Mobile phone elastic piece resonant frequency optimization method

Abstract

The invention relates to a mobile phone elastic piece resonant frequency optimization method in the field of mobile phone elastic pieces, which comprises the following steps: acquiring elastic piece material and structure design parameters, and establishing a relation model of elastic piece resonant frequency, material and structure; according to the working frequency range of the mobile phone and the target interval of the resonant frequency of the preset elastic sheet, determining the optimization direction of the elastic sheet material and the structural design; a finite element analysis method is adopted to simulate vibration modes of the elastic sheet under different materials and structural designs, and the inherent frequency and vibration characteristics of the elastic sheet are obtained; if the inherent frequency of the elastic sheet exceeds the target interval, adjusting the material thickness or structural design parameters of the elastic sheet, and performing finite element analysis again until the inherent frequency falls into the target interval; verifying whether the resonant frequency and the vibration mode of the elastic sheet accord with a preset target or not through an experimental test, and obtaining comparative analysis of experimental data and a simulation result; and according to the optimized elastic sheet material and structural design parameters, elastic sheet manufacturing process parameters are generated and used for accurate manufacturing in the actual production process.

Description

Technical Field

[0001] The present invention relates to the field of information technology, in particular to a spring for a mobile phone, and specifically to a method for optimizing the resonance frequency of a mobile phone spring. Background Art

[0002] In the precise manufacturing of the multi-contact structure of mobile phone shrapnel, the control of the resonance characteristics of the shrapnel is a key technical problem. Differences in the material and structural design of the shrapnel will cause changes in its natural frequency and vibration mode, which will in turn affect the signal transmission quality and anti-interference ability of the mobile phone. In the actual production process, how to balance the contradiction between the strength, elasticity and resonant frequency of the shrapnel is a technical challenge that needs to be solved urgently. Ideally, the shrapnel should ensure sufficient strength and elasticity while its resonant frequency can match the operating frequency of the mobile phone and maintain a stable vibration mode. However, due to the complexity of the shrapnel structure and the differences in material properties, it is difficult to meet these requirements at the same time in actual production. At present, engineers are constantly conducting experiments and tests by optimizing the selection of shrapnel materials and structural design, trying to find an optimal balance between strength, elasticity and resonance characteristics, in order to obtain a high-quality and high-reliability mobile phone shrapnel multi-contact structure. However, how to achieve this goal under the dual pressure of production efficiency and cost control is still a technical problem to be overcome. Summary of the invention

[0003] The present invention provides a method for optimizing the resonance frequency of a mobile phone spring, the method comprising:

[0004] Obtain the material and structural design parameters of the shrapnel, and establish a relationship model between the shrapnel resonant frequency and the material and structure;

[0005] According to the operating frequency range of the mobile phone, the target range of the spring resonant frequency is preset to determine the optimization direction of the spring material and structural design;

[0006] Finite element analysis method is used to simulate the vibration mode of the shrapnel under different materials and structural designs to obtain the natural frequency and vibration characteristics of the shrapnel;

[0007] If the natural frequency of the spring is beyond the target range, adjust the spring material thickness or structural design parameters and re-perform finite element analysis until the natural frequency falls within the target range;

[0008] According to the simulation results of the vibration mode of the spring, determine whether the vibration of the spring is stable. If there is unstable vibration, optimize the spring structure design to reduce unnecessary vibration and noise;

[0009] Through experimental testing, verify whether the resonant frequency and vibration mode of the spring meet the preset goals, and obtain comparative analysis between experimental data and simulation results;

[0010] If there is a deviation between the experimental data and the simulation results, adjust the shrapnel material and structural design parameters, and re - conduct finite - element analysis and experimental tests until the shrapnel resonance frequency and vibration mode reach the preset target;

[0011] Generate shrapnel manufacturing process parameters according to the optimized shrapnel material and structural design parameters for precise manufacturing in the actual production process;

[0012] Adopt machine - learning algorithms to train the experimental data of the shrapnel resonance frequency and vibration mode, and establish a shrapnel resonance characteristic prediction model for rapid optimization in subsequent shrapnel design and manufacturing processes.

[0013] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0014] The present invention discloses a method for optimizing the resonance frequency of a mobile - phone shrapnel. This method first establishes a relationship model between the shrapnel resonance frequency and materials and structures, presets a target interval according to the mobile - phone operating frequency range, simulates the shrapnel vibration mode through finite - element analysis to obtain the natural frequency and vibration characteristics. If the natural frequency exceeds the target interval, adjust the material thickness or structural parameters and re - analyze. The present invention also verifies the resonance frequency and vibration mode through experimental tests, compares and analyzes the experimental data with the simulation results. If there is a deviation, perform parameter adjustment and re - testing until the preset target is reached. Finally, the present invention uses machine - learning algorithms to establish a resonance characteristic prediction model for rapid optimization in subsequent design and manufacturing. This method can effectively optimize the shrapnel resonance frequency, improve the performance of mobile phones and user experience, and at the same time enhance the design and manufacturing efficiency of shrapnels. Brief Description of the Drawings

[0015] Figure 1 It is a flowchart of a method for optimizing the resonance frequency of a mobile - phone shrapnel according to the present invention. Detailed Embodiments

[0016] The technical solutions of the present invention will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0017] Such as Figure 1 , a method for optimizing the resonance frequency of a mobile - phone shrapnel in this embodiment may specifically include:

[0018] Step S101: Obtain the shrapnel material and structural design parameters, and establish a relationship model between the shrapnel resonance frequency and materials and structures.

[0019] According to the shrapnel material and structural design parameters, the finite element analysis method is used to simulate the vibration response of the shrapnel at different excitation frequencies, and the natural frequency and vibration mode data of the shrapnel are obtained. According to the obtained natural frequency and vibration mode data of the shrapnel, combined with the theory of material mechanics and the theory of vibration mechanics, a mathematical model describing the relationship between the resonant frequency of the shrapnel and the material parameters and structural parameters is established. The multiple regression analysis algorithm is used to identify the parameters and fit the coefficients of the established mathematical model, and the quantitative relationship expression between the resonant frequency of the shrapnel and the material parameters and structural parameters is obtained. According to the obtained quantitative relationship expression by fitting, the sensitivity analysis method is used to calculate the influence degree of different material parameters and structural parameters on the resonant frequency of the shrapnel, and the key influencing parameters are judged. For the judged key influencing parameters, the optimization algorithm is used to search for the optimal parameter combination that makes the resonant frequency of the shrapnel reach the predetermined target value within the given parameter value range. The optimized optimal parameter combination is substituted into the finite element analysis model for simulation verification to determine the feasibility and effectiveness of the optimization results. According to the simulation verification results, the optimized material parameters and structural parameters are appropriately adjusted to obtain the final design parameters that meet the requirements of the shrapnel resonant frequency, and the optimization design of the shrapnel is completed.

[0020] Specifically, as in this embodiment, an exemplary vibration analysis of the shrapnel is carried out. First, the vibration response of the shrapnel at different excitation frequencies is simulated by the finite element analysis method. For example, for a rectangular steel shrapnel, a three-dimensional model can be established, the material properties such as elastic modulus and Poisson's ratio are set, and then the boundary conditions and excitation forces are applied. By solving the characteristic equation, the natural frequency and vibration mode data of the shrapnel can be obtained. After obtaining these data, a mathematical model is established by combining the theory of material mechanics and vibration mechanics. For example, using the Euler-Bernoulli beam theory, the shrapnel can be simplified into a cantilever beam, and an equation describing the relationship between its resonant frequency and material parameters (such as density, elastic modulus) and structural parameters (such as length, width, thickness) is established. Next, the multiple regression analysis algorithm is used to identify the parameters and fit the coefficients of the model. Suppose a series of experimental data are obtained, including the resonant frequencies under different material and structural parameters. By methods such as the least squares method, a quantitative relationship expression can be fitted, such as f = a*E^b*ρ^c*L^d*W^e*T^f, where f is the resonant frequency, E is the elastic modulus, ρ is the density, and L, W, T are the length, width, and thickness respectively. After obtaining this expression, sensitivity analysis can be carried out. By calculating partial derivatives or using the Monte Carlo simulation method, the influence degree of each parameter on the resonant frequency can be determined. For example, it may be found that the thickness T has the greatest influence on the frequency, while the influence of the width W is relatively small. For the parameters with greater influence, an optimization algorithm can be used to search for the optimal parameter combination. For example, if the goal is to adjust the resonant frequency to 500 Hz, a genetic algorithm or a particle swarm optimization algorithm can be used to search for the best solution within the given parameter range. This may result in a set of optimized parameter values, such as E = 200 GPa, ρ = 7800 kg / m 3 , L = 100 mm, W = 20 mm, T = 2 mm. Finally, this set of optimized parameters is substituted into the finite element model for verification. If the simulation results show that the resonant frequency is close to the target value and other performance indicators such as stress distribution also meet the requirements, then this set of parameters can be used as the final design scheme. If there are deviations, fine-tuning may be required, such as slightly increasing the thickness or decreasing the length, to achieve more precise frequency control. This optimization design method can not only be applied to simple rectangular shrapnels, but also to more complex shapes and structures. Through this systematic analysis and optimization process, while ensuring performance, the material usage can be minimized to the greatest extent, and the cost performance and reliability of the product can be improved.

[0021] Step S102, according to the working frequency range of the mobile phone, preset the target interval of the resonant frequency of the shrapnel, and determine the optimization direction of the shrapnel material and structure design.

[0022] Obtain the frequency range data of the mobile phone's operation and determine the target interval parameters of the resonant frequency of the shrapnel. According to the target interval parameters, establish a mapping model between the shrapnel material properties and the resonant frequency. Use the mapping model to screen candidate shrapnel materials that meet the resonant frequency requirements. Based on the candidate shrapnel materials, construct a simulation model of the shrapnel structure and the resonant frequency. Adopt the genetic algorithm to optimize the shrapnel structure parameters in the simulation model so that the resonant frequency falls within the target interval. According to the optimized shrapnel material and structure parameters, generate the manufacturing process flow of the shrapnel. Integrate the shrapnel into the mobile phone, test its resonant performance and reliability within the mobile phone's operating frequency range, and verify the effectiveness of the design scheme.

[0023] Step S103: Adopt the finite element analysis method to simulate the vibration modes of the shrapnel under different material and structure designs, and obtain the natural frequency and vibration characteristics of the shrapnel.

[0024] According to the material properties and structural design parameters of the shrapnel, establish a finite element analysis model, perform mesh division and boundary condition setting on the shrapnel. Adopt the modal analysis method to calculate the natural frequency and vibration modes of the shrapnel under different vibration modes, and obtain the vibration characteristic curves of the shrapnel. By changing the material parameters and structural dimensions of the shrapnel, conduct multiple groups of simulation calculations to analyze the influence laws of material properties and structural design on the vibration characteristics of the shrapnel. According to the vibration characteristic curves and influence laws, optimize the material selection and structural design of the shrapnel so that its natural frequency avoids the external excitation frequency and reduces the resonance risk. Adopt the random vibration analysis method to apply random excitation loads to the shrapnel model, and calculate the vibration response and stress distribution of the shrapnel in the actual working environment. According to the random vibration response results, judge the vibration reliability of the shrapnel under different materials and structures, and screen out the optimized design scheme that meets the vibration performance requirements. Through the method of combining simulation and experiment, verify the optimized shrapnel design scheme to ensure that it has good anti-vibration performance in actual use.

[0025] Specifically, as an exemplary finite element analysis, it is an effective method to study the vibration characteristics of the shrapnel. First, a geometric model of the shrapnel is established, such as a rectangular thin plate, and material properties such as elastic modulus and density are defined. Then, mesh division is carried out. Tetrahedral or hexahedral elements can be used, and the mesh size needs to be small enough to ensure the calculation accuracy. The boundary condition settings usually include fixed-end constraints and free ends. Modal analysis can reveal the natural frequencies and vibration modes of the shrapnel. For example, for a stainless steel shrapnel with a length of 20 mm, a width of 5 mm, and a thickness of 0.5 mm, its first natural frequency may be around 200 Hz, and the vibration mode presents a typical first-order bending mode. By changing the thickness of the shrapnel, the changing trend of the natural frequency can be observed. An increase in thickness will lead to an increase in frequency. Material properties have a significant impact on vibration characteristics. For example, if stainless steel is replaced with aluminum alloy, due to the decrease in density, the natural frequency under the same dimensions will increase. In terms of structural design, increasing the length of the shrapnel will reduce the natural frequency, while increasing the width has a smaller impact. These rules can guide the optimization design to make the natural frequency of the shrapnel avoid the external excitation frequency, such as the working frequency range of the mobile phone vibration motor. Random vibration analysis simulates the actual working environment. A power spectral density function that conforms to the mobile phone usage scenario can be set, such as a stationary random excitation in the range of 20 - 2000 Hz. The analysis results include the root mean square displacement and stress distribution of each point of the shrapnel. For example, in a certain design scheme, the maximum displacement at the free end of the shrapnel may reach 0.1 mm, and the maximum stress is concentrated near the fixed end, about 100 MPa. By comparing the random vibration responses of different design schemes, the best scheme can be selected. For example, in a certain scheme, while ensuring sufficient stiffness, the maximum stress is much lower than the material fatigue limit, indicating good vibration reliability. Finally, samples can be made for vibration table tests to verify the accuracy of the simulation results and ensure that the shrapnel can work reliably for a long time in actual use.

[0026] Step S104, if the natural frequency of the shrapnel exceeds the target range, adjust the material thickness or structural design parameters of the shrapnel, and re-perform the finite element analysis until the natural frequency falls within the target range.

[0027] Obtain the initial material thickness and structural design parameters of the shrapnel as the input conditions for the finite element analysis; calculate the natural frequency of the shrapnel through the finite element analysis, and determine whether it falls within the preset target range; if the natural frequency is not within the target range, determine the adjustment amplitude of the material thickness or structural design parameters according to the degree of deviation of the frequency from the target range; adjust one or more attributes of the material thickness or structural design parameters to obtain new input conditions for the finite element analysis; input the adjusted material thickness and structural design parameters into the finite element analysis model, and recalculate the natural frequency of the shrapnel; determine whether the adjusted natural frequency falls within the target range. If it does not fall within the range, continue to adjust the material thickness or structural design parameters; when the natural frequency falls within the target range, output the current material thickness and structural design parameters as the optimal design scheme of the shrapnel.

[0028] Step S105: According to the simulation results of the shrapnel vibration mode, determine whether the shrapnel vibration is stable. If there is unstable vibration, optimize the shrapnel structure design to reduce unnecessary vibration and noise.

[0029] Obtain the shrapnel vibration simulation result data, perform frequency-domain analysis on the vibration data to obtain the vibration frequency and amplitude information. According to the preset stable vibration frequency and amplitude thresholds, determine whether the shrapnel vibration is stable. If the vibration frequency or amplitude exceeds the threshold range, it is determined as unstable vibration. For the shrapnel with unstable vibration, use the finite element analysis method to model and simulate the shrapnel structure, and identify the key structure parameters causing the unstable vibration. Through optimization algorithms such as genetic algorithm or particle swarm algorithm, search for the optimal combination of shrapnel structure parameters to obtain the optimized shrapnel structure design scheme. According to the optimized shrapnel structure design scheme, remodel and simulate the shrapnel to verify whether the vibration stability of the optimized shrapnel meets the requirements. If the vibration stability of the optimized shrapnel still does not meet the requirements, return to the fourth step and continue to optimize the shrapnel structure design until the vibration stability requirements are met. Process the sample of the optimized shrapnel structure and conduct physical vibration tests, measure the actual vibration frequency and amplitude, confirm the coincidence with the simulation results, and verify the feasibility and effectiveness of the shrapnel structure design.

[0030] Specifically, obtaining the data of the simulation results of the shrapnel vibration, as an example, usually involves sensor measurement and data acquisition systems. For example, an acceleration sensor or a displacement sensor can be used and installed at the key positions of the shrapnel, and the vibration signals are recorded through a high-speed data acquisition card. The obtained time-domain data is converted into frequency-domain data through the fast Fourier transform (FFT), so as to obtain the vibration frequency and amplitude information. When judging the vibration stability of the shrapnel, it is necessary to preset the frequency and amplitude thresholds of the stable vibration in advance. Suppose the stable vibration frequency range of a certain shrapnel is 45 - 55 Hz, and the amplitude threshold is 0.5 mm. If the analysis result shows that the main vibration frequency of the shrapnel is 62 Hz and the amplitude is 0.8 mm, it is determined as unstable vibration. For the shrapnel with unstable vibration, the finite element analysis method is used for in-depth research. First, a geometric model of the shrapnel is established, and material properties such as elastic modulus and density are defined. Then, mesh generation is carried out, and boundary conditions and loads are set. Through modal analysis, the natural frequency and vibration mode of the shrapnel can be obtained, and through static analysis, the stress distribution can be obtained. These results help to identify the key structural parameters causing unstable vibration, such as the thickness, width of the shrapnel or the stiffness of a specific area. The application of the optimization algorithm aims to find the optimal combination of the shrapnel structural parameters. Taking the genetic algorithm as an example, the key dimensional parameters of the shrapnel can be encoded as "genes", such as the thickness range of 1 - 3 mm and the width range of 10 - 20 mm. New parameter combinations are generated through operations such as crossover and mutation, and the performance of each group of parameters is evaluated using finite element analysis. The fitness function can be set to minimize the deviation between the vibration frequency and the target frequency. After multiple generations of iteration, the algorithm will converge to the optimal or approximately optimal parameter combination. The optimized shrapnel structural design scheme needs to be verified. Re-establish the finite element model and conduct modal analysis and transient dynamics analysis. If the analysis result shows that the vibration frequency has fallen within the range of 45 - 55 Hz and the amplitude is less than 0.5 mm, it is considered that the optimization is successful. Otherwise, it is necessary to adjust the parameters or constraint conditions of the optimization algorithm and re-perform the optimization process. Finally, it is crucial to process the samples of the optimized shrapnel and conduct physical tests. The samples can be made using numerical control processing equipment and then tested on a vibration table. A laser vibrometer or an acceleration sensor is used to measure the actual vibration frequency and amplitude. Compare the test results with the simulation results. If the deviation is within the acceptable range (such as the frequency deviation is less than 5% and the amplitude deviation is less than 10%), the feasibility of the design is verified. This process not only confirms the optimization result but also helps to improve the simulation model and enhance the accuracy of future designs.

[0031] Step S106, through experimental tests, verify whether the resonant frequency and vibration mode of the shrapnel meet the preset targets, and obtain the comparative analysis of the experimental data and the simulation results.

[0032] According to the preset resonant frequency and vibration mode target, design an experimental scheme to determine the test parameters and conditions. Build an experimental platform, select appropriate sensors and data acquisition devices, and conduct vibration tests on the shrapnel. Collect the vibration response data of the shrapnel under different excitation conditions, including parameters such as amplitude, frequency, and damping. Preprocess the collected experimental data to remove noise and outliers and extract effective vibration signals. Based on the material properties and geometric dimensions of the shrapnel, establish a finite element model and conduct modal analysis and harmonic response analysis. Compare the resonant frequency and vibration mode obtained from the simulation analysis with the experimental results, and calculate the relative error and correlation coefficient. If the experimental results deviate significantly from the simulation prediction, adjust the finite element model or experimental conditions and iterate and optimize until the preset target is met.

[0033] Specifically, as an exemplary vibration test of the shrapnel is a key step in evaluating its performance and reliability. When designing the experimental scheme, the working environment and intended use of the shrapnel need to be considered. For example, for the shrapnel, it may be necessary to simulate high-temperature and high-frequency vibration conditions; while for the shrapnel in precision instruments, the influence of low-frequency and micro-vibrations may be more concerned. The construction of the experimental platform involves the selection of various devices. Commonly used sensors include acceleration sensors, strain gauges, and laser vibrometers, etc. Taking the acceleration sensor as an example, its sensitivity and frequency response range need to match the expected vibration characteristics of the shrapnel. The data acquisition device needs to have a sufficient sampling rate and resolution to accurately capture the vibration details of the shrapnel. During the vibration response data acquisition process, by changing parameters such as the excitation frequency and amplitude, the performance of the shrapnel under different working conditions can be obtained. For example, a frequency sweep test is conducted on a certain shrapnel, the frequency range is 0 - 500 Hz, and the excitation force is gradually increased from 10 N to 100 N, and the vibration response of each point of the shrapnel is recorded. Data preprocessing is an important link to ensure the reliability of the analysis results. Common preprocessing methods include filtering, detrending, and outlier detection, etc. Taking the test data of a certain time as an example, a band-pass filter is used to remove the low-frequency interference below 50 Hz and the high-frequency noise above 1000 Hz, and then the data curve is smoothed by the moving average method. The establishment of the finite element model needs to accurately reflect the geometric characteristics and material properties of the shrapnel. Taking a certain steel shrapnel as an example, the Shell 63 element can be used for mesh division, and the material parameters are set as elastic modulus 210 GPa, Poisson's ratio 0.3, and density 7850 kg / m 3Modal analysis can obtain the natural frequencies and vibration modes of the shrapnel, while harmonic response analysis can predict the amplitude and phase responses of the shrapnel under different frequency excitations. The comparative analysis of experimental results and simulation predictions is the key to verifying the accuracy of the model. For example, the measured value of the first natural frequency of a certain shrapnel is 152 Hz, while the finite element analysis result is 158 Hz, and the relative error is 3.9%. By calculating the MAC values (Modal Assurance Criterion) of each order of mode, the correlation between the experimental mode and the theoretical mode can be further evaluated. If a large deviation is found between the experimental results and the simulation predictions, model correction or experimental condition adjustment is required. For example, if it is found that the stiffness of the actual shrapnel is lower than expected, it may be necessary to re-measure the material properties or check whether there are problems in the connection parts such as welding. Through iterative optimization, the prediction accuracy of the model is continuously improved, and finally the preset resonant frequency and vibration mode targets are achieved. The purpose of this series of steps is to ensure that the shrapnel design meets the actual application requirements and improve the product reliability and service life. Through precise vibration analysis, possible resonance problems can be predicted and avoided, and the vibration damping performance of the shrapnel can be optimized, thereby improving the stability and efficiency of the overall system.

[0034] Step S107, if there is a deviation between the experimental data and the simulation results, adjust the material and structural design parameters of the shrapnel, and re-conduct finite element analysis and experimental tests until the resonant frequency and vibration mode of the shrapnel reach the preset targets.

[0035] According to the experimental data and the simulation results, determine whether there is a deviation between the two. If there is a deviation, obtain the material parameters and structural design parameters of the shrapnel, and adjust and optimize these parameters. Input the adjusted material parameters and structural design parameters of the shrapnel into the finite element analysis model, and through the finite element analysis method, simulate and calculate the resonant frequency and vibration mode of the adjusted shrapnel. Compare the resonant frequency and vibration mode obtained by the finite element analysis with the preset targets to determine whether the performance of the adjusted shrapnel meets the requirements. If not, return to the parameter adjustment step and continue to optimize the material and structural parameters. When the finite element analysis results show that the resonant frequency and vibration mode of the adjusted shrapnel meet the preset requirements, apply the optimized material and structural parameters of the shrapnel to the actual sample production and conduct experimental test verification. During the experimental test process, collect the vibration response data of the shrapnel under different excitation conditions, obtain the measured resonant frequency and vibration mode data, and compare and analyze them with the preset targets. If there is a large deviation between the measured data and the preset targets, feedback the measured data to the finite element analysis model, and further correct the material parameters and structural parameters through the data assimilation method, and cycle through the analysis and testing. Continuously iterate and optimize the material, structure, analysis, and testing processes until the experimentally measured resonant frequency and vibration mode of the shrapnel are highly consistent with the preset targets, and complete the design optimization and performance matching of the shrapnel.

[0036] Specifically, the design optimization of the shrapnel is an iterative process that requires continuous adjustment of parameters to achieve the desired performance. When there is a deviation between the experimental data and the simulation results, it is first necessary to obtain and adjust the material parameters and structural design parameters of the shrapnel. For example, for the suspension shrapnel, it may be necessary to adjust the elastic modulus, yield strength of its material, or the thickness, curvature, etc. of the structure. Minor changes in these parameters may significantly affect the performance of the shrapnel. Inputting the adjusted parameters into the finite element analysis model is a key step in the optimization process. Taking a certain titanium alloy shrapnel for aviation as an example, the adjusted material parameters may include the elastic modulus increasing from 110 GPa to 115 GPa, and the Poisson's ratio being adjusted from 0.33 to 0.32. In terms of structural parameters, the thickness may be increased from 2.5 mm to 2.8 mm while the length remains unchanged. These adjustments are aimed at improving the stiffness of the shrapnel, thereby changing its resonant frequency and vibration mode. Finite element analysis is an effective tool for predicting the performance of the shrapnel. Through simulation calculations, the resonant frequency and vibration mode of the adjusted shrapnel can be obtained. For example, for a shrapnel in a precision instrument, the first natural frequency before adjustment is 280 Hz, and after adjustment, it may rise to 295 Hz, getting closer to the design target of 300 Hz. At the same time, the vibration mode diagram may show minor changes in the node positions, which is of great significance for reducing stress concentration. Comparing the analysis results with the preset target is the key to judging the optimization effect. If the adjusted performance still does not meet the requirements, it is necessary to return to the parameter adjustment step. This process may require multiple iterations. When the analysis results meet the requirements, the optimized parameters need to be applied to the production of actual samples. This step verifies the reliability of the theoretical analysis. For example, for a shrapnel on a certain spacecraft, the optimized design parameters may include using a new type of titanium alloy material, increasing the thickness by 0.2 mm, and adopting a special chamfer treatment at the edge, etc. These improvements are aimed at enhancing the fatigue life and vibration damping performance of the shrapnel. Experimental testing is the ultimate means to verify the optimization effect. By collecting the vibration response data of the shrapnel under different excitation conditions, the measured resonant frequency and vibration mode can be obtained. For example, a sweep frequency test is conducted on a micro shrapnel in an electronic device, with the frequency range from 0 to 2000 Hz and the excitation force gradually increasing from 0.1 N to 1 N, and the vibration responses of each key point of the shrapnel are recorded. These data will be used for comparative analysis with the preset target. If there is a large deviation between the measured data and the preset target, the measured data needs to be fed back into the finite element analysis model. Through the data assimilation method, the material parameters and structural parameters can be further corrected. This process helps to improve the accuracy and prediction ability of the model. For example, if the measured natural frequency of a support shrapnel is 5% lower than the predicted value, through feedback adjustment, it may be found that the elastic modulus of the actual material is slightly lower than the initial setting value, and the model parameters need to be adjusted accordingly. The entire optimization process is an iterative cycle involving multiple aspects such as materials, structures, analysis, and testing. Through this systematic method, the performance of the shrapnel can be gradually improved, making its resonant frequency and vibration mode highly consistent with the preset target.This not only ensures the reliability of the shrapnel in practical applications but also provides valuable experience and data support for the development of future similar products.

[0037] Step S108: Generate shrapnel manufacturing process parameters based on the optimized shrapnel material and structural design parameters for precise manufacturing in the actual production process.

[0038] Obtain the elastic modulus data of the shrapnel material in the finite element analysis software, combine it with the material ratio data in the structural design parameters to generate guiding material ratio feeding information, and determine the batching parameters according to the material ratio feeding information. According to the batching parameters, obtain shrapnel materials of different batches. Based on the yield strength data in the pre-established material test database, select the furnace temperature control parameters to obtain several groups of heat treatment process combinations including furnace temperature control parameters. According to the heat treatment process combinations, determine the final heat treatment state. Based on the final heat treatment state, obtain the heat-treated shrapnel semi-finished products. Combine the preset rolling direction database and perform rolling processing on the heat-treated shrapnel semi-finished products in different directions. According to the grain size of the semi-finished products after rolling processing, determine whether the grain size meets the preset range. If the grain size meets the preset range, obtain the shrapnel semi-finished products with qualified grains. According to the pre-established relationship library between stamping angle and springback control, combine the die clearance data to generate multiple groups of stamping process parameters, and determine the optimal stamping scheme according to the multiple groups of stamping process parameters. According to the optimal stamping scheme, obtain the stamped shrapnel. Use a laser confocal microscope to obtain the surface topography of the stamped shrapnel, calculate the surface roughness data based on the surface topography, and determine whether the surface roughness meets the design requirements. If the surface roughness meets the design requirements, obtain the shrapnel with qualified surface. Combine the fatigue life test results and use the support vector machine regression algorithm to predict the fatigue life of the shrapnel under different lubrication conditions, and obtain the optimal lubrication conditions according to the prediction results. According to the optimal lubrication conditions, obtain the lubricated shrapnel finished products. Use the Monte Carlo method to simulate the production process, combine the production rhythm and yield data, and obtain the optimized process parameters by constructing a digital twin model of the production process.

[0039] Specifically, the elastic modulus of the shrapnel material is a key parameter determining its performance. Obtaining this data through finite element analysis software can provide an important basis for subsequent material proportioning. For example, the elastic modulus of a certain alloy shrapnel is 210 GPa. Combining with the proportioning data of 75% iron, 20% nickel, and 5% chromium in the structural design, specific feeding information can be generated. Such precise batching parameters can ensure the consistency and controllability of material properties. After the material is prepared, heat treatment is required to optimize its performance. According to the pre-established material test database, assuming the yield strength of this alloy is 800 MPa, a furnace temperature range of 850 °C - 900 °C can be selected for heat treatment. By adjusting the holding time and cooling rate, multiple groups of heat treatment process combinations can be obtained. The finally determined heat treatment state may be air cooling after holding at 900 °C for 2 hours, which can obtain an ideal grain structure and mechanical properties. The semi-finished shrapnel after heat treatment needs to be rolled. Assuming the pre-set rolling direction database includes three directions: longitudinal, transverse, and 45° oblique, rolling the semi-finished product in different directions can control its anisotropy. Through metallographic analysis, if it is found that the grain size after longitudinal rolling is 20 μm, meeting the pre-set range of 15 - 25 μm, it can be considered that the grains of this semi-finished product are qualified. Stamping is a key process for shrapnel forming. According to the pre-established relationship library between stamping angle and springback control, combined with the die clearance data of 0.1 mm, multiple groups of stamping process parameters can be generated. For example, when the stamping angles are 30°, 45°, and 60° respectively, the corresponding springback angles may be 2°, 3°, and 4°. By comparing the forming effects of these parameter combinations, the optimal stamping scheme can be determined, such as a stamping angle of 45° combined with a die clearance of 0.1 mm. The surface quality of the shrapnel after stamping directly affects its service performance. Using a laser confocal microscope to obtain the surface topography, the calculated surface roughness Ra value is 0.4 μm. If the design requirement is Ra ≤ 0.5 μm, it can be determined that the surface quality is qualified. This precise surface detection method can effectively ensure the friction characteristics and fatigue life of the shrapnel. Finally, through the support vector machine regression algorithm, combined with the fatigue life test results, the fatigue life of the shrapnel under different lubrication conditions can be predicted. For example, under dry friction, oil lubrication, and solid lubricant conditions, the predicted fatigue lives are 10^5, 10^6, and 5×10^5 cycles respectively. Based on this prediction result, oil lubrication can be determined as the best lubrication condition, thereby optimizing the service life of the shrapnel. By simulating the entire production process using the Monte Carlo method, combined with the production rhythm of 100 pieces per hour and the yield rate data of 98%, constructing a digital twin model can further optimize the process parameters. This method can evaluate the influence of different parameter combinations in a virtual environment, thus achieving a more efficient and stable manufacturing process in actual production.

[0040] Step S109: Use a machine learning algorithm to train the experimental data of the resonant frequency and vibration mode of the shrapnel, and establish a prediction model for the resonant characteristics of the shrapnel, which is used for rapid optimization in the subsequent shrapnel design and manufacturing processes.

[0041] Obtain the experimental data of the resonant frequency and vibration mode of the shrapnel, and preprocess the data, including operations such as data cleaning, feature extraction, and data standardization, to obtain a dataset suitable for machine learning algorithm training. According to the preprocessed dataset, select a suitable machine learning algorithm, such as support vector machine, random forest, or neural network, etc., to construct a prediction model for the resonant characteristics of the shrapnel. Use methods such as cross-validation to evaluate the prediction model, and optimize the model performance by adjusting the model hyperparameters to obtain the optimal prediction model. Apply the optimized prediction model to the shrapnel design and manufacturing processes. By inputting parameters such as the material properties and geometric dimensions of the shrapnel, predict the resonant frequency and vibration mode of the shrapnel. According to the prediction results, judge whether the shrapnel design meets the performance requirements. If not, adjust the shrapnel design parameters and re-enter them into the prediction model until the requirements are met. Transmit the shrapnel design parameters that meet the performance requirements to the manufacturing link to guide the processing and production of the shrapnel, ensuring that the actual resonant characteristics of the shrapnel are consistent with the prediction results. Establish a database for the resonant characteristics of the shrapnel, store the shrapnel design parameters, prediction results, and actual test data, which is used for the continuous optimization and iterative update of the prediction model to improve the generalization ability and prediction accuracy of the model.

[0042] Specifically, the construction of a prediction model for the resonant characteristics of a shrapnel is a complex process involving multiple technical aspects. First of all, obtaining experimental data is a crucial step. For example, a laser Doppler vibrometer can be used to measure the vibration response of the shrapnel under different excitation conditions and record its frequency and amplitude. During the data preprocessing stage, noise interference may be encountered, and filtering techniques need to be adopted to remove high-frequency noise and extract effective features. In the process of feature extraction, time-domain features (such as root mean square value, peak value) and frequency-domain features (such as main frequency, power spectral density) can be considered. Data standardization can adopt the Z-score method to make features with different dimensions comparable. For example, convert the shrapnel thickness (unit: millimeter) and resonant frequency (unit: hertz) into dimensionless standard scores. The selection of machine learning algorithms needs to consider data characteristics and prediction goals. Support vector machines are suitable for dealing with high-dimensional feature spaces and can be used to predict continuous resonant frequency values. Random forests are good at dealing with non-linear relationships and can be used to classify vibration modes. Neural networks have strong feature learning capabilities and are suitable for large-scale data sets. In the model evaluation stage, the K-fold cross-validation method can be adopted. For example, divide the data set into 5 parts, use 4 parts for training and 1 part for validation each time, loop 5 times, and take the average performance as the model evaluation index. Hyperparameter optimization can use grid search or Bayesian optimization methods, such as adjusting the kernel function parameters of support vector machines or the number of layers of neural networks. In practical applications, the optimized prediction model can input the material properties (such as elastic modulus, density) and geometric parameters (such as length, width, thickness) of the shrapnel to predict its resonant characteristics. For example, for a steel shrapnel with a length of 100 mm, a width of 10 mm, and a thickness of 0.5 mm, the model may predict its fundamental frequency to be 500 Hz and the second-order modal frequency to be 1500 Hz. If the prediction result does not meet the design requirements, such as the target frequency is 600 Hz, the stiffness can be changed by adjusting the shrapnel thickness or material, thereby adjusting the resonant frequency. This process may require multiple iterations until the design parameters that meet the requirements are found. In the manufacturing process, the output of the prediction model can guide the machining accuracy requirements. For example, if the model shows that a 0.1 mm change in thickness will cause a 50 Hz frequency shift, then the thickness tolerance needs to be controlled within ±0.05 mm during machining to ensure that the actual resonant frequency falls within the acceptable range. Establishing a database of resonant characteristics not only helps model optimization but also provides a reference for new product development. By comparing the predicted values with the measured values, the limitations of the model can be found, such as low prediction accuracy within certain parameter ranges, so as to collect more data or improve the algorithm targeted. This continuous optimization process can improve the generalization ability of the model and make it applicable to a wider range of shrapnel design scenarios.

[0043] The above-disclosed is only the preferred embodiment of the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A method for optimizing the resonance frequency of a mobile phone shrapnel, characterized in that: The method comprises: Step S101, obtaining the material and structural design parameters of the spring, and establishing a relationship model between the spring resonance frequency and the material and structure; Step S102, according to the operating frequency range of the mobile phone, preset the target range of the spring resonant frequency, and determine the optimization direction of the spring material and structure design; Step S103, using a finite element analysis method to simulate the vibration modes of the spring under different materials and structural designs, and obtaining the natural frequency and vibration characteristics of the spring; Step S104, if the natural frequency of the spring piece exceeds the target range, adjust the spring piece material thickness or structural design parameters, and re-perform finite element analysis until the natural frequency falls within the target range; Step S105, judging whether the vibration of the spring is stable according to the simulation result of the spring vibration mode, and if there is unstable vibration, optimizing the spring structure design to reduce vibration and noise; Step S106, verifying whether the resonant frequency and vibration mode of the spring meet the preset target through experimental testing, and obtaining comparative analysis between the experimental data and the simulation results; Step S107, if there is a deviation between the experimental data and the simulation results, the spring material and structural design parameters are adjusted, and the finite element analysis and experimental test are repeated until the spring resonance frequency and vibration mode reach the preset target; Step S108, generating spring manufacturing process parameters according to the optimized spring material and structural design parameters for accurate manufacturing in the actual production process; Step S109, using a machine learning algorithm to train the experimental data of the spring resonant frequency and vibration mode, and establish a spring resonant characteristic prediction model for rapid optimization in the subsequent spring design and manufacturing process.

2. The method according to claim 1, characterized in that The step S101 comprises: According to the material and structural design parameters of the shrapnel, the finite element analysis method is used to simulate the vibration response of the shrapnel under different excitation frequencies to obtain the natural frequency and vibration mode data of the shrapnel; according to the obtained natural frequency and vibration mode data of the shrapnel, combined with the material mechanics theory and the vibration dynamics theory, a mathematical model describing the relationship between the resonant frequency of the shrapnel and the material parameters and structural parameters is established; the multivariate regression analysis algorithm is used to perform parameter identification and coefficient fitting on the established mathematical model to obtain the quantitative relationship expression between the resonant frequency of the shrapnel and the material parameters and structural parameters; According to the quantitative relationship expression obtained by fitting, the sensitivity analysis method is used to calculate the influence of different material parameters and structural parameters on the resonant frequency of the shrapnel and determine the key influencing parameters; According to the identified key influencing parameters, an optimization algorithm is used to search for the optimal parameter combination that makes the resonant frequency of the shrapnel reach the predetermined target value within a given parameter value range; the optimal parameter combination obtained by optimization is substituted into the finite element analysis model for simulation verification to determine the feasibility and effectiveness of the optimization results; according to the simulation verification results, the optimized material parameters and structural parameters are appropriately adjusted to obtain the final design parameters that meet the resonant frequency requirements of the shrapnel, thereby completing the optimization design of the shrapnel.

3. The method according to claim 1, characterized in that The step S102 comprises: Obtain the frequency range data of the mobile phone's operation and determine the target interval parameters of the shrapnel's resonant frequency; establish a mapping model between the shrapnel material properties and the resonant frequency based on the target interval parameters; use the mapping model to screen candidate shrapnel materials that meet the resonant frequency requirements; build a simulation model of the shrapnel structure and resonant frequency based on the candidate shrapnel materials; use a genetic algorithm to optimize the shrapnel structure parameters in the simulation model so that the resonant frequency falls within the target interval; generate a manufacturing process flow for the shrapnel based on the optimized shrapnel material and structural parameters; integrate the shrapnel into the mobile phone, test its resonant performance and reliability within the mobile phone's operating frequency range, and verify the effectiveness of the design.

4. The method according to claim 1, characterized in that: The step S103 comprises: According to the material properties and structural design parameters of the shrapnel, a finite element analysis model is established, and the shrapnel is meshed and boundary conditions are set; the modal analysis method is used to calculate the natural frequency and vibration mode of the shrapnel in different vibration modes, and the vibration characteristic curve of the shrapnel is obtained; by changing the material parameters and structural dimensions of the shrapnel, multiple groups of simulation calculations are carried out to analyze the influence of material properties and structural design on the vibration characteristics of the shrapnel; according to the vibration characteristic curve and the influence law, the material selection and structural design of the shrapnel are optimized to make its natural frequency avoid the external excitation frequency and reduce the risk of resonance; the random vibration analysis method is used to apply random excitation loads in the shrapnel model, and the vibration response and stress distribution of the shrapnel in the actual working environment are calculated; according to the random vibration response results, the vibration reliability of the shrapnel under different materials and structures is judged, and the optimization design scheme that meets the vibration performance requirements is screened out; the optimized shrapnel design scheme is verified by combining simulation and experiment to ensure that it has good anti-vibration performance in actual use.

5. The method according to claim 1, characterized in that: The step S104 comprises: Obtain the initial material thickness and structural design parameters of the shrapnel as input conditions for finite element analysis; calculate the natural frequency of the shrapnel through finite element analysis to determine whether it falls within a preset target range; If the natural frequency is not within the target range, the adjustment range of the material thickness or structural design parameters is determined according to the degree of frequency deviation from the target range; one or more attributes in the material thickness or structural design parameters are adjusted to obtain new finite element analysis input conditions; The adjusted material thickness and structural design parameters are input into the finite element analysis model, and the natural frequency of the shrapnel is recalculated; it is determined whether the adjusted natural frequency falls into the target interval, and if not, the material thickness or structural design parameters are further adjusted; when the natural frequency falls into the target interval, the material thickness and structural design parameters at this time are output as the optimal design solution for the shrapnel.

6. The method according to any one of claims 1 to 5, characterized in that: The step S105 comprises: Acquire the vibration simulation result data of the shrapnel, perform frequency domain analysis on the vibration data, and obtain the vibration frequency and amplitude information; determine whether the vibration of the shrapnel is stable according to the preset stable vibration frequency and amplitude thresholds, and if the vibration frequency or amplitude exceeds the threshold range, it is determined to be unstable vibration; for the shrapnel with unstable vibration, use the finite element analysis method to model and simulate the shrapnel structure to identify the key structural parameters that cause unstable vibration; search for the optimal combination of shrapnel structural parameters through genetic algorithm or particle swarm algorithm to obtain the optimized shrapnel structural design scheme; according to the optimized shrapnel structural design scheme, re-model and simulate the shrapnel to verify whether the optimized shrapnel vibration stability meets the requirements; if the optimized shrapnel vibration stability still does not meet the requirements, return to the fourth step and continue to optimize the shrapnel structural design until the vibration stability requirements are met; perform sample processing and physical vibration testing on the optimized shrapnel structure, measure the actual vibration frequency and amplitude, confirm the degree of consistency with the simulation results, and verify the feasibility and effectiveness of the shrapnel structural design.

7. The method according to any one of claims 1 to 5, characterized in that: The step S106 comprises: Design the experimental plan and determine the test parameters and conditions according to the preset resonant frequency and vibration mode targets; build the experimental platform, select appropriate sensors and data acquisition equipment, and conduct vibration tests on the shrapnel; collect the vibration response data of the shrapnel under different excitation conditions, including amplitude, frequency and damping parameters; pre-process the collected experimental data to remove noise and outliers and extract effective vibration signals; establish a finite element model based on the material properties and geometric dimensions of the shrapnel, and perform modal analysis and harmonic response analysis; compare the resonant frequency and vibration mode obtained from the simulation analysis with the experimental results, and calculate the relative error and correlation coefficient; If there is a deviation between the experimental results and the simulation predictions, the finite element model or experimental conditions are adjusted and iteratively optimized until the preset goals are met.

8. The method according to any one of claims 1 to 5, characterized in that: The step S107 comprises: According to the experimental data and simulation results, determine whether there is a deviation between the two. If there is a deviation, obtain the material parameters and structural design parameters of the shrapnel, and adjust and optimize these parameters; input the adjusted material parameters and structural design parameters of the shrapnel into the finite element analysis model, and use the finite element analysis method to simulate and calculate the resonant frequency and vibration mode of the adjusted shrapnel; compare the resonant frequency and vibration mode obtained by the finite element analysis with the preset target to determine whether the performance of the adjusted shrapnel meets the requirements. If not, return to the parameter adjustment step and continue to optimize the material and structural parameters; when the finite element analysis results show that the adjusted resonant frequency and vibration mode of the shrapnel meet the preset requirements, apply the optimized shrapnel material and structural parameters to the actual sample production, and carry out experimental testing and verification; During the experimental test, the vibration response data of the shrapnel under different excitation conditions are collected, and the measured resonant frequency and vibration mode data are obtained, which are compared and analyzed with the preset targets. If there is a deviation between the measured data and the preset target, the measured data is fed back to the finite element analysis model, and the material parameters and structural parameters are further corrected through the data assimilation method, and the analysis and testing are carried out in a cycle. The material, structure, analysis and testing process are continuously iterated and optimized until the experimentally measured resonant frequency and vibration mode of the shrapnel are highly consistent with the preset target, completing the design optimization and performance matching of the shrapnel.

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