Industrial master digital prototype collaborative design method and device based on MBSE

Abstract

The invention discloses an MBSE-based industrial master digital prototype collaborative design method and device, and belongs to the technical field of industrial master modeling. The method comprises the following steps: S1, constructing a design model library of an industrial master digital prototype; the design model library comprises a plurality of different types of demand models and a plurality of large models for various aspects of demands of the digital prototype; s2, selecting a plurality of appropriate demand models from a design model library according to targets and demands, and calling the correspondingly matched large models by utilizing an artificial intelligence technology to construct an industrial master structure digital prototype model, an industrial master electrical digital prototype model, an industrial master hydraulic digital prototype model and / or an industrial master process digital prototype model; and S3, adjusting and optimizing the digital prototype model to obtain an industrial master digital prototype model meeting the precision requirement. According to the method, digital prototype design and multi-model fusion can be carried out from four dimensions, and the design efficiency and precision of the industrial master are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial machine tool modeling, and in particular relates to a method and device for collaborative design of a digital prototype of an industrial machine tool based on MBSE. Background Art

[0002] The application of model-based system engineering (MBSE) in the modeling of industrial mother machine digital prototypes is a complex process that combines system engineering methods with advanced modeling technology. By using standard system modeling languages ​​(such as SysML) to build demand models, functional models, and architecture models, the decomposition and allocation from demand to function can be achieved. This methodology emphasizes model-centered system analysis and design to ensure that the entire system can achieve the predetermined engineering goals.

[0003] As the core equipment of the manufacturing industry, the design and manufacturing process of industrial machine tools requires a high degree of accuracy and reliability. Digital prototype modeling technology provides strong support for the design and optimization of industrial machine tools. Through digital prototypes, products can be simulated and analyzed in a virtual environment, so that potential problems can be discovered in advance and improvements can be made, greatly reducing the cost and risk of physical trial production.

[0004] At present, the implementation schemes for modeling digital prototypes of industrial mother machines through MBSE mostly rely on empirical design, without forming a modular model construction, and generally involve multiple design tools and simulation tools. The discrete use of tools makes it difficult to coordinate, which imposes certain limitations on modeling. In addition, the existing digital prototype schemes are mostly targeted at aerospace, and there is no systematic digital prototype design method for industrial mother machines. Summary of the invention

[0005] The present invention aims to solve one of the technical problems in the above-mentioned related art at least to a certain extent.

[0006] To this end, the purpose of the present invention is to provide a method and device for collaborative design of digital prototypes of industrial mother machines based on MBSE, which can perform collaborative design of digital prototypes from four dimensions, integrate multiple models, and improve the design efficiency and accuracy of industrial mother machines.

[0007] In order to solve the above-mentioned technical problems, the present invention is achieved as follows: The embodiment of the present invention provides a method for collaborative design of digital prototype of industrial mother machine based on MBSE, the method comprising: S1. Construct a design model library for the digital prototype of an industrial machine; the design model library includes several different types of demand models and several large models for various aspects of the digital prototype; S2. According to the objectives and requirements, select several appropriate demand models from the design model library, match them to appropriate large models according to the matching principle, and build the industrial mother machine structure digital prototype model, industrial mother machine electrical digital prototype model, industrial mother machine hydraulic digital prototype model and / or industrial mother machine process digital prototype model; S3. Adjust and optimize the constructed digital prototype model based on the experience of engineers to obtain a digital prototype model of the industrial mother machine that meets the requirements.

[0008] The demand model in the present invention is a model constructed for target demand.

[0009] In addition, the MBSE-based industrial mother machine digital prototype collaborative design method according to the present invention may also have the following additional technical features: In some implementations thereof, the step of constructing a design model library of the industrial mother machine digital prototype in step S1 includes: Determine goals and requirements: clarify the specific goals and requirements of digital prototype design for different types of industrial mother machines, including the function, accuracy and scope of application of the model; Data collection and cleaning: Collect relevant historical data from multiple sources, clean the collected data, and remove invalid, erroneous or redundant information; Feature engineering: extract feature data useful for model building from the cleaned data and preprocess the feature data to improve the training efficiency and performance of the model; Model selection and training: Select the appropriate model architecture according to specific needs, use the processed feature data to train the respective models, and obtain various types of models by continuously adjusting the parameters and structure of the model; Model verification and optimization: Use the preset verification data set to evaluate the trained model, and then optimize the model based on the evaluation results to obtain optimized models of various types; Modular construction: The optimized models of various types are modularly constructed to form a model library containing different types of industrial mother machine digital prototype design models.

[0010] In some of the embodiments, the industrial mother machine structure digital prototype model includes a multi-level part model, a mechanism model, an assembly model, a contact model and a multi-body dynamics model.

[0011] In some of the embodiments, when constructing the part model, according to the existing design parameters and target indicators, a suitable large model is matched according to the matching principle to generate an initial part model, and the initial part model is optimized and adjusted in combination with the experience of engineers to obtain a part model that meets the requirements; When constructing the mechanism model, according to the connection and motion relationship of the industrial mother machine, the initial mechanism model is generated by matching the appropriate large model according to the matching principle; the initial mechanism model is optimized and adjusted based on the experience of engineers to obtain a mechanism model that meets the requirements; When constructing the assembly model, according to the constructed part model and mechanism model, a suitable large model is matched according to the matching principle to obtain an initial assembly model, and the initial assembly model is adjusted and optimized based on the experience of engineers to obtain a final assembly model; When constructing the contact model, according to the constructed part model and assembly model, based on the part geometric features and the collision detection algorithm, an initial contact model is generated by matching a suitable large model according to the matching principle, and the initial contact model is adjusted and optimized in combination with the engineer's experience to obtain a contact model that can reflect the contact relationship between the parts; When constructing the multi-body dynamics model, according to the constructed part model, mechanism model and assembly model, a suitable large model is matched according to the matching principle to obtain an initial multi-body dynamics model; the initial multi-body dynamics model is adjusted and optimized according to the motion characteristics and force conditions to obtain the final multi-body dynamics model.

[0012] In some of the embodiments, the industrial mother machine electrical digital prototype model includes a control model, a detection model and / or a fault prediction model.

[0013] In some embodiments, when constructing the control model, according to the matching principle, combined with the required parameters and objectives related to electrical control, a suitable large model is matched to generate an initial control model; then, the initial control model is adjusted and optimized based on the experience of engineers to obtain the desired control model; When constructing the detection model, an initial detection model is generated by matching a suitable large model according to the detection requirements and matching principles; and the initial detection model is further optimized and adjusted based on the experience of engineers to obtain the required detection model; Fault prediction model: Based on the constructed control model and detection model, the initial fault prediction model is generated by matching the appropriate large model according to the matching principle. The initial fault prediction model is further optimized and adjusted based on the engineer's experience to obtain the required fault prediction model.

[0014] In some of the embodiments, the construction content of the industrial machine hydraulic digital prototype model includes: According to the hydraulic control requirements described in the demand model, the initial hydraulic control model is generated by matching the appropriate large model according to the matching principle. The initial hydraulic control model is further optimized and adjusted based on the experience of engineers to ensure that the hydraulic control model meets the requirements in the demand model and obtain the final hydraulic control model.

[0015] In some of the embodiments, the construction content of the industrial mother machine process digital prototype model includes: According to the obtained process requirement model, the appropriate large model is matched according to the matching principle to generate the initial industrial mother machine process digital prototype model, and then the initial industrial mother machine process digital prototype model is further adjusted and optimized based on the engineer's experience to obtain the final industrial mother machine process digital prototype model.

[0016] In some of the embodiments, the relevant historical data includes design drawings, performance parameters and usage records of the industrial machine tool; The characteristic data include the geometric shape, material properties and assembly relationship of the parts; The preprocessing of feature data includes normalization and standardization; Optimizing the model based on the evaluation results includes adjusting model parameters and / or improving the model structure.

[0017] An embodiment of the present invention also provides an MBSE-based industrial mother machine digital prototype collaborative design device, including a processor and a memory, wherein a software program is stored on the memory, and when the processor runs the software program, the content of the MBSE-based industrial mother machine digital prototype collaborative design method as described in any one of the above items can be implemented.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A block diagram of a collaborative design method for digital prototype of an industrial mother machine based on MBSE disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0022] See also Figure 1As shown, in some embodiments of the present invention, a collaborative design method for digital prototypes of industrial mother machines based on MBSE is provided. First, according to the design requirements of industrial mother machines, the classification includes additive manufacturing, equal material manufacturing and subtractive manufacturing, and each type of manufacturing corresponds to several demand models.

[0023] According to the demand model, the modular construction of the digital prototype of the industrial mother machine structure is carried out, including multi-level part models, mechanism models, assembly models, contact models and multi-body dynamics models.

[0024] According to the demand model, the modular construction of the electrical digital prototype of the industrial mother machine is carried out, including the control model, detection model and fault prediction model.

[0025] According to the demand model, the modular construction of the hydraulic digital prototype of the industrial mother machine is carried out.

[0026] According to the demand model, the modular construction of the industrial mother machine process digital prototype is carried out. The typical process model library data is used to match the demand model, and the modular construction of the industrial mother machine process digital prototype is carried out, including the process parameter model of the same type of industrial mother machine.

[0027] The process of establishing a demand model includes: The first step is to clarify the model objectives and functional definitions.

[0028] The second step is data collection and preparation. Collect design-related data: Collect a large number of demand description samples from different customers about sand mold 3D printing equipment, covering various size ranges, accuracy requirements, structural characteristics and processing time requirements. At the same time, these demand descriptions are manually annotated to clarify the various design parameters contained therein for training the demand analysis module. Design model data: Organize various design models related to sand mold 3D printing equipment, record the function, applicable scenario, input parameters and output results of each design model, and build a design model library. Digital prototype data: Collect existing digital prototype cases and complete data of sand mold 3D printing equipment for learning and reference of automatic design and modeling process. Perform data preprocessing, including demand data cleaning and design model data organization.

[0029] The third step is to design the model architecture. Fine-tuning based on pre-trained language models: Based on pre-trained language models such as BERT and GPT, fine-tuning is performed using the collected demand sample data. By designing appropriate downstream tasks, such as named entity recognition (NER), key entities such as size, accuracy, structure, and time in the demand description are identified; existing text classification technology is used to classify the demand types in order to better match the design model. Post-processing and parameter extraction: Post-process the results output by the pre-trained language model to convert the identified entities into the precise design parameter format required by the design model. For example, the size values ​​described in the text are converted into specific length, width, and height values, and the units are attached. Model matching and parameter determination module: Build a design model knowledge base: Build the sorted design model data into a knowledge base, which can be stored in the form of knowledge graphs for fast query and retrieval. In the knowledge graph, nodes represent design models and their related attributes, and edges represent the relationships between models and applicable conditions. Matching algorithm design: Design a matching algorithm based on rules and machine learning. According to the design parameters extracted by the demand parsing module, first use rule matching to screen out design models that meet the basic conditions. Then, the screened models are further sorted and selected through machine learning algorithms (such as decision trees, support vector machines, etc.) to determine the best matching design model. At the same time, the input parameters required for each matching model are extracted from the knowledge base.

[0030] The fourth step is model training and optimization, which includes dividing the data set, setting training goals and optimization algorithms, and model evaluation and improvement.

[0031] Step 5: Model deployment and maintenance. Deploy the trained demand model to the actual business system. Develop corresponding interfaces and user interfaces to facilitate users to input demand information and obtain the design solution and digital prototype model output by the model. With the development of technology and changes in customer needs, continuously collect new demand sample data, design model data, and digital prototype data. Regularly retrain and optimize the model to ensure that the model can adapt to new needs and design requirements in a timely manner. At the same time, establish a model monitoring mechanism to monitor the model's operating status and performance indicators in real time, and promptly discover and solve problems that arise during the model's operation.

[0032] The model building process is as follows: 1. Establish a digital prototype design model library for industrial mother machines: Determine goals and requirements: clarify the specific goals and requirements of the digital prototype design model library for different types of industrial mother machines, including the model's function, accuracy, and scope of application.

[0033] Data collection and cleaning: Collect relevant historical data from various sources, such as industrial machine design drawings, performance parameters, usage records, etc. Clean the collected data to remove invalid, erroneous or redundant information to ensure data accuracy and consistency.

[0034] Feature engineering: Extract feature data useful for model building from the cleaned data, such as the geometry of parts, material properties, assembly relationships, etc. Preprocess the feature data, such as normalization and standardization, to improve the training efficiency and performance of the model.

[0035] Model selection and training: Select the appropriate model architecture based on specific needs. Use historical data to train the model, and continuously adjust the model's parameters and structure so that the model can accurately describe and predict the performance and behavior of industrial mother machines.

[0036] Model validation and optimization: Use the validation data set to evaluate the trained model and check the accuracy and reliability of the model. Optimize the model based on the evaluation results, such as adjusting parameters and improving the model structure, to improve the performance of the model.

[0037] Modular construction: The trained models are modularized to form a digital prototype design model library for different types of industrial mother machines. The model library includes all Figure 1 The models in the library form a model library containing digital prototype design models of different types of industrial mother machines.

[0038] The digital prototype design of industrial mother machines can be customized using the industrial mother machine digital prototype design model library. The modules in the model library are combined and configured according to needs. Professional engineers further optimize and adjust the modules they are responsible for, generate customized models, and finally generate the required digital prototype of the industrial mother machine.

[0039] 2. Description and introduction of each model construction process: 1. The modular construction process of the aforementioned industrial mother machine structure digital prototype is as follows: Multi-level part model: According to the matching principle, the appropriate large model is matched and called, and a new part design scheme is generated according to the existing design parameters and target indicators. The designer can optimize and adjust the generated design scheme and finally obtain a part model that meets the requirements. Due to the large variety of parts or components in the industry, the matching principles of each part and component are different. The present invention does not make specific limitations on them. The matching principles of specific parts and components can refer to the prior art. When matching according to the matching principle, the existing matching model that integrates the matching principle can be used to achieve it, such as a matching model based on artificial intelligence technology. The present invention does not make too many limitations.

[0040] The digital prototype design of industrial mother machines is related to the safety and reliability of products in actual use, and any design defects may lead to serious consequences. Designers or engineers are required to review the generated design schemes from the perspective of ethics and safety. And although the matching model can process a large amount of data, it is not as good as the professional knowledge and experience accumulated by human engineers in long-term practice in the early stage of design model application and self-learning, or even for a long time. For example, in the design of sand mold 3D printing equipment, engineers can intuitively judge the rationality of the matching model generation scheme based on past project experience for the strength requirements of sand molds under different casting processes and the feasibility of demolding of complex structures. For high-precision casting sand molds such as aircraft engine blades, engineers know that the support structure design needs to be strengthened in some key parts to ensure that the sand mold will not be deformed due to high temperature and metal liquid impact during the casting process. This is difficult for the matching model to automatically match and generate based on data alone. Adjustment and optimization based on engineer experience can be carried out from several perspectives such as functional integrity, safety, compatibility, performance, cost, and simulation verification. The modular construction of several other industrial mother machine digital prototypes listed below also has similar adjustments and optimizations.

[0041] The following takes a common structure of an industrial machine tool - the transmission system as an example to introduce the process of building a multi-level part model. The transmission system is the core component of the industrial machine tool, responsible for transmitting the power of the motor to the processing system to realize the processing of the workpiece. The design steps of the present invention include: Step 1: Identify transmission system requirements First, it is necessary to establish a demand model for the transmission system, including parameters such as transmission efficiency, power, speed, working environment, etc. These requirements will directly affect the design parameters and material selection of the part model.

[0042] Step 2: Large model selection and application Choose a large model: Based on the needs of the transmission system, choose a large model with strong computing power and high accuracy, such as a deep learning model. These models should be able to handle complex geometries, physical properties, and interactions.

[0043] Data preparation: Collect data related to the transmission system, including the size, shape, material properties, working conditions, etc. of the parts. These data will be used to train the big model and generate the part model.

[0044] Model training and optimization: Use the collected data to train the large model, and evaluate and optimize the model through cross-validation, performance indicator evaluation and other methods. Ensure that the model can accurately reflect the characteristics and behavior of the transmission system. Store the trained model in the digital prototype model library Step 3: Multi-level part model construction Parts hierarchy division: According to the demand model, the parts are divided into different levels. For example, it can be divided into base, transmission rod, transmission rod, gear, motor and other levels.

[0045] Part model generation: Generate part models at each level using the large model. For complex parts, they can be further subdivided into smaller sub-parts and their models generated separately.

[0046] Model integration and optimization: Integrate the generated part models to form a complete transmission system model. During the integration process, attention should be paid to the matching relationship between parts, dimensional accuracy and consistency of physical properties. Designers select the optimal model based on experience, and then use the optimization capability of the large model to optimize the parameters and perform simulation tests on the overall model to improve the performance and reliability of the transmission system.

[0047] Mechanism model: Based on the connection and motion relationship of the industrial mother machine, the artificial intelligence matching model is used to generate the initial design of the mechanism model. Engineers can further optimize and adjust the design scheme generated by the model to ensure that the mechanism model meets the requirements of the demand model.

[0048] Assembly model: Based on the part model and mechanism model generated by the artificial intelligence matching model, the initial assembly model is automatically generated. Engineers can further adjust and optimize the assembly model to ensure the correctness of the assembly between the various parts.

[0049] Contact model: According to the part model and assembly model generated by the artificial intelligence matching model, the contact relationship model between parts is automatically generated. This can be achieved by learning the part geometric features and collision detection algorithms through the model to ensure the correct contact of parts during movement.

[0050] Multi-body dynamics model: Generate an initial multi-body dynamics model using existing part models, mechanism models, and assembly models. Engineers can make further adjustments and optimizations based on the motion characteristics and force conditions generated by the model to ensure the accuracy and reliability of the model. The multi-body dynamics model enables virtual assembly of parts and can be used to analyze the characteristics, states, and specific parameter values ​​of each part during the force and motion process to design actual prototypes.

[0051] 2. The modular construction process of the electrical digital prototype of the aforementioned industrial mother machine is as follows: Control model: Use the corresponding artificial intelligence matching model to generate the initial control model design according to the required parameters and goals related to electrical control. Engineers can adjust and optimize the design generated by the model to ensure that the control requirements in the required model are met. Taking the sand mold 3D printing equipment as an example, this article briefly introduces how engineers can make adjustments and optimizations. Engineers can make optimization adjustments in six aspects: 1) Functional integrity: On the one hand, carefully check the demand model. If the nozzle movement speed needs to be adjusted in real time according to the sand mold part, the solution lacks the corresponding control logic and needs to be supplemented. On the other hand, remove redundant functions such as special fault alarms that rarely occur and do not affect the main functions. 2) Performance optimization: In terms of response speed, check bottlenecks such as signal transmission path and controller operation speed, such as upgrading low-bandwidth data transmission bus or optimizing protocol. In terms of stability, evaluate the selection of electrical components and circuit layout, such as replacing unstable power supply modules, adding voltage stabilization and filtering circuits, and doing a good job of cable layout and shielding to enhance anti-interference capabilities. 3) Compatibility: In terms of hardware compatibility, ensure that the electrical control solution matches the hardware interfaces and electrical parameters such as mechanical structure and sensors, and design signal conversion circuits. At the same time, consider future expansion needs such as adding nozzle types or improving accuracy, and reserve interfaces and control resources. 4) Cost control: Conduct a cost-benefit analysis on the selection of electrical components, replace expensive models with controllers with similar functions and reasonable prices, simplify complex circuit designs, and reduce unnecessary use of relays and contactors. 5) Safety optimization: In terms of electrical safety protection, improve leakage, overcurrent, and short-circuit protection, such as adding leakage protection switches. In terms of operational safety design, reasonably set the position and method of the emergency stop button, and set warning and confirmation links for dangerous operations. 6) Simulation verification: First use simulation software to simulate the operation of the equipment under different working conditions, and check the control logic and performance indicators. Then test on the actual equipment, collect current, voltage and other data, and fine-tune the plan based on the results to ensure that the control requirements are met.

[0052] Detection model: Based on the detection requirements of the industrial machine tool described in the demand model, the corresponding artificial intelligence matching model is used to generate the initial design of the detection model. Engineers can further optimize and adjust the design scheme generated by the model to ensure that the detection model meets the requirements in the demand model.

[0053] The construction process of the detection model includes: Step 1: Testing requirements As the core equipment in intelligent manufacturing, the performance and quality of industrial machine tools have a significant impact on production efficiency and product quality. Therefore, the testing requirements for industrial machine tools mainly include the following aspects: Precision testing: Ensure that the processing accuracy of industrial mother machines meets the design requirements, including positioning accuracy, repeated positioning accuracy, geometric accuracy, etc.

[0054] Performance testing: Evaluate the overall performance of the industrial machine tool, such as cutting speed, feed speed, spindle power, etc., to ensure that it can meet production needs.

[0055] Fault diagnosis: By detecting the operating status of industrial mother machines, potential faults can be discovered and diagnosed in a timely manner to avoid production interruptions and equipment damage.

[0056] Safety inspection: Ensure that the safety protection devices of industrial mother machines are intact and effective to prevent operators from being injured during the production process.

[0057] Step 2: Detect relevant parameters In order to meet the testing requirements, a series of parameters need to be set to evaluate the performance and quality of industrial mother machines. The following are some key parameters: Accuracy parameters: Positioning accuracy: measures the accuracy of positioning of industrial machine tools during processing.

[0058] Repeatability: Evaluate the consistency of industrial machine tools during multiple positioning.

[0059] Geometric accuracy: reflects the relative position and shape accuracy between the various components of the industrial mother machine.

[0060] Performance parameters: Cutting speed: The speed of an industrial machine tool during the cutting process.

[0061] Feed speed: The speed at which an industrial machine tool feeds during processing.

[0062] Spindle power: The output power of the spindle motor reflects the cutting capacity of the industrial machine tool.

[0063] Fault diagnosis parameters: Vibration signal: Analyze the operating status of the equipment by detecting the vibration signal of the industrial mother machine.

[0064] Temperature signal: monitor the temperature changes of various components of industrial mother machines and detect abnormal conditions in time.

[0065] Noise signal: Analyze the noise during the operation of industrial mother machines to determine whether there is any equipment failure.

[0066] Safety parameters: Integrity of safety protection devices: Check whether the safety protection devices are complete and effective.

[0067] Reliability of the emergency stop button: Ensure that the emergency stop button can quickly stop equipment operation in an emergency.

[0068] Step 3: Large model application Based on the above detection requirements and parameters, a detection model for industrial mother machines can be constructed. The model construction process is as follows: Data collection and preprocessing: Collect historical data of industrial mother machines, including accuracy test data, performance test data, fault diagnosis data, safety test data, etc. Preprocess the data, including data cleaning, data conversion, and data standardization, to improve data quality and availability.

[0069] Feature extraction and selection: Extract features related to detection requirements from raw data, such as accuracy features, performance features, fault diagnosis features, and safety features. Use feature selection algorithms to screen out key features that have a greater impact on detection results to improve the accuracy and efficiency of the model.

[0070] Model training and optimization: Select appropriate artificial intelligence algorithms, such as deep learning, machine learning, etc., use preprocessed data and key features to train models, and adjust model parameters to optimize performance. Evaluate and optimize models through cross-validation, performance indicator evaluation and other methods to ensure that the model can accurately reflect the characteristics and behaviors of industrial mother machines.

[0071] Write the trained model into the industrial mother machine digital prototype model library.

[0072] Step 4: Detection model construction Generate an initial design of the detection model that meets the requirements based on the required model and model library. Engineers can further optimize and adjust the design based on the model generated. For example: Adjust feature weights: According to actual needs, adjust the weights of each feature to improve the accuracy and sensitivity of the model; Optimize algorithm parameters: Adjust algorithm parameters, such as learning rate, number of iterations, etc., to further optimize the performance of the model; Introducing new features: Introducing new features based on new detection requirements and technological developments to expand the application scope and detection capabilities of the model.

[0073] Fault prediction model: Use the established control model and detection model to generate an initial fault prediction model. Engineers can further optimize and adjust the fault prediction algorithm generated by the model based on their experience to improve the accuracy and reliability of fault prediction.

[0074] 3. The modular construction process of the aforementioned industrial machine hydraulic digital prototype is as follows: According to the hydraulic control requirements described in the demand model, the corresponding artificial intelligence matching model is used to match the appropriate large model to generate the initial design of the hydraulic control model. Engineers can further optimize and adjust the design scheme generated by the model based on previous experience to ensure that the hydraulic control model meets the requirements in the demand model.

[0075] 4. The modular construction process of the aforementioned industrial mother machine process digital prototype is as follows: According to the process parameters and process characteristics, the demand model of the appropriate process type is selected from the model library, and the model is further adjusted and optimized in combination with the engineer's experience to obtain the final process demand model; based on the obtained process demand model, the corresponding artificial intelligence matching model is used to match the appropriate large model to generate the initial industrial mother machine process digital prototype model, and the initial industrial mother machine process digital prototype model is further adjusted and optimized in combination with the engineer's experience to obtain the final process demand model.

[0076] After the model is built, the digital prototype of the industrial machine tool can be simulated, including the simulation of the industrial machine tool structure digital prototype, the industrial machine tool electrical digital prototype, the industrial machine tool hydraulic digital prototype, and the industrial machine tool process digital prototype. When simulating the industrial machine tool structure digital prototype, the statics, kinematics, and dynamics of the industrial machine tool structure digital prototype are solved, and kinematic and dynamic analysis is performed. Mechanical analysis such as modal analysis, fatigue analysis, and thermodynamic analysis can also be performed according to specific needs. These analyses together form the basis for a comprehensive evaluation of the structural performance of the industrial machine tool and for optimal design. Virtual assembly and analysis of the industrial machine tool structure digital prototype, including assembly modeling, tolerance analysis, and mechanism motion analysis, can be performed to identify problems in assembly design.

[0077] In some embodiments of the present invention, physical field analysis and visualization can also be performed on the working process of the industrial machine tool, including visualization of the analysis object, numerical simulation of the temperature field, and defect prediction of the processed product. The physical model in the application process of the industrial machine tool is described and analyzed in a visual way, and the relationship between the various parameters of the industrial machine tool and its physical properties and physical performance is displayed in a dynamic way. The analysis of these physical fields can help to gain a deep understanding of the working principle and performance characteristics of the industrial machine tool, help engineers understand the changes in product characteristics during the actual working process, increase the controllability and predictability of the product manufacturing and use process, and understand and optimize the performance of the industrial machine tool.

[0078] In some embodiments of the present invention, when simulating the electrical digital prototype of an industrial mother machine, it includes electrical integrated system simulation and power supply and distribution system simulation; the effectiveness of the electrical system design scheme is verified through the integrated technology of electrical system design and simulation based on the digital prototype, thereby shortening the R&D cycle of the industrial mother machine, reducing costs, and improving the design accuracy and reliability of the industrial mother machine.

[0079] The contents of the collaborative design of electrical digital prototypes for industrial mother machines include: modular design of digital prototype models, the use of multi-domain parallel and collaborative design methods, and collaborative simulation, and joint simulation analysis of the same product at the system level by different personnel / tools. Customized development of various tool modules based on the characteristics and processes of industrial mother machine design.

[0080] In the process of digital prototype design of industrial machine tools, engineers from different fields need to work closely together to complete the task. Through the sharing function provided by the platform, engineers from different fields can easily access, modify and generate models. This enables them to design and simulate on the same basis, avoiding duplication of work and waste of resources. In other words, collaboration is reflected in the fact that personnel from multiple fields can collaborate on a unified platform to complete the entire digital prototype design of industrial machine tools. The platform can centralize a variety of simulation tools, and engineers from different fields can perform collaborative design and collaborative simulation. They can communicate and discuss in real time through the communication tools provided by the platform to jointly solve problems encountered in the design process. At the same time, the platform also supports multi-field parallel simulation analysis, allowing engineers to have a more comprehensive understanding of the performance and behavioral characteristics of the digital prototype of the industrial machine tool.

[0081] After the digital prototype of the industrial mother machine is designed, it can be optimized and adjusted: During the design and simulation process, engineers from different fields need to adjust and optimize the model according to the simulation results and optimization suggestions. They can use the optimization tools provided by the platform to adjust parameters, modify the model, and other operations to improve the performance and reliability of the digital prototype.

[0082] After the optimization and adjustment are completed, the digital prototype of the industrial mother machine can also be reviewed and fed back regularly: in order to ensure the smooth progress of the design process and the quality of the design results, regular review and feedback are required. Engineers from different fields can participate in the review meeting together to evaluate and discuss the design results and put forward improvement opinions and suggestions. This can ensure that problems in the design process are discovered and solved in a timely manner.

[0083] There is a close connection and interactive relationship between the platform and the digital prototype design model library of industrial machine tools. The platform provides storage, management, integration, collaboration, optimization and sharing support for the model library; engineers from different fields achieve close collaboration and communication through the platform and jointly complete the digital prototype design task of industrial machine tools.

[0084] Parts not described in detail in the present invention may refer to the prior art or are known to those skilled in the art, and the present invention will not go into details.

[0085] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A collaborative design method for digital prototype of industrial mother machine based on MBSE, characterized in that: The method comprises: S1. Construct a design model library for the digital prototype of an industrial machine; the design model library includes several different types of demand models and several large models for various aspects of the digital prototype; S2. According to the objectives and requirements, select several appropriate demand models from the design model library, match them to appropriate large models according to the matching principle, and build the industrial mother machine structure digital prototype model, industrial mother machine electrical digital prototype model, industrial mother machine hydraulic digital prototype model and / or industrial mother machine process digital prototype model; S3. Adjust and optimize the constructed digital prototype model based on the experience of engineers to obtain a digital prototype model of the industrial mother machine that meets the requirements.

2. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 1 is characterized in that: The steps of constructing the design model library of the industrial mother machine digital prototype in step S1 include: Determine goals and requirements: clarify the specific goals and requirements of digital prototype design for different types of industrial mother machines, including the function, accuracy and scope of application of the model; Data collection and cleaning: Collect relevant historical data from multiple sources, clean the collected data, and remove invalid, erroneous or redundant information; Feature engineering: extract feature data useful for model building from the cleaned data and preprocess the feature data to improve the training efficiency and performance of the model; Model selection and training: Select the appropriate model architecture according to specific needs, use the processed feature data to train the respective models, and obtain various types of models by continuously adjusting the parameters and structure of the model; Model verification and optimization: Use the preset verification data set to evaluate the trained model, and then optimize the model based on the evaluation results to obtain optimized models of various types; Modular construction: The optimized models of various types are modularly constructed to form a model library containing different types of industrial mother machine digital prototype design models.

3. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 1 is characterized in that: The industrial mother machine structure digital prototype model includes a multi-level parts model, a mechanism model, an assembly model, a contact model and a multi-body dynamics model.

4. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 3 is characterized in that: When constructing the part model, according to the existing design parameters and target indicators, a suitable large model is matched according to the matching principle to generate an initial part model, and the initial part model is optimized and adjusted in combination with the engineer's experience to obtain a part model that meets the requirements; When constructing the mechanism model, according to the connection and motion relationship of the industrial mother machine, the initial mechanism model is generated by matching the appropriate large model according to the matching principle; the initial mechanism model is optimized and adjusted based on the experience of engineers to obtain a mechanism model that meets the requirements; When constructing the assembly model, according to the constructed part model and mechanism model, a suitable large model is matched according to the matching principle to obtain an initial assembly model, and the initial assembly model is adjusted and optimized based on the experience of engineers to obtain a final assembly model; When constructing the contact model, according to the constructed part model and assembly model, based on the part geometric features and the collision detection algorithm, an initial contact model is generated by matching a suitable large model according to the matching principle, and the initial contact model is adjusted and optimized in combination with the engineer's experience to obtain a contact model that can reflect the contact relationship between the parts; When constructing the multi-body dynamics model, according to the constructed part model, mechanism model and assembly model, a suitable large model is matched according to the matching principle to obtain an initial multi-body dynamics model; the initial multi-body dynamics model is adjusted and optimized according to the motion characteristics and force conditions to obtain the final multi-body dynamics model.

5. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 1 is characterized in that: The electrical digital prototype model of the industrial mother machine includes a control model, a detection model and / or a fault prediction model.

6. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 5 is characterized in that: When constructing the control model, according to the matching principle, combined with the required parameters and objectives related to electrical control, a suitable large model is matched to generate an initial control model; then, the initial control model is adjusted and optimized based on the experience of engineers to obtain the required control model; When constructing the detection model, an initial detection model is generated by matching a suitable large model according to the detection requirements and matching principles; and the initial detection model is further optimized and adjusted based on the experience of engineers to obtain the required detection model; Fault prediction model: Based on the constructed control model and detection model, the initial fault prediction model is generated by matching the appropriate large model according to the matching principle. The initial fault prediction model is further optimized and adjusted based on the engineer's experience to obtain the required fault prediction model.

7. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 1 is characterized in that: The construction content of the industrial mother machine hydraulic digital prototype model includes: According to the hydraulic control requirements described in the demand model, the initial hydraulic control model is generated by matching the appropriate large model according to the matching principle. The initial hydraulic control model is further optimized and adjusted based on the experience of engineers to ensure that the hydraulic control model meets the requirements in the demand model and obtain the final hydraulic control model.

8. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 1 is characterized in that: The construction content of the industrial mother machine process digital prototype model includes: According to the obtained process requirement model, the appropriate large model is matched according to the matching principle to generate the initial industrial mother machine process digital prototype model, and then the initial industrial mother machine process digital prototype model is further adjusted and optimized based on the engineer's experience to obtain the final industrial mother machine process digital prototype model.

9. The MBSE-based collaborative design method for industrial mother machine digital prototype according to claim 2 is characterized in that: Relevant historical data include the design drawings, performance parameters and usage records of industrial mother machines; The characteristic data include the geometric shape, material properties and assembly relationship of the parts; The preprocessing of feature data includes normalization and standardization; Optimizing the model based on the evaluation results includes adjusting model parameters and / or improving the model structure.

10. An industrial machine tool digital prototype collaborative design device based on MBSE, comprising a processor and a memory, wherein a software program is stored in the memory, characterized in that: When the processor runs the software program, it can implement the content of the MBSE-based industrial mother machine digital prototype collaborative design method as described in any one of claims 1 to 9.

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