Power core machine hot-state assembly deviation prediction method and device, equipment and medium

Abstract

The application provides a power core engine hot-state assembly deviation prediction method, device, equipment and medium, a first model for core engine assembly deviation virtual prototype simulation and analysis is established; tolerance analysis is performed on the first model to determine the key features affecting the assembly deviation of each component of the core engine, the obtained 2D and 3D fusion data of the key features are compared with the corresponding component model to obtain a first correction model; according to the working temperature distribution of the core engine and the thermal expansion coefficients of the materials of each component of the core engine, a second model for core engine temperature field and thermal deformation simulation is established to extract the three-dimensional thermal deformation amount of the key features; the three-dimensional thermal deformation amount is introduced into the first correction model to form a third model, and tolerance analysis is repeatedly performed to predict the assembly deviation and interference of the core engine under the hot state. The application can realize the prediction of the assembly deviation and interference of the core engine under the hot state, and has important economic value and application prospect.

Description

Technical Field

[0001] This invention relates to the field of digital assembly simulation technology. In particular, it relates to a method, apparatus, equipment, and medium for predicting hot assembly deviations of a power core engine. Background Technology

[0002] The core engine is the most critical component of power plants such as gas turbine engines and aero engines. It includes rotor components like the compressor and high-pressure turbine, as well as key high-temperature components like the combustion chamber. The core engine is designed, processed, manufactured, and assembled in a cold state, and then rotates at high speed in a hot environment with high temperature and pressure. Components expand when heated by the high-temperature gas. Because different materials have different coefficients of expansion, and each component's temperature rise and rate of rise vary, interference may occur during the hot assembly of the core engine. Interference is mainly divided into two types. The first is beneficial interference, where thermal expansion can tighten the connections of certain components in the core engine, allowing the gas turbine to operate normally. For example, many components in the combustion chamber rely on thermal expansion for fastening. However, if the expansion is miscalculated, the connections between components may not be strong enough, potentially causing components to detach during operation, damaging the core engine, or even causing a serious accident. The second type is adverse interference. Differences in the axiality, end-face parallelism, flatness, and end-face and radial runout of rotor components such as the core engine compressor and high-pressure turbine, especially thermal deformation of the core engine under hot conditions, will lead to assembly deviations and interference due to rotor-stator clearance and runout. For example, interference may occur between the rotor and the compressor casing or turbine guide vanes. This can seriously affect the dynamic performance of the core engine and even lead to safety accidents such as blade tip collisions.

[0003] Today, with the advancement of computer technology, more and more equipment is using digital assembly methods for deviation simulation and prediction during the production and assembly process. However, traditional digital assembly deviation prediction mainly focuses on the study of assembly under cold conditions, without simulating the impact of thermal deformation on assembly deviations and interference under the actual working conditions of the core machine. It also fails to integrate the amount of thermal deformation into the actual model, making it impossible to accurately predict the assembly deviations and interference of the power core machine. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, device, equipment and medium for predicting hot assembly deviations of a power core engine, in order to solve the problem that the prior art does not conduct simulation studies on the effect of thermal deformation on assembly deviations and interference under the actual working conditions of the core engine, does not integrate the amount of thermal deformation into the actual model, and therefore cannot accurately predict the assembly deviations and interferences of the power core engine.

[0005] To achieve the above and other related objectives, this invention provides a method for predicting hot assembly deviations of a core machine. The method includes: establishing a first model for virtual prototype simulation and analysis of core machine assembly deviations; performing tolerance analysis on the first model to determine key features affecting the assembly deviations of various core machine components; performing 2D and 3D precision measurements and scans on the core machine components; comparing the obtained 2D and 3D fused data of the key features with the corresponding component models to obtain a first correction model; establishing a second model for simulating the temperature field and thermal deformation of the core machine based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of various core machine components, to extract the three-dimensional thermal deformation of the key features of each core machine component; importing the three-dimensional thermal deformation into the first correction model to form a third model for core machine assembly deviation analysis; and repeating tolerance analysis to predict assembly deviations and interferences of the core machine under hot conditions.

[0006] In one embodiment of the present invention, the establishment of a first model for simulation and analysis of virtual prototype of core machine assembly deviation includes: establishing a CAD model of a component according to the design drawings and assembling it; marking three-dimensional tolerance dimensions in the CAD model of the component according to the design drawings; and importing the assembled and marked CAD model of the component into tolerance analysis software to form a first model for simulation and analysis of virtual prototype of core machine assembly deviation based on design tolerance.

[0007] In one embodiment of the present invention, the method includes: establishing assembly features in the first model according to actual assembly requirements, and selecting assembly processes and planning assembly sequences; establishing feature measurements in the first model to measure assembly gaps or interferences that meet design specifications.

[0008] In one embodiment of the present invention, the step of performing tolerance analysis on the first model to determine the key features affecting the assembly deviation of each component of the core machine includes: performing tolerance analysis on the first model to output analysis data on the impact of the design tolerance of each component on assemblability and assembly accuracy, and identifying the source and degree of influence of the assembly deviation, thereby determining the key features affecting the assembly deviation of each component of the core machine.

[0009] In one embodiment of the present invention, the method for obtaining the 2D and 3D fused data includes: performing 3D scanning and measurement on key features that affect the assembly deviation of various components of the core machine; extracting 3D tolerance data related to the key features from the 3D scanning data and fusing it with 2D data.

[0010] In one embodiment of the present invention, the method includes: identifying and prioritizing the assembly mating surfaces of each component of the core machine; scanning and measuring the key features of each actual component that affect the assembly deviation of the core machine, and collecting point cloud data of the key features of the assembly mating surfaces of the actual components through 3D scanning; deleting noise and redundant patches from the point cloud data, and obtaining a triangulated mesh model based on the measured point cloud using a triangulation mesh algorithm; performing best fitting between the triangulated mesh model and the component CAD model, and obtaining the key parameters of the contour, size, and shape of the actual component model using the component CAD model as a reference; extracting key 3D tolerance data related to the key features of the assembly mating surfaces of the actual components, and fusing it with 2D data.

[0011] In one embodiment of the present invention, the step of establishing a second model for simulating the temperature field and thermal deformation of the core machine based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of each component of the core machine includes: assigning the thermal expansion coefficient, elastic modulus, density, and deformation curve of each component material corresponding to high-temperature deformation in CAE software based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of each component material; and establishing the core machine temperature field, thermal expansion coefficient, elastic modulus, density, and deformation curve of each component based on finite element method to obtain the thermal deformation distribution of each component.

[0012] To achieve the above and other related objectives, the present invention provides a core machine hot assembly deviation prediction device, the device comprising: a first model module, used to establish a first model for virtual prototype simulation and analysis of core machine assembly deviation; a first correction model module, used to perform tolerance analysis on the first model to determine the key features affecting the assembly deviation of each component of the core machine, perform 2D and 3D precision measurement and scanning on the core machine components, and compare the obtained 2D and 3D fused data of the key features with the corresponding component model to obtain a first correction model; a second model module, used to establish a second model for core machine temperature field and thermal deformation simulation based on the operating temperature distribution of the core machine and the thermal expansion coefficient of the materials of each component of the core machine, so as to extract the three-dimensional thermal deformation amount of the key features of each component of the core machine; and a third model module, used to import the three-dimensional thermal deformation amount into the first correction model to form a third model for core machine assembly deviation analysis, and repeatedly perform tolerance analysis to predict the assembly deviation and interference of the core machine under hot conditions.

[0013] To achieve the above and other related objectives, the present invention provides a computer device, the device comprising: a memory and a processor; the memory storing a computer program, and the processor executing the computer program to implement the method described above.

[0014] To achieve the above and other related objectives, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0015] As described above, the present invention provides a method, apparatus, device, and medium for predicting hot assembly deviations of a core machine. This involves establishing a first model for virtual prototype simulation and analysis of core machine assembly deviations; performing tolerance analysis on the first model to determine key features affecting the assembly deviations of various core machine components; comparing the obtained 2D and 3D fused data of the key features with the corresponding component models to obtain a first correction model; establishing a second model for core machine temperature field and thermal deformation simulation based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of various core machine components to extract the three-dimensional thermal deformation of the key features of each core machine component; importing the three-dimensional thermal deformation into the first correction model to form a cold-state model for core machine assembly deviation analysis; and repeating tolerance analysis to predict assembly deviations and interferences under hot conditions of the core machine.

[0016] It has the following functions:

[0017] This application focuses on the core engine of a gas turbine power engine, and establishes a hot assembly deviation analysis model for the core engine based on parameters such as component machining dimensional deviations, form and position tolerances, three-dimensional contours and deformations in the cold state, and material thermal expansion coefficients and thermal deformations in the hot state. This model enables the prediction of assembly deviations and interferences such as gaps and runouts in the core engine under hot operating conditions, which has significant economic value and application prospects. Attached Figure Description

[0018] Figure 1 The diagram shown is a flowchart illustrating a core machine hot assembly deviation prediction method according to one embodiment of the present invention.

[0019] Figure 2 The diagram shown is a schematic diagram of a component CAD model of a gas turbine rotor as an example in one embodiment of the present invention.

[0020] Figure 3 The diagram shown is a schematic diagram of a micro-engine assembly deviation analysis model in one embodiment of the present invention.

[0021] Figure 4 The diagram shown is a schematic representation of a three-dimensional scanned point cloud according to one embodiment of the present invention.

[0022] Figure 5 This diagram illustrates a comparison of deviations in key feature values ​​according to one embodiment of the present invention.

[0023] Figure 6 The diagram shown is a schematic representation of the thermal deformation model of a micro gas turbine rotor in one embodiment of the present invention.

[0024] Figure 7 The diagram shows a flowchart of core machine hot assembly deviation prediction and optimization in one embodiment of the present invention.

[0025] Figure 8 The diagram shown is a schematic representation of a core machine hot assembly deviation prediction device according to one embodiment of the present invention.

[0026] Figure 9 The diagram shown is a structural schematic of a computer device according to one embodiment of the present invention. Detailed Implementation

[0027] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0028] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0029] With the advancement of computer technology, more and more equipment is using digital assembly methods for deviation simulation and prediction during the production and assembly process. However, traditional digital assembly deviation prediction mainly focuses on the study of assembly under cold conditions, without simulating the thermal deformation under the actual working conditions of the core machine, and without integrating the thermal deformation amount into the actual model, thus failing to accurately predict the assembly deviation and interference of the power core machine.

[0030] To address this issue, this application proposes a method for predicting hot assembly deviations of the core engine. Specifically for the core engine of a gas turbine power engine, a hot assembly deviation analysis model is established based on parameters such as component machining dimensional deviations, geometric tolerances, three-dimensional contours and deformations in the cold state, and material thermal expansion coefficients and thermal deformations in the hot state. This model enables the prediction of assembly deviations and interferences such as gaps and runouts in the core engine under hot operating conditions, possessing significant economic value and application prospects.

[0031] like Figure 1 The diagram shown illustrates a flowchart of the core machine hot assembly deviation prediction method according to one embodiment of the present invention.

[0032] As shown in the figure, the method includes:

[0033] Step S101: Establish a virtual prototype simulation and analysis model for core machine assembly deviation based on design tolerances.

[0034] In one embodiment of this application, step S101 includes:

[0035] A. Create CAD models of the components based on the design drawings and assemble them;

[0036] B. Mark the three-dimensional tolerance dimensions in the CAD model of the parts according to the design drawings;

[0037] C. Import the assembled and labeled CAD models of the components into tolerance analysis software to form a first model for simulation and analysis of virtual prototypes of core machine assembly deviations based on design tolerances.

[0038] Specifically, the construction process of the first model used for virtual prototype simulation and analysis of core machine assembly deviations mainly includes: establishing CAD models of components in a computer-aided system based on design drawings, and assembling the component models based on the design. For example... Figure 2 As shown, taking a gas turbine rotor as an example, the diagram is a schematic CAD model of the component.

[0039] In each component model, the three-dimensional tolerance dimensions are marked according to the design drawings. After marking, the assembled component models are imported into the tolerance analysis software to establish a virtual prototype simulation and analysis model of the core machine assembly deviation based on the design tolerance.

[0040] For example, tolerance analysis software can include DTAS 3D or 3DCS Variation Analyst Multi-CAD. DTAS 3D (Dimensional Tolerance Analysis System 3D), a domestically developed 3D tolerance analysis software, is based on Monte Carlo principles. It models the product according to its tolerances and assembly relationships, then performs analytical and simulation calculations to predict whether the product design can meet its critical dimensional requirements, predict the product pass rate, and conduct root cause analysis. It also incorporates AI and FEA functions, making tolerance analysis modeling more efficient and applicable to a wider range of scenarios. 3DCS Variation Analyst Multi-CAD is a powerful tolerance analysis software, an independent CAD-neutral software system used to model variations caused by part tolerances and manufacturing and assembly processes. 3DCS Multi-CAD can be used with native CAD files, STEP, and IGES.

[0041] In some feasible embodiments, the method includes:

[0042] A. Based on the actual assembly requirements, establish assembly features in the first model, and select assembly processes and plan assembly sequences;

[0043] B. Establish feature measurements in the first model to measure assembly gaps or interferences that meet design specifications.

[0044] Specifically, after establishing a virtual prototype simulation and analysis model of core machine assembly deviation based on design tolerances, assembly features are established according to actual assembly, assembly processes are selected, assembly sequence is planned, and feature measurements are established in the first model, mainly used to measure the assembly gaps or interferences that need to be guaranteed as specified in the design.

[0045] like Figure 3 As shown, this application presents an assembly deviation analysis model for a micro gas turbine core engine in one embodiment. Key assembly features of components are represented using meshes and nodes. Assembly relationships, dimensional tolerances, and geometric tolerances are assigned to the meshes and nodes, and the propagation of tolerances during the assembly process is achieved through the network and nodes. Assembly deviations are analyzed by measuring key assembly indicators. Representing key assembly features of parts using networks and nodes allows for a lightweight analysis model.

[0046] Step S102: Perform tolerance analysis on the first model to determine the key features that affect the assembly deviation of each component of the core machine. Compare the obtained 2D and 3D fusion data of the key features with the corresponding component models to obtain the first corrected model.

[0047] In one embodiment of this application, the step of performing tolerance analysis on the first model to determine the key features affecting the assembly deviations of various core components includes:

[0048] Tolerance analysis is performed on the first model to output data on the impact of design tolerances of each component on assemblability and assembly accuracy, and to identify the sources and degree of influence of assembly deviations, thereby determining the key characteristics affecting the assembly deviations of each component of the core machine.

[0049] In simple terms, the simulation and analysis model of the virtual prototype of the core machine assembly deviation based on design tolerances is subjected to tolerance analysis. The results include the impact of the design tolerances of each part on the assemblability and assembly accuracy of the equipment, the influencing factors of assembly deviations, and can accurately identify the source and degree of influence of assembly deviations. This allows us to determine the key 2D and 3D dimensions, geometric tolerances, and other factors that affect the assembly deviations of each component of the core machine.

[0050] In one embodiment of this application, the method for obtaining the 2D and 3D fused data includes:

[0051] A. Perform 3D scanning and measurement on the key features that affect the assembly deviation of various components of the core machine.

[0052] Further, step A includes:

[0053] A1. Identify and prioritize the features of the mating surfaces of the core components;

[0054] A2. Scan and measure the key features that affect the assembly deviation of the core machine corresponding to each actual component, and collect point cloud data of the key features of the assembly mating surfaces of the actual components through 3D scanning.

[0055] Primarily, precision measurement technology is used to perform 2D and 3D scanning and measurement of key features affecting assembly deviations of various core components. Specifically, feature identification and key segmentation are performed on the mating surfaces of components. Digital inspection equipment is used to scan and measure the key features of actual components that affect core component assembly deviations. 3D scanning is used to collect point cloud data of the key features of the mating surfaces of actual components. For example... Figure 4 The image shown is a schematic diagram of a 3D scanned point cloud.

[0056] B. Extract 3D tolerance data related to key features from 3D scanning data and fuse it with 2D data.

[0057] Furthermore, step B includes:

[0058] B1. Remove noise and redundant patches from the point cloud data, and use a triangulation meshing algorithm to obtain a triangulated mesh model based on the measured point cloud.

[0059] B2. Perform optimal fitting between the triangulated mesh model and the component CAD model, and obtain the key parameters of the actual component model's outline, dimensions, and shape and position using the component CAD model as a reference.

[0060] B3. Extract key 3D tolerance data related to the key features of the actual component assembly surfaces, and fuse them with 2D data to obtain 2D and 3D fused data.

[0061] Primarily, key parameters such as contour and deformation are extracted from the 3D scan data and fused with 2D data. Specifically, the measured point cloud data obtained through scanning is processed, removing noise and redundant faces, and a triangulation meshing algorithm is used to obtain a triangulated mesh model based on the measured point cloud. The triangulated model obtained from the actual component scan is best-fitted with the component design CAD model. After fitting, the contour, deformation, and other key parameters of the actual model are obtained using the theoretical CAD model as a reference. Key 3D tolerance data related to the key features of the assembly mating surfaces of the actual components are extracted, and then fused with 2D data to obtain fused 2D and 3D data.

[0062] In one or more embodiments of this application, the step of comparing the obtained 2D and 3D fused data of the key features with the corresponding component model to obtain a first corrected model includes:

[0063] A. Compare the obtained 2D and 3D fused data of the key features with the corresponding component models to identify discrepancies. For example... Figure 5 As shown, this is a schematic diagram comparing the deviations of key feature values.

[0064] B. Taking into account the inherent accuracy error of the measuring instrument, the deviation is converted into data that can be recognized by the tolerance analysis software and then imported into the CAD model of the component. This is then integrated into the first correction model based on the analysis of the virtual prototype of the core assembly deviation of the core machine based on actual measurement and design tolerance.

[0065] C. Repeat the tolerance analysis on the first model to determine the key characteristics affecting the assembly deviation of each component of the core machine. Use the measured values ​​instead of the design values ​​and the instrument measurement uncertainty instead of the design tolerance for tolerance analysis and optimization.

[0066] Step S103: Based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of each component of the core machine, establish a second model for the simulation of the temperature field and thermal deformation of the core machine, so as to extract the three-dimensional thermal deformation of the key features of each component of the core machine.

[0067] In some feasible embodiments, the method for measuring the coefficient of thermal expansion of the materials of each component of the core machine includes: assigning a working temperature to each component according to the actual working conditions, placing a sample of the key material or newly developed material corresponding to each component into a coefficient of thermal expansion device, and accurately measuring the coefficient of thermal expansion of the material.

[0068] In one or more embodiments, step S103 specifically includes:

[0069] A. Based on the operating temperature distribution of the core machine and the thermal expansion coefficients of the materials of each component of the core machine, assign the thermal expansion coefficients, elastic modulus, density, and deformation curves of each component material corresponding to high-temperature deformation in the CAE software.

[0070] B. Establish the core machine temperature field, thermal expansion coefficient, elastic modulus, density, and deformation curves of each core machine component based on the finite element method, so as to obtain the thermal deformation distribution of each core machine component.

[0071] In simple terms, based on the operating temperature distribution of the core machine and the measured coefficient of thermal expansion, factors such as the coefficient of thermal expansion, elastic modulus, density, and deformation curve of the corresponding material under high temperature deformation are assigned in CAE software. The thermal deformation distribution of the core machine components is then obtained by establishing the core machine temperature field and coefficient of thermal expansion of each component based on the finite element method.

[0072] like Figure 6 The diagram shown illustrates the thermal deformation model of the turbine rotor of a micro gas turbine core unit according to one embodiment of this application. During the daily operation of the gas turbine, the materials of the heated components differ, resulting in different coefficients of expansion. Furthermore, the heating conditions and rates of each component vary, which may lead to a mismatch in the expansion and deformation of the rotor and stator, changes in rotor blade tip clearance, and the fact that parts that can be assembled in a cold state do not necessarily maintain sufficient clearance during hot operation. Therefore, simulation is needed to predict the deformation of the gas turbine components under hot conditions.

[0073] Taking a turbine rotor as an example, the turbine rotor and compressor rotor together form a gas turbine rotor. The outer surface of the turbine rotor is exposed to high-temperature, high-pressure gas, while the air around the compressor rotor is cooler. The large temperature difference between the two rotors and the uneven temperature distribution cause different expansion and contraction deformations within the elastic body. To maintain continuity, thermal stress is generated within the gas turbine rotor. Heat is mainly transferred through the connection between the two rotors, resulting in a large internal temperature difference. Simultaneously, the turbine rotor is exposed to high-temperature, high-pressure gas. Due to the highest temperature at the turbine inlet and the lowest at the outlet, a temperature difference exists, leading to varying degrees of thermal expansion and deformation in the turbine rotor. CAE software simulation can obtain the thermal deformation at each point of the turbine. The simulation results show that the thermal deformation is greatest at the turbine blade inlet and outlet, and smallest at the turbine impeller disk. Post-processing of the data yields the coordinates of key points before and after deformation. Based on the difference in coordinates before and after deformation, a thermal deformation simulation model is established on the theoretical model.

[0074] Step S104: Import the three-dimensional thermal deformation amount into the first correction model to form the third model for core machine assembly deviation analysis, and repeat the tolerance analysis to predict the assembly deviation and interference of the core machine under hot state.

[0075] Primarily, the thermal deformation of key features of each component is extracted from the second model used for core engine temperature field and thermal deformation simulation. This thermal deformation is then imported into the corresponding parts of the first corrected model, which is based on a virtual prototype analysis of assembly deviations using a fusion of measured and design tolerances. This forms a cold-state assembly corrected model containing hot-state assembly information. Tolerance analysis is repeatedly performed on the first model to determine the key features affecting the assembly deviations of each core engine component, and to predict the assembly deviations and interference under hot conditions of the core engine.

[0076] like Figure 7 The diagram illustrates a flowchart of core machine hot assembly deviation prediction and optimization in one embodiment of this application. The following detailed description, in conjunction with specific embodiments, will help those skilled in the art to further understand this application, but does not limit the application in any way.

[0077] 1) Taking the core engine of a gas turbine as an example, predict and analyze the interference of components in the combustion chamber that are fastened by thermal expansion and the gap between the rotor and the stator.

[0078] 2) Based on the design tolerances of core components of the gas turbine, such as the compressor, turbine, and combustion chamber, establish a tolerance analysis model for assembly deviations.

[0079] Based on the design drawings, CAD models of the parts are created in a computer-aided system, and the parts are assembled according to the design. After marking the three-dimensional tolerance dimensions in each part model according to the design drawings, the assembly model is imported into tolerance analysis software. Assembly features are established, assembly processes are selected, and the assembly sequence is planned based on the actual assembly. Feature measurements are also established in the model, mainly measuring the assembly clearances or interferences that need to be guaranteed as specified in the design. A virtual prototype simulation and analysis model of the core machine assembly deviation based on design tolerances is established.

[0080] 3) Based on the tolerance analysis model, determine the key factors affecting the assembly accuracy of the core machine, such as assembly clearance and runout.

[0081] Tolerance analysis was performed on a virtual prototype simulation and analysis model of the core machine assembly deviation based on design tolerances. The results included the impact of each part's design tolerances on the assemblability and assembly accuracy of the equipment, the influencing factors of assembly deviations, and the ability to accurately identify the sources and degrees of influence of assembly deviations. This revealed the key 2D and 3D factors affecting the core machine assembly deviations. Based on the analysis results, key features and parameters affecting assembly performance were determined. During the design phase, tolerance allocation optimization can be performed to maximize part tolerances while meeting design performance requirements.

[0082] 4) Precision measuring equipment such as coordinate measuring machines, laser trackers, articulated arms, and high-precision 3D scanners are used to accurately measure the key features and parameters that affect the assembly deviation of the core machine, as determined by the analysis.

[0083] 5) Extract key parameters such as contour and deformation from 3D scanned point cloud data.

[0084] The measured data obtained through scanning is processed, and after removing noise and redundant patches, a triangulation mesh model based on the measured point cloud is obtained using a triangulation mesh algorithm. The triangulated model obtained from scanning the actual part is then optimally fitted to the part's CAD design model. After fitting, using the theoretical CAD model as a reference, key parameters such as the contour and deformation of the actual model are obtained. Deviation data related to key features of the assembly and mating surfaces of the actual part are extracted, such as... Figure 2 By combining 2D data such as part dimensions, complete 2D / 3D deviation data of key features affecting assembly performance can be obtained.

[0085] 6) Establish a core machine assembly deviation analysis model based on the measured 2D / 3D data.

[0086] Based on the measured 2D / 3D deviation data of key features of the parts, combined with the measurement uncertainty of the measuring instrument, the data is transformed into data that can be recognized by the tolerance analysis model. Then, the assembly tolerance analysis model is assembled to obtain the assembly deviation and interference based on the measured data of the machined parts.

[0087] 7) Establish a core machine temperature field and thermal deformation simulation model based on finite element method.

[0088] Based on the operating temperature distribution of the core machine, factors such as the thermal expansion coefficient, elastic modulus, density, and deformation curve of the corresponding materials are assigned in CAE software to establish a finite element-based simulation model of the core machine temperature field and thermal deformation for each component.

[0089] 8) High-precision thermocouples and high-precision laser interferometers are used to accurately measure the temperature and thermal deformation of key and new materials, and to accurately obtain the coefficient of thermal expansion of the materials.

[0090] 9) Correct the core machine thermal deformation simulation model based on the accurate measured data of temperature field, thermal deformation, and thermal expansion coefficient of materials obtained from the test.

[0091] By substituting the measured coefficient of thermal expansion of the material into the core machine temperature field and thermal deformation simulation model based on the finite element method, a corrected simulation model of temperature field and thermal deformation based on the finite element method is obtained.

[0092] 10) Extract the three-dimensional thermal deformation of key features of each component of the core machine from the core machine thermal deformation simulation model.

[0093] By correcting the core machine's thermal deformation simulation model based on measured data, the thermal deformation amount of each key feature can be obtained. Furthermore, because the temperature sensor in the thermal expansion coefficient measuring device has inherent instrument errors, the thermal deformation value also fluctuates within the temperature error range. This range of deformation values ​​approximates the tolerance fluctuation of the part under high and low temperatures.

[0094] 11) The obtained three-dimensional thermal deformation is used as the measured data under hot conditions and imported into the third model of the core machine assembly deviation analysis to predict the assembly deviation and interference of the core machine under hot conditions.

[0095] The hot-state tolerances are imported into the corresponding parts of the assembly deviation virtual prototype analysis and correction model based on the fusion of measured and design tolerances, forming a cold-state assembly correction model that includes hot-state assembly information. The model predicts the assembly deviations and interferences of the core machine under hot conditions, and performs tolerance analysis and optimization on the results.

[0096] Compared with existing technologies, this application has the following advantages: The method of this application focuses on the analysis and prediction of hot assembly deviations in the core engine, addressing the lack of research on thermal deformation during current digital assembly research, which prevents the prediction of assembly deviations and interferences such as gaps and runouts in practical applications. A cold-state analysis model of core engine assembly deviations based on 2D and 3D data fusion is established. The key characteristic thermal deformation of the core engine under hot conditions is extracted in three dimensions, and the thermal deformation is corrected using experimental data, resulting in a more accurate and reliable model. Furthermore, based on the cold-state assembly deviation analysis model, assembly deviations and interferences under hot conditions are predicted, achieving the prediction objective while improving efficiency and saving costs, demonstrating significant economic value and application prospects.

[0097] like Figure 8 The figure shows a schematic diagram of a core machine hot assembly deviation prediction device according to an embodiment of the present invention. As shown, the core machine hot assembly deviation prediction device 800 includes:

[0098] The first model module 801 is used to establish the first model for simulation and analysis of virtual prototypes for core machine assembly deviations;

[0099] The first correction model module 802 is used to perform tolerance analysis on the first model to determine the key features that affect the assembly deviation of each component of the core machine, and to compare the obtained 2D and 3D fusion data of the key features with the corresponding component model to obtain the first correction model.

[0100] The second model module 803 is used to establish a second model for the simulation of the core machine temperature field and thermal deformation based on the operating temperature distribution of the core machine and the thermal expansion coefficient of the materials of each component of the core machine, so as to extract the three-dimensional thermal deformation of the key features of each component of the core machine.

[0101] The third model module 804 is used to import the three-dimensional thermal deformation into the first correction model to form the third model for core machine assembly deviation analysis, and repeat the tolerance analysis to predict the assembly deviation and interference of the core machine under hot conditions.

[0102] It is understandable that the device 800, through the operation of its various modules, can achieve the following: Figure 1 The aforementioned method for predicting hot assembly deviations of the power core machine.

[0103] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the third model module 804 can be a separate processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and its functions can be called and executed by a processing element of the above device. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0104] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0105] like Figure 9 The figure shows a schematic diagram of a computer device according to an embodiment of the present invention. As shown, the computer device 900 includes a memory 901 and a processor 902. The memory 901 stores a computer program, and the processor 902 executes the computer program to implement the following: Figure 1 The aforementioned method for predicting hot assembly deviations of the power core machine.

[0106] The memory 901 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0107] The processor 902 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0108] In one embodiment of the present invention, a computer-readable storage medium stores a data transmission program thereon, which, when executed by a processor, implements as follows: Figure 1 The aforementioned method for predicting hot assembly deviations of the power core machine.

[0109] As will be understood by those skilled in the art, all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned image processing program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0110] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0112] In summary, the present invention provides a method, apparatus, equipment, and medium for predicting hot assembly deviations of a power core machine, which can effectively overcome the various shortcomings of the prior art and has high industrial application value.

[0113] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for predicting hot assembly deviation of a power core engine, characterized in that, The method comprises: establishing a first model for core engine assembly deviation virtual prototype simulation and analysis; performing tolerance analysis on the first model to determine the key features affecting the assembly deviation of each core engine part, comparing the obtained 2D and 3D fusion data of the key features with the corresponding part model to obtain a first modified model; establishing a second model for core engine temperature field and thermal deformation simulation according to the working temperature distribution of the core engine and the thermal expansion coefficient of the material of each core engine part, to extract the three-dimensional thermal deformation of the key features of each core engine part; introducing the three-dimensional thermal deformation into the first modified model to form a third model for core engine assembly deviation analysis, and repeatedly performing tolerance analysis to predict the assembly deviation and interference of the core engine under thermal state; The method for obtaining 2D and 3D fusion data comprises: performing feature recognition and key division on the assembly fit surfaces of each core engine part; scanning and measuring the key features of each actual part affecting the assembly deviation of the core engine, and collecting point cloud data of the key features on the actual part assembly fit surface through 3D scanning; deleting noise points and redundant facets from the point cloud data, and obtaining a triangulation grid model based on the measured point cloud by using a triangulation grid algorithm; best fitting the triangulation grid model and the part CAD model, and obtaining the key parameters of the profile, size and shape of the actual part model based on the part CAD model as the reference datum; extracting the key 3D tolerance data related to the key features of the actual part assembly fit surface, combining the inherent accuracy error of the measuring instrument, and fusing it with the 2D data; The second model for core engine temperature field and thermal deformation simulation according to the working temperature distribution of the core engine and the thermal expansion coefficient of the material of each core engine part comprises: According to the working temperature distribution of the core engine and the thermal expansion coefficient of the material of each core engine part, give the core engine each part material corresponding high temperature deformation thermal expansion coefficient, elastic modulus, density, deformation curve in CAE software; Establish the core engine temperature field, thermal expansion coefficient, elastic modulus, density, deformation curve of each core engine part based on finite element, to obtain the thermal deformation distribution of each core engine part.

2. The method of claim 1, wherein, The first model for core engine assembly deviation virtual prototype simulation and analysis comprises: establishing a part CAD model according to the design drawing and assembling; annotating the three-dimensional tolerance size in the part CAD model according to the design drawing; importing the assembled and annotated part CAD model into the tolerance analysis software to form a first model for core engine assembly deviation virtual prototype simulation and analysis based on design tolerance.

3. The method of claim 1 or 2, wherein, The method comprises: establishing assembly features in the first model according to actual assembly requirements, and selecting assembly process and planning assembly sequence; establishing feature measurement in the first model to measure the assembly clearance or interference that meets the design specification requirements.

4. The method of claim 2, wherein, The tolerance analysis on the first model to determine the key features affecting the assembly deviation of each core engine part comprises: The first model is tolerance analyzed to output influence analysis data of design tolerances of each part on assemblability and assembly accuracy, and to identify sources and influence degrees of assembly deviations, so as to judge key features influencing assembly deviations of each part of the core engine.

5. The method of claim 1, wherein, The method for obtaining the 2D and 3D fusion data comprises: The key features influencing assembly deviations of each part of the core engine are 3D scanned and measured; 3D tolerance data related to the key features are extracted from the 3D scanning data and fused with the 2D data.

6. A core machine hot assembly deviation prediction device, characterized in that, The device comprises: A first model module for establishing a first model for virtual prototype simulation and analysis of assembly deviations of the core engine; A first correction model module for tolerance analyzing the first model to judge the key features influencing assembly deviations of each part of the core engine, and for comparing the obtained 2D and 3D fusion data of the key features with the model of the corresponding part to obtain a first correction model; The method for obtaining the 2D and 3D fusion data comprises: Feature recognition and key division are performed on assembly fit surfaces of each part of the core engine; The key features of each actual part influencing assembly deviations of the core engine are scanned and measured, and point cloud data of the key features of the assembly fit surfaces of the actual parts are collected through 3D scanning; The point cloud data is deleted for removing noise points and redundant facets, and a triangulation grid model based on the measured point cloud is obtained through a triangulation grid algorithm; The triangulation grid model and the CAD model of the part are best fitted, and the key parameters of the profile, size and shape of the actual part model are obtained with the CAD model of the part as a reference datum; Key 3D tolerance data related to the key features of the assembly fit surfaces of the actual part are extracted, and the data are fused with the 2D data in combination with inherent accuracy errors of the measuring instrument; A second model module for establishing a second model for temperature field and thermal deformation simulation of the core engine according to a working temperature distribution of the core engine and thermal expansion coefficients of materials of each part of the core engine, so as to extract three-dimensional thermal deformation amounts of the key features of each part of the core engine; The second model for temperature field and thermal deformation simulation of the core engine is established according to the working temperature distribution of the core engine and the thermal expansion coefficients of the materials of each part of the core engine, and comprises: According to the working temperature distribution of the core engine and the thermal expansion coefficients of the materials of each part of the core engine, the thermal expansion coefficients, elastic modulus, density and deformation curves of the materials of each part of the core engine corresponding to high-temperature deformation are assigned in a CAE software; The core engine temperature field, thermal expansion coefficients, elastic modulus, density and deformation curves of each part of the core engine are established based on finite elements, so as to obtain thermal deformation distribution of each part of the core engine; A third model module for importing the three-dimensional thermal deformation amounts into the first correction model to form a third model for assembly deviation analysis of the core engine, and repeatedly performing tolerance analysis to predict assembly deviations and interferences of the core engine under a thermal state.

7. A computer device, comprising: The device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A computer program product comprising a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of claims 1-5. A computer program product comprising a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of claims

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