Dynamic and static integrated digital twin modeling method for multi-physical association open type assembly station

Abstract

The invention discloses a dynamic and static integrated digital twin modeling method for a multi-physical association open type assembly station, which comprises the following steps of: performing comprehensive and systematic digital description on geometric and physical attributes and assembly information of the assembly station through a multi-physical and multi-layer static model construction method, and constructing to obtain a static model. According to a dynamic and static integrated model construction method based on correlation analysis, a mapping relation between static parameters and dynamic behaviors is established, uncertainty factors in the assembly process are considered, and finally an integrated digital twinborn model supporting high-fidelity simulation and analysis is formed. According to the method, multi-physical and multi-level characteristics of the assembly station can be comprehensively considered, effective integration of dynamic and static models is realized, meanwhile, a deviation accumulation effect is considered, static information such as an assembly data set and an assembly process attribute set of a product can be comprehensively and accurately described, and a solid foundation is provided for subsequent dynamic simulation and analysis.

Description

Technical Field

[0001] This invention relates to the technical field of mechanical assembly, and in particular to a method for modeling a dynamic and static integrated digital twin of an open assembly station with multiple physical associations. Background Technology

[0002] In modern mechanical assembly processes, open assembly stations serve as key production units, and their efficiency and precision directly impact product quality and production costs. However, existing digital twin modeling methods for open assembly stations suffer from the following prominent problems when applied to mechanical assembly scenarios:

[0003] 1) Existing modeling methods fail to fully integrate and reflect the multi-physical (e.g., mechanical, electrical, hydraulic, control, etc.) and multi-level (e.g., product parts, assembly units, the entire workstation, process flow, etc.) characteristics of complex assembly station systems, resulting in the inability of the constructed digital twin to comprehensively and accurately simulate the real mechanical assembly process and equipment behavior.

[0004] 2) The integration between static mechanical structure models (such as CAD models and MBD information) and dynamic assembly process models (such as kinematic, dynamic, fluid, and electrical control models) is low, lacking an effective mapping and coupling mechanism, making it difficult to accurately predict the impact of changes in static design parameters on dynamic assembly performance (such as positioning accuracy, stress conditions, and cycle time).

[0005] 3) Existing models are difficult to effectively handle the inherent uncertainties in the mechanical assembly process. In particular, they ignore the cumulative effects of factors such as component geometric deviations, assembly sequence, and clamping force changes on the final assembly accuracy and performance. This leads to significant deviations between the simulation calculation results of the digital twin and the actual mechanical assembly results, reducing the reliability of the model's prediction and analysis.

[0006] 4) Due to the insufficient accuracy and comprehensiveness of digital twin models, it is difficult to effectively support the simulation of refined assembly operations, key performance (such as accuracy, efficiency, and reliability) analysis, process optimization, and fault prediction and maintenance in open assembly stations, thus limiting its application potential in actual mechanical assembly. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-physical association open assembly station dynamic and static integrated digital twin modeling method that can comprehensively consider the multi-physical and multi-level characteristics of the assembly station, achieve effective integration of dynamic and static models, and consider the cumulative effect of deviation.

[0008] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0009] A multi-physical association open assembly station dynamic and static integrated digital twin modeling method includes:

[0010] By using a multi-physics, multi-level static model construction method, a comprehensive and systematic digital description of the geometric and physical properties and assembly information of the assembly station is obtained, thus constructing a static model.

[0011] The dynamic-static integrated model construction method based on correlation analysis establishes the mapping relationship between static parameters and dynamic behavior, and considers the uncertainties in the assembly process, ultimately forming an integrated digital twin model that supports high-fidelity simulation and analysis.

[0012] Furthermore, a static model is constructed, including:

[0013] S1-1. Using 3D modeling software and based on MBD technology, geometric models are created for the entities in the open assembly station, including all parts, fixtures, and robot end effectors. Product manufacturing information is directly integrated into the 3D model, and the shape, size, and key geometric tolerance requirements of the parts are annotated in detail. These geometric models with product manufacturing information form the basis of the static model, providing geometric data for subsequent assembly path planning, interference checking, and accuracy analysis.

[0014] S1-2. Based on the geometric model, integrate the characteristics of various physical fields involved in the assembly station;

[0015] S1-3. In 3D modeling software, establish an assembly tree, define assembly constraints and connection types, thereby constructing the product assembly hierarchy structure;

[0016] S1-4. Define the process information and attributes related to the assembly process to obtain the static model.

[0017] Furthermore, based on the geometric model, the characteristics of various physical domains involved in the assembly station are integrated, including:

[0018] Structural characteristic modeling is performed on components and assemblies, defining material properties to provide input for mechanical performance analysis; electrical characteristic modeling is performed on electrical components, describing their electrical parameters and behavior; fluid characteristic modeling is performed on hydraulic components, defining their flow-pressure characteristics; and electromagnetic characteristic modeling is performed on electromagnetic components.

[0019] Furthermore, define the process information and attributes related to the assembly process, including:

[0020] Plan detailed assembly sequences and steps;

[0021] Define the precise zero point and positioning method of key components;

[0022] Set the speed and acceleration of the robot or operator at each step;

[0023] Specify the applied force / torque value and the clamping force magnitude;

[0024] Define other relevant process constraints and performance constraints.

[0025] Furthermore, the method for constructing dynamic and static integrated models based on correlation analysis includes:

[0026] S2-1. Constructing a dynamic analysis model:

[0027] Establish various dynamic analysis models of the dynamic behavior of assembly stations and products during the assembly process;

[0028] S2-2. Establishing a mapping based on association analysis:

[0029] The key parameters defined in the static model are used as input or output parameters of the dynamic analysis model. The correlation analysis method is used to deeply explore and quantify the influence of factors including the geometric features, physical properties and process parameters of the open assembly station on the key performance indicators of assembly accuracy, assembly efficiency and product performance, and generate a grey relational order to identify the key parameters that have the most significant impact on assembly performance.

[0030] S2-3. Determine the main influencing parameters:

[0031] By comprehensively analyzing the grey relational order results obtained from different data normalization processing methods, the geometric, physical, and process parameters that rank highly in multiple analysis results are identified as the main parameters affecting various assembly performance indicators of the product. These main parameters are the focus of subsequent model optimization and deviation control.

[0032] S2-4. Considering the cumulative effect of deviation and extending the theoretical model:

[0033] By incorporating geometric deviations and their transmission relationship during the assembly process into the static and dynamic analysis models, the theoretical formulas are re-derived and the model parameters are corrected, thereby expanding the theoretical model and making it more accurately reflect the deviation accumulation and transmission laws in the real assembly system.

[0034] S2-5. Construct an integrated digital twin model:

[0035] After completing the construction of the dynamic analysis model, correlation analysis mapping, determination of key influencing parameters, and expansion of the theoretical model, the static model, dynamic analysis model, and expanded theoretical model are seamlessly integrated with the help of an advanced engineering system modeling and simulation platform to construct a comprehensive mechatronic digital twin model that reflects the physical behavior and assembly process of the open assembly station.

[0036] Furthermore, the established analytical models for the dynamic behavior of assembly stations and products during the assembly process include:

[0037] System flow dynamic models describing the dynamic response of fluid-driven components; product performance mathematical models describing the mechanical behavior and deformation of products during assembly; pressure characteristic mathematical models describing the pressure change law in hydraulic systems; and mechatronic system models integrating the interactive behaviors of mechanical, electrical, and hydraulic systems.

[0038] Compared with existing technologies, the principles and advantages of this technical solution are as follows:

[0039] 1. In response to the complex mechanical structure, diverse physical phenomena, and intricate assembly processes of open assembly stations, this technical solution employs a multi-physics, multi-level static model construction method to comprehensively and accurately describe the static information such as the product's assembly dataset and assembly process attribute set, providing a solid foundation for subsequent dynamic simulation and analysis.

[0040] 2. A dynamic-static integrated model construction method based on correlation analysis establishes a mapping relationship between static parameters and dynamic behaviors, and considers uncertainties in the assembly process. This ultimately forms an integrated digital twin model that supports high-fidelity simulation and analysis. Through this integrated digital twin model, precise assembly operation simulation calculations, performance analysis, process verification, and optimization can be carried out, providing a scientific, reliable, and highly valuable basis for decision-making in actual assembly. This helps achieve efficient, accurate, and intelligent assembly production goals and effectively improves the performance and reliability of the entire assembly system. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the services required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the principle of the multi-physical association open assembly station dynamic and static integrated digital twin modeling method of the present invention;

[0043] Figure 2 The flowchart of the principle of the multi-physics, multi-level static model construction method is shown in the flowchart of the multi-physics association open assembly station dynamic and static integrated digital twin modeling method of the present invention.

[0044] Figure 3 The flowchart illustrates the principle of the dynamic and static integration model construction method based on correlation analysis in the multi-physical association open assembly station dynamic and static integration digital twin modeling method of this invention. Detailed Implementation

[0045] The present invention will be further described below with reference to specific embodiments:

[0046] like Figure 1 As shown in this embodiment, the method for modeling a dynamic and static integrated digital twin of a multi-physical association open assembly station includes the following steps:

[0047] S1. By using a multi-physics, multi-level static model construction method, a comprehensive and systematic digital description of the geometric and physical properties and assembly information of the assembly station is carried out to construct a static model.

[0048] S2. A dynamic-static integrated model construction method based on correlation analysis establishes a mapping relationship between static parameters and dynamic behavior, and considers the uncertainties in the assembly process, ultimately forming an integrated digital twin model that supports high-fidelity simulation and analysis.

[0049] In the above, such as Figure 2 As shown, the specific process of step S1 is as follows:

[0050] S1-1, Geometric Characteristic Modeling: Utilizing advanced 3D CAD modeling software and based on MBD technology, precise geometric modeling is performed on all entities in the open assembly station, including components, fixtures, and robot end effectors. Product manufacturing information is directly integrated into the 3D model, with detailed annotations of component shapes, dimensions, and key geometric tolerances. These geometric models containing product manufacturing information form the basis of the static model, providing accurate geometric data for subsequent assembly path planning, interference checks, and accuracy analysis.

[0051] S1-2. Integration of Multiple Physical Properties: Based on the geometric model, the model integrates properties from multiple physical domains involved in the assembly station. This includes: structural property modeling of components and assembly structures, defining material properties (such as elastic modulus, Poisson's ratio, density, etc.) to provide input for mechanical performance analysis (such as stress deformation, stress distribution); electrical property modeling of electrical components (such as motors, sensors, controllers), describing their electrical parameters and behavior; fluid property modeling of hydraulic components (such as hydraulic cylinders, valves, pumps), defining their flow-pressure characteristics, etc.; and electromagnetic property modeling of electromagnetic components. This integrated modeling of multiple physical properties ensures that the digital twin can reflect the actual physical behavior of the assembly station.

[0052] S1-3. Product Assembly Information Definition and Management: This involves meticulously recording all components, parts, and components that make up the final product, as well as their assembly relationships, mating methods, constraints, and connection methods. This is achieved by creating an assembly tree in 3D CAD software, defining assembly constraints (such as mating, alignment, and fixing), and connection types (such as bolted connections, welding, and riveting). Constructing a clear product assembly hierarchy facilitates modular management and assembly process decomposition for complex products.

[0053] S1-4. Assembly Process Attribute Set Description: Defines the process information and attributes related to the assembly process. This includes, but is not limited to: planning detailed assembly sequences and steps; defining the precise zero-position and positioning methods of key components; setting the movement speed and acceleration of the robot or operator in each step; specifying the applied force / torque values ​​and clamping force magnitudes; and defining other related process constraints and performance constraints (such as temperature and humidity requirements). These process attributes are associated with the 3D model to guide subsequent dynamic simulation and process verification.

[0054] Through the above steps, this invention achieves a comprehensive, multi-physical, and multi-layered digital description of the physical entity of an open assembly station and its assembly-related information, constructing a high-fidelity static model and laying a solid foundation for subsequent dynamic and static integration. This multi-dimensional and multi-layered information definition and integration facilitates efficient hierarchical and refined management of static data and models, providing a solid foundation for building a comprehensive digital twin.

[0055] In the above, such as Figure 3 As shown, the specific process of step S2 is as follows:

[0056] S2-1. Constructing a dynamic analysis model:

[0057] Establish various analytical models that can accurately describe the dynamic behavior of assembly stations and products during the assembly process. These models may include: dynamic flow mathematical models describing the dynamic response of fluid-driven components (such as hydraulic cylinders and pneumatic cylinders); product performance mathematical models describing the mechanical behavior and deformation of products during assembly; pressure characteristic mathematical models describing the pressure change law in hydraulic systems; and mechatronics integrated mathematical models that integrate the interactive behavior of multiple fields such as mechanics, electrical, and hydraulics.

[0058] S2-2. Establishing a mapping based on association analysis:

[0059] Key parameters defined in the static model (such as the geometric dimensions, tolerances, material properties, assembly relationships, and process parameters of components) are used as input or output parameters for the dynamic analysis model. Correlation analysis methods (such as grey relational analysis) are employed to deeply explore and quantify the influence of the geometric features, physical properties, and process parameters of the open assembly station on key performance indicators such as assembly accuracy, assembly efficiency, and product performance. A grey relational order is generated to identify the key parameters that have the most significant impact on assembly performance.

[0060] S2-3. Determine the main influencing parameters:

[0061] By comprehensively analyzing the grey relational order results obtained from different data normalization methods, geometric, physical, and technological parameters that consistently rank highly in multiple analyses were identified as the main parameters affecting various assembly performance indicators of the product. These main parameters will be the focus of subsequent model optimization and deviation control.

[0062] S2-4. Considering the Cumulative Effect of Deviations and Extending the Theoretical Model: Recognizing that in actual mechanical assembly processes, minute differences in geometric deviations in component manufacturing and positioning, as well as in assembly sequence and clamping forces, can interact and accumulate within complex assembly chains, ultimately leading to substandard product assembly performance (i.e., the cumulative effect of deviations). To improve the accuracy of the digital twin model and its predictive ability for actual assembly processes, this invention incorporates geometric deviations and their transmission relationships during assembly into static and dynamic analysis models. Theoretical formulas are re-derived, and model parameters are corrected, thereby extending the theoretical model to more accurately reflect the cumulative and transmission patterns of deviations in real assembly systems.

[0063] S2-5. Construct an integrated digital twin model:

[0064] After completing the construction of the dynamic analysis model, correlation analysis mapping, determination of key influencing parameters, and expansion of the theoretical model, the static model, dynamic analysis model, and expanded theoretical model are seamlessly integrated using an advanced engineering system modeling and simulation platform to construct a comprehensive mechatronic digital twin model that reflects the physical behavior and assembly process of an open assembly station. This model highly integrates the interactive behaviors of multiple fields such as mechanics, electrical systems, and hydraulics, and can accurately simulate various dynamic behaviors and physical characteristics during the assembly process. Based on this highly realistic digital twin model, precise assembly operation simulation calculations, performance analysis, process verification, and optimization can be carried out, providing a scientific, reliable, and highly valuable basis for decision-making in actual assembly, helping to achieve efficient, precise, and intelligent assembly production goals, and effectively improving the performance and reliability of the entire assembly system.

[0065] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for modeling a dynamic and static integrated digital twin of an open assembly station with multi-physical association, characterized in that: include: By using a multi-physics, multi-level static model construction method, a comprehensive and systematic digital description of the geometric and physical properties and assembly information of the assembly station is obtained, thus constructing a static model. The dynamic-static integrated model construction method based on correlation analysis establishes the mapping relationship between static parameters and dynamic behavior, and considers the uncertainties in the assembly process, ultimately forming an integrated digital twin model that supports high-fidelity simulation and analysis.

2. The multi-physical association open assembly station dynamic and static integrated digital twin modeling method according to claim 1, characterized in that, Constructing a static model includes: S1-1. Using 3D modeling software and based on MBD technology, geometric models are created for the entities in the open assembly station, including all parts, fixtures, and robot end effectors. Product manufacturing information is directly integrated into the 3D model, and the shape, size, and key geometric tolerance requirements of the parts are annotated in detail. These geometric models with product manufacturing information form the basis of the static model, providing geometric data for subsequent assembly path planning, interference checking, and accuracy analysis. S1-2. Based on the geometric model, integrate the characteristics of various physical fields involved in the assembly station; S1-3. In 3D modeling software, establish an assembly tree, define assembly constraints and connection types, thereby constructing the product assembly hierarchy structure; S1-4. Define the process information and attributes related to the assembly process to obtain the static model.

3. The method for modeling a dynamic and static integrated digital twin of a multi-physical association open assembly station according to claim 2, characterized in that, Based on the geometric model, the characteristics of various physical fields involved in the assembly station are integrated, including: Structural characteristic modeling is performed on components and assemblies, defining material properties to provide input for mechanical performance analysis; electrical characteristic modeling is performed on electrical components, describing their electrical parameters and behavior; fluid characteristic modeling is performed on hydraulic components, defining their flow-pressure characteristics; and electromagnetic characteristic modeling is performed on electromagnetic components.

4. The multi-physical association open assembly station dynamic and static integrated digital twin modeling method according to claim 2, characterized in that, Define process information and attributes related to the assembly process, including: Plan detailed assembly sequences and steps; Define the precise zero point and positioning method of key components; Set the speed and acceleration of the robot or operator at each step; Specify the applied force / torque value and the clamping force magnitude; Define other relevant process constraints and performance constraints.

5. The method for modeling a dynamic and static integrated digital twin of a multi-physical association open assembly station according to claim 1, characterized in that, Methods for constructing dynamic and static integrated models based on correlation analysis include: S2-1. Constructing a dynamic analysis model: Establish various dynamic analysis models of the dynamic behavior of assembly stations and products during the assembly process; S2-2. Establishing a mapping based on association analysis: The key parameters defined in the static model are used as input or output parameters of the dynamic analysis model. The correlation analysis method is used to deeply explore and quantify the influence of factors including the geometric features, physical properties and process parameters of the open assembly station on the key performance indicators of assembly accuracy, assembly efficiency and product performance, and generate a grey relational order to identify the key parameters that have the most significant impact on assembly performance. S2-3. Determine the main influencing parameters: By comprehensively analyzing the grey relational order results obtained from different data normalization processing methods, the geometric, physical, and process parameters that rank highly in multiple analysis results are identified as the main parameters affecting various assembly performance indicators of the product. These main parameters are the focus of subsequent model optimization and deviation control. S2-4. Considering the cumulative effect of deviation and extending the theoretical model: By incorporating geometric deviations and their transmission relationship during the assembly process into the static and dynamic analysis models, the theoretical formulas are re-derived and the model parameters are corrected, thereby expanding the theoretical model and making it more accurately reflect the deviation accumulation and transmission laws in the real assembly system. S2-5. Construct an integrated digital twin model: After completing the construction of the dynamic analysis model, correlation analysis mapping, determination of key influencing parameters, and expansion of the theoretical model, the static model, dynamic analysis model, and expanded theoretical model are seamlessly integrated with the help of an advanced engineering system modeling and simulation platform to construct a comprehensive mechatronic digital twin model that reflects the physical behavior and assembly process of the open assembly station.

6. The method for modeling a dynamic and static integrated digital twin of a multi-physical association open assembly station according to claim 5, characterized in that, The established analytical models for the dynamic behavior of assembly stations and products during the assembly process include: System flow dynamic models describing the dynamic response of fluid-driven components; product performance mathematical models describing the mechanical behavior and deformation of products during assembly; pressure characteristic mathematical models describing the pressure change law in hydraulic systems; and mechatronic system models integrating the interactive behaviors of mechanical, electrical, and hydraulic systems.

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