Tolerance semantic representation model-based product assembly method, apparatus and device, and storage medium

Abstract

The invention discloses a product assembly method and device based on a tolerance semantic representation model, equipment and a storage medium, and the method comprises the steps: obtaining the part feature information of a to-be-assembled product in a product assembly stage; establishing a mathematical model according to the part feature information to obtain a tolerance propagation model; establishing a tolerance semantic representation model according to the tolerance propagation model; generating an assembly dimension chain of the to-be-assembled product according to the tolerance semantic representation model; performing tolerance analysis on the assembly dimension chain by using a Monte Carlo simulation method to obtain probability distribution of an assembly result; and when the probability distribution of the assembly result meets the design requirement, obtaining a product assembly tolerance value based on the assembly dimension chain, carrying out tolerance integration on the product assembly tolerance value through a preset distribution strategy to obtain the tolerance of each component ring, and completing product assembly according to the tolerance of each component ring. The tolerance value of each composition ring can be reasonably distributed, and the accuracy and efficiency of product assembly are improved.

Description

Technical Field

[0001] The present application relates to the technical field of product assembly, and in particular to a product assembly method, device, equipment and storage medium based on a tolerance semantic representation model. Background Art

[0002] In modern manufacturing, the accuracy and quality of product assembly directly affect the function and service life of the product. In order to ensure that the product meets the predetermined performance requirements, tolerance design, analysis and optimization of each component in the assembly process must be carried out. Tolerance modeling, analysis and synthesis are key links to ensure assembly accuracy, improve product quality and reduce production costs.

[0003] Although existing product assembly methods based on tolerance semantic representation models (such as vector ring model, TTRS model, etc.) can describe the geometric relationship between parts more accurately, due to the complexity of product design and assembly process, the models are often too complex and require a lot of calculations, especially in the case of multi-part assembly and complex assembly relationships. The calculation efficiency is low and it is difficult to obtain accurate results quickly. Summary of the invention

[0004] The main purpose of this application is to provide a product assembly method, device, equipment and storage medium based on a tolerance semantic representation model, aiming to solve the technical problems of low accuracy and efficiency of product assembly.

[0005] To achieve the above objectives, the present application proposes a product assembly method based on a tolerance semantic representation model, the product assembly method based on a tolerance semantic representation model comprising:

[0006] In the product assembly stage, obtain the part feature information of the product to be assembled;

[0007] Establishing a tolerance propagation model according to the part feature information;

[0008] Establishing a tolerance semantic representation model based on the tolerance propagation model;

[0009] Generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model;

[0010] Using the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain, and obtain a probability distribution of assembly results;

[0011] When the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is obtained based on the assembly dimension chain, and the product assembly tolerance value is tolerance integrated through a preset allocation strategy to obtain the tolerance of each component ring, and the product assembly is completed according to the tolerance of each component ring.

[0012] In one embodiment, the step of establishing a tolerance propagation model according to the part feature information includes:

[0013] According to the part feature information, the basic geometric parameters, size restrictions, relative position relationship between the parts and the matching type of the parts are obtained, wherein the basic geometric parameters are represented by a set of variables G={g1, g2, ..., gn}, wherein each gi represents a geometric attribute, and the geometric attributes include diameter and length; the relative position relationship is represented by a coordinate transformation matrix T, T ij represents the position and direction of the i-th part relative to the j-th part; the fit type is determined by the clearance fit parameter C or the interference fit parameter I, respectively, by the formula C = D hole -D shaft and I = D shaft -D hole Indicates that D hole and D shaft are the nominal diameters of the hole and shaft, respectively;

[0014] A mathematical model is established based on the basic geometric parameters, the size restrictions, the relative position relationship and the matching type to obtain a tolerance propagation model, wherein for the basic geometric parameters, a normal distribution model is used. where gi is a geometric property, σ gi is the standard deviation, and the relative position relationship is expressed by the matrix equation P i =T ij *P j Modeling, where P i and P j are the position vectors of part i and part j respectively.

[0015] In one embodiment, the step of establishing a tolerance semantic representation model according to the tolerance propagation model includes:

[0016] Construct knowledge graphs corresponding to different tolerance types;

[0017] Extracting tolerance features and target tolerance types of products to be assembled based on the part feature information;

[0018] Mapping the tolerance feature into a corresponding tolerance relationship network through the tolerance propagation model;

[0019] Searching for a corresponding tolerance semantic representation in the knowledge graph corresponding to the target tolerance type;

[0020] Constructing an influence relationship matrix according to the tolerance semantic representation and the tolerance relationship network;

[0021] Determine the assembly accuracy influencing path between different sizes based on the influencing relationship matrix;

[0022] A tolerance semantic representation model is constructed based on the assembly accuracy impact path between the different sizes.

[0023] In one embodiment, the step of generating the assembly dimension chain of the product to be assembled according to the tolerance semantic representation model includes:

[0024] The critical dimension path affecting assembly accuracy is predicted based on the tolerance semantic representation model, wherein the tolerance semantic representation model is:

[0025]

[0026] Among them, K ij The key dimension D i To the critical dimension D j The critical dimension path of g is the function of the influence of the dimension feature on the assembly accuracy, W ij The key dimension D i To the critical dimension D j The influence weight of A ij The key dimension D i For key dimension D j The degree of influence, T i The key dimension D i Tolerance, D i is the i-th key dimension, Dj is the j-th key dimension, max(T 1 ,T 2 ,...,T n ) represents a set of tolerances T 1 ,T 2 ,...,T n The maximum value in T n The key dimension D n Tolerance;

[0027] Arranging the key dimensions in order based on the key dimension path to form a target dimension chain diagram;

[0028] Calculate the cumulative error in the dimensional chain based on the target dimensional chain diagram;

[0029] The cumulative error is evaluated, and when the cumulative error meets the evaluation requirements, an assembly dimension chain of the product to be assembled is generated.

[0030] In one embodiment, the step of performing tolerance analysis on the assembly dimension chain using the Monte Carlo simulation method to obtain a probability distribution of assembly results comprises:

[0031] Using Monte Carlo simulation method to obtain the key dimensions and tolerance range of each part in the assembly dimension chain;

[0032] Determining a probability distribution model according to a tolerance range of the critical dimension;

[0033] Randomly generate a set of values ​​according to the probability distribution model, wherein the total number of value sets is N, and for each size di, generate a set of values ​​{di,1,di,2,...,di,N} of size N;

[0034] Calculate assembly results based on the sequence relationship in the dimension chain diagram;

[0035] A probability distribution of the assembly results is constructed based on the data of the assembly results.

[0036] In one embodiment, when the probability distribution of the assembly result meets the design requirements, a product assembly tolerance value is obtained based on the assembly dimension chain, and the product assembly tolerance value is tolerance integrated through a preset allocation strategy to obtain the tolerance of each component ring, and the step of completing product assembly according to the tolerance of each component ring includes:

[0037] When the probability distribution of the assembly result meets the design requirements, calculating the product assembly tolerance value based on the variance of the assembly dimension chain;

[0038] By using a preset allocation strategy, the product assembly tolerance value is allocated to each component ring to obtain the tolerance of each component ring. The product assembly is completed according to the tolerance of each component ring. The tolerance of each component ring is calculated as follows:

[0039]

[0040] Among them, a i is the proportion of each component ring’s contribution to the total variance, σ i is the total variance, CT i is the tolerance of each component ring, and CT is the product assembly tolerance value.

[0041] In one embodiment, the method further comprises:

[0042] determining a target key connection point in the assembly dimension chain as a virtual joint;

[0043] Defining a Jacobian matrix for the virtual joint, wherein the Jacobian matrix represents the influence of the virtual joint position on other dimensions;

[0044] The influence of the virtual joint position on the assembly dimension chain is calculated based on the Jacobian matrix, and the calculation is as follows:

[0045] ΔX j =J j *δ j

[0046] Among them, Jj is the Jacobian matrix, δ j is the change caused by virtual joint j, ΔX j is the influence of the virtual joint position on the assembly dimension chain;

[0047] Calculate the total error accumulation based on the impacts;

[0048] The parameters in the Jacobian matrix are adjusted based on the total error accumulation to optimize the total error, and the product assembly is adjusted based on the optimized total error.

[0049] In addition, to achieve the above-mentioned purpose, the present application also proposes a product assembly device based on a tolerance semantic representation model, and the product assembly device based on a tolerance semantic representation model comprises:

[0050] An acquisition module is used to obtain the part feature information of the product to be assembled during the product assembly stage;

[0051] An establishment module is used to establish a tolerance propagation model according to the part feature information;

[0052] The establishment module is further used to establish a tolerance semantic representation model based on the tolerance propagation model;

[0053] A generating module, used for generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model;

[0054] An analysis module, used for performing tolerance analysis on the assembly dimension chain using a Monte Carlo simulation method to obtain a probability distribution of assembly results;

[0055] The tolerance synthesis module is used to obtain the product assembly tolerance value based on the assembly dimension chain when the probability distribution of the assembly result meets the design requirements, and to perform tolerance synthesis on the product assembly tolerance value through a preset allocation strategy to obtain the tolerance of each component ring, and complete the product assembly according to the tolerance of each component ring.

[0056] In addition, to achieve the above-mentioned objectives, the present application also proposes a product assembly device based on a tolerance semantic representation model, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the product assembly method based on the tolerance semantic representation model as described above.

[0057] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the product assembly method based on the tolerance semantic representation model as described above are implemented.

[0058] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the product assembly method based on the tolerance semantic representation model as described above are implemented.

[0059] One or more technical solutions proposed in this application use tolerance propagation models and tolerance semantic representation models to accurately describe part features and their interrelationships, ensuring that tolerance factors that may affect product quality can be fully considered in the design stage. This helps to identify and optimize critical dimension chains, thereby improving the quality of the final product. The tolerance semantic representation model established based on the tolerance propagation model allows users to flexibly adjust parameters according to actual needs. This method is not only suitable for standardized production processes, but also for customized or complex-shaped product designs. The Monte Carlo simulation method can be used for tolerance analysis to predict the probability distribution of assembly results without actual physical testing. This method greatly reduces the number and cost of experiments and speeds up the product development cycle. Through the precise analysis of the assembly dimension chain, the tolerance values ​​of each component ring can be reasonably allocated. This preset allocation strategy takes into account factors such as manufacturing process and cost, which helps to use resources more effectively and reduce production costs. When determining the assembly dimension chain and performing tolerance synthesis, full consideration is given to whether the probability distribution of the assembly result meets the design requirements. This method ensures that even if there is a certain degree of manufacturing error, most products can meet the design standards and improve the consistency and reliability of assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0062] Figure 1 A flowchart diagram of a first embodiment of a product assembly method based on a tolerance semantic representation model of the present application;

[0063] Figure 2 A flow chart of a second embodiment of a product assembly method based on a tolerance semantic representation model of the present application;

[0064] Figure 3 A flowchart diagram of a third embodiment of a product assembly method based on a tolerance semantic representation model of the present application;

[0065] Figure 4 This is a schematic diagram of the module structure of a product assembly device based on a tolerance semantic representation model according to an embodiment of the present application;

[0066] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the product assembly method based on the tolerance semantic representation model in the embodiment of the present application.

[0067] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0069] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0070] The main solution of the embodiment of the present application is: in the product assembly stage, obtaining part feature information of the product to be assembled; establishing a tolerance propagation model based on the part feature information; establishing a tolerance semantic representation model based on the tolerance propagation model; generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model; using the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain to obtain a probability distribution of assembly results; when the probability distribution of the assembly results meets the design requirements, obtaining a product assembly tolerance value based on the assembly dimension chain, and performing tolerance synthesis on the product assembly tolerance value through a preset allocation strategy to obtain the tolerances of each component ring, and completing product assembly according to the tolerances of each component ring.

[0071] Because the models of product assembly in existing technologies are often too complex and require a lot of calculations, especially in the case of multi-part assemblies and complex assembly relationships, the calculation efficiency is low and it is difficult to quickly obtain accurate results.

[0072] The present application provides a solution, which first performs tolerance modeling, generates an assembly dimension chain after modeling, and performs tolerance analysis based on the assembly dimension chain, thereby analyzing the influence of the component rings (part size, shape and position accuracy) in the assembly dimension chain on the closed ring, and performs tolerance synthesis, that is, tolerance allocation, based on the analysis results, thereby obtaining the tolerances of each part, completing product assembly, and improving the efficiency and accuracy of product assembly.

[0073] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a product assembly device based on a tolerance semantic representation model, etc. The following takes a product assembly device based on a tolerance semantic representation model as an example to illustrate this embodiment and the following embodiments.

[0074] Based on this, the embodiment of the present application provides a product assembly method based on a tolerance semantic representation model, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the product assembly method based on the tolerance semantic representation model of the present application.

[0075] In this embodiment, the product assembly method based on the tolerance semantic representation model includes steps S10 to S60:

[0076] Step S10: In the product assembly stage, the part feature information of the product to be assembled is obtained.

[0077] It should be noted that during the product assembly or parts processing planning stage, the product tolerance needs to be designed to improve the efficiency of product assembly. The products to be assembled may be automotive parts, electronic product parts, and may also include other types of parts, which are not limited in this embodiment.

[0078] Part feature information may include the size, position, direction of part features, basic assembly parameters of parts, and the type of fit of parts.

[0079] Part feature information can be obtained by querying design documents or scanning the parts using 3D scanning equipment and analyzing them using specific software.

[0080] Step S20: establishing a tolerance propagation model according to the part feature information.

[0081] It is understandable that a mathematical model can be established based on the specific features in the part feature information. The mathematical model may include the definition of the part, size restrictions, etc., and may also define the tolerance distribution principle. The completed data model is the tolerance propagation model.

[0082] In a feasible implementation, step S20 may include: obtaining basic geometric parameters, size restrictions, relative positional relationships between parts, and matching types of parts according to the part feature information, wherein the basic geometric parameters are represented by a set of variables G={g1, g2, ..., gn}, wherein each gi represents a geometric attribute, and the geometric attributes include diameter and length; the relative positional relationship is represented by a coordinate transformation matrix T, T ijrepresents the position and direction of the i-th part relative to the j-th part; the fit type is determined by the clearance fit parameter C or the interference fit parameter I, respectively, by the formula C = D hole -D shaft and I = D shaft -D hole Indicates that D hole and D shaft are the nominal diameters of the hole and shaft, respectively;

[0083] A mathematical model is established based on the basic geometric parameters, the size restrictions, the relative position relationship and the matching type to obtain a tolerance propagation model, wherein for the basic geometric parameters, a normal distribution model is used. where gi is a geometric property, σ gi is the standard deviation, and the relative position relationship is expressed by the matrix equation P i =T ij *P j Modeling, where P i and P j are the position vectors of part i and part j respectively.

[0084] It should be noted that the basic set parameters of the parts, the size restriction parameters, the relative position relationship between the parts, and the matching type between the parts can be obtained based on the part feature information.

[0085] The basic set parameters can be expressed as a set of variables G, where each gi in the variable G represents a specific geometric attribute, which may include attributes such as diameter, length, width, and other attributes. The size limit is defined as the upper and lower limits, which can be expressed by L = {Lmin, Lmax}, so as to ensure that all dimensions meet the design specifications. The relative position relationship between the parts can be expressed by the coordinate transformation matrix T, T ij Represents the position and orientation of the i-th part relative to the j-th part.

[0086] In a specific implementation, the fit type is determined by a clearance fit parameter C or an interference fit parameter I, which can be obtained by calculating the clearance fit parameter C or the interference fit parameter I using the nominal diameters of the hole and the shaft in the part, respectively.

[0087] It can be understood that a mathematical model can be established based on basic geometric parameters, dimensional restrictions, relative position relationships, and fit types to obtain a tolerance propagation model. For basic geometric parameters, a normal distribution model can be used. gi is geometric mathematics, σgi is the standard deviation, thus establishing the tolerance propagation model of basic geometric parameters. For dimensional restrictions, constraints can be directly applied to ensure that each dimensional parameter is within the specified range. For relative position relationships, modeling can be performed through matrix equations, P i=T ij *P j Modeling, where P i and P j are the position vectors of part i and part j respectively. Through different types of fit, the probability density function f(x; C / I, μ, σ) can be used to represent the probability distribution of clearance or interference, where μ is the mean and σ is the standard deviation.

[0088] The tolerance propagation model is a mathematical model used to describe how tolerances propagate in part feature information. During the product assembly process, due to the fit and relative position relationship between parts, the tolerance of one part will affect the size and performance of other parts and even the entire assembly. The tolerance propagation model accurately describes this influence relationship through mathematical methods, allowing designers to predict and evaluate the tolerance of the assembly during the design stage. In the tolerance propagation model, the basic geometric parameters, dimensional restrictions, relative position relationships, and fit types of parts are all taken into consideration and together form the basis of the model. Among them, the basic geometric parameters are the inherent properties of the parts, such as diameter, length, etc., which determine the basic shape and size of the parts. The dimensional restrictions are further constraints on these basic geometric parameters to ensure that the size of the parts meets the design specifications. The relative position relationship describes the relative position and direction between parts, which is one of the key factors to be considered during the assembly process. The fit type determines the fit between parts, such as clearance fit or interference fit, which directly affects the dimensional accuracy and performance of the assembly.

[0089] Step S30: establishing a tolerance semantic representation model according to the tolerance propagation model.

[0090] In a specific implementation, the tolerance semantic representation model is established based on the tolerance propagation model, and the tolerance semantic representation model can represent and display the initial tolerance value of each part.

[0091] In a feasible implementation, step S30 may include: constructing a knowledge graph corresponding to different tolerance types; extracting the tolerance features and the target tolerance type of the product to be assembled based on the part feature information; mapping the tolerance features into a corresponding tolerance relationship network through the tolerance propagation model; searching for the corresponding tolerance semantic representation in the knowledge graph corresponding to the target tolerance type; constructing an influence relationship matrix based on the tolerance semantic representation and the tolerance relationship network; determining the assembly accuracy influence path between different sizes based on the influence relationship matrix; and constructing a tolerance semantic representation model based on the assembly accuracy influence path between different sizes.

[0092] It should be noted that the knowledge graph corresponding to the tolerance type is pre-built, which contains common tolerance types and their corresponding tolerance semantic representations. By building such a knowledge graph, it is easy to find and reference tolerance semantic representations, improving the efficiency and accuracy of modeling.

[0093] It is understandable that when extracting the tolerance features of the product to be assembled, the type and value of the tolerance features can be determined based on the specific data in the part feature information, such as size, position, fit type, etc. Then, these tolerance features are mapped to the corresponding tolerance relationship network through the tolerance propagation model. The tolerance relationship network describes the mutual influence relationship between the tolerance features and is the basis for establishing the tolerance semantic representation model.

[0094] It is worth noting that after determining the target tolerance type, the corresponding tolerance semantic representation can be found in the corresponding knowledge graph. The tolerance semantic representation is an abstraction and generalization of the tolerance characteristics. According to the tolerance semantic representation and the tolerance relationship network, an influence relationship matrix can be constructed. The influence relationship matrix describes the assembly accuracy influence path between different dimensions, that is, how the tolerance change of one dimension affects the tolerance of other dimensions. By constructing the influence relationship matrix, the mutual influence relationship between the dimensions can be clearly understood, providing a basis for subsequent tolerance synthesis.

[0095] Based on the assembly accuracy impact path between different dimensions, a tolerance semantic representation model can be constructed. The tolerance semantic representation model comprehensively considers the mutual influence relationship between dimensions and can accurately describe the tolerance propagation and change during the product assembly process. By establishing such a model, it can provide strong support for subsequent tolerance analysis and synthesis, thereby improving the efficiency and accuracy of product assembly.

[0096] Step S40: generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model.

[0097] It should be understood that the assembly dimension chain of the product to be assembled can be generated based on the tolerance semantic representation model. The assembly dimension chain refers to a closed dimension group composed of a series of part dimensions that directly or indirectly affect a specific assembly accuracy (such as clearance, position, etc.) in a machine or component.

[0098] It should be noted that the influence on assembly accuracy can be analyzed by extracting the closed loop and component loop elements of the assembly dimension chain of the product to be assembled. Since the parts themselves have manufacturing errors, the matching between the parts during assembly will inevitably have accumulated errors, which will affect the assembly accuracy. The relationship between the assembly accuracy requirements and the manufacturing errors of each part is the relationship between the component loop error and the closed loop error reflected by the dimension chain.

[0099] It should be noted that after the step of generating the assembly dimension chain, if it is found that the cumulative error exceeds the allowable range, the step of establishing the tolerance propagation model is returned to re-evaluate the tolerance allocation strategy until the design specification is met.

[0100] In a feasible implementation, step S40 may include: predicting a critical dimension path that affects assembly accuracy based on the tolerance semantic representation model; arranging critical dimensions in sequence based on the critical dimension path to form a target dimension chain diagram; calculating the cumulative error in the dimension chain based on the target dimension chain diagram; evaluating the cumulative error, and generating the assembly dimension chain of the product to be assembled when the cumulative error meets the evaluation requirements.

[0101] It should be noted that the tolerance semantic representation model refers to the ability to accurately predict the accuracy of assembly results through detailed analysis of the size and shape accuracy of each part during the assembly process. This model not only considers the tolerance of a single part, but also comprehensively considers the interaction between parts and the impact of assembly sequence on the final accuracy. The tolerance semantic representation model is:

[0102]

[0103] Among them, K ij The key dimension D i To the critical dimension D j The critical dimension path of g is the function of the influence of the dimension feature on the assembly accuracy, W ij The key dimension D i To the critical dimension D j The influence weight of A ij The key dimension D i For key dimension D j The degree of influence, T i The key dimension D i Tolerance, D i is the i-th key dimension, Dj is the j-th key dimension, max(T 1 ,T 2 ,...,T n ) represents a set of tolerances T 1 ,T 2 ,...,T n The maximum value in T n The key dimension D n Tolerance.

[0104] The critical dimension path can be identified by analyzing the interaction between the various parts. Assume that the critical dimension path consists of a series of dimensions d1, d2, ..., dn, where each di represents a specific dimension or distance.

[0105] The key dimensions can be arranged in the order of their impact on the assembly, which can be expressed as a vector D = [d1, d2, ..., dn]. This vector reflects the sequential connection of the key dimensions from the first part to the last part. The key dimensions after sequential arrangement can form a target dimension chain diagram, which is a complete dimension chain diagram.

[0106] After obtaining the target dimension chain diagram, the cumulative error in the dimension chain can be calculated to evaluate whether it meets the design requirements. The cumulative error is calculated as follows:

[0107]

[0108] Among them, Δ i is the tolerance range of the i-th critical dimension, Δ total It is the cumulative error in the entire dimension chain, and the tolerance range is the difference between the maximum and minimum values.

[0109] In the specific implementation, if the correlation between the dimensions is considered, the variance propagation formula is used:

[0110]

[0111] is the variance of the i-th dimension, and cov(i,j) is the covariance between dimensions i and j.

[0112] It should be noted that after the cumulative error is calculated, the cumulative error can be evaluated. The evaluation standard of the cumulative error can be a pre-set allowable error range [ε min , ε max ], if Δ total or total If it falls within this range, it is considered to meet the design requirements. When the cumulative error meets the evaluation requirements, the final assembly dimension chain can be generated.

[0113] Step S50: using the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain to obtain a probability distribution of assembly results.

[0114] It should be noted that Monte Carlo Simulation is a computational algorithm that obtains numerical solutions through repeated random sampling. Monte Carlo Simulation is used to perform tolerance analysis on assembly dimension chains to obtain the probability distribution of assembly results. The probability distribution of assembly results is used to indicate whether the size and tolerance of the closed loop after assembly can meet the overall functional design requirements.

[0115] It should be noted that after obtaining the assembly results, all assembly results can be grouped according to intervals, and the frequency of each interval can be counted to draw a histogram, or the probability density function of the assembly results can be estimated by a non-parametric method to obtain the probability distribution of the assembly results.

[0116] The tolerance analysis of the assembly dimension chain can be performed by Monte Carlo simulation method to determine the probability distribution of the assembly results, and whether the requirements are met based on the probability distribution of the assembly results.

[0117] Step S60: When the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is obtained based on the assembly dimension chain, and the product assembly tolerance value is tolerance integrated through a preset allocation strategy to obtain the tolerance of each component ring, and the product assembly is completed according to the tolerance of each component ring.

[0118] It should be noted that if the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value can be allocated to the tolerance of each part according to certain criteria. This process is tolerance allocation (tolerance synthesis). Tolerance synthesis mainly allocates tolerances from the perspective of economy and rationality. Traditional tolerance allocation methods include: equal tolerance method, equal precision method, equal influence method, comprehensive factor method and equal process capability method. Traditional tolerance allocation design methods do not fully consider factors such as processing methods, process capabilities, and processing costs. By reasonably allocating the tolerances of related parts or their process size tolerances, the product can achieve the best technical and economic benefits.

[0119] In specific implementations, the preset allocation strategy can be a linear programming method, a genetic algorithm, a simulated annealing algorithm, an equal tolerance allocation, an equal precision allocation or other custom strategies. By using the preset allocation strategy to allocate tolerances, the size and tolerance of the closed loop after assembly, i.e., the tolerance of each component loop, can be obtained, thereby completing product assembly.

[0120] It is worth noting that completing product assembly according to the tolerance of each component ring means accurately manufacturing and assembling the parts according to the tolerance requirements of each component ring. During the manufacturing process, the tolerance range of each part is strictly controlled to ensure that they meet the design requirements. During the assembly process, attention is paid to the matching mode and relative position relationship between the parts to ensure that the assembly accuracy meets expectations. By comprehensively considering the tolerance of each component ring, it can be ensured that the size and performance of the entire assembly meet the expected goals, thereby improving the quality and reliability of the product.

[0121] This embodiment provides a product assembly method based on a tolerance semantic representation model, which uses a tolerance propagation model and a tolerance semantic representation model to accurately describe part features and their interrelationships, ensuring that tolerance factors that may affect product quality can be fully considered in the design stage. This helps to identify and optimize critical dimension chains, thereby improving the quality of the final product. The tolerance semantic representation model established based on the tolerance propagation model allows users to flexibly adjust parameters according to actual needs. This method is not only suitable for standardized production processes, but also suitable for customized or complex-shaped product designs. The Monte Carlo simulation method is used for tolerance analysis to predict the probability distribution of assembly results without actual physical testing. This method greatly reduces the number and cost of experiments and speeds up the product development cycle. Through the precise analysis of the assembly dimension chain, the tolerance values ​​of each component ring can be reasonably allocated. This preset allocation strategy takes into account factors such as manufacturing process and cost, which helps to use resources more effectively and reduce production costs. When determining the assembly dimension chain and performing tolerance synthesis, full consideration is given to whether the probability distribution of the assembly result meets the design requirements. This method ensures that even if there is a certain degree of manufacturing error, most products can meet the design standards, thereby improving the consistency and reliability of assembly.

[0122] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S50 includes steps S501 to S505:

[0123] Step S501: using the Monte Carlo simulation method to obtain the critical dimensions and tolerance range of each part in the assembly dimension chain.

[0124] It should be noted that the Monte Carlo simulation method can be used to obtain the critical dimensions di and tolerance range of each part in the assembly dimension chain.

[0125] Step S502: determining a probability distribution model according to the tolerance range of the critical dimension.

[0126] In the specific implementation, a probability distribution model can be formulated for the tolerance range of each key dimension. If the probability distribution is a normal distribution, the mean μ of each dimension is set i and standard deviation σ i .

[0127] Step S503: randomly generating a set of numerical values ​​according to the probability distribution model.

[0128] Among them, the total number of value sets is N, then for each size di, a value set {di,1,di,2,...,di,N} of size N is generated.

[0129] It should be noted that random generation can be performed through the Monte Carlo simulation method. Specifically, a random number generator can be used to generate a numerical set of key dimensions of each probability distribution model. The numerical set is a random sample of each key dimension. Suppose the total number of samples (the total number of numerical sets) is N. For each dimension di, a numerical set {di,1,di,2,...,di,N} of size N is generated.

[0130] Step S504: Calculate the assembly result based on the sequence relationship in the dimension chain diagram.

[0131] It should be noted that for each set of values, the assembly result can be calculated based on the sequence relationship in the dimension chain diagram. The specific calculation is as follows:

[0132]

[0133] Among them, j represents the jth value set, that is, all key sizes are accumulated.

[0134] Step S505: constructing a probability distribution of the assembly result based on the data of the assembly result.

[0135] It should be noted that the probability distribution of the assembly results can be constructed based on the data of the assembly results, specifically by grouping all Rj by intervals and counting the frequency of each interval to draw a histogram; or, the probability density function (pdf) of the assembly results can be estimated by a non-parametric method.

[0136] When calculating the assembly results, for the assembly dimension chain consisting of three key dimensions, use the formula Rj = d 1,j +d 2,j +d 3,j To calculate the assembly results of each sample combination, where d 1,j ,d 2,j ,d 3,j are the j-th values ​​of the first, second and third critical dimensions respectively.

[0137] This embodiment uses the Monte Carlo simulation method to obtain the critical dimensions and tolerance ranges of each part in the assembly dimension chain; determines the probability distribution model based on the tolerance range of the critical dimensions; randomly generates a numerical set based on the probability distribution model, where the total number of numerical sets is N, and for each dimension di, generates a numerical set {di,1,di,2,...,di,N} of size N; calculates the assembly result based on the sequential relationship in the dimension chain diagram; constructs the probability distribution of the assembly result based on the data of the assembly result. By constructing the probability distribution of the assembly result, it can be clearly seen which areas of the assembly result are more likely to exceed the specified tolerance range, which helps to identify potential risk points in advance. Based on the simulation results, designers can adjust the tolerance range of parts or reconsider the assembly sequence to improve the product's pass rate and reduce the scrap rate. By accurately controlling the tolerance of critical dimensions and ensuring that the assembly results meet the expected standards, the reliability and stability of the entire product are improved.

[0138] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S60 includes steps S601 to S602:

[0139] Step S601: when the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is calculated based on the variance of the assembly dimension chain.

[0140] It should be noted that the probability distribution of the assembly results meets the design requirements to prove whether the basic dimensions and upper and lower deviations of each component ring marked can meet the overall functional requirements after processing. If the final performance does not meet the requirements, it is necessary to modify the tolerances of each component ring again, and after repeated trial calculations, until the performance requirements are met.

[0141] If the probability distribution of the assembly results meets the design requirements, the product assembly tolerance value can be calculated based on the variance of the assembly dimension chain. The product assembly tolerance value is calculated as follows:

[0142] CT=k*σ R

[0143] Where k is the safety factor, σ R is the variance of the assembly dimension chain;

[0144] In the above formula, CT is the product assembly tolerance value, k is the safety factor, and the safety factor can be set in advance, such as 0.8, 0.9, etc. The safety factor is used to consider the uncertainty and risk in the production and assembly process.

[0145] Step S602: allocating the product assembly tolerance value to each component ring through a preset allocation strategy to obtain the tolerance of each component ring, and completing product assembly according to the tolerance of each component ring.

[0146] It should be noted that the preset allocation strategy in this embodiment is an equal-precision allocation strategy, and the equal-precision allocation strategy can be used to allocate the difference of product assembly to each component ring, thereby obtaining the tolerance of each component ring and completing product assembly.

[0147] In a specific implementation, the tolerance of each component ring is calculated as follows:

[0148]

[0149] Among them, a i is the proportion of each component ring’s contribution to the total variance, σ i is the total variance, CT i is the tolerance of each component ring, CT is the product assembly tolerance value, and the tolerance value of each component ring can be calculated through the above formula, so as to achieve a reasonable distribution of the tolerance of each component ring.

[0150] In a specific implementation, the tolerance value CT of each component ring can be i Adjust production process parameters or select appropriate processing methods to ensure that the assembly quality of the final product meets the design requirements. At the same time, record and analyze the data generated during the assembly process to facilitate subsequent optimization and improvement.

[0151] In a feasible implementation, the concept of virtual joints can be introduced during the product assembly process, and the Jacobian matrix can be introduced into tolerance-related calculations to improve assembly accuracy.

[0152] Therefore, the method further includes: determining a target key connection point in the assembly dimension chain as a virtual joint; defining a Jacobian matrix for the virtual joint, wherein the Jacobian matrix represents the influence of the virtual joint position on other dimensions; and calculating the influence of the virtual joint position on the assembly dimension chain based on the Jacobian matrix, as follows: ΔX j =J j *δ j , where J j is the Jacobian matrix, δ j is the change caused by virtual joint j, ΔX j The influence of the virtual joint position on the assembly dimension chain is calculated; the total error accumulation is calculated according to the influence; the parameters in the Jacobian matrix are adjusted based on the total error accumulation to optimize the total error, and the product assembly is adjusted based on the optimized total error.

[0153] It should be noted that when introducing virtual joints, key connection points that can be simplified into rotational or translational motions can be determined in the assembly dimension chain as virtual joints, and the target key connection points are key connection points that can be simplified into rotational or translational motions.

[0154] A corresponding Jacobian matrix can be defined for each virtual joint. The Jacobian matrix can be combined with the error accumulation in the dimensional chain to accurately calculate the tolerance changes due to the virtual joints.

[0155] It should be understood that the Jacobian matrix is ​​used to represent the influence of the virtual joint position on other dimensions. Therefore, the influence of the virtual joint position on the assembly dimension chain can be calculated based on the Jacobian matrix, as follows:

[0156] ΔX j =J j *δ j

[0157] In the above formula, ΔX j is the influence of the virtual joint position on the assembly dimension chain, J j is the Jacobian matrix, δ j is the change caused by the virtual joint j.

[0158] By combining the effects of all virtual joints, the total error accumulation can be calculated, where m is the number of virtual joints, which can be directly obtained. The total error accumulation plays a vital role in assembly dimension chain analysis and tolerance design. It is mainly used to evaluate and control the accumulated errors in the entire assembly process to ensure that the assembly accuracy of the final product meets the design requirements. The total error accumulation provides an important basis for tolerance allocation. Through the analysis of the total error accumulation, the tolerance values ​​of each component ring can be reasonably allocated to ensure that the production cost and complexity are reduced as much as possible while meeting the assembly accuracy requirements. For example, stricter tolerances can be assigned to critical dimensions, while minor dimensions can be appropriately relaxed. The total error accumulation is calculated as follows:

[0159]

[0160] Where m is the number of virtual joints, ΔX total The total error

[0161] When virtual joints are introduced, the position and motion trajectory of each virtual joint can also be visualized through 3D modeling software to help designers better understand and optimize the assembly process.

[0162] After the total error accumulation is obtained, the tolerance allocation can be optimized by adjusting the Jacobian matrix parameters to ensure that all components can be correctly assembled within the new tolerance range and meet the functional requirements.

[0163] It should be noted that in order to find the best tolerance allocation solution, the parameters of the Jacobian matrix can be adjusted and an iterative algorithm can be used to minimize the total assembly error or maximize production efficiency. The optimization objective function is expressed as follows:

[0164]

[0165] Among them, Y target is the most desired assembly result, Y i is a series of assembly results obtained from the Monte Carlo simulation, N is the total number of critical dimensions, and the optimal tolerance allocation solution is found by continuously adjusting the Jacobian matrix parameters so that E reaches the minimum value.

[0166] In this embodiment, when the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is calculated based on the variance of the assembly dimension chain; the product assembly tolerance value is allocated to each component ring through a preset allocation strategy to obtain the tolerance of each component ring, and the product assembly is completed according to the tolerance of each component ring. The tolerance of each component ring is calculated as follows: Among them, a i is the proportion of each component ring’s contribution to the total variance, σ i is the total variance, CT i is the tolerance of each component ring, and CT is the product assembly tolerance value. By calculating and allocating the tolerance values ​​of each component ring, it is ensured that the assembly result of the final product meets the design requirements. This method can accurately control the dimensional variation of each component, thereby improving the overall product quality.

[0167] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the product assembly method based on the tolerance semantic representation model of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0168] This application also provides a product assembly device based on a tolerance semantic representation model, please refer to Figure 4 , the product assembly device based on the tolerance semantic representation model includes:

[0169] The acquisition module 10 is used to acquire the part feature information of the product to be assembled during the product assembly stage.

[0170] The establishing module 20 is used to establish a tolerance propagation model according to the part feature information.

[0171] The establishing module 20 is further used to establish a tolerance semantic representation model according to the tolerance propagation model.

[0172] The generating module 30 is used to generate the assembly dimension chain of the product to be assembled according to the tolerance semantic representation model.

[0173] The analysis module 40 is used to perform tolerance analysis on the assembly dimension chain using a Monte Carlo simulation method to obtain a probability distribution of assembly results.

[0174] The tolerance synthesis module 50 is used to obtain the product assembly tolerance value based on the assembly dimension chain when the probability distribution of the assembly result meets the design requirements, and to perform tolerance synthesis on the product assembly tolerance value through a preset allocation strategy to obtain the tolerance of each component ring, and complete the product assembly according to the tolerance of each component ring.

[0175] The product assembly device based on the tolerance semantic representation model provided by the present application adopts the product assembly method based on the tolerance semantic representation model in the above-mentioned embodiment, which can solve the technical problems of low accuracy and efficiency of product assembly. Compared with the prior art, the beneficial effects of the product assembly device based on the tolerance semantic representation model provided by the present application are the same as the beneficial effects of the product assembly method based on the tolerance semantic representation model provided by the above-mentioned embodiment, and the other technical features of the product assembly device based on the tolerance semantic representation model are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0176] In one embodiment, the establishment module 20 is further used to obtain basic geometric parameters, size restrictions, relative positional relationships between parts, and matching types of parts according to the part feature information, wherein the basic geometric parameters are represented by a set of variables G={g1, g2, ..., gn}, wherein each gi represents a geometric attribute, and the geometric attributes include diameter and length; the relative positional relationship is represented by a coordinate transformation matrix T, T ij represents the position and direction of the i-th part relative to the j-th part; the fit type is determined by the clearance fit parameter C or the interference fit parameter I, respectively, by the formula C = D hole -D shaft and I = D shaft -D hole Indicates that D hole and D shaft are the nominal diameters of the hole and shaft, respectively;

[0177] A mathematical model is established based on the basic geometric parameters, the size restrictions, the relative position relationship and the matching type to obtain a tolerance propagation model, wherein for the basic geometric parameters, a normal distribution model is used. where gi is a geometric property, σ gi is the standard deviation, and the relative position relationship is expressed by the matrix equation P i =T ij *Pj Modeling, where P i and P j are the position vectors of part i and part j respectively.

[0178] In one embodiment, the establishment module 20 is also used to construct a knowledge graph corresponding to different tolerance types; extract the tolerance characteristics of the product to be assembled and the target tolerance type based on the part feature information; map the tolerance characteristics into a corresponding tolerance relationship network through the tolerance propagation model; search for the corresponding tolerance semantic representation in the knowledge graph corresponding to the target tolerance type; construct an influence relationship matrix based on the tolerance semantic representation and the tolerance relationship network; determine the assembly accuracy influence path between different sizes based on the influence relationship matrix; and construct a tolerance semantic representation model based on the assembly accuracy influence path between different sizes.

[0179] In one embodiment, the generating module 30 is further used to predict the critical dimension path affecting the assembly accuracy based on the tolerance semantic representation model, wherein the tolerance semantic representation model is:

[0180]

[0181] Among them, K ij The key dimension D i To the critical dimension D j The critical dimension path of g is the function of the influence of the dimension feature on the assembly accuracy, W ij The key dimension D i To the critical dimension D j The influence weight of A ij The key dimension D i For key dimension D j The degree of influence, T i The key dimension D i Tolerance, D i is the i-th key dimension, Dj is the j-th key dimension, max(T 1 ,T 2 ,...,T n ) represents a set of tolerances T 1 ,T 2 ,...,T n The maximum value in T n The key dimension D n Tolerance;

[0182] Arranging the key dimensions in order based on the key dimension path to form a target dimension chain diagram;

[0183] Calculate the cumulative error in the dimensional chain based on the target dimensional chain diagram;

[0184] The cumulative error is evaluated, and when the cumulative error meets the evaluation requirements, an assembly dimension chain of the product to be assembled is generated.

[0185] In one embodiment, the analysis module 40 is further used to obtain the critical dimensions and tolerance range of each part in the assembly dimension chain using a Monte Carlo simulation method;

[0186] Determining a probability distribution model according to a tolerance range of the critical dimension;

[0187] Randomly generate a set of values ​​according to the probability distribution model, wherein the total number of value sets is N, and for each size di, generate a set of values ​​{di,1,di,2,...,di,N} of size N;

[0188] Calculate assembly results based on the sequence relationship in the dimension chain diagram;

[0189] A probability distribution of the assembly results is constructed based on the data of the assembly results.

[0190] In one embodiment, the tolerance synthesis module 50 is further used to calculate the product assembly tolerance value based on the variance of the assembly dimension chain when the probability distribution of the assembly result meets the design requirements;

[0191] By using a preset allocation strategy, the product assembly tolerance value is allocated to each component ring to obtain the tolerance of each component ring. The product assembly is completed according to the tolerance of each component ring. The tolerance of each component ring is calculated as follows:

[0192]

[0193] Among them, a i is the proportion of each component ring’s contribution to the total variance, σ i is the total variance, CT i is the tolerance of each component ring, and CT is the product assembly tolerance value.

[0194] In one embodiment, the apparatus further comprises an adjustment module, the adjustment module being used to determine a target key connection point in the assembly dimension chain as a virtual joint;

[0195] Defining a Jacobian matrix for the virtual joint, wherein the Jacobian matrix represents the influence of the virtual joint position on other dimensions;

[0196] The influence of the virtual joint position on the assembly dimension chain is calculated based on the Jacobian matrix, and the calculation is as follows:

[0197] ΔX j =J j *δ j

[0198] Among them, J j is the Jacobian matrix, δ j is the change caused by virtual joint j, ΔX j is the influence of the virtual joint position on the assembly dimension chain;

[0199] Calculate the total error accumulation based on the impacts;

[0200] The parameters in the Jacobian matrix are adjusted based on the total error accumulation to optimize the total error, and the product assembly is adjusted based on the optimized total error.

[0201] The present application provides a product assembly device based on a tolerance semantic representation model, and the product assembly device based on the tolerance semantic representation model includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the product assembly method based on the tolerance semantic representation model in the above-mentioned embodiment one.

[0202] Reference below Figure 5 , which shows a schematic diagram of a product assembly device based on a tolerance semantic representation model suitable for implementing an embodiment of the present application. The product assembly device based on a tolerance semantic representation model in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The product assembly equipment based on the tolerance semantic representation model shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0203] like Figure 5As shown, the product assembly equipment based on the tolerance semantic representation model may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 to the RAM (Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the product assembly equipment based on the tolerance semantic representation model are also stored. The processing device 1001, the ROM 1002 and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, an LCD (Liquid Crystal Display), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the product assembly equipment based on the tolerance semantic representation model to communicate with other equipment wirelessly or by wire to exchange data. Although the figure shows a product assembly equipment based on the tolerance semantic representation model with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0204] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0205] The product assembly equipment based on the tolerance semantic representation model provided by the present application adopts the product assembly method based on the tolerance semantic representation model in the above-mentioned embodiment, which can solve the technical problems of low accuracy and efficiency of product assembly. Compared with the prior art, the beneficial effects of the product assembly equipment based on the tolerance semantic representation model provided by the present application are the same as the beneficial effects of the product assembly method based on the tolerance semantic representation model provided by the above-mentioned embodiment, and the other technical features in the product assembly equipment based on the tolerance semantic representation model are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0206] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0207] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0208] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the product assembly method based on the tolerance semantic representation model in the above-mentioned embodiment.

[0209] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory, Erasable Programmable Read Only Memory or Flash Memory), optical fiber, CD-ROM (CD-Read Only Memory, portable compact disk read-only memory), optical storage device, magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0210] The computer-readable storage medium may be included in the product assembly device based on the tolerance semantic representation model; or may exist independently without being assembled into the product assembly device based on the tolerance semantic representation model.

[0211] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a product assembly device based on a tolerance semantic representation model, the product assembly device based on the tolerance semantic representation model: in the product assembly stage, obtains part feature information of the product to be assembled; establishes a tolerance propagation model according to the part feature information; establishes a tolerance semantic representation model according to the tolerance propagation model; generates an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model; uses the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain to obtain a probability distribution of assembly results; when the probability distribution of the assembly results meets the design requirements, obtains a product assembly tolerance value based on the assembly dimension chain, and performs tolerance synthesis on the product assembly tolerance value through a preset allocation strategy to obtain the tolerances of each component ring, and completes the product assembly according to the tolerances of each component ring.

[0212] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or it can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0213] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0214] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0215] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned product assembly method based on the tolerance semantic representation model, and can solve the technical problems of low accuracy and efficiency of product assembly. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the product assembly method based on the tolerance semantic representation model provided by the above-mentioned embodiment, and will not be repeated here.

[0216] The present application also provides a computer program product, including a computer program, which implements the steps of the product assembly method based on the tolerance semantic representation model as described above when the computer program is executed by a processor.

[0217] The computer program product provided by the present application can solve the technical problems of low accuracy and efficiency of product assembly. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the product assembly method based on the tolerance semantic representation model provided by the above embodiment, and will not be described in detail here.

[0218] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A product assembly method based on a tolerance semantic representation model, characterized in that: Product assembly methods based on tolerance semantic representation models include: In the product assembly stage, obtain the part feature information of the product to be assembled; Establishing a tolerance propagation model according to the part feature information; Establishing a tolerance semantic representation model based on the tolerance propagation model; Generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model; Using the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain, and obtain a probability distribution of assembly results; When the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is obtained based on the assembly dimension chain, and the product assembly tolerance value is tolerance integrated through a preset allocation strategy to obtain the tolerance of each component ring, and the product assembly is completed according to the tolerance of each component ring.

2. The method according to claim 1, characterized in that The step of establishing a tolerance propagation model according to the part feature information comprises: According to the part feature information, the basic geometric parameters, size restrictions, relative position relationship between the parts and the matching type of the parts are obtained, wherein the basic geometric parameters are represented by a set of variables G={g1, g2, ..., gn}, wherein each gi represents a geometric attribute, and the geometric attributes include diameter and length; the relative position relationship is represented by a coordinate transformation matrix T, T ij represents the position and direction of the i-th part relative to the j-th part; the fit type is determined by the clearance fit parameter C or the interference fit parameter I, respectively, by the formula C = D hole -D shaft and I = D shaft -D hole Indicates that D hole and D shaft are the nominal diameters of the hole and shaft, respectively; A mathematical model is established based on the basic geometric parameters, the size restrictions, the relative position relationship and the matching type to obtain a tolerance propagation model, wherein for the basic geometric parameters, a normal distribution model is used. where gi is a geometric property, σ gi is the standard deviation, and the relative position relationship is expressed by the matrix equation P i =T ij *P j Modeling, where P i and P j are the position vectors of part i and part j respectively.

3. The method according to claim 1, characterized in that The step of establishing a tolerance semantic representation model according to the tolerance propagation model comprises: Construct knowledge graphs corresponding to different tolerance types; Extracting tolerance features and target tolerance types of products to be assembled based on the part feature information; Mapping the tolerance feature into a corresponding tolerance relationship network through the tolerance propagation model; Searching for a corresponding tolerance semantic representation in the knowledge graph corresponding to the target tolerance type; Constructing an influence relationship matrix according to the tolerance semantic representation and the tolerance relationship network; Determine the assembly accuracy influencing path between different sizes based on the influencing relationship matrix; A tolerance semantic representation model is constructed based on the assembly accuracy impact path between the different sizes.

4. The method according to claim 1, characterized in that The step of generating the assembly dimension chain of the product to be assembled according to the tolerance semantic representation model comprises: The critical dimension path affecting assembly accuracy is predicted based on the tolerance semantic representation model, wherein the tolerance semantic representation model is: Among them, K ij The key dimension D i To the critical dimension D j The critical dimension path of g is the function of the influence of the dimension feature on the assembly accuracy, W ij The key dimension D i To the critical dimension D j The influence weight of A ij The key dimension D i For key dimension D j The degree of influence, T i The key dimension D i Tolerance, D i is the i-th key dimension, Dj is the j-th key dimension, max(T1,T2,...,T n ) represents a set of tolerances T1, T2, ..., T n The maximum value in T n The key dimension D n Tolerance; Arranging the key dimensions in order based on the key dimension path to form a target dimension chain diagram; Calculate the cumulative error in the dimensional chain based on the target dimensional chain diagram; The cumulative error is evaluated, and when the cumulative error meets the evaluation requirements, an assembly dimension chain of the product to be assembled is generated.

5. The method according to claim 1, characterized in that The step of using the Monte Carlo simulation method to perform tolerance analysis on the assembly dimension chain to obtain a probability distribution of the assembly result comprises: Using Monte Carlo simulation method to obtain the key dimensions and tolerance range of each part in the assembly dimension chain; Determining a probability distribution model according to a tolerance range of the critical dimension; Randomly generate a set of values ​​according to the probability distribution model, wherein the total number of value sets is N, and for each size di, generate a set of values ​​{di,1,di,2,...,di,N} of size N; Calculate assembly results based on the sequence relationship in the dimension chain diagram; A probability distribution of the assembly results is constructed based on the data of the assembly results.

6. The method according to claim 1, characterized in that When the probability distribution of the assembly result meets the design requirements, the product assembly tolerance value is obtained based on the assembly dimension chain, and the product assembly tolerance value is subjected to tolerance synthesis through a preset allocation strategy to obtain the tolerance of each component ring. The step of completing the product assembly according to the tolerance of each component ring includes: When the probability distribution of the assembly result meets the design requirements, calculating the product assembly tolerance value based on the variance of the assembly dimension chain; By using a preset allocation strategy, the product assembly tolerance value is allocated to each component ring to obtain the tolerance of each component ring. The product assembly is completed according to the tolerance of each component ring. The tolerance of each component ring is calculated as follows: Among them, a i is the proportion of each component ring’s contribution to the total variance, σ i is the total variance, CT i is the tolerance of each component ring, and CT is the product assembly tolerance value.

7. The method according to claim 1, characterized in that The method further comprises: determining a target key connection point in the assembly dimension chain as a virtual joint; Defining a Jacobian matrix for the virtual joint, wherein the Jacobian matrix represents the influence of the virtual joint position on other dimensions; The influence of the virtual joint position on the assembly dimension chain is calculated based on the Jacobian matrix, and the calculation is as follows: ΔX j =J j *δ j Among them, J j is the Jacobian matrix, δ j is the change caused by virtual joint j, ΔX j is the influence of the virtual joint position on the assembly dimension chain; Calculate the total error accumulation based on the impacts; The parameters in the Jacobian matrix are adjusted based on the total error accumulation to optimize the total error, and the product assembly is adjusted based on the optimized total error.

8. A product assembly device based on a tolerance semantic representation model, characterized in that: The device comprises: An acquisition module is used to obtain the part feature information of the product to be assembled during the product assembly stage; An establishment module is used to establish a tolerance propagation model according to the part feature information; The establishment module is further used to establish a tolerance semantic representation model based on the tolerance propagation model; A generating module, used for generating an assembly dimension chain of the product to be assembled according to the tolerance semantic representation model; An analysis module, used for performing tolerance analysis on the assembly dimension chain using a Monte Carlo simulation method to obtain a probability distribution of assembly results; The tolerance synthesis module is used to obtain the product assembly tolerance value based on the assembly dimension chain when the probability distribution of the assembly result meets the design requirements, and to perform tolerance synthesis on the product assembly tolerance value through a preset allocation strategy to obtain the tolerance of each component ring, and complete the product assembly according to the tolerance of each component ring.

9. A product assembly device based on a tolerance semantic representation model, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the product assembly method based on the tolerance semantic representation model as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the product assembly method based on the tolerance semantic representation model as described in any one of claims 1 to 7 are implemented.

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