A method for optimizing the resonant frequency of a mobile phone spring

Abstract

The application relates to a mobile phone spring piece resonance frequency optimization method in the field of mobile phone spring pieces, which comprises the following steps: obtaining spring piece material and structure design parameters, establishing a spring piece resonance frequency and material and structure relationship model; according to a mobile phone working frequency range, presetting a target interval of the spring piece resonance frequency, determining the optimization direction of the spring piece material and structure design; adopting a finite element analysis method to simulate the vibration mode of the spring piece under different materials and structure designs, obtaining the inherent frequency and vibration characteristics of the spring piece; if the inherent frequency of the spring piece exceeds the target interval, adjusting the thickness of the spring piece material or the structure design parameters, and re-performing finite element analysis until the inherent frequency falls into the target interval; through experimental testing, verifying whether the spring piece resonance frequency and the vibration mode meet the preset target, obtaining comparative analysis of experimental data and simulation results; according to the optimized spring piece material and structure design parameters, generating spring piece manufacturing process parameters, which are used for accurate manufacturing in the actual production process.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more particularly to a spring for mobile phones, specifically to a method for optimizing the resonant frequency of a mobile phone spring. Background Technology

[0002] In the precise manufacturing of multi-contact spring structures for mobile phones, controlling the resonant characteristics of the springs is a key technical challenge. Differences in spring materials and structural design can lead to changes in their natural frequency and vibration modes, thus affecting the quality of mobile phone signal transmission and anti-interference capabilities. In actual production, balancing the contradictions between spring strength, elasticity, and resonant frequency is a pressing technical challenge. Ideally, the spring should ensure sufficient strength and elasticity while its resonant frequency matches the mobile phone's operating frequency and maintains a stable vibration mode. However, due to the complexity of the spring structure and the differences in material properties, it is difficult to simultaneously meet these requirements in actual production. Currently, engineers are continuously experimenting and testing to optimize spring material selection and structural design, attempting to find the optimal balance between strength, elasticity, and resonant characteristics to obtain high-quality, high-reliability multi-contact spring structures for mobile phones. However, achieving this goal under the dual pressures of production efficiency and cost control remains a technical challenge to be overcome. Summary of the Invention

[0003] This invention provides a method for optimizing the resonant frequency of a mobile phone spring contact, the method comprising:

[0004] Obtain the material and structural design parameters of the spring sheet, and establish a model relating the spring sheet resonant frequency to the material and structure.

[0005] Based on the mobile phone's operating frequency range, the target range of the spring resonant frequency is preset, and the optimization direction of the spring material and structural design is determined.

[0006] The finite element method was used to simulate the vibration modes of the spring under different material and structural designs, and the natural frequency and vibration characteristics of the spring were obtained.

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

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

[0009] Through experimental testing, we verified whether the resonant frequency and vibration mode of the spring met the preset targets, and obtained a comparative analysis of the experimental data and simulation results.

[0010] If there is a deviation between the experimental data and the simulation results, the material and structural design parameters of the spring sheet should be adjusted, and the finite element analysis and experimental tests should be carried out again until the resonant frequency and vibration mode of the spring sheet reach the preset target.

[0011] Based on the optimized spring material and structural design parameters, spring manufacturing process parameters are generated for precise manufacturing in the actual production process;

[0012] Machine learning algorithms were used to train experimental data on the resonant frequency and vibration mode of the spring, and a predictive model of the spring's resonant characteristics was established for rapid optimization in the subsequent spring design and manufacturing process.

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

[0014] This invention discloses a method for optimizing the resonant frequency of a mobile phone spring contactor. The method first establishes a model relating the spring contactor's resonant frequency to materials and structure. A target range is preset based on the mobile phone's operating frequency range. Finite element analysis is used to simulate the spring contactor's vibration mode, obtaining its natural frequency and vibration characteristics. If the natural frequency exceeds the target range, the material thickness or structural parameters are adjusted, and the analysis is repeated. This invention also verifies the resonant frequency and vibration mode through experimental testing, comparing and analyzing experimental data with simulation results. If deviations exist, parameters are adjusted and retesting is performed until the preset target is reached. Finally, this invention uses machine learning algorithms to establish a resonant characteristic prediction model for rapid optimization in subsequent design and manufacturing. This method effectively optimizes the spring contactor's resonant frequency, improves mobile phone performance and user experience, and simultaneously enhances the efficiency of spring contactor design and manufacturing. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for optimizing the resonant frequency of a mobile phone spring contact according to the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1 This embodiment of a method for optimizing the resonant frequency of a mobile phone spring contact can specifically include:

[0018] Step S101: Obtain the material and structural design parameters of the spring sheet, and establish a model relating the spring sheet resonant frequency to the material and structure.

[0019] Based on the material and structural design parameters of the spring, the finite element method (FEM) is used to simulate the vibration response of the spring under different excitation frequencies, obtaining its natural frequencies and mode shapes. Based on the obtained natural frequencies and mode shapes, and combining materials mechanics and vibration dynamics theories, a mathematical model describing the relationship between the spring's resonant frequency and material and structural parameters is established. A multivariate regression analysis algorithm is used to identify parameters and fit coefficients to the established mathematical model, obtaining a quantitative expression for the relationship between the spring's resonant frequency and the material and structural parameters. Based on the fitted quantitative expression, sensitivity analysis is used to calculate the influence of different material and structural parameters on the spring's resonant frequency, identifying key influencing parameters. For the identified key influencing parameters, an optimization algorithm is used to search for the optimal parameter combination within a given parameter range to achieve the predetermined target resonant frequency. The optimized parameter combination is substituted into the finite element analysis model for simulation verification to determine the feasibility and effectiveness of the optimization results. Based on the simulation verification results, the optimized material and structural parameters are appropriately adjusted to obtain the final design parameters that meet the spring's resonant frequency requirements, completing the optimized design of the spring.

[0020] Specifically, as an example of this embodiment, the vibration analysis of a spring is performed. First, the vibration response of the spring under different excitation frequencies is simulated using the finite element method. For example, for a rectangular steel spring, a three-dimensional model can be established, material properties such as elastic modulus and Poisson's ratio can be set, and then boundary conditions and excitation forces can be applied. By solving the characteristic equation, the natural frequencies and mode shapes of the spring can be obtained. After obtaining these data, a mathematical model is established by combining the theories of mechanics of materials and vibration dynamics. For example, the Euler-Bernoulli beam theory can be used to simplify the spring into a cantilever beam, and an equation describing the relationship between its resonant frequency and material parameters (such as density and elastic modulus) and structural parameters (such as length, width, and thickness) can be established. Next, a multivariate regression analysis algorithm is used to identify parameters and fit coefficients to the model. It is assumed that a series of experimental data are obtained, including resonant frequencies under different material and structural parameters. Using methods such as least squares, 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, and T are the length, width, and thickness, respectively. After obtaining this expression, sensitivity analysis can be performed. By calculating partial derivatives or using Monte Carlo simulation, the influence of each parameter on the resonant frequency can be determined. For example, it might be found that the thickness T has the greatest impact on the frequency, while the width W has a relatively smaller impact. For parameters with a significant impact, optimization algorithms can be used to search for the optimal parameter combination. For example, if the goal is to adjust the resonant frequency to 500Hz, a genetic algorithm or particle swarm optimization algorithm can be used to search for the best solution within a given parameter range. This might yield a set of optimized parameter values, such as E = 200 GPa, ρ = 7800 kg / m³. 3 The parameters are: L = 100mm, W = 20mm, and T = 2mm. Finally, these optimized parameters are 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 meet the requirements, then these parameters can be used as the final design scheme. If there are deviations, fine-tuning may be necessary, such as slightly increasing the thickness or decreasing the length, to achieve more precise frequency control. This optimization design method can be applied not only to simple rectangular springs but also to more complex shapes and structures. Through this systematic analysis and optimization process, material usage can be minimized while ensuring performance, thereby improving the product's cost-effectiveness and reliability.

[0021] Step S102: Based on the mobile phone's operating frequency range, preset the target range of the spring resonant frequency and determine the optimization direction of the spring material and structural design.

[0022] The frequency range data of the mobile phone's operation is obtained to determine the target range parameters for the spring's resonant frequency. Based on these target range parameters, a mapping model between the spring's material properties and the resonant frequency is established. Using this mapping model, candidate spring materials that meet the resonant frequency requirements are screened. Based on these candidate spring materials, a simulation model of the spring structure and resonant frequency is constructed. A genetic algorithm is used to optimize the spring structure parameters in the simulation model, ensuring the resonant frequency falls within the target range. Based on the optimized spring material and structure parameters, a manufacturing process flow for the spring is generated. The spring is integrated into the mobile phone, and its resonant performance and reliability within the mobile phone's operating frequency range are tested to verify the effectiveness of the design.

[0023] Step S103: Using the finite element analysis method, the vibration modes of the spring sheet under different material and structural designs are simulated to obtain the natural frequency and vibration characteristics of the spring sheet.

[0024] Based on the material properties and structural design parameters of the spring, a finite element analysis model was established, and the spring was meshed and boundary conditions were set. Modal analysis was used to calculate the natural frequencies and mode shapes of the spring under different vibration modes, obtaining its vibration characteristic curves. Multiple simulations were performed by changing the material parameters and structural dimensions of the spring to analyze the influence of material properties and structural design on its vibration characteristics. Based on the vibration characteristic curves and influence patterns, the material selection and structural design of the spring were optimized to ensure its natural frequencies avoid external excitation frequencies, reducing the risk of resonance. Random vibration analysis was used to apply random excitation loads to the spring model, calculating the vibration response and stress distribution of the spring under actual working conditions. Based on the random vibration response results, the vibration reliability of the spring under different materials and structures was assessed, and an optimized design scheme that meets the vibration performance requirements was selected. The optimized spring design scheme was verified through a combination of simulation and experimentation to ensure its good vibration resistance in practical use.

[0025] Specifically, finite element analysis (FEM), as an example, is an effective method for studying the vibration characteristics of springs. First, a geometric model of the spring, such as a rectangular thin plate, is established, defining material properties such as elastic modulus and density. Then, a mesh is created, using tetrahedral or hexahedral elements; the mesh size needs to be small enough to ensure computational accuracy. Boundary conditions typically include fixed-end constraints and free-end constraints. Modal analysis reveals the natural frequencies and mode shapes of the spring. For example, a stainless steel spring with a length of 20mm, a width of 5mm, and a thickness of 0.5mm might have a first-order natural frequency around 200Hz, exhibiting a typical first-order bending mode. By changing the spring's thickness, the trend of natural frequency variation can be observed; increasing the thickness leads to an increase in frequency. Material properties have a significant impact on vibration characteristics. For example, replacing stainless steel with aluminum alloy will increase the natural frequency for the same dimensions due to the reduced density. In terms of structural design, increasing the spring's length will decrease the natural frequency, while increasing the width has a smaller impact. These principles can guide optimized design, ensuring that the spring's natural frequency avoids external excitation frequencies, such as the operating frequency range of a mobile phone vibration motor. Random vibration analysis simulates the actual working environment. A power spectral density function suitable for mobile phone usage scenarios can be set, such as a stationary random excitation within the 20-2000Hz range. Analysis results include the root mean square displacement and stress distribution at various points on the spring. For example, in a certain design, the maximum displacement at the free end of the spring might reach 0.1mm, while the maximum stress is concentrated near the fixed end, approximately 100MPa. By comparing the random vibration responses of different design schemes, the optimal scheme can be selected. For instance, a certain scheme, while ensuring sufficient stiffness, exhibits a maximum stress far below the material fatigue limit, indicating good vibration reliability. Finally, a sample can be fabricated for vibration table testing to verify the accuracy of the simulation results and ensure the spring can operate reliably for extended periods in actual use.

[0026] Step S104: If the natural frequency of the spring exceeds the target range, adjust the material thickness or structural design parameters of the spring, and perform finite element analysis again until the natural frequency falls into the target range.

[0027] The initial material thickness and structural design parameters of the spring are obtained as input conditions for finite element analysis (FEM). The natural frequency of the spring is calculated through FEM, and it is determined whether it falls within the 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. One or more attributes of the material thickness or structural design parameters are adjusted to obtain new FEM input conditions. The adjusted material thickness and structural design parameters are input into the FEM model to recalculate the natural frequency of the spring. It is determined whether the adjusted natural frequency falls within the target range. If it does not, the material thickness or structural design parameters are adjusted further. When the natural frequency falls within the target range, the material thickness and structural design parameters at this time are output as the optimal design scheme for the spring.

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

[0029] The simulation results of the spring vibration are obtained, and frequency domain analysis is performed on the vibration data to obtain vibration frequency and amplitude information. Based on preset stable vibration frequency and amplitude thresholds, the stability of the spring vibration is determined. If the vibration frequency or amplitude exceeds the threshold range, it is considered unstable vibration. For unstable spring vibrations, finite element analysis is used to model and simulate the spring structure, identifying key structural parameters causing unstable vibrations. Optimization algorithms, such as genetic algorithms or particle swarm optimization, are used to search for the optimal combination of spring structure parameters, resulting in an optimized spring structure design. Based on the optimized spring structure design, the spring is remodeled and simulated to verify whether the optimized spring vibration stability meets the requirements. If the optimized spring vibration stability still does not meet the requirements, the process returns to step four to continue optimizing the spring structure design until the vibration stability requirements are met. A prototype of the optimized spring structure is fabricated and subjected to physical vibration testing to measure the actual vibration frequency and amplitude, confirming the agreement with the simulation results and verifying the feasibility and effectiveness of the spring structure design.

[0030] Specifically, acquiring simulation results of spring vibration, as an example, typically involves sensor measurement and data acquisition systems. For instance, accelerometers or displacement sensors can be installed at key locations on the spring, and vibration signals can be recorded using a high-speed data acquisition card. The obtained time-domain data is converted to frequency-domain data via Fast Fourier Transform (FFT) to obtain vibration frequency and amplitude information. When determining the stability of the spring vibration, it is necessary to pre-set the frequency and amplitude thresholds for stable vibration. Assuming the stable vibration frequency range of a certain spring is 45-55Hz, and the amplitude threshold is 0.5mm, if the analysis results show that the main vibration frequency of the spring is 62Hz and the amplitude is 0.8mm, it is determined to be unstable vibration. For springs with unstable vibration, the finite element analysis method is used for in-depth study. First, a geometric model of the spring is established, and material properties such as elastic modulus and density are defined. Then, mesh generation is performed, and boundary conditions and loads are set. Modal analysis can obtain the natural frequencies and mode shapes of the spring, and static analysis can obtain the stress distribution. These results help identify key structural parameters causing unstable vibration, such as the thickness, width, or stiffness of specific areas of the spring. The application of optimization algorithms aims to find the optimal combination of spring sheet structural parameters. Taking a genetic algorithm as an example, the key dimensional parameters of the spring sheet can be encoded as "genes," such as a thickness range of 1-3 mm and a width range of 10-20 mm. New parameter combinations are generated through operations such as crossover and mutation, and the performance of each parameter group 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 iterations, the algorithm converges to the optimal or near-optimal parameter combination. The optimized spring sheet structure design needs to be verified. A new finite element model is established, and modal analysis and transient dynamic analysis are performed. If the analysis results show that the vibration frequency has fallen within the 45-55 Hz range and the amplitude is less than 0.5 mm, the optimization is considered successful. Otherwise, the parameters or constraints of the optimization algorithm need to be adjusted, and the optimization process needs to be repeated. Finally, it is crucial to process and test the optimized spring sheet. Samples can be fabricated using CNC machining equipment and then tested on a vibration table. The actual vibration frequency and amplitude are measured using a laser vibrometer or an accelerometer. By comparing the test results with the simulation results, if the deviation is within an acceptable range (e.g., frequency deviation less than 5%, amplitude deviation less than 10%), the feasibility of the design is verified. This process not only confirms the optimization results but also helps improve the simulation model and enhance the accuracy of future designs.

[0031] Step S106: Through experimental testing, verify whether the resonant frequency and vibration mode of the spring meet the preset target, and obtain a comparative analysis of the experimental data and simulation results.

[0032] Based on the preset resonant frequency and vibration mode targets, an experimental scheme was designed, and test parameters and conditions were determined. An experimental platform was built, and suitable sensors and data acquisition equipment were selected to conduct vibration tests on the spring sheet. Vibration response data of the spring sheet under different excitation conditions were collected, including parameters such as amplitude, frequency, and damping. The collected experimental data were preprocessed to remove noise and outliers, and the effective vibration signals were extracted. A finite element model was established based on the material properties and geometry of the spring sheet, and modal analysis and harmonic response analysis were performed. The resonant frequency and vibration mode obtained from the simulation analysis were compared with the experimental results, and the relative error and correlation coefficient were calculated. If the experimental results deviated significantly from the simulation predictions, the finite element model or experimental conditions were adjusted, and iterative optimization was performed until the preset targets were met.

[0033] Specifically, vibration testing of a spring, as an example, is a crucial step in evaluating its performance and reliability. When designing the experimental scheme, the working environment and intended use of the spring must be considered. For example, for a spring, it may be necessary to simulate high-temperature, high-frequency vibration conditions; while for springs in precision instruments, the impact of low-frequency, minute vibrations may be more important. The experimental platform setup involves the selection of various equipment. Commonly used sensors include accelerometers, strain gauges, and laser vibrometers. Taking an accelerometer as an example, its sensitivity and frequency response range must match the expected vibration characteristics of the spring. Data acquisition equipment must have sufficient sampling rate and resolution to accurately capture the vibration details of the spring. During vibration response data acquisition, the performance of the spring under different operating conditions can be obtained by changing parameters such as excitation frequency and amplitude. For example, a frequency sweep test can be performed on a spring with a frequency range of 0-500Hz, and the excitation force can be gradually increased from 10N to 100N, recording the vibration response at various points on the spring. Data preprocessing is an important step in ensuring the reliability of the analysis results. Common preprocessing methods include filtering, detrending, and outlier detection. Taking a test as an example, a bandpass filter was used to remove low-frequency interference below 50Hz and high-frequency noise above 1000Hz, and then the data curve was smoothed using a moving average method. The establishment of the finite element model needs to accurately reflect the geometric characteristics and material properties of the spring fragment. Taking a steel spring fragment as an example, Shell 63 elements can be used for mesh generation, with material parameters set to an elastic modulus of 210 GPa, Poisson's ratio of 0.3, and density of 7850 kg / m³. 3Modal analysis yields the natural frequencies and mode shapes of the spring, while harmonic response analysis predicts its amplitude and phase response under different frequency excitations. Comparing experimental results with simulation predictions is crucial for verifying model accuracy. For example, the measured first-order natural frequency of a spring is 152Hz, while the finite element analysis result is 158Hz, a relative error of 3.9%. Calculating the MAC values ​​(modal guarantee criteria) for each mode further evaluates the correlation between experimental and theoretical modes. If a significant deviation is found between experimental results and simulation predictions, model correction or experimental condition adjustments are necessary. For instance, if the actual spring stiffness is lower than expected, it may be necessary to remeasure material properties or check for problems in welded or other connection parts. Through iterative optimization, the model's prediction accuracy is continuously improved, ultimately achieving the preset resonant frequency and vibration mode targets. This series of steps aims to ensure the spring design meets practical application requirements, improving product reliability and lifespan. Accurate vibration analysis can predict and avoid potential resonance problems, optimize the spring's vibration damping performance, and thus improve the overall system stability and efficiency.

[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 spring, and repeat the finite element analysis and experimental testing until the resonant frequency and vibration mode of the spring reach the preset target.

[0035] Based on experimental data and simulation results, it is determined whether there is a deviation between the two. If a deviation exists, the material parameters and structural design parameters of the spring are obtained and adjusted and optimized. The adjusted material parameters and structural design parameters are input into the finite element analysis model, and the resonant frequency and vibration mode of the adjusted spring are simulated and calculated using the finite element analysis method. The resonant frequency and vibration mode obtained from the finite element analysis are compared with the preset target to determine whether the performance of the adjusted spring meets the requirements. If the requirements are not met, the parameter adjustment step is returned to continue optimizing the material and structural parameters. When the finite element analysis results show that the resonant frequency and vibration mode of the adjusted spring meet the preset requirements, the optimized material and structural parameters of the spring are applied to the fabrication of actual prototypes and experimental testing is carried out for verification. During the experimental testing, vibration response data of the spring under different excitation conditions are collected, and the measured resonant frequency and vibration mode data are obtained and compared with the preset target. If there is a large deviation between the measured data and the preset target, the measured data is fed back into the finite element analysis model, and the material and structural parameters are further corrected through data assimilation methods. The analysis and testing are carried out cyclically. The materials, structure, analysis, and testing processes are continuously iterated and optimized until the experimentally measured resonant frequency and vibration mode of the spring are highly consistent with the preset target, thus completing the design optimization and performance matching of the spring.

[0036] Specifically, spring design optimization is an iterative process that requires continuous parameter adjustments to achieve the desired performance. When there are discrepancies between experimental data and simulation results, it is first necessary to obtain and adjust the material and structural design parameters of the spring. For example, for suspension springs, it may be necessary to adjust the elastic modulus, yield strength of the material, or the thickness and curvature of the structure. Small changes in these parameters can significantly affect the performance of the spring. Inputting the adjusted parameters into the finite element analysis model is a key step in the optimization process. Taking a certain aerospace titanium alloy spring as an example, the adjusted material parameters may include increasing the elastic modulus from 110 GPa to 115 GPa and adjusting the Poisson's ratio 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 aim to improve the stiffness of the spring, thereby changing its resonant frequency and vibration mode. Finite element analysis is an effective tool for predicting spring performance. Through simulation calculations, the resonant frequency and vibration mode of the adjusted spring can be obtained. For example, the first natural frequency of a spring in a precision instrument might be 280Hz before adjustment, but after adjustment, it could rise to 295Hz, closer to the design target of 300Hz. Simultaneously, the mode shape diagram might show subtle changes in node positions, which is crucial for reducing stress concentration. Comparing the analysis results with the preset target is key to judging the optimization effect. If the adjusted performance still does not meet the requirements, the parameter adjustment process needs to be repeated. This process may require multiple iterations. When the analysis results meet the requirements, the optimized parameters need to be applied to the fabrication of an actual prototype. This step verifies the reliability of the theoretical analysis. For example, for a spring in a spacecraft, optimized design parameters might include using a new titanium alloy material, increasing the thickness by 0.2mm, and using special chamfering treatment on the edges. These improvements aim to improve the fatigue life and vibration damping performance of the spring. Experimental testing is the ultimate means of verifying the optimization effect. By collecting vibration response data of the spring under different excitation conditions, the measured resonant frequency and vibration mode can be obtained. For example, a frequency sweep test is performed on a miniature spring in an electronic device, with a frequency range of 0-2000Hz and an excitation force gradually increasing from 0.1N to 1N. The vibration response at key points of the spring is recorded. This data will be used for comparative analysis with a preset target. If there is a significant deviation between the measured data and the preset target, the measured data needs to be fed back into the finite element analysis model. Through data assimilation methods, material and structural parameters can be further corrected. This process helps improve the accuracy and predictive ability of the model. For example, if the measured natural frequency of a support spring is 5% lower than the predicted value, feedback adjustment may reveal that the elastic modulus of the actual material is slightly lower than the initial set value, requiring corresponding adjustments to the model parameters. The entire optimization process is an iterative cycle involving multiple aspects such as materials, structure, analysis, and testing. Through this systematic approach, the performance of the spring can be gradually improved, ensuring that its resonant frequency and vibration mode closely match 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 similar products in the future.

[0037] Step S108: Based on the optimized spring material and structural design parameters, generate spring manufacturing process parameters for precise manufacturing in the actual production process.

[0038] The elastic modulus data of the spring material is obtained from the finite element analysis software. Combined with the material proportioning data in the structural design parameters, guiding material proportioning and feeding information is generated. The feeding parameters are determined based on this information. Different batches of spring material are obtained based on the feeding parameters. Using yield strength data from a pre-established material testing database, furnace temperature control parameters are selected, resulting in several heat treatment process combinations including these parameters. The final heat treatment state is determined based on these combinations. Based on the final heat treatment state, heat-treated semi-finished spring materials are obtained. Using a pre-established rolling direction database, the heat-treated semi-finished spring materials are rolled in different directions. The grain size of the rolled semi-finished material is then assessed to determine if it meets a preset range. If the grain size meets the preset range, a qualified semi-finished spring material is obtained. Based on a pre-established stamping angle and springback control relationship database, combined with die clearance data, multiple sets of stamping process parameters are generated. The optimal stamping scheme is determined based on these multiple stamping process parameters. Based on the optimal stamping scheme, stamped spring sheets are obtained. The surface morphology of the stamped spring sheets is acquired using a laser confocal microscope. Surface roughness data is calculated based on the surface morphology to determine if the surface roughness meets design requirements. If the surface roughness meets the design requirements, a qualified spring sheet is obtained. Combining fatigue life test results, a support vector machine regression algorithm is used to predict the fatigue life of the spring sheet under different lubrication conditions. The optimal lubrication conditions are obtained based on the prediction results. Based on the optimal lubrication conditions, lubricated spring sheet products are obtained. The production process is simulated using the Monte Carlo method. Combining production cycle time and yield data, a digital twin model of the production process is constructed to obtain optimized process parameters.

[0039] Specifically, the elastic modulus of the spring material is a key parameter determining its performance. Obtaining this data through finite element analysis software provides crucial information for subsequent material proportioning. For example, if the elastic modulus of a certain alloy spring is 210 GPa, combined with the proportions of 75% iron, 20% nickel, and 5% chromium in the structural design, specific material feeding information can be generated. These precise feeding parameters ensure the consistency and controllability of material properties. After material preparation, heat treatment is required to optimize its performance. Based on a pre-established material testing database, assuming the alloy's yield strength is 800 MPa, a furnace temperature range of 850℃-900℃ can be selected for heat treatment. By adjusting the holding time and cooling rate, multiple heat treatment process combinations can be obtained. The final determined heat treatment state might be holding at 900℃ for 2 hours followed by air cooling, which yields ideal grain structure and mechanical properties. The heat-treated spring semi-finished product then needs to be rolled. Assuming a pre-defined rolling direction database includes three directions—longitudinal, transverse, and 45° oblique—rolling the semi-finished product in different directions can control its anisotropy. Metallographic analysis shows that if the grain size after longitudinal rolling is 20 μm, meeting the pre-defined range of 15-25 μm, the semi-finished product's grain size is considered acceptable. Stamping is a crucial process in spring forming. Based on a pre-established database of stamping angle and springback control relationships, combined with 0.1 mm die clearance data, multiple sets of stamping process parameters can be generated. For example, stamping angles of 30°, 45°, and 60° might correspond to springback angles of 2°, 3°, and 4°. By comparing the forming effects of these parameter combinations, the optimal stamping scheme can be determined, such as a 45° stamping angle with a 0.1 mm die clearance. The surface quality of the stamped spring directly affects its performance. Using a laser confocal microscope to obtain the surface morphology, the calculated surface roughness Ra value is 0.4 μm. If the design requirement is Ra ≤ 0.5 μm, the surface quality can be considered acceptable. This precise surface inspection method effectively ensures the frictional characteristics and fatigue life of the spring sheet. Finally, by using a support vector machine regression algorithm combined with fatigue life test results, the fatigue life of the spring sheet 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, oil lubrication can be determined as the optimal lubrication condition, thereby optimizing the service life of the spring sheet. By simulating the entire production process using the Monte Carlo method, combined with a production cycle of 100 pieces per hour and a yield rate of 98%, a digital twin model can be constructed to further optimize process parameters. This method can evaluate the impact of different parameter combinations in a virtual environment, thereby achieving a more efficient and stable manufacturing process in actual production.

[0040] Step S109: Using machine learning algorithms, the experimental data of the spring resonant frequency and vibration mode are trained to establish a spring resonant characteristic prediction model for rapid optimization in subsequent spring design and manufacturing processes.

[0041] Experimental data on the resonant frequency and vibration modes of spring-loaded contact springs are acquired and preprocessed, including data cleaning, feature extraction, and data standardization, to obtain a dataset suitable for training machine learning algorithms. Based on the preprocessed dataset, a suitable machine learning algorithm, such as support vector machine, random forest, or neural network, is selected to construct a predictive model for the spring-loaded contact spring's resonant characteristics. The predictive model is evaluated using methods such as cross-validation, and its performance is optimized by adjusting hyperparameters to obtain the optimal predictive model. The optimized predictive model is applied to the spring-loaded contact spring design and manufacturing process. By inputting parameters such as the spring's material properties and geometric dimensions, the resonant frequency and vibration modes of the spring are predicted. Based on the prediction results, it is determined whether the spring-loaded contact spring design meets performance requirements. If not, the spring-loaded contact spring design parameters are adjusted and re-input into the predictive model until the requirements are met. The spring-loaded contact spring design parameters that meet performance requirements are then passed to the manufacturing process to guide the processing and production of the spring, ensuring that the actual resonant characteristics of the spring are consistent with the predicted results. A database of spring-loaded contact spring resonant characteristics is established to store spring-loaded contact spring design parameters, prediction results, and actual test data for continuous optimization and iterative updates of the predictive model, improving its generalization ability and prediction accuracy.

[0042] Specifically, constructing a predictive model for the resonant characteristics of a spring is a complex process involving multiple technical steps. First, acquiring experimental data is a crucial step. For example, a laser Doppler vibrometer can be used to measure the vibration response of the spring under different excitation conditions, recording its frequency and amplitude. During data preprocessing, noise interference may occur, requiring filtering techniques to remove high-frequency noise and extract effective features. In feature extraction, time-domain features (such as root mean square value and peak value) and frequency-domain features (such as dominant frequency and power spectral density) can be considered. Data standardization can employ the Z-score method to make features of different dimensions comparable. For example, the spring thickness (unit: millimeters) and resonant frequency (unit: Hertz) can be converted into dimensionless standard scores. The selection of machine learning algorithms needs to consider the data characteristics and prediction objectives. Support vector machines are suitable for handling high-dimensional feature spaces and can be used to predict continuous resonant frequency values. Random forests excel at handling nonlinear relationships and can be used to classify vibration modes. Neural networks have powerful feature learning capabilities and are suitable for large-scale datasets. In the model evaluation stage, K-fold cross-validation can be used. For example, the dataset can be divided into 5 parts, with 4 parts used for training and 1 part for validation each time, repeated 5 times, and the average performance used as the model evaluation metric. Hyperparameter optimization can use grid search or Bayesian optimization methods, such as adjusting the kernel function parameters of the support vector machine or the number of layers in the neural network. In practical applications, the optimized prediction model can predict the resonant characteristics of a spring by inputting its material properties (such as elastic modulus and density) and geometric parameters (such as length, width, and thickness). For example, for a steel spring that is 100mm long, 10mm wide, and 0.5mm thick, the model may predict its fundamental frequency as 500Hz and its second-order modal frequency as 1500Hz. If the prediction results do not meet the design requirements, such as a target frequency of 600Hz, the stiffness can be changed by adjusting the spring's 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 processing accuracy requirements. For example, if the model shows that a 0.1mm change in thickness leads to a 50Hz frequency shift, then during manufacturing, the thickness tolerance needs to be controlled within ±0.05mm to ensure the actual resonant frequency falls within an acceptable range. Establishing a resonant characteristic database not only aids in model optimization but also provides a reference for new product development. By comparing predicted and measured values, limitations of the model can be identified, such as lower prediction accuracy within certain parameter ranges, allowing for targeted collection of more data or improvement of the algorithm. This continuous optimization process enhances the model's generalization ability, making it applicable to a wider range of spring design scenarios.

[0043] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for optimizing the resonant frequency of a mobile phone spring contactor, characterized in that, The method includes: Step S101: Obtain the material and structural design parameters of the spring sheet, and establish a model relating the spring sheet resonant frequency to the material and structure. Step S102, based on the mobile phone's operating frequency range, presets the target range of the spring resonant frequency and determines the optimization direction of the spring material and structural design; it also includes: acquiring the mobile phone's operating frequency range data and determining the target range parameters of the spring resonant frequency; establishing a mapping model between the spring material properties and the resonant frequency based on the target range parameters; using the mapping model to screen candidate spring materials that meet the resonant frequency requirements; constructing a simulation model of the spring structure and resonant frequency based on the candidate spring materials; using a genetic algorithm to optimize the spring structure parameters in the simulation model so that the resonant frequency falls within the target range; generating the spring manufacturing process flow based on the optimized spring material and structural parameters; integrating the spring into the mobile phone and testing its resonant performance and reliability within the mobile phone's operating frequency range to verify the effectiveness of the design scheme; Step S103: Using the finite element analysis method, the vibration modes of the spring sheet under different material and structural designs are simulated to obtain the natural frequency and vibration characteristics of the spring sheet. Step S104: If the natural frequency of the spring exceeds the target range, adjust the material thickness or structural design parameters of the spring, and perform the finite element analysis again until the natural frequency falls into the target range. Step S105: Based on the simulation results of the spring vibration mode, determine whether the spring vibration is stable. If unstable vibration exists, optimize the spring structure design to reduce vibration and noise. Step S106: Through experimental testing, verify whether the resonant frequency and vibration mode of the spring meet the preset target, and obtain a comparative analysis of the experimental data and simulation results; Step S107: If there is a deviation between the experimental data and the simulation results, adjust the material and structural design parameters of the spring, and re-perform the finite element analysis and experimental test until the resonant frequency and vibration mode of the spring reach the preset target. Step S108: Based on the optimized spring material and structural design parameters, generate spring manufacturing process parameters for precise manufacturing in the actual production process; Step S109: Using machine learning algorithms, the experimental data of the spring resonant frequency and vibration mode are trained to establish a spring resonant characteristic prediction model for rapid optimization in subsequent spring design and manufacturing processes.

2. The method according to claim 1, characterized in that, Step S101 includes: Based on the material and structural design parameters of the spring, the finite element method is used to simulate the vibration response of the spring under different excitation frequencies, and the natural frequency and mode shape data of the spring are obtained. Based on the obtained natural frequency and mode shape data of the spring, combined with the theory of mechanics of materials and the theory of vibration dynamics, a mathematical model describing the relationship between the resonant frequency of the spring and the material and structural parameters is established. The multivariate regression analysis algorithm is used to identify parameters and fit coefficients to the established mathematical model, and the quantitative relationship expression between the resonant frequency of the spring and the material and structural parameters is obtained. Based on the quantitative relationship expression obtained from the fitting, the sensitivity analysis method is used to calculate the degree of influence of different material parameters and structural parameters on the resonant frequency of the spring sheet, and to determine the key influencing parameters. For the identified key influencing parameters, an optimization algorithm is used to search for the optimal parameter combination that enables the spring resonant frequency to reach the predetermined target value within the given parameter value range. The optimized parameter combination is then substituted into the finite element analysis model for simulation verification to determine the feasibility and effectiveness of the optimization results. Based on the simulation verification results, the optimized material and structural parameters are appropriately adjusted to obtain the final design parameters that meet the spring resonant frequency requirements, thus completing the optimized design of the spring.

3. The method according to claim 1, characterized in that, Step S103 includes: Based on the material properties and structural design parameters of the spring, a finite element analysis model was established, and the spring was meshed and boundary conditions were set. Modal analysis was used to calculate the natural frequencies and mode shapes of the spring under different vibration modes, obtaining its vibration characteristic curves. Multiple simulations were performed by changing the material parameters and structural dimensions of the spring to analyze the influence of material properties and structural design on its vibration characteristics. Based on the vibration characteristic curves and influence patterns, the material selection and structural design of the spring were optimized to ensure its natural frequencies avoid external excitation frequencies, reducing the risk of resonance. Random vibration analysis was used to apply random excitation loads to the spring model, calculating the vibration response and stress distribution of the spring under actual working conditions. Based on the random vibration response results, the vibration reliability of the spring under different materials and structures was assessed, and an optimized design scheme that meets the vibration performance requirements was selected. The optimized spring design scheme was verified through a combination of simulation and experimentation to ensure its good vibration resistance in practical use.

4. The method according to claim 1, characterized in that, Step S104 includes: The initial material thickness and structural design parameters of the spring are obtained as input conditions for finite element analysis; the natural frequency of the spring is calculated through finite element analysis to determine whether it falls within the 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; adjustments are made to one or more properties of the material thickness or structural design parameters to obtain new finite element analysis input conditions. Input the adjusted material thickness and structural design parameters into the finite element analysis model and recalculate the natural frequency of the spring. Determine whether the adjusted natural frequency falls within the target range. If it does not, continue to adjust the material thickness or structural design parameters. When the natural frequency falls within the target range, output the material thickness and structural design parameters at this time as the optimal design scheme for the spring.

5. The method according to any one of claims 1-4, characterized in that, Step S105 includes: The process involves acquiring simulation data of the spring's vibration, performing frequency domain analysis to obtain vibration frequency and amplitude information, determining the stability of the spring's vibration based on preset stable vibration frequency and amplitude thresholds, and classifying the vibration as unstable if the frequency or amplitude exceeds the threshold range. For unstable springs, finite element analysis is used to model and simulate the spring structure, identifying key structural parameters causing unstable vibration. A genetic algorithm or particle swarm optimization algorithm is used to search for the optimal combination of spring structure parameters, resulting in an optimized spring structure design. Based on the optimized spring structure design, the spring is remodeled and simulated to verify whether the optimized spring vibration stability meets the requirements. If the optimized spring vibration stability still does not meet the requirements, the process returns to step S104 to continue optimizing the spring structure design until the vibration stability requirements are met. Finally, the optimized spring structure is sampled and subjected to physical vibration testing to measure the actual vibration frequency and amplitude, confirming the agreement with the simulation results and verifying the feasibility and effectiveness of the spring structure design.

6. The method according to any one of claims 1-4, characterized in that, Step S106 includes: Based on the preset resonant frequency and vibration mode targets, an experimental scheme is designed, and test parameters and conditions are determined; an experimental platform is built, and appropriate sensors and data acquisition equipment are selected to conduct vibration tests on the spring sheet; vibration response data of the spring sheet under different excitation conditions are collected, including amplitude, frequency, and damping parameters; the collected experimental data are preprocessed to remove noise and outliers and extract effective vibration signals; a finite element model is established based on the material properties and geometric dimensions of the spring sheet, and modal analysis and harmonic response analysis are performed; the resonant frequency and vibration mode obtained from the simulation analysis are compared with the experimental results, and the relative error and correlation coefficient are calculated; If the experimental results deviate from the simulation predictions, the finite element model or experimental conditions should be adjusted and iteratively optimized until the preset target is met.

7. The method according to any one of claims 1-4, characterized in that, Step S107 includes: Based on the experimental data and simulation results, it is determined whether there is a deviation between the two. If a deviation exists, the material parameters and structural design parameters of the spring are obtained and adjusted and optimized. The adjusted material parameters and structural design parameters are input into the finite element analysis model, and the resonant frequency and vibration mode of the adjusted spring are simulated and calculated using the finite element analysis method. The resonant frequency and vibration mode obtained from the finite element analysis are compared with the preset target to determine whether the performance of the adjusted spring meets the requirements. If the requirements are not met, the parameter adjustment step is returned to continue optimizing the material and structural parameters. When the finite element analysis results show that the resonant frequency and vibration mode of the adjusted spring meet the preset requirements, the optimized spring material and structural parameters are applied to the actual sample fabrication and experimental testing is carried out for verification. During the experimental testing, vibration response data of the spring under different excitation conditions were collected to obtain the measured resonant frequency and vibration mode data, which were then compared and analyzed with the preset target. If there was a deviation between the measured data and the preset target, the measured data was fed back into the finite element analysis model. Through data assimilation methods, the material parameters and structural parameters were further corrected, and the analysis and testing were carried out iteratively. The material, structure, analysis, and testing processes were continuously optimized until the experimentally measured resonant frequency and vibration mode of the spring were highly consistent with the preset target, thus completing the design optimization and performance matching of the spring.

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