Data-driven electronic equipment parameterized model automatic generation method

Abstract

The invention relates to the technical field of parameterization design and automatic modeling, in particular to a data-driven electronic equipment parameterization model automatic generation method. The method comprises the following steps: acquiring electronic equipment data, and performing size anomaly analysis to obtain size anomaly data; performing size optimization on the electronic equipment data based on the size abnormal data, generating electronic equipment optimization data, and constructing an electronic equipment model; extracting a heat dissipation structure based on the electronic equipment optimization data, and performing hotspot accumulation analysis on the electronic equipment model to obtain hotspot accumulation structure data; performing heat dissipation enhancement based on the hotspot accumulation structure data to generate heat dissipation enhanced structure data; and performing multi-band resonance analysis on the electronic equipment model to obtain vibration data. Based on parameterization design and an automatic modeling technology, the optimization rate of electronic equipment design is remarkably improved, and the heat dissipation efficiency, the structural strength and the stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of parametric design and automated modeling, and in particular to a data-driven method for automatically generating a parametric model of an electronic device. Background Art

[0002] Traditional data-driven methods for automatically generating parametric models of electronic devices suffer from low parameter optimization efficiency. They primarily employ single-objective optimization approaches, such as optimizing only for size or material. This makes it difficult to simultaneously address multiple design objectives, such as structural strength, heat dissipation efficiency, and manufacturing cost. The optimization process relies on manual parameter adjustment, resulting in high computational complexity, long iteration cycles, and difficulty in rapidly responding to design changes. Traditional methods lack intelligent optimization capabilities and rely primarily on pre-set rules and formulas for calculations. They are unable to implement dynamic adjustments and adaptive optimization, and fail to fully utilize artificial intelligence algorithms such as deep learning and reinforcement learning. This results in a low degree of automation and still requires significant manual intervention, especially in optimizing heat dissipation structures and adjusting air duct layouts. Computational cost is also a major concern. Traditional methods typically employ finite element analysis (FEA) and computational fluid dynamics (CFD) simulations, requiring hours or even days for a single optimization run. For complex structures, such as multi-duct heat dissipation layouts and multi-layer heat sink designs, the computational complexity is enormous, and the optimization process is prone to falling into local optima. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a data-driven method for automatically generating a parametric model of an electronic device to solve at least one of the above technical problems.

[0004] To achieve the above object, a data-driven method for automatically generating a parametric model of an electronic device includes the following steps:

[0005] Step S1: acquiring electronic device data and performing dimensional anomaly analysis to obtain dimensional anomaly data; optimizing the electronic device data based on the dimensional anomaly data to generate electronic device optimization data, and constructing an electronic device model;

[0006] Step S2: extracting a heat dissipation structure based on the electronic device optimization data, and performing hotspot accumulation analysis on the electronic device model to obtain hotspot accumulation structure data; performing heat dissipation enhancement based on the hotspot accumulation structure data to generate heat dissipation enhancement structure data;

[0007] Step S3: performing multi-band resonance analysis on the electronic device model to obtain vibration data; performing stress point fracture analysis on the electronic device model based on the vibration data to obtain stress point fracture data; performing multi-scale stress structure optimization on the stress point fracture data to obtain multi-scale stress structure optimization data;

[0008] Step S4: parameterizing the electronic device model according to the heat dissipation enhancement structure data and the multi-scale force structure optimization data to generate a parameterized model of the electronic device.

[0009] By acquiring electronic device data and performing dimensional anomaly analysis, the present invention can accurately identify unreasonable dimensions within the design, ensuring that designs that do not meet structural requirements are avoided during the optimization process, thereby achieving dimensional optimization. Generating optimized data and constructing an electronic device model provides a reliable foundation for subsequent analysis and optimization, enabling faster response to design changes and avoiding the frequent manual intervention required by traditional methods. By extracting optimized data and performing hotspot accumulation analysis, high-temperature areas within the electronic device can be precisely identified, enabling timely enhancement of the heat dissipation structure and optimization of the heat dissipation design, effectively improving heat dissipation efficiency. This process not only improves heat dissipation performance but also ensures stable operation of the device under high loads or harsh environments, reducing the reliance on manual adjustments to the heat dissipation structure required in traditional methods. In terms of vibration analysis, multi-band resonance analysis can help identify potential vibration issues, and stress point fracture analysis can accurately calculate potential structural risks to the device. Multi-scale load-bearing structure optimization allows for stress analysis at multiple levels, improving the strength and stability of the support structure and ensuring efficient and stable performance under various operating conditions. By integrating the heat dissipation enhancement structure data with the multi-scale load-bearing structure optimization data, the electronic device model is parameterized to generate a dynamically adjustable electronic device model. This intelligent design model can adaptively optimize based on real-time data and demand, which not only greatly improves design efficiency, but also ensures that the equipment achieves the optimal balance in multiple aspects such as structural strength, heat dissipation efficiency and manufacturing cost, thereby significantly reducing computing costs and avoiding the defects of traditional optimization methods such as huge computational complexity and easy to fall into local optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 A schematic flow chart of the steps of a data-driven method for automatically generating a parametric model of an electronic device according to the present invention;

[0012] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, 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 creative efforts are within the scope of protection of the present invention.

[0014] 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.

[0015] 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.

[0016] To achieve this, please refer to Figure 1 The present invention provides a data-driven method for automatically generating a parametric model of an electronic device, the method comprising the following steps:

[0017] Step S1: acquiring electronic device data and performing dimensional anomaly analysis to obtain dimensional anomaly data; optimizing the electronic device data based on the dimensional anomaly data to generate electronic device optimization data, and constructing an electronic device model;

[0018] In this embodiment, electronic equipment data is obtained and dimensional anomaly analysis is performed. First, geometric parameters are extracted from the CAD (computer-aided design) file of the existing electronic equipment, including external dimensions (such as length, width, and height), inner cavity dimensions (such as width, height, and depth of the internal space), hole positions (such as hole positions, diameters, and depths), and joint positions (such as thickness and angles of the joints). Ensure that all values ​​are consistent with the design drawings when extracting data to avoid introducing errors in the subsequent analysis process. Then, a dimensional tolerance analysis method is used for anomaly detection. Tolerance analysis detects whether the dimensions of each component meet the design requirements by setting a maximum allowable tolerance (for example, 0.5 mm). Specifically, each measured value is compared with the set tolerance range to identify abnormal dimensional data that exceeds the tolerance range. Error values ​​exceeding 0.5 mm are dimensional anomaly data. Based on the dimensional anomaly data, automated optimization tools are used for dimensional optimization. Commonly used tools include design methods based on topology optimization. Topology optimization optimizes the structure by modifying the distribution of materials. The goal is to minimize material waste while ensuring structural stability. The topology optimization algorithm adjusts the size and shape according to the set design constraints (such as strength, heat dissipation requirements and weight). During the optimization process, the specific objective function is set to minimize material usage while maximizing structural strength and meeting heat dissipation requirements. Constraints include the maximum allowable weight, maximum heat dissipation surface area (for example, by increasing the size of the heat sink or optimizing the air duct to improve heat dissipation efficiency), and compressive strength (for example, strength requirements when bearing maximum load). The optimization process uses finite element analysis (FEA) to simulate different design schemes to ensure that the optimized design can meet the performance requirements such as strength, stiffness and heat resistance in actual use. After the optimization is completed, the CAD model is updated according to the optimization results to generate new electronic equipment optimization data, and finally an electronic equipment model that meets all design requirements is generated.

[0019] Step S2: extracting a heat dissipation structure based on the electronic device optimization data, and performing hotspot accumulation analysis on the electronic device model to obtain hotspot accumulation structure data; performing heat dissipation enhancement based on the hotspot accumulation structure data to generate heat dissipation enhancement structure data;

[0020] In this embodiment, the heat dissipation structure in the electronic device optimization data is first extracted. The heat dissipation structure includes geometric information of key components such as heat sinks, air ducts, and heat dissipation holes. During the extraction process, the focus is on the dimensions of the heat sink (such as length, width, and height), the distribution and size of the heat dissipation holes, and parameters such as the shape, length, and width of the air ducts. By comparing with the CAD model, it is ensured that the heat dissipation structure components meet the design requirements, and the preliminary geometric data required for heat dissipation performance optimization is obtained. Next, the electronic device model is simulated using computational fluid dynamics (CFD) software. The CFD simulation process includes inputting the optimized electronic device model into CFD software (such as ANSYS Fluent or COMSOL Multiphysics), performing meshing, and constructing a computational model of the thermal flow field. During the simulation, the internal heat source distribution of the device (such as the heat generated by electronic components) is used as input data to simulate the airflow path, temperature distribution, etc. The heat source distribution data is typically determined based on the heat data generated during device use (for example, through calculation of the device's power consumption) to ensure that the model accurately reflects actual operating conditions. The airflow path is set by the configuration of the fan and air duct, and the temperature distribution is simulated based on the geometric information of heat dissipation components such as the heat sink and air duct. During the simulation, heat flow data is analyzed to identify high-temperature areas within the device. These areas are typically caused by concentrated heat sources or inefficient heat dissipation. To perform hotspot accumulation analysis, a temperature threshold, typically 85°C, is set. Areas exceeding 85°C are considered hotspots. CFD software uses thermodynamic simulation results to identify areas exceeding this threshold and generates geometric features of these high-temperature areas, such as their location, area, and shape. Based on this hotspot accumulation data, the device's heat dissipation structure is then enhanced. Strategies for heat dissipation enhancement include increasing the surface area of ​​the heat sink, optimizing the duct layout, and improving fan configuration. Specifically, increasing the surface area of ​​the heat sink can be achieved by increasing the number or area of ​​the fins or modifying their shape. This increases the contact area between the fins and the surrounding air, thereby improving heat dissipation efficiency. CFD simulation results can be used to guide optimization of the duct layout, adjusting the shape, location, and size of the ducts to ensure efficient airflow through the hotspots and remove excess heat. Fan configuration improvements can also be made by increasing fan speed or changing the number and location of fans to increase air flow and further enhance heat dissipation.

[0021] Step S3: performing multi-band resonance analysis on the electronic device model to obtain vibration data; performing stress point fracture analysis on the electronic device model based on the vibration data to obtain stress point fracture data; performing multi-scale stress structure optimization on the stress point fracture data to obtain multi-scale stress structure optimization data;

[0022] In this embodiment, the electronic equipment model is subjected to vibration simulation. When establishing the vibration simulation model, it is necessary to make preliminary settings based on the structure, material properties and external environmental parameters (such as operating temperature, vibration frequency, etc.) of the equipment. Structural parameters include material type (such as aluminum alloy, steel, etc.), shape (such as rectangular parallelepiped, cylindrical, etc.) and connection method (such as bolt connection, welding, etc.). Material properties include elastic modulus, Poisson's ratio, density, etc., which can be obtained by consulting material manuals or experimental data. External environmental parameters include operating temperature range and vibration frequency range, for example, the operating temperature is -20°C to 50°C, the vibration frequency is 10Hz to 1000Hz, the vibration amplitude is ±2mm, etc. With this information, a vibration simulation model is established, and vibration simulation is performed using finite element analysis (FEA) software (such as ANSYS, ABAQUS, etc.). The geometric shape, material properties and boundary conditions (such as fixed support or connection interface) are input into the FEA software, meshing is performed and a vibration analysis model is constructed. During the simulation, the vibration amplitude range was set to ±2mm, and the vibration frequency range was 10Hz to 1000Hz, covering the vibration conditions likely to be encountered in actual operation. Harmonic response analysis was used during the simulation to calculate the vibration response at different frequencies, including displacement, acceleration, and stress. The simulation results yielded vibration data, including stress distribution, displacement response, and acceleration response at different frequencies and vibration amplitudes. This data can be used to identify areas within the device subject to excessive stress, particularly those located at edges, joints, or weak structures. These areas may experience fatigue failure or stress concentration due to vibration, impacting the long-term stability of the device. Based on this vibration data, fracture analysis of the stress points was performed. Fracture mechanics theory (such as the calculation of the stress intensity factor (K)) was used to analyze the stress points, assess the stress concentration at each stress point, and determine the area where fracture would occur. In specific implementation, the vibration data (including stress distribution and deformation) was combined with the fracture mechanics model to calculate the stress intensity factor (K) at each stress point. For areas with higher material brittleness (such as welding points, around holes, etc.), if the stress intensity factor exceeds the critical value of the material (such as the Kc value), crack extension or fracture may occur. These areas are the fracture data of the stress points. Based on the fracture data of the stress points, the structural optimization method is used to optimize the stress structure. During the optimization process, the structural reinforcement of key parts of the equipment is analyzed, such as increasing the wall thickness, optimizing the material distribution, adding reinforcement ribs, etc. to improve the strength of the stress points. During structural optimization, constraints are set, such as maximum bearing capacity, vibration frequency, material strength, etc., to ensure that the optimized structure meets the strength requirements while not causing excessive vibration or instability.Specifically, constraints may include maximum load-bearing capacity to ensure that the equipment does not suffer structural instability or damage during use; vibration frequency to ensure that vibrations within the operating frequency range do not cause resonance, which usually requires adjusting the stiffness of key components or modifying the connection method; material strength, using high-strength materials (such as aluminum alloys, steel plates, etc.) to strengthen stress points to ensure that the material can withstand greater stress and the influence of the external environment.

[0023] Step S4: parameterizing the electronic device model according to the heat dissipation enhancement structure data and the multi-scale force structure optimization data to generate a parameterized model of the electronic device.

[0024] In this embodiment, the electronic device model is parameterized. At this stage, the geometric parameters, material selection and performance indicators of all key structures need to be clearly defined and input. For the geometric parameters, the key dimensions of the heat dissipation structure, such as the wind speed in the air duct, the size of the heat sink, the distribution and thickness of the heat dissipation holes, etc., are first determined. In terms of material selection, suitable materials are selected according to performance requirements (such as thermal conductivity, strength, weight, etc.), such as aluminum alloy for heat sinks, steel for frames, etc. Performance indicators include heat dissipation efficiency, mechanical strength, vibration tolerance, etc. The electronic device model is parameterized using parametric modeling tools (such as the parametric design module of SolidWorks or CATIA). Using these tools, the basic geometric model of the electronic device is first established, including the appearance and internal structure. Then, each design parameter is set as an adjustable parameter to ensure that each design element can be adjusted and optimized in actual application. For example, the heat sink size can be set within a parameterized range (e.g., 100mm to 200mm), the duct speed can be set within a parameterized range based on cooling requirements (e.g., 1m / s to 3m / s), and the thickness can be adjusted between 1mm and 5mm. Constraints must be set within the parametric model. For the cooling system, these constraints might include ensuring that the duct speed is at least 1.5m / s to ensure effective air circulation; that the heat sink size must meet minimum heat exchange efficiency requirements; and that the thickness must maintain reasonable stiffness within the design weight. For load-bearing structures, these constraints might include ensuring that the maximum external pressure does not exceed the material's yield strength; and that the vibration frequency at operating temperature must avoid the resonant frequency range. A parametric optimization algorithm is used to optimize the model as a whole. Common optimization algorithms include genetic algorithms and particle swarm optimization, which are capable of finding the optimal solution within a multidimensional design space. Genetic algorithms, for example, simulate the processes of natural selection and genetic variation to continuously optimize the design parameter combination to meet the design objectives. During the optimization process, the algorithm adjusts the parameter combination to explore the balance between multiple factors, such as cooling performance, strength, and cost. In specific operations, it is first necessary to set the objective function, such as minimizing the total cost or maximizing the heat dissipation performance. The objective function will be customized according to different design requirements and involves the weighted summation of multiple design objectives. During the optimization process, the algorithm will repeatedly generate multiple design schemes, each time adjusting the design parameters (such as the size of the heat sink, the layout of the air duct, the wall thickness, etc.), and evaluating its performance in terms of heat dissipation, strength and cost. For example, the heat dissipation enhancement structure data will optimize the heat sink and air duct to ensure the best heat conduction efficiency; and the multi-scale force structure optimization data will guide the optimization of the distribution and thickness of the material to ensure that it can maintain sufficient strength when subjected to vibration and external pressure. The result generated by the parametric optimization algorithm will be a final parametric model of an electronic device. This model not only meets the current design requirements, but also has a high degree of flexibility and can be quickly adjusted in different design scenarios.Through this optimization process, the design can meet the personalized requirements of different applications while considering multiple dimensions such as heat dissipation, strength, and cost. For example, in high-power devices, heat dissipation efficiency is optimized to maximize, while in lightweight designs, thickness is minimized to ensure cost-effectiveness.

[0025] Preferably, step S1 is specifically as follows:

[0026] Step S11: Acquire electronic device data. When the following conditions occur simultaneously, it is determined to be a dimensional deviation abnormality and the dimensional deviation abnormality data is obtained: the wall thickness deviation exceeds the set tolerance range of ±0.2mm, the key installation hole position offset exceeds 0.5mm, the bending angle error exceeds ±2°, and the overall dimensional deviation exceeds the set threshold of 1%;

[0027] In this embodiment, it is necessary to extract the geometric data of the electronic device from the CAD (computer-aided design) file, including wall thickness, mounting hole position, bending angle, and overall dimensions. These data are extracted from the CAD model using specialized analytical software (such as AutoCAD or SolidWorks) to ensure the accuracy and completeness of all geometric parameters. Each extracted parameter needs to be checked one by one to determine whether there is any abnormal dimensional deviation. First, for wall thickness, the tolerance range is set to ±0.2mm, and each position is measured. If the measured wall thickness deviation exceeds this tolerance range, it is determined to be an abnormal dimensional deviation. Secondly, the offset of the key mounting hole position is checked, and the offset tolerance is set to 0.5mm. For each mounting hole, calculate the offset of its position relative to the design drawing. If the offset exceeds 0.5mm, it is recorded as abnormal data. In addition, the bending angle error must be within ±2°, and the angle is calibrated using a dedicated angle measurement tool. If the error exceeds this range, it is considered abnormal. Finally, the overall dimensional deviation must be controlled within 1%. A laser rangefinder is used to measure the external dimensions. Any deviation outside the 1% range is recorded as abnormal data. Through the above checks, all detected abnormal dimensional data is collected and saved as dimensional deviation abnormal data for subsequent optimization.

[0028] Step S12: When the following conditions occur simultaneously, it is determined to be an assembly interference anomaly and assembly interference anomaly data is obtained: the minimum safety distance between components is less than the set threshold of 0.5mm, the screw holes are not aligned with a deviation exceeding 0.3mm, and the heat dissipation duct or ventilation hole size deviation exceeds ±10%;

[0029] In this embodiment, when performing an assembly interference anomaly check, it is first necessary to measure the minimum safety distance between each component, and the set safety distance threshold is 0.5mm. The internal space is scanned by a three-dimensional measuring tool (such as a laser scanner) to obtain the actual distance between each component. If the measured distance is less than 0.5mm, it is determined to be an interference anomaly. Next, check the alignment of the screw holes, use precision measuring tools (such as CNC probes) to position the screw holes, and measure their deviations relative to the design drawings. If the deviation exceeds 0.3mm, it is recorded as abnormal data. Finally, check the dimensional deviation of the heat dissipation duct or ventilation hole, and the set tolerance is ±10%. Use a dimensional measuring instrument to measure the actual size of the air duct or ventilation hole and compare it with the design drawing. If the dimensional deviation exceeds ±10%, it is determined to be assembly interference anomaly data. All these measurement results will be recorded and summarized into assembly interference anomaly data for further analysis and processing.

[0030] Step S13: Integrate the dimension deviation abnormality data and the assembly interference abnormality data to obtain dimension abnormality data;

[0031] In this embodiment, for the dimensional deviation abnormality data, the data sources include wall thickness deviation, hole position offset, angular error and overall dimensional deviation. Each abnormality requires detailed recording of the following key information: specific location, measurement value, difference from the design standard, and whether it exceeds the predetermined tolerance range. For example, in the wall thickness deviation data, the actual measurement value of each position is recorded and compared with the design drawing. If the deviation exceeds ±0.2mm, it is marked as an abnormality, and the specific measurement value and position number are attached; similarly, for hole position offset, the measurement value of the hole position is recorded, and the relative deviation from the designed hole position is indicated. If it exceeds the range of ±0.5mm, it is also marked as an abnormality. For assembly interference abnormality data, focus on recording the minimum safety spacing between components, screw hole alignment deviation, and dimensional deviation of the heat dissipation duct or ventilation hole. The actual spacing of each component and part needs to be measured by a three-dimensional measuring tool (such as a laser scanner or CNC probe). Record the spacing between all components. If the spacing is less than 0.5mm, it indicates an interference anomaly, and record the position, measurement value, and the difference from the designed spacing; for the offset of the screw hole, record the relative position error of the hole. If the error exceeds 0.3mm, it is also marked as abnormal data. Data integration requires the use of a database management system (such as MySQL or Microsoft SQL Server) to ensure that the classification and storage of all abnormal data are organized and traceable. By entering all dimensional deviation anomalies and assembly interference anomaly data into the database, use data integration tools (such as Excel's VLOOKUP function or database query language SQL) to sort, filter and classify the data. For each abnormal data, attach the data item category (dimensional deviation or assembly interference), abnormality type (such as wall thickness deviation, hole position offset, etc.), specific value and difference relative to the design standard. In addition, each abnormal data must be marked with the location where it occurred, such as the specific location number or component serial number, so that the area that needs to be processed can be accurately located during subsequent optimization.

[0032] Step S14: Optimizing the size of the electronic device data based on the size abnormality data to generate electronic device optimized data;

[0033] In this embodiment, optimization is performed using parametric optimization tools. SolidWorks' optimization module or Ansys' structural optimization tool can be used. These tools can intelligently adjust the design based on the integrated dimensional deviation anomaly data. Dimensional deviation anomaly data (such as wall thickness deviation, hole position offset, angular error, etc.) is obtained in step S13 and serves as the basis for optimization. This data set records the deviation of each dimension in detail. Therefore, during optimization, this data set can be used as a basis to ensure that the optimization solution can eliminate or reduce anomalies. The main goals of optimization include: reducing unnecessary material waste, improving structural stability, and ensuring that each dimensional tolerance is within a set acceptable range. During the optimization process, the optimization goal must first be defined. For example, the optimization goal for wall thickness is to remove excess material based on actual needs while maintaining sufficient structural strength. The optimization goal for hole position offset is to adjust the hole position to reduce interference during assembly. For angular error, the goal is to ensure that the bending angle error is less than ±2° to ensure assembly accuracy. For wall thickness optimization, the objective function function in SolidWorks or Ansys is used to define the optimization goal as minimizing wall thickness while meeting strength requirements. The optimal balance between strength and material usage can be achieved by setting wall thickness ranges and constraints (such as a tolerance of ±0.2mm). For hole position optimization, based on abnormal dimensional deviation data (such as hole position offset exceeding 0.5mm), an optimization tool is used to automatically adjust the hole positions to ensure that the distance and accuracy between each hole meet the design requirements, thereby avoiding assembly interference. During the hole position optimization process, the maximum deviation range of the hole positions is set to 0.5mm to ensure a smooth assembly process. For angular error optimization, the optimization objective function is set to ensure that the deviation of the bending angle does not exceed ±2°. During this process, finite element analysis (FEA) is used to simulate and analyze the bending part, and the bending angle is automatically adjusted based on the optimization objective of the angular error. The constraint is that the bending angle error must remain within the set range. For overall dimensional deviation, the optimization goal is to ensure that the overall dimensional error does not exceed 1% to ensure compatibility with other equipment and components. The calculations in the optimization process can use a variety of algorithms, such as gradient-based optimization algorithms (such as the steepest descent method) or genetic algorithms. Gradient methods are suitable for precise searches for feasible solutions, while genetic algorithms are suitable for more complex design spaces, effectively avoiding local optima and finding the optimal design solution globally. During calculations, optimization algorithms use predefined constraints to adjust the design, ensuring that the design solution meets the requirements after each iteration.

[0034] Step S15: constructing an electronic device model according to the electronic device optimization data.

[0035] In this embodiment, CAD software (such as SolidWorks or CATIA) is used to build an optimized model. First, open the CAD software, select the appropriate design file template, and prepare to import the optimized data. When importing the data, ensure that all key parameters in the optimized data (such as wall thickness, hole position, angle, etc.) are accurately extracted and match the design requirements. Use the modeling tools in the CAD software, take the optimized data as input, and gradually adjust the geometric shape to ensure that it meets the new design requirements. For the adjustment of wall thickness, use the parametric modeling function in CAD to directly modify the wall thickness parameters of each part to ensure that it meets the optimized standard. For the adjustment of the angle, use the angle control tool of CAD to accurately adjust the bending angle to ensure that its error is controlled within ±2°, so as to meet the optimization target. For the optimization of the hole position, use the hole position tool of the CAD software or manually adjust the relative position of the hole to ensure that the position and size of all mounting holes are consistent with the assembly requirements, especially the hole position offset must be within ±0.5mm to avoid assembly interference problems. In addition, adjust the external dimensions to ensure that the overall dimensional error is within 1%, which meets the requirements in the optimization target. Throughout the modeling process, each step is ensured to be strictly aligned with the optimized data, accurately reflecting the optimized design. After modeling is complete, the optimized electronic device model is saved and archived as a reference blueprint for production and assembly. Before archiving, basic collision checks and assembly simulations can be performed using CAD software to ensure smooth assembly of all components. The optimized model not only serves as a design blueprint for production but also provides precise geometric parameters and structural configurations for subsequent production processes and assembly. This ensures that production proceeds according to the optimized plan and reduces potential problems caused by design deviations.

[0036] Preferably, step S14 is specifically as follows:

[0037] Step S141: dividing the electronic device data into abnormal structures based on the size abnormality data to obtain electronic device abnormal structure data;

[0038] In this embodiment, it is necessary to obtain the integrated dimensional abnormality data. These data include information such as wall thickness deviation, hole position offset, angular error, etc. By analyzing the data, first identify the areas where abnormal structures exist, such as parts where the wall thickness deviation exceeds ±0.2mm, parts where the hole position offset exceeds 0.5mm, parts where the angular error exceeds ±2°, etc. Then, these abnormal data are divided into regions, and areas with similar problems are marked to form different structural categories. Data processing tools (such as Excel or database management systems) can be used to screen and group abnormal data to ensure that each abnormal structure data has a clear identification. The key to this step is to classify each part according to the set tolerance and deviation standards, and generate abnormal structure data for the electronic device as the basis for subsequent optimization.

[0039] Step S142: Optimizing the wall thickness of the abnormal structure data of the electronic device to obtain optimized wall thickness structure data;

[0040] In this embodiment, all wall thickness deviation information is first extracted from the abnormal structure data. For each area where there is a wall thickness deviation, the optimization goal is set to reduce unnecessary wall thickness while ensuring the strength of the structure. When using a parametric optimization tool (such as the optimization module of SolidWorks), the tolerance range of the wall thickness is set to ±0.2mm, and optimization adjustments are made within this range. During the optimization process, a wall thickness optimization algorithm (such as an optimization algorithm based on the minimum material method) is used to calculate the optimal wall thickness of each part, and factors such as mechanical strength and structural stability are taken into consideration to ensure that the overall performance is not affected while reducing the wall thickness. During the optimization process, finite element analysis (FEA) is used to evaluate the structural strength. Ultimately, structural data after wall thickness optimization is generated to provide data support for the next step of hole position adjustment.

[0041] Step S143: performing hole position adjustment on the abnormal structure data of the electronic device to obtain hole position adjustment structure data;

[0042] In this embodiment, all hole position deviation information is extracted, especially the part where the hole position offset exceeds 0.5mm. The offset hole positions are adjusted by CAD software (such as SolidWorks or CATIA) to ensure that the hole positions are consistent with the assembly requirements. In the specific operation, the hole position editing function of CAD is used to modify the position and size of the hole to ensure that the safety distance between the hole and other holes and components meets the assembly requirements. For the adjusted hole positions, their new relative positions and offsets are recorded to ensure that they meet the assembly tolerance requirements. Each adjusted hole position needs to be rechecked to ensure that the modified hole position no longer interferes and can smoothly cooperate with the installation of other components. After the adjustment is completed, new hole position adjustment structure data is generated as input data for subsequent stress analysis.

[0043] Step S144: performing stress analysis on the hole position adjustment structure data according to the wall thickness optimization structure data to obtain stress data, and performing stress statistics, wherein a high stress threshold is set to 70%-80% of the yield strength to obtain high stress data;

[0044] In this embodiment, finite element analysis (FEA) software (such as Ansys or Abaqus) is used to simulate and analyze the optimized structure to calculate the stress distribution of each part. During the stress analysis process, the high stress threshold is set to 70%-80% of the yield strength to ensure reliability during normal use. The stress of each component is calculated one by one to obtain stress data, with special attention paid to areas that exceed the set threshold. After completing the stress analysis, the stress values ​​are statistically analyzed, high stress data are extracted, and areas that exceed the set threshold are marked. These high stress areas will provide a basis for subsequent high stress area identification and support structure optimization.

[0045] Step S145: performing region identification on the electronic device data according to the high stress data to obtain high stress region data;

[0046] In this embodiment, the regional information in the stress data is used to determine whether the stress value of each area exceeds the set high stress threshold. For areas exceeding the set threshold, they are marked as high stress areas, and the specific location and stress value are recorded. Data processing tools (such as Excel, MATLAB or database management systems) can be used to filter the stress data and extract regional information that exceeds the threshold. According to the set standards, high stress areas are identified, and the geometric positions, stress values ​​and potential problems of these areas are marked. The identification of high stress areas provides a basis for subsequent support structure optimization, ensuring that these high stress areas can be effectively processed.

[0047] Step S146: Optimize the electronic device support structure based on the high stress area data to obtain electronic device optimization data.

[0048] In this embodiment, CAD software (such as SolidWorks or CATIA) is used to perform structural optimization based on the high stress area data. According to the location of the identified high stress area, support structures are added or adjusted in these areas. In specific operations, the structural enhancement function of the CAD software is used to add support components such as reinforcing ribs and support columns to disperse the load in high stress areas. During the optimization process, the shape, size and position of the support structure are adjusted according to the results of the mechanical analysis to ensure that the optimized support structure can effectively reduce local stress. When optimizing the support structure, the selection and distribution of materials used are considered to ensure that the overall strength and stability are improved. After the optimization is completed, the optimized data of the support structure is generated to provide a basis for subsequent production and assembly.

[0049] Preferably, step S146 is specifically as follows:

[0050] Perform structural recognition on high stress area data to obtain high stress area structural data;

[0051] In this embodiment, stress data is extracted. By setting a high stress threshold (usually 70%-80% of the yield strength), all areas where the stress value exceeds the threshold are screened out. These areas can be marked by data processing tools (such as Excel or MATLAB). Further, these areas are geometrically identified in the CAD software. Based on structural features, such as pore size, wall thickness and shape information, different high stress structure types are distinguished. Through geometric graphics analysis, the geometric shape of each area is defined, and the area that needs to be strengthened is automatically determined by the optimization algorithm. After the structural identification is completed, the specific position, size and shape of each high stress area are recorded, and the high stress area structural data is generated. These data will serve as the basis for subsequent lattice structure filling and optimization.

[0052] Filling the lattice structure based on the high stress area structure data, wherein the lattice structure type is set to body-centered cubic and the unit size range is set to 1.5mm-3mm, to obtain the lattice structure data;

[0053] In the present embodiment, CAD software (such as SolidWorks) or optimization software (such as OptiStruct) is used to fill the high stress area with a lattice structure. The lattice structure type is set to body-centered cubic (BCC), and the size range of the lattice unit is set to between 1.5mm and 3mm. In order to maintain the overall strength and stability, the automatic filling function can be selected in the software to gradually fill each high stress area to ensure that the lattice structure is evenly distributed. Through this process, a body-centered cubic lattice structure will be filled in each high stress area to further evenly distribute stress and reduce stress concentration. The size range of the lattice structure (1.5mm-3mm) is determined by comprehensively considering factors such as size, use environment, and load requirements. After completing the lattice structure filling, the structural data is saved and prepared for the next step of adding ribs.

[0054] Add ribs to the lattice structure data, where the hinge thickness is 0.8mm-2.0mm, the hinge width range is 5mm-15mm, and the hinge bending radius is 2mm-6mm, to obtain the rib data;

[0055] In this embodiment, the ribs are used to increase the rigidity of the structure and reduce the deformation caused by external forces. First, the design parameters of the hinge are determined: the hinge thickness is set to 0.8mm to 2.0mm, the hinge width range is set to 5mm to 15mm, and the hinge bending radius is set to 2mm to 6mm. According to these parameters, the rib design module in the CAD software (such as the support structure design tool of AutoCAD or SolidWorks) is used to design the parts of each lattice structure where ribs need to be added. The design of the ribs should take into account the relationship between the bending radius and thickness of the hinge to ensure that the ribs can enhance the support capacity of the local area without affecting the overall structural strength. During operation, the optimal position of the ribs is automatically calculated by the software and added to the lattice structure. After completing the rib design, save the rib data to provide data support for the next step of elastic hinge design.

[0056] Perform elastic hinge design on the lattice structure data to obtain elastic hinge data;

[0057] In this embodiment, the purpose of the elastic hinge is to improve the response performance under a specific load by using a material with variable stiffness. First, the parameters of the hinge are set: the hinge thickness is between 0.8mm and 2.0mm, the hinge width is between 5mm and 15mm, and the hinge bending radius is between 2mm and 6mm. Use elastic materials (such as steel, aluminum alloy or composite materials) to design the elastic properties of the hinge to ensure that the hinge has sufficient elasticity when subjected to force. The elastic design module in the CAD software (such as the elastomer design function of SolidWorks) can optimize the design by simulating the deformation of the hinge under different load conditions. During the design process, the thickness and bending radius of the hinge need to be continuously adjusted to obtain the best elastic response. According to the set thickness, width and bending radius, the elastic hinge data is finally generated, and its size, material and performance data are recorded for subsequent support structure optimization.

[0058] The electronic equipment support optimization structure is integrated according to the rib data and elastic hinge data to obtain the electronic equipment optimization data.

[0059] In this embodiment, CAD software is used to perform an integrated design of ribs and elastic hinges to ensure that the various optimized components cooperate with each other and enhance the overall supporting force. First, ribs are added to weak areas (such as high stress areas) based on the rib data, and at the same time, the adjustable supporting force is enhanced according to the elastic hinge design. During the integration process, mechanical analysis software (such as Ansys) is used to simulate the optimized support structure to ensure that the new design can withstand the stress distribution under various working environments. During the optimization process, the thickness, position, size and material of the ribs must be strictly controlled to ensure that the supporting performance is improved without affecting the assembly and use of other components. After the integration is completed, electronic equipment optimization data is generated as reference data for subsequent production and assembly.

[0060] Preferably, the hotspot accumulation analysis in step S2 includes:

[0061] Optimize data extraction and heat dissipation structure based on electronic equipment;

[0062] In this embodiment, the electronic device optimization data is used to first extract the heat dissipation structure. This process requires importing the optimized design data into CAD software (such as SolidWorks, AutoCAD), and then identifying the areas involved in heat dissipation based on the geometric shape, component layout and material properties. The heat dissipation structure is usually related to components such as the radiator, heat sink, and fan position. Therefore, the system extracts the size, position and layout of these components from the optimization data. The design requirements of the heat dissipation system are determined based on the operating environment temperature and the heating conditions of the internal components. The material of the radiator is usually selected from a material with higher thermal conductivity (such as aluminum alloy or copper). Through analysis such as heat conduction and convection, heat dissipation structure data suitable for electronic equipment is finally generated. This data will provide a basis for subsequent thermal flow simulation and temperature field analysis.

[0063] Conduct thermal flow simulation based on heat dissipation structure;

[0064] In this embodiment, after the heat dissipation structure is extracted, a heat flow simulation is performed to simulate the distribution and dissipation process of the internal heat. CFD (computational fluid dynamics) software (such as Ansys Fluent, COMSOL Multiphysics) is used for simulation analysis. First, the boundary conditions of the heat dissipation structure are set in the simulation environment, and the heat source data of each internal component, including power density, working environment temperature, etc., are input. According to the heat dissipation system design, the heat flow boundary conditions of heat conduction, convection and radiation are set. During the simulation process, it is necessary to define the air flow pattern, including parameters such as the density, viscosity and temperature convection coefficient of the fluid. The change in temperature field is related to the intensity of the heat source, the air flow conditions and the effectiveness of the radiator. The calculation during the simulation process will provide temperature distribution data of each component and area, providing data support for subsequent temperature field analysis.

[0065] Calculate the temperature during thermal flow simulation and draw the temperature field distribution diagram;

[0066] In this embodiment, the temperature data of each internal point is extracted according to the heat flow simulation results. In the simulation software, the temperature value of each calculation node can be output through the heat flow calculation module. The temperature data is visualized by a computer program and converted into a temperature field distribution diagram. In order to improve the accuracy and readability of the data, a heat map (HeatMap) is usually used to display different temperature areas in different colors, such as red for high temperature areas and blue for low temperature areas. During the calculation process, a temperature threshold is set to effectively identify high temperature areas, which usually require additional heat dissipation measures. The main parameters used in the temperature calculation include ambient temperature, heat source power, thermal conductivity of the material, air flow velocity in the air duct, etc. These parameters can be obtained by measurement, experiment or calculation. Based on these calculation results, a temperature field distribution diagram inside the electronic device is drawn.

[0067] Identify the high-temperature model structure of electronic equipment models based on temperature field distribution diagrams;

[0068] In this embodiment, by setting a high temperature threshold (for example, 80°C), areas where the temperature exceeds the threshold are screened out from the temperature field distribution map, and these areas are high-temperature model structures. By using a graphical analysis tool (such as the image processing toolbox in MATLAB), the temperature field distribution map is converted into a heat map to further identify the specific location, size, and shape of these high-temperature areas. These high-temperature areas are usually located near heating elements (such as circuit boards, processors), or are areas where the temperature rises due to insufficient heat dissipation. Through further analysis of these high-temperature areas, it is determined whether additional heat dissipation improvement measures are needed, such as adding heat sinks, fans, or changing the airflow path.

[0069] Calculate the airflow velocity in the duct of the high-temperature model structure;

[0070] In this embodiment, the airflow velocity in the duct directly affects the internal heat dissipation effect. The airflow velocity is calculated using CFD simulation software (such as Ansys Fluent and OpenFOAM). First, the boundary conditions of the duct are set, including the fan power, wind speed, inlet and outlet locations, etc. Then, based on the internal fluid dynamics characteristics, the flow of air inside is simulated, focusing on the airflow velocity near the high-temperature area. The calculation of airflow velocity involves multiple parameters, such as air density, flow viscosity, fan power, and internal duct shape. The calculated duct airflow velocity data will help determine whether the current heat dissipation system can effectively remove heat, especially near the high-temperature area. When calculating the airflow velocity in the duct of the high-temperature model structure, it is first necessary to set the boundary conditions of the duct. This includes the fan power, wind speed, and the inlet and outlet locations of the internal duct. The fan power and wind speed are input according to the fan parameters in the design. The wind speed is usually determined by the fan's rated power and design conditions, and the wind speed value is generally meters per second (m / s). The inlet and outlet locations of the duct also need to be specified in the simulation software. The inlet position is set as the inflow point of normal temperature air, and the outlet is the outflow point of the fluid. Next, a 3D model is created in CFD simulation software (such as Ansys Fluent or OpenFOAM), the geometry of the duct is imported, and the air density (e.g. 1.225 kg / m 3 ) and flow viscosity (usually 1.81×10^-5Pa·s), these physical parameters have an important influence on the flow characteristics of the airflow. Use fluid dynamics equations (such as the Navier-Stokes equations) to simulate the flow conditions including the airflow, focusing on analyzing the velocity distribution of the airflow near the high-temperature area. During the simulation process, through numerical calculations, the CFD software will solve the velocity field of the airflow and obtain the airflow velocity data at each position, especially the airflow velocity in the high-temperature area, which will help to determine whether the heat can be effectively taken away to ensure the heat dissipation effect. Ultimately, the calculation results of the airflow velocity will be used to evaluate the performance of the current cooling system. If the airflow velocity is insufficient, the air duct design or fan configuration needs to be adjusted to optimize the heat dissipation effect.

[0071] The hot spot accumulation structure of the high temperature model structure is identified based on the air flow velocity in the duct to generate the hot spot accumulation structure data.

[0072] In this embodiment, hot spot accumulation usually occurs in areas where the air flow velocity is low and heat is difficult to dissipate. Through CFD simulation software, the flow of air inside is analyzed, and the areas with low air flow velocity are found. Combined with the high temperature areas, the hot spot structure with heat accumulation is identified. This process requires setting the air flow velocity and temperature thresholds. When the air flow velocity is lower than the set value (such as 0.1m / s) and the temperature exceeds the set high temperature value (such as 80°C), the area is considered to be a hot spot accumulation area. The data of the hot spot accumulation structure is obtained by calculation, including the location, size and air flow velocity distribution of the hot spot area. These data can be used to further optimize the heat dissipation structure to improve the stability and performance of the equipment.

[0073] Preferably, the heat dissipation enhancement in step S2 includes:

[0074] Adjust the air duct layout of hotspot accumulation structure data;

[0075] In this embodiment, the temperature data of the welding area is collected by a sensor, and the optimal flow path of the air duct is determined by combining the morphological data of the hot spot accumulation area. Fluid dynamics simulation software (such as ANSYS Fluent) is used to simulate the flow characteristics of the air duct, and the impact of different air duct layouts on the hot spot accumulation area is analyzed. According to the simulation results, the shape and size of the air duct are adjusted to ensure that the air duct can cover all hot spot areas while avoiding unnecessary interference with other parts of the welding equipment. Specifically, the inlet and outlet positions of the air duct need to be set to a minimum distance of 50 mm from the welding point, and the cross-sectional area of ​​the air duct should be optimized according to the flow rate requirements to ensure that the airflow can effectively take away the heat from the welding area.

[0076] Classify the air duct layout to obtain positive pressure air duct layout and negative pressure air duct layout;

[0077] In this embodiment, in the classification process of the air duct layout, the air duct is first distinguished between positive pressure and negative pressure by setting different air duct pressure standards. The design of the positive pressure air duct requires that the pressure at the air duct inlet be higher than the ambient pressure of the welding area. The pressure at the air duct inlet is usually set to be 0.1Pa-0.5Pa higher than the ambient pressure to ensure that the driving force of the air flow in the air duct is sufficient. The design of the negative pressure air duct requires that the pressure at the air duct inlet be lower than the ambient pressure, and the hot air flow is attracted by generating negative pressure to take away the heat. During the design, the pressure difference is further adjusted by simulation software to ensure that both layouts can effectively circulate air flow and ensure sufficient air flow under different operating conditions. The fan speed of the positive pressure air duct layout is relatively high, generally set in the range of 1200-3000 revolutions per minute (rpm). The air outlet position of the negative pressure air duct is optimized through fluid analysis to ensure that it can work effectively under specific negative pressure conditions.

[0078] Optimize the fan speed of the positive pressure duct layout;

[0079] In this embodiment, the temperature change data of the welding area is obtained by a temperature sensor, and the wind speed required for the air duct is calculated in combination with the simulation software. The wind speed should be set to 2-4m / s to ensure that the air flow speed in the air duct can cover all hot spots without affecting the welding process. The fan speed can be adjusted by a PID control algorithm to ensure that the fan maintains a constant speed during actual operation. The specific range of the fan speed is set to 1200rpm to 2500rpm. If the speed is too low, the air flow will not be able to effectively carry away the heat, and if the speed is too high, it will cause excessive noise and energy waste. The fan speed is further adjusted according to the fluid simulation results to ensure that the system operates under optimal conditions.

[0080] Optimize the air outlet position of the negative pressure air duct layout, where the position range is set to 50mm-200mm from the air outlet to the heat source and the air outlet opening rate is 40%-70%;

[0081] In this embodiment, in the negative pressure air duct layout, the position of the air outlet is crucial to the efficiency of the air duct. The temperature distribution simulation is performed by thermal flow analysis software (such as COMSOL), and the center position of the hot spot accumulation area is first determined. Then, the position of the air outlet is optimized according to the parameters in the range of 50mm to 200mm from the heat source. The opening rate of the air outlet is required to be between 40% and 70% to ensure that the air flow can pass through effectively, while avoiding excessive concentration of air flow and resulting in reduced efficiency. The size and opening rate of the air outlet can further optimize the flow path of the air flow by adjusting the structural parameters around the air outlet, such as the number and spacing of the heat dissipation fins. The specific position of the air outlet is required to be set near the welding area, and the distance from the heat source should be fine-tuned according to the simulation results to ensure that the negative pressure air duct can maximize the heat absorption of the hot spot area.

[0082] The air duct isolation design is based on the fan speed and air outlet position, where the thickness of the isolation plate is set to 0.8mm-3mm and the isolation channel spacing is ≥20mm;

[0083] In this embodiment, the thickness of the isolation plate should be set between 0.8mm and 3mm, and the specific value is determined according to the air flow rate and airflow requirements in the air duct. The material of the isolation plate should be selected from materials with high thermal conductivity, such as aluminum alloy or copper, to ensure the heat conduction effect of the isolation plate. The spacing between the isolation channels needs to be set to more than 20mm to prevent interference between airflows and ensure that each air duct can effectively take away the heat from the corresponding area. Flow field analysis is performed using CFD (computational fluid dynamics) software to adjust the position and size of the isolation plate to ensure that each air duct can maintain sufficient airflow intensity while being independently isolated.

[0084] Added heat deflector cover with hotspot accumulation structure data, where the thickness of the heat deflector cover is set to 0.5mm-2mm and the tilt angle is set to 30°-60°;

[0085] In this embodiment, the thermal deflector is used to guide the heat to flow toward the air duct outlet in the air duct design, thereby improving the overall heat dissipation effect. The thickness of the thermal deflector should be set to 0.5mm to 2mm, and optimized according to different welding heat loads and the size of the air duct. The material of the hood needs to be an alloy material with high thermal conductivity, and the inclination angle of the hood should be adjusted according to the heat source distribution in the welding area, usually selecting an angle range of 30° to 60°. Through simulation analysis, the optimal angle and thickness of the hood are determined to ensure that the hood can effectively guide heat without interfering with the welding process. The installation position of the thermal deflector needs to be coordinated with the air duct to ensure that the heat can be smoothly carried away through the air duct.

[0086] The heat dissipation enhancement structure is integrated according to the isolation air duct and the heat deflector to generate the heat dissipation enhancement structure data.

[0087] In this embodiment, during the integration of the heat dissipation enhancement structure, the parameters of the duct isolation and the thermal deflector are comprehensively optimized to generate complete heat dissipation structure data. First, the duct and the thermal deflector are linked and analyzed using CFD simulation software to determine the optimal duct layout, isolation plate material, and thermal deflector design. Secondly, the duct isolation design is combined with the thermal deflector to ensure that each duct unit can maximize the heat removal. The final design of the heat dissipation enhancement structure should take into account the interaction between heat flow and air flow to ensure that the efficiency of the entire heat dissipation system is maximized. Finally, based on the thermal load requirements of the welding process, the duct layout, isolation plate thickness, and thermal deflector thickness and angle are integrated to generate detailed heat dissipation enhancement structure data to ensure that overheating does not occur during the welding process.

[0088] Preferably, the multi-band resonance analysis in step S3 includes:

[0089] Assigning materials to the electronic equipment model to obtain a material assignment model;

[0090] In this embodiment, when assigning materials to the electronic device model, the geometric model that needs to be obtained first is usually generated by computer-aided design (CAD) software, such as using SolidWorks or AutoCAD for three-dimensional modeling. During the modeling process, the size, shape and design details of each component are ensured to be consistent with the actual product, and material differences are considered according to the functional requirements of different structural parts. Then, in the CAD software, appropriate materials are selected or customized for each component of the model for assignment. Commonly used materials include aluminum alloy, steel plate, plastic, etc. The material selection should be comprehensively judged based on factors such as strength, weight, thermal conductivity, and corrosion resistance. The physical performance parameters of each material, such as density, Young's modulus, Poisson's ratio, thermal expansion coefficient, etc., need to be extracted from the material database, standard literature or material supplier's data manual, and the corresponding physical properties are input through the material library or custom material function of the CAD software to ensure that it matches the component. During the assignment process, appropriate material properties are assigned to the components based on their functions and performance requirements in the structure. Upon completion, a material assignment model can be obtained. This model contains the geometric and physical property data of all components, which can truly reflect the performance of each part in subsequent finite element analysis and provide an accurate and reliable physical basis for natural frequency calculation, heat conduction simulation, and stress-strain analysis.

[0091] Determine the natural vibration frequency of the material assignment model;

[0092] In this embodiment, when determining the natural vibration frequency of the material assignment model, it is first necessary to perform finite element analysis on the three-dimensional geometric model with completed material assignment. This process is usually implemented using finite element analysis software such as ANSYS and ABAQUS. First, the geometric model containing complete material properties is imported into the software to ensure that the physical parameters of each component in the model (such as density, Young's modulus, Poisson's ratio, etc.) are accurate; then, the boundary conditions of the model are defined according to the actual working environment, including the position of the fixed support point, whether there are external loads or constraints, etc., to truly simulate the stress conditions of the structure during use. After the boundary conditions are set, the modal analysis is started. The modal analysis module uses numerical methods to solve the free vibration response of the structure, ultimately obtaining multiple orders of natural frequencies. Each order frequency corresponds to a different vibration mode, among which the first-order natural frequency usually represents the frequency at which the structure is most likely to vibrate in the absence of external force excitation. The modal analysis calculation process comprehensively considers material properties and structural configuration, geometric characteristics and imposed constraints to ensure the high accuracy of the solved natural frequencies. After the analysis is completed, the obtained natural frequency data can be used in subsequent engineering applications such as resonance risk assessment, structural vibration reduction design and performance optimization, providing key parameter support for dynamic reliability analysis of electronic equipment.

[0093] Determine the operating vibration frequency of the material assignment model;

[0094] In this embodiment, when determining the working vibration frequency of the material assignment model, it is first necessary to simulate its vibration conditions in the actual use environment, because the working vibration frequency usually comes from external vibration sources in the working environment of the electronic equipment, such as mechanical vibrations caused by running components such as motors, fans or pumps. In order to accurately obtain the vibration frequency under working conditions, two main technical paths can be adopted: one is based on finite element simulation, and the other is to obtain it through on-site measurement. The simulation method needs to apply mechanical excitation loads that conform to the actual situation to the material assignment model. These loads include periodic excitation (such as periodic vibration caused by rotating equipment) or random excitation (such as structural impact or environmental disturbance, etc.). According to the structural characteristics and operating conditions of the equipment, in the finite element analysis software (such as ANSYS or ABAQUS), By conducting time domain or frequency domain analysis and processing the frequency spectrum of the response data, the vibration response characteristics of the model at different frequencies can be obtained, and then the main vibration frequency range under the working state can be identified; the on-site test method requires the installation of vibration measurement devices such as accelerometers on the equipment, and the vibration frequency characteristics of the equipment are analyzed by measuring the vibration data of the equipment during operation. The test process usually selects multiple key locations to deploy sensors to capture the global response, and the actual working vibration frequency in operation is obtained through spectrum analysis. The measured results can be used to verify the accuracy of the simulation model, or provide reference data for subsequent modeling. Finally, combined with the simulation analysis and field test results, the working vibration frequency of the material assignment model in actual use is obtained, which provides an important basis for subsequent resonance analysis, structural optimization and dynamic stability improvement.

[0095] Resonance judgment is performed based on the natural vibration frequency and the working vibration frequency to obtain resonance data;

[0096] In this embodiment, when making a resonance judgment based on the natural vibration frequency and the working vibration frequency, it is first necessary to compare and analyze the natural vibration frequency data obtained above with the working vibration frequency data, wherein the natural vibration frequency refers to the free vibration frequency of the structure when it is not externally excited, reflecting the dynamic characteristics of the structure itself, and the working vibration frequency is the external excitation frequency generated by the equipment during operation, such as the periodic disturbance caused by rotating parts such as motors and fans. In the resonance judgment, it is necessary to focus on whether the working frequency is close to or coincides with a certain order natural frequency of the structure. When the frequency difference between the two is small or even coincides, resonance will occur, causing the vibration response of the structure at this frequency point to be sharply amplified, which may cause structural fatigue damage, loose connections or even malfunction shutdown. In order to accurately identify whether it is To determine whether resonance occurs, the frequency response analysis method can be used. The external excitation frequency can be gradually adjusted through frequency domain scanning technology, the structural response curve can be analyzed, and whether there is frequency overlap at the response peak. By comparing the difference between each order natural frequency and the working excitation frequency, and combining the amplitude-frequency response curve, it can be determined whether it has entered the resonance range. In this process, it is necessary to record key data such as the resonant frequency point, resonant amplitude and vibration mode in detail. The resonant frequency refers to the frequency point where the working frequency matches or is close to the natural frequency, the vibration amplitude reflects the strength of the structural response at this frequency point, and the vibration mode shows the main deformation form of the structure in the resonant state. The collection and analysis of the above resonance data can provide an accurate basis for further structural optimization, dynamic stability assessment and vibration suppression design.

[0097] Perform impact vibration simulation on the material assignment model to obtain impact vibration data;

[0098] In this embodiment, when performing shock vibration simulation on the material assignment model, it is first necessary to clarify the type and parameters of the shock load. Usually, the shock conditions are set with reference to standards such as IEC 60068-2-27. This standard provides a test basis for the shock environment that electronic equipment may be subjected to during transportation or use. Common shock waveforms include half-sine waves, trapezoidal waves, or rectangular waves. Shock parameters include peak acceleration, pulse duration, and number of shocks. After determining the shock load, use finite element analysis (FEA) software such as ABAQUS to set the loading conditions at key parts of the model to ensure that the direction, amplitude, and position of the load are consistent with the actual use environment. At the same time, the model should have completed the material assignment operation and have the necessary physical parameters such as density, Young's modulus, and Poisson's ratio to ensure simulation accuracy. The transient dynamic analysis method is adopted in the process, that is, the structural response during the impact process is simulated and calculated through explicit or implicit time integration technology, and the time history response data under the impact load is obtained, including the displacement, velocity and acceleration distribution at different time points. These data reflect the dynamic response characteristics of the structure during the impact process. Through time domain analysis, the vibration response of the structure in the early, middle and late stages of the impact can be observed, so as to evaluate the stability and safety of the model under impact. The final impact vibration data provides an important basis for subsequent impact resistance design, and can be used to evaluate the bearing limit, weak points of the structure and whether it is necessary to adjust the structural layout or replace the material to improve its impact resistance.

[0099] The resonance data and the impact vibration data are integrated to obtain the vibration data.

[0100] In this embodiment, when integrating resonance data and impact vibration data, it is first necessary to ensure the consistency of the two types of data in terms of time scale, frequency range and physical quantity dimension. The resonance data usually includes natural frequencies of different orders, corresponding vibration modes and vibration response characteristics at each order of resonance frequency, while the impact vibration data mainly describes the time-varying response process of the structure's acceleration, displacement, velocity, etc. under the action of impact load. In order to integrate the two into unified vibration data, it is necessary to first perform frequency domain processing on the impact vibration data. A commonly used method is to convert it from the time domain to the frequency domain through Fourier transform, so as to obtain the vibration response amplitude and phase information at different frequencies. The obtained frequency components are then compared with the resonance frequency to analyze whether the impact event has excited the natural modes of the structure, especially those modes in the frequency range close to the resonance frequency. Whether an amplification effect occurs. This frequency overlap usually means that the structure may enter a resonant state under the action of an impact, resulting in strong local or overall vibration. In order to intuitively express the integrated analysis results, data processing software such as MATLAB or Excel can be used to unify the two types of data according to the frequency dimension and construct a unified vibration response table or graphical results. The data table can include frequency points, vibration amplitudes, response times, vibration mode numbers, resonance state marks, etc. Graphical presentation can use frequency response curves, modal participation coefficient diagrams, frequency amplification factor diagrams under impact excitation, etc. The final integrated vibration data will accurately reflect the dynamic response characteristics of the model under various working and extreme conditions, providing comprehensive data support and decision-making basis for subsequent structural optimization, vibration suppression design and reliability assessment.

[0101] Preferably, the stress point fracture analysis in step S3 includes:

[0102] Count the vibration magnitude of vibration data and obtain high vibration data;

[0103] In this embodiment, when counting the vibration magnitude of vibration data to identify high vibration data, it is first necessary to comprehensively consider the vibration responses generated under different working conditions, including the vibration amplification effect under the resonant state and the instantaneous strong vibration caused by the impact load. The acquisition of vibration data is usually carried out by installing acceleration sensors or displacement sensors at key parts of the structure for real-time collection. The data obtained contains multiple dimensional information such as vibration acceleration, displacement, frequency and duration. In order to comprehensively analyze the vibration intensity, it is necessary to adopt a combination of time domain analysis and frequency domain analysis. The time domain analysis is used to obtain the changes in the vibration signal on the time axis, such as peak value, average value and effective value (RMS), while the frequency domain analysis is used to evaluate the distribution of vibration energy in different frequency ranges. The original vibration signal can be converted into a spectrum by tools such as fast Fourier transform (FFT), so as to identify high amplitude responses at specific frequencies. During the analysis process, the vibration amplitude of each sensor measuring point can be quantified by calculating its maximum value or effective value and comparing it with a preset vibration threshold. The threshold can be based on international standards such as IEC It can be formulated in accordance with 60068 or MIL-STD-810, or it can be comprehensively set based on historical data statistical results combined with factors such as material properties, working environment and structural tolerance. For example, parameters such as maximum allowable acceleration, vibration duration and possible fatigue effects on the structure can be considered. If the vibration amplitude at a certain frequency point exceeds the set threshold, the corresponding data will be marked as high vibration data, and its corresponding key attributes such as frequency, amplitude, time and position will be recorded at the same time. The extraction of these high vibration data not only helps to determine whether the system exceeds the design load range, but also provides an important data basis for subsequent high stress area positioning, fatigue life assessment and structural strengthening design, effectively improving the safety and reliability of the overall structure.

[0104] Based on the high vibration data, the electronic equipment model is region-identified to obtain the high vibration area;

[0105] In this embodiment, when performing regional identification on the electronic device model based on high vibration data, it is first necessary to import the statistically obtained high vibration data into finite element analysis (FEA) software, such as ANSYS or ABAQUS, to build a geometric model of the electronic device, and define the corresponding boundary conditions and load conditions in combination with the actual application scenario, including fixed support points, external excitation forces during operation, and simulation of environmental factors. At the same time, the model is given corresponding material parameters, such as density, Young's modulus and Poisson's ratio, etc. These factors will jointly affect the final vibration response calculation accuracy. After the modeling is completed, frequency response analysis is performed based on the input high vibration data to simulate the vibration behavior of the device within the actual operating frequency range, thereby obtaining the response amplitude and distribution at different frequencies. By analyzing the response results and setting a reasonable vibration threshold, all In the frequency response results, areas where the vibration amplitude exceeds the threshold are marked as high-vibration areas. The set threshold can be determined in combination with the amplitude range and standard specifications of the previous high-vibration data to ensure the scientific and practical nature of the identification process. In the frequency response diagram, the high-vibration area is usually manifested as a distribution band with concentrated vibration amplitude peaks, reflecting the resonance trend of the structure under a specific frequency or the location of local stress concentration. These areas are prone to fatigue damage or structural deformation due to long-term vibration or cyclic loads in actual operation. Therefore, the identified high-vibration area will be presented in the form of color heat map, vector response distribution or numerical annotation. The final output includes information such as the location of the high-vibration area, the vibration amplitude of the corresponding frequency point and the response time history, providing a key basis for engineers to further carry out structural reinforcement design, reliability assessment and high-stress point correction.

[0106] Analyze high stress areas in high vibration areas;

[0107] In this embodiment, when analyzing the high stress area in the high vibration area, it is necessary to use finite element analysis software (such as ABAQUS or ANSYS) to perform static and dynamic analysis on the electronic equipment model. By establishing a geometric model and giving the material physical properties (such as elastic modulus, Poisson's ratio, yield strength, etc.), a basis is provided for evaluating the stress response under vibration. When setting the boundary conditions, fixed supports and external loads should be applied in combination with the actual working state, including impact loads or periodic vibration loads, etc. At the same time, the high vibration data obtained in the early stage is loaded onto the model as the input working condition. During the dynamic analysis process, the software will simulate the stress response of the structure under vibration excitation and output stress distribution diagrams at different time steps or frequencies, thereby revealing the stress that may occur during the vibration process. The key areas of concentration are high stress areas. These high stress areas often appear at locations with large vibration amplitudes, connection points, locations with sudden geometric structure changes (such as hole edges, sharp corners, transition sections, etc.), or nodes where external loads are concentrated. The analysis should focus on areas where stress values ​​reach or exceed the yield strength or fatigue strength of the material, so as to identify weak points with potential failure risks. On this basis, fatigue life analysis can also be carried out in combination with the fatigue performance curve of the material to evaluate the damage evolution process of these high stress areas under long-term periodic vibration or repeated impact. Through the output results such as stress time history diagrams and equivalent stress cloud diagrams, the locations where fatigue cracks are prone to initiation and their stress levels can be clearly identified, thereby providing a decision-making basis for subsequent structural reinforcement, design optimization and reliability improvement.

[0108] Perform fatigue life analysis on high stress areas to obtain life data of high stress areas;

[0109] In this embodiment, when performing fatigue life analysis on a high-stress region, it is first necessary to determine the SN curve (stress-life curve) of the material used. This curve is obtained experimentally and describes the fatigue life that the material can withstand under different stress amplitudes and reflects its physical properties such as fatigue limit, yield strength, and tensile strength. Subsequently, the stress amplitude and corresponding number of stress cycles of the high-stress region under vibration load are combined to obtain the stress time history response of each high-stress region under different working conditions through finite element analysis. These stress cycles are substituted into fatigue damage theory. Commonly used evaluation methods include the Palmgren-Miner linear cumulative damage method or the rain flow counting method. These methods can convert the effect of each stress amplitude on fatigue life into a damage factor and accumulate all cyclic damage. In the specific calculation process, each stress peak-valley pair is regarded as a cycle. By corresponding to the life value in the SN curve, the fatigue damage corresponding to the cycle is obtained. Then, all cyclic damages are accumulated to obtain a total damage value. When the accumulated damage reaches 1, it is considered that fatigue failure has occurred in the region. From this, the service life of each high-stress region can be derived. The life data is usually expressed in units of stress cycles, for example, 106 times or 10 7 times, and can be further converted into failure time under normal operating frequency and time conditions. These high-stress area life data can reveal the fatigue reliability of the structure under actual loads and provide an important basis for optimized design, life prediction and maintenance plan.

[0110] Predict crack probability from life data in high stress areas;

[0111] In this embodiment, predicting the crack probability in the life data of the high stress area requires combining fatigue life data and the stress state of the high vibration area for analysis. First, finite element analysis is used to obtain stress data under high vibration conditions to reveal the stress distribution in the high stress area. Subsequently, based on the fatigue properties of the material, such as fatigue limit and yield strength, a fatigue damage model is established to calculate the initial probability of crack occurrence in the high stress area. Next, the Paris law is applied, which describes the relationship between the crack growth rate and the stress intensity factor. The crack growth rate is proportional to the stress intensity factor at the crack tip. Combined with the stress-life curve of the material, the stress change in each fatigue cycle is converted into the probability of crack initiation. In this process, the initial size of the crack (usually determined by microscopic inspection or assuming the initial microcrack size) is used as an input parameter to calculate the crack expansion in different fatigue cycles. The growth of the crack in each cycle corresponds to a certain crack initiation probability, and this calculation depends on the stress intensity factor and the fatigue properties of the material. By accumulating the crack initiation probability of multiple fatigue cycles, the probability of crack occurrence in the high stress area can be obtained. Accurately predicting the probability of crack occurrence requires understanding several key parameters: stress intensity factor, material fatigue life data (such as the SN curve), and initial crack size. These parameters are input into a crack growth model and combined with actual stress-life analysis to derive crack initiation probability and growth trends, thereby predicting potential crack risks in high-stress areas. Throughout this process, fatigue damage accumulation calculations and crack growth predictions provide a basis for subsequent reliability analysis and maintenance decisions.

[0112] Calculate crack growth rate based on crack probability;

[0113] In this embodiment, the calculation of the crack growth rate is usually predicted using the Paris law or other applicable crack propagation models. In this process, the crack growth rate is mainly determined by the change of the stress intensity factor, and the Paris law links the crack growth rate to the change of the stress intensity factor. In the high stress area, the stress intensity factor of the crack in each loading cycle is calculated based on the vibration data and the fatigue life analysis of the material. The stress intensity factor is a measure of the stress field at the crack tip and is affected by the external loading conditions, crack geometry and material properties. In the high vibration area, the change of this factor is usually accompanied by a periodic change of stress, so the crack growth rate also depends on the change of the stress intensity factor, that is, the stress fluctuations to which the crack tip is subjected. By obtaining the crack propagation parameters of the material (such as C and m values), the crack growth rate can be quantified. The C and m values ​​are the material constants and stress intensity factor indexes of the material, respectively, and are usually obtained through experimental data or literature. The C value reflects the crack propagation sensitivity of the material, while the m value describes the relationship between the crack propagation rate and the change of the stress intensity factor. In high-stress regions, the crack growth rate varies with the stress intensity factor (SIF). Therefore, the crack growth rate within each loading cycle is calculated based on the change in SIF during that cycle. The entire crack growth rate calculation process requires detailed tracking of each stage of crack growth, typically divided into the initial initiation phase, the stable growth phase, and the final accelerated growth phase. At each stage, changes in the SIF and the fatigue properties of the material will affect the crack growth rate. Therefore, to accurately predict crack growth, a comprehensive analysis of factors such as stress changes in the high-stress region, loading conditions, and initial crack size is necessary to ensure accurate crack growth rate prediction.

[0114] Analyze crack propagation paths based on crack growth rate;

[0115] In this embodiment, finite element analysis software is used to perform fracture mechanics analysis to accurately calculate the crack propagation path. In this process, high stress areas need to be simulated by detailed mechanical models, especially the analysis of stress distribution during crack initiation and propagation. Finite element analysis can capture stress concentration points and simulate the entire process of cracks from initial initiation to propagation. Cracks usually propagate along the direction where stress is most concentrated. Specifically, the crack propagation direction is affected by the stress intensity factor (usually K_I), which characterizes the intensity of the stress field at the crack tip. The size and change of the stress intensity factor determine whether the crack will propagate and the rate of crack propagation. In finite element simulation, by calculating the stress state of each node, the change of the stress intensity factor can be obtained, which provides a basis for determining the crack propagation path. The crack propagation path usually follows the weakest point of the material, which is usually an area with low fracture toughness of the material. In high stress areas, the crack path often passes through the microstructural weaknesses of the material, such as grain boundaries, holes or impurity areas of metal materials, or internal defect areas caused by welding or forming processes. By combining numerical methods and gradually calculating the stress intensity factor in finite element analysis, it is possible to accurately simulate the expansion of cracks in different directions. Accurate prediction of the crack propagation path also requires consideration of factors such as the crack initiation position, the crack geometric characteristics, and the fracture toughness of the material. During the simulation process, the initial position of the crack is crucial because the crack propagation path will start directly from this position. By accurately setting these parameters during the simulation process, the complete path of the crack from the starting point to the propagation point can be simulated. The fracture toughness of the material plays a key role in the resistance to crack propagation. In areas with higher strength or better toughness, the crack propagates more slowly and the path may be deflected. Therefore, the simulation of the crack propagation path not only relies on the calculation of the stress intensity factor, but also requires a comprehensive consideration of the structural properties and mechanical response of the material to accurately predict the crack propagation route.

[0116] The stress point fracture of the crack propagation path is identified to obtain the stress point fracture data.

[0117] In this embodiment, finite element analysis (FEA) is used to simulate the crack propagation process to obtain stress distribution data along the crack path. During the crack propagation process, the locations where stress concentration occurs usually become key points for crack propagation. These locations have geometric defects, material inhomogeneities, or process defects, leading to local stress enhancement. Through simulation, these stress concentration points can be clearly identified. These points are usually the source of crack propagation or potential fracture locations. The identification of stress concentration points not only depends on the calculation of the stress intensity factor, but also requires further analysis in combination with the specific conditions of the material's fracture toughness and the crack propagation path. These stress points are usually located on the crack propagation path, especially near the crack tip or turning point. In the finite element model, the calculation of the stress state can accurately predict the changes in the stress points during crack propagation. These changes reveal that the crack propagates along the direction of greater stress in certain areas. In the process of identifying stress points, fracture toughness analysis is an effective means. Fracture toughness reflects the ability of a material to resist fracture during crack propagation and is usually closely related to the stress intensity factor. By calculating the stress intensity factor of each point on the crack path, the stress point most likely to fracture can be accurately located. Analysis of stress intensity factors can help determine whether cracks will propagate at specific stress points, especially in areas of stress concentration. Through further stress analysis, the stress points that lead to fracture during crack propagation can be identified, and the role of these stress points in the crack propagation path can be analyzed. These stress points not only reflect the local stress state of the material, but also reveal the starting points of brittle fracture or fatigue fracture that occur during crack propagation. By analyzing these stress points, detailed fracture data at the stress points can be obtained, including the specific location of the fracture, the load conditions, and the stress value that triggers the fracture. In addition, the identification of stress points also provides important data support for subsequent crack prediction, structural optimization, and fatigue life analysis.

[0118] Preferably, the multi-scale force-bearing structure optimization in step S3:

[0119] Extract fracture structure data of stress point fracture data;

[0120] In this embodiment, it is necessary to calculate the stress distribution on the crack propagation path through a finite element analysis (FEA) model. In order to obtain accurate crack propagation data, the crack propagation path needs to be modeled in detail, taking into account the surface and internal geometric structure, material properties and loading conditions. Through these modeling, the stress intensity factor on the crack propagation path, the stress field at the crack tip and other factors affecting crack propagation can be calculated. In the finite element model, after defining the boundary conditions and load input parameters (such as load amplitude, loading frequency, loading direction, etc.), the distribution of cracks at the stress points can be obtained. Using these calculation results, the fracture structure data on the crack path can be extracted, which reflects the interaction between stress, material properties and geometric properties during the crack propagation process.

[0121] Calculate the maximum stress from fracture structure data;

[0122] In this embodiment, it is necessary to calculate the stress field of the crack propagation path by the finite element analysis (FEA) method. In the finite element analysis model, the geometric model of the crack propagation path must first be accurately constructed, which involves a detailed description of the initial position of the crack, the propagation direction, and the boundary conditions and load conditions of the structure. To this end, it is necessary to define the load conditions (such as tension, compression or shear force) and the direction and amplitude of the loading. Next, based on the relative relationship between the crack position and the surrounding structure, meshing is performed, and the stress field distribution is solved by finite element analysis software (such as ANSYS, Abaqus, etc.). This step will calculate the stress value at each node, including stress types such as normal stress and shear stress. After obtaining the stress field, it is necessary to extract the local stress distribution of each node on the crack propagation path, with special attention to the crack tip and the area of ​​stress concentration. The crack tip is usually the key position of stress concentration, so it is particularly necessary to accurately calculate the stress value in these areas. By calculating the stress distribution of the crack tip or the area of ​​stress concentration, the position of the maximum stress value can be clearly identified. At this point, stress intensity factors (such as K_I, K_II) can be used to further quantify the stress state around the crack. The stress intensity factor is an important parameter that describes the stress intensity at the crack tip. By calculating the stress intensity factor value at the crack tip, the magnitude of the maximum stress can be further determined. When selecting the maximum stress, it is necessary to compare the stress values ​​of all nodes and select the point with the largest stress value as the maximum stress of the fracture structure. In this process, the stress state of different regions must be considered to ensure that the maximum stress value can accurately reflect the vulnerable points of the structure, especially the stress state at the crack initiation or expansion location. The maximum stress data obtained in this way can provide reliable stress parameters for subsequent crack propagation analysis and fracture prediction. Ultimately, these calculated maximum stress values ​​will become key parameters in the fracture structure data, used to further analyze the possibility of crack propagation and the reliability of the structure.

[0123] Calculate structural stiffness from fractured structural data;

[0124] In this embodiment, stress-strain calculations are performed using a finite element analysis model. During this process, a geometric model of the structure must first be constructed, including consideration of the crack propagation region and other features that affect the structural stiffness. Within the geometric model, the material properties, structure dimensions, and boundary conditions must be accurately defined to ensure the accuracy of the analysis results. In the model, the crack propagation region typically manifests as stress concentration and localized structural deformation. In particular, the area near the crack tip should be finely meshed to facilitate detailed calculation of the deformation. Loading simulations are performed based on known load conditions, which are typically derived from actual operating conditions, such as tensile, compressive, or bending loads. Under load, the structure deforms, and the deformation values ​​can be calculated using finite element analysis software (such as ANSYS, Abaqus, etc.). Deformation is typically expressed as strain or displacement, reflecting the degree of deformation of various parts of the structure under the external load. The stiffness of the structure is closely related to the elastic modulus and geometric structural properties of the material, and therefore needs to be calculated based on the geometric parameters of the structure (such as cross-sectional area, aspect ratio, etc.) and the Young's modulus of the material. When calculating stiffness, the deformation of the structure and the applied external load are usually used to infer its stiffness. The deformation obtained by finite element calculation, combined with the external load, can be used to solve the stiffness using the force-displacement relationship formula. For structures with cracks, special attention should be paid to the changes in stiffness near the crack propagation area, because cracks will cause a decrease in local stiffness and affect the overall deformation behavior of the structure. In these areas, the specific impact of cracks on the stiffness of the structure should be considered in the calculation, which is usually manifested as a decrease in the stiffness coefficient. In the calculation, it should be ensured that the existence of cracks is correctly considered in the stress-strain analysis to ensure that the calculation results accurately reflect the specific impact of cracks on the stiffness of the structure.

[0125] The fracture structure data are classified according to the structural stiffness and maximum stress, and the material defect fracture data and high stress concentration fracture data are obtained;

[0126] In this embodiment, based on the results of finite element analysis, the stress data of the entire structure under load and the deformation of the structure are obtained. By classifying these stress data, it is possible to clearly identify which areas are high stress concentration areas. These areas are usually potential starting points for crack propagation. Based on the crack propagation path and the stress state at the crack tip, the location of the stress concentration area can be further determined. These areas usually have obvious stress gradients, which are manifested as higher stress intensity factors (such as K_I or K_II) values. Once the high stress areas are determined, it is necessary to further determine whether the crack propagation in these areas is related to material defects. Material defects, such as pores, inclusions, grain boundary inhomogeneities, etc., usually trigger crack initiation and propagation in these high stress areas. Therefore, the next step is to identify whether the starting point of crack propagation corresponds to the known material defect location through detailed inspection of the structural geometry and analysis of the material properties. If the crack propagation starting point coincides with a material defect, it can be determined that the crack propagation is caused by a material defect. In this case, it is necessary to record the type, location, size, and other information of the defect in detail. During the classification process, the data should be divided according to the crack propagation location and stress state. Some data is classified as fractures caused by material defects, indicating that crack propagation is directly related to defects or inhomogeneities within the material. Another portion is classified as fractures caused by high stress concentration, indicating that crack propagation is primarily influenced by localized high stress concentrations, rather than internal material defects. Detailed statistics and analysis are performed on the data for each region to ensure accurate classification and clearly distinguish between fracture behaviors caused by material defects and those caused by high stress concentrations. This classification enables a better understanding of the failure mechanisms of fractured structures, providing data support for subsequent material optimization and structural design.

[0127] Perform material optimization on fracture structure data based on material defect fracture data to obtain material optimization data;

[0128] In this embodiment, the first step of material optimization is to analyze the cause of the fracture and the defect location based on the material defect fracture data. Through experimental or simulation data, combined with the geometric shape, position and material properties of the defect, the influencing factors of crack propagation under these conditions can be inferred. An appropriate material optimization algorithm (such as topology optimization or strength-based optimization method) is used to optimize the design for the material defect area. This process involves changing the composition and thickness of the material or using different alloy materials to ensure that the optimized material has higher fatigue resistance near the crack propagation point. During the optimization process, it is necessary to consider the parameters such as strength, toughness, plasticity of the material in detail, and use these parameters to perform local material optimization to obtain improved material data.

[0129] Based on the high stress concentration fracture data, the fracture structure data is subjected to fillet design to obtain fillet design data;

[0130] In this embodiment, adding fillet design to high stress concentration areas is an effective means to reduce stress concentration and improve fracture toughness. In this process, it is first necessary to identify the sharp corners with high stress in the structure based on the high stress concentration fracture data. For these sharp corners, the angles of these areas are gradually smoothed by means of fillet design to reduce the stress concentration effect. During design, the radius value of the fillet is used as the main parameter for optimizing the design. Usually, the radius needs to be determined based on the actual working conditions, stress field distribution and material properties of the structure. The stress distribution after fillet design is simulated by finite element analysis tools to ensure that the fillet design can effectively reduce the stress value in the stress concentration area and reduce the risk of crack initiation and expansion. Finally, the fillet design data obtained contains the new design radius value and the corresponding stress distribution.

[0131] Integrate material optimization data and fillet design data to obtain multi-scale force-bearing structure optimization data.

[0132] In this embodiment, it is ensured that the data after material optimization and the data after rounded corner design can be applied simultaneously in the same structure to avoid conflicts or incompatibilities between each other. During the integration process, the strength and stiffness of the material and the influence of the rounded corner design on the stress distribution are analyzed to ensure that the two can jointly improve the durability and crack resistance of the structure. Finite element analysis tools are used to perform further stress analysis and deformation analysis on the integrated structure to verify the performance of the optimized structure under actual load. Ultimately, the obtained multi-scale force structure optimization data will include optimized material properties, improved rounded corner design solutions and corresponding stress distribution data, providing a more robust structural design solution.

[0133] Preferably, step S4 is specifically as follows:

[0134] Step S41: identifying heat dissipation structure parameters of the electronic device model according to the heat dissipation enhancement structure data to obtain heat dissipation structure parameters, including heat dissipation fin arrangement parameters and heat dissipation channel parameters;

[0135] In this embodiment, a heat dissipation enhancement structure is designed according to the geometric shape of the electronic device and the external environment. By analyzing the heat dissipation area, the finite element analysis tool is used to simulate the heat flow distribution to obtain the heat transfer conditions of each part. The layout parameters of the heat dissipation fins include the number of fins, size (such as length, width, thickness), arrangement (such as parallel arrangement or staggered arrangement) and spacing between fins. These parameters are optimized and calculated based on the heat flow distribution and space constraints. The heat dissipation channel parameters include the size, shape, position and wind flow direction of the channel. Through heat conduction and convection analysis, the area where the heat dissipation fins need to be added and the position of the heat dissipation channel are determined to achieve the best heat dissipation effect.

[0136] Step S42: performing support structure stability analysis on the electronic device model based on the multi-scale force structure optimization data to obtain support structure stability data;

[0137] In this embodiment, multi-scale force structure optimization data is used. These data come from the previous analysis of the force state of the model, including the stress distribution under static load and dynamic load. The stress and deformation of each supporting component under different loads are calculated by finite element analysis (FEA), with special attention paid to the deformation resistance and buckling resistance of the supporting structure. Key parameters include the material strength of the support column, the cross-sectional area, the contact force distribution with the connection, and the distance between the support points. The stability of the support structure is verified by analyzing the stress and strain data of the key support points to ensure that the support structure does not deform excessively or become unstable within the design load range.

[0138] Step S43: performing heat dissipation performance analysis on the electronic device model according to the heat dissipation fin arrangement parameters and the heat dissipation channel parameters to obtain heat dissipation performance data;

[0139] In this embodiment, the heat dissipation performance is further evaluated based on the heat dissipation fin arrangement parameters and the heat dissipation channel parameters. A three-dimensional thermal analysis is performed using thermal flow analysis software (such as ANSYS or COMSOL). The input parameters such as material, ambient temperature, thermal conductivity of the heat dissipation fins, etc. are used to calculate the heat dissipation efficiency under different working conditions. The heat dissipation performance data includes the heat dissipation capacity of the fins, heat flux density, temperature distribution, and thermal resistance characteristics of the channel. By adjusting the spacing of the heat dissipation fins, increasing the number of heat dissipation channels, or optimizing the channel layout, it is possible to analyze in detail how these parameters affect the overall thermal management performance to ensure that the appropriate temperature range can be maintained during operation.

[0140] Step S44: integrating the electronic device performance parameters into the electronic device performance parameters according to the support structure stability data and the heat dissipation performance data;

[0141] In this embodiment, the support structure stability data and heat dissipation performance data will be used as input for multi-objective performance integration. Through weight distribution, based on machine learning methods or weighted average models, the stability and heat dissipation performance of the support structure are comprehensively evaluated. First, each performance data needs to be standardized and converted into a unified evaluation index. For example, the stability of the support structure can be quantified by the maximum deformation or buckling stress, and the heat dissipation performance can be quantified by thermal resistance or temperature uniformity. According to these standardized indicators, combined with actual design requirements (such as high stability, low temperature rise, etc.), various performances are comprehensively calculated to obtain the final performance parameters.

[0142] Step S45: parameterizing the electronic device model based on the electronic device performance parameters to generate a parameterized electronic device model.

[0143] In this embodiment, the design is first parameterized based on the performance parameters of the electronic device. Through CAD modeling software (such as SolidWorks or AutoCAD), the geometric dimensions of the device, the layout of the support structure, the parameters of the heat sink fins and the channel are converted into adjustable parametric variables. These variables include but are not limited to the overall dimensions of the device, the position of the support columns, the layout of the fins, the shape of the channel, etc. By setting appropriate parametric constraints, a model that can be automatically adjusted according to demand is generated. All parameter settings are based on the performance data obtained from the above analysis to ensure that the generated model can meet the requirements of stability and heat dissipation performance.

[0144] 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 data-driven method for automatically generating a parametric model of an electronic device, characterized in that: The following steps are involved: Step S1: acquiring electronic device data and performing dimensional anomaly analysis to obtain dimensional anomaly data; Optimize the size of the electronic device data based on the size abnormality data, generate electronic device optimization data, and build an electronic device model; Step S2: extracting the heat dissipation structure based on the electronic device optimization data, and performing hot spot accumulation analysis on the electronic device model to obtain hot spot accumulation structure data; Perform heat dissipation enhancement based on hotspot accumulation structure data to generate heat dissipation enhancement structure data; Step S3: performing multi-band resonance analysis on the electronic device model to obtain vibration data; performing stress point fracture analysis on the electronic device model based on the vibration data to obtain stress point fracture data; performing multi-scale stress structure optimization on the stress point fracture data to obtain multi-scale stress structure optimization data; Step S4: parameterizing the electronic device model according to the heat dissipation enhancement structure data and the multi-scale force structure optimization data to generate a parameterized model of the electronic device.

2. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire electronic device data. When the following conditions occur simultaneously, it is determined to be a dimensional deviation abnormality and the dimensional deviation abnormality data is obtained: the wall thickness deviation exceeds the set tolerance range of ±0.2mm, the key installation hole position offset exceeds 0.5mm, the bending angle error exceeds ±2°, and the overall dimensional deviation exceeds the set threshold of 1%; Step S12: When the following conditions occur simultaneously, it is determined to be an assembly interference anomaly and assembly interference anomaly data is obtained: the minimum safety distance between components is less than the set threshold of 0.5mm, the screw holes are not aligned with a deviation exceeding 0.3mm, and the heat dissipation duct or ventilation hole size deviation exceeds ±10%; Step S13: Integrate the dimension deviation abnormality data and the assembly interference abnormality data to obtain dimension abnormality data; Step S14: Optimizing the size of the electronic device data based on the size abnormality data to generate electronic device optimized data; Step S15: constructing an electronic device model according to the electronic device optimization data.

3. The data-driven electronic device parameter model automatic generation method according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: dividing the electronic device data into abnormal structures based on the size abnormality data to obtain electronic device abnormal structure data; Step S142: Optimizing the wall thickness of the abnormal structure data of the electronic device to obtain optimized wall thickness structure data; Step S143: performing hole position adjustment on the abnormal structure data of the electronic device to obtain hole position adjustment structure data; Step S144: performing stress analysis on the hole position adjustment structure data according to the wall thickness optimization structure data to obtain stress data, and performing stress statistics, wherein a high stress threshold is set to 70%-80% of the yield strength to obtain high stress data; Step S145: performing region identification on the electronic device data according to the high stress data to obtain high stress region data; Step S146: Optimize the electronic device support structure based on the high stress area data to obtain electronic device optimization data.

4. The data-driven electronic device parameter model automatic generation method according to claim 3, characterized in that: Step S146 is specifically as follows: Perform structural recognition on high stress area data to obtain high stress area structural data; Filling the lattice structure based on the high stress area structure data, wherein the lattice structure type is set to body-centered cubic and the unit size range is set to 1.5mm-3mm, to obtain the lattice structure data; Add ribs to the lattice structure data, where the hinge thickness is 0.8mm-2.0mm, the hinge width range is 5mm-15mm, and the hinge bending radius is 2mm-6mm, to obtain the rib data; Perform elastic hinge design on the lattice structure data to obtain elastic hinge data; The electronic equipment support optimization structure is integrated according to the rib data and elastic hinge data to obtain the electronic equipment optimization data.

5. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: The hotspot accumulation analysis in step S2 includes: Optimize data extraction and heat dissipation structure based on electronic equipment; Conduct thermal flow simulation based on heat dissipation structure; Calculate the temperature during thermal flow simulation and draw the temperature field distribution diagram; Identify the high-temperature model structure of electronic equipment models based on temperature field distribution diagrams; Calculate the airflow velocity in the duct of the high-temperature model structure; The hot spot accumulation structure of the high temperature model structure is identified based on the air flow velocity in the duct to generate the hot spot accumulation structure data.

6. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: The heat dissipation enhancement in step S2 includes: Adjust the air duct layout of hotspot accumulation structure data; Classify the air duct layout to obtain positive pressure air duct layout and negative pressure air duct layout; Optimize the fan speed of the positive pressure duct layout; Optimize the air outlet position of the negative pressure air duct layout, where the position range is set to 50mm-200mm from the air outlet to the heat source and the air outlet opening rate is 40%-70%; The air duct isolation design is based on the fan speed and air outlet position, where the thickness of the isolation plate is set to 0.8mm-3mm and the isolation channel spacing is ≥20mm; Added heat deflector cover with hotspot accumulation structure data, where the thickness of the heat deflector cover is set to 0.5mm-2mm and the tilt angle is set to 30°-60°; The heat dissipation enhancement structure is integrated according to the isolation air duct and the heat deflector to generate the heat dissipation enhancement structure data.

7. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: The multi-band resonance analysis in step S3 includes: Assigning materials to the electronic equipment model to obtain a material assignment model; Determine the natural vibration frequency of the material assignment model; Determine the operating vibration frequency of the material assignment model; Resonance judgment is performed based on the natural vibration frequency and the working vibration frequency to obtain resonance data; Perform impact vibration simulation on the material assignment model to obtain impact vibration data; The resonance data and the impact vibration data are integrated to obtain the vibration data.

8. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: The stress point fracture analysis in step S3 includes: Count the vibration magnitude of vibration data and obtain high vibration data; Based on the high vibration data, the electronic equipment model is region-identified to obtain the high vibration area; Analyze high stress areas in high vibration areas; Perform fatigue life analysis on high stress areas to obtain life data of high stress areas; Predict crack probability from life data in high stress areas; Calculate crack growth rate based on crack probability; Analyze crack propagation paths based on crack growth rate; The stress point fracture of the crack propagation path is identified to obtain the stress point fracture data.

9. The data-driven electronic device parameter model automatic generation method according to claim 1, characterized in that: The multi-scale force-bearing structure optimization in step S3 includes: Extract fracture structure data of stress point fracture data; Calculate the maximum stress from fracture structure data; Calculate structural stiffness from fractured structural data; The fracture structure data are classified according to the structural stiffness and maximum stress, and the material defect fracture data and high stress concentration fracture data are obtained; Perform material optimization on fracture structure data based on material defect fracture data to obtain material optimization data; Based on the high stress concentration fracture data, the fracture structure data is subjected to fillet design to obtain fillet design data; Integrate material optimization data and fillet design data to obtain multi-scale force-bearing structure optimization data.

10. The data-driven automatic generation method of electronic device parametric models according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: identifying heat dissipation structure parameters of the electronic device model according to the heat dissipation enhancement structure data to obtain heat dissipation structure parameters, including heat dissipation fin arrangement parameters and heat dissipation channel parameters; Step S42: performing support structure stability analysis on the electronic device model based on the multi-scale force structure optimization data to obtain support structure stability data; Step S43: performing heat dissipation performance analysis on the electronic device model according to the heat dissipation fin arrangement parameters and the heat dissipation channel parameters to obtain heat dissipation performance data; Step S44: integrating the electronic device performance parameters into the electronic device performance parameters according to the support structure stability data and the heat dissipation performance data; Step S45: parameterizing the electronic device model based on the electronic device performance parameters to generate a parameterized electronic device model.

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