Structural error tolerance model construction method for connecting and positioning compartment body

Abstract

The invention relates to the technical field of structural design and optimization, and particularly discloses a structural error tolerance model construction method for compartment connection positioning, which comprises the following steps of: obtaining real point cloud data of a bolt connection pair through three-dimensional laser scanning, and realizing parameterized digital geometric modeling in combination with a deep neural network; material attributes are given, and self-adaptive grid division is carried out; generating a dynamic load sequence based on a vehicle driving condition, applying the dynamic load sequence to the model, and extracting full-field displacement vector data by using a digital image correlation technology; further identifying a micro-slip area of the contact interface, and calculating an energy dissipation index of the micro-slip area; a quantitative mapping relation between energy dissipation and fatigue life is established through experimental calibration, key design parameters are iteratively optimized by adopting a pattern search method with the target life as a constraint, and finally a connection structure scheme meeting the durability requirement is output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural design and optimization, and particularly relates to a structure error tolerance model construction method for compartment connection positioning. BACKGROUND

[0002] The connection between the compartment and the base body usually adopts a bolt connection pair to realize detachable assembly and positioning of the structure. This kind of connection structure long-term bears complex dynamic loads in the service process, including inertial force in vehicle driving, vibration caused by road excitation and alternating stress caused by working condition change. Under the action of these loads, the connection interface is prone to small relative slip (i.e. micro-slip), which causes interface friction, energy dissipation and local stress concentration, and further induces micro-motion fatigue damage, which is one of the main causes of connection structure failure. The traditional connection structure design relies on empirical formula or static strength checking, and it is difficult to accurately reflect the nonlinear contact behavior and dynamic response characteristics under actual working conditions. Although in recent years, digital image correlation (DIC) technology has been used for experimental measurement of deformation field, it is high in cost, difficult to repeat and difficult to integrate with the design process. At the same time, the existing simulation method still has deficiencies in modeling accuracy, load authenticity and life prediction accuracy, especially lacking a quantitative analysis model that systematically relates actual geometric error, dynamic load, micro-slip behavior and fatigue life. Therefore, there is an urgent need for a connection structure design optimization method that can comprehensively consider manufacturing deviation, real working conditions and mechanical response to improve connection reliability and realize error tolerance design.

[0003] The technical problem solved by the present application is: In the prior art, the design of the bolt connection structure cannot accurately predict its micro-slip behavior and fatigue life under complex dynamic loads, which leads to the premature failure of the connection pair due to fretting wear and energy accumulation and dissipation in actual use, affecting the safety and durability of the overall structure. SUMMARY

[0004] The purpose of the present application is to provide a structure error tolerance model construction method for compartment connection positioning to solve the problems in the above background.

[0005] The purpose of the present application can be achieved by the following technical solutions: A structure error tolerance model construction method for compartment connection positioning, comprising the following steps: S1: constructing a parameterized digital geometric model containing a bolt, a connecting plate and a base body, and assigning corresponding material properties to each component; S2: generating a reference digital speckle image with random speckle features based on the parameterized digital geometric model, and generating a dynamic load sequence simulating vehicle driving working conditions; S3: gradually applying the dynamic load sequence to the parameterized digital geometric model for mechanical solving, calculating the deformed digital speckle image of the surface of the parameterized digital geometric model after each load step, non-contact full-field comparing the deformed digital speckle image with the reference digital speckle image, and extracting full-field displacement vector data of the connecting pair under each load step; S4: identifying the micro-slip region on the connecting interface of the connecting pair based on the full-field displacement vector data, and calculating the energy dissipation index corresponding to the micro-slip region in each load cycle; S5: establishing a quantitative mapping relationship between the energy dissipation index and the fatigue life according to the energy dissipation index, taking a preset target life as a constraint, and iteratively optimizing the design parameters of the parameterized digital geometric model to make the energy dissipation index of the key region meet the life requirement, and outputting the final optimized connecting structure scheme.

[0006] As a further scheme of the application: the construction process of the parameterized digital geometric model is: Obtain the real point cloud data of the connecting pair by three-dimensional laser scanning, denoise and simplify the point cloud data, and establish an initial parameterized digital geometric model reflecting the actual manufacturing tolerance and surface topography; Perform feature recognition and parameterized reconstruction on the initial parameterized digital geometric model based on a deep neural network, define the key dimensions in the initial parameterized digital geometric model as adjustable design variables, wherein the input is a feature vector of the point cloud data, and the output is a structure topology and size constraint relationship of the parameterized digital geometric model; Perform adaptive mesh generation on the parameterized digital geometric model by a multi-scale mesh generation technique, generate encrypted surface meshes in the contact area, and adopt sparse meshes in the non-key area, to establish a parameterized digital geometric model suitable for high-precision contact analysis.

[0007] As a further scheme of the application: the dynamic load sequence simulating the vehicle driving working condition specifically comprises: Based on the geometric properties and material properties of the parameterized digital geometric model, automatically extract the mass distribution characteristics and structural stiffness characteristics, calculate the overall mass matrix and stiffness matrix; According to the kinematic parameters of the typical working condition of the vehicle, combining the mass matrix and the stiffness matrix, the basic inertia load component at the connecting interface is calculated through the Newton-Euler equation; Taking the road spectrum excitation data as input, performing load transfer path analysis through the stiffness matrix, and calculating the dynamic additional load component caused by the road excitation; Superimposing the basic inertia load component and the dynamic additional load component in the time domain to synthesize a long-time sequence dynamic load sequence matched with the characteristics of the parameterized digital geometric model.

[0008] As a further scheme of the present application: the dynamic load sequence is gradually applied to the parametric digital geometric model for mechanical solving, and specifically includes: In the mechanical solving process of each incremental load step, the full Newton-Raphson iteration method is used to solve the equilibrium equation considering geometric nonlinearity and contact nonlinearity, so as to ensure the convergence and accuracy of the solving result; After the solving of each load step is converged, the spatial coordinate variation of all surface nodes of the parametric digital geometric model is extracted, the discrete node displacement is reconstructed into a continuous model surface deformation field, and a deformed digital speckle image is generated accordingly; The phase correlation method is used for initial value estimation of the speckle images before and after deformation, and then the zero-mean normalized cross-correlation function is used for fine matching to calculate the two-dimensional displacement components of each pixel; By integrating the displacement increments of all load steps, the time sequence full-field displacement vector data of the connecting pair in the entire dynamic load history are constructed.

[0009] As a further scheme of the present application: the deformed digital speckle image is compared with the reference digital speckle image in a non-contact full-field manner, and specifically includes: A regular grid array is defined on the reference digital speckle image as the center of the calculation sub-area to be tracked; For the deformed digital speckle image under each load step, an inverse combined Gauss-Newton algorithm based on gray gradient optimization is used to iteratively calculate each calculation sub-area to solve the displacement field that minimizes the gray difference between the two images; The displacement field smoothing constraint based on the quadratic B-spline function is introduced to regularize the initial displacement field obtained by solving to suppress the non-physical fluctuations caused by noise; The spatial gradient of the displacement field of adjacent calculation sub-areas is calculated to obtain the full-field continuous strain tensor distribution, and high-precision full-field displacement vector data are output in combination with the displacement field.

[0010] As a further scheme of the present application: the micro-slip region on the contact interface of the connecting pair is identified, and specifically includes: The tangential displacement time history data of the contact interface nodes within a complete load cycle are extracted, and the displacement amplitude and phase angle distribution of each node are calculated; The displacement characteristics of the nodes are automatically classified, and the nodes with similar amplitude and phase characteristics are merged into the same slip mode region; The displacement direction consistency coefficient of each clustering region is calculated, and the region with a direction consistency coefficient lower than a first set threshold is preliminarily determined as a micro-slip region; The preliminarily determined micro-slip region is subjected to a morphological closing operation processing to eliminate discrete noise points and fill the internal cavities of the region, and a continuous and complete micro-slip region boundary is generated.

[0011] As a further scheme of the present application: the calculation of the energy dissipation index corresponding to the micro-slippage region in each load cycle specifically comprises: Within the identified micro-slippage region, the tangential stress time history curve and the relative slip velocity time history curve of each node in the entire load cycle are extracted; The tangential stress time history curve and the relative slip velocity time history curve of each node are multiplied point by point to obtain the instantaneous friction power time history curve of the corresponding node; The instantaneous friction power time history curve of each node is subjected to numerical integration to calculate the energy dissipation value of a single node in one load cycle; The energy dissipation values of all nodes in the micro-slippage region are weighted and accumulated, wherein the weights are allocated according to the contact areas represented by the nodes to obtain the total energy dissipation index of the corresponding micro-slippage region.

[0012] As a further scheme of the present application: the establishment of the quantitative mapping relationship with fatigue life specifically comprises: Through experiments on standard fretting fatigue test pieces of multiple materials, a data set of failure cycle numbers corresponding to different energy dissipation index levels is obtained; a nonlinear regression analysis method is used to fit the experimental data set to establish a damage accumulation model with the energy dissipation index as the independent variable and the failure cycle number as the dependent variable; based on the damage equivalence principle, the complex cycles in the actual load spectrum are simplified as equivalent constant amplitude cycles to determine the critical energy dissipation index threshold; an interpolation method is used to construct a complete quantitative mapping relationship curve of energy dissipation-life as a criterion for life prediction.

[0013] As a further scheme of the present application: the iterative optimization of the design parameters of the parameterized digital geometric model specifically comprises: The thickness of the connecting plate, the bolt pre-tightening force, and the curvature radius of the contact surface are determined as key design variables, and their engineering feasible value ranges are set; a pattern search method is used to generate several candidate parameter combinations in the neighborhood of the current design point; by re-executing the mechanical solving and energy dissipation calculation process, the energy dissipation index values corresponding to each candidate combination are directly obtained; according to the critical energy dissipation threshold corresponding to the target life, the candidate schemes that meet the requirements are selected, and the one with the optimal index is taken as the new reference design point for the next round of iteration; when consecutive multiple iterations cannot find a better solution, the calculation is terminated, and the current optimal parameter combination and the corresponding connecting structure scheme are output.

[0014] The present application has the following advantages: (1) By constructing precise parametric digital geometric models, simulating complex mechanical behavior, and predicting fatigue life based on energy dissipation indices, this invention can effectively identify and optimize the design parameters of key areas. This not only improves the anti-microslip capability of the connection structure under dynamic loads but also ensures its stability and safety throughout its service life. Therefore, connection structures designed and optimized using the method of this invention can significantly improve their durability and long-term reliability.

[0015] (2) Traditional design methods rely on numerous physical experiments to verify and optimize design schemes, which is both time-consuming and expensive. This invention utilizes advanced numerical simulation technology to complete the entire process from modeling to optimization in a virtual environment, significantly reducing the need for physical prototypes. Furthermore, the application of optimization strategies such as pattern search allows design parameters to be explored efficiently over a wider range, quickly finding the optimal design scheme that meets the target lifespan requirements. This method not only accelerates the product development cycle but also reduces R&D costs, saving valuable resources for enterprises. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1 As shown, this invention provides a method for constructing a structural error tolerance model for compartment connection and positioning, comprising the following steps: S1: Construct a parametric digital geometric model that includes bolts, connecting plates and the base, and assign corresponding material properties to each component; S2: Generate a benchmark digital speckle image with random speckle characteristics based on a parametric digital geometric model, and simultaneously generate a dynamic load sequence simulating vehicle driving conditions. S3: Apply the dynamic load sequence step by step to the parametric digital geometric model for mechanical solution. After each load step, calculate the deformed digital speckle image on the surface of the parametric digital geometric model. Compare the deformed digital speckle image with the reference digital speckle image in a non-contact full-field comparison and extract the full-field displacement vector data of the connecting pair under each load step. S4: Identify the micro-slip region on the contact interface of the connecting pair based on the full-field displacement vector data, and calculate the energy dissipation index corresponding to the micro-slip region in each load cycle; S5: Establish a quantitative mapping relationship between the energy dissipation index and the fatigue life, and use the preset target life as a constraint to optimize the design parameters of the parameterized digital geometric model through iteration, so that the energy dissipation index of the key region meets the life requirement, and the final optimized connection structure scheme is output.

[0020] In S1, a parameterized digital geometric model containing a bolt, a connecting plate and a base is constructed, and corresponding material properties are assigned to each component, specifically including: The real point cloud data of the bolt connecting pair is obtained by three-dimensional laser scanning technology. The actual bolt, connecting plate and base components are scanned comprehensively using a three-dimensional laser scanner, and appropriate scanning distance and angle overlap rate are maintained during the scanning process to ensure that complete and high-precision surface point cloud data is obtained. The original point cloud data collected is pre-processed, including outlier removal, noise filtering and point cloud simplification. The statistical filtering algorithm is used for outlier removal, which calculates the average distance of each point and its adjacent points, and removes the points whose distance exceeds three times the standard deviation. The Gaussian filtering algorithm is used for noise filtering, which smoothes random noise by weighted average of point cloud. The method based on curvature characteristics is used for point cloud simplification, which reduces the point density in flat areas and maintains higher point density in feature-rich areas, ensuring accuracy while improving processing efficiency. The processed point cloud data retains the actual manufacturing tolerance and micro-topography features of the part surface, providing accurate input data for subsequent modeling.

[0021] The pre-processed point cloud data is identified and parameterized reconstructed based on deep neural network. A deep neural network with encoder-decoder structure is constructed, the encoder part is composed of five convolutional layers, which is used to extract the global and local features of the point cloud. The decoder part contains three fully connected layers, which map the extracted features to the control parameters of the parameterized model. The input of the network is the three-dimensional coordinates and normal vector information of each point in the point cloud, and the output is the structure topology relationship and size constraint conditions of the parameterized digital geometric model. The training process uses a point cloud dataset containing multiple types of bolt connecting pairs, each sample is labeled with key dimensions and topology relationships. The network learns the mapping relationship between point cloud features and parameterized models, and can automatically identify the thread features of the bolt, the hole position of the connecting plate and the geometric features of the contact surface, and convert these features into adjustable design variables.

[0022] Adaptive mesh generation is performed on the parametric digital geometry model by using multi-scale mesh generation techniques. Different mesh density strategies are adopted according to the contact analysis requirements of different regions of the model. In the contact regions between the bolt and the connecting plate, and between the connecting plate and the base, a curved surface adaptive encryption algorithm is used to generate high-density meshes, with mesh sizes controlled within 0.1 mm to accurately capture the contact stress distribution. In non-critical parts away from the contact region, a relatively sparse mesh division is adopted, and the mesh size can be increased to 1 mm to improve the calculation efficiency. During the mesh generation process, the surface triangulation of the geometry model is first processed to ensure that the mesh quality meets the analysis requirements, with the edge length ratio of the triangular element controlled within 1.5 and the maximum internal angle not exceeding 120 degrees. Then, according to the needs of contact analysis, multi-layer mesh refinement is performed in the encryption region to ensure that there are enough grid nodes at the contact interface to accurately describe the contact behavior.

[0023] The final parametric digital geometry model contains complete geometric features, material properties and mesh information, and can accurately reflect the geometric characteristics of the actual bolted joint. This model not only retains the manufacturing tolerances and surface topography of the actual parts, but also realizes flexible adjustment of key dimensions through parametric design variables, providing a reliable digital foundation for subsequent contact mechanics analysis. The mesh division scheme of the model not only ensures the analysis accuracy of the contact region, but also takes into account the overall calculation efficiency, making it suitable for numerical simulation analysis under various working conditions. The digital model established by this method provides effective technical support for the design optimization and performance prediction of bolted joints.

[0024] In S2, a reference digital speckle image with random speckle features is generated based on the parametric digital geometry model, and a dynamic load sequence simulating the vehicle driving conditions is also generated, which specifically includes: Based on the geometric properties and material properties of the parametric digital geometry model, the mass distribution characteristics and structural stiffness characteristics are automatically extracted, and the overall mass matrix and stiffness matrix are calculated. The geometric properties include the three-dimensional size, volume and surface area of each component, which are directly obtained through the parametric model. The material properties include parameters such as elastic modulus, Poisson's ratio and density, which are set according to the actual material type. The calculation of mass distribution characteristics is based on the volume and material density of each component, and the mass contribution of each grid node is obtained through numerical integration method, and finally the overall mass matrix is assembled. The calculation of structural stiffness characteristics uses the finite element method, according to the grid division results and material elastic parameters, the stiffness matrix of each element is calculated, and then the overall stiffness matrix is formed through element assembly process. In this process, the dimensions of the mass matrix and the stiffness matrix are consistent with the number of degrees of freedom of the model, ensuring the accuracy of the dynamics analysis.

[0025] According to the kinematic parameters of typical vehicle working conditions, combined with the mass matrix and stiffness matrix, the basic inertia load components at the connection interface are calculated through the Newton-Euler equation. The typical vehicle working conditions include uniform motion, acceleration, braking, turning and other basic motion states, each of which corresponds to different kinematic parameters such as acceleration, angular velocity, radius of curvature, etc. The Newton-Euler equation describes the relationship between rigid body motion and force. By regarding the vehicle as a multi-rigid body system, the system motion equation is established. During the calculation process, first, the acceleration and angular acceleration of the mass center of each component are calculated according to the kinematic parameters, and then the inertia force and inertia torque generated by each component are calculated by combining the mass matrix and the moment of inertia parameters. Finally, through the force transmission relationship, the components of these inertia loads at the bolt connection interface are calculated to form the basic inertia load time series.

[0026] The road spectrum excitation data is taken as input, and the load transfer path analysis is performed through the stiffness matrix to calculate the dynamic additional load components caused by road excitation. The road spectrum excitation data is derived from actual road measurement results and contains vibration excitation information caused by road roughness, usually in the form of power spectral density. Load transfer path analysis is based on structural dynamics theory and describes the relationship between excitation points and response points through frequency response function. During the calculation process, first, the road spectrum excitation data is converted to the time domain to generate the road excitation time series. Then, the frequency response characteristics of the system are calculated using the overall stiffness matrix and mass matrix. Finally, the dynamic response of the road excitation at the connection interface is calculated through convolution operation to obtain the dynamic additional load components.

[0027] The basic inertia load components and dynamic additional load components are superimposed in the time domain to generate a long-time dynamic load sequence that matches the characteristics of the parametric digital geometric model. The superposition process is performed in the time domain to ensure the synchronization of the two load components in time. The basic inertia load components reflect the quasi-static load generated by the change of vehicle motion state, while the dynamic additional load components reflect the dynamic load generated by road excitation. In the synthesis process, first, the time steps of the two load components are aligned to ensure consistent sampling frequency. Then, the two load components at the same time are combined by vector addition to obtain the total load value at that time. By traversing the entire time sequence, a long-time dynamic load sequence is generated. The generated load sequence contains the size, direction and action point information of the load, and completely matches the characteristics of the parametric digital geometric model, providing accurate boundary conditions for subsequent contact analysis.

[0028] In S3, the dynamic load sequence is applied to the parameterized digital geometric model for mechanical solving, and the deformed digital speckle image of the surface of the parameterized digital geometric model is calculated after each load step. The deformed digital speckle image is compared with the reference digital speckle image in a non-contact full-field manner, and the full-field displacement vector data of the connecting pair at each load step is extracted, specifically including: In the mechanical solving process of each incremental load step, the full Newton-Raphson iteration method is used to solve the equilibrium equation considering geometric nonlinearity and contact nonlinearity. The dynamic load sequence is discretized into multiple incremental steps, and the load increment of each incremental step is 1% to 5% of the total load. In each iteration, the tangent stiffness matrix and the residual force vector of the structure are first calculated according to the current displacement state. The tangent stiffness matrix considers both the geometric stiffness effect and the contact state change. The geometric nonlinearity is represented by including the stress stiffening effect, and the contact nonlinearity is handled by the contact algorithm based on the penalty function method. During the iteration process, the displacement increment is updated by solving a linear equation system until the norm of the residual force is less than the specified tolerance or the maximum number of iterations is reached. This iteration method can effectively handle strong nonlinear problems, ensuring that the solving process converges within a reasonable number of iterations and obtaining accurate mechanical response results.

[0029] After the solution converges at each load step, the spatial coordinate changes of all surface nodes of the parameterized digital geometric model are extracted, and the discrete node displacement is reconstructed into a continuous model surface deformation field. The node displacement data is directly output from the finite element solver, including the displacement components of each node in three coordinate directions. In order to obtain a continuous deformation field, an interpolation method based on shape functions is used to convert the discrete node displacement into the displacement values of any point on the model surface. For triangular or quadrilateral surface elements, linear or bilinear shape functions are used for interpolation calculation. According to the reconstructed continuous displacement field, combined with the initial reference digital speckle image, the corresponding deformed digital speckle image is generated through the image deformation algorithm. The image deformation process considers the continuity and smoothness of the displacement field, ensuring that the generated deformed speckle image truly reflects the deformation state of the model surface, providing accurate input data for subsequent digital image correlation analysis.

[0030] The phase correlation method is used to estimate the initial value of the deformed speckle image, and then the zero-mean normalized cross-correlation function is used for fine matching. The phase correlation method first performs fast Fourier transform on the reference image and the deformed image, and obtains the integer pixel level displacement initial value by calculating the mutual power spectrum and inverse transform. This step can quickly estimate the overall displacement in a large range, providing a good initial condition for subsequent fine matching. After obtaining the initial value, the zero-mean normalized cross-correlation function is used for sub-region matching, which has good robustness to light changes and noise. In the calculation process, a certain size of sub-region is selected centered on the measured point, and the position that makes the cross-correlation function maximum is searched in the deformed image, so as to obtain the sub-pixel level displacement estimation.

[0031] By integrating the displacement increments of all load steps, the time series full-field displacement vector dataset of the joint pair under the entire dynamic load history is constructed. The displacement field calculated for each load step is the incremental displacement of that step relative to the initial state. To obtain the cumulative displacement, the displacement increments of each load step need to be vector superimposed. The superimposition process is based on the consistency of the node numbering of the finite element model, ensuring that the displacement history of each node is correctly recorded. The final dataset contains the displacement time series of each surface node under all load steps, and the data format is a complete record of the three-dimensional vector field changing over time. This dataset completely describes the deformation history of the joint pair under dynamic loading, providing basic data for subsequent mechanical property evaluation and fatigue analysis.

[0032] A regular grid of lattice points is defined on the reference digital speckle image as the center of the calculation sub-area to be tracked. The spacing of the grid is set according to the measurement accuracy requirements and computational resources, which is five to fifteen pixels. Each grid point corresponds to the center position of a calculation sub-area, and the size of the sub-area is determined according to the speckle feature size. The arrangement of the grid covers the entire area of interest, ensuring that the complete deformation field information can be captured. When arranging the grid points, areas with unclear features should be avoided to ensure that each sub-area contains enough speckle features for reliable matching.

[0033] For the deformed digital speckle image under each load step, an inverse combined Gauss-Newton algorithm based on gray level gradient optimization is used for iterative calculation. This algorithm solves the displacement field by minimizing the gray level difference between the reference image sub-area and the deformed image sub-area. In each iteration, the gray level gradient matrix of the reference image sub-area is first calculated, then the Hessian matrix is constructed and the displacement correction is solved. The feature of the inverse combination method is to fix the gray level gradient calculation on the reference image, avoiding the re-computation of the gradient of the deformed image in each iteration, which improves the calculation efficiency. The iteration process continues until the norm of the displacement correction is less than the set threshold (usually zero point zero one pixel) or the maximum number of iterations is reached.

[0034] A displacement field smoothing constraint based on quadratic B-spline function is introduced to regularize the initial displacement field obtained by solving. Due to the influence of image noise and speckle quality, the directly calculated displacement field may contain non-physical fluctuations. In order to suppress these noises, a parameterized representation of the displacement field is established using quadratic B-spline function. The control point spacing of the B-spline function is set according to the smoothness requirement of the displacement field, usually five to ten calculation point spacings. The discrete displacement measurements are fitted to the B-spline function by least squares method, realizing the smoothing processing of the displacement field.

[0035] The full-field continuous strain tensor distribution is obtained by calculating the spatial gradient of the displacement field of adjacent calculation sub-regions. The spatial gradient of the displacement field is calculated by the central difference method. For each internal point on the regular grid, the strain components are calculated using the displacement values of its adjacent points. The strain tensor in the plane strain state includes three independent components: the normal strain in the x direction, the normal strain in the y direction, and the shear strain. The continuity and differential consistency of the displacement field are considered during the strain calculation process, and the least squares method is used to improve the accuracy of the strain calculation. The final strain field is represented in tensor form, fully describing the strain distribution state on the joint surface. Combined with the displacement field data, the complete mechanical response data set containing displacement and strain information is output, providing a comprehensive basis for the mechanical performance analysis of the joint.

[0036] In S4, the micro-slip region on the contact interface of the joint is identified based on the full-field displacement vector data, and the energy dissipation index corresponding to the micro-slip region in each load cycle is calculated, including: The tangential displacement time history data of the contact interface nodes in a complete load cycle is extracted, and the displacement amplitude and phase angle distribution of each node is calculated. The tangential displacement time history data is obtained from the full-field displacement vector data set, which contains the tangential displacement value of each node at each time step in the load cycle period. The displacement amplitude and phase angle corresponding to the fundamental frequency component are extracted by fast Fourier transform analysis of the displacement time history data. The displacement amplitude represents the range of motion of the node in the tangential plane, and the phase angle reflects the time delay of the node motion relative to the reference signal. During the calculation process, the displacement time history of each node is de-trended to eliminate the influence of rigid body displacement and ensure that the analyzed displacement data truly reflect the relative slip motion. Through this step, the motion characteristic parameters of all nodes on the contact interface are obtained, providing a data basis for subsequent region identification.

[0037] The displacement characteristics of the nodes are automatically classified, and nodes with similar amplitude and phase characteristics are merged into the same slip mode region. A density-based clustering algorithm is used to analyze the amplitude and phase characteristics of the nodes. The algorithm first constructs a feature space, and each node is represented as a two-dimensional data point by its displacement amplitude and phase angle. By calculating the Euclidean distance between data points, regions with higher density are identified as clustering centers. During the clustering process, the neighborhood radius parameter is set to zero point one, and the minimum sample number parameter is five, ensuring that only regions with significant statistical characteristics are identified as valid clusters. Each clustering region represents a specific slip mode with similar displacement amplitude and phase characteristics. This automatic classification process can effectively distinguish different slip behavior regions on the contact interface.

[0038] A direction consistency coefficient is calculated for each cluster region, and a region with a direction consistency coefficient below a first set threshold is preliminarily determined as a micro-slip region. The direction consistency coefficient is evaluated by calculating the average angle of the direction vectors of the node displacements in the region. First, the direction of each node displacement is expressed as a unit vector, and then the average of all the direction vectors of the nodes is calculated. The direction consistency coefficient is defined as the magnitude of the average vector, and has a value ranging from 0 to 1. The closer the coefficient is to 1, the more consistent the motion directions of the nodes in the region are; the closer the coefficient is to zero, the more dispersed the motion directions are. The first set threshold is set to 0.6, and when the direction consistency coefficient of a region is below the threshold, it indicates that the motion directions of the nodes in the region are not consistent, and there is complex relative slip behavior, so the region is preliminarily determined as a micro-slip region.

[0039] The preliminarily determined micro-slip regions are subjected to a morphological closing operation to eliminate discrete noise points and fill in internal cavities. The morphological closing operation includes a sequential combination of dilation and erosion operations. First, a dilation operation is performed on the region using a structure element, and the structure element is a circular template with a radius of 3 nodes. The dilation operation can connect adjacent small regions and fill small-sized cavities. Subsequently, an erosion operation is performed to restore the original boundary of the region while maintaining the connectivity of the region. This processing process can effectively eliminate isolated points caused by measurement noise while maintaining the integrity of the main micro-slip region. After morphological processing, a continuous and complete micro-slip region boundary is obtained, providing an accurate spatial range definition for subsequent energy dissipation calculation.

[0040] Within the identified micro-slip regions, the tangential stress time history curve and the relative slip velocity time history curve of each node over the entire load cycle period are extracted. The tangential stress time history data is obtained from the finite element solution results, including the tangential stress value of each node at each time step during the load cycle. The relative slip velocity time history data is obtained by numerically differentiating the tangential displacement time history, and the central difference method is used to calculate the instantaneous velocity at each time step. In the numerical differentiation process, the time step is determined according to the load cycle period and the sampling frequency to ensure the accuracy of the velocity calculation. The data extraction range is limited within the identified micro-slip region, focusing on the regions with significant relative slip.

[0041] The tangential stress time history curve of each node is multiplied point by point with the relative slip velocity time history curve to obtain the instantaneous friction power time history curve of the corresponding node. The multiplication operation is performed in the time domain, and the stress value and the velocity value corresponding to the same time point are multiplied to obtain the instantaneous friction power value at that time point. The instantaneous friction power represents the energy dissipation rate per unit contact area and reflects the instantaneous energy conversion efficiency in the micro-slip process. During the calculation process, it is ensured that the stress data and the velocity data have the same time step and time alignment to avoid calculation errors caused by different time synchronization. Through this operation, the mechanical parameters are converted into energy parameters, which prepares for the subsequent energy integral calculation.

[0042] The instantaneous friction power time history curve of each node is numerically integrated to calculate the energy dissipation value of a single node within a load cycle. The numerical integration adopts the composite Simpson rule, which divides the load cycle period into an even number of equally spaced subintervals. In each subinterval, the Simpson formula is applied to calculate the integral approximation value, and then the results of all subintervals are accumulated to obtain the total integral value. The integral step is determined according to the frequency characteristics of the load cycle, and is usually set to 1% of the period length. The numerical integration process takes into account the nonlinear characteristics of the power time history curve and can accurately calculate the total energy dissipation within each load cycle. The calculated energy dissipation value represents the total amount of energy consumed by the node through friction in a complete load cycle.

[0043] The energy dissipation values of all nodes in the micro-slip region are weighted and accumulated, with the weights being allocated according to the contact area represented by the nodes. The weight of each node is determined by the contact area it controls, which is calculated through the area allocation of the element to which the node belongs. For triangular elements, the node weight is one-third of the element area; for quadrilateral elements, the bilinear shape function is used for area allocation. The weighted accumulation process first multiplies the energy dissipation value of each node by its corresponding weight, and then sums the weighted energy dissipation values of all nodes to obtain the total energy dissipation index of the micro-slip region. This index quantifies the total friction energy loss of the region within a load cycle, providing an important basis for the fatigue life assessment and thermal effect analysis of the joint pair.

[0044] In S5, a quantitative mapping relationship between the energy dissipation index and the fatigue life is established, and the design parameters of the parameterized digital geometry model are iteratively optimized to meet the life requirements of the key region with a preset target life as a constraint, and the final optimized connection structure scheme is output, including: Through experiments on standard fretting fatigue specimens of multiple materials, a data set of failure cycle numbers corresponding to different energy dissipation levels is obtained. The standard fretting fatigue specimens are used in the experiment, and the materials of the specimens cover structural steel and aluminum alloy commonly used in the connection pair. During the experiment, a constant normal load and a cyclic tangential displacement are applied on a dedicated fretting fatigue testing machine, and the energy dissipation index of the contact interface of the specimen is measured. By changing the displacement amplitude and the normal load level, a series of fatigue test data at different energy dissipation levels are obtained. Each experimental condition is repeated 3 to 5 times to ensure the statistical reliability of the data. The experiment continues until the specimen shows obvious cracks or the stiffness decreases significantly, and the cycle number at this time is recorded as the failure cycle number. Finally, a complete experimental data set containing the energy dissipation index and the corresponding failure cycle number is obtained, providing an experimental basis for establishing a life prediction model.

[0045] A damage accumulation model with the energy dissipation index as the independent variable and the failure cycle number as the dependent variable is established by fitting the experimental data set using a nonlinear regression analysis method. The nonlinear regression analysis uses the least squares method to determine the model parameters through iterative optimization. The damage accumulation model adopts a power function form, in which the failure cycle number is proportional to the negative power of the energy dissipation index. During the regression analysis, the experimental data are first logarithmically transformed to convert the nonlinear relationship into a linear relationship for preliminary fitting, and then the Gauss-Newton method is used for accurate parameter estimation. The goodness of fit of the model is evaluated by the coefficient of determination, which is required to be above 0.9 to ensure the prediction accuracy of the model. The established damage accumulation model can quantitatively describe the relationship between the energy dissipation index and the fatigue life, providing a mathematical model basis for life prediction.

[0046] Based on the damage equivalence principle, the complex cycles in the actual load spectrum are simplified to equivalent constant amplitude cycles, and the critical energy dissipation index threshold is determined. The damage equivalence principle uses the Miner linear cumulative damage theory to equivalent the fatigue damage under variable amplitude load to the damage under constant amplitude load. In the specific implementation process, first, the rainflow counting analysis is performed on the actual load spectrum to identify all complete load cycles and their amplitude distribution. Then, according to the damage accumulation model, the damage degree caused by each load cycle is calculated, and the total damage is obtained by linearly superimposing all damage degrees. Finally, according to the target life requirement, the allowed equivalent constant amplitude energy dissipation index value is calculated as the critical threshold. This threshold represents the maximum allowed energy dissipation level that the connection pair can safely withstand within the target life period.

[0047] The complete energy dissipation-life quantitative mapping curve is constructed by interpolation method as the criterion of life prediction. Within the range of experimental data, the cubic spline interpolation method is used to construct the continuous mapping curve. For the area outside the range of experimental data, the logarithmic linear extrapolation method is used for appropriate extension. In the interpolation process, the monotonic decreasing property of the curve is ensured, which conforms to the basic law that the greater the energy dissipation, the shorter the life. The final mapping curve covers from the long-life area of low energy dissipation to the short-life area of high energy dissipation, providing comprehensive criterion basis for the design of connecting pairs with different life requirements. The curve is stored in the form of chart, which is convenient for quick query and application in the optimization process.

[0048] The thickness of the connecting plate, the bolt pre-tightening force and the contact surface curvature radius are determined as the key design variables, and their engineering feasible value ranges are set. The thickness of the connecting plate is set to vary from 5 mm to 20 mm according to the structural space constraints and process requirements. The bolt pre-tightening force is set to vary from 20,000 N to 100,000 N according to the bolt specifications and material strength. The contact surface curvature radius is set to vary from 50 mm to 200 mm according to the matching requirements and processing capacity. The value range of each design variable is verified for engineering feasibility to ensure that the optimization results have good realizability. These design variables have a significant impact on the stiffness characteristics and contact state of the connecting pair, and are the key factors affecting the energy dissipation index.

[0049] The pattern search method is used to generate several candidate parameter combinations in the neighborhood of the current design point. The energy dissipation index values corresponding to each candidate combination are directly obtained by re-executing the mechanical solving and energy dissipation calculation process. The pattern search method generates test points around the current design point according to a specific pattern, which is usually the coordinate direction or the simplex vertex direction. In each iteration step, 5 to 10 candidate parameter combinations are generated, each representing a specific design variable value. For each candidate combination, the complete finite element analysis process is re-executed, including load application, mechanical solving, micro-slip identification and energy dissipation calculation, to directly obtain the key area energy dissipation index corresponding to the design. Although this direct calculation method has a higher computational cost, it can accurately reflect the influence of design variable changes on energy dissipation.

[0050] According to the critical energy dissipation threshold corresponding to the target life, the candidate schemes meeting the requirements are screened out, and the one with the optimal index is taken as the new benchmark design point for the next round of iteration. The screening process first excludes candidate schemes whose energy dissipation index exceeds the critical threshold to ensure that the life requirement is met. Among the qualified schemes, the one with the smallest energy dissipation index is selected as the current optimal solution. If the energy dissipation index of all candidate schemes exceeds the threshold, the one with the smallest index is selected as the new benchmark point. The new benchmark design point will serve as the center for the next round of pattern search, and new candidate parameter combinations will be generated in its neighborhood for further exploration. In this way, the optimization process gradually moves towards the design direction that meets the life requirement and has the smallest energy dissipation.

[0051] The calculation is terminated when consecutive multiple rounds of iteration fail to find a better solution, and the current optimal parameter combination and the corresponding connection structure scheme are output. The termination condition is set as the improvement of the best objective function value of the last three rounds of iteration being less than 1%, indicating that the optimization process has converged to a local optimal solution. Before terminating the calculation, the current optimal design is subjected to final verification, including complete mechanical performance evaluation and manufacturing process inspection. The final output optimization results include specific design parameter values, corresponding energy dissipation index prediction values, and reliability evaluation reports that meet the target life. Detailed three-dimensional geometric models and process parameter requirements are also provided to ensure that the optimization results can be directly used for engineering implementation.

[0052] The working principle of the present application is as follows: a parameterized digital geometric model containing bolts, connecting plates and a base is constructed, and material properties are assigned to each component; a reference digital speckle image with random speckle characteristics and a dynamic load sequence simulating vehicle driving conditions are generated based on the model; the dynamic load is gradually applied to the model for mechanical solution, the deformed digital speckle image is calculated and compared with the reference image, and the full-field displacement vector data is extracted; the micro-slip region is identified based on the displacement vector data and the energy dissipation index is calculated; according to the relationship between the energy dissipation index and the fatigue life, the design parameters are iteratively optimized to meet the pre-set target life requirement, and the optimized connection structure scheme is output.

[0053] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still fall within the scope of the present application.

Claims

1. A method for constructing a structural error tolerance model for compartment connection and positioning, characterized in that, Includes the following steps: S1: Construct a parametric digital geometric model that includes bolts, connecting plates and the base, and assign corresponding material properties to each component; S2: Generate a benchmark digital speckle image with random speckle characteristics based on a parametric digital geometric model, and simultaneously generate a dynamic load sequence simulating vehicle driving conditions. S3: Apply the dynamic load sequence step by step to the parametric digital geometric model for mechanical solution. After each load step, calculate the deformed digital speckle image on the surface of the parametric digital geometric model. Compare the deformed digital speckle image with the reference digital speckle image in a non-contact full-field comparison and extract the full-field displacement vector data of the connecting pair under each load step. S4: Identify the micro-slip regions on the contact interface of the connecting pair based on the full-field displacement vector data, and calculate the energy dissipation index corresponding to the micro-slip regions in each load cycle; S5: Establish a quantitative mapping relationship between energy dissipation index and fatigue life. With the preset target life as a constraint, iteratively optimize the design parameters of the parameterized digital geometric model to ensure that the energy dissipation index of the key area meets the life requirements, and output the final optimized connection structure scheme.

2. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The process of constructing the parametric digital geometric model is as follows: The actual point cloud data of the connector is obtained by 3D laser scanning. The point cloud data is then denoised and simplified to establish an initial parametric digital geometric model that reflects the actual manufacturing tolerances and surface morphology. Based on deep neural networks, feature recognition and parametric reconstruction of the initial parametric digital geometric model are performed. The key dimensions in the initial parametric digital geometric model are defined as adjustable design variables, where the input is the feature vector of point cloud data and the output is the structural topology and dimensional constraint relationship of the parametric digital geometric model. The parametric digital geometric model is adaptively meshed using multi-scale mesh generation technology. A dense surface mesh is generated in the contact area, while a sparse mesh is used in non-critical areas, thus establishing a parametric digital geometric model suitable for high-precision contact analysis.

3. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The generation of the dynamic load sequence simulating vehicle driving conditions specifically includes: Based on the geometric and material properties of the parametric digital geometric model, the mass distribution characteristics and structural stiffness characteristics are automatically extracted, and the overall mass matrix and stiffness matrix are calculated. Based on the kinematic parameters of the vehicle under typical operating conditions, and combined with the mass matrix and stiffness matrix, the basic inertial load components at the connection interface are calculated using the Newton-Euler equations. Using road spectrum excitation data as input, load transfer path analysis is performed through stiffness matrix to calculate the dynamic additional load components caused by road excitation. The basic inertial load components and the dynamic additional load components are superimposed and synthesized in the time domain to generate a long-term dynamic load sequence that matches the characteristics of the parametric digital geometric model.

4. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The stepwise application of dynamic load sequences to a parametric digital geometric model for mechanical solution specifically includes: In the mechanical solution process of each incremental load step, the complete Newton-Rafaelsen iteration method is used to solve the equilibrium equations considering geometric nonlinearity and contact nonlinearity to ensure the convergence and accuracy of the solution results. After the solution converges in each load step, the spatial coordinate changes of all surface nodes of the parameterized digital geometric model are extracted, the discrete node displacements are reconstructed into a continuous model surface deformation field, and a deformed digital speckle image is generated accordingly. The initial value prediction of the speckle images before and after deformation is performed using the phase correlation method, and then fine matching is performed by combining the zero-mean normalized cross-correlation function to calculate the two-dimensional displacement component of each pixel. By integrating the displacement increments of all load steps, a time-series full-field displacement vector dataset of the connection pair throughout the entire dynamic load history is constructed.

5. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The non-contact full-field comparison of the deformed digital speckle image and the reference digital speckle image specifically includes: Define a regular grid of dots on the reference digital speckle image as the center of the computational sub-region to be tracked; For the deformed digital speckle image under each load step, the inverse combination Gauss-Newton algorithm based on gray-level gradient optimization is used to iteratively calculate each calculation sub-region and solve the displacement field that minimizes the gray-level difference between the sub-regions in the two images. A displacement field smoothing constraint based on a quadratic B-spline function is introduced to regularize the solved initial displacement field in order to suppress non-physical fluctuations caused by noise. By calculating the spatial gradient of the displacement field in adjacent computational sub-regions, the continuous strain tensor distribution of the entire field is obtained, and high-precision full-field displacement vector data is output by combining the displacement field.

6. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The identification of micro-slippage areas on the contact interface of the connector specifically includes: Extract the tangential displacement time history data of the contact interface nodes within a complete load cycle, and calculate the displacement amplitude and phase angle distribution of each node; The displacement characteristics of nodes are automatically classified, and nodes with similar amplitude and phase characteristics are grouped into the same slip mode region; Calculate the displacement direction consistency coefficient within each cluster region, and preliminarily determine the regions with a direction consistency coefficient lower than a first set threshold as micro-slip regions; Morphological closing operations are performed on the initially identified microslip regions to eliminate discrete noise points and fill the voids inside the regions, generating continuous and complete microslip region boundaries.

7. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The calculation of the energy dissipation index corresponding to the microslip region within each load cycle specifically includes: Within the identified microslip region, the tangential stress time history curve and the relative slip velocity time history curve of each node are extracted throughout the entire load cycle. The tangential stress time history curve of each node is multiplied point by point with the relative slip velocity time history curve to obtain the instantaneous friction power time history curve of the corresponding node. Numerical integration is performed on the instantaneous friction power time history curve of each node to calculate the energy dissipation value of a single node in one load cycle. The energy dissipation values ​​of all nodes within the microslip region are weighted and accumulated, with the weights allocated according to the contact area represented by each node, to obtain the total energy dissipation index of the corresponding microslip region.

8. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The establishment of a quantitative mapping relationship between fatigue life and fatigue life specifically includes: Experiments were conducted on standard fretting fatigue specimens of various materials to obtain data sets of failure cycle counts corresponding to different energy dissipation index levels. Nonlinear regression analysis was used to fit the experimental data sets, establishing a damage accumulation model with energy dissipation index as the independent variable and failure cycle count as the dependent variable. Based on the damage equivalence principle, the complex cycles in the actual load spectrum were simplified into equivalent constant amplitude cycles, and the critical energy dissipation index threshold was determined. A complete quantitative mapping relationship curve between energy dissipation and lifetime was constructed using interpolation methods, serving as a criterion for lifetime prediction.

9. The method for constructing a structural error tolerance model for compartment connection and positioning according to claim 1, characterized in that, The iterative optimization of the design parameters of the parametric digital geometric model specifically includes: The thickness of the connecting plate, the bolt preload, and the radius of curvature of the contact surface are determined as key design variables, and their feasible value ranges are set. A pattern search method is used to generate several candidate parameter combinations in the neighborhood of the current design point. By re-executing the mechanical solution and energy dissipation calculation process, the energy dissipation index values ​​corresponding to each candidate combination are directly obtained. Based on the critical energy dissipation threshold corresponding to the target life, the candidate schemes that meet the requirements are selected, and the one with the best index is used as the new benchmark design point for the next round of iteration. When no better solution can be found in several consecutive iterations, the calculation is terminated, and the current optimal parameter combination and the corresponding connection structure scheme are output.

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