality improvement service stage considering thermal deformation error is shown in the example.

[0072] Figure 23 The Sysml state machine diagram of the gear hobbing machine thermal deformation error prediction model in the fusion model of the processing quality improvement service stage considering thermal deformation error is shown in the example.

[0073] Figure 24 The Sysml state machine diagram is shown in the fusion model for predicting the in-process accuracy of gear hobbing in the processing quality improvement service stage considering thermal deformation error in the example. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The processes or components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0075] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0076] A method for fusing virtual models of manufacturing equipment for digital twin services, characterized by comprising the following steps:

[0077] Step 1: Hierarchical division of the virtual model of manufacturing equipment based on S3C2 architecture:

[0078] (11) Divide the heterogeneous sub-models based on application scenarios to meet the integrated application of different functional services to the sub-models;

[0079] (12) Heterogeneous sub-models within the same scene are hierarchically divided on a time scale to satisfy the ability of specific functional services to express the correct content and hide irrelevant information at different stages.

[0080] Step 2: Determine the fusion mechanism of the sub-models;

[0081] Step 3: Determine the sub-model fusion method;

[0082] Step 4: Perform sub-model fusion verification;

[0083] Step 5: Based on the changes in digital twin services, perform sub-model fusion and updates.

[0084] The digital twin virtual model of the manufacturing equipment is characterized by multiple dimensions, multiple domains, multiple levels, and multiple scenarios. For different application scenarios and requirements, the digital twin virtual model of the manufacturing equipment involves multiple different heterogeneous sub-models. These heterogeneous sub-models together constitute the digital twin virtual model of the manufacturing equipment, which is a collection of models describing various aspects of the manufacturing equipment.

[0085] Furthermore, the virtual model fusion of the manufacturing equipment, through information interaction and dynamic collaboration between models in multiple scenarios, can solve the problems of insufficient mapping accuracy, limited functional services, and difficulties in information interaction that exist in a single model, thereby improving the manufacturing equipment's ability to handle complex tasks.

[0086] See Figure 1 Step 1 involves the hierarchical partitioning of the virtual model of manufacturing equipment based on the S3C2 architecture. The S3C2 architecture is based on the platform-independent model (PIM) and platform-specific model (PSM) concepts within Model-Driven Architecture (MDA). Specifically, S3 represents the platform-independent model with a higher level of abstraction, specifically representing the digital twin service, time scale, and application scenario. C2 represents the platform-specific model, containing detailed technical implementation specifications, specifically representing the heterogeneous sub-model components and implementation code. Figure 1It is known that a digital twin virtual model of manufacturing equipment is a set of heterogeneous sub-models describing its various aspects and interconnected. Therefore, research on the fusion of virtual models of manufacturing equipment for digital twin services has its own unique complexities: the complexity of the virtual model itself, the complexity of the context of the sub-model fusion process, and the complexity of the digital twin service itself. The complexity of the virtual model is reflected in the multifaceted heterogeneity of its sub-models. The complexity of the context of the sub-model fusion process is mainly due to the fact that the construction of many sub-models focuses only on specific application requirements, resulting in loose coupling relationships, unclear hierarchical structures, and chaotic calling relationships between sub-models. The complexity of digital twin services is reflected in the large number of service types, unclear service granularity, and the dynamic nature of services being adjusted on demand. Therefore, the hierarchical division of the virtual model of manufacturing equipment based on the S3C2 architecture can effectively reduce the complexity of the fusion of virtual models of manufacturing equipment, clarify the relationships between models, between models and their context, between models and services, and between services. This allows model fusion to shield irrelevant information and express the correct content at the appropriate time scale, thereby providing a supporting foundation for subsequent model fusion based on digital twin services and improving the efficiency of model fusion.

[0087] In step 1, (11) involves dividing the virtual model of the manufacturing equipment based on the S3C2 architecture into different application scenarios according to different modeling research objects. The virtual model after scenario division can support the functional implementation of different digital twin services.

[0088] In step 1, (12) the sub-models built for different application scenarios still have differences in time scale. Therefore, the heterogeneous sub-models in the same scenario are further divided into granular sub-models in time scale. The division in time scale also indirectly supports the granular division of digital twin services, so that services can shield irrelevant information and express the correct content in the corresponding time scale.

[0089] Step 2: Design a fusion mechanism that meets the interaction characteristics and topology of heterogeneous sub-models during the fusion process of virtual models of manufacturing equipment for digital twin services. This includes: concurrent fusion mechanism, sequential fusion mechanism, selective fusion mechanism, interrupted fusion mechanism, embedded fusion mechanism, and hybrid fusion mechanism.

[0090] The virtual model fusion for manufacturing equipment oriented towards digital twin services realizes the general characteristics of digital twin virtual models, namely scalability, interoperability, extensibility, and fidelity. It provides the virtual model of manufacturing equipment with the ability to perceive physical objects on a time scale, i.e., the scalability of the digital twin virtual model; it realizes the expression of the interactive behavior of heterogeneous sub-models in multiple scenarios, i.e., the interoperability of the digital twin virtual model; and it improves the scalability of the manufacturing equipment virtual model by reusing and configuring sub-models according to different task requirements. Therefore, it solves the problem of insufficient mapping accuracy of a single model, and achieves dynamic description and accurate simulation of the behavior of physical objects by reusing and configuring different sub-models, thus achieving a truly comprehensive, systematic, and reasonable high-fidelity mapping.

[0091] Step 2 determines the virtual model fusion process for manufacturing equipment oriented towards digital twin services, and designs the virtual model fusion process for manufacturing equipment oriented towards digital twin services through micro-analysis and macro-synthesis.

[0092] See Figure 2 The concurrent fusion mechanism is represented by the symbol "|", denoted as Sub_M1|Sub_M2, where Sub-M1 and Sub-M2 represent sub-model 1 and sub-model 2 participating in the fusion, respectively. The concurrent fusion mechanism means that the participating sub-models run in parallel and each has its own independent running thread, entering through control transfer. During the fusion process, the heterogeneous sub-models collaborate based on specific needs through synchronous or asynchronous interaction. Therefore, in the concurrent fusion mechanism, the collaborative working mode between heterogeneous sub-models is a concurrent mechanism where each has its own independent running thread, and the information exchange between models takes the form of synchronous or asynchronous interaction.

[0093] See Figure 3 The sequential fusion mechanism is represented by the symbol "∶", denoted as Sub_M1:Sub_M2, where Sub-M1 and Sub-M2 represent sub-model 1 and sub-model 2 participating in the fusion, respectively. The sequential fusion mechanism indicates that the participating sub-models collaborate with each other in a sequential execution order. During the fusion process, control transfer is used, and different heterogeneous sub-models complete specific tasks within their respective problem domains, ultimately achieving complete functional services in a workflow-like manner. Therefore, in the sequential fusion mechanism, the collaborative working mode between heterogeneous sub-models is a workflow with a sequential execution order, and the information exchange between models is a sequential transmission.

[0094] See Figure 4The fusion mechanism is represented by the symbol "+", denoted as Sub_M1+Sub_M2, where Sub-M1 and Sub-M2 represent sub-model 1 and sub-model 2 participating in the fusion, respectively. The fusion mechanism means that participating sub-models selectively execute the function of one of their sub-models based on specific needs, thereby completing the collaborative operation between sub-models. During the fusion process, control transfer occurs. Heterogeneous sub-models select a specific sub-model to implement the corresponding service based on external environment information, input event types, or specific functional requirements, and completely transfer operational control to the selected sub-model. Therefore, in the fusion mechanism, the collaborative working mode between heterogeneous sub-models is based on specific requirements, selecting one sub-model to execute the corresponding function.

[0095] See Figure 5 The interruption fusion mechanism is based on symbols. Indicated, denoted as Here, Sub-M1 and Sub-M2 represent sub-model 1 and sub-model 2 participating in the fusion, respectively. During the fusion process, the interruption fusion mechanism first executes the functional behavior of sub-model Sub-M1 through control transfer. If sub-model Sub-M2 participating in the fusion starts executing due to an external event, the execution of sub-model Sub-M1 is terminated. After sub-model Sub-M2 completes execution, the return to execution of sub-model Sub-M1 through control switch depends on the specific requirements of the fusion process. Therefore, in the interruption fusion mechanism, whether a sub-model executes during collaborative work depends on the corresponding external event stimulus; similarly, whether interrupted sub-model 1 returns to execution also depends on specific requirements.

[0096] See Figure 6 The embedding fusion mechanism is represented by the symbol "←", denoted as Sub_M1←Sub_M2, where Sub-M1 and Sub-M2 represent sub-model 1 and sub-model 2 participating in the fusion, respectively. During the fusion process, through control transfer, the embedding fusion mechanism indicates that sub-model Sub-M1 will delegate some computational tasks to sub-model Sub-M2 for execution. Sub-model Sub-M2 does not expose specific functional behaviors to the outside world, but only runs in the context provided by sub-model Sub-M1. Therefore, in the embedding fusion mechanism, the collaborative working mode of heterogeneous sub-models is that sub-model 2 is completely embedded in the execution environment of sub-model 1, and whether it executes depends entirely on whether sub-model 1 delegates relevant computational tasks.

[0097] See Figure 7A hybrid fusion mechanism refers to the combined use of the aforementioned different mechanisms across multiple sub-models. This mechanism can address more complex fusion needs, thereby providing more powerful digital twin services. Hybrid fusion mechanisms take various forms depending on the specific problem. Therefore, a hybrid fusion mechanism is a typical example of a hybrid of sequential and embedded fusion mechanisms.

[0098] Step 3 proposes a virtual model fusion method for manufacturing equipment based on the system modeling language SysML and process algebra π calculus, used to express the specific interactive behaviors between sub-models. This method not only highlights the advantages of SysML's high readability and ease of understanding but also establishes a rigorous mathematical foundation for the fusion process. The core idea of ​​this combined method is to endow the semi-formal SysML language with a formal specification possessing strict π calculus semantics through designed transformation rules. Then, formal methods are used to analyze and verify the transformed fusion model with precise semantics.

[0099] See Figure 8 process P1 and process P2 represent processes, channel represents a channel, state1 and state2 represent states, and event represents an event.

[0100] The π-calculus semantics of the sequential transitions in the SysML state machine diagram are shown in Equation (1), where P1 and P2 represent process 1 and process 2, respectively. in Indicates the input channel. This represents the event set, where e1 represents the specific event 1, and [x = e1] is the event matching construct.

[0101]

[0102] Therefore, the sequential transitions in the SysML state machine diagram can be converted into the corresponding formal specifications described by π calculus semantics according to formula (1).

[0103] See Figure 9 process P1 and process P2 represent processes, channel1 represents a channel, output represents the guard condition mapped to the output prefix action in the π calculus, state1 and state2 represent states, event represents an event, and guard represents the guard condition.

[0104] The π-calculus semantics of the sequential transitions with guarded conditions in the SysML state machine diagram are shown in Equation (2), where P1 and P2 represent process 1 and process 2, respectively. in Indicates the input channel, eve out Indicates the output channel. Let x represent the event set, e1 represent the specific event 1, and [x = e1] be the event matching construct, which maps the guard conditions to the output prefix actions in the π calculus. Only when the condition is true can the object proceed with subsequent matching construction and state transition.

[0105]

[0106] Depend on Figure 9 It can be seen that the sequence transitions with guard conditions in the SysML state machine diagram can be converted into the corresponding formal specifications described by π calculus semantics according to formula (2).

[0107] See Figure 10 process P1 to process P5 represent processes, channel1 to channel14 represent channels, state1 to state5 represent states, and event1 to event4 represent events.

[0108] The π-calculus semantics of the selection transition in the SysML state machine diagram are shown in formula (3): P1 to P5 represent processes 1 to 5 respectively, eve in Indicates the input channel. Represents an event set, e i Let i represent a specific event, e1 to e4 represent events 1 to 4 respectively, [x = e i ] is used for event matching construction.

[0109]

[0110] Depend on Figure 10 It can be seen that the selection transitions in the SysML state machine diagram can be converted into the corresponding formal specifications described by π calculus semantics according to formula (3).

[0111] See Figure 11 , process P1, process P2, process P 3f process P 4f process P 5f process P 3s process P 4s process P 5s This represents a process, channel1 and channel2 represent channels, and state1, state2, and state3 represent processes. 3f state 4f state 5f state 3s state 4s state5s The states are represented by event1 and event2.

[0112] The π-calculus semantics of the parallel assembly of SysML state machine diagrams are shown in equations (4) and (5): {P1, P2} represent the pre-bidding state and the post-merging state, {P 3s P 4s P 5s} represent the initial entry states of the three orthogonal regions, respectively, {P join} represents the state when orthogonal regions merge, eve in Indicates the input channel, eve in Indicates the input channel. Represents an event set, e i Let i represent a specific event, e1 and e2 represent events 1 and 2, respectively, [x = e i ] is used for event matching construction.

[0113]

[0114]

[0115] Depend on Figure 11 It can be seen that the parallel assembly in the SysML state machine diagram can be converted into the corresponding formal specification described by π calculus semantics according to formulas (4) and (5).

[0116] See Figure 12 and Figure 13 process Q1 and process Q2 represent processes, channel and channel2 represent channels, object1 represents an object, and message1 and message2 represent messages.

[0117] The π-calculus semantics of asynchronous message passing in SysML sequence graphs are shown in equations (6) and (7): Q1 and Q2 represent process 1 and process 2, respectively. in Indicates the input channel, mes out Indicates the output channel. Let m1 and m2 represent message set, respectively, and [x = m1] be the message matching construct.

[0118]

[0119]

[0120] Depend on Figure 12 and Figure 13 It can be seen that asynchronous message transmission of SysML sequence graphs can be converted into the corresponding formal specifications described by π calculus semantics according to formulas (6) and (7).

[0121] See Figure 14 and Figure 15 process Q 11 process Q 12 process Q 13 process Q 21 processQ 22 process Q 23 The first character represents a process, the second character represents a channel, the third character represents an object, and the fourth character represents a message.

[0122] The π-calculus semantics of synchronous message passing in SysML sequence graphs are shown in equations (8) and (9): {Q 11 Q 12 Q 13} and {Q 21 Q 22 Q 23} represents a set of processes, mes in Indicates the input channel, mes out Indicates the output channel. Represents a message set, m i Indicates specific messages i, m i m2 represents specific message 1 and message 2, [x = m i ] is used for message matching construction.

[0123]

[0124]

[0125]

[0126]

[0127] Depend on Figure 14 and Figure 15 It can be seen that the synchronous message transmission of SysML sequence graphs can be converted into the corresponding formal specifications described by π calculus semantics according to formulas (8) and (9).

[0128] See Figure 16 process Q1, process Q2, process Q3, and process Q4 represent processes; channel, channel2, and channel13 represent channels; object1 represents an object; and message1, message2, and message3 represent messages.

[0129] The π-calculus semantics of the selected branch transmission in SysML sequence graphs are shown in equation (10): Q i Let Q1 represent process i, Q4 represent processes 1 through 4 respectively, and mes in Indicates the input channel. Represents a message set, m i Let i represent a specific message, m1 to m4 represent messages 1 to 4 respectively, [x = m i ] is used for message matching construction.

[0130]

[0131] Depend on Figure 16 It can be seen that the selected branch transmission of the SysML sequence graph can be converted into the corresponding formal specification described by the semantics of π calculus according to formula (10).

[0132] See Figure 17 and Figure 18 process Q 11 process Q 12 process Q 13 process Q 21 processQ 22 process Q 23 The first character represents a process, the second character represents a channel, the third character represents an object, and the fourth character represents a message.

[0133] The π-calculus semantics of concurrent message passing in SysML sequence graphs are shown in equations (11) and (12): {Q 11 Q 12 Q 13} and {Q 21 Q 22 Q 23} represents a set of processes, mes in Indicates the input channel, mes out Indicates the output channel. Represents a message set, m i Let i represent specific message, m1 and m2 represent specific messages 1 and 2, respectively, [x = m i ] is the message matching construction, where messages r1 and r2 are process Q. 22 The semaphore is set to true when the branch action is completed; therefore, when the semaphore for each branch is true, process Q... 22 Through the passage Send feedback message m return And trigger the conversion to enter process Q 23 .

[0134]

[0135]

[0136]

[0137]

[0138] Depend on Figure 17 and Figure 18 It can be seen that concurrent message transmission of SysML sequence graphs can be converted into the corresponding formal specifications described by π calculus semantics according to formulas (11) and (12).

[0139] In step 4 of this invention: the mutual simulation equivalence theory of π calculus and activity detection are used to provide verification for the fusion of digital twin virtual models of manufacturing equipment for digital twin services, and to realize semantic consistency checks and system activity analysis between different aspects of SysML subgraphs during the fusion of digital twin virtual models of manufacturing equipment.

[0140] In step 5 of this invention: based on the changes in the digital twin service type and service time granularity, the corresponding sub-model is selected and merged and updated.

[0141] The manufacturing equipment described in this invention includes, but is not limited to, the main manufacturing equipment used in the manufacturing process, such as CNC machine tools, industrial robots, additive manufacturing equipment, molding manufacturing equipment, precision instruments and meters, and special manufacturing equipment. The digital twin services described mainly refer to the production and manufacturing services provided by the manufacturing equipment during its use, such as: visual human-computer interaction, real-time monitoring, manufacturing process control, dynamic scheduling optimization, predictive maintenance, fault diagnosis, and quality control.

[0142] Example 1

[0143] This embodiment provides a method for fusing digital twin virtual models of gear-making machine tools to improve machining quality, including the following steps:

[0144] The heterogeneous sub-models defined by the fusion of digital twin virtual models of gear-making machine tools for improving machining quality include: a pre-optimization model of gear hobbing process parameters considering accuracy, cost, and efficiency; a mid-process prediction model of gear hobbing machining accuracy; a gear hobbing tool optimization model; a gear hobbing tool wear state prediction model; a gear hobbing tool life prediction model; and a gear hobbing machine thermal deformation error prediction model.

[0145] Step 1: Based on the S3C2 architecture, perform hierarchical partitioning of the heterogeneous sub-models defined in the virtual model of the gear-making machine tool for improving machining quality.

[0146] Furthermore, the aforementioned heterogeneous sub-models are divided into coarse-grained categories for workpiece application scenarios, tool application scenarios, and gear-making machine tool application scenarios, respectively.

[0147] Furthermore, the aforementioned heterogeneous sub-models are divided into different time scales in the integrated application aimed at improving machining quality. For example, the pre-process optimization model for gear hobbing process parameters mainly focuses on optimizing process parameters before machining; the in-process prediction model for gear hobbing machining accuracy mainly focuses on changes in machining quality during the machining process; the prediction model for thermal deformation error of gear hobbing machine mainly focuses on the impact of nonlinear elastic deformation on machining quality during the process of gear hobbing machine tool cooling to thermal equilibrium; and the prediction model for gear hobbing tool wear state mainly focuses on the impact of hobbing cutter wear on machining quality due to long-term continuous machining.

[0148] Furthermore, the differences in time scale among the aforementioned heterogeneous sub-models indirectly support the granularity of processing quality improvement services across time scales.

[0149] Furthermore, the machining quality improvement service for gear-making machine tools is divided into two phases on a time scale: the machining quality improvement service phase that considers thermal deformation error (cold machine to thermal equilibrium phase) and the machining quality improvement service phase that considers hob wear (thermal equilibrium to long-term continuous machining phase).

[0150] The hierarchical partitioning of the virtual model of the gear-making machine tool based on the S3C2 architecture can effectively reduce the complexity of fusion, thereby providing support for subsequent model fusion aimed at improving processing quality and improving model fusion efficiency.

[0151] Step 2: Determine the virtual model fusion mechanism for gear-making machine tools that is gear-oriented for improving processing quality. This mechanism is a hybrid fusion mechanism that includes sequential, concurrent, and embedded methods.

[0152] Furthermore, the first step is to consider the fusion of virtual models of gear hobbing machine tools during the machining quality improvement service stage (cold machine to thermal equilibrium stage) considering thermal deformation errors. The defined fusion model includes: a pre-optimization model of hobbing process parameters considering accuracy, cost, and efficiency; a mid-process prediction model of hobbing machining accuracy; a hobbing tool optimization model; and a hobbing machine thermal deformation error prediction model.

[0153] Furthermore, the relationships between sub-models are described using block definition graphs in the Sysml language, such as... Figure 19 As shown. The sequential relationships and dynamics of information interaction between the fusion sub-models are described by sequence diagrams in Sysml language, specifically as follows. Figure 20As shown. The states possessed by each sub-model during the fusion process, and the state transitions caused by different events, are described by a state machine diagram in Sysml language, as shown in the figure. Figure 21 , 22 As shown in Figures 23 and 24.

[0154] Furthermore, after describing the fusion model of the processing quality improvement service stage considering thermal deformation error using the Sysml modeling language, it is necessary to formally describe the sequence diagram and state machine diagram of the fusion model based on the π calculus transformation rule to support subsequent fusion verification.

[0155] Furthermore, the first step is to formally describe the π-calculus of the sequence graph of the fusion model, using the more complex formal description of the π-calculus of the Sysml behavioral graph as an example. The formal description of the π-calculus of the pre-optimization model of the gear hobbing process parameters in the sequence graph of the fusion model (denoted as Model1) is shown in Equation (13), and the π-calculus formula is shown in Equation (14). Wherein, WorkpieceArr represents the arrival of the workpiece to be processed, GearDesPara represents the design parameters of the gear to be processed, ToolPara represents the preferred tool parameters, StartPro represents the start of processing, NoOptReq represents no need for dynamic optimization of process parameters, and OptReq represents the need for dynamic optimization of process parameters. in Indicates the input channel, mes out Indicates the output channel. Represents a message set. These represent the corresponding processes.

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[0160] Furthermore, the formal description of the π calculus of the gear hobbing machine thermal deformation error prediction model (denoted as Model3) in the sequence diagram of the fusion model is shown in Equation (15), and the π calculus formula is shown in Equation (16). Wherein, WorkpieceArr indicates the arrival of the workpiece to be processed, StartPro indicates the start of processing, PredVal indicates the threshold determination of the predicted value, NoOptReq indicates no need for dynamic optimization of process parameters, InProQuaPre indicates in-process quality prediction, OptReq indicates the need for dynamic optimization of process parameters, ParaAdj indicates the need for process parameter adjustment, and NoParaAdj indicates no need for process parameter adjustment. in Indicates the input channel, mesout Indicates the output channel. Represents a message set. These represent the corresponding processes.

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[0165] Furthermore, the π-calculus formal description of the state diagram of the pre-optimization model of gear hobbing process parameters in the fusion model (denoted as Model1) is shown in Equation (17), and the π-calculus formula is shown in Equation (18). Wherein, WorkpieceArr represents the arrival of the workpiece to be processed, GearDesPara represents the design parameters of the gear to be processed, ToolPara represents the preferred tool parameters, InitialComp represents initialization completion, StartPro represents the start of processing, ReachTherBal represents reaching thermal equilibrium, NoReaTherBal represents not reaching thermal equilibrium, NoOptReq represents no need for dynamic optimization of process parameters, OptReq represents the need for dynamic optimization of process parameters, and ContinueOpt represents continued optimization. in Indicates the input channel, eve out Indicates the output channel. Represents a set of events. These represent the corresponding processes.

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[0173] Furthermore, the π-calculus formal description of the state diagram of the gear hobbing machine thermal deformation error prediction model (denoted as Model3) in the fusion model is shown in Equation (19), and the π-calculus formula is shown in Equation (20). Wherein, StartPro indicates the start of processing, InitialComp indicates initialization completion, PredVal indicates the prediction threshold determination, NoOptReq indicates no need for dynamic optimization of process parameters, InProQuaPre indicates in-process quality prediction, OptReq indicates the need for dynamic optimization of process parameters, ParaAdj indicates the need for process parameter adjustment, NoParaAdj indicates no need for process parameter adjustment, and ContinueOpt indicates continued optimization. in Indicates the input channel, eve out Indicates the output channel. Represents a set of events. These represent the corresponding processes.

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[0183] Step 4: The mutual simulation equivalence theory of π-calculus and activity detection are used to verify the model fusion process for the stage considering the improvement of processing quality by thermal deformation errors. Based on the mutual simulation equivalence theory, the semantic consistency between each heterogeneous sub-model in the fusion model sequence diagram and its corresponding state diagram is verified. The system deadlock of the fusion model sequence diagram is verified based on activity detection. These verifications provide support for the correctness of the fusion model construction.

[0184] Step 5: After the gear hobbing machine tool reaches thermal equilibrium from cold machining, the machining quality improvement service changes over time from considering thermal deformation errors to considering hob wear (from thermal equilibrium to long-term continuous machining). Correspondingly, the heterogeneous sub-models to be integrated become: a pre-optimization model for hobbing process parameters considering accuracy, cost, and efficiency; a mid-process prediction model for hobbing machining accuracy; a hobbing tool optimization model; a hobbing tool wear state prediction model; and a hobbing tool life prediction model.

[0185] Furthermore, for the updated model fusion of the machining quality improvement service stage that takes into account hob wear, we carried out the following: hierarchical division of the manufacturing equipment virtual model based on S3C2 architecture, heterogeneous sub-model fusion mechanism, heterogeneous sub-model fusion method based on Sysml and π calculus, and heterogeneous sub-model fusion verification.

[0186] Therefore, this invention realizes the fusion of virtual models of manufacturing equipment for digital twin services, making up for the shortcomings of existing modeling methods for virtual models of manufacturing equipment digital twins. Specifically, the S3C2-based hierarchical architecture of the virtual model effectively reduces the complexity of virtual model fusion and improves fusion efficiency; the interactive structure of the fusion mechanism provides support during the fusion process; and the heterogeneous sub-model fusion method and model fusion verification method based on Sysml and π-calculus have advantages such as high readability, ease of understanding, rigorous mathematical foundation, and fusion model analysis capabilities, thus improving the correctness of model fusion.

[0187] Furthermore, the manufacturing equipment model fusion method of the present invention solves the problems of single heterogeneous sub-models having limited functional services, information barriers between sub-models, difficulties in dynamic interaction, and inability to work collaboratively. Through model fusion, more accurate related service results can be obtained.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A manufacturing equipment virtual model fusion method for digital twin service, characterized in that, Includes the following steps: Step 1: Hierarchical division of the virtual model of manufacturing equipment based on S3C2 architecture; Step 2: Determine the fusion mechanism of the sub-models; Step 3: Determine the sub-model fusion method; Step 4: Perform sub-model fusion verification; Step 5: Based on changes in digital twin services, merge and update the sub-models; Among them, the method for determining the sub-model fusion in step 3 is a virtual model fusion method for manufacturing equipment based on the combination of the system modeling language SysML and the process algebra π calculus, which is used to realize the expression of specific interactive behaviors between sub-models; The manufacturing equipment virtual model fusion method combining the SysML and process algebra pi calculus comprises a sequential conversion of a SysML state machine diagram and a pi calculus semantic: and represents a process 1 and a process 2, represents an input channel, represents an output channel, is a guard condition judgment, and true and false are true and false respectively, represents an event set, represents a specific event 1, is an event matching construction; ； ； π-calculus semantics of selection transitions in SysML state machine diagrams: to These represent processes 1 through 5 respectively. Indicates the input channel. Represents a set of events. Represents a specific event i, to These represent events 1 through 4 respectively. Construct for event matching; ； π-calculus semantics for parallel assembly of SysML state machine diagrams: This represents the state before the fork and the state after the merge. These represent the initial state of the three orthogonal regions. This indicates the state when orthogonal regions converge. Indicates the input channel. Represents a set of events. Represents a specific event i, and Indicates events 1 and 2. Construct for event matching; ； ； π-calculus semantics for asynchronous message passing in SysML sequence graphs: and Indicates process 1 and process 2, Indicates the input channel. Indicates the output channel. Represents a message set. and This refers to specific messages 1 and 2. Construct for message matching; ； ； π-calculus semantics for synchronous message passing in SysML sequence graphs: and Represents a set of processes. Indicates the input channel. Indicates the output channel. Represents a message set. Indicates the specific message i, and This refers to specific messages 1 and 2. Construct for message matching; ； ； π-calculus semantics of selected branch transmission in SysML sequence graphs: Indicates process i, to These represent processes 1 through 4 respectively. Indicates the input channel. Represents a message set. Indicates the specific message i, to These represent messages 1 through 4 respectively. Construct for message matching; ； π-calculus semantics for concurrent message passing in SysML sequence graphs: and Represents a set of processes. Indicates the input channel. Indicates the output channel. Represents a message set. Indicates the specific message i, and This refers to specific messages 1 and 2. For message matching construction, message and For process The semaphore is set to true when the branch action is completed; Therefore, when the semaphore determination for each branch is true, the process... Through the passage Send feedback message And trigger the conversion process. ; ； 。 2. The virtual model fusion method for manufacturing equipment according to claim 1, characterized in that, The hierarchical division of the manufacturing equipment virtual model based on the S3C2 architecture in step 1 includes: (11) dividing the heterogeneous sub-models based on application scenarios to meet the fusion application of different functional services to the sub-models; (12) dividing the heterogeneous sub-models in the same scenario into hierarchical divisions on a time scale to meet the ability of specific functional services to express the correct content and hide irrelevant information at different stages.

3. The virtual model fusion method for manufacturing equipment according to claim 2, characterized in that, The hierarchical division of the virtual model of manufacturing equipment based on the S3C2 architecture includes coarse-grained division of different application scenarios according to different modeling research objects, and the virtual model after scenario division supports the functional implementation of different digital twin services.

4. The virtual model fusion method for manufacturing equipment according to claim 1, characterized in that, The fusion mechanism for determining the sub-model includes: concurrent fusion mechanism, sequential fusion mechanism, selective fusion mechanism, interrupted fusion mechanism, embedded fusion mechanism, and hybrid fusion mechanism.

5. The virtual model fusion method for manufacturing equipment according to claim 1, characterized in that, In step 4: the mutual simulation equivalence theory of π calculus and activity detection are used to provide verification for the fusion of digital twin virtual models of manufacturing equipment for digital twin services, and to realize semantic consistency checks and system activity analysis between different aspects of SysML subgraphs during the fusion of digital twin virtual models of manufacturing equipment.

6. The virtual model fusion method for manufacturing equipment according to claim 1, characterized in that, In step 5, the corresponding sub-model is selected for fusion and updating based on the changes in the digital twin service type and service time granularity.

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