A method, device, equipment and medium for simulating part assembly by integrating shape error and contact deformation

Abstract

The application discloses a part assembly simulation method and device fusing shape error and contact deformation, equipment and medium, and relates to the technical field of part assembly. The method comprises the following steps: taking the contact pressure between the mating surfaces of the upper part and the lower part as a constraint, which is not more than the yield strength of the target material; calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud by using the boundary element method, and determining the point set formed by the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as the current updated point set; determining the target function of assembly accuracy according to the initial point set, the current updated point set, the current translation matrix and the current rotation matrix; optimizing the target function with the minimum assembly accuracy as the optimization target, and outputting the optimized translation matrix and rotation matrix; and performing pose transformation on the upper part through iteration until the iteration stopping condition is reached to output the final pose of the part. The application can provide more accurate assembly simulation results.

Description

Technical Field

[0001] The present application relates to the technical field of parts assembly, and in particular to a parts assembly simulation method, device, equipment and medium that integrates shape error and contact deformation. Background Art

[0002] Assembly simulation is a key step in tolerance analysis of mechanical products. By simulating various mechanical factors in the assembly process, such as force, deformation, and contact, it evaluates assembly accuracy, thereby guiding the optimization of product design and manufacturing. However, current assembly simulation methods have some limitations, mainly in the treatment of flexible deformation. Traditional assembly simulation methods usually adopt a two-step approach: first determine the rigid assembly pose, and then calculate the flexible deformation. However, this method often oversimplifies the flexible contact deformation and ignores the actual behavior of the flexible material and the deformation of the contact surface. Therefore, the flexible assembly simulation method based on the rigid assembly results cannot accurately describe the impact of flexible deformation on the actual assembly pose. Summary of the Invention

[0003] The purpose of this application is to provide a part assembly simulation method, device, equipment and medium that integrates shape error and contact deformation, which can provide more accurate assembly simulation results.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a part assembly simulation method that integrates shape error and contact deformation, comprising:

[0006] Acquiring point cloud data of parts to be assembled, wherein the parts to be assembled include upper parts and lower parts;

[0007] Performing error processing on the point cloud data to obtain an upper part surface point cloud and a lower part surface point cloud;

[0008] For each point cloud in the upper part surface point cloud, a point pair is formed along the assembly direction to the nearest point cloud in the lower part surface point cloud, and each point pair constitutes an initial point set;

[0009] Initialize the translation matrix and rotation matrix of the upper part movement;

[0010] With the constraint that the contact pressure between the mating surfaces of the upper part and the lower part does not exceed the yield strength of the target material, the boundary element method is used to calculate the deformation of the current upper part surface point cloud and the current lower part surface point cloud, and a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud is determined, which is recorded as the current updated point set; the deformed upper part surface point cloud and the lower part surface point cloud are updated to the current upper part surface point cloud and the current lower part surface point cloud;

[0011] determining an objective function of assembly accuracy according to the initial point set, the current updated point set, the current translation matrix and the current rotation matrix, optimizing the objective function with the minimum assembly accuracy as the optimization target, and outputting the optimized translation matrix and rotation matrix; and updating the optimized translation matrix and rotation matrix as the current translation matrix and the current rotation matrix;

[0012] judging whether the current iteration meets an iteration stop condition;

[0013] if yes, determining that the assembly simulation is ended, and outputting the current translation matrix and the current rotation matrix, wherein the output current translation matrix and the current rotation matrix are used to represent the assembly pose of the upper part;

[0014] if no, returning to the step of calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud by using the boundary element method with the contact pressure between the mating surfaces of the upper part and the lower part not exceeding the yield strength of the target material as a constraint, and determining a point set composed of the deformed upper part surface point cloud and the deformed lower part surface point cloud, which is recorded as the current updated point set.

[0015] Optionally, the iteration stop condition is that the assembly accuracy of the current iteration is less than a set value or the iteration number reaches a set iteration number.

[0016] Optionally, the point cloud data is subjected to error processing to obtain the upper part surface point cloud and the lower part surface point cloud, and the error processing specifically includes:

[0017] the point cloud data is subjected to filtering processing by using a Fourier filtering method to obtain a systematic error;

[0018] a Gaussian random field is subjected to nonlinear transformation to obtain a random error with a non-Gaussian distribution of kurtosis and skewness;

[0019] the systematic error and the random error are superimposed on the point cloud data to obtain the upper part surface point cloud and the lower part surface point cloud.

[0020] Optionally, the objective function is represented as:

[0021]

[0022] wherein Min f(R, T) is the objective function value, R is the current rotation matrix, T is the current translation matrix, M is the number of sampling points in the length direction, N is the number of sampling points in the width direction, p iu is the i-th point on the upper part surface point cloud in the current updated point set, q iu is the corresponding point on the lower part surface point cloud in the current updated point set. iu corresponding point.

[0023] Optionally, the constraint condition of the objective function is expressed as:

[0024] |R·p i +Tq i |·n i ≥0;

[0025] Among them, p i is the i-th point on the upper part surface point cloud in the initial point set, q i The initial point is concentrated on the surface point cloud of the lower part and p i The corresponding point, n i is the direction vector along the assembly direction.

[0026] Optionally, optimizing the objective function with minimization of assembly accuracy as the optimization goal specifically includes:

[0027] Taking minimization of assembly accuracy as the optimization goal, the particle swarm algorithm is used to solve the objective function to obtain the optimized translation matrix and rotation matrix.

[0028] In a second aspect, the present application provides a parts assembly simulation device that integrates shape error and contact deformation, comprising:

[0029] A point cloud data acquisition module is used to acquire point cloud data of parts to be assembled, wherein the parts to be assembled include upper parts and lower parts;

[0030] An error processing module is used to perform error processing on the point cloud data to obtain an upper part surface point cloud and a lower part surface point cloud;

[0031] An initial point set determination module is used to form a point pair from each point cloud in the upper part surface point cloud to the nearest point cloud in the lower part surface point cloud along the assembly direction, and each point pair constitutes an initial point set;

[0032] A translation matrix and rotation matrix initialization module, used to initialize the translation matrix and rotation matrix of the upper part movement;

[0033] An updated point set determination module is configured to calculate the deformation of the current upper part surface point cloud and the current lower part surface point cloud using a boundary element method, with the contact pressure between the mating surfaces of the upper part and the lower part not exceeding the yield strength of the target material as a constraint, and determine a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as the current updated point set; and update the deformed upper part surface point cloud and the lower part surface point cloud to the current upper part surface point cloud and the current lower part surface point cloud;

[0034] an optimization module configured to determine a target function of assembly accuracy according to the initial point set, the current updated point set, the current translation matrix and the current rotation matrix, optimize the target function with the minimum assembly accuracy as an optimization target, and output an optimized translation matrix and an optimized rotation matrix; and update the optimized translation matrix and the optimized rotation matrix as the current translation matrix and the current rotation matrix.

[0035] a judgment module configured to judge whether the current iteration meets an iteration stop condition;

[0036] a pose output module configured to, if the judgment module outputs yes, determine that the assembly simulation is ended, and output the current translation matrix and the current rotation matrix, wherein the output current translation matrix and the output current rotation matrix are used to represent the assembly pose of the upper part.

[0037] an iteration module configured to, if the judgment module outputs no, return to a step of calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud by using the boundary element method with the constraint that the contact pressure between the mating surfaces of the upper part and the lower part does not exceed the yield strength of a target material, and determining a point set formed by the deformed upper part surface point cloud and the deformed lower part surface point cloud, denoted as a current updated point set.

[0038] In a third aspect, the present application provides a computer device, comprising a memory and a processor to store a computer program on the memory and run the computer program on the processor, and the processor executes the computer program to implement the assembly method of the flexible part according to any one of the above.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the assembly method of the flexible part according to any one of the above.

[0040] According to the embodiments provided in the present application, the following technical effects are achieved:

[0041] The present application provides a part assembly simulation method and device fusing shape error and contact deformation, and when the boundary element method is used to calculate the deformation of the current upper part surface point cloud and the current lower part surface point cloud, the constraint that the contact pressure between the mating surfaces of the upper part and the lower part does not exceed the yield strength of a target material is used, and the physical factors in the assembly process are more comprehensively considered, i.e., the flexible contact deformation is considered, so that more accurate assembly simulation results can be provided. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0043] Figure 1 A flowchart of a part assembly simulation method integrating shape errors and contact deformations provided by an embodiment of the present application is shown in the figure.

[0044] Figure 2 A flowchart of a part assembly simulation method integrating shape errors and contact deformations provided by another embodiment of the present application is shown in the figure.

[0045] Figure 3 A tolerance condition diagram provided by an embodiment of the present application is shown in the figure.

[0046] Figure 4 A diagram of generating a discrete meshed skin sample from random errors and systematic errors provided by an embodiment of the present application is shown in the figure.

[0047] Figure 5 A contact state diagram provided by an embodiment of the present application is shown in the figure.

[0048] Figure 6 A functional module diagram of a part assembly simulation device integrating shape errors and contact deformations provided by an embodiment of the present application is shown in the figure.

[0049] Figure 7 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0051] The above purposes, features and advantages of the present application will be more apparent and easy to understand. The present application will be described in further detail below with reference to the drawings and specific embodiments.

[0052] A part assembly simulation method integrating shape errors and contact deformations provided by an embodiment of the present application is shown in the figure. Figure 1 The part assembly simulation method integrating shape errors and contact deformations includes:

[0053] Step 101: Obtain point cloud data of a part to be assembled, the part to be assembled comprising an upper part and a lower part.

[0054] Step 102: Perform error processing on the point cloud data to obtain an upper part surface point cloud and a lower part surface point cloud.

[0055] Step 103: Form a point pair by aligning each point cloud in the upper part surface point cloud to the nearest point cloud in the lower part surface point cloud along an assembly direction, each point pair forming an initial point set.

[0056] Step 104: Initialize a translation matrix and a rotation matrix of movement of the upper part.

[0057] Step 105: Calculate deformation of a current upper part surface point cloud and a current lower part surface point cloud using a boundary element method with a constraint that a contact pressure between mating surfaces of the upper part and the lower part does not exceed a yield strength of a target material, and determine a point set formed by the deformed upper part surface point cloud and the deformed lower part surface point cloud, denoted as a current updated point set; update the deformed upper part surface point cloud and the deformed lower part surface point cloud to the current upper part surface point cloud and the current lower part surface point cloud.

[0058] Step 106: Determine an objective function of assembly accuracy according to the initial point set, the current updated point set, a current translation matrix, and a current rotation matrix, optimize the objective function with a minimization of assembly accuracy as an optimization objective, and output an optimized translation matrix and an optimized rotation matrix; update the optimized translation matrix and the optimized rotation matrix to the current translation matrix and the current rotation matrix.

[0059] Step 107: Determine whether a current iteration meets an iteration stop condition.

[0060] If the determination of step 107 is yes, perform step 108.

[0061] Step 108: Determine that assembly simulation is complete, and output the current translation matrix and the current rotation matrix, the output current translation matrix and the output current rotation matrix being used to represent an assembly pose of the upper part.

[0062] If the determination of step 107 is no, return to step 105.

[0063] The iteration stop condition is that assembly accuracy of a current iteration is less than a set value or a number of iterations reaches a set iteration number.

[0064] An application scenario of the present application is to assemble an upper part to a lower part through translation and rotation.

[0065] In step 101, the point cloud data of the actual part surface is detected by a measuring instrument. The skin surface model of the upper part and the lower part is represented by the point cloud data.

[0066] In step 102, in order to more accurately represent the surface shape error, the point cloud data of the actual part surface detected by the measuring instrument is used to generate the skin surface model. By analyzing the characteristics and distribution of the point cloud data, a surface shape error model can be established. The error model includes two parts: systematic error and random error.

[0067] Systematic error usually manifests as a shift or shape error along the overall or local area of the surface.

[0068] Random error refers to random disturbance caused by factors such as measurement noise and instrument accuracy. For most parts, the distribution of random error is usually not a simple Gaussian distribution, but a non-Gaussian distribution with certain kurtosis and skewness. In order to more realistically simulate the actual situation, the skewness and kurtosis of the small error of the surface point cloud data are calculated, and a non-Gaussian distributed random error with measured kurtosis and skewness is obtained by nonlinear transformation of the random Gaussian random field.

[0069] Step 102 specifically includes:

[0070] The point cloud data is filtered using a Fourier filtering method to obtain the systematic error, specifically including: calculating a transfer function according to the surface power spectral density of the point cloud data; performing Fourier convolution on a Gaussian distributed random surface and the filtering coefficient of the transfer function to obtain the systematic error.

[0071] A Gaussian random field is nonlinearly transformed to obtain a random error with non-Gaussian distribution of kurtosis and skewness.

[0072] The systematic error and the random error are superimposed on the point cloud data to obtain the upper part surface point cloud and the lower part surface point cloud.

[0073] The present application considers the systematic error and the random error together, which can be superimposed to generate the final error model. This error model describes the error of each point on the part surface, and the shape defects of the surface can be modeled by integrating them.

[0074] After step 102, the present application further includes determining the initial positional relationship between the two parts according to the ideal model, i.e. obtaining the initial positional relationship of the two key assembly surfaces.

[0075] In step 103, for the feature p i on the surface S1 of the upper part, the feature q iCalculate each data point p in the feature point cloud of the upper part to be assembled i The closest point q along the assembly direction to the lower part assembly feature surface S2 i The corresponding point set is formed, which is the initial point set.

[0076] Step 104 also includes initializing the number of iterations and the iterative assembly accuracy. The translation matrix and the rotation matrix are used to represent the translation and rotation amounts during the assembly of the upper part to the lower part.

[0077] Among them, step 105 calculates the local deformation of the assembly surface using the improved boundary element method, under the premise of constraining the assembly features of the two parts to not interfere with each other. The improvement of the improved boundary element method is that it takes into account the elastic-plastic effect when calculating the local deformation. It is necessary to limit the contact pressure between the mating surfaces to not exceed the yield strength of the material, which will ensure that the material deforms within the elastic range and does not exceed its plastic deformation limit. This can more accurately simulate the elastic-plastic behavior of the material. The initial characteristic points p of the two surfaces are i With q i , after calculating the local deformation of the surface, the key feature points of the upper and lower parts are p iu With q iu .

[0078] This application calculates the corresponding key feature p based on the known point pairs in the current updated point set iu With q iu The distance between the two parts is the distance between the key features of the assembly as the corresponding assembly accuracy measure, where the assembly features of the two parts do not interfere with each other, constraining the initial feature points p of the two surfaces i With q i Along the assembly direction n i The distance is not less than zero.

[0079] The objective function is expressed as:

[0080] Among them, Min f(R,T) is the objective function value, R is the current rotation matrix, T is the current translation matrix, M is the number of sampling points in the length direction, and N is the number of sampling points in the width direction. That is, the size of the point cloud is M*N, p iu is the i-th point on the surface point cloud of the upper part in the current updated point set, q iu The updated point set is the point cloud of the lower part surface and the point p iu The corresponding point.

[0081] The constraint condition of the objective function is expressed as: |R·p i +Tq i |·n i ≥0.

[0082] The purpose of the objective function optimization is to update the translation matrix and the rotation matrix, so as to optimize the assembly accuracy.

[0083] wherein p i is the i th point on the surface point cloud of the upper part in the initial point set, q i is the corresponding point of p i on the surface point cloud of the lower part in the initial point set, and n i is the direction vector along the assembly direction.

[0084] In step 106, the objective function is optimized with the minimization of the assembly accuracy as the optimization target, specifically including: using the particle swarm algorithm to solve the objective function with the minimization of the assembly accuracy as the optimization target, to obtain the optimized translation matrix and the rotation matrix.

[0085] According to the above solving process, the final assembly pose of the upper part and the lower part can be calculated. For the problem of solving the optimal transformation matrix in each iteration, the particle swarm algorithm is used to solve the nonlinear constraint optimization problem, and the nonlinear constraint optimization problem is formalized as a minimization problem of an objective function. By constructing a penalty function, the constrained optimization problem can be converted into an unconstrained optimization problem, which can constrain the parts from interfering with each other for local deformation analysis under the action of the penalty coefficient. Further, the newly constructed objective function of the particle swarm is used for solving, so as to calculate the optimal assembly pose in the iterative search process. The optimal assembly pose is the assembly pose relative to the initial positions of the upper part and the lower part.

[0086] In the actual assembly process, flexible materials will elastically deform under stress, and the contact surface will also have a small displacement. These factors will affect the final assembly result. Therefore, the application can consider both the geometric shape defects of the part surface and the flexible characteristics of the material, i.e., the deformation of the contact surface. By directly integrating the flexible contact deformation into the assembly simulation process, the assembly process can be more accurately simulated, thereby improving the accuracy and reliability of the simulation result.

[0087] The application directly integrates the contact deformation into the optimization, which can more comprehensively consider the physical factors in the assembly process, including rigid assembly and flexible contact deformation. It can provide more accurate and realistic assembly simulation results. At the same time, dynamic adjustment, and the iterative calculation of the contact deformation in the optimization allows dynamic adjustment of the rigid assembly pose during the optimization process, in response to the actual contact deformation.

[0088] In an exemplary embodiment, as Figure 2 shown, the application also provides a part assembly simulation method that fuses shape error and contact deformation. The method includes the following steps.

[0089] Step 1. First, determine the tolerance conditions and material parameters of the parts, such as the flatness and position of the parts, as shown in Figure 3 , Figure 3 The height of the two cuboid parts (one as the upper part and one as the lower part) is 25 mm, the flatness is 0.8, and the position is 0.8; the material of the parts is polylactic acid, and the specific parameters include elastic modulus, Poisson's ratio, yield strength, etc.; then use a laser spectrum confocal microscope to measure the surface error to obtain the accurate morphology of the mating surface of the two parts.

[0090] Step 2. Based on the measured discrete point cloud data of the machined surface, generate system error (system deviation) based on power spectral density and random error (random deviation) with kurtosis and skewness non-Gaussian distribution based on point cloud data, thereby generating a discrete gridded skin surface sample as shown in Figure 4 , The system deviation and random deviation are superimposed to obtain the shape error (surface shape error model), and it is judged whether the shape error meets the tolerance requirements. If it meets the requirements, the skin model sample is generated, and if it does not meet the requirements, it is scaled according to the tolerance.

[0091] Step 3. The feature on the surface S1 corresponding to the upper part is p i , and the feature on the surface S2 corresponding to the lower part is q i . Calculate the distance from each data point p i in the upper part feature point cloud to the nearest point q i in the lower part assembly feature surface S2 along the assembly direction to form a corresponding point set.

[0092] Step 4. Initialization, set the number of iterations and iteration accuracy, give the translation matrix T and rotation matrix R.

[0093] Step 5. Under the premise that the assembly features of the two parts do not interfere, calculate the local deformation of the assembly surface by the improved boundary element method, and the key feature points of the upper and lower parts after deformation are p iu and q iu .

[0094] Step 6. According to the known point pair, calculate the distance between the corresponding key features p iu and q iu , that is, take the distance between the key features of the two parts as the corresponding assembly accuracy measurement.

[0095] Step 7. If the result of the objective function obtained by solving satisfies the initial iteration accuracy, that is, the average distance of the corresponding feature points is minimum, then jump out of the iteration, otherwise go to step 4 and continue iteration until the maximum number of iterations is reached.

[0096] Step 8. The contact state obtained by the above algorithm is as shown in Figure 5As shown, the shape error and the local deformation are comprehensively considered to improve the assembly accuracy, make the assembly posture more consistent with the actual assembly, and be beneficial to subsequent tolerance analysis.

[0097] In an exemplary embodiment, a part assembly simulation device integrating shape error and contact deformation is also provided, as shown in Figure 6 As shown, the part assembly simulation device integrating shape error and contact deformation comprises:

[0098] A point cloud data acquisition module 201 is configured to acquire point cloud data of parts to be assembled, the parts to be assembled including upper parts and lower parts.

[0099] An error processing module 202 is configured to perform error processing on the point cloud data to obtain upper part surface point clouds and lower part surface point clouds.

[0100] An initial point set determination module 203 is configured to form point pairs by connecting each point cloud in the upper part surface point clouds to the nearest point cloud in the lower part surface point clouds along an assembly direction, and each point pair forms an initial point set.

[0101] A translation matrix and rotation matrix initialization module 204 is configured to initialize a translation matrix and a rotation matrix of movement of the upper part, and a current translation matrix and a current rotation matrix output by the module are used to represent an assembly posture of the upper part.

[0102] An updated point set determination module 205 is configured to calculate deformation of current upper part surface point clouds and current lower part surface point clouds by using a boundary element method with a constraint that a contact pressure between mating surfaces of the upper part and the lower part does not exceed a yield strength of a target material, and to determine a point set formed by the deformed upper part surface point clouds and the deformed lower part surface point clouds, denoted as a current updated point set. The deformed upper part surface point clouds and the deformed lower part surface point clouds are updated to the current upper part surface point clouds and the current lower part surface point clouds.

[0103] An optimization module 206 is configured to determine a target function of assembly accuracy according to the initial point set, the current updated point set, the current translation matrix, and the current rotation matrix, to optimize the target function with a minimum assembly accuracy as an optimization target, and to output an optimized translation matrix and an optimized rotation matrix. The optimized translation matrix and the optimized rotation matrix are updated to the current translation matrix and the current rotation matrix.

[0104] A judgment module 207 is configured to judge whether a current iteration meets an iteration stop condition.

[0105] A posture output module 208 is configured to determine that assembly simulation is completed and to output the current translation matrix and the current rotation matrix if the judgment module outputs yes.

[0106] The iteration module 209 is configured to return to the step of calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud by using the boundary element method and determining a point set formed by the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as a current updated point set, if the output of the determination module is no.

[0107] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram of the computer device can be as shown in FIG. 1. Figure 7 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store assembly simulation data of parts. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a part assembly simulation method that fuses shape error and contact deformation.

[0108] Those skilled in the art can understand that the structure shown in the above Figure 7 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0109] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0110] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0111] In an exemplary embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0112] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0113] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0114] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0115] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0116] The principles and implementations of the present application are described in detail with specific examples in this paper, and the above examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed. Therefore, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A part assembly simulation method integrating shape error and contact deformation, characterized in that: The part assembly simulation method integrating shape error and contact deformation includes: Acquiring point cloud data of parts to be assembled, wherein the parts to be assembled include upper parts and lower parts; Performing error processing on the point cloud data to obtain an upper part surface point cloud and a lower part surface point cloud; For each point in the upper part surface point cloud, a point pair is formed from the nearest point in the lower part surface point cloud along the assembly direction, and each point pair constitutes an initial point set; Initialize the translation matrix and rotation matrix of the upper part movement; With the constraint that the contact pressure between the mating surfaces of the upper part and the lower part does not exceed the yield strength of the target material, the boundary element method is used to calculate the deformation of the current upper part surface point cloud and the current lower part surface point cloud, and a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud is determined, which is recorded as the current updated point set; the deformed upper part surface point cloud and the lower part surface point cloud are updated to the current upper part surface point cloud and the current lower part surface point cloud; Determining an objective function of assembly accuracy based on the initial point set, the current updated point set, the current translation matrix, and the current rotation matrix; optimizing the objective function with minimization of assembly accuracy as the optimization goal, and outputting the optimized translation matrix and rotation matrix; and updating the optimized translation matrix and rotation matrix as the current translation matrix and current rotation matrix; Determine whether the current iteration meets the iteration stop condition; If so, the assembly simulation is determined to be finished, and the current translation matrix and the current rotation matrix are output. The output current translation matrix and the current rotation matrix are used to represent the assembly posture of the upper part; If not, return to the step of calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud using the boundary element method, with the contact pressure between the mating surfaces of the upper part and the lower part not exceeding the yield strength of the target material as a constraint, and determining a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as the current updated point set; The objective function is expressed as: Among them, f(R,T) is the objective function value, R is the current rotation matrix, T is the current translation matrix, M is the number of sampling points in the length direction, N is the number of sampling points in the width direction, and p iu is the i-th point on the surface point cloud of the upper part in the current updated point set, q iu The updated point set is the point cloud of the lower part surface and the point p iu corresponding points; The constraints of the objective function are expressed as: |R·p i +T-q i |·n i ≥0; Among them, p i is the i-th point on the upper part surface point cloud in the initial point set, q i The initial point is concentrated on the surface point cloud of the lower part and p i The corresponding point, n i is the direction vector along the assembly direction.

2. The part assembly simulation method integrating shape error and contact deformation according to claim 1 is characterized in that: The iteration stopping condition is that the assembly accuracy of the current iteration is less than the set value or the number of iterations reaches the set number of iterations.

3. The part assembly simulation method integrating shape error and contact deformation according to claim 1 is characterized in that: Error processing is performed on the point cloud data to obtain the upper part surface point cloud and the lower part surface point cloud, specifically including: Using Fourier filtering method to filter the point cloud data to obtain a system error; Perform nonlinear transformation on a Gaussian random field to obtain a random error with non-Gaussian distribution and kurtosis; The systematic error and the random error are superimposed on the point cloud data to obtain an upper-level part surface point cloud and a lower-level part surface point cloud.

4. The part assembly simulation method integrating shape error and contact deformation according to claim 1, characterized in that: The objective function is optimized with the minimization of assembly accuracy as the optimization goal, specifically including: Taking minimization of assembly accuracy as the optimization goal, the particle swarm algorithm is used to solve the objective function to obtain the optimized translation matrix and rotation matrix.

5. A parts assembly simulation device integrating shape error and contact deformation, characterized in that: The part assembly simulation device integrating shape error and contact deformation comprises: A point cloud data acquisition module is used to acquire point cloud data of parts to be assembled, wherein the parts to be assembled include upper parts and lower parts; An error processing module is used to perform error processing on the point cloud data to obtain an upper part surface point cloud and a lower part surface point cloud; An initial point set determination module is used to form a point pair from each point in the upper part surface point cloud to the nearest point in the lower part surface point cloud along the assembly direction, and each point pair constitutes an initial point set; A translation matrix and rotation matrix initialization module, used to initialize the translation matrix and rotation matrix of the upper part movement; An updated point set determination module is configured to calculate the deformation of the current upper part surface point cloud and the current lower part surface point cloud using a boundary element method, with the contact pressure between the mating surfaces of the upper part and the lower part not exceeding the yield strength of the target material as a constraint, and determine a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as the current updated point set; and update the deformed upper part surface point cloud and the lower part surface point cloud to the current upper part surface point cloud and the current lower part surface point cloud; an optimization module for determining an objective function of assembly accuracy based on the initial point set, the current updated point set, the current translation matrix, and the current rotation matrix, optimizing the objective function with minimization of assembly accuracy as the optimization goal, outputting the optimized translation matrix and rotation matrix; and updating the optimized translation matrix and rotation matrix as the current translation matrix and current rotation matrix; A judgment module is used to judge whether the current iteration meets the iteration stop condition; A posture output module is used to determine that the assembly simulation is completed if the output of the judgment module is yes, and output the current translation matrix and the current rotation matrix. The output current translation matrix and the current rotation matrix are used to represent the assembly posture of the upper part; an iterative module, configured to, if the output of the judgment module is negative, return to the step of calculating the deformation of the current upper part surface point cloud and the current lower part surface point cloud using the boundary element method, with the contact pressure between the mating surfaces of the upper part and the lower part not exceeding the yield strength of the target material as a constraint, and determine a point set consisting of the deformed upper part surface point cloud and the lower part surface point cloud, which is recorded as the current updated point set; The objective function is expressed as: Among them, f(R,T) is the objective function value, R is the current rotation matrix, T is the current translation matrix, M is the number of sampling points in the length direction, N is the number of sampling points in the width direction, and p iu is the i-th point on the surface point cloud of the upper part in the current updated point set, q iu The updated point set is the point cloud of the lower part surface and the point p iu corresponding points; The constraints of the objective function are expressed as: |R·p i +T-q i |·n i ≥0; Among them, p i is the i-th point on the upper part surface point cloud in the initial point set, q i The initial point is concentrated on the surface point cloud of the lower part and p i The corresponding point, n i is the direction vector along the assembly direction.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the part assembly simulation method for integrating shape error and contact deformation as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the part assembly simulation method for integrating shape error and contact deformation according to any one of claims 1 to 4 is implemented.

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