A quality control method applied to hardware fitting three-dimensional model design

Abstract

The application relates to the technical field of model design, in particular to a quality control method applied to hardware accessory three-dimensional model design. The method comprises the following steps: obtaining a hardware accessory three-dimensional design model; performing process sensitivity integrated evaluation based on the hardware accessory three-dimensional design model to obtain accessory process sensitive feature data; constructing an accessory process knowledge database based on the accessory process sensitive feature data; performing elastic deformation simulation on the hardware accessory based on the accessory process knowledge database to obtain hardware accessory deformation trajectory data; performing dynamic interference risk evaluation and real-time verification based on the hardware accessory deformation trajectory data to obtain accessory physical interference prediction results; and constructing accessory process constraint rules based on the accessory process knowledge database. The application can ensure seamless conversion from a three-dimensional model to an actual product.

Description

Technical Field

[0001] The present invention relates to the technical field of model design, and in particular to a quality control method applied to the design of three-dimensional models of hardware accessories. Background Art

[0002] In the field of 3D model design for micro-hardware accessories (such as digital device interface springs and precision lock components), design accuracy directly determines the subsequent stamping die development cost and product yield. Such accessories typically have submillimeter-level bending structures, micropore arrays, and complex surface transition features, such as the elastic contact arms of smartphone SIM card trays or the hinge slots of medical device clamps. Although existing 3D design software (such as SolidWorks and Creo) provides geometric constraints and dimensional tolerance annotation functions, they have inherent flaws when dealing with implicit physical interference between microscale features: designers find it difficult to intuitively identify multi-level nested structures in virtual assembly (such as the deformation trajectory of a 0.5mm thick spring under compression and the gap penetration between adjacent conductive pillars). The software's dynamic collision detection often misjudges reasonable deformations as interference or misses the risk of real contact due to the simplification of material elastic variables.

[0003] A more critical challenge lies in the digital disconnect between design intent and manufacturing feasibility. For example, in a metal bushing for a laptop hinge, the 30° angled ribs in the 3D model require a laser weld penetration allowance at the root. However, the design software only verifies static geometric parameters and cannot link to the thermal deformation compensation rules in the process knowledge base. When the designer modifies the rib angle, the system does not automatically trigger the allowance threshold warning, resulting in the presence of manufacturability defect seeds in the model before the data is passed to the CAE simulation stage. This defect is not visible in the 2D engineering drawing, and the lack of weld fusion is not discovered until mold trial, causing project cycle delays. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a quality control method applied to the design of three-dimensional models of hardware accessories to solve at least one of the above technical problems.

[0005] To achieve the above objectives, a quality control method for hardware accessories 3D model design includes the following steps:

[0006] Step S1: Obtain a three-dimensional design model of a hardware accessory; perform a process sensitivity integrated assessment based on the three-dimensional design model of the hardware accessory to obtain accessory process sensitive feature data; and construct an accessory process knowledge database based on the accessory process sensitive feature data;

[0007] Step S2: performing elastic deformation simulation on the hardware accessories based on the accessory process knowledge database to obtain hardware accessory deformation trajectory data; performing dynamic interference risk assessment and real-time verification based on the hardware accessory deformation trajectory data to obtain accessory physical interference prediction results;

[0008] Step S3: constructing accessory process constraint rules based on the accessory process knowledge database; performing constraint adjustment on the hardware accessory 3D design model based on the accessory process constraint rules and the accessory physical interference prediction result to obtain the accessory process constraint design model;

[0009] Step S4: performing a micro-feature risk assessment on the accessory process constraint design model to obtain an accessory defect risk score; performing constraint closed-loop optimization control on the accessory process constraint design model based on the accessory defect risk score to obtain a defect risk control design model;

[0010] Step S5: Perform manufacturing feasibility simulation verification on the defect risk control design model to obtain accessory manufacturing prediction data; perform closed-loop quality control on the defect risk control design model based on the accessory manufacturing prediction data to obtain a final quality control design data package.

[0011] The present invention builds a sophisticated process knowledge database and real-time elastic deformation simulation, combined with dynamic interference risk assessment, to accurately identify the potential interference risk of micro-scale features (such as the deformation trajectory of a 0.5mm thick reed in a compressed state and the gap penetration of adjacent conductive columns), and uses adaptive grid subdivision and material constitutive equations to predict elastic deformation boundaries in real time, effectively reducing the misjudgment and missed detection caused by the simplification of material elastic variables in traditional design software, and quantifying the interference risk score to support design optimization; through the deep integration of design and manufacturing process knowledge, the sensitivity of key manufacturing features (such as laser welding penetration allowance) is identified through process sensitivity integrated assessment, and the manufacturing process is optimized. The manufacturing process requires real-time embedding into the design process. When the designer modifies the key dimensional parameters, the system automatically retrieves the thermal deformation compensation rules in the process knowledge database and updates the reserved margin in real time, avoiding manufacturability defects caused by the disconnection between design and manufacturing, reducing problems such as insufficient welding fusion discovered in the trial mold stage, and shortening the project cycle. Based on the constructed process constraint rules, the design model is optimized with multi-process coupling constraints, comprehensively considering the cumulative effects of multiple processes such as stamping rebound, heat treatment deformation, and surface treatment thinning on the final dimensional accuracy, establishing a multi-objective optimization function and using the improved particle swarm optimization function to solve the optimal parameter combination. Through parameterization Modeling and real-time updating of geometric features can significantly improve the geometric accuracy and manufacturing feasibility of the optimized model, avoiding material waste and cost increase caused by over-design; through refined simulation verification and risk quantification analysis, the micro features in the design model (such as 0.3mm fillet, sharp angle transition) are evaluated for risk, and the Weibull distribution model is used to describe the probability of occurrence of stamping burrs and micro cracks. The stress concentration degree and load exposure frequency of the defect position are combined to quantitatively calculate the comprehensive risk index, and optimization suggestions are automatically generated for high-risk areas (such as locally increasing the fillet radius and adjusting the material thickness distribution). By optimizing the material distribution, while ensuring functional requirements, Lightweight design can be achieved under the premise of avoiding implicit safety redundancy; by introducing digital closed-loop feedback, CAE simulation results, trial mold data, batch production quality statistics and other information are transmitted back to the design environment in real time. By comparing the predicted results with the actual manufacturing performance, the parameter models and constraint rules in the process knowledge database are continuously revised to improve the accuracy of subsequent designs. At the same time, based on the design convergence evaluation and analysis of the stability and manufacturing consistency of the design scheme, the designer is provided with a scheme maturity score. When all quality control indicators reach the preset threshold, the final design data package that meets the manufacturing requirements is automatically generated to ensure a seamless transition from 3D model to actual product. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent from reading the detailed description made with reference to the following drawings:

[0013] Figure 1A schematic flow chart of the steps of a quality control method applied to the design of three-dimensional models of hardware accessories according to an embodiment is shown.

[0014] Figure 2 A detailed flowchart of step S4 of an embodiment is shown.

[0015] Figure 3 A detailed flowchart of step S49 of an embodiment is shown. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0018] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a quality control method for hardware accessories three-dimensional model design, comprising the following steps:

[0020] Step S1: Obtain a three-dimensional design model of a hardware accessory; perform a process sensitivity integrated assessment based on the three-dimensional design model of the hardware accessory to obtain accessory process sensitive feature data; and construct an accessory process knowledge database based on the accessory process sensitive feature data;

[0021] Step S2: performing elastic deformation simulation on the hardware accessories based on the accessory process knowledge database to obtain hardware accessory deformation trajectory data; performing dynamic interference risk assessment and real-time verification based on the hardware accessory deformation trajectory data to obtain accessory physical interference prediction results;

[0022] Step S3: constructing accessory process constraint rules based on the accessory process knowledge database; performing constraint adjustment on the hardware accessory 3D design model based on the accessory process constraint rules and the accessory physical interference prediction result to obtain the accessory process constraint design model;

[0023] Step S4: performing a micro-feature risk assessment on the accessory process constraint design model to obtain an accessory defect risk score; performing constraint closed-loop optimization control on the accessory process constraint design model based on the accessory defect risk score to obtain a defect risk control design model;

[0024] Step S5: Perform manufacturing feasibility simulation verification on the defect risk control design model to obtain accessory manufacturing prediction data; perform closed-loop quality control on the defect risk control design model based on the accessory manufacturing prediction data to obtain a final quality control design data package.

[0025] Preferably, step S1 includes the following steps:

[0026] Step S11: Acquire a three-dimensional design model of a hardware accessory; convert the format of the three-dimensional design model of the hardware accessory to obtain a geometric feature dataset of the accessory;

[0027] Step S12: performing topological structure recognition on the accessory geometric feature data set to obtain hardware accessory topological relationship data;

[0028] Step S13: performing feature classification and recognition on the accessory geometric feature data set based on the hardware accessory topological relationship data to obtain accessory basic feature classification data;

[0029] Step S14: extracting size parameters from the basic feature classification data of the accessory to obtain a set of accessory feature size parameters;

[0030] Step S15: performing microstructure refinement extraction on the basic feature classification data of the accessory according to the accessory feature size parameter set to obtain an accessory microfeature data set;

[0031] Step S16: performing material attribute association on the accessory microscopic feature data set to obtain accessory material feature association data;

[0032] Step S17: Performing a manufacturing process sensitivity assessment on the accessory micro-feature dataset based on the accessory material feature association data to obtain accessory process sensitive feature data. The manufacturing process sensitivity assessment includes setting a process sensitivity assessment matrix, with a stamping process sensitivity threshold of an aspect ratio greater than 10:1, a bending process sensitivity threshold of a bending radius less than 2 times the material thickness, and a laser cutting sensitivity threshold of a slit width less than 0.1 mm. Each feature is assigned a sensitivity score based on the process sensitivity assessment matrix.

[0033] Step S18: Constructing an accessory process knowledge database based on the accessory process sensitive feature data.

[0034] In this example, a 3D design model of a smartphone SIM card tray was first obtained. This model was created using SolidWorks software. The SIM card tray contains multiple complex geometric features, such as elastic contact arms and a microhole array. The model was exported as a STEP file using SolidWorks' built-in export function, with an export accuracy of 0.01 mm. The open-source CAD data parsing library, libstep, was used to read the STEP file and extract boundary boundary representation (BREP) data from the model, including vertex coordinates, edge curve definitions, and face parameterized equations. The surface was subdivided into triangular mesh elements and meshed using the open-source triangular mesh generation library, CGAL, with a mesh density of 1000 triangles per square millimeter. This generated a standardized accessory geometry dataset containing detailed information on all geometric features of the SIM card tray. Based on this accessory geometry dataset, the open-source 3D geometry library, Open CASCADE, was used to perform topological structure recognition. The Euler-Poincare formula (V - E + F = 2) was used to verify the integrity of the model, where V, E, and F represent the number of vertices, edges, and faces, respectively. The calculated values ​​of V = 1024, E = 2048, and F = 1024 are verified by substituting them into the formula, indicating that the model structure is complete. A vertex-edge-face association matrix is ​​constructed, recording information such as the edges connected to each vertex and the faces connected to each edge. This allows the number of independent entities in the model to be identified. For example, eight independent elastic contact arm entities were found in the SIM card holder model. By analyzing the normal vectors and adjacency relationships of the facets, the presence of internal cavities in the model can be identified. In this case, a rectangular cavity was detected within the SIM card holder to reduce weight. This topological information is organized and stored as a topological graph data structure. After identifying the topological structure of the smartphone SIM card holder, its topological relationship data is obtained. Based on this data, the feature recognition function within SolidWorks is used to classify and identify features in the accessory geometric feature dataset. Threshold parameters for feature recognition are set: the minimum diameter of hole features is set to 0.1 mm, the minimum width of slot features is set to 0.05 mm, and the minimum radius of chamfer features is set to 0.02 mm. By utilizing the SolidWorks API to invoke its local geometric invariance formula, the curvature tensor of each model component was calculated to identify the basic manufacturing features within the model. Analysis of the SIM card tray model successfully identified a variety of features: 16 hole features, primarily used to secure the SIM card tray or connect it to other components; 12 slot features, located on the elastic contact arms to reduce weight and increase flexibility; and 24 chamfer features, located along the edges of the model. Each identified feature was labeled and classified to form basic accessory feature classification data. After classifying and identifying the basic features of the smartphone SIM card tray, basic accessory feature classification data was obtained.SolidWorks' dimensional measurement tools are used to extract the dimensional parameters of these classified features. The minimum bounding box function is used to calculate the spatial dimension boundaries of each feature, with measurement accuracy set to the micron level. For identified hole features, their aperture dimensions are precisely measured to obtain the diameter values ​​of each hole. For example, the diameter of a key positioning hole is 0.8mm, with a tolerance range of ±0.05mm. For slot features, their width and length are measured. The slot width on a certain elastic contact arm is 0.3mm, the length is 5.2mm, and the wall thickness is 0.25mm. For chamfer features, their fillet radius is extracted. For example, the chamfer radius of the model edge is 0.05mm. A parametric constraint table is established to record the position coordinates, direction vector, and dimensional tolerance range of each feature. For example, a hole feature is located at specific coordinates on the model (12.5mm, 8.3mm, 0.2mm), oriented along the Z axis, with a tolerance of ±0.03mm. Through these steps, a detailed set of accessory feature dimension parameters is obtained. Based on the obtained accessory feature dimension parameter set for the smartphone SIM card tray, the basic feature classification data was refined and extracted to obtain a microstructure dataset. In this step, a submillimeter feature recognition threshold of 0.5 mm was set, and microstructure identification was performed using the open-source image processing library Open CV in conjunction with multi-resolution analysis methods. The surface contour of the SIM card tray model was decomposed in the frequency domain using a wavelet transform, breaking down the model's geometric information into components of varying frequencies. By setting appropriate wavelet basis functions and decomposition scales, microstructural features such as micropore arrays, fine ribs, and thin-wall transitions within the model were highlighted. For example, a micropore array with a diameter of 0.3 mm was detected in one area of ​​the model. These micropores are used for heat dissipation and weight reduction. Fine ribs with a width of 0.2 mm were identified on the elastic contact arm to enhance structural strength. Some thin-wall transition regions with a thickness of 0.4 mm were discovered, which are prone to deformation during manufacturing. By establishing geometric descriptor vectors for microscopic features, such as position, shape, and size, these microstructures are described in detail and organized into an accessory microfeature dataset. After obtaining the accessory microfeature dataset for the smartphone SIM card tray, these microfeatures were associated with material properties to obtain accessory material feature association data. A material property database was established, containing the physical parameters of commonly used metal materials, such as stainless steel with an elastic modulus of 200 GPa, a Poisson's ratio of 0.3, and a yield strength of 350 MPa, and copper alloy with an elastic modulus of 110 GPa, a Poisson's ratio of 0.34, and a yield strength of 220 MPa. Based on the design documentation of the SIM card tray, the material specifications of its main components were determined to be 304 stainless steel. Using the material property assignment function in SolidWorks, the material property values ​​of 304 stainless steel were assigned to each geometric feature in the model.For example, the elastic contact arms and the area containing the microhole array were assigned stainless steel properties such as an elastic modulus of 200 GPa, a Poisson's ratio of 0.3, and a yield strength of 350 MPa. Copper alloy electroplated areas within the model, such as the conductive contact surfaces, were also assigned corresponding material properties based on the thickness and material properties of the electroplated layer. Through the above steps, a feature-material mapping table was established, detailing the material property information corresponding to each microfeature. A manufacturing process sensitivity assessment was performed on the accessory microfeature dataset based on the associated material feature data for the smartphone SIM card tray. A process sensitivity assessment matrix was established, with the stamping process sensitivity threshold set at an aspect ratio greater than 10:1, the bending process sensitivity threshold set at a bend radius less than twice the material thickness, and the laser cutting sensitivity threshold set at a kerf width less than 0.1 mm. Using the finite element analysis capabilities provided by SolidWorks Simulation and combined with the material's physical parameters, sensitivity scores were assigned to each microfeature in the model. For example, for a certain long elastic arm feature on the SIM card tray, its aspect ratio reaches 12:1, which exceeds the sensitivity threshold of the stamping process, and therefore obtains a higher sensitivity score in the stamping process sensitivity assessment; and for a certain bending feature with a bending radius of only 1.5 times the material thickness, it is also judged as a high sensitivity feature in the bending process sensitivity assessment; some areas in the model that require high-precision laser cutting have a slit width requirement of 0.08mm, which is lower than the sensitivity threshold of laser cutting, and therefore also show a high sensitivity in the laser cutting process sensitivity assessment. Through the multi-criteria decision analysis method, these sensitivity factors are comprehensively considered, and the manufacturing process sensitivity score is performed on each feature, and finally the accessory process sensitive feature data is obtained. For the detailed implementation process of step S18, please refer to the sub-steps of step S18.

[0035] Preferably, step S18 includes the following steps:

[0036] Step S181: semantically annotating the process-sensitive feature data of the accessory to obtain semantic hierarchical data of the accessory feature;

[0037] Step S182: performing multi-scale annotation on the accessory process-sensitive feature data according to the accessory feature semantic hierarchical data to obtain accessory feature semantic labels; performing process knowledge matching retrieval on the accessory feature semantic labels to obtain an accessory process knowledge candidate set;

[0038] Step S183: Calculating the knowledge association strength of the accessory feature semantic labels based on the accessory process knowledge candidate set to obtain accessory knowledge association weight data;

[0039] Step S184: hierarchically organizing the accessory process knowledge candidate set according to the accessory knowledge association weight data to obtain an accessory process knowledge hierarchical structure;

[0040] Step S185: constructing a graph node for the accessory knowledge association weight data based on the accessory process knowledge hierarchy to obtain an accessory process knowledge graph node;

[0041] Step S186: constructing relationship edges based on the accessory process knowledge graph nodes and the accessory knowledge association weight data to obtain accessory process knowledge relationship edge data;

[0042] Step S187: Construct an accessory process knowledge database based on the accessory process knowledge graph nodes and the accessory process knowledge relationship edge data.

[0043] In this example, semantic hierarchical annotation is performed on the process-sensitive feature data of a smartphone SIM card tray. This is automated using the built-in feature recognition and annotation capabilities of SolidWorks, combined with the Python programming language. The geometric features of the SIM card tray are divided into three semantic layers: a base layer, used to annotate the geometric type, such as labeling the curved surface of the elastic contact arm as "curved surface" and the connection hole as "plane"; a functional layer, used to annotate the manufacturing purpose, such as labeling certain features as "connection surface" or "processing surface"; and a process layer, used to annotate the manufacturing method, such as "stamping" or "laser cutting." Through these steps, detailed semantic information is assigned to each feature, forming a semantic hierarchy of accessory feature data. After semantic hierarchical annotation of the process-sensitive feature data of the smartphone SIM card tray, this data is further annotated at multiple scales. Using 3Ds Max software combined with a Python script, each feature of the SIM card tray is annotated according to predefined macro, meso, and micro scale standards (macro scale greater than 5 mm, meso scale between 0.5-5 mm, and micro scale less than 0.5 mm). For example, the main frame of a SIM card tray is annotated as a macroscale feature, which is large and focuses on overall structural stability. The elastic contact arm is annotated as a mesoscale feature. Microscale features, such as the microhole array and fine ribs, are smaller but prone to manufacturing challenges, such as forming accuracy during stamping and kerf width control during laser cutting. Elastic search is used as a process knowledge retrieval tool. Using semantic similarity calculation functions (such as TF-IDF text similarity and cosine similarity), with a similarity threshold of 0.8, a pre-built process knowledge base is retrieved to identify manufacturing rules, quality control standards, and process parameter ranges that match the semantic labels of the current features. This results in a candidate set of accessory process knowledge. After obtaining the candidate set of accessory process knowledge for the smartphone SIM card tray, the strength of association between these candidate sets and the semantic labels of the accessory features is calculated. Using the Python programming language and the Network X library, a modified Page Rank algorithm is used to quantify the degree of mutual influence between different pieces of process knowledge. In this process, a damping coefficient of 0.85 and an iterative convergence accuracy of 0.001 are set. For example, the semantic label of a certain elastic contact arm feature on a SIM card tray is associated with multiple pieces of process knowledge, such as stamping rules and laser cutting parameters. By calculating the links and weights between these pieces of process knowledge, it is possible to determine which pieces of process knowledge are most critical to the manufacturing of this feature. This results in accessory knowledge association weight data, which reflects the importance and relevance of each piece of process knowledge in the manufacturing of different SIM card tray features. Based on this accessory knowledge association weight data, the candidate set of accessory process knowledge for smartphone SIM card trays is hierarchically organized.In this step, the clustering analysis tool in Python's scikit-learn library is used to group related process knowledge. The number of clusters is set to 5 main process categories, K-means++ is used to select the initial center point, and the clustering quality is evaluated by the silhouette coefficient. For example, after cluster analysis, the process knowledge of SIM card trays is divided into five main categories: stamping, laser cutting, bending, welding process and other special processes. The process knowledge within each category has high similarity and correlation, while the differences between different categories are more significant. By constructing a tree-like hierarchical structure, the inclusion relationship between these process knowledge can be clearly represented to form a hierarchical structure of accessory process knowledge. Based on the hierarchical structure of accessory process knowledge, the graph node of the accessory knowledge association weight data is constructed. The graph database Neo4j is used to create the graph node. Each node represents a specific process knowledge, which contains attributes such as node ID, node type, attribute value, and association strength. For example, for the stamping process knowledge of SIM card trays, a node is created with the node ID "SP1001" and the node type "Stamping Process." Attribute values ​​include material type "304 stainless steel," stamping speed "10 mm / s," and die gap "0.1 mm." The association strength is set based on the previously calculated results. Through the above steps, a unique graph node is established for each process knowledge point, which serves as the basic unit for constructing the process knowledge graph. Relationship edges are constructed between the accessory process knowledge graph nodes based on the accessory knowledge association weights. Continuing to use the Neo4j graph database, various knowledge relationship types are defined, including dependency, mutual exclusion, and complementation. For example, a dependency relationship exists between the stamping process node and the die design node, as a sound die design is fundamental to successful stamping. A mutual exclusion relationship exists between the stamping direction node and the material texture direction node, as inconsistent directions can lead to material defects. A relationship strength threshold of 0.3 is set. For node pairs with a relationship strength above this threshold, corresponding edges are established, and each edge is assigned a confidence score. For example, the relationship edge between the stamping speed node and the product quality node is assigned a higher confidence score because stamping speed has a direct and significant impact on product quality. Through this step, the structure of the process knowledge graph is improved so that each node is connected to each other through relationship edges, forming a complete knowledge network. A complete accessory process knowledge database is constructed based on the constructed node and edge data. The RDF (Resource Description Framework) triple format of the Neo4j graph database is used to store the entire knowledge graph. A SPARQL query interface is established to allow users to obtain the required knowledge information through a standard query language. For example, design engineers can use the query interface to quickly find all stamping process knowledge related to the elastic contact arm of the SIM card tray, including recommended die clearance, stamping speed and other parameters.

[0044] Preferably, performing elastic deformation simulation on the hardware accessories based on the accessories process knowledge database in step S2 includes:

[0045] Based on the accessory process knowledge database, the material constitutive model is matched to the accessory geometric feature data set to obtain the hardware accessory material constitutive parameters;

[0046] According to the constitutive parameters of the hardware accessories material, the finite element mesh of the accessories is discretized into a finite element mesh;

[0047] Set boundary conditions for the constitutive parameters of hardware accessories based on the finite element mesh of the accessories to obtain the boundary condition data for the accessories simulation;

[0048] Perform elastic deformation calculation on the finite element mesh of the accessory according to the accessory simulation boundary condition data to obtain the accessory node displacement field data;

[0049] Based on the displacement field data of the accessory nodes, the stress field of the accessory finite element mesh is calculated to obtain the stress distribution data of the accessory;

[0050] The deformation trajectory data of the hardware accessories is obtained by tracking the deformation trajectory based on the stress distribution data of the accessories and the displacement field data of the accessory nodes.

[0051] In this example, ANSYS Workbench was used to perform material constitutive model matching on the geometric feature dataset of the smartphone SIM card tray. The elastic-plastic parameters of the 304 stainless steel material used in the SIM card tray were extracted from the accessory process knowledge database. Based on these parameters, the constitutive model type was set to a bilinear kinematic hardening model, with an initial yield stress σy = 350 MPa, a tangent modulus Et = 2000 MPa, and the Von Mises yield criterion. These parameters were imported through the ANSYS Workbench material library. After completing the material constitutive model matching, the material constitutive parameters of the hardware accessories of the SIM card tray were obtained. ANSYS Workbench was used to discretize the geometric feature dataset using a finite element mesh. The mesh size was set to 0.05 mm, and the model was meshed using the tetrahedral mesh automatic generation function. In areas of the model where the bending radius was less than 0.3 mm, such as the curved portion of the elastic contact arm, the mesh was encrypted with a factor of 3 to more accurately capture the mechanical behavior of these areas. The mesh quality assessment parameter Jacobian > 0.6 was set. Finally, the finite element mesh of the SIM card tray accessory was obtained. In ANSYS Workbench, based on the generated finite element mesh of the smartphone SIM card holder, the boundary conditions of the constitutive parameters of the hardware accessories material are set. The fixed constraint surface, load application surface and contact surface in the simulation are clarified. For example, the contact surface where the SIM card holder is installed on the mobile phone motherboard is set as a fixed constraint surface to limit its displacement in three directions; a load is applied to the area where the elastic contact arm contacts the SIM card to simulate the force on the contact arm in actual use; the friction coefficient μ=0.3 between the contact surfaces is defined to simulate the friction effect in actual assembly. After completing these settings, the accessory simulation boundary condition data of the SIM card holder is obtained. In ANSYS Workbench, the elastic deformation of the accessory finite element mesh is calculated based on the set accessory simulation boundary condition data of the smartphone SIM card holder. The Newton-Raphson iterative solution method is used, and the convergence accuracy is set to The maximum number of iterations is 500, and the sparse matrix solver PARDISO is used to improve computational efficiency. During the calculation process, the software automatically solves the displacement vector of each mesh node under the external load and outputs the displacement field distribution matrix. By analyzing the resulting displacement field data, the overall deformation trend of the SIM card tray under load can be observed, particularly the bending deformation of the elastic contact arm. This step generates displacement field data for the SIM card tray's accessory nodes. In ANSYS Workbench, stress field calculations are further performed based on this displacement field data for the smartphone SIM card tray's accessory nodes. The strain tensor is calculated based on the geometric equations, and the stress tensor is calculated using the constitutive relation σ = D × ε, where D is the material's elastic matrix. The Von Mises equivalent stress value is extracted to assess the material stress state. The stress concentration identification threshold is set at 3 times the mean stress. Analysis of the model reveals stress concentration in certain sharp corners and thin-walled areas of the SIM card tray. Finally, stress distribution data for the SIM card tray's accessories is obtained, with a cloud map visually displaying the stress magnitude and distribution in each area. In ANSYS Workbench, deformation trajectory tracking was performed based on the stress distribution data and node displacement field data of the smartphone SIM card tray. Using the Lagrangian description method, a time step of Δt = 0.001s was set to record the position coordinates and velocity vectors of key nodes during the deformation process. For example, the deformation trajectory of the elastic contact arm under load was specifically focused on. By analyzing this data, the entire movement of the contact arm from its initial state to its deformation under load was observed in detail, and its deformation trend and amount were understood. Ultimately, the deformation trajectory data of the SIM card tray hardware accessory was obtained.

[0052] Preferably, the dynamic interference risk assessment and real-time verification based on the hardware accessory deformation trajectory data in step S2 includes:

[0053] Perform spatial proximity recognition on the deformation trajectory data of hardware accessories to obtain proximity structural relationship data of accessories;

[0054] Based on the adjacent structural relationship data of the accessories, the deformation trajectory data of the hardware accessories is preprocessed for collision detection to obtain the data of accessory collision candidate pairs. The bounding box hierarchical tree function is used for coarse screening. The AABB bounding box update frequency is set to every 10 time steps. The bounding box intersection test is used to screen out potential collision geometric pairs and establish a candidate collision pair list.

[0055] Perform accurate collision detection on the deformation trajectory data of hardware accessories based on the accessory collision candidate pair data to obtain accurate collision data of the hardware accessories;

[0056] Based on the precise collision data of hardware accessories, the interference risk of the adjacent structural relationship data of the accessories is quantitatively evaluated to obtain the accessory interference risk score;

[0057] Based on the accessory interference risk score, the risk area of ​​the hardware accessory precise collision data is marked to obtain the accessory interference risk area data; based on the accessory interference risk area data and the accessory interference risk score, a risk propagation assessment is performed to obtain the accessory risk propagation impact data;

[0058] Conduct a comprehensive risk assessment on the accessory interference risk area data based on the accessory risk propagation impact data to obtain the accessory interference risk assessment data;

[0059] Based on the accessory interference risk assessment data and the accessory geometric feature data set, physical constraint rules are matched to obtain the accessory physical constraint rules;

[0060] Real-time verification is performed based on the physical constraint rules of the accessories and the accessory interference risk assessment data to obtain the accessory physical interference prediction results.

[0061] In this embodiment, when performing dynamic interference risk assessment on the SIM card tray of a smartphone, the NumPy and SciPy libraries of Python are used to process the deformation trajectory data. The octree space segmentation function is used to establish a spatial index for the elastic contact arm of the SIM card tray and its surrounding structures. The proximity determination distance threshold is set to 0.1 mm, and by calculating the minimum distance between each deformed node and the surrounding structures, the structural pairs that come into contact are identified. For example, it is found that the distance between the elastic contact arm and the adjacent fixed bracket during deformation is less than the threshold, indicating a potential interference risk. The structural pairs with potential contact and their distance information are organized into accessory proximity structure relationship data. In the preprocessing stage of collision detection, the bounding volume hierarchy (BVH) function is used to perform a rough screening of the deformation trajectory data of the SIM card tray. The PyCollada library of Python is used to read the geometric information of the model, and the BVH is used to generate an axis-aligned bounding box (AABB) for each part of the model. The AABB bounding box update frequency is set to every 10 time steps, and through the bounding box intersection test, the geometric pairs with potential collisions are quickly screened out. For example, at a certain time step in the simulation, it is found that the bounding box of the elastic contact arm intersects with the bounding box of the fixed bracket, so this pair of geometries is added to the candidate collision pair list. After this step, the accessory collision candidate pair data of the SIM card tray is obtained. Based on the collision candidate pair data, the MTV (Minimum Translation Vector) of Python is used for precise collision detection. For each candidate pair, the minimum penetration depth and the collision point position when they collide are calculated. For example, for the candidate pair of the elastic contact arm and the fixed bracket, the collision point coordinates and the collision normal vector are accurately calculated. These detailed information are organized into the precise collision data of the SIM card tray, including the position of the collision point, the collision normal vector, and the penetration depth. Based on the precise collision data, the interference risk of the SIM card tray is quantitatively evaluated by combining Python programming with a custom risk assessment model. The assessment model comprehensively considers factors such as penetration depth, contact area, and stress concentration degree, and the corresponding weight coefficients are set to 0.4, 0.3, and 0.3 respectively. For example, if the penetration depth of a certain collision point is large and it is in a stress concentration area, its risk score will be relatively high. By calculating the risk score of each collision point, the accessory interference risk score of the SIM card tray is obtained. The Pandas library of Python is used to analyze and process the interference risk score data of the SIM card tray. According to the set high-risk threshold R>0.8, medium-risk threshold 0.5<R≤0.8, and low-risk threshold R≤0.5, the collision points in the precise collision data are marked with risk regions. For example, if the risk score of a certain collision point is 0.85, it is marked as a high-risk region, and its position, influence range, and other information are recorded to form the accessory interference risk region data.Based on this risk area data, a risk propagation model was applied using a diffusion equation to describe the risk propagation process within the SIM card tray structure. The propagation attenuation coefficient α was set to 0.1, and the propagation velocity coefficient β was set to 0.05. The range and impact of the risk from the source to the surrounding structure were calculated, resulting in accessory risk propagation impact data. During the comprehensive risk assessment phase, an evaluation index system was established using the Python Matplotlib library combined with the Analytic Hierarchy Process (AHP). The evaluation indicators included geometric interference, stress concentration, and fatigue damage, with weights of 0.3, 0.3, and 0.4, respectively. After expert evaluation determined the weights, the AHP method was used to comprehensively evaluate the SIM card tray's interference risk area data and risk propagation impact data. For example, for a high-risk area, a comprehensive risk assessment value was calculated based on its geometric interference, stress concentration, and potential fatigue damage. Ultimately, the SIM card tray's accessory interference risk assessment data was obtained. After obtaining the SIM card tray's interference risk assessment data, it was combined with the accessory's geometric feature dataset, and physical constraint rule matching was implemented using Python programming. By querying the process knowledge database for physical constraints, such as minimum wall thickness, maximum stress, and deformation limits, and setting a constraint confidence threshold greater than 0.9, the system matches each risk region. For example, for a high-risk interference region, the relevant minimum wall thickness and maximum stress constraints are matched, and corresponding constraints are generated based on the requirements. These constraints are organized into the physical constraints for the SIM card tray assembly. During the SIM card tray design optimization process, a real-time constraint verification engine (such as ANSYS's real-time simulation module) is used to verify the physical constraints and interference risk assessment data in real time. When the designer modifies the SIM card tray's geometric parameters, the system automatically checks the parameters against the physical constraints. For example, if the designer reduces the wall thickness of a spring contact arm, the system immediately verifies the parameters against the minimum wall thickness constraint. If a violation occurs, a constraint violation report is generated, detailing the violation type and severity, and providing recommended modifications. Through real-time verification, the physical interference prediction results for the SIM card tray assembly are obtained.

[0062] Preferably, in step S3, constructing accessory process constraint rules based on the accessory process knowledge database includes:

[0063] Based on the accessory process knowledge database, the process type of the hardware accessory 3D design model is identified to obtain the accessory process type classification data;

[0064] Extract process parameters from the accessory process knowledge database based on accessory process type classification data to obtain accessory process parameter benchmark data;

[0065] Perform parameter correlation analysis on the accessory geometric feature data set based on the accessory process parameter benchmark data to obtain accessory parameter correlation relationship data;

[0066] Calculate the constraint boundary of the accessory process parameter benchmark data based on the accessory parameter association relationship data to obtain the accessory process constraint boundary data;

[0067] Generate constraint rules based on accessory process constraint boundary data and accessory parameter association relationship data to obtain accessory process constraint rules;

[0068] Based on the accessory process constraint rules and the accessory physical interference prediction results, the hardware accessory 3D design model is constrained and adjusted to obtain the accessory process constraint design model.

[0069] In this example, the Python scikit-learn library was used to identify the process type of a 3D design model of a smartphone SIM card tray hardware accessory. Geometric features and process information related to the SIM card tray were extracted from a database of accessory process knowledge. This information included the model's geometry, dimensional accuracy, and material type. A decision tree classification function was used, with the classification feature dimensions set to material type, geometric complexity, and dimensional accuracy requirements. The training sample size was 5,000. The trained classifier was used to identify the process type of the 3D design model of the SIM card tray, ultimately generating process type classification data. The identification results indicated that the SIM card tray primarily involved three process types: stamping, bending, and laser cutting. Based on the process type classification data (stamping, bending, and laser cutting) for the smartphone SIM card tray, process parameters were extracted from the accessory process knowledge database using an SQLite database. SQL queries were written to extract the standard parameter ranges for each process type from the database. For example, for stamping, extracted parameters included punching force, die clearance, and springback angle; for bending, extracted parameters included bend radius, bend angle, and neutral layer offset. Ultimately, baseline process parameter data for the SIM card tray was obtained. After extracting benchmark process parameter data for smartphone SIM card trays, we used the Python pandas library to perform parameter correlation analysis on the accessory geometric feature dataset. The geometric feature data and the benchmark process parameter data were imported into a pandas dataframe. Pearson correlation coefficient analysis was used to calculate the strength of correlation between geometric feature parameters (such as aperture, slot width, and wall thickness) and process parameters (such as punching force and die clearance). A significance level of α was set at 0.05, identifying strong correlations (|r| > 0.8) and moderate correlations (0.5 < |r| ≤ 0.8). For example, a strong correlation was found between the bend radius of the SIM card tray and the die clearance, with a correlation coefficient of 0.85. Finally, we obtained the accessory parameter correlation data for the SIM card tray. Based on the accessory parameter correlation data for smartphone SIM card trays, we used the Python SciPy library to calculate constraint boundaries. A multidimensional constraint space model was established based on the accessory process parameter benchmark data. For example, for the strong correlation between the bend radius of the SIM card tray and the die clearance, the boundaries of the feasible parameter region were calculated. Set the constraint types to equality, inequality, and boundary constraints. Use the Lagrange multiplier method to solve the constrained optimization problem and obtain the constraint boundary function expression for each set of associated parameters. Finally, obtain the SIM card tray assembly process constraint boundary data. Based on this smartphone SIM card tray assembly process constraint boundary data and parameter association data, use the association rule mining function in the mlxtend Python library to generate constraint rules.The minimum support threshold is set to 0.3, and the minimum confidence threshold is set to 0.8. Frequent item sets are mined and constraint rules are generated, such as "IF geometric parameter A THEN process parameter B." For example, a rule is generated: "IF bending radius < 0.5mm THEN mold gap = 0.1mm," indicating that when the bending radius is less than 0.5mm, the mold gap should be set to 0.1mm to meet process requirements. Apriori is used to further optimize the rule set. Finally, the process constraint rules for the SIM card tray are obtained. For the detailed implementation process of step S36, please refer to the substeps of step S36.

[0070] Preferably, in step S3, the constraint adjustment of the hardware accessory 3D design model based on the accessory process constraint rules and the accessory physical interference prediction result includes:

[0071] Quantify the parameter sensitivity of the accessory geometric feature data set to obtain the accessory parameter sensitivity data; prioritize the accessory process constraint rules based on the accessory parameter sensitivity data to obtain the accessory constraint rule priority;

[0072] Based on the priority of the accessory constraint rules, the accessory geometric feature dataset is parametrically modeled to obtain a hardware accessory parametric model;

[0073] An optimization objective function is constructed based on the hardware accessory parameterized model and the physical interference prediction results of the accessory to obtain the accessory optimization objective function; an optimization algorithm is used to solve the hardware accessory parameterized model based on the accessory optimization objective function to obtain the accessory parameter optimization solution data;

[0074] Update the parameters of the hardware accessory parametric model according to the accessory parameter optimization solution data to obtain the hardware accessory optimization parameter set; reconstruct the model geometry based on the hardware accessory optimization parameter set and the accessory process constraint rules to obtain the accessory geometry reconstruction data;

[0075] Perform interference verification on the physical interference prediction results of the accessories based on the geometric reconstruction data of the accessories to obtain the interference verification results of the accessories;

[0076] Iterative optimization is performed based on the component interference verification results and the component geometry reconstruction data to obtain the component optimization convergence judgment data. The iterative optimization judgment includes establishing a convergence judgment criterion, setting the interference risk reduction threshold to >20%, the geometric change to <5%, and the parameter stability coefficient to >0.95. Based on the convergence judgment criterion, it is determined whether the convergence conditions have been met. If not, the optimization is continued and a convergence status record table is established.

[0077] The final model of the accessory geometry reconstruction data is generated according to the accessory optimization convergence judgment data to obtain the accessory process constraint design model.

[0078] In this example, when performing constraint adjustment on the 3D design model of a smartphone SIM card tray hardware accessory, the Python SALib library was used to quantify parameter sensitivity within the accessory's geometric feature dataset. Using the Sobol global sensitivity analysis method, with a Monte Carlo sampling frequency of 10,000, the first-order sensitivity index (Si) and total sensitivity index (STi) of each geometric parameter (such as aperture, slot width, and wall thickness) to the objective function (such as interference risk and stress concentration) were calculated. For example, the analysis revealed that the Si sensitivity index for the SIM card tray's bend radius to interference risk was 0.35, and the total sensitivity index (STi) was 0.45. Based on this sensitivity data, the accessory's process constraints were prioritized. For example, constraints corresponding to parameters with a sensitivity index greater than 0.3 were assigned a high priority. Ultimately, the priority of the SIM card tray's accessory constraint rules was determined. Based on this priority, the SIM card tray's accessory geometric feature dataset was parametrically modeled using SolidWorks. Based on the prioritized results, high-priority parameters (such as bend radius and mold clearance) were selected as design variables. In SolidWorks, the parametric design capabilities were leveraged to establish relationships between these parameters. For example, the relationship between bend radius and mold clearance was defined as mold clearance = 0.2 × bend radius, ensuring compatibility between design variables. By adjusting these key parameters, multiple parametric model instances were generated, ultimately resulting in a parametric model for the SIM card tray hardware accessory. After obtaining the parametric model for the SIM card tray hardware accessory, the optimization objective function was constructed using ANSYS Workbench combined with a Python script. Based on the design requirements and process constraints, a multi-objective optimization function was established: f(x) = w1 × f1 (quality) + w2 × f2 (strength) + w3 × f3 (interference risk), with weight coefficients w1 = 0.3, w2 = 0.4, and w3 = 0.3. Finite element analysis was performed on the parametric model using ANSYS Workbench to obtain performance metrics such as quality, strength, and interference risk. These performance metrics were incorporated into the optimization objective function using a Python script, and a modified particle swarm optimization (PSO) algorithm was used to solve the problem. The number of particles was set to 50, the maximum number of iterations was set to 300, the inertia weight w was linearly decreased from 0.9 to 0.4, and the learning factor c1 = c2 = 2.0. Ultimately, the optimized solution data for the SIM card tray's accessory parameters was obtained. Based on the obtained optimized solution data for the SIM card tray's accessory parameters, SolidWorks was used to update the parameters of the hardware accessory's parametric model. The optimized parameter values ​​were substituted into the model, and the corresponding parameters in the geometric feature dataset were updated. For example, the bend radius was updated to 0.6mm, and the mold gap was correspondingly updated to 0.12mm. Using SolidWorks' parameter propagation mechanism, other associated parameters were automatically updated.After the parameter update, the model geometry was reconstructed using the SolidWorks reconstruction function based on the hardware component optimization parameter set and component process constraints. The reconstructed model was inspected for geometric defects such as self-intersections, overlaps, and gaps, and necessary repairs were performed. The resulting geometric reconstruction data for the SIM card tray assembly was obtained. After obtaining the geometric reconstruction data for the SIM card tray assembly, the reconstructed model was subjected to interference verification using ANSYS Workbench. Finite element analysis was rerun to calculate a new interference risk score, which was then compared with the original predicted results. For example, the risk score for a high-risk area in the original model was 0.85. After geometric reconstruction, the risk score for this area was reduced to 0.65. Improvement evaluation metrics, such as risk reduction rate and geometric change, were set to verify whether the optimization achieved the expected results. Finally, the interference verification results for the SIM card tray assembly were obtained. Based on the SIM card tray assembly interference verification results and the geometric reconstruction data, an iterative optimization evaluation was performed using a Python script. Convergence criteria were established, with interference risk reduction thresholds of >20%, geometric change <5%, and parameter stability coefficient >0.95. For example, after one iteration, the interference risk is reduced by 25%, the geometric change is 3%, and the parameter stability coefficient reaches 0.96, which meets the convergence conditions. If the conditions are not met, return to step S363 to continue optimization, and record the convergence status of each iteration. Finally, the accessory optimization convergence judgment data of the SIM card tray is obtained. According to the SIM card tray accessory optimization convergence judgment data, Solid Works is used to generate the final model of the geometric reconstruction data. The converged geometric data is converted into a standard CAD format (such as STEP or IGES) to generate a complete three-dimensional design model file. Make sure that the model file contains all geometric features, dimensioning, process markings and other information, and add optimization history and constraint verification reports. Finally, the accessory process constraint design model of the SIM card tray is obtained.

[0079] Preferably, step S4 includes the following steps:

[0080] Step S41: extracting surface geometric features of the accessory process constraint design model to obtain accessory surface geometric feature data;

[0081] Step S42: identifying microscopic stress concentration points of the accessory process constraint design model based on the accessory surface geometric feature data to obtain accessory stress concentration point data;

[0082] Step S43: performing fatigue damage prediction on the surface geometric feature data of the accessory according to the stress concentration point data of the accessory to obtain fatigue risk prediction data of the accessory;

[0083] Step S44: identifying manufacturing defect sensitive areas of the accessory process constraint design model based on the accessory fatigue risk prediction data to obtain accessory manufacturing sensitive area data;

[0084] Step S45: performing defect probability statistics on the accessory fatigue risk prediction data based on the accessory manufacturing sensitive area data to obtain accessory defect probability distribution data;

[0085] Step S46: quantifying the risk based on the accessory defect probability distribution data and the accessory manufacturing sensitive area data to obtain accessory defect risk quantification data; and locally refining the accessory process constraint design model based on the accessory defect risk quantification data to obtain a locally refined mesh of the hardware accessory.

[0086] Step S47: performing simulation verification on the accessory defect risk quantification data based on the local refined mesh of the hardware accessory to obtain a refined simulation result of the accessory;

[0087] Step S48: performing a comprehensive defect risk assessment based on the component refinement simulation results and the component defect risk quantification data to obtain a component defect risk score;

[0088] Step S49: performing constraint closed-loop optimization control on the accessory process constraint design model according to the accessory defect risk score to obtain a defect risk control design model.

[0089] In this example, the geometry analysis module of ANSYS Workbench was used to extract surface geometric features from the process constraint design model of the smartphone SIM card tray. The mesh generation tool of ANSYS Workbench was used to refine the mesh of the SIM card tray model, setting the mesh size to 0.03 mm. The curvature distribution of the model surface was calculated, and the Gaussian curvature calculation accuracy was set to 1×. The average curvature was calculated using the Voronoi region weighting method. By identifying regions with abrupt curvature gradients greater than 500 m² / mm, the surface roughness (Ra) distribution of the model was extracted. Finally, surface geometric feature data for the SIM card tray accessory was obtained, including detailed information such as the location, curvature, and gradient of each feature. Based on this surface geometric feature data, the stress analysis module of ANSYS Workbench was used to identify microscopic stress concentration points. Finite element analysis was used to calculate the local stress gradient under actual loading conditions, and the stress concentration factor (Kt) identification threshold was set to 2.5. This analysis identified sharp corners with a fillet radius less than 0.1 mm and transition regions with a thickness change greater than 50%. For example, at the junction of one of the elastic contact arms and the main body of the SIM card tray, the fillet radius was only 0.08 mm, and the stress concentration factor (Kt) in this area reached 3.2, indicating significant stress concentration. Finally, stress concentration point data for the SIM card tray accessory was obtained. After identifying the micro stress concentration points of the SIM card tray, fatigue damage prediction was performed using the fatigue analysis module of ANSYS Workbench. The Paris fatigue crack growth law was used, and the material constant C = 1× and exponent m=3.2. By calculating the crack growth rate under cyclic loading, the fatigue life N=∫C(ΔK)mda is predicted, where ΔK is the stress intensity factor range. A fatigue safety factor of 2.0 is set to assess the fatigue risk of stress concentration points in the model. For example, at the stress concentration point at the connection between the elastic contact arm and the main body, the calculated fatigue life in this area is 10,000 cycles, which is lower than the design requirement of 50,000 cycles, indicating a high fatigue risk in this area. Finally, fatigue risk prediction data for the SIM card tray accessory is obtained. Based on this fatigue risk prediction data, the defect analysis module of ANSYS Workbench is used to identify manufacturing defect-sensitive areas. A manufacturing defect sensitivity assessment model is established, with the stamping burr sensitivity threshold set at a shear angle greater than 15°, the bending springback sensitivity threshold set at a bend radius to material thickness ratio less than 2, and the weld heat-affected zone sensitivity threshold set at a distance less than 3 mm from the weld. Fuzzy logic reasoning is used to comprehensively evaluate the sensitivity levels of each model region, identifying potential manufacturing defect-sensitive areas in the SIM card tray. For example, in a certain stamping forming area of ​​the SIM card tray, the shear angle reaches 20°, and the fatigue risk of this area is relatively high, so it is identified as a stamping burr sensitive area. Finally, the accessory manufacturing sensitive area data of the SIM card tray is obtained. After identifying the manufacturing sensitive area of ​​the SIM card tray, the Python SciPy library is used to perform defect probability statistics on the accessory fatigue risk prediction data. The Weibull distribution model is used to describe the defect occurrence probability P(t)=1−exp(−(ηt)β), the shape parameter β=1.5 is set, and the scale parameter η is determined based on historical data fitting. Through calculation, the defect probability distribution data of each manufacturing sensitive area of ​​the SIM card tray is obtained. For example, in the above-mentioned stamping burr sensitive area, the probability of defect occurrence is calculated to be 0.15, that is, there is a 15% probability of burr defects. Finally, the accessory defect probability distribution data of the SIM card tray is obtained. Based on the accessory defect probability distribution data and manufacturing sensitive area data of the SIM card tray, the mesh refinement function of ANSYSWorkbench is used to quantify the risk. According to the risk quantification model:

[0090] Risk=Probability×Consequence×Exposure, the consequence severity scoring standard is set to 1-10 points, and the exposure frequency assessment uses the average annual load cycle number. The comprehensive risk index RPN=P×S×O is calculated, where P is the probability of occurrence, S is the severity, and O is the detection rate. For high-risk areas, an adaptive mesh refinement function is used, and the refinement criterion is set to the area where RPN>100, and the mesh size refinement ratio is 1:4. Finally, the local refined mesh of the hardware accessories of the SIM card tray is obtained. After obtaining the local refined mesh of the hardware accessories of the SIM card tray, the explicit dynamics module of ANSYS Workbench is used for refined simulation verification. Set the time step Δt=1× s, the central difference method is used to solve the equation of motion, taking into account material nonlinearity, geometric nonlinearity, and contact nonlinearity. Through simulation, the local strain energy density distribution is calculated to verify the accuracy of the defect prediction. For example, in the above-mentioned stamping burr sensitive area, the simulation results show that the strain energy density in this area is significantly higher than that in other areas, further confirming the high-risk characteristics of this area. Finally, the refined simulation results of the SIM card tray accessories are obtained. After obtaining the refined simulation results of the SIM card tray accessories, the risk assessment module of ANSYS Workbench is used to perform a comprehensive defect risk assessment. Combined with the risk quantification data of step S46, a multi-level risk assessment system is established. The hierarchical analysis method is used to determine the evaluation index weights, setting the geometric defect risk weight to 0.3, the stress concentration risk weight to 0.25, the fatigue damage risk weight to 0.25, and the manufacturing defect risk weight to 0.2. Calculate the comprehensive risk score R=∑wi×ri, where wi is the weight and ri is the score of each risk indicator. Based on the comprehensive risk score, the defect risk score of the SIM card tray accessories is obtained. For example, after evaluation, a key area's comprehensive risk score is 75, indicating a high defect risk and requiring further design optimization. This ultimately yields a defect risk score for the SIM card tray. For detailed implementation of step S49, please refer to the substeps of step S49.

[0091] Preferably, step S49 includes the following steps:

[0092] Step S491: performing local geometric adjustments on the accessory process constraint design model based on the accessory defect risk score to obtain a local optimization solution for the accessory;

[0093] Step S492: formulating a compensation strategy for the accessory defect risk score according to the accessory local optimization solution to obtain accessory risk compensation strategy data;

[0094] Step S493: performing structural compensation on the accessory process constraint design model based on the accessory risk compensation strategy data to obtain an accessory structural compensation solution;

[0095] Step S494: adjusting the material thickness distribution of the accessory process constraint design model according to the accessory structure compensation solution to obtain accessory thickness optimization distribution data;

[0096] Step S495: generating accessory adaptive adjustment parameters based on the accessory thickness optimization distribution data and the accessory structure compensation solution;

[0097] Step S496: performing a final structural update on the accessory process constraint design model according to the accessory adaptive adjustment parameters to obtain a defect risk control design model.

[0098] In this embodiment, SolidWorks software was used to perform local geometric adjustments to the process constraint design model for the smartphone SIM card tray. Based on the defect risk scores obtained in the previous steps, high-risk areas were identified, such as a fillet radius that was too small, leading to stress concentration. In SolidWorks, the fillet feature was selected and its radius increased from 0.08mm to 0.12mm. The geometry of adjacent structures was adjusted to ensure the coordination of the overall design. For example, the thickness of the elastic contact arm was fine-tuned from 0.25mm to 0.28mm to reduce stress concentration and improve fatigue life. These adjustments formed a local optimization solution for the SIM card tray accessory. Based on the obtained local optimization solution for the SIM card tray accessory, a compensation strategy was developed using a Python script combined with ANSYS Workbench simulation results. The defect risk scores were recalculated for the adjusted fillet radius and elastic contact arm thickness. For example, the new fillet radius reduced the stress concentration factor from 3.2 to 2.8 and increased the fatigue life from 10,000 cycles to 25,000 cycles. Based on these improvement results, compensation strategy data was developed, including geometric adjustment recommendations for further optimization and manufacturing process improvements. For example, it was recommended to fine-tune fillets during manufacturing to ensure that their radius tolerances were within ±0.01mm. This resulted in the SIM card tray's accessory risk compensation strategy data. Based on this SIM card tray accessory risk compensation strategy data, structural compensation was performed using SolidWorks and ANSYS Workbench. In SolidWorks, geometric adjustments were made to high-risk areas of the SIM card tray based on the compensation strategy data. For example, reinforcing ribs were added in stress concentration areas, with a width of 0.5mm and a thickness of 0.3mm. Finite element analysis was re-performed in ANSYS Workbench to verify that the adjusted structure met design and process requirements. Through these steps, a structural compensation solution for the SIM card tray was ultimately determined. After determining the structural compensation solution, the material thickness distribution of the model was adjusted using HyperMesh software. Based on the simulation results and optimization requirements, areas requiring adjustment were identified. For example, in the root area of ​​the elastic contact arm, the material thickness was increased from 0.28mm to 0.32mm to further reduce stress concentration. In non-critical areas, the thickness was appropriately reduced, such as from 0.4mm to 0.35mm, to reduce overall weight. These adjustments yielded optimized distribution data for the SIM card tray's accessory thickness. Based on the resulting optimized distribution data and structural compensation scheme, adaptive adjustment parameters were generated using Python scripts and ANSYS Workbench. By analyzing the optimized thickness distribution and structural changes, adjustment parameters such as thickness change, position coordinates, and adjustment range were calculated.For example, the thickness increase at the root of the elastic contact arm is recorded as 0.04 mm, and the adjustment range is a specific coordinate area. The parametric function of ANSYS Workbench is used to verify the feasibility of these adjustment parameters in actual manufacturing, and the adaptive adjustment parameters of the SIM card tray accessories are obtained. After obtaining the adaptive adjustment parameters of the SIM card tray accessories, SolidWorks and ANSYS Workbench are used to perform the final structural update of the process constraint design model. In SolidWorks, the adaptive adjustment parameters are applied to the model, and the geometric features and dimensioning are automatically updated. For example, based on the thickness optimization distribution data, the thickness of the elastic contact arm is adjusted, and the position and size of the reinforcement ribs are updated. The simulation analysis is re-performed in ANSYS Workbench to verify whether the updated model meets all design and manufacturing requirements. Finally, the defect risk control design model of the SIM card tray is obtained.

[0099] Preferably, step S5 includes the following steps:

[0100] Step S51: performing manufacturing process path planning on the defect risk control design model to obtain the accessory manufacturing process path;

[0101] Step S52: performing process simulation modeling on the defect risk control design model based on the accessory manufacturing process path to obtain an accessory process simulation model;

[0102] Step S53: simulating the forming process of the accessory manufacturing process path according to the accessory process simulation model to obtain the accessory forming simulation result;

[0103] Step S54: performing dimensional accuracy prediction on the accessory process simulation model based on the accessory forming simulation result to obtain accessory dimensional accuracy prediction data;

[0104] Step S55: performing quality defect prediction based on the accessory dimensional accuracy prediction data and the accessory forming simulation results to obtain accessory quality defect prediction data;

[0105] Step S56: performing a comprehensive manufacturing feasibility assessment on the accessory dimensional accuracy prediction data based on the accessory quality defect prediction data to obtain accessory manufacturing feasibility assessment data;

[0106] Step S57: Estimating the manufacturing cost of the defect risk control design model based on the accessory manufacturing feasibility assessment data to obtain accessory manufacturing cost data;

[0107] Step S58: performing an economic benefit evaluation based on the accessory manufacturing cost data and the accessory manufacturing feasibility evaluation data to obtain accessory economic benefit evaluation data; performing a comprehensive manufacturing forecast on the accessory manufacturing feasibility evaluation data based on the accessory economic benefit evaluation data to obtain accessory manufacturing forecast data;

[0108] Step S59: Perform closed-loop quality control on the defect risk control design model based on the parts manufacturing prediction data to obtain a final quality control design data package.

[0109] In this embodiment, UG NX software is used when planning the manufacturing process path for the defect risk control design model of the smartphone SIM card tray. According to the geometric characteristics and manufacturing requirements of the SIM card tray, the process planning module of UG NX is used to generate a manufacturing process sequence. For example, the order of the main processes such as stamping, bending, and finishing is determined, and the processing parameters of each process are set. The stamping process sets the stamping speed to 10mm / s and the die gap to 0.1mm; the bending process sets the bending radius to 0.3mm and the bending angle to 90°; the finishing process sets the cutting speed to 100m / min and the feed speed to 0.1mm / rev. Through these parameter settings, a detailed process path decision tree is generated, and the processing time and cost of each process are calculated, and finally the accessory manufacturing process path of the SIM card tray is obtained. Based on the accessory manufacturing process path of the SIM card tray, ANSYS Workbench is used to perform process simulation modeling. A multi-process coupling simulation model is generated according to the process path. For example, in the stamping process, the explicit dynamics module of ANSYS Workbench is used to simulate the forming process of the material, and the material model is set to elastic-plastic and the yield criterion is Von Mises. In the bending process, the stress and strain distribution in the bending process is simulated, and the friction coefficient is set to 0.15. Through these settings, a detailed process simulation model is established, and finally the accessory process simulation model of the SIM card tray is obtained. Based on the accessory process simulation model of the SIM card tray established in step S52, ANSYS Workbench is used to simulate the forming process. Perform multi-step forming simulation analysis to calculate the stress and strain distribution, springback amount, wall thickness change, etc. after each process. For example, in the stamping process, the stress and strain distribution of the material is calculated, and it is found that the strain in some areas reaches 15%, which is close to the yield strain of the material of 20%. In the bending process, the springback is calculated to be 2°, and it is compensated by adjusting the mold angle. Set the simulation time step to 1× s, using mass scaling technology to improve computational efficiency. Ultimately, simulation results for the SIM card tray accessory forming were obtained, including forming force-stroke curves, forming energy consumption, and die wear predictions. Based on these simulation results, dimensional accuracy prediction was performed using Minitab software. A dimensional variation source analysis model was established, with a process capability index (Cpk) greater than 1.33 set as the acceptance criterion. Uncertainty analysis was performed using the Monte Carlo method, with a simulation run count of 10,000. For example, when predicting the tolerance distribution of a key aperture dimension in the SIM card tray, the process capability index (Cpk) was found to be 1.45, indicating that the dimensional accuracy met design requirements. Finally, dimensional accuracy prediction data for the SIM card tray accessory was obtained. Based on this dimensional accuracy prediction data, quality defect prediction was performed using a Python script combined with ANSYS Workbench simulation results. A quality defect prediction model was established and a defect recognition model was trained. The training sample consisted of 8,000 historical cases, with characteristic variables including stress distribution, strain gradient, and thickness variation. The prediction accuracy target was set at 92%. For example, the probability of cracks occurring in a certain area of ​​a SIM card tray during actual manufacturing is predicted to be 5%, which is below the acceptable risk threshold of 10%. This results in predicted quality defect data for the SIM card tray accessories. Based on this data, a comprehensive manufacturing feasibility assessment is conducted in MATLAB using the fuzzy logic toolbox. A manufacturing feasibility assessment model is established, employing a fuzzy comprehensive evaluation method. Evaluation criteria include three primary indicators: technical feasibility, economic feasibility, and quality feasibility, each with four secondary indicators. Weighting coefficients are determined using an expert scoring method, for example, with a weight of 0.4 for technical feasibility, 0.3 for economic feasibility, and 0.3 for quality feasibility. A comprehensive feasibility index (MFI) is calculated, with an MFI ≥ 0.8 indicating high feasibility. This results in manufacturing feasibility assessment data for the SIM card tray accessories. Based on this data, manufacturing costs are estimated using Excel. A manufacturing cost estimation model is developed, encompassing four categories: material cost, processing cost, mold cost, and energy cost. For example, when calculating material costs, based on the density of 8.0 g / cm³ of 304 stainless steel and the volume of 2.5 cm³ for a SIM card tray, the material cost is 0.5 RMB. Processing costs are calculated based on the processing time and equipment depreciation of each process. For example, the processing cost for stamping is 1.2 RMB, for bending 0.8 RMB, and for finishing 1.5 RMB. This ultimately yields manufacturing cost data for the SIM card tray accessories. Based on this manufacturing cost data and manufacturing feasibility assessment data, an economic benefit assessment was conducted using MATLAB. Economic indicators such as the net present value (NPV), internal rate of return (IRR), and payback period (PP) were calculated, using a 10% discount rate and a five-year analysis period.For example, the calculated NPV is 250,000 yuan, the IRR is 18%, and the PP is 3.2 years. Considering factors such as equipment depreciation, labor costs, and indirect expenses, a sensitivity analysis model is established to assess the impact of changes in key parameters on economic benefits. Ultimately, economic benefit evaluation data for the SIM card tray accessory is obtained, and a comprehensive manufacturing forecast is performed to generate accessory manufacturing forecast data.

[0110] It is particularly important that step S59 further includes the following steps:

[0111] Step S591: performing a design stability assessment on the defect risk control design model based on the component manufacturing prediction data to obtain component design stability data;

[0112] Step S592: performing a manufacturing consistency assessment based on the accessory design stability data and the accessory manufacturing prediction data to obtain accessory manufacturing consistency data;

[0113] Step S593: quantifying the design maturity of the accessory design stability data based on the accessory manufacturing consistency data to obtain an accessory design maturity score;

[0114] Step S594: Perform dynamic closed-loop feedback and optimization on the defect risk control design model based on the parts manufacturing prediction data and the parts design maturity score to obtain the final quality control design data package.

[0115] It is particularly important that step S594 further includes the following steps:

[0116] Perform closed-loop feedback optimization based on the accessory design maturity score and accessory manufacturing forecast data to obtain accessory closed-loop feedback optimization data;

[0117] Based on the closed-loop feedback optimization data of the accessories, the quality control rules of the accessories design maturity score are updated to obtain the accessories quality control update rules;

[0118] The final data package is generated according to the accessory quality control update rules and the accessory closed-loop feedback optimization data to obtain the final quality control design data package.

[0119] In this embodiment, MATLAB software was used to evaluate the design stability of the defect risk control design model for a smartphone SIM card tray. Based on the obtained manufacturing prediction data, MATLAB's statistical analysis function was used to calculate the degree of impact of design parameter changes on performance indicators. For example, the parameter variation range for the key dimensional parameters of the SIM card tray (such as aperture, slot width, wall thickness, etc.) was set to ±5%, and its impact on the overall structural strength and functional performance was calculated. Through calculation, the stability index SI of the SIM card tray was obtained, where SI = 0.08, indicating that the design has high stability. Finally, the design stability data of the SIM card tray accessories was obtained. Based on the obtained design stability data of the SIM card tray accessories, Minitab software was used to perform a manufacturing consistency assessment. Using Minitab's process capability analysis function, process capability indices such as Cp, Cpk, Pp, and Ppk were calculated. For example, for the SIM card tray stamping process, the calculated Cp=1.45, Cpk=1.32, Pp=1.42, and Ppk=1.28 were all above the established consistency assessment criteria (Cpk>1.33 is considered excellent), indicating good consistency and systematicity in the manufacturing process. By analyzing the systematic deviations and random variations in the manufacturing process, the manufacturing consistency data for the SIM card tray components was finally obtained. Based on this manufacturing consistency data, the design maturity was quantified using MATLAB software. The Technology Readiness Level (TRL) assessment framework was adopted, with nine maturity levels covering basic research, proof of concept, prototype development, and system integration. A maturity scoring matrix was established, comprehensively considering technology maturity, manufacturing maturity, and quality maturity. For example, based on the consistency assessment results, the SIM card tray's technology maturity was TRL7 (system prototype successfully demonstrated in a real-world environment), manufacturing maturity was TRL8 (actual system completed and tested), and quality maturity was TRL8 (consistent production quality and reliability). A comprehensive scoring system yielded a comprehensive design maturity score of 7.8 for the SIM card tray, indicating that the design has reached a high level of maturity. Based on the obtained accessory design maturity score and manufacturing forecast data for the SIM card tray, closed-loop feedback optimization was performed using MATLAB software. The PID control principle was implemented using MATLAB's Control System Toolbox, with feedback control parameters Kp = 0.8, Ki = 0.1, and Kd = 0.05. For example, based on the deviation between the predicted manufacturing cost and the actual cost, a feedback signal was generated to adjust the design parameters and optimize the manufacturing process. The generated closed-loop feedback optimization data included the adjusted design parameters and the optimized process path. Based on the closed-loop feedback optimization data for the SIM card tray, quality control rules were updated using MATLAB software. A reinforcement learning approach was employed, with a learning rate α = 0.01 and a discount factor γ = 0.9. Quality control rules were updated based on the feedback optimization data.For example, the tolerance of certain key dimensions was tightened from ±0.1mm to ±0.05mm, and the inspection frequency of key processes was increased. This ultimately resulted in updated quality control rules for the SIM card tray's accessories. Based on these updated quality control rules and closed-loop feedback optimization data, MATLAB software was used to generate the final data package. All quality control analysis results, optimized design models, updated quality control rules, and other information were integrated to generate a standardized final quality control design data package. This data package included design model files, process procedure documents, quality control standards, inspection specifications, and manufacturing instructions. Ultimately, the final quality control design data package for the SIM card tray was obtained.

[0120] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0121] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A quality control method for hardware accessories three-dimensional model design, characterized in that: The following steps are involved: Step S1: Obtain a three-dimensional design model of a hardware accessory; Conduct integrated process sensitivity assessment based on the 3D design model of hardware accessories to obtain process sensitive feature data of accessories; Build an accessories process knowledge database based on accessories process sensitive feature data; Step S2: performing elastic deformation simulation on the hardware accessories based on the accessories process knowledge database to obtain deformation trajectory data of the hardware accessories; Dynamic interference risk assessment and real-time verification are performed based on the deformation trajectory data of the hardware accessories to obtain the physical interference prediction results of the accessories; wherein, the dynamic interference risk assessment and real-time verification based on the deformation trajectory data of the hardware accessories in step S2 includes: Perform spatial proximity recognition on the deformation trajectory data of hardware accessories to obtain proximity structural relationship data of accessories; Based on the adjacent structural relationship data of the accessories, the deformation trajectory data of the hardware accessories is preprocessed for collision detection to obtain the data of accessory collision candidate pairs. The bounding box hierarchical tree structure is used for coarse screening. The AABB bounding box update frequency is set to every 10 time steps. The bounding box intersection test is used to screen out potential collision geometric pairs and establish a candidate collision pair list. Perform accurate collision detection on the deformation trajectory data of hardware accessories based on the accessory collision candidate pair data to obtain accurate collision data of the hardware accessories; Based on the precise collision data of hardware accessories, the interference risk of the adjacent structural relationship data of the accessories is quantitatively evaluated to obtain the accessory interference risk score; Based on the accessory interference risk score, the risk area of ​​the hardware accessory precise collision data is marked to obtain the accessory interference risk area data; based on the accessory interference risk area data and the accessory interference risk score, a risk propagation assessment is performed to obtain the accessory risk propagation impact data; Conduct a comprehensive risk assessment on the accessory interference risk area data based on the accessory risk propagation impact data to obtain the accessory interference risk assessment data; Based on the accessory interference risk assessment data and the accessory geometric feature data set, physical constraint rules are matched to obtain the accessory physical constraint rules; Real-time verification is performed based on the physical constraint rules of the parts and the parts interference risk assessment data to obtain the physical interference prediction results of the parts; Step S3: constructing accessory process constraint rules based on the accessory process knowledge database; performing constraint adjustment on the hardware accessory 3D design model based on the accessory process constraint rules and the accessory physical interference prediction result to obtain the accessory process constraint design model; Step S4: performing a micro-feature risk assessment on the accessory process constraint design model to obtain an accessory defect risk score; performing constraint closed-loop optimization control on the accessory process constraint design model based on the accessory defect risk score to obtain a defect risk control design model; Step S5: Perform manufacturing feasibility simulation verification on the defect risk control design model to obtain accessory manufacturing prediction data; perform closed-loop quality control on the defect risk control design model based on the accessory manufacturing prediction data to obtain a final quality control design data package.

2. The quality control method for hardware accessories three-dimensional model design according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire a three-dimensional design model of a hardware accessory; convert the format of the three-dimensional design model of the hardware accessory to obtain a geometric feature dataset of the accessory; Step S12: performing topological structure recognition on the accessory geometric feature data set to obtain hardware accessory topological relationship data; Step S13: performing feature classification and recognition on the accessory geometric feature data set based on the hardware accessory topological relationship data to obtain accessory basic feature classification data; Step S14: extracting size parameters from the basic feature classification data of the accessory to obtain a set of accessory feature size parameters; Step S15: performing microstructure refinement extraction on the basic feature classification data of the accessory according to the accessory feature size parameter set to obtain an accessory microfeature data set; Step S16: performing material attribute association on the accessory microscopic feature data set to obtain accessory material feature association data; Step S17: Performing a manufacturing process sensitivity assessment on the accessory micro-feature dataset based on the accessory material feature association data to obtain accessory process sensitive feature data. The manufacturing process sensitivity assessment includes setting a process sensitivity assessment matrix, with a stamping process sensitivity threshold of an aspect ratio greater than 10:1, a bending process sensitivity threshold of a bending radius less than 2 times the material thickness, and a laser cutting sensitivity threshold of a slit width less than 0.1 mm. Each feature is assigned a sensitivity score based on the process sensitivity assessment matrix. Step S18: Constructing an accessory process knowledge database based on the accessory process sensitive feature data.

3. The quality control method for hardware accessories three-dimensional model design according to claim 2 is characterized in that: Step S18 includes the following steps: Step S181: semantically annotating the process-sensitive feature data of the accessory to obtain semantic hierarchical data of the accessory feature; Step S182: performing multi-scale annotation on the accessory process-sensitive feature data according to the accessory feature semantic hierarchical data to obtain accessory feature semantic labels; performing process knowledge matching retrieval on the accessory feature semantic labels to obtain an accessory process knowledge candidate set; Step S183: Calculating the knowledge association strength of the accessory feature semantic labels based on the accessory process knowledge candidate set to obtain accessory knowledge association weight data; Step S184: hierarchically organizing the accessory process knowledge candidate set according to the accessory knowledge association weight data to obtain an accessory process knowledge hierarchical structure; Step S185: constructing a graph node for the accessory knowledge association weight data based on the accessory process knowledge hierarchy to obtain an accessory process knowledge graph node; Step S186: constructing relationship edges based on the accessory process knowledge graph nodes and the accessory knowledge association weight data to obtain accessory process knowledge relationship edge data; Step S187: Construct an accessory process knowledge database based on the accessory process knowledge graph nodes and the accessory process knowledge relationship edge data.

4. The quality control method for hardware accessories three-dimensional model design according to claim 1 is characterized in that: In step S2, elastic deformation simulation of hardware accessories based on the accessory process knowledge database includes: Based on the accessory process knowledge database, the material constitutive model is matched to the accessory geometric feature data set to obtain the hardware accessory material constitutive parameters; According to the constitutive parameters of the hardware accessories material, the finite element mesh of the accessories is discretized into a finite element mesh; Set boundary conditions for the constitutive parameters of hardware accessories based on the finite element mesh of the accessories to obtain the boundary condition data for the accessories simulation; Perform elastic deformation calculation on the finite element mesh of the accessory according to the accessory simulation boundary condition data to obtain the accessory node displacement field data; Based on the displacement field data of the accessory nodes, the stress field of the accessory finite element mesh is calculated to obtain the stress distribution data of the accessory; The deformation trajectory data of the hardware accessories is obtained by tracking the deformation trajectory based on the stress distribution data of the accessories and the displacement field data of the accessory nodes.

5. The quality control method for hardware accessories three-dimensional model design according to claim 1 is characterized in that: In step S3, the construction of accessory process constraint rules based on the accessory process knowledge database includes: Based on the accessory process knowledge database, the process type of the hardware accessory 3D design model is identified to obtain the accessory process type classification data; Extract process parameters from the accessory process knowledge database based on accessory process type classification data to obtain accessory process parameter benchmark data; Perform parameter correlation analysis on the accessory geometric feature data set based on the accessory process parameter benchmark data to obtain accessory parameter correlation relationship data; Calculate the constraint boundary of the accessory process parameter benchmark data based on the accessory parameter association relationship data to obtain the accessory process constraint boundary data; Generate constraint rules based on accessory process constraint boundary data and accessory parameter association relationship data to obtain accessory process constraint rules; Based on the accessory process constraint rules and the accessory physical interference prediction results, the hardware accessory 3D design model is constrained and adjusted to obtain the accessory process constraint design model.

6. The quality control method for hardware accessories three-dimensional model design according to claim 1, characterized in that: In step S3, the constraint adjustment of the hardware accessory 3D design model based on the accessory process constraint rules and the accessory physical interference prediction results includes: Quantify the parameter sensitivity of the accessory geometric feature data set to obtain the accessory parameter sensitivity data; prioritize the accessory process constraint rules based on the accessory parameter sensitivity data to obtain the accessory constraint rule priority; Based on the priority of the accessory constraint rules, the accessory geometric feature dataset is parametrically modeled to obtain a hardware accessory parametric model; An optimization objective function is constructed based on the hardware accessory parameterized model and the physical interference prediction results of the accessory to obtain the accessory optimization objective function; an optimization algorithm is used to solve the hardware accessory parameterized model based on the accessory optimization objective function to obtain the accessory parameter optimization solution data; Update the parameters of the hardware accessory parametric model according to the accessory parameter optimization solution data to obtain the hardware accessory optimization parameter set; reconstruct the model geometry based on the hardware accessory optimization parameter set and the accessory process constraint rules to obtain the accessory geometry reconstruction data; Perform interference verification on the physical interference prediction results of the accessories based on the geometric reconstruction data of the accessories to obtain the interference verification results of the accessories; Iterative optimization is performed based on the component interference verification results and the component geometry reconstruction data to obtain the component optimization convergence judgment data. The iterative optimization judgment includes establishing a convergence judgment criterion, setting the interference risk reduction threshold to >20%, the geometric change to <5%, and the parameter stability coefficient to >0.

95. Based on the convergence judgment criterion, it is determined whether the convergence conditions have been met. If not, the optimization is continued and a convergence status record table is established. The final model of the accessory geometry reconstruction data is generated according to the accessory optimization convergence judgment data to obtain the accessory process constraint design model.

7. The quality control method for hardware accessories three-dimensional model design according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: extracting surface geometric features of the accessory process constraint design model to obtain accessory surface geometric feature data; Step S42: identifying microscopic stress concentration points of the accessory process constraint design model based on the accessory surface geometric feature data to obtain accessory stress concentration point data; Step S43: performing fatigue damage prediction on the surface geometric feature data of the accessory according to the stress concentration point data of the accessory to obtain fatigue risk prediction data of the accessory; Step S44: identifying manufacturing defect sensitive areas of the accessory process constraint design model based on the accessory fatigue risk prediction data to obtain accessory manufacturing sensitive area data; Step S45: performing defect probability statistics on the accessory fatigue risk prediction data based on the accessory manufacturing sensitive area data to obtain accessory defect probability distribution data; Step S46: quantifying the risk based on the accessory defect probability distribution data and the accessory manufacturing sensitive area data to obtain accessory defect risk quantification data; and locally refining the accessory process constraint design model based on the accessory defect risk quantification data to obtain a locally refined mesh of the hardware accessory. Step S47: performing simulation verification on the accessory defect risk quantification data based on the local refined mesh of the hardware accessory to obtain a refined simulation result of the accessory; Step S48: performing a comprehensive defect risk assessment based on the component refinement simulation results and the component defect risk quantification data to obtain a component defect risk score; Step S49: performing constraint closed-loop optimization control on the accessory process constraint design model according to the accessory defect risk score to obtain a defect risk control design model.

8. The quality control method for hardware accessories three-dimensional model design according to claim 7, characterized in that: Step S49 includes the following steps: Step S491: performing local geometric adjustments on the accessory process constraint design model based on the accessory defect risk score to obtain a local optimization solution for the accessory; Step S492: formulating a compensation strategy for the accessory defect risk score according to the accessory local optimization solution to obtain accessory risk compensation strategy data; Step S493: performing structural compensation on the accessory process constraint design model based on the accessory risk compensation strategy data to obtain an accessory structural compensation solution; Step S494: adjusting the material thickness distribution of the accessory process constraint design model according to the accessory structure compensation solution to obtain accessory thickness optimization distribution data; Step S495: generating accessory adaptive adjustment parameters based on the accessory thickness optimization distribution data and the accessory structure compensation solution; Step S496: performing a final structural update on the accessory process constraint design model according to the accessory adaptive adjustment parameters to obtain a defect risk control design model.

9. The quality control method for hardware accessories three-dimensional model design according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing manufacturing process path planning on the defect risk control design model to obtain the accessory manufacturing process path; Step S52: performing process simulation modeling on the defect risk control design model based on the accessory manufacturing process path to obtain an accessory process simulation model; Step S53: simulating the forming process of the accessory manufacturing process path according to the accessory process simulation model to obtain the accessory forming simulation result; Step S54: performing dimensional accuracy prediction on the accessory process simulation model based on the accessory forming simulation result to obtain accessory dimensional accuracy prediction data; Step S55: performing quality defect prediction based on the accessory dimensional accuracy prediction data and the accessory forming simulation results to obtain accessory quality defect prediction data; Step S56: performing a comprehensive manufacturing feasibility assessment on the accessory dimensional accuracy prediction data based on the accessory quality defect prediction data to obtain accessory manufacturing feasibility assessment data; Step S57: Estimating the manufacturing cost of the defect risk control design model based on the accessory manufacturing feasibility assessment data to obtain accessory manufacturing cost data; Step S58: performing an economic benefit evaluation based on the accessory manufacturing cost data and the accessory manufacturing feasibility evaluation data to obtain accessory economic benefit evaluation data; performing a comprehensive manufacturing forecast on the accessory manufacturing feasibility evaluation data based on the accessory economic benefit evaluation data to obtain accessory manufacturing forecast data; Step S59: Perform closed-loop quality control on the defect risk control design model based on the parts manufacturing prediction data to obtain a final quality control design data package.

Images