A method and system for generating a parts assembly scheme

Abstract

The application relates to the technical field of component assembly, in particular to a component assembly scheme generation method and system, which comprises the following steps: acquiring part information corresponding to assembly parts, and constructing a multi-physical field model corresponding to the assembly parts based on finite element software; identifying key control points in the multi-physical field model; generating simulation rules based on part batches, and adjusting assembly working conditions corresponding to the multi-physical field model according to the simulation rules; updating the key control points based on simulated assembly working conditions, and generating an initial population set based on a genetic algorithm technology; dynamically adjusting deviation weight coefficients corresponding to the key control points based on the assembly working conditions by adopting a preset weight mechanism; and optimizing and iterating the initial population set according to the deviation weight coefficients and iteration parameters, so as to obtain an actual assembly scheme. The preset weight mechanism is used to automatically adjust the deviation weight coefficients of the key control points according to the assembly working conditions, so that the problem that the prior art lacks dynamic adaptability is solved.

Description

A method and system for generating component assembly and adjustment schemes Technical Field

[0001] This application relates to the technical field of component assembly and adjustment, and in particular to a method and system for generating component assembly and adjustment schemes. Background Technology

[0002] In the industrial manufacturing sector, the assembly and debugging of components are core aspects affecting product quality and production efficiency. Currently, component assembly involves high-precision parts such as aerospace turbine blades and automotive engines. The assembly accuracy of high-precision components is affected by assembly conditions and minute dimensional differences in parts during the production process, resulting in a low overall pass rate after assembly.

[0003] To address these issues, current methods primarily rely on the experience of assembly workers to determine the assembly sequence and tolerance compensation, and to design theoretical dimensions based on the drawings. Standardized assembly processes are then developed based on these theoretical dimensions. After assembly is complete, equipment such as coordinate measuring machines is used to conduct random inspections of the assembled assemblies.

[0004] The aforementioned traditional method involves assembling components according to a standard assembly process based on the design drawings, without considering the assembly conditions. It fails to dynamically adjust the assembly strategy based on actual working conditions, leading to problems between the actual assembly and the standard assembly process, thus reducing assembly efficiency. Summary of the Invention

[0005] In order to dynamically adjust strategies according to actual working conditions and improve the assembly efficiency of parts, this application provides a method and system for generating parts assembly and adjustment schemes.

[0006] Firstly, this application provides a method for optimizing the assembly and adjustment of components, employing the following technical solution:

[0007] A method for generating a component assembly and adjustment scheme includes the following steps:

[0008] Obtain the part information corresponding to the assembly and adjustment parts, including the part batch, and construct a multiphysics model corresponding to the assembly and adjustment parts based on finite element software;

[0009] Identify key control points in the multiphysics model, whereby the key control points represent regions that affect the assembly accuracy of assembled parts.

[0010] Simulation rules are generated based on the batch of parts, and the assembly and adjustment conditions corresponding to the multiphysics model are adjusted according to the simulation rules.

[0011] The key control points are updated based on the simulated assembly conditions, and an initial population set is generated based on genetic algorithm technology. The initial population set includes several initial population schemes.

[0012] A preset weighting mechanism is used to dynamically adjust the deviation weighting coefficients corresponding to the key control points based on the assembly and adjustment conditions.

[0013] The initial population set is optimized and iterated based on the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme.

[0014] By adopting the above technical solution, a multiphysics model corresponding to the assembled parts is constructed based on finite element software, and key control points in the multiphysics model are identified. The assembly and adjustment conditions of the multiphysics model are adjusted based on simulation rules, and the key control points are updated based on the simulated assembly and adjustment conditions. An initial population set is generated based on genetic algorithm technology, and the deviation weight coefficients corresponding to the key control points are dynamically adjusted based on the assembly and adjustment conditions using a preset weight mechanism. The initial population set is then iteratively optimized based on the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme. This allows for dynamic adjustment of strategies according to actual working conditions, improving the assembly efficiency of parts. By dynamically identifying key control points and fusing data, the risk of deviation accumulation caused by individual differences in parts can be predicted and avoided in advance, thus solving the problem of low pass rate. The preset weight mechanism automatically adjusts the deviation weight coefficients of key control points according to the assembly and adjustment conditions, solving the problem of lack of dynamic adaptability in existing technologies. Based on swarm intelligence optimization methods such as genetic algorithm and particle swarm optimization, the optimal assembly and adjustment combination is selected before assembly and adjustment, reducing the inefficiency caused by manual trial and error and rework.

[0015] In some embodiments, the part information includes dimensional deviations, and identifying key control points in the multiphysics model includes the following steps:

[0016] Based on the local distribution of the material optimized by variable density, preliminary key points in the multiphysics model are identified and marked as potential key points.

[0017] Based on the batch of parts, quantitative parameters are obtained, and based on the quantitative parameters and dimensional deviations, sensitivity analysis is performed on the potential critical points to obtain a sensitivity list;

[0018] The potential key points are filtered based on the sensitivity list to obtain the key control points in the multiphysics model.

[0019] By adopting the above technical solution, the multiphysics model is locally distributed based on the variable density optimized material to obtain potential key points that affect assembly accuracy. Then, sensitivity analysis is performed on the potential key points based on quantitative parameters to obtain a sensitivity list. Based on the sensitivity list and the potential key points, the corresponding key control points are obtained. This enables the acquisition of the corresponding key control points of the assembled parts, which facilitates the subsequent full-size scanning of the key control points, improves the prediction of the assembly accuracy of the assembled parts, and predicts and avoids the risk of cumulative deviation caused by individual differences of parts in advance, thereby solving the problem of low pass rate.

[0020] In some embodiments, the deviation weight coefficients corresponding to the key control points are dynamically adjusted based on a preset weighting mechanism, including the following steps:

[0021] Based on finite element parameter perturbation analysis, the geometric sensitivity coefficients corresponding to the key control points are obtained, and the mechanical sensitivity coefficients corresponding to the key control points are obtained based on stress influence weights.

[0022] Based on the geometric sensitivity coefficient and the mechanical sensitivity coefficient, an initial weight coefficient corresponding to the key control point is generated according to a preset judgment matrix;

[0023] A preset weighting mechanism is used to dynamically adjust the deviation weighting coefficients corresponding to the key control points based on the initial weighting coefficients.

[0024] By adopting the above technical solution, the deviation weight coefficients of each key control point of the assembled parts are dynamically adjusted based on the preset weight mechanism. This allows for adjustments based on the actual assembly and adjustment conditions. By adjusting the weight coefficients of different key control points, it is convenient to adjust the assembly strategy according to different assembly and adjustment conditions, thereby improving the assembly efficiency of the assembled parts and reducing the problem of manual repetitive assembly trial and error.

[0025] In some embodiments, generating simulation rules based on the part batch includes the following steps:

[0026] Based on the batch of parts, obtain the adjacent assembly parts corresponding to the assembly and adjustment parts, and obtain the assembly stress area based on the assembly parts and the adjacent assembly parts;

[0027] Assembly rules are generated based on the assembly stress region, and simulation rules are generated based on the assembly rules.

[0028] In some embodiments, after performing optimal iterations on the initial population set based on the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme, the following steps are further included:

[0029] A full-size scan of key control points is performed using a blue light 3D scanner to obtain scanned point cloud data.

[0030] Based on the ICP algorithm, the scanned point cloud data is correlated with the CAD model to obtain the three-dimensional model corresponding to the assembled parts.

[0031] The actual assembly scheme is simulated based on the three-dimensional model to obtain the assembly deviation.

[0032] By adopting the above technical solution, the key control points can be fully scanned using a blue light 3D scanner, which can more accurately obtain the areas of the assembly parts that need to be controlled. The point cloud data is then matched with the CAD model to obtain the corresponding 3D model of the assembly parts. Based on the 3D model, the actual assembly scheme is simulated to obtain the assembly deviation. In this way, the assembly parts can be simulated according to the actual assembly conditions, saving the cost of manual assembly trial and error and improving the assembly efficiency of the assembly parts.

[0033] In some embodiments, after performing optimal iterations on the initial population set based on the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme, the following steps are further included:

[0034] Real-time acquisition of monitoring data from assembly line sensors, including vibration data and temperature values;

[0035] Based on the vibration data and temperature values, determine whether there is corresponding matching data in the preset database;

[0036] If not, the preset database is updated based on the vibration data and temperature values, and the three-dimensional model is updated based on the preset database.

[0037] In some embodiments, obtaining quantization parameters based on the part batch includes the following steps:

[0038] Heuristic screening based on quantization parameters is used to obtain a visual theory matrix, and priorities are set based on the visual theory matrix.

[0039] The quantization parameters are grouped by physical mechanism to obtain a parameter set, and the quantization parameters are set accordingly based on the parameter set.

[0040] By adopting the above technical solution, the quantization parameters are grouped by physical mechanism to avoid processor resource occupation during operation. Through the set physical mechanism grouping, the processor can perform synchronous operation on the parameter set or operate on the parameter set one by one, saving the time of generating quantization parameters.

[0041] In some embodiments, after setting the quantization parameters based on the parameter set, the following steps are also included:

[0042] The assembly deviation is subjected to adversarial verification, an ignoring parameter is obtained based on the assembly deviation, and the quantization parameter is updated according to the ignoring parameter.

[0043] By adopting the above technical solution, the assembly deviation is subjected to adversarial verification, the neglected parameters are obtained based on the assembly deviation, and the quantization parameters are updated according to the neglected parameters, thereby stimulating a more rigorous mesh convergence analysis.

[0044] Secondly, this application provides a system for generating component assembly and adjustment schemes, which adopts the following technical solution:

[0045] A system for generating component assembly and adjustment schemes, comprising executing the method for generating component assembly and adjustment schemes as described in the first aspect, including:

[0046] The model building module is used to obtain the part information corresponding to the assembly and adjustment parts, including the part batch, and to build a multiphysics model corresponding to the assembly and adjustment parts based on finite element software.

[0047] A key point identification module is used to identify key control points in the multiphysics model. The key control points represent areas that affect the assembly accuracy of the assembled parts.

[0048] The working condition simulation module generates simulation rules based on the batch of parts and adjusts the assembly and adjustment working conditions corresponding to the multiphysics model according to the simulation rules.

[0049] The key point update module updates the key control points based on simulated assembly conditions and generates an initial population set based on genetic algorithm technology. The initial population set includes several initial population schemes.

[0050] A coefficient generation module, which uses a preset weighting mechanism to dynamically adjust the deviation weighting coefficients corresponding to the key control points based on the assembly and adjustment conditions.

[0051] The scheme generation module performs optimization iterations on the initial population set based on the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme.

[0052] In some embodiments, the storage module is used to store part information, key control points corresponding to the part information, and deviation weighting coefficients corresponding to the key control points.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. A multiphysics model corresponding to the assembled parts is constructed based on finite element method (FEM) software, and key control points in the FEM model are identified. The assembly and adjustment conditions of the FEM model are adjusted based on simulation rules, and the key control points are updated based on the simulated assembly and adjustment conditions. An initial population set is generated based on genetic algorithm technology, and the deviation weight coefficients corresponding to the key control points are dynamically adjusted based on the assembly and adjustment conditions using a preset weight mechanism. The initial population set is then iteratively optimized based on the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme. This allows for dynamic adjustment of strategies according to actual working conditions, improving the assembly efficiency of parts. By dynamically identifying key control points and fusing data, the risk of deviation accumulation caused by individual differences in parts can be predicted and avoided in advance, thus solving the problem of low pass rate.

[0055] 2. By using a preset weighting mechanism to automatically adjust the deviation weighting coefficients of key control points according to the assembly and adjustment conditions, the problem of lack of dynamic adaptability in existing technologies is solved;

[0056] 3. Based on swarm intelligence optimization methods such as genetic algorithms and particle swarm optimization, the optimal assembly and adjustment combination is selected before assembly and adjustment, thereby reducing the inefficiency caused by manual trial and error and rework. Attached Figure Description

[0057] Figure 1 is a block diagram of the method for generating a component assembly and adjustment scheme provided in an embodiment of this application;

[0058] Figure 2 is a block diagram of the method for obtaining the deviation weight coefficient provided in an embodiment of this application;

[0059] Figure 3 is another method block diagram provided in an embodiment of this application;

[0060] Figure 4 is a block diagram of the method for obtaining quantization parameters provided in an embodiment of this application;

[0061] Figure 5 is a schematic diagram of the structure of the component assembly and adjustment scheme generation system provided in this embodiment.

[0062] Figure labeling: 10, Model building module; 20, Key point identification module; 30, Working condition simulation module; 40, Key point update module; 50, Coefficient generation module; 60, Scheme generation module. Detailed Implementation

[0063] To better understand the purpose, technical solutions, and advantages of this application, it has been described and illustrated below with reference to the accompanying drawings and embodiments. However, those skilled in the art should understand that this application can be implemented without these details. In some cases, to avoid obscuring various aspects of this application due to unnecessary description, well-known methods, processes, systems, components, and / or circuits already described at a higher level will not be elaborated upon. It will be apparent to those skilled in the art that various modifications can be made to the embodiments disclosed in this application, and the general principles defined in this application can be applied to other embodiments and application scenarios without departing from the principles and scope of this application. Therefore, this application is not limited to the illustrated embodiments, but conforms to the broadest scope consistent with the scope of protection claimed in this application.

[0064] This application discloses a method for generating component assembly and adjustment schemes, which is applied to a component assembly and adjustment scheme generation system. The system includes a processor. The processor models the assembly and adjustment parts based on finite element software and analyzes them through assembly and adjustment conditions to adjust the modeling and analysis of the assembly and adjustment parts by the finite element software, thereby obtaining corresponding key control points. The key control points are updated based on simulated assembly conditions, and an initial population set is generated based on genetic algorithm technology. The initial population set is iteratively optimized to obtain the actual assembly and adjustment scheme.

[0065] As shown in Figure 1, the method for generating a component assembly and adjustment scheme includes the following steps:

[0066] S100: Obtain the part information corresponding to the assembly and adjustment parts, and construct the multiphysics model corresponding to the assembly and adjustment parts based on finite element software.

[0067] Among these, the assembled parts are components that require assembly and adjustment. Part information includes the corresponding part batch and dimensional deviations. Multiphysics deviations refer to the coupled models of thermal, force, and flow fields. Finite element method (FE) software is primarily used to simulate the structural, thermal, fluid, and electromagnetic multiphysics behaviors of the assembled parts. FE software options include ANSYS, COMSOL, ABAQUS, and Altair HyperWorks. Since ANSYS, COMSOL, ABAQUS, and Altair HyperWorks all utilize existing technologies, this embodiment does not modify the corresponding ANSYS, COMSOL, ABAQUS, and Altair HyperWorks programs; therefore, further details will not be provided here.

[0068] It's important to note that the process begins with model preparation, involving geometric modeling followed by the definition of multiple material properties and the setting of multiphysics parameters, including thermal, force, and flow fields. Coupling settings for these multiphysics fields are then implemented, such as selecting coupling types, boundary conditions and loads, and establishing fluid-thermal and thermal-mechanical coupling. After coupling is established, a meshing strategy is selected based on the desired coupling type. For flow field meshes, refinement is required at the boundary layer and near the wall; for solid meshes, refinement is needed in the thermal gradient region and refinement is required in the force field region of interest. Interface meshes must ensure fluid-solid interface mesh matching or use interpolation to transfer data. After meshing the assembled parts, the solver is configured, with specific configuration steps tailored to the coupling type. Finally, results are extracted, with different results extracted based on different physical parameters, and the results are verified through coupling effects.

[0069] It should be noted that when performing mesh generation, operators need to adjust the mesh generation rules according to different components, which is relatively slow. The processor can obtain the historical mesh generation rules corresponding to the assembled parts from the preset database, generate a mesh generation signal based on the historical mesh generation rules, and control the finite element software to perform mesh generation on the assembled parts based on the mesh generation signal, so as to quickly obtain a multiphysics model.

[0070] For example, the finite element method (FEM) software used is ANSYS. The ANSYS Mechanical APDL or ACT (Application Customization Toolkit) provides a Python / Matlab interface, allowing for scripting via Python / Matlab to facilitate signal interaction between the processor and the FEM software. When ANSYS needs to mesh the assembled part, it sends a meshing rule signal to the processor. The processor receives the meshing rule signal and filters it in a preset database to obtain historical meshing rules corresponding to the assembled part. Based on these historical rules, it generates a meshing signal and sends it to the FEM software. Finally, the FEM software adjusts the meshing of the assembled part based on the meshing signal.

[0071] For assembly parts that cannot be found in the preset database, the batch of parts for that assembly part can be analyzed to obtain similar matching parts from the preset database. Then, meshing can be performed according to the historical partitioning rules corresponding to the matching parts. Specifically, the similarity of matching parts is determined based on the part structure, size, and function. There are no restrictions on whether similarity is required for all three aspects (structure, size, and function) or just one.

[0072] This embodiment employs a three-step process: first, similarity matching is performed based on part function; second, similarity matching is performed based on part structure; and finally, similarity matching is performed based on part size. Only when all three criteria are met is the assembled part considered similar to the matching part. For assembled parts that do not exhibit similarity, finite element analysis (FEM) software is used to attempt mesh generation for the part. The generation time, accuracy, and effect are analyzed step-by-step to obtain the optimal mesh generation for that part. The generation results can be correlated with subsequent assembly errors in the assembly software. By adjusting the mesh generation type and verifying the final assembly error, a relationship between mesh generation and assembly error is established, leading to the optimal mesh generation rule for the assembled part. The assembled part and its corresponding mesh generation rule are then stored in a preset database.

[0073] Regarding the extraction of different results based on different physical parameters, for example, for flow fields, velocity contour maps, pressure distribution, and turbulence intensity can be used. For thermal fields, temperature distribution and heat flux density can be used. For force fields, stress / strain, displacement, plastic deformation, etc., can be used.

[0074] Finally, after verification of the coupling effect, the main focus was on energy conservation checks. This involved verifying the force balance and conducting experimental benchmarking by integrating the heat flux from the flow field into the solid with the solid temperature field. The existing scheme was adopted, so I won't go into too much detail here.

[0075] S200 identifies key control points in multiphysics models.

[0076] Among them, key control points represent the areas that affect the assembly accuracy of assembled parts. Performing full-dimensional analysis and calculation for assembled parts is too costly. Therefore, by identifying the key control points corresponding to the assembled parts and focusing on the positions where the influence on assembly accuracy exceeds 0.8, the amount of calculation for dimensional analysis of the assembled parts can be reduced.

[0077] To identify key control points in the multiphysics model, this embodiment employs topology optimization analysis. By optimizing the material distribution using variable density, key control points with an impact coefficient exceeding 0.8 on assembly accuracy are identified. Variable density material distribution optimization is an efficient design method based on topology optimization. It controls the distribution of materials in space using continuous variables, ultimately resulting in a lightweight, high-performance structure.

[0078] In one embodiment, identifying key control points in a multiphysics model includes the following steps:

[0079] S210 uses variable density optimized materials for local distribution to identify preliminary key points in a multiphysics model and marks these preliminary key points as potential key points.

[0080] S220 obtains quantization parameters based on part batches and performs sensitivity analysis on potential critical points based on quantization parameters and dimensional deviations to obtain a sensitivity list.

[0081] S230 filters potential key points based on a sensitivity list to obtain key control points in the multiphysics model.

[0082] The quantification parameters include geometric parameters, material parameters, boundary conditions, and numerical parameters. First, based on the principle of a single variable, variable density optimization materials are used to locally distribute the material across the assembled parts, identifying preliminary key points in the multiphysics model. These preliminary key points are then marked as potential key points. Next, corresponding quantification parameters are obtained based on the part batch. Sensitivity analysis is performed on the potential key points based on the quantification parameters and corresponding dimensional deviations, resulting in a sensitivity list. This list is then sorted in descending order, and the potential control points at the top of the sensitivity list are designated as critical control points.

[0083] It should be noted that the number of critical control points selected from the sorted sensitivity list is based on the part type and function. Specifically, for parts that are small in size and serve an auxiliary function, the corresponding number of tests can be obtained based on historical test data. For parts that are large in size and / or serve a primary function, potential critical control points with a sensitivity exceeding 0.8 can be selected as critical control points.

[0084] Furthermore, the specific formation method of the material optimized by the variable density method can be set according to existing distribution habits, or the corresponding material distribution rules can be obtained from the historical data of the currently assembled parts in a preset database, thereby enabling the rapid acquisition of key control points of the assembled parts. By obtaining corresponding matching data through the historical material distribution data of the assembled parts, the material distribution time of the finite element software is reduced. The processor acquires key point identification signals, filters corresponding matching data in the preset database based on the key point identification signals, and adjusts the material distribution rules of the finite element software according to the matching data, thereby accurately acquiring the key control points corresponding to the assembled parts and improving the efficiency of acquiring key control points.

[0085] It should be noted that the interaction between the finite element software and the processor can be achieved by providing a corresponding software interface through the finite element software, and then writing control scripts based on the software to realize information exchange between the finite element software and the processor.

[0086] For example, the finite element method (FEM) software used is ANSYS. ANSYS Mechanical APDL or ACT (Application Customization Toolkit) provides a Python / Matlab interface, allowing for scripting and signal interaction between the finite element method and the software. When ANSYS needs to model the assembled part and identify key control points, it generates key point identification signals and sends them to the processor. The processor receives these signals and responds with material distribution signals. Based on these signals, it filters a pre-set database to obtain matching data corresponding to the assembled part. Based on this matching data, it generates material distribution signals and sends them to the finite element method. Finally, the finite element method adjusts the material distribution rules for the assembled part based on these material distribution signals.

[0087] It should be noted that different finite element software uses different interfaces, which will not be elaborated here.

[0088] S300 generates simulation rules based on part batches and adjusts the assembly and adjustment conditions corresponding to the multiphysics model according to the simulation rules.

[0089] The simulation rules refer to how to conduct the working condition simulation, whether to use single-variable simulation or multi-variable cross-simulation. Based on the simulation rules, the assembly and adjustment conditions of the multiphysics model are adjusted, which in turn allows for dynamic adjustment of key control points based on the actual assembly conditions of the assembled parts. Through dynamic identification and data fusion of key control points, the risk of accumulated deviations caused by individual differences in assembled parts can be predicted and avoided in advance, thus solving the problem of low pass rates.

[0090] It should be noted that the simulation rules are generated based on the current part batch. The simulation is performed according to the specific structure of the assembled parts corresponding to the batch. The specific steps for generating the simulation rules are as follows: First, obtain the part type and corresponding historical assembly data based on the part batch. Second, obtain the assembly temperature range and humidity range of the assembled parts based on the historical assembly data. Third, obtain the material properties based on the part type, such as elastic modulus, Poisson's ratio, and yield strength. Fourth, obtain the expected load spectrum based on the historical assembly data. Finally, establish a load condition matrix based on the assembly temperature range, humidity range, material properties, and expected load spectrum. Finally, simulate the functional verification, strength analysis, life prediction, and failure analysis of the assembled parts based on the load condition matrix.

[0091] The assembly and adjustment temperature range mentioned here refers to the operating temperature suitable for the assembled parts. Simulations based on the actual operating temperature of the assembled parts result in simulations that more closely reflect reality. Furthermore, by considering actual operating conditions, simulation time for the assembled parts can be saved, conserving overall simulation resources. The handling of the assembly and adjustment humidity range is the same or similar to that of the assembly and adjustment temperature range, and will not be elaborated upon here. Material properties are obtained based on the part type. Specifically, the material of the currently assembled part is identified based on its type, and then the corresponding elastic modulus, Poisson's ratio, and yield strength are set. Elastic modulus, Poisson's ratio, and yield strength are inherent properties of the assembled parts, and can be obtained from material manuals, supplier data, experimental data, and simulation software databases. It should be noted that the processor obtains the part type and, through identification, filters it in a preset database to obtain the corresponding material properties.

[0092] The assembly and adjustment conditions corresponding to the multiphysics model are adjusted according to the simulation rules. The steps are as follows: An assembly and adjustment condition matrix is ​​established based on the assembly and adjustment temperature range, assembly and adjustment moderate range, material properties, and expected load spectrum. Specifically, the key variables affecting the assembly and adjustment conditions are first identified, such as environmental conditions, material properties, mechanical loads, and coupling effects. These key variables are then set accordingly. Next, the assembly and adjustment condition matrix is ​​constructed using orthogonal experimental methods or the most stringent condition method. The assembly and adjustment condition matrix can use conditions such as high temperature, high temperature, and maximum mechanical load; low temperature, dryness, and vibration load; and room temperature, moderate cycling, and fatigue load.

[0093] Based on the assembly and adjustment condition matrix, the functional verification, strength analysis, life prediction, and failure analysis of the assembled parts can be simulated. Specifically, data simulation can be performed using finite element software, and the simulation structure can be obtained through a processor. The simulation results can then be analyzed to obtain the required functional verification, strength analysis, life prediction, and failure analysis.

[0094] For example, using ANSYS as an example of finite element software, firstly, the load case matrix is ​​parametrically input. This is done through APDL scripts or Workbench parametric design, batch importing the load case matrix and converting the measured vibration data into PSD or time-history loads. Next, multiple public data points are solved in batches, using DesignXplorer or optiSLang to automatically traverse the load case matrix combinations. The processor automatically evaluates the results, monitors the displacement of key points, compares the maximum Von Mises stress with the material yield strength in advance, and calls nCode to calculate fatigue damage accumulation. Finally, failure risk is visualized.

[0095] It should be noted that nCode is a deep parsing software mainly used for fatigue life prediction, durability analysis, and signal processing. Regarding the use of nCode by finite element software, those skilled in the art can implement it themselves based on the finite element software and nCode, so we will not go into details here.

[0096] Specifically, for functional verification, finite element software can perform kinematic simulation and contact analysis, while functional criteria require the processor to call the criterion rules in the preset database and also needs to call multibody dynamics software. For strength analysis, finite element software performs static and nonlinear analyses, and the processor needs to set safety factors and material failure criteria, etc.

[0097] Here is an example of how the processor interacts with the finite element software. The finite element software performs functional verification on the assembled parts and sends rule signals to the processor. The processor receives the rule signals and filters the judgment rules in the preset database according to the rule signals. It then analyzes the simulation data of the currently assembled parts according to the judgment rules to generate the corresponding functional verification results.

[0098] S400 updates key control points based on simulated assembly conditions and generates an initial population set based on genetic algorithm technology. The initial population set includes several initial population schemes.

[0099] In this process, the assembly and adjustment parts are simulated under assembly conditions to obtain the regions where the assembly and adjustment parts affect the assembly accuracy. Based on these regions, the key control points are updated. The regions where the parts are affected are calculated to determine the sensitivity of each region of the assembly and adjustment parts. The specific calculation method for the sensitivity is based on existing methods, which will not be elaborated on here.

[0100] It should be noted that the key control points are dynamically adjusted based on the simulated assembly conditions. For example, when the engine operating temperature is greater than 150 degrees, the conditions for the newly added key control points are as shown in formula (1).

[0101] (1);

[0102] in, The sensitivity coefficient representing node i after the update is the normalized coefficient. The displacement vector difference between node i and its neighboring node j is represented; 0.1 mm is the recommended initial threshold, which can be adjusted from 0.05 to 0.2 mm according to the assembly accuracy requirements.

[0103] The threshold for the displacement difference between adjacent nodes is dynamically adjusted according to the assembly accuracy, and the control conditions for deleting key control points are as shown in formula (2).

[0104] (2);

[0105] in, Characterizes the displacement modulus of node i at the current temperature. The reference temperature is used to characterize the temperature, such as room temperature (25 degrees Celsius). 0.5 is the displacement attenuation threshold, which can be increased to 0.7 in sensitive areas.

[0106] The initial population is generated using a genetic algorithm, and the specific method for generating the initial population will not be elaborated upon here. The initial population set includes several initial population schemes.

[0107] The S500 uses a preset weighting mechanism to dynamically adjust the deviation weighting coefficients of key control points based on the assembly and adjustment conditions.

[0108] Among them, the preset weighting mechanism is a real-time adjustment mechanism based on the assembly and adjustment conditions. Specifically, based on environmental variables such as temperature and pressure, different coefficients are assigned to key control points in the optimization objective function or error assessment. Under different environmental conditions, the error tolerance or optimization priority of each key control point is dynamically allocated to achieve a more robust and adaptive design or control.

[0109] The preset weighting mechanism includes Fitness = ∑ (deviation i × weight i) + process constraint penalty term. The design types of process constraint penalty term include assembly force constraints, interference checks, and positioning accuracy, etc., and different constraint types correspond to different constraint formulas. The constraint formulas here adopt existing technology, so they will not be elaborated on further.

[0110] Referring to Figure 2, in one embodiment, the deviation weight coefficients corresponding to key control points are dynamically adjusted based on a preset weighting mechanism, including the following steps:

[0111] S510 uses finite element parameter disturbance analysis to obtain the geometric sensitivity coefficients corresponding to key control points, and uses stress influence weights to obtain the mechanical sensitivity coefficients corresponding to key control points.

[0112] S520 generates initial weight coefficients corresponding to key control points based on a preset judgment matrix, using geometric and mechanical sensitivity coefficients.

[0113] S530 employs a preset weighting mechanism to dynamically adjust the deviation weighting coefficients corresponding to key control points based on initial weighting coefficients.

[0114] The geometric sensitivity coefficient is calculated through perturbation analysis using finite element parameters, while the mechanical sensitivity coefficient refers to the weighting of stress influence. The preset judgment matrix is ​​a pre-defined judgment matrix, specifically determined through expert scoring or obtained from the judgment matrices corresponding to previously assembled parts with high precision.

[0115] It should be noted that the calculation methods for the geometric sensitivity coefficient and the mechanical sensitivity coefficient are based on existing calculation methods, which will not be elaborated upon here. In this embodiment, the preset judgment matrix can be generated from expert data and historical assembly matrices. The expert mode and historical assembly matrix can be weighted according to the historical assembly accuracy to generate the preset judgment matrix corresponding to this embodiment. The preset judgment matrix is ​​as follows:

[0116] ;

[0117] in, , Characterizes the amount of change in assembly error. The displacement disturbance of the i-th critical control point is generally recommended to be 0.01 mm. , The stress on the i-th key point, The area of ​​the affected region corresponding to the key feature point. This is the process reliability coefficient, which can be determined by expert evaluation.

[0118] S600 optimizes the initial population set by using the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme.

[0119] The optimization iteration of the initial population set based on the deviation weight coefficient and iteration parameters is obtained using swarm intelligence optimization methods such as genetic algorithm and particle swarm optimization. These are all existing algorithms used for optimization iteration, so we will not go into too much detail here.

[0120] For example, in this embodiment, the genetic algorithm is used as the main algorithm. The actual assembly and adjustment scheme is based on the genetic algorithm to set crossover and mutation operations to evolve the population until it converges to the optimal solution. The optimal solution is used as the actual assembly and adjustment scheme. Here, the crossover probability is set to 0.8 and the mutation probability is set to 0.05.

[0121] In one embodiment, generating simulation rules based on part batches includes the following steps:

[0122] S310: Obtain the adjacent assembly parts corresponding to the assembled parts based on the parts batch, and obtain the assembly stress area based on the assembled parts and the adjacent assembly parts.

[0123] S320 generates assembly rules based on assembly stress regions and generates simulation rules based on assembly rules.

[0124] Since the assembly parts are interconnected during assembly, when simulating the rules for the assembly parts, the corresponding adjacent assembly parts can be obtained through the batch of the parts. Based on the adjacent assembly parts and the assembly parts, the assembly stress areas that exist between them can be obtained. Assembly rules are generated from these assembly stress areas, and the simulation rules generated based on the assembly rules are updated. In this way, the actual assembly conditions corresponding to the assembly parts can be obtained, and more accurate key control points can be obtained.

[0125] It should be noted that swarm intelligence optimization methods such as particle swarm optimization can be used to select the optimal assembly and adjustment combination before assembly and adjustment, thereby reducing the inefficiency caused by manual trial and error and rework.

[0126] Referring to Figure 3, in one embodiment, after generating an initial population based on a genetic algorithm and dynamically evaluating the deviation according to a weight formula to obtain the actual assembly and adjustment scheme, the following steps are also included:

[0127] The S700 uses a blue light 3D scanner to perform full-size scanning of key control points to obtain scanned point cloud data.

[0128] The S800 uses the ICP algorithm to map scanned point cloud data to CAD models in order to obtain the 3D model of the assembled parts.

[0129] The S900 simulates the assembly of the actual assembly scheme based on a 3D model to obtain the assembly deviation.

[0130] By constructing a virtual assembly and adjustment closed-loop technical solution through full-size 3D scanning data acquisition and multiphysics simulation, the problem of weak data-driven capability in existing technologies is solved. High-precision 3D scanning equipment is used, combined with point cloud registration algorithms, such as the ICP algorithm, to achieve high-precision matching between measured data and CAD models during the parts warehousing process.

[0131] In one embodiment, after performing optimal iterations on the initial population set based on the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme, the following steps are further included:

[0132] The S910 acquires real-time monitoring data from assembly line sensors.

[0133] S920 determines whether corresponding matching data exists in the preset database based on vibration data and temperature values.

[0134] S930, if not, updates the preset database based on vibration data and temperature values, and updates the 3D model based on the preset database.

[0135] Specifically, based on real-time monitoring data from assembly line sensors, including vibration and temperature data, corresponding matching data is obtained from a preset database. This is achieved through multi-dimensional similarity calculations, with a similarity score of 90% or higher. If a matching data match is found, the processor compares the assembly result corresponding to the monitoring results with the estimated result. If the error between the estimated and assembly results is within acceptable limits, the generated actual assembly and adjustment scheme is considered accurate. If the error is outside acceptable limits, the actual assembly and adjustment scheme needs to be regenerated. This regeneration can be achieved by obtaining the ignored parameters in step S223, updating the quantization parameters based on the ignored parameters, and then executing the aforementioned steps again to regenerate the actual assembly and adjustment scheme. If no matching data is found, the preset database is updated based on the vibration and temperature data.

[0136] Referring to Figure 4, in one embodiment, obtaining quantization parameters based on part batches includes the following steps:

[0137] S221 uses heuristic filtering based on quantization parameters to obtain a visual theory matrix, and sets priorities based on the visual theory matrix.

[0138] S222, group the quantization parameters by physical mechanism to obtain a parameter set, and set the quantization parameters accordingly based on the parameter set.

[0139] Specifically, the visual theory matrix is ​​a matrix that uses importance and uncertainty settings. It allows for quick focus on key parameters. For example, in thermo-mechanical coupling analysis, engineers will instinctively focus on areas with large temperature gradients, such as hot spot effects.

[0140] The physical mechanism grouping categorizes multiphysics parameters according to their physical mechanisms, employing methods such as thermal correlation, force correlation, and flow correlation to reduce cognitive load. Thermal group: thermal conductivity, convective heat transfer coefficient, emissivity. Force group: elastic modulus, Poisson's ratio, coefficient of thermal expansion. Flow group: viscosity, inlet velocity, turbulence intensity.

[0141] In one embodiment, after setting the quantization parameters based on the parameter set, the following steps are also included:

[0142] S223, perform adversarial verification on assembly deviations, obtain neglected parameters based on assembly deviations, and update quantization parameters based on neglected parameters.

[0143] Adversarial verification primarily involves identifying potentially overlooked parameters, such as neglected contact thermal resistance, similar to red team exercises. For example, in electronic heat dissipation simulations, engineers might default to ignoring the effect of air humidity, but expert consultation using the Delphi method reveals that it contributes to a 10% temperature deviation. Quantifying the consequences of this error into economic losses motivates more rigorous mesh convergence analysis.

[0144] This application also discloses a system for generating component assembly and adjustment schemes.

[0145] As shown in Figure 5, the component assembly and adjustment scheme generation system includes a model building module 10, a key point identification module 20, a working condition simulation module 30, a key point update module 40, a coefficient generation module 50, and a scheme generation module 60. The model building module 10 acquires the component information corresponding to the components to be assembled, including component batches, and constructs a multiphysics model of the components based on finite element software. The key point identification module 20 identifies key control points in the multiphysics model; key control points represent areas that affect the assembly accuracy of the components. The working condition simulation module 30 generates simulation rules based on component batches and adjusts the assembly and adjustment working conditions corresponding to the multiphysics model according to the simulation rules. The key point update module 40 updates the key control points based on the simulated assembly working conditions and generates an initial population set based on genetic algorithm technology, which includes several initial population schemes. The coefficient generation module 50 uses a preset weighting mechanism to dynamically adjust the deviation weight coefficients corresponding to the key control points based on the assembly and adjustment working conditions. The scheme generation module 60 performs optimal iteration on the initial population set based on the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme.

[0146] The other functions performed in the above-mentioned model building module 10, key point identification module 20, working condition simulation module 30, key point update module 40, coefficient generation module 50, and scheme generation module 60, as well as the technical details of each function, are the same or similar to the corresponding features in the component assembly and adjustment scheme generation method described above, so they will not be repeated here.

[0147] In one embodiment, the component assembly and adjustment scheme generation system further includes a storage module, which stores component information, key control points corresponding to the component information, and deviation weight coefficients corresponding to the key control points.

[0148] The implementation principle is as follows:

[0149] The processor models the assembled parts using finite element method (FEM) software and analyzes the assembly conditions to adjust the modeling and analysis, thereby obtaining corresponding key control points (CNCs). These CNCs are updated based on simulated assembly conditions, and an initial population set is generated using a genetic algorithm. The processor dynamically adjusts the deviation weight coefficients corresponding to the CNCs based on the assembly conditions using a preset weighting mechanism. The initial population set is iteratively optimized based on the deviation weight coefficients and iteration parameters to obtain the actual assembly scheme. A full-size scan of the CNCs is performed using a blue light 3D scanner to obtain scanned point cloud data. The scanned point cloud data is mapped to the CAD model using an ICP algorithm to obtain a 3D model. The actual assembly scheme is simulated based on the 3D model to obtain assembly deviations.

[0150] It should be understood that although the steps in the flowcharts in the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order in which these steps are performed, and they may be performed in other orders.

[0151] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for generating a component assembly and adjustment scheme, characterized in that, Includes the following steps: Obtain the part information corresponding to the assembly and adjustment parts, including the part batch, and construct a multiphysics model corresponding to the assembly and adjustment parts based on finite element software. The multiphysics model includes thermal field, force field and flow field; identify the key control points in the multiphysics model, which represent the regions that affect the assembly accuracy of the assembly and adjustment parts. The process of generating simulation rules based on the part batches and adjusting the assembly and adjustment conditions corresponding to the multiphysics model according to the simulation rules includes the following steps: obtaining the assembly adjacent parts corresponding to the assembly and adjustment parts according to the part batches, and obtaining the assembly stress region according to the assembly and adjustment parts and the assembly adjacent parts; generating assembly rules based on the assembly stress region, and generating simulation rules based on the assembly rules; updating the key control points based on the simulated assembly conditions, and generating an initial population set based on genetic algorithm technology. The initial population set includes several initial population schemes. The initial population is generated based on the genetic algorithm. The actual assembly and adjustment scheme is based on the genetic algorithm to set crossover and mutation operations to evolve the population until it converges to the optimal solution, and the optimal solution is taken as the actual assembly and adjustment scheme; dynamically adjusting the deviation weight coefficients corresponding to the key control points based on the assembly and adjustment conditions using a preset weight mechanism; performing optimization iteration on the initial population set according to the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme; the part information includes dimensional deviations. Identifying key control points in the multiphysics model includes the following steps: based on variable density optimization. The material is locally distributed to identify preliminary key points in the multiphysics model, and these preliminary key points are marked as potential key points. Quantitative parameters are obtained based on the part batch, and sensitivity analysis is performed on the potential key points based on the quantitative parameters and dimensional deviations to obtain a sensitivity list. The potential key points are then filtered based on the sensitivity list to obtain key control points in the multiphysics model. The deviation weight coefficients corresponding to the key control points are dynamically adjusted based on a preset weighting mechanism, including the following steps: obtaining the geometric sensitivity coefficients corresponding to the key control points based on finite element parameter perturbation analysis, and obtaining the mechanical sensitivity coefficients corresponding to the key control points based on stress influence weights; generating initial weight coefficients corresponding to the key control points based on the geometric and mechanical sensitivity coefficients using a preset judgment matrix; and dynamically adjusting the deviation weight coefficients corresponding to the key control points using a preset weighting mechanism based on the initial weight coefficients. The preset weighting mechanism includes Fitness = ∑(deviation i × weight i) + process constraint penalty term, where the process constraint penalty term design types include assembly force constraints, interference checks, and positioning accuracy.

2. The method for generating a component assembly and adjustment scheme according to claim 1, characterized in that, After performing optimization iterations on the initial population set based on the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme, the following steps are also included: performing full-size scanning of key control points based on a blue light 3D scanner to obtain scanned point cloud data; matching the scanned point cloud data with a CAD model based on the ICP algorithm to obtain a 3D model corresponding to the assembly and adjustment parts; and simulating assembly based on the 3D model of the actual assembly and adjustment scheme to obtain assembly deviations.

3. The method for generating a component assembly and adjustment scheme according to claim 2, characterized in that, After performing optimization iterations on the initial population set based on the deviation weight coefficient and iteration parameters to obtain the actual assembly and adjustment scheme, the following steps are also included: acquiring monitoring data from assembly line sensors in real time, the monitoring data including vibration data and temperature values; determining whether there is corresponding matching data in the preset database based on the vibration data and temperature values; if not, updating the preset database based on the vibration data and temperature values, and updating the three-dimensional model based on the preset database.

4. The method for generating a component assembly and adjustment scheme according to claim 2, characterized in that, The process of obtaining quantization parameters based on the batch of parts includes the following steps: performing heuristic filtering based on the quantization parameters to obtain a visual theory matrix, and setting priorities based on the visual theory matrix; grouping the quantization parameters by physical mechanism to obtain a parameter set, and setting the quantization parameters accordingly based on the parameter set.

5. The method for generating a component assembly and adjustment scheme according to claim 4, characterized in that, After setting the quantization parameters based on the parameter set, the method further includes the following steps: performing adversarial verification on the assembly deviation, obtaining the neglected parameters based on the assembly deviation, and updating the quantization parameters according to the neglected parameters.

6. A system for generating component assembly and adjustment schemes, characterized in that, A method for generating a component assembly and adjustment scheme according to any one of claims 1-5 includes: a model building module (10), which is used to acquire component information corresponding to the assembly and adjustment components, the component information including component batches, and construct a multiphysics model corresponding to the assembly and adjustment components based on finite element software; a key point identification module (20), which is used to identify key control points in the multiphysics model, the key control points representing areas that affect the assembly accuracy of the assembly and adjustment components; and a working condition simulation module (30), which generates simulation rules based on the component batches and according to the... The simulation rules adjust the assembly and adjustment conditions corresponding to the multiphysics model; the key point update module (40) updates the key control points based on the simulated assembly and adjustment conditions, and generates an initial population set based on genetic algorithm technology, the initial population set including several initial population schemes; the coefficient generation module (50) adopts a preset weight mechanism to dynamically adjust the deviation weight coefficients corresponding to the key control points based on the assembly and adjustment conditions; the scheme generation module (60) performs optimization iteration on the initial population set according to the deviation weight coefficients and iteration parameters to obtain the actual assembly and adjustment scheme.

7. The component assembly and adjustment scheme generation system according to claim 6, characterized in that, It also includes a storage module, which is used to store part information, key control points corresponding to the part information, and deviation weighting coefficients corresponding to the key control points.

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