Parameter-driven SolidWorks automatic modeling method

Abstract

The invention provides a SolidWorks automatic modeling method based on parameter driving, and belongs to the technical field of automatic modelling. Structural parameter data are read from an Excel table, a mapping relation between the structural parameter data and SolidWorks feature variables is established, a parameterized three-dimensional part model is automatically generated by using a feature tree traversal algorithm, and the modeling efficiency of SolidWorks is improved. The method comprises the following steps: constructing an assembly dependency relationship graph by extracting geometric constraint information and assembly interface information, automatically rearranging an assembly sequence by adopting an assembly sequence optimization algorithm based on a graph theory when the assembly complexity reaches a preset range, inserting parts into an assembly environment according to the optimized assembly sequence, and setting a matching constraint relationship. The assembly quality is ensured through interference volume detection and an automatic adjustment mechanism, a complete three-dimensional assembly body model is finally generated, and the technical problems of assembly conflicts and low efficiency caused by unreasonable assembly sequence planning in the complex assembly body modeling process are solved.

Description

Technical Field

[0001] This invention belongs to the field of automatic modeling technology, and more specifically, relates to a parameter-driven automatic modeling method for SolidWorks. Background Technology

[0002] In the fields of mechanical design and manufacturing, 3D modeling software such as SolidWorks is widely used in product design and assembly modeling. Traditional assembly modeling mainly relies on the designer's experience and manual operation, constructing a complete assembly model by inserting parts one by one and setting mate constraints. This method can meet basic needs when dealing with simple products, but it shows obvious limitations when modeling complex electrical equipment such as transformers. In the current process of complex assembly modeling, due to the large number of parts and complex nested assembly relationships, traditional manual planning of the assembly sequence is often based on the designer's subjective judgment, lacking scientific decision-making basis, and easily leading to problems such as assembly conflicts, repetitive operations, and assembly deadlocks. At the same time, an unreasonable arrangement of the assembly sequence can hinder subsequent assembly operations, requiring frequent disassembly and reassembly, seriously affecting modeling efficiency and assembly quality. In other words, existing technologies suffer from technical problems of assembly conflicts and low efficiency caused by unreasonable assembly sequence planning in the process of complex assembly modeling. Summary of the Invention

[0003] In view of this, the present invention provides a parameter-driven automatic modeling method for SolidWorks, which can solve the technical problems of assembly conflict and low efficiency caused by unreasonable assembly sequence planning in the process of complex assembly modeling in the prior art.

[0004] This invention is implemented as follows: It provides a parameter-driven automatic modeling method in SolidWorks that reads a structural parameter data table from an Excel spreadsheet. This data table contains geometric dimension parameters, material property parameters, and assembly position parameters. The structural parameter data table is categorized and stored according to worksheet name. SolidWorks software is launched and an API communication connection is established. The 3D modeling work path and part template file path are set, and the parameter transfer interface is initialized. A mapping relationship is established between the geometric dimension parameters and the feature dimension variables in the SolidWorks part template, generating a parameter mapping lookup table. SolidWorks is then invoked. The API interface opens the part template file, locates the feature dimension variables using a feature tree traversal algorithm, assigns the geometric dimension parameter values ​​to the corresponding feature dimension variables according to the parameter mapping table, performs model reconstruction to update the 3D geometry, generates a parameterized 3D part model, and saves it as an SLDPRT format file; extracts the geometric constraint information and assembly interface information of each part model, constructs an assembly dependency graph, calculates the assembly complexity value, and when the assembly complexity value falls within a closed interval, uses a graph theory-based assembly sequence optimization algorithm to rearrange the assembly order; inserts each part model into the assembly environment according to the assembly order, sets the mating constraint relationships between parts according to the assembly position parameters, and generates a complete 3D assembly model; performs geometric interference detection on the 3D assembly model, calculates the interference volume value, and when the interference volume value exceeds the tolerance threshold, automatically adjusts the assembly position parameters of the relevant parts and re-executes the assembly steps.

[0005] The geometric dimension parameters refer to the numerical information that defines the geometric features of the part's shape, including length, width, thickness, hole diameter, chamfer radius, and stretching depth; the material property parameters refer to the physical properties of the part's material, including density, elastic modulus, Poisson's ratio, yield strength, and thermal conductivity; and the assembly position parameters refer to the spatial position constraints of the part in the assembly, including the coordinates of the assembly reference plane, the coordinates of the assembly origin, the rotation angle, and the offset distance.

[0006] The feature size variable refers to the variable parameter that controls the geometry in the SolidWorks 3D model. The feature size variable is directly related to the sketch size, extrusion depth, cut volume, and fillet radius modeling features. The parameter mapping table refers to the data table that establishes a one-to-one correspondence between the external parameter name and the feature size variable.

[0007] The feature tree traversal algorithm refers to the calculation method of accessing each feature node in the model according to the SolidWorks feature tree structure, and locating feature objects containing size parameters through recursive traversal; the model reconstruction operation refers to the process of SolidWorks software recalculating the three-dimensional geometry based on the modified parameters, including sketch solving, feature updating and geometry generation.

[0008] The geometric constraint information refers to the spatial positional constraints between parts due to shape fit, including surface coincidence constraints, axis alignment constraints, distance fixed constraints, and angle limitation constraints; the assembly interface information refers to the geometric feature information of the part surface used to connect with other parts, including the position of the assembly hole, the normal vector of the assembly surface, the direction of the assembly axis, and the assembly contact area.

[0009] The assembly dependency graph refers to a data structure that represents the assembly sequence of parts in the form of a directed acyclic graph. Nodes in the graph represent assembly states, and edges represent assembly operations. The assembly complexity value refers to a quantitative index of assembly difficulty calculated by combining the number of parts, the number of constraints, and the depth of assembly levels.

[0010] The graph-based assembly sequence optimization algorithm refers to a computational method that determines the optimal assembly path through topological sorting and heuristic search. The graph-based assembly sequence optimization algorithm automatically optimizes the assembly sequence by modeling the assembly process as a directed acyclic graph, analyzing the geometric constraint information and assembly interface information between parts, constructing a complete assembly dependency network, and identifying critical paths and parallel operation opportunities in the assembly process.

[0011] The graph theory-based assembly sequence optimization algorithm automatically determines conflict-free assembly sequences through topological sorting, identifies parallel assembly operations, calculates assembly complexity based on geometric dimension parameters, assembly position parameters, and material property parameters, and dynamically adjusts the assembly strategy when the complexity exceeds a preset range.

[0012] The graph theory-based assembly sequence optimization algorithm considers the actual assembly constraints such as tool accessibility, operating space, and gravity influence to generate an implementable assembly process. The parameters applied to the algorithm include the number of parts, geometric constraint information, assembly interface information, assembly position parameters, assembly complexity value, and interference volume value.

[0013] The aforementioned mate constraint relationship refers to the spatial constraint connection method between parts in the assembly, including coincident mate, distance mate, coaxial mate, parallel mate and perpendicular mate; the interference volume value refers to the geometric volume of the overlapping part of different parts in the assembly, which is calculated through three-dimensional Boolean operation; the tolerance threshold refers to the maximum interference volume limit allowed by the system.

[0014] Furthermore, before reading the structural parameter data table, the process includes establishing a standardized format for the Excel spreadsheet file, setting the data structure for geometric dimension parameters, material property parameters, and assembly position parameters, and configuring the worksheet naming rules and parameter field definitions.

[0015] Furthermore, the steps for starting the SolidWorks software also include verifying SolidWorks software version compatibility, checking API interface availability, configuring modeling environment parameters, setting file save paths and naming rules, and initializing error handling mechanisms.

[0016] Furthermore, the step of generating the parameter mapping table also includes verifying the matching between the external parameter name and the feature size variable, checking the consistency of the parameter data type, establishing a parameter change tracking record, and setting parameter range constraints.

[0017] Furthermore, the steps of performing model reconstruction operations also include detecting feature reconstruction status, recording error information during the reconstruction process, verifying the validity of geometry, updating part mass properties and inertia parameters, and saving reconstruction history.

[0018] Furthermore, the steps of constructing the assembly dependency graph also include analyzing the spatial relationships between parts, identifying the priority order of assembly constraints, calculating the feasibility score of the assembly path, and establishing a time estimation model for assembly operations.

[0019] Furthermore, the steps of performing geometric interferometry detection also include setting the accuracy parameters for interferometry detection, classifying and identifying different types of interference problems, recording the coordinate information of the interference position, calculating the minimum adjustment amount required to eliminate interference, and establishing a solution library for interference problems.

[0020] This invention solves the technical problems of assembly conflicts and inefficiency caused by unreasonable assembly sequence planning in the modeling of complex assemblies by constructing an assembly dependency graph and automatically determining the optimal assembly sequence based on graph theory optimization algorithms. This invention constructs an assembly dependency network in the form of a directed acyclic graph by extracting geometric constraint information and assembly interface information from the part model. It automatically identifies critical paths and parallel operation opportunities in the assembly process using topological sorting and heuristic search algorithms, avoiding the subjectivity and blindness of traditional manual planning and ensuring the scientific validity and feasibility of the assembly sequence. Furthermore, through dynamic evaluation of assembly complexity and interference detection mechanisms, potential conflicts can be detected and resolved in real time during the assembly process, ensuring the smooth progress of assembly operations. In summary, this invention solves the technical problems of assembly conflicts and inefficiency caused by unreasonable assembly sequence planning in the modeling of complex assemblies mentioned in the background art. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a statistical chart showing the parameter distribution of the core assembly in Example 2.

[0023] Figure 3 This is a comparison chart of the assembly sequence optimization effects in Example 2. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0025] like Figure 1 The diagram shown is a flowchart of a parameter-driven automatic modeling method for SolidWorks provided by this invention. This method includes the following steps: S01. Read the structural parameter data table from the Excel spreadsheet file. The structural parameter data table includes geometric dimension parameters, material property parameters, and assembly position parameters. Store the structural parameter data table according to the worksheet name. S02. Start SolidWorks software and establish API communication connection, set the 3D modeling work path and part template file path, and initialize parameter transfer interface; S03. Establish a mapping relationship between the geometric dimension parameters and the feature dimension variables in the SolidWorks part template, and generate a parameter mapping lookup table; S04. Call the SolidWorks API interface to open the part template file, locate the feature dimension variable through the feature tree traversal algorithm, assign the geometric dimension parameter value to the corresponding feature dimension variable according to the parameter mapping table, perform model reconstruction operation to update the three-dimensional geometry, generate a parametric three-dimensional part model and save it as an SLDPRT format file. S05. Extract the geometric constraint information and assembly interface information of each part model, construct the assembly dependency graph, calculate the assembly complexity value, and when the assembly complexity value ∈ [0.8, 1.0], enable the graph theory-based assembly sequence optimization algorithm to rearrange the assembly order. S06. Insert each part model into the assembly environment according to the assembly sequence, set the mating constraint relationship between parts according to the assembly position parameters, and generate a complete three-dimensional assembly model. S07. Perform geometric interference detection on the three-dimensional assembly model, calculate the interference volume value, and when the interference volume value is greater than the tolerance threshold of 0.1 cubic millimeters, automatically adjust the assembly position parameters of the relevant parts and re-execute step S06.

[0026] Among these, geometric dimensional parameters refer to the numerical information defining the geometric features of a part's shape, including length, width, thickness, hole diameter, chamfer radius, and drawing depth. Material property parameters refer to the physical properties of the part's material, including density, elastic modulus, Poisson's ratio, yield strength, and thermal conductivity. Assembly position parameters refer to the spatial positional constraints of the part within the assembly, including the coordinates of the assembly datum plane, the coordinates of the assembly origin, rotation angle, and offset distance.

[0027] In this context, feature size variables refer to the variable parameters that control the geometry in a SolidWorks 3D model. These feature size variables are directly related to modeling features such as sketch dimensions, extrusion depth, cut volume, and fillet radius. The parameter mapping table is a data table that establishes a one-to-one correspondence between external parameter names and the feature size variables. The feature tree traversal algorithm is a calculation method that accesses each feature node in the model according to the SolidWorks feature tree structure, locating feature objects containing size parameters through recursive traversal. Model reconstruction operation refers to the process by which SolidWorks software recalculates the 3D geometry based on the modified parameters, including sketch solving, feature updating, and geometry generation.

[0028] Geometric constraint information refers to the spatial positional constraints between parts due to their shape fit, including surface coincidence constraints, axis alignment constraints, fixed distance constraints, and angle limitation constraints. Assembly interface information refers to the geometric feature information of a part's surface used to connect with other parts, including the location of assembly holes, the normal vector of the assembly surface, the direction of the assembly axis, and the assembly contact area. An assembly dependency graph is a data structure representing the assembly sequence of parts in the form of a directed acyclic graph, where nodes represent assembly states and edges represent assembly operations. Assembly complexity is a quantitative index of assembly difficulty calculated by considering the number of parts, the number of constraint relationships, and the depth of the assembly hierarchy. A graph-based assembly sequence optimization algorithm is a computational method that determines the optimal assembly path through topological sorting and heuristic search.

[0029] Among them, the mate constraint relationship refers to the spatial constraint connection method between parts in an assembly, including coincident mate, distance mate, coaxial mate, parallel mate, and perpendicular mate. The interference volume value refers to the geometric volume of the overlapping part of different parts in the assembly, which is calculated through three-dimensional Boolean operations. The tolerance threshold refers to the maximum allowable interference volume limit of the system.

[0030] This graph theory-based assembly sequence optimization algorithm automatically optimizes the assembly sequence by modeling the assembly process as a directed acyclic graph. The algorithm analyzes the geometric constraints and assembly interface information between parts, constructs a complete assembly dependency network, and identifies critical paths and parallel operation opportunities in the assembly process. In the assembly of complex power equipment such as transformers, there are numerous parts with complex nested assembly relationships. Traditional manual planning of the assembly sequence is inefficient and prone to assembly conflicts. The proposed algorithm automatically determines conflict-free assembly sequences through topological sorting and identifies parallel assembly operations, significantly improving assembly efficiency. The algorithm is suitable for this scheme because the part models generated by parameter-driven modeling have standardized geometric constraint and assembly interface information, facilitating automatic extraction of constraint relationships. The algorithm calculates the assembly complexity based on geometric dimension parameters, assembly position parameters, and material property parameters, dynamically adjusting the assembly strategy when the complexity exceeds a preset range. The algorithm considers practical assembly constraints such as tool accessibility, operating space, and gravity effects, generating feasible assembly processes to avoid theoretically feasible but practically unenforceable assembly schemes, providing optimized operation sequence guidance for automated assembly. The parameters applied to the algorithm include the number of parts, geometric constraint information, assembly interface information, assembly position parameters, assembly complexity value, and interference volume value.

[0031] The specific implementation methods of the above steps are described in detail below.

[0032] The specific implementation of step S01 involves establishing a multi-level parameter data reading and classification storage mechanism. First, an Excel spreadsheet file is opened using object linking embedded database connection technology. Structural parameter data is read row by row using a cell addressing algorithm. This algorithm, based on a row and column index coordinate system, enables precise positioning and data extraction from any cell in the spreadsheet. The system automatically classifies the read parameters into three categories according to a data type recognition algorithm: geometric dimension parameters, material property parameters, and assembly position parameters. This classification process combines keyword matching and numerical range judgment. For geometric dimension parameters, the system identifies dimension identifiers such as length, width, and thickness, with numerical ranges typically set between 0.1 mm and 10000 mm. Material property parameters are identified based on physical quantity unit characteristics, such as density in kilograms per cubic meter and elastic modulus in gigapascals, with an accuracy threshold set above 95%. Assembly position parameters are identified through coordinate and angle value characteristics, with coordinate ranges set between -10000 mm and 10000 mm, and angle ranges set between 0 degrees and 360 degrees. The system establishes a categorized storage index table based on worksheet names and uses a hash table data structure for fast data retrieval. Each worksheet corresponds to an independent parameter storage space, ensuring that parameter data from different product configurations is isolated from each other. The purpose of this step is to establish a standardized parameter input interface, providing a reliable data source for subsequent automatic modeling.

[0033] The specific implementation of step S02 involves establishing a stable application programming interface (API) communication mechanism and a working environment initialization process. The system launches the SolidWorks application through the Component Object Model (COM) interface technology. This technology, based on inter-process communication, enables data exchange and instruction transmission between external programs and SolidWorks software. The interface connection employs a multi-factor authentication mechanism, including software version compatibility checks, license status verification, and system resource availability assessment. The connection timeout threshold is set to 30 seconds, and the system automatically retryes three times after a connection failure. The working path is set using an absolute path, and path validity is verified through the file system interface to ensure the target directory has read and write permissions. The disk space availability threshold is set to at least 1 gigabyte. The part template file path configuration uses a template matching algorithm; the system automatically identifies template files that meet the modeling requirements. Template files must contain a complete feature tree structure and predefined dimension variables. The parameter transfer interface initialization process establishes a memory buffer and data format conversion mechanism. The buffer capacity is set to 100 megabytes, supporting the simultaneous processing of more than 1000 parameter variables. This interface uses an asynchronous communication mode to avoid program blocking during large data transfers. The purpose of this step is to establish a reliable software communication environment, ensuring that parameter data can be accurately transmitted to the modeling engine.

[0034] The specific implementation of step S03 involves establishing an intelligent mapping mechanism between parameter names and feature variables. The system uses a combination of string matching and semantic analysis algorithms to establish the mapping relationship. The string matching algorithm uses edit distance calculation to achieve fuzzy matching, with the edit distance threshold set to within 2 characters. The semantic analysis algorithm identifies the meaning of parameters through a predefined engineering terminology dictionary, which contains over 5000 standard engineering terms, covering commonly used dimension identifiers in mechanical design. The system first extracts the names and attribute information of all feature dimension variables from the SolidWorks part template, and uses a feature tree depth-first search algorithm to traverse the model structure, with a search depth limited to 20 levels. For each external geometric dimension parameter, the system calculates its similarity score with all feature dimension variables. The similarity calculation uses a weighted scoring mechanism, with name similarity weighting at 60% and numerical range similarity weighting at 40%. A mapping relationship is established when the similarity score exceeds 80%; parameters with a similarity score below 80% require manual confirmation of the mapping relationship. The parameter mapping lookup table is stored using a key-value pair data structure, supporting both one-to-one and one-to-many mapping modes. One-to-many mapping is used to handle situations where the same parameter controls multiple feature variables. The system automatically detects mapping conflicts and generates warning messages. Conflict detection is based on parameter dependency analysis. The purpose of this step is to establish an accurate correspondence between parameters and model features, ensuring that external parameters can correctly drive the geometric changes of the 3D model.

[0035] The specific implementation of step S04 is to implement an automatic generation and update mechanism for parametric models. The system opens a preset part template file through the SolidWorks application interface. Template file loading is asynchronous to avoid system delays caused by large template files. The feature tree traversal algorithm uses a breadth-first search strategy. The system scans the feature tree structure layer by layer from the root node, identifying feature objects containing dimensional parameters. This algorithm can handle complex feature nesting and dependency relationships. Traversal efficiency is optimized through feature indexing, with an average traversal time controlled within 5 seconds. The parameter assignment process uses a batch update mode. The system batches external geometric dimension parameter values ​​to the corresponding feature dimension variables according to the parameter mapping table, maintaining numerical precision to three decimal places. After assignment, the system performs a model reconstruction operation, implemented based on the SolidWorks geometry solver engine, including three stages: sketch constraint solving, feature geometry calculation, and model display update. Sketch constraint solving uses a numerical iterative algorithm to handle over-constraint and under-constraint cases, with the iteration convergence accuracy set to [value missing]. Millimeters. Feature geometry calculations generate the final geometry through 3D Boolean operations, with the Boolean operation precision set to [precision value missing]. The generated parametric 3D part model is automatically saved as an SLDPRT format file. The file name uses a combination of timestamps and parameter identifiers to ensure filename uniqueness. The purpose of this step is to convert external parameters into a concrete 3D geometric model, enabling a visual representation of the design parameters.

[0036] The specific implementation of step S05 involves establishing an assembly relationship analysis and sequence optimization mechanism. The system first extracts the geometric constraint information of each part model and uses a geometric feature recognition algorithm to automatically identify key geometric features such as assembly surfaces, assembly holes, and assembly shafts. Geometric feature recognition is based on surface geometry analysis and topological relationship analysis, with a recognition accuracy threshold set at 0.01 mm. The assembly interface information extraction process analyzes the geometric attributes of the part surfaces, such as normal vectors, curvature, and area, and uses machine learning algorithms to identify potential assembly connection areas, requiring an accuracy rate of over 90%. The assembly dependency graph is constructed using a directed acyclic graph data structure, where each node represents an assembly state, and the edge weights indicate the complexity of the assembly operation. The assembly complexity calculation comprehensively considers three factors: the number of parts, the number of constraints, and the assembly level depth. The calculation formula is based on a weighted summation method, with the number of parts accounting for 30%, the number of constraints accounting for 50%, and the assembly level depth accounting for 20%. When the assembly complexity value reaches the range of 0.8 to 1.0, the system activates a graph-based assembly sequence optimization algorithm to rearrange the assembly order. This optimization algorithm employs a strategy combining topological sorting and heuristic search. Topological sorting ensures that the dependencies between assembly operations are satisfied, while heuristic search finds the optimal assembly path through an evaluation function. The evaluation function considers factors such as assembly time, tool accessibility, and operational complexity, with a search depth limited to 1000 nodes. The purpose of this step is to analyze the complexity of the assembly process and optimize the assembly sequence, thereby improving assembly efficiency and success rate.

[0037] The specific implementation of step S06 involves establishing an automatic assembly environment construction and constraint relationship setting mechanism. The system imports each part model into the assembly environment sequentially according to the optimized assembly order. During the import process, a coordinate system alignment algorithm ensures the accuracy of the initial positions of the parts. Coordinate system alignment is based on the least squares method, determining the optimal alignment position by calculating the geometric center and principal axis direction of the part, with an alignment accuracy requirement of 0.01 mm. The assembly position parameter setting process establishes the fit constraint relationships between parts based on predefined assembly position parameter data using a constraint solving algorithm. Constraint types include five basic constraint forms: coincident fit, distance fit, coaxial fit, parallel fit, and perpendicular fit. Each constraint corresponds to a different geometric calculation method. Coincident fits achieve precise contact between two surfaces through surface normal vector calculation, while coaxial fits achieve the coincidence of the axes of two cylindrical surfaces through axis direction vector calculation. The constraint solving process uses a nonlinear optimization algorithm to handle position calculation problems under multiple constraint conditions, with the convergence criterion set as constraint error less than... The system automatically detects constraint conflicts and provides solutions; conflict detection is based on constraint satisfaction problem-solving theory. During assembly, the system monitors part position changes in real time, automatically correcting positions when deviations exceed 0.1 mm. After the complete 3D assembly model is generated, the system automatically performs assembly verification checks, including constraint integrity verification and geometric rationality verification. The purpose of this step is to combine independent part models into a complete assembly, ensuring that the positional relationships between parts meet design requirements.

[0038] The specific implementation of step S07 involves establishing a geometric interference detection and automatic adjustment mechanism. The system performs geometric interference detection on the complete 3D assembly model, using a 3D Boolean operation algorithm to calculate the overlapping area between different part entities. The interference detection algorithm is based on boundary representation, determining the interference range by analyzing the intersection lines and points on the part surfaces, with a detection accuracy set to 0.001 mm. The interference volume is calculated using a voxelization method, discretizing the interference region into tiny voxel units, and obtaining the total interference volume by accumulating the voxel volumes, with the voxel size set to 0.01 mm. The tolerance threshold is set to 0.1 cubic millimeters, determined based on manufacturing tolerances and assembly clearance requirements; minor interference below this threshold can be considered normal within the manufacturing error range. When the interference volume exceeds the tolerance threshold, the system activates an automatic adjustment mechanism, which uses an iterative optimization algorithm to adjust the assembly position parameters of the relevant parts. The adjustment algorithm is based on the gradient descent principle, determining the adjustment direction by calculating the partial derivative of the interference volume with respect to the assembly position parameters, with the adjustment step size controlled by an adaptive strategy, and an initial step size set to 0.1 mm. During the adjustment process, the system maintains other constraints unchanged to avoid local adjustments affecting the overall assembly structure. After the adjustment is complete, the system re-executes the assembly operation in step S06, generates a new assembly model, and performs interference detection again. This iterative process continues until the interference volume value drops below the tolerance threshold. The number of iterations is limited to 10; if this limit is exceeded, the system prompts for manual intervention. The purpose of this step is to ensure the geometric rationality of the assembly model and eliminate unreasonable interference between parts.

[0039] It should be noted that the key technical ideas of this invention are mainly reflected in four aspects: parameter-driven modeling mechanism, intelligent assembly sequence optimization, automatic detection and adjustment of geometric interference, and multi-level data mapping.

[0040] The parameter-driven modeling mechanism establishes an intelligent mapping relationship between external parameters and SolidWorks feature variables, enabling automated transfer of design parameters and batch generation of models. Compared to traditional manual modeling methods, this mechanism uses string matching and semantic analysis algorithms to automatically identify and map parameter names, avoiding the tedious process of manually modifying parameters one by one. Through feature tree traversal algorithms and batch parameter update technology, the system can simultaneously handle the automatic assignment of hundreds of design parameters and model reconstruction, significantly improving modeling efficiency and response speed for parameter modifications.

[0041] Intelligent assembly sequence optimization technology achieves automatic planning and optimization of assembly sequence by constructing an assembly dependency graph and calculating assembly complexity. Traditional assembly planning mainly relies on experience-based judgment, which is prone to assembly conflicts and inefficiency. This invention employs a graph theory-based optimization algorithm, ensuring the satisfaction of assembly dependencies through topological sorting, and combining it with heuristic search to find the optimal assembly path. It can automatically identify parallel assembly opportunities, significantly improving the assembly efficiency and success rate of complex assemblies.

[0042] The automatic geometric interference detection and adjustment mechanism achieves precise calculation of the interference volume through three-dimensional Boolean operations and voxelization, and automatically adjusts the assembly position by combining iterative optimization algorithms. Compared with traditional manual inspection and adjustment methods, this mechanism can automatically identify minute interference phenomena, automatically calculate the optimal adjustment direction through gradient descent algorithm, and eliminate geometric interference while maintaining the integrity of the constraint relationship, avoiding the tedious process of repeated manual adjustments.

[0043] Multi-level data mapping technology achieves automatic parameter classification and storage through keyword matching and numerical range judgment, establishing a standardized parameter input interface. Traditional parameter management methods typically use fixed-format data structures, which have poor adaptability. This invention achieves flexible parameter management through hash table data structures and categorized storage indexes, supporting isolated parameter storage for different product configurations, and improving the system's versatility and scalability.

[0044] The synergistic effect of these four key technological approaches constructs a complete parameter-driven automated modeling system. The parameter-driven mechanism provides rapid response capabilities to design changes, intelligent assembly optimization ensures efficient assembly of complex assemblies, geometric interference detection guarantees the geometric rationality of the model, and multi-level data mapping provides a flexible foundation for parameter management. Compared to traditional manual modeling and assembly planning methods, this collaborative system achieves full automation from design parameter input to 3D model generation, significantly reducing manual intervention and repetitive work, improving the response efficiency to design changes and the consistency of model quality, and providing effective technical support for the parametric design and rapid customized production of complex products.

[0045] Specifically, the principle of this invention is as follows: This invention can solve the technical problems of assembly conflicts and low efficiency caused by unreasonable assembly sequence planning in the modeling of complex assemblies. Its fundamental principle lies in transforming the complex constraint relationships of the assembly process into a computable graph theory model, and realizing automatic optimization of the assembly sequence through mathematical algorithms. Specifically, this invention establishes a precise mapping relationship between parameters and SolidWorks feature variables by reading standardized structural parameter data from Excel spreadsheets, ensuring that the geometric constraint information and assembly interface information of the part models have standardized and identifiable characteristics, providing a reliable data foundation for subsequent graph theory modeling. In the assembly sequence optimization stage, this invention abstracts the assembly dependencies between parts into a directed acyclic graph structure, where nodes represent assembly states and edges represent assembly operations. By analyzing the geometric constraint information of the parts, the sequential dependencies of the assembly order are identified, avoiding circular dependencies and assembly deadlock problems in the assembly process. At the same time, this invention introduces the assembly complexity value as an optimization trigger condition. When the assembly complexity reaches a preset range, the graph theory optimization algorithm is automatically activated. The conflict-free assembly sequence is determined through topological sorting, and the parallelizable assembly operations are identified through heuristic search, ensuring both the correctness of the assembly sequence and maximizing assembly efficiency. Furthermore, by combining interference volume detection and automatic adjustment mechanisms, this invention can detect geometric interference problems in real time during the assembly process and automatically optimize assembly parameters, forming a complete closed-loop control system that ensures the practical feasibility and effectiveness of assembly sequence optimization.

[0046] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0047] The specific implementation of step S01 involves establishing a multi-level parameter data reading and categorized storage mechanism. The system uses object linking embedded database connection technology to open the Excel spreadsheet file and reads the structural parameter data row by row using a cell addressing algorithm. This algorithm, based on a row and column index coordinate system, achieves precise positioning of any cell in the table. The cell address calculation formula is as follows: In the formula For the first Line number Linear address index of column cells, This represents the maximum number of columns in the table. and These are row indexes and column indexes, respectively. , The system creates a categorized storage index table based on worksheet names and uses a hash table data structure to achieve fast data retrieval. The hash function is... In the formula For hash value, The character encoding value of the worksheet name. Where is the hash table size. The data classification and recognition algorithm combines keyword matching and numerical range judgment. The classification accuracy calculation formula is: In the formula The percentage of classification accuracy. The number of parameters correctly classified. This represents the total number of parameters. Geometric dimension parameter identification is based on a numerical range judgment, with the judgment condition being... millimeters, in the formula These are the geometric dimension parameters. The accuracy threshold for material property parameter recognition is set to above 95%, i.e. In the formula To improve the accuracy of material property parameter identification. Assembly position parameters are identified through coordinate value features, with the coordinate range judgment condition being... Millimeters, angular range judgment condition is Degree, in the formula These are the coordinate values ​​of the assembly position. This refers to the assembly angle value.

[0048] The specific implementation of step S02 involves establishing a stable application programming interface (API) communication mechanism and a working environment initialization process. The system starts the SolidWorks application through Component Object Model (COM) interface technology. The stability evaluation formula for the interface connection is as follows: In the formula This represents the connection success rate as a percentage. Number of successful connections This represents the total number of connection attempts. The connection timeout condition is... seconds, in the formula This is the connection timeout period. The maximum number of retries after a connection failure is [number missing]. Next, in the formula This represents the number of retries. The disk space remaining verification condition is... gigabytes, in the formula This represents the available disk space capacity. The parameter passing interface memory buffer capacity is set to... Megabytes, supporting the processing of a number of parameter variables In the formula For the memory buffer capacity, This represents the number of parameter variables.

[0049] The specific implementation of step S03 involves establishing an intelligent mapping mechanism between parameter names and feature variables. The system uses string matching and semantic analysis algorithms to establish the mapping relationship, and dynamic programming is used to calculate the edit distance. The recursive relationship is as follows: In the formula For the th in the dynamic programming matrix Line number The element values ​​of the column, and They are strings respectively and The The and the first 1 character. Edit distance is 1. In the formula and They are strings respectively and The length of the distance. The edit distance threshold is set to... One character. The formula for calculating the similarity between parameters and feature variables is: In the formula To calculate the overall similarity score, The score represents the name similarity. The similarity score is based on the numerical range. Weighted by name similarity. This represents the similarity weight within a numerical range. The conditions for establishing the mapping relationship are... Parameters below this threshold require manual verification of the mapping relationship. The feature tree search depth is limited to [value missing]. Layer, in the formula The depth level is the number of layers to search.

[0050] The specific implementation of step S04 is to implement an automatic generation and update mechanism for the parameterized model. The feature tree traversal algorithm adopts a breadth-first search strategy, and the traversal time control condition is... Seconds. The formula for maintaining the precision of parameter assignment values ​​is as follows: In the formula This is the precise parameter value after assignment. The input parameter values ​​are [values ​​to be inserted here]. The sketch constraint solution uses a numerical iterative algorithm, with the convergence accuracy criterion being [criterion to be inserted here]. millimeters, in the formula The Euclidean norm of the constraint error vector is used. Feature geometry calculations are performed using three-dimensional Boolean operations, with the Boolean operation precision set to [value missing]. millimeters, in the formula This is the tolerance for Boolean operations.

[0051] The specific implementation of step S05 involves establishing an assembly relationship analysis and sequence optimization mechanism. The geometric feature recognition accuracy threshold is set to... Millimeters. The required accuracy for assembly interface information recognition is [missing information]. The formula for calculating assembly complexity is as follows: In the formula This represents the assembly complexity value. For the number of parts, To constrain the number of relations, For assembly level depth, Weighted by the number of parts. To constrain the number of relation weights, The assembly level depth weight is used. The assembly sequence optimization initiation condition is... In graph theory-based assembly sequence optimization algorithms, the search depth is limited to... Each node.

[0052] The specific implementation of step S06 involves establishing an automatic assembly environment construction and constraint relationship setting mechanism. The system imports each part model into the assembly environment sequentially according to the optimized assembly order. During the import process, a coordinate system alignment algorithm ensures the accuracy of the initial positions of the parts. The coordinate system alignment algorithm is based on the least squares method, and the objective function is... In the formula For the sum of squared alignment errors, For the number of alignment points, for Rotation matrix, for Translation vector, and The first A pair of corresponding points Coordinate vectors. Optimal transformation parameters are obtained by solving... and Obtain, in the formula Let be the partial derivative of the objective function with respect to the rotation matrix. Let be the partial derivative of the objective function with respect to the translation vector. The alignment accuracy requirement is... millimeters, in the formula This is the coordinate system alignment accuracy threshold. The assembly position parameter setting process establishes the mating constraint relationships between parts based on predefined assembly position parameter data and a constraint solving algorithm. The constraint solving employs a nonlinear optimization algorithm, and the convergence criterion is... millimeters, in the formula To constrain the error vector, This represents the Euclidean norm. The position deviation monitoring conditions are as follows: millimeters, in the formula This is the positional deviation vector.

[0053] The specific implementation of step S07 involves establishing a geometric interferometry detection and automatic adjustment mechanism. The interferometry detection accuracy is set to... Millimeters. The interference volume was calculated using a voxelization method, with the voxel size set to [value missing]. Millimeters. Tolerance threshold set to The condition for determining if the interference volume exceeds the limit is as follows: The iterative optimization algorithm is based on the gradient descent method, and the initial value of the adjusted step size is set to... Millimeters, gradient calculation formula is In the formula Let V be the gradient vector of the interference volume with respect to the assembly position parameters. This is the assembly position parameter vector. The number of iterations is limited to... Second-rate.

[0054] It should be explained that the graph theory-based assembly sequence optimization algorithm in this scheme achieves automatic optimization of the assembly order by constructing an assembly dependency graph. The assembly dependency graph is represented as follows: In the formula This is a set of assembly status nodes. This is the set of edges for assembly operations. The formula for calculating the node weights in the graph is as follows: In the formula The node weight value. For the number of assembly operations. For assembly time, To determine the assembly difficulty level, , , The weighting coefficients are and satisfy the following conditions: The topological sorting algorithm determines the assembly sequence, and the sorting result matrix is... In the formula For the first The assembly state corresponding to each assembly step. Let be the total number of assembly steps. The heuristic search evaluation function is: In the formula For nodes The overall evaluation value, From the starting node to the node The actual cost, For the node Heuristic cost estimation to the target node. The algorithm dynamically adjusts the assembly strategy and automatically optimizes the assembly path when the assembly complexity exceeds a preset range. It takes into account practical constraints such as tool accessibility and operating space to generate an implementable assembly process.

[0055] Cell addressing algorithm formula Based on the principle of matrix linearization storage, the coordinates of a two-dimensional table are converted into a one-dimensional linear address, enabling fast location and data extraction of any cell in an Excel spreadsheet, which improves data reading efficiency compared to the traditional row-by-row scanning method.

[0056] Edit distance recursive relation The optimal solution is constructed step by step using a dynamic programming strategy. By comparing the costs of four operations—character matching, insertion, deletion, and replacement—the minimum value is selected to achieve accurate calculation of string similarity. Formula for calculating parameter similarity A weighted fusion strategy is adopted, which comprehensively considers the matching degree of names and the matching degree of numerical ranges. The importance of different factors is adjusted by weight coefficients, thereby improving the accuracy and robustness of parameter mapping.

[0057] Assembly complexity calculation formula A quantitative evaluation model for assembly difficulty was established, and an objective measurement of assembly complexity was achieved through multi-factor comprehensive evaluation, providing a scientific basis for assembly sequence optimization. Coordinate system aligned least squares objective function The optimal rotation and translation transformation parameters are solved by minimizing the sum of squared distances between all corresponding point pairs, ensuring accurate alignment of the part coordinate system; Convergence Criterion for Constraint Solution The millimeter uses the Euclidean norm to measure constraint errors, ensuring that assembly constraints are accurately met and improving assembly accuracy and stability.

[0058] Gradient descent optimization formula By calculating the partial derivative of the interference volume with respect to the assembly parameters, the optimal adjustment direction is determined, thereby automatically eliminating interference problems and avoiding the inefficient process of manual trial and error. Node weight calculation formula The importance of assembly nodes is quantified by linearly weighted combination of three key factors, providing a basis for determining node priority in graph theory algorithms; Heuristic search evaluation function By combining actual costs and heuristic estimations, search efficiency is improved while ensuring search integrity, enabling rapid optimization of assembly paths and significantly shortening assembly sequence planning time.

[0059] To better understand and implement this invention, Example 2, a specific application scenario, is provided below: A technical team needed to perform parametric modeling of the core assembly of a 110kV oil-immersed power transformer. This transformer core assembly consists of 82 components, including core plates, clamps, insulating pads, tie plates, and pressure plates. Traditional manual modeling methods are time-consuming and prone to dimensional errors. The technical team decided to use a parameter-driven SolidWorks automatic modeling method to solve this problem.

[0060] The technical team first established a structural parameter data table, storing all parameter information of the transformer core assembly in an Excel spreadsheet file. For example... Figure 2As shown, the parameter data table contains five worksheets, corresponding to the core laminations, clamping system, insulation structure, fastening components, and assembly relationships, respectively. The core lamination worksheet includes geometric dimensional parameters such as the length of the silicon steel sheet (1847 mm), width (1523 mm), thickness (0.3 mm), and stacking height (1680 mm), as well as the silicon steel sheet density (7650). Magnetic permeability 4500 Loss coefficient 1.2 Material property parameters, etc. The clamping system worksheet includes the main clamp's length (1980mm), width (320mm), and thickness (25mm), and the auxiliary clamp's assembly position parameters such as coordinates (158,0,0), rotation angle (0°), and offset distance (15mm). The insulation structure worksheet defines the insulating pad's length (180mm), width (160mm), and thickness (8mm), and the insulating paper's dielectric strength (25). Dielectric constant 3.7, volume resistivity Parameters such as these.

[0061] The system uses object linking embedded database connection technology to open Excel spreadsheet files and employs a cell addressing algorithm to accurately locate each parameter value using a row and column index coordinate system. A data type recognition algorithm automatically categorizes the read parameters into three main categories: geometric dimensions, material properties, and assembly location parameters, achieving an accuracy rate of 98.7%. The system establishes a categorized storage index table based on worksheet names and uses a hash table data structure for data retrieval. Each worksheet corresponds to an independent parameter storage space, ensuring that parameter data for different components is isolated from each other.

[0062] Next, the system launches the SolidWorks 2024 application via the Component Object Model (COM) interface technology, establishing an API communication connection. The interface connection employs a multi-factor authentication mechanism: software version compatibility checks pass, license status verification is successful, and system resource availability assessment shows 8.2GB of free memory, 12% CPU utilization, and a connection time of 18 seconds. The working path is set to D:, the part template file path is configured to D:, and the parameter passing interface initializes with an 85MB memory buffer, supporting the simultaneous processing of 1240 parameter variables.

[0063] The system employs string matching and semantic analysis algorithms to establish parameter mapping relationships. The string matching algorithm uses edit distance calculation to achieve fuzzy matching, with an edit distance threshold set to 2 characters. The semantic analysis algorithm identifies the meaning of parameters using a predefined dictionary containing 5300 standard engineering terms. The system extracts feature dimension variable information from SolidWorks part templates and uses a feature tree depth-first search algorithm to traverse the model structure at a depth of 16 layers. For the core plate length parameter of 1847mm, the system calculates a similarity score of 94.2% with the "CoreLength" variable in the template, establishing a mapping relationship. For the clamp width parameter of 320mm, the system calculates a similarity score of 91.8% with the "ClampWidth" variable in the template, also establishing a mapping relationship. The parameter mapping comparison table is shown in Table 1. Table 1. Parameter Mapping Reference Table for Iron Core Assembly

[0064] The system calls the SolidWorks API to open the core-level sheet template file CoreLamination.SLDPRT and locates feature size variables using a feature tree traversal algorithm. The traversal algorithm employs a breadth-first search strategy, scanning the feature tree structure from the root node to identify feature objects containing size parameters, with an average traversal time of 3.7 seconds. Parameter assignment uses a batch update mode, where the system batches external geometric dimension parameter values ​​to the corresponding feature size variables, maintaining numerical precision to three decimal places. Model reconstruction is then performed, and the sketch constraint solution uses a numerical iterative algorithm with an iterative convergence accuracy of [missing value]. mm, the feature geometry calculation generates the final geometric shape through three-dimensional Boolean operations, with a Boolean operation precision of [value missing]. mm. The generated parametric core-stage model is saved as a CoreLamination_20250825_v2.SLDPRT format file.

[0065] Following the same process, the system sequentially generates 82 component models, including ClampAssembly.SLDPRT, InsulationPad.SLDPRT, TensionPlate.SLDPRT, and PressurePlate.SLDPRT. The generation time for each component model is controlled within 45 seconds, resulting in an overall modeling efficiency improvement of 74.3% compared to traditional manual methods.

[0066] The system extracts geometric constraint information and assembly interface information from each part model to construct an assembly dependency graph. Geometric constraints include four types: surface coincidence constraints, axis alignment constraints, fixed distance constraints, and angle-limited constraints. Assembly interface information includes assembly hole positions, assembly surface normals, assembly axis directions, and assembly contact areas. The assembly dependency graph uses a directed acyclic graph (DAG) data structure, containing 82 nodes representing assembly states and 156 edges representing assembly operations. The assembly complexity calculation comprehensively considers the number of parts (82), the number of constraints (248), and the assembly level depth (6 levels), resulting in a complexity of 0.87, falling within the range [0.8, 1.0]. The system then employs a graph theory-based assembly sequence optimization algorithm.

[0067] The optimization algorithm employs a strategy combining topological sorting and heuristic search. Topological sorting ensures that the dependencies between assembly operations are satisfied, while heuristic search finds the optimal assembly path through an evaluation function. The evaluation function considers three factors: assembly time, tool accessibility, and operational complexity, with weights of 40%, 35%, and 25%, respectively. The search depth is 847 nodes. Figure 3 As shown, the optimized assembly sequence increases the number of parallel operation opportunities from 12 to 23, and the assembly efficiency is expected to improve by 38.6%.

[0068] The system imports the part models into the assembly environment according to the optimized assembly sequence. First, the core plate model is inserted as the assembly datum. The coordinate system alignment algorithm, based on the least squares method, determines the optimal alignment position by calculating the geometric center and main axis direction of the part, with an alignment accuracy of 0.008mm. Next, the main clamping component model is inserted, and coincident fit constraints are set according to the assembly position parameters. The bottom surface of the clamping component coincides with the bottom surface of the core plate, and the side distance constraint is 15mm. Then, auxiliary clamping components are inserted, and coaxial fit constraints are set, with the axis coinciding with the central axis of the core, and the angle constraint at 0°. Insulating pads, pull plates, pressure plates, and other components are then inserted sequentially. The assembly position of each component is determined by a constraint solving algorithm. A nonlinear optimization algorithm is used to handle multiple constraints, and the convergence criterion is that the constraint error is less than... mm.

[0069] The assembly process monitors part position changes in real time, with positional deviations controlled within 0.08mm. After the complete 3D assembly model is generated, the system automatically performs assembly verification checks. The constraint integrity verification pass rate is 99.2%, and the geometric rationality verification found 3 minor gaps exceeding the tolerance, all within 0.05mm, which is within the acceptable range.

[0070] The system performs geometric interference detection on a complete iron core assembly model. The interference detection algorithm is based on boundary representation, determining the interference range by analyzing the intersection lines and points on the parts' surfaces, with a detection accuracy of 0.001 mm. The interference volume is calculated using a voxelization method, with a voxel size of 0.008 mm. Local interference was detected between the tension plate and the pressure plate, with an interference volume value of 0.23. Exceeding the tolerance threshold of 0.1 The system initiates an automatic adjustment mechanism, employing an iterative optimization algorithm to adjust the assembly position parameters of relevant parts. The adjustment algorithm is based on the gradient descent method, determining the adjustment direction by calculating the partial derivative of the interference volume with respect to the assembly position parameters, with an initial adjustment step size of 0.08 mm. After four iterations, the Y-direction position parameter of the pull plate was adjusted from 126.5 mm to 126.2 mm, the Z-direction angle parameter of the pressure plate was adjusted from 15.3° to 15.0°, and the interference volume value was reduced to 0.07. It is below the tolerance threshold.

[0071] The technical team conducted a quality assessment of the final generated core assembly model, as shown in Table 2: Table 2 Quality Assessment Results of Core Assembly Model

[0072] The final generated core assembly model contains 82 parts, with a total assembly file size of 287MB, 1456 features, and 248 constraints. The model supports parametric modification; when the core dimensions need to be adjusted, simply modify the corresponding values ​​in the Excel parameter table, and the system can automatically complete the update of the entire model and assembly reconstruction within 15 minutes.

[0073] This invention represents a fundamental breakthrough in technical principles compared to traditional manual modeling methods. Traditional methods rely on manually creating the 3D features of each part one by one, requiring manual setting of dimensional parameters and establishment of assembly constraints. This process is prone to human error and inefficient. This invention establishes an automatic mapping mechanism between parameters and model features, enabling external data to directly drive the 3D geometry, fundamentally eliminating manual intervention. The assembly sequence optimization algorithm, based on graph theory, automatically analyzes the constraint dependencies between components, determining the optimal assembly path through topological sorting and heuristic search, exhibiting stronger logic and accuracy compared to traditional empirical assembly planning. The geometric interference detection and automatic adjustment mechanism employs 3D Boolean operations and iterative optimization algorithms, accurately identifying geometric conflicts in the assembly and automatically adjusting component positions, avoiding the inefficient practices of visual inspection and manual adjustment in traditional methods. The parameter mapping lookup table establishes a standardized data interface, allowing modifications to design parameters to be automatically transmitted to all relevant geometric features, achieving true parametric design. The entire technical solution forms a complete automated closed loop from data input, feature recognition, model generation to assembly verification, significantly improving the efficiency and accuracy of complex assembly modeling.

[0074] It should be noted that the variables involved in this invention are explained in detail in Tables 3 and 4.

[0075] Table 3. Variable Explanation Table (Part 1)

[0076] Table 4. Variable Explanation Table (Part Two)

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A parameter-driven SolidWorks automatic modeling method, characterized in that, A structure parameter data table is read from an Excel table file, the structure parameter data table contains geometric dimension parameters, material attribute parameters and assembly position parameters, and the structure parameter data table is stored in classified manner according to worksheet names; SolidWorks software is started and an API communication connection is established, a three-dimensional modeling work path and a part template file path are set, and a parameter transmission interface is initialized; A geometric dimension parameter and a feature size variable in a SolidWorks part template are mapped to each other to generate a parameter mapping table, the SolidWorks API interface is called to open the part template file, the feature size variable is located through a feature tree traversal algorithm, the geometric dimension parameter value is assigned to the corresponding feature size variable according to the parameter mapping table, a model reconstruction operation is performed to update the three-dimensional geometric shape, a parameterized three-dimensional part model is generated and saved as an SLDPRT format file, geometric constraint information and assembly interface information of each part model are extracted, an assembly dependency graph is constructed, an assembly complexity value is calculated, and when the assembly complexity value belongs to a closed interval, a graph theory-based assembly sequence optimization algorithm is used to rearrange the assembly order; According to the assembly order, each part model is inserted into an assembly environment, a fitting constraint relationship between parts is set according to the assembly position parameters, and a complete three-dimensional assembly model is generated; geometric interference detection is performed on the three-dimensional assembly model, an interference volume value is calculated, and when the interference volume value is greater than a tolerance threshold, the assembly position parameters of the related parts are automatically adjusted and the assembly step is re-executed.

2. The parameter-driven SolidWorks automatic modeling method according to claim 1, wherein, The geometric dimension parameter refers to numerical information defining the geometric features of the part shape, including length, width, thickness, hole diameter, chamfer radius and drawing depth; the material attribute parameter refers to the physical property value of the part material, including density, elastic modulus, Poisson's ratio, yield strength and thermal conductivity; the assembly position parameter refers to the spatial position constraint information of the part in the assembly, including the assembly reference surface coordinates, the assembly origin coordinates, the rotation angle and the offset distance.

3. The parameter-driven SolidWorks automatic modeling method according to claim 2, wherein, The feature size variable refers to a variable parameter in a SolidWorks three-dimensional model that controls the geometric shape, and the feature size variable is directly associated with the sketch size, the drawing depth, the cutout volume and the fillet radius modeling feature; The parameter mapping table refers to a data table establishing a one-to-one correspondence between the external parameter name and the feature size variable.

4. The parameter-driven SolidWorks automatic modeling method according to claim 3, wherein, The feature tree traversal algorithm refers to a calculation method for sequentially accessing each feature node in the model according to the SolidWorks feature tree structure, and the feature object containing the size parameter is located through recursive traversal; the model reconstruction operation refers to the process of recalculating the three-dimensional geometric shape by SolidWorks software according to the modified parameters, including sketch solving, feature updating and geometry generation.

5. The parameter-driven SolidWorks automatic modeling method according to claim 4, wherein, The geometric constraint information refers to the spatial position restriction conditions between parts due to the shape fit, including face coincidence constraint, axis alignment constraint, distance fixed constraint and angle limitation constraint; the assembly interface information refers to the geometric feature information of the part surface for connecting with other parts, including assembly hole position, assembly face normal vector, assembly axis direction and assembly contact area.

6. The parameter-driven SolidWorks automatic modeling method according to claim 5, wherein, The assembly dependency graph refers to a data structure representing the assembly sequence of parts in the form of a directed acyclic graph, where the nodes represent the assembly state and the edges represent the assembly operation; the assembly complexity value refers to a quantitative index of assembly difficulty calculated by comprehensively considering the number of parts, the number of constraint relationships and the assembly level depth.

7. The parameter-driven SolidWorks automatic modeling method according to claim 6, wherein, The graph-based assembly sequence optimization algorithm refers to a calculation method for determining the optimal assembly path through topological sorting and heuristic search, which realizes the automatic optimization of assembly sequence by modeling the assembly process as a directed acyclic graph, analyzes the geometric constraint information and assembly interface information between parts, constructs a complete assembly dependency relationship network, and identifies the critical path and parallel operation opportunity in the assembly process.

8. The parameter-driven SolidWorks automatic modeling method according to claim 7, wherein, The graph-based assembly sequence optimization algorithm automatically determines the conflict-free assembly sequence through topological sorting, while identifying the parallel assembly operations, calculates the assembly complexity value according to the geometric size parameters, assembly position parameters and material attribute parameters, and dynamically adjusts the assembly strategy when the complexity exceeds the preset range.

9. The parameter-driven SolidWorks automatic modeling method according to claim 8, wherein, The graph-based assembly sequence optimization algorithm considers the actual assembly constraints of tool accessibility, operation space and gravity influence, generates an implementable assembly process flow, and the parameters applied to the algorithm include the number of parts, geometric constraint information, assembly interface information, assembly position parameters, assembly complexity value and interference volume value.

10. The parameter-driven SolidWorks automatic modeling method according to claim 9, wherein, The fitting constraint relationship refers to the spatial constraint connection mode between parts in the assembly body, including coincidence fitting, distance fitting, coaxial fitting, parallel fitting and vertical fitting; the interference volume value refers to the geometric volume of the overlapping part of different parts in the assembly body, which is calculated by three-dimensional Boolean operation; the tolerance threshold refers to the maximum interference volume limit value allowed by the system.

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