A method and system for optimizing the service performance of assembly connection structures

Abstract

This invention discloses a method and system for optimizing the service performance of assembly connection structures, relating to the fields of mechanical design and precision assembly optimization technology. The method includes the following steps: using multiple assembly parameter combinations as decision variables, iterating through the decision variables with the minimization of corresponding multiple service performance indicators as the optimization objective to obtain multiple candidate assembly parameter combinations; comprehensively ranking the multiple candidate assembly parameter combinations based on the weights and values ​​of the multiple service performance indicators, and obtaining the optimal assembly parameter combination based on the ranking result; constructing a finite element model based on the optimal assembly parameter combination, and performing iterative optimization of the interface morphology of the finite element model based on service performance indicators characterizing the uniformity of contact pressure distribution to obtain the optimized assembly interface morphology. The optimal assembly parameter combination and the optimized interface morphology output by this invention together form the optimal service characteristics of the assembly interface, improving the overall performance of the connection structure.

Description

Technical Field

[0001] This invention relates to the field of mechanical design and precision assembly optimization technology, and in particular to a method and system for optimizing the service performance of assembly connection structures. Background Technology

[0002] Assembly connection structures refer to the parts formed between assembled mechanical parts, components, and sub-components, and are widely found in mechanical equipment. The service performance of these structures mainly includes the uniformity of contact pressure distribution, the proportion of effective contact area, and connection characteristics related to structural deformation and interface heat transfer. These performance indicators directly determine the load-bearing capacity, operational stability, and service life of the equipment.

[0003] The distribution of contact characteristics at the assembly interface is a crucial factor affecting the connection performance of assembled structures, primarily influenced by both assembly parameters and interface morphology. Existing technologies typically attempt to improve contact characteristics through assembly parameter optimization or morphology design. However, existing methods for optimizing assembly parameters are often fragmented, usually focusing only on adjusting assembly process parameters (such as preload, assembly sequence, assembly batches, interference fit parameters, etc.) or controlling surface quality parameters (such as surface roughness grade, surface hardening state, etc.). This uncoupling, one-sided optimization approach usually presupposes an ideally smooth interface. When the effect of micro-morphology cannot be ignored, relying solely on assembly connection process optimization is insufficient to fundamentally eliminate the influence of morphology errors on the contact state.

[0004] On the other hand, the design of the assembly interface morphology is an important means of controlling and improving the distribution of contact characteristics at the assembly interface. A typical solution method is to transform the morphology design into an iterative modification problem of the contact node coordinates, and to control the distribution of contact characteristics by iteratively updating the contact node positions. However, the morphology design of the assembly interface is usually based on an ideal mechanical state, which is detached from the actual assembly environment. Since the coupling effect of assembly process (such as the macroscopic stress deformation of the structure caused by assembly load) and surface quality parameters on the connection performance is not considered, the optimal morphology designed may deviate from the expectation due to structural warping after actual assembly.

[0005] In summary, existing technologies suffer from the problem that "assembly parameter optimization" and "interface morphology design" are disconnected from each other and deviate from the coupling constraints of actual working conditions, making it difficult to achieve precise control and coordinated optimization of the service performance of the assembly connection structure. Summary of the Invention

[0006] In view of the defects of the prior art, the present invention provides a method and system for optimizing the service performance of assembly connection structures, which solves the existing problems.

[0007] The present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the service performance of an assembly connection structure, comprising the following steps: Obtain multiple combinations of assembly parameters for the assembly connection structure, including assembly process parameters and surface quality parameters; Multiple assembly parameter combinations are used as decision variables. The decision variables are iterated with the optimization objective of minimizing the corresponding multiple service performance indicators to obtain multiple candidate assembly parameter combinations. The weights of multiple service performance indicators are obtained through the values ​​of multiple service performance indicators corresponding to multiple candidate assembly parameter combinations. Based on the weights and values ​​of multiple service performance indicators, multiple candidate assembly parameter combinations are comprehensively ranked. The optimal assembly parameter combination is obtained based on the ranking result. A finite element model is constructed based on the optimal assembly parameter combination. The interface morphology of the finite element model is iteratively optimized based on the service performance index characterizing the uniformity of contact pressure distribution to obtain the optimal assembly interface morphology. The optimal assembly parameter combination, the optimal assembly interface morphology, and the corresponding service performance index values ​​are output.

[0008] Preferably, the step of iterating the decision variables with the minimization of multiple corresponding service performance indicators as the optimization objective to obtain multiple candidate assembly parameter combinations specifically includes the following steps: The decision variables are initialized to obtain an initial population, where each individual represents a combination of assembly parameters; Based on the agent model group, multiple service performance index values ​​are obtained for each individual, and the corresponding optimization target value is obtained based on the multiple service performance index values; Multiple individuals are ranked based on multiple optimization objective values; A new generation of population is generated based on the ranking results, and the corresponding optimization target value is obtained; Iterate until the termination condition is met and output the Pareto front solution set, which is a combination of multiple candidate assembly parameters.

[0009] Preferably, the construction of the agent model group includes the following steps: Multiple sets of assembly parameter combinations are generated within the design space, and finite element analysis is performed in the finite element numerical analysis model of the connection structure to obtain the corresponding multiple service performance index values. Multiple surrogate models are trained by combining multiple sets of assembly parameters and corresponding service performance index values ​​to obtain a surrogate model group; each surrogate model is used to output a service performance index.

[0010] Preferably, the construction of the finite element numerical analysis model specifically includes the following steps: Based on the geometric dimensions of the connection structure, a geometric model of the connection structure is created in 3D modeling software; The geometric model of the connection structure is meshed using the finite element method, material properties are set, loads and boundary conditions are applied, and a finite element numerical analysis model is obtained.

[0011] Preferably, obtaining the weights of multiple service performance indicators through multiple service performance indicator values ​​corresponding to multiple candidate assembly parameter combinations specifically includes the following steps: Multiple service performance index values ​​corresponding to multiple candidate assembly parameter combinations are constructed into a decision matrix, and the decision matrix is ​​dimensionless to obtain a normalized matrix. The weights corresponding to the normalized matrix are obtained based on the entropy weight method. The information entropy of each service performance indicator is obtained based on the weights. The weights of multiple service performance indicators are obtained based on the information entropy.

[0012] Preferably, multiple candidate assembly parameter combinations are comprehensively ranked based on the weights and values ​​of multiple service performance indicators, and the optimal assembly parameter combination is obtained according to the ranking result. This specifically includes the following steps: A weighted normalized matrix is ​​constructed based on the weights and normalized matrices of multiple service performance indicators; The positive and negative ideal solutions are determined based on the weighted normalization matrix; The relative proximity is determined based on the distance between the positive and negative ideal solutions; Based on the relative proximity, multiple candidate assembly parameter combinations are comprehensively ranked, and the candidate assembly parameter combination with the highest relative proximity is taken as the optimal assembly parameter combination.

[0013] Preferably, the interface morphology iterative optimization of the finite element model based on service performance indicators characterizing the uniformity of contact pressure distribution specifically includes the following steps: Calculate the node normal displacement increment based on the effective contact node data; Determine whether each node will exceed the design domain after modification. If it does, abandon the modification of the node; otherwise, modify the normal coordinates of the node. The modified finite element model was solved again, and the assembly interface node data was extracted. Calculate the evaluation metrics for this iteration based on the node data obtained again; Determine if the evaluation metric has converged. If it has converged, end the optimization. If it has not converged, check again if the number of iterations has reached the preset maximum number of iterations. If the maximum number of iterations has been reached, end the optimization. If it has not been reached, adjust the adaptive relaxation factor of the node.

[0014] Preferably, the effective contact node data includes node coordinates, node contact pressure, or node contact status.

[0015] Preferably, the assembly process parameters include interference fit, bolt preload, bolt tightening sequence or bolt tightening batch; the surface quality parameters include surface roughness or surface hardness; and the service performance indicators include maximum contact pressure, contact pressure distribution uniformity, effective contact area ratio, structural deformation, temperature, or interface heat transfer related indicators.

[0016] In a second aspect, the present invention provides a service performance optimization system for an assembly connection structure, comprising: The acquisition module is used to acquire various combinations of assembly parameters for the assembly connection structure. The assembly parameter combinations include assembly process parameters and surface quality parameters. The first optimization module is used to take multiple assembly parameter combinations as decision variables, and to iterate the decision variables with the minimization of the corresponding multiple service performance indicators as the optimization objective to obtain multiple candidate assembly parameter combinations; to obtain the weights of multiple service performance indicators through the values ​​of multiple service performance indicators corresponding to the multiple candidate assembly parameter combinations, to comprehensively rank the multiple candidate assembly parameter combinations based on the weights and values ​​of multiple service performance indicators, and to obtain the optimal assembly parameter combination based on the ranking result. The second optimization module is used to construct a finite element model based on the optimal assembly parameter combination, perform iterative optimization of the interface morphology of the finite element model based on the service performance index characterizing the uniformity of contact pressure distribution, obtain the optimal assembly interface morphology, and output the optimal assembly parameter combination, the optimal assembly interface morphology, and the corresponding service performance index values.

[0017] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects: This invention uses assembly process parameters and surface quality parameters as design variables, and iteratively optimizes the optimal combination of assembly parameters by minimizing multiple service performance indicators. Compared to existing technologies that optimize only assembly process parameters or only control surface quality parameters, this invention achieves coupled optimization of assembly process and surface quality, enabling the optimal combination of assembly parameters to achieve a reasonable trade-off among multiple service performance indicators. Furthermore, after obtaining the optimal combination of assembly parameters, iterative optimization of the interface morphology is performed under this combination, ensuring that the morphology design fully considers the actual influence of assembly process parameters and surface quality parameters. Compared to existing technologies that solve morphology based on ideal mechanical states, this invention avoids structural warping problems caused by neglecting assembly loads, ensuring that the optimized interface morphology achieves the expected results after actual assembly, further improving the uniformity of contact pressure distribution and the effective contact area ratio. The optimal combination of assembly parameters and the optimized interface morphology ultimately output by this invention form the optimal service characteristics of the assembly interface, significantly improving the overall performance of the connection structure. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for optimizing the service performance of an assembly connection structure according to the present invention; Figure 2 This is a geometric model diagram of the spindle core rotor assembly and connection structure provided in an embodiment of the present invention; Figure 3 This is a weighted contribution rate diagram of performance indicators after assembly process optimization provided in an embodiment of the present invention. Figure 4 The Pareto front solutions and optimal parameter combinations for each performance metric provided in the embodiments of the present invention; in, Figure 4 (a): Axial deformation and contact pressure at the large end. Figure 4 (b): Radial deformation of the large end and contact pressure, Figure 4 (c): Maximum temperature and contact pressure of the shaft core. Figure 4 (d): Radial deformation of the large end and the highest temperature of the shaft core. Figure 4 (e): Axial deformation of the large end and the highest temperature of the shaft core. Figure 4 (f): Radial deformation and axial deformation of the large end; Figure 5 The evolution process of the standard deviation and maximum value of contact pressure in the interface morphology optimization process provided in the embodiments of the present invention; Figure 6 This is a spatiotemporal distribution diagram of the evolution of morphology modification amount during the morphology optimization process provided in an embodiment of the present invention; Figure 7 This is an optimized morphology modification diagram provided in an embodiment of the present invention; Figure 8 A comparison diagram of interface contact pressure distribution before and after morphology optimization provided for embodiments of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a method for optimizing the service performance of an assembly connection structure, specifically involving a collaborative optimization method for assembly process, surface quality, and interface morphology of the connection structure, referring to... Figure 1 Specifically, it includes the following steps: S1: Establish a finite element numerical analysis model of the assembly connection structure, determine the assembly process parameters and surface quality parameters as the set of design variable parameters, and determine the set of service performance indicators.

[0022] Based on the geometric dimensions of the assembly connection structure, a geometric model of the assembly connection structure is established in 3D modeling software. The geometric model is meshed using the finite element method, material properties are set, loads and boundary conditions are applied, and a finite element numerical analysis model of the assembly connection structure is obtained.

[0023] Assembly process parameters include at least one of interference fit, bolt preload, bolt tightening sequence, and bolt tightening batch; surface quality parameters include at least one of surface roughness and surface hardness. The set of service performance indicators includes two or more of the following: maximum contact pressure, contact pressure distribution uniformity, effective contact area ratio, structural deformation, temperature, and interface heat transfer related indicators; among which, the contact pressure distribution uniformity indicator can be contact pressure variance, contact pressure standard deviation, contact pressure distribution normalized entropy, or a combination thereof.

[0024] For details, please refer to Figure 2 When establishing the finite element numerical analysis model of the shaft-rotor assembly connection structure, a geometric model is created in 3D modeling software based on the geometric dimensions of the assembly connection structure, and meshing is performed; material properties are set; loads and boundary conditions are applied; and a preset range is selected around the assembly interface as the design domain. The design domain is the range within which the topography nodes can change during topography optimization; changes in topography during the optimization process can only occur within this range.

[0025] In this embodiment, the assembly process parameters in the shaft-rotor assembly connection structure include interference fit, the surface quality parameters include surface roughness and surface hardness, and the service performance index set includes average contact pressure, large end axial deformation, large end radial deformation, and maximum shaft temperature.

[0026] S2: Generate sample schemes within the preset design space and perform simulation calculations to obtain the initial dataset.

[0027] Sample schemes can be generated using space-filling sampling methods, including at least one of Latin hypercube sampling and orthogonal experimental design.

[0028] In this embodiment, an orthogonal experimental design method is used to generate sample schemes for the shaft-rotor assembly connection structure, and simulation analysis is performed in finite element analysis software to obtain performance indicators. For each sample scheme, the response values ​​corresponding to the performance indicator set are extracted to form a multidimensional dataset for surrogate model training and decision ranking.

[0029] S3: Build a group of proxy models based on the initial dataset to quickly predict the set of performance metrics and evaluate the prediction error.

[0030] The surrogate model group includes one or more of the following: Kriging model, Gaussian process regression model, radial basis function model, support vector regression model, and neural network regression model. Cross-validation is used to evaluate the prediction error of the surrogate models, obtaining the predictive ability of each surrogate model on different metrics to determine the target prediction value for optimization. When the surrogate model error exceeds a preset threshold, supplementary sample schemes are used and the initial dataset is updated to improve prediction accuracy. Each performance metric has a specific surrogate model for prediction.

[0031] In this embodiment, a group of Kriging surrogate models is constructed based on the initial dataset of the shaft-rotor assembly connection structure to quickly predict the set of performance indicators of the shaft-rotor assembly connection structure. Cross-validation is used to evaluate the prediction error of the surrogate models for calculating the performance of the shaft-rotor assembly connection structure, and to obtain the prediction capability of each surrogate model on different indicators.

[0032] Furthermore, the surrogate model group can adopt a strategy of "multi-model parallelism + optimal fusion", that is, multiple surrogate models provide prediction results for the same indicator, and the optimal prediction is selected or weighted fusion is performed based on the error evaluation results to obtain the target prediction value.

[0033] S4: Based on the performance index prediction results of the surrogate model group, a multi-objective optimization algorithm is used to iteratively optimize the set of design variable parameters to obtain the Pareto front solution set.

[0034] The multi-objective optimization algorithm can be a population-based evolutionary optimization algorithm, including at least one of the non-dominated sorting genetic algorithm and its variants. In this embodiment, the non-dominated sorting genetic algorithm is used in the shaft core-rotor assembly connection structure.

[0035] The set of design variable parameters is denoted as the decision vector. The set of performance metrics is denoted as the response vector. The multi-objective optimization problem is constructed as follows: ; Among them, the cost-type indicators correspond to Directly take the predicted value or its monotonic transformation; benefit-type indicators can be transformed into a minimization form by taking the negative sign or monotonic transformation.

[0036] The iterative process of the genetic algorithm includes: initializing the population, predicting the target vector based on the surrogate model, non-dominated sorting and crowding distance calculation, selection / crossover / mutation to generate a new generation population, iterating to the termination condition and outputting the Pareto front solution set; the termination condition includes at least one of reaching a preset number of generations or the target change satisfying a preset threshold. In this embodiment, the termination condition in the shaft core-rotor assembly connection structure is reaching the preset number of generations.

[0037] S5: Determine the index weights based on the Pareto front solution set, and use TOPSIS to perform comprehensive ranking based on the index weights to obtain the optimal combination of design variable parameters.

[0038] The weight determination method includes at least one of preset weights and objective weights determined based on the entropy weight method. The entropy weight method includes: performing dimensionless processing on the multi-index decision matrix and calculating the information entropy and difference coefficient of each index to obtain the objective weight of each index.

[0039] The comprehensive ranking using TOPSIS includes the following steps: constructing a weighted normalized matrix based on index weights, determining the positive and negative ideal solutions, calculating the distance between each Pareto solution and the positive and negative ideal solutions, and determining the optimal combination of design variable parameters based on the relative proximity.

[0040] In this embodiment, in the shaft core-rotor assembly connection structure, objective weights determined based on the entropy weight method are used, and TOPSIS is used for comprehensive ranking based on index weights to obtain the optimal combination of design variable parameters. The implementation steps are as follows: The performance metrics of the Pareto front solution set are used to construct a decision matrix. ,in Indicates the candidate solutions, The indicators are represented. The indicators are then dimensionless to obtain a normalized matrix. To avoid logarithmic aberrations, tiny positive numbers are introduced. .

[0041] The proportion of entropy weight method The calculation is as follows: ; In the formula, n The total number of candidate solutions.

[0042] Information entropy The calculation is as follows: ; Coefficient of difference With objective weight They are respectively: ; Determining the weight vector Then, construct a weighted normalized matrix. : ; Determine the ideal solution With negative ideal solution : ; Calculate the distance to the positive and negative ideal solutions. and relative closeness : ; ; In the formula, m This represents the total number of performance metrics.

[0043] Select The candidate solution corresponding to the maximum value is taken as the optimal combination of design variable parameters.

[0044] See Figure 3 The figure shows the objective weights of each performance index of the shaft core-rotor assembly connection structure determined by the entropy weight method.

[0045] See Figure 4 The figure shows the Pareto front solution set of each index and the trade-off matrix between each index obtained by the non-dominated sorting genetic algorithm for the shaft core-rotor assembly connection structure. The red stars in the figure mark the optimal parameter combination obtained by TOPSIS comprehensive sorting.

[0046] S6: Under the optimal combination of design variable parameters, solve the effective contact node data of the assembly interface using finite element software, and perform iterative optimization of the interface morphology within the design domain.

[0047] Effective contact node data includes at least the node's spatial coordinates, node contact pressure, and node contact status; an effective contact node is a node whose contact status meets the preset effective contact conditions.

[0048] The assembly interface is discretized into a set of finite element contact nodes. The coordinate positions of the contact nodes characterize the morphology of the assembly interface. By modifying the coordinate positions of the contact nodes, the active design of the assembly interface morphology can be achieved. The assembly interface morphology optimization model can be described as follows: ; In the formula, Represents the set of contact nodes. Representing the Spatial location of each contact node Represents the optimization goal. This represents the range of the feasible region for morphological optimization. This represents the number of nodes that are touched on the interface.

[0049] The uniformity evaluation function (index) for contact pressure distribution is defined as follows: ; In the formula, represent Contact pressure at the contact nodes of the location. This represents the average value of all contact pressures.

[0050] The position of each contact node is adjusted based on the pressure magnitude. The normal displacement increment of each contact node is calculated as follows:

[0051] ; In the formula, Represents a node normal displacement increment, This represents the adaptive relaxation factor during the morphology iteration process. Represents a node Contact pressure, This indicates the minimum pressure at the contact point. This indicates the maximum pressure at the contact node. This represents the normal vector of a node.

[0052] In two adjacent iterations, the contact interface on the first... The coordinate changes of each contact node can be expressed as: ; judge Check if the node constraints are met. If they are met, update; otherwise, return to the state before modification.

[0053] No. The node modification coefficient of the next iteration An adaptive update rule is adopted: the node modification coefficient is increased when the evaluation metric improves and decreased when the evaluation metric deteriorates. This increase and decrease are achieved through expansion and contraction factors. The update method employs the following strategy:

[0054] ; In the formula, Indicates the acceleration factor for morphological modification. The shrinkage factor represents the morphological modification. This represents the current iteration number.

[0055] Perform iterative optimization of the interface appearance within the preset design domain. The specific implementation steps are as follows: S61: Calculate the node normal displacement increment based on the extracted effective contact node data of the assembly interface.

[0056] S62: Determine whether each node will exceed the design domain after modification. If it does, abandon the modification of the node; otherwise, modify the normal coordinates of the node.

[0057] S63: Solve the modified finite element model again and extract the assembly interface node data.

[0058] S64: Calculate the evaluation metrics for this iteration based on the node data obtained again.

[0059] S65: Determine whether the evaluation index has converged. If it has converged, end the optimization. If it has not converged, determine again whether the number of iterations has reached the preset maximum number of iterations. If it has reached the maximum number of iterations, end the optimization. If it has not reached the maximum number of iterations, adjust the adaptive relaxation factor of the node.

[0060] S66: Then begin the next iteration of optimization, repeating S61-S66 until the end.

[0061] S7: During the iteration process, the node modification coefficients are dynamically updated based on the evaluation index (uniformity evaluation function) until the evaluation index converges or the set number of iterations is reached, and the optimized combination of design variable parameters and interface morphology distribution are output.

[0062] After modification, continue iterative optimization until the evaluation index converges or the set number of iterations is reached. If the change in the evaluation index is less than the preset threshold or the maximum number of iterations is reached, stop; otherwise, continue to execute the next iteration optimization.

[0063] After the termination condition is met, the optimal combination of assembly process parameters and the optimized assembly interface morphology distribution are output.

[0064] Please see Figure 5 The figure shows the evolution of the evaluation indicators (standard deviation of contact pressure and maximum value of contact pressure) of the shaft core-rotor assembly connection structure during the morphology optimization process. As the iterative optimization proceeds, the standard deviation and maximum value of contact pressure gradually decrease until they tend to converge.

[0065] Please see Figure 6 The figure shows the spatiotemporal distribution of morphological changes in the shaft core-rotor assembly connection structure during the morphological optimization process. It can be seen that the axial profile of the shaft core is gradually optimized from a flat shape to a non-flat shape.

[0066] Please see Figure 7 The figure shows the morphological distribution of the optimized shaft-rotor assembly connection structure. It can be seen that the optimized morphology presents a shape that is high on both sides and low in the middle, with a maximum height difference of about 7 micrometers between the morphologies.

[0067] Please see Figure 8 The figure shows a comparison of the axial distribution of contact pressure before and after the optimization of the shaft core-rotor assembly connection structure. It can be seen that before the optimization, the axial distribution of contact pressure was uneven and there were stress concentration points. After the optimization, the axial distribution of contact pressure is uniform and the stress concentration phenomenon disappears.

[0068] Based on the same concept, the present invention also provides a service performance optimization system for assembly connection structures, including an acquisition module, a first optimization module, and a second optimization module.

[0069] The acquisition module is used to acquire various combinations of assembly parameters for the assembly connection structure. These combinations include assembly process parameters and surface quality parameters.

[0070] The first optimization module is used to take multiple assembly parameter combinations as decision variables, and to iterate the decision variables with the minimization of the corresponding multiple service performance indicators as the optimization objective, so as to obtain multiple candidate assembly parameter combinations. The weights of multiple service performance indicators are obtained through the values ​​of multiple service performance indicators corresponding to the multiple candidate assembly parameter combinations. Based on the weights and values ​​of multiple service performance indicators, the multiple candidate assembly parameter combinations are comprehensively ranked, and the optimal assembly parameter combination is obtained according to the ranking result.

[0071] The second optimization module is used to construct a finite element model based on the optimal assembly parameter combination, and to perform iterative optimization of the interface morphology of the finite element model based on the service performance index characterizing the uniformity of contact pressure distribution, so as to obtain the optimal assembly interface morphology and output the optimal assembly parameter combination, the optimal assembly interface morphology and the corresponding service performance index values.

[0072] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0073] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the service performance of an assembly connection structure, characterized in that, Includes the following steps: Obtain multiple combinations of assembly parameters for the assembly connection structure, including assembly process parameters and surface quality parameters; Multiple assembly parameter combinations are used as decision variables. The decision variables are iterated with the optimization objective of minimizing the corresponding multiple service performance indicators to obtain multiple candidate assembly parameter combinations. The weights of multiple service performance indicators are obtained through the values ​​of multiple service performance indicators corresponding to multiple candidate assembly parameter combinations. Based on the weights and values ​​of multiple service performance indicators, multiple candidate assembly parameter combinations are comprehensively ranked. The optimal assembly parameter combination is obtained based on the ranking result. A finite element model is constructed based on the optimal assembly parameter combination. The interface morphology of the finite element model is iteratively optimized based on the service performance index characterizing the uniformity of contact pressure distribution to obtain the optimal assembly interface morphology. The optimal assembly parameter combination, the optimal assembly interface morphology, and the corresponding service performance index values ​​are output.

2. The method for optimizing the service performance of an assembly connection structure as described in claim 1, characterized in that, The step of iterating the decision variables with the optimization objective of minimizing multiple corresponding service performance indicators to obtain multiple candidate assembly parameter combinations specifically includes the following steps: The decision variables are initialized to obtain an initial population, where each individual represents a combination of assembly parameters; Based on the agent model group, multiple service performance index values ​​are obtained for each individual, and the corresponding optimization target value is obtained based on the multiple service performance index values; Multiple individuals are ranked based on multiple optimization objective values; A new generation of population is generated based on the ranking results, and the corresponding optimization target value is obtained; Iterate until the termination condition is met and output the Pareto front solution set, which is a combination of multiple candidate assembly parameters.

3. The method for optimizing the service performance of an assembly connection structure as described in claim 2, characterized in that, The construction of the agent model group includes the following steps: Multiple sets of assembly parameter combinations are generated within the design space, and finite element analysis is performed in the finite element numerical analysis model of the connection structure to obtain the corresponding multiple service performance index values. Multiple surrogate models are trained by combining multiple sets of assembly parameters and corresponding service performance index values ​​to obtain a surrogate model group; each surrogate model is used to output a service performance index.

4. The method for optimizing the service performance of an assembly connection structure as described in claim 3, characterized in that, The construction of the finite element numerical analysis model specifically includes the following steps: Based on the geometric dimensions of the connection structure, a geometric model of the connection structure is created in 3D modeling software; The geometric model of the connection structure is meshed using the finite element method, material properties are set, loads and boundary conditions are applied, and a finite element numerical analysis model is obtained.

5. The method for optimizing the service performance of an assembly connection structure as described in claim 1, characterized in that, The step of obtaining the weights of multiple service performance indicators by combining multiple candidate assembly parameters and corresponding multiple service performance index values ​​specifically includes the following steps: Multiple service performance index values ​​corresponding to multiple candidate assembly parameter combinations are constructed into a decision matrix, and the decision matrix is ​​dimensionless to obtain a normalized matrix. The weights corresponding to the normalized matrix are obtained based on the entropy weight method. The information entropy of each service performance indicator is obtained based on the weights. The weights of multiple service performance indicators are obtained based on the information entropy.

6. The method for optimizing the service performance of an assembly connection structure as described in claim 5, characterized in that, Based on the weights and values ​​of multiple service performance indicators, multiple candidate assembly parameter combinations are comprehensively ranked, and the optimal assembly parameter combination is obtained according to the ranking result. The specific steps include: A weighted normalized matrix is ​​constructed based on the weights and normalized matrices of multiple service performance indicators; The positive and negative ideal solutions are determined based on the weighted normalization matrix; The relative proximity is determined based on the distance between the positive and negative ideal solutions; Based on the relative proximity, multiple candidate assembly parameter combinations are comprehensively ranked, and the candidate assembly parameter combination with the highest relative proximity is taken as the optimal assembly parameter combination.

7. The method for optimizing the service performance of an assembly connection structure as described in claim 1, characterized in that, The interface morphology is iteratively optimized based on service performance indicators characterizing the uniformity of contact pressure distribution in the finite element model, specifically including the following steps: Under the optimal combination of assembly parameters, solve for the effective contact node data of the assembly interface; Calculate the node normal displacement increment based on the effective contact node data; Determine whether each node will exceed the design domain after modification. If it does, abandon the modification of the node; otherwise, modify the normal coordinates of the node. The modified finite element model was solved again, and the assembly interface node data was extracted. Calculate the evaluation metrics for this iteration based on the node data obtained again; Determine if the evaluation metric has converged. If it has converged, end the optimization. If it has not converged, check again if the number of iterations has reached the preset maximum number of iterations. If the maximum number of iterations has been reached, end the optimization. If it has not been reached, adjust the adaptive relaxation factor of the node.

8. The method for optimizing the service performance of an assembly connection structure as described in claim 7, characterized in that, The effective contact node data includes node coordinates, node contact pressure, or node contact status.

9. The method for optimizing the service performance of an assembly connection structure as described in claim 1, characterized in that, The assembly process parameters include interference fit, bolt preload, bolt tightening sequence or bolt tightening batch; the surface quality parameters include surface roughness or surface hardness; the service performance indicators include maximum contact pressure, contact pressure distribution uniformity, effective contact area ratio, structural deformation, temperature or interface heat transfer related indicators.

10. A service performance optimization system for an assembly connection structure, characterized in that, include: The acquisition module is used to acquire various combinations of assembly parameters for the assembly connection structure. The assembly parameter combinations include assembly process parameters and surface quality parameters. The first optimization module is used to take multiple assembly parameter combinations as decision variables, and to iterate the decision variables with the minimization of the corresponding multiple service performance indicators as the optimization objective to obtain multiple candidate assembly parameter combinations; to obtain the weights of multiple service performance indicators through the values ​​of multiple service performance indicators corresponding to the multiple candidate assembly parameter combinations, to comprehensively rank the multiple candidate assembly parameter combinations based on the weights and values ​​of multiple service performance indicators, and to obtain the optimal assembly parameter combination based on the ranking result. The second optimization module is used to construct a finite element model based on the optimal assembly parameter combination, perform iterative optimization of the interface morphology of the finite element model based on the service performance index characterizing the uniformity of contact pressure distribution, obtain the optimal assembly interface morphology, and output the optimal assembly parameter combination, the optimal assembly interface morphology, and the corresponding service performance index values.

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