Transmission gear NVH (Noise Vibration and Harshness) characteristic optimization and noise control method

Abstract

The invention belongs to the technical field of transmission gears, and discloses a transmission gear NVH characteristic optimization and noise control method. Comprising the following steps: acquiring multi-working-condition vibration noise data of a transmission gear system, and performing spectral decomposition and frequency band optimization processing to obtain a gear system vibration noise feature sequence; then modal characteristic analysis is carried out on the characteristic sequence, a vibration transmission path model is constructed, further modeling and processing are carried out, and a gear engagement dynamic contact model is established; meanwhile, gear manufacturing process parameter data are obtained, and a manufacturing precision portrait is constructed; on this basis, NVH key influence factor analysis and adaptive optimization decision making are carried out, and a process optimization strategy is constructed; and finally, dynamic parameter adjustment is implemented according to the NVH risk level. According to the method, multi-working-condition data analysis and microcosmic contact dynamics are combined, an association mechanism is established, and a complete technical system from data acquisition to optimization control is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission gears, and more specifically, to a method for optimizing the NVH characteristics and controlling the noise of a transmission gear. Background Art

[0002] Gear transmission is one of the most commonly used mechanical transmission forms in the industrial field, and is widely used in fields such as automobiles, aerospace, and construction machinery. With the rapid development of industrial technology and the continuous improvement of market demands, higher requirements are put forward for the NVH (noise, vibration, and harshness) performance of gear transmission systems. Especially in the field of new energy vehicles, due to the removal of the masking effect of traditional engines, the vibration and noise problems of gear transmission systems are more prominent, directly affecting the comfort and market competitiveness of the whole vehicle.

[0003] At present, in the research on the NVH characteristics of gear transmission systems, there are studies on the vibration characteristics during gear meshing by establishing a gear dynamics model, and there are also discussions on the influence of machining accuracy on the NVH performance of gears from the perspective of manufacturing processes. However, most of these studies are limited to a specific aspect and lack a systematic optimization method. In practical engineering applications, traditional gear optimization methods mainly rely on experience for parameter adjustment, which is difficult to meet the performance requirements under different working conditions, and the optimization effect is often not satisfactory.

[0004] With the development of intelligent manufacturing technology, the design and manufacturing level of gear transmission systems has been continuously improved. However, in the design and manufacturing process of gear transmission systems, there is generally a problem that the vibration and noise control effect is not ideal. Especially in high-speed and heavy-load equipment such as automotive transmissions and construction machinery, abnormal vibrations and noises often occur in gear systems, which not only affect the normal operation of the equipment, but also lead to a reduction in service life and an increase in energy consumption. Existing technologies often adopt empirical optimization methods to deal with these problems, lacking systematicness and scientificity, and are difficult to cope with complex and changeable working conditions. At the same time, in actual production, due to the lack of effective correlation analysis between manufacturing processes and performance indicators, the product quality fluctuates greatly, and the maintenance cost is high. Especially in fields with high NVH performance requirements such as new energy vehicles, existing optimization methods are difficult to meet the increasingly high performance requirements. In addition, traditional gear optimization methods often ignore the dynamic characteristics at the micro scale and cannot accurately grasp the root causes of vibration and noise generation, resulting in a significant reduction in the optimization effect, seriously restricting the technological progress and product upgrade of related industries.

[0005] In view of this, the present invention proposes a method for optimizing the NVH characteristics and controlling the noise of a transmission gear to solve the above problems. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solutions: A method for optimizing the NVH characteristics and controlling the noise of a transmission gear, comprising: Step S1: Obtain the multi-condition vibration and noise data of the transmission gear system; perform spectral decomposition and frequency band optimization processing on the multi-condition vibration and noise data to obtain the vibration and noise characteristic sequence of the gear system; Step S2: Perform modal characteristic analysis on the vibration and noise characteristic sequence of the gear system to generate a multi-dimensional vibration modal characteristic matrix; perform transmission path optimization identification on the multi-dimensional vibration modal characteristic matrix to generate a vibration transmission path model of the gear system; Step S3: Perform microscopic contact dynamics modeling on the vibration transmission path model of the gear system to obtain a gear meshing dynamic contact model; perform non-linear parameterization processing on the gear meshing dynamic contact model to obtain a gear meshing non-linear parameterization model; Step S4: Obtain the gear manufacturing process parameter data; excavate the accuracy influencing factors of the gear manufacturing process parameter data to construct a gear manufacturing accuracy profile; Step S5: Analyze the key NVH influencing factors of the gear manufacturing accuracy profile and perform adaptive optimization decision-making to construct a gear manufacturing process optimization strategy; Step S6: Dynamically adjust the parameters of the gear meshing non-linear parameterization model based on the gear manufacturing process optimization strategy to construct a transmission gear NVH optimization control model.

[0007] Preferably, the specific steps of Step S1 are: Obtain the multi-condition vibration and noise data of the transmission gear system; perform time-domain signal and frequency-domain signal separation processing on the multi-condition vibration and noise data to obtain a time-domain data set and a frequency-domain data set; Perform instantaneous feature extraction on the time-domain data set to identify transient impact feature points; perform time-domain envelope analysis on the time-domain data set according to the transient impact feature points to obtain the time-domain characteristic data of the gear system; Perform harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized feature data; Perform time-frequency joint analysis processing on the time-domain characteristic data and the frequency-domain optimized feature data of the gear system to obtain the vibration and noise characteristic sequence of the gear system.

[0008] Preferably, the performing harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized feature data includes: Perform meshing frequency characteristic analysis on the frequency-domain data set to obtain the gear meshing frequency characteristic; Extract harmonic components according to the gear meshing frequency characteristic to obtain the gear system harmonic sequence; Perform frequency distribution analysis on the gear system harmonic sequence to generate harmonic distribution data; Perform parametric band energy statistics on the frequency-domain data set based on harmonic distribution data to generate a parametric band energy distribution; Identify abnormal bands in the frequency-domain data set based on the parametric band energy distribution and mark the abnormal band points; Perform band optimization processing on the abnormal band points to generate frequency-domain optimized feature data.

[0009] Preferably, the specific steps of step S2 are as follows: Perform multi-dimensional modal decomposition on the vibration and noise characteristic sequence of the gear system to generate vibration sequences of different modes; Perform modal characteristic analysis on the vibration sequences of different modes to obtain multiple vibration modal characteristics; Perform modal superposition synthesis based on multiple vibration modal characteristics to generate a multi-dimensional vibration modal characteristic matrix; Calculate the transfer function of the multi-dimensional vibration modal characteristic matrix to obtain vibration transfer function data; Perform structural response sensitivity analysis on the multi-dimensional vibration modal characteristic matrix to generate a structural response sensitivity matrix; Perform transfer path optimization identification on the structural response sensitivity matrix based on the vibration transfer function data to generate a vibration transfer path model of the gear system.

[0010] Preferably, the specific steps of step S3 are as follows: Calculate the tooth surface contact stiffness of the vibration transfer path model of the gear system to obtain the tooth surface contact stiffness distribution; Perform microscopic contact dynamics modeling on the tooth surface contact stiffness distribution to obtain a gear meshing dynamic contact model; perform meshing parameter sensitivity analysis on the gear meshing dynamic contact model to obtain a meshing parameter sensitivity network; Perform non-linear parameterization on the meshing parameter sensitivity network to obtain a non-linear parameterized model of gear meshing.

[0011] Preferably, the specific steps of step S4 are as follows: Monitor the entire gear manufacturing process flow to obtain gear manufacturing process parameter data; Perform process correlation analysis on the gear manufacturing process parameter data to generate process correlation data; Mine the accuracy influencing factors from the process correlation data to generate manufacturing accuracy influencing factors; Perform global portrait fitting on the manufacturing accuracy influencing factors to construct a gear manufacturing accuracy portrait.

[0012] Preferably, the mining of the accuracy influencing factors from the process correlation data to generate manufacturing accuracy influencing factors includes: Extract multiple key process parameters based on the process correlation data; Calculate the parameter sensitivity of the gear manufacturing process parameter data based on multiple key process parameters to obtain the process parameter sensitivity; Statistically analyze the precision contribution of each process to the process correlation data to obtain the precision contribution value of each process; Conduct a precision transfer chain analysis on the precision contribution value of each process according to the process parameter sensitivity to obtain the precision transfer characteristic data; Identify the key control points of the manufacturing process based on the process correlation data; Conduct a process parameter distribution analysis based on the key control points of the manufacturing process to obtain the process parameter distribution data; Conduct a comprehensive influencing factor analysis on the process parameter distribution data and the precision transfer characteristic data to generate the influencing factors of manufacturing precision.

[0013] Preferably, the specific steps of step S5 are as follows: Extract the key precision features from the gear manufacturing precision portrait to obtain the key precision feature data; Conduct a full-spectrum analysis of the NVH characteristics of the gear system vibration and noise characteristic sequence to obtain the full-spectrum data of the NVH characteristics; Conduct a correlation analysis on the key precision feature data based on the full-spectrum data of the NVH characteristics to obtain the NVH key influencing factor data; Quantify the contribution of the NVH key influencing factor data to generate the NVH influencing factor contribution value; Conduct an adaptive optimization decision based on the NVH influencing factor contribution value to construct an optimization strategy for the gear manufacturing process.

[0014] Preferably, the specific steps of step S6 are as follows: Conduct a parameter sensitivity analysis on the gear meshing non-linear parametric model to generate a parameter sensitivity matrix; map the optimization target to the parameter sensitivity matrix based on the gear manufacturing process optimization strategy to construct a parameter optimization framework; Conduct dynamic parameter adjustment on the parameter optimization framework to construct a NVH optimization control model for the transmission gear; The specific dynamic parameter adjustment is as follows: identify the NVH risk level of the current gear system based on the gear manufacturing process optimization strategy; The NVH risk levels include: high-risk area, medium-risk area and low-risk area; When the NVH risk level of the gear system is in the high-risk area, the parameter optimization framework performs tooth surface micro-topography reconstruction processing; When the NVH risk level of the gear system is in the medium-risk area, the parameter optimization framework performs meshing parameter dynamic compensation processing; When the NVH risk level of the gear system is in the low-risk area, the parameter optimization framework performs conventional meshing parameter optimization.

[0015] Preferably, the tooth surface micro-topography reconstruction process includes: Measure the micro-topography of the tooth surface to obtain micro-topography data; Construct a three-dimensional micro-model of the tooth surface based on the micro-topography data; Calculate the contact stress distribution of the three-dimensional micro-model of the tooth surface to obtain a contact stress distribution map; Identify the stress concentration area according to the contact stress distribution map; Conduct micro-topography reconstruction design for the stress concentration area to generate an optimized micro-topography model; Perform precision machining on the tooth surface based on the optimized micro-topography model to achieve tooth surface micro-topography reconstruction; The meshing parameter dynamic compensation process includes: Obtain the real-time operation data of the gear system; Analyze the meshing state of the real-time operation data of the gear system to identify meshing deviations; Calculate the meshing parameter compensation amount based on the meshing deviation; Dynamically correct the meshing parameter compensation amount to generate dynamic compensation parameters; Feed the dynamic compensation parameters back to the gear manufacturing process to achieve meshing parameter dynamic compensation; The conventional meshing parameter optimization process includes: Analyze the mapping relationship between the meshing parameters and NVH characteristics of the gear system; Construct a meshing parameter optimization model based on the mapping relationship; Perform multi-objective optimization calculation on the meshing parameter optimization model to obtain an optimized meshing parameter combination; Apply the optimized meshing parameter combination to the gear manufacturing process to achieve meshing parameter optimization.

[0016] The technical effects and advantages of a method for optimizing the NVH characteristics and controlling noise of a transmission gear according to the present invention: The present invention can significantly improve the operation reliability and smoothness of the gear transmission system, effectively reduce the vibration and noise levels during operation, and enhance the operation comfort of the transmission system. It can adapt to different working conditions and operation conditions, and has strong practicability and adaptability. By establishing a complete optimization control system, the collaborative optimization of manufacturing processes and performance indicators is achieved, greatly improving the manufacturing precision and quality stability of gear products. It can not only reduce the energy loss of the gear transmission system and extend its service life, but also significantly improve the smoothness of the whole machine operation and enhance the market competitiveness of the product. In addition, it can effectively solve the vibration and noise problems of the transmission system under extreme working conditions such as high speed and heavy load, improve the reliability and durability of the system, and reduce the maintenance cost at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. is a schematic diagram of a method for optimizing the NVH characteristics and controlling the noise of a transmission gear according to the present invention; Figure 2 FIG. is a schematic diagram of a system for optimizing the NVH characteristics and controlling the noise of a transmission gear according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for optimizing the NVH characteristics and controlling the noise of a transmission gear in the embodiments of the present application includes: Step S1: Obtain the multi-condition vibration and noise data of the transmission gear system; perform spectral decomposition and frequency band optimization processing on the multi-condition vibration and noise data to obtain the vibration and noise characteristic sequence of the gear system; It can be understood that the execution subject of the present application can be a system for optimizing the NVH characteristics and controlling the noise of a transmission gear, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application are described by taking the server as the execution subject as an example.

[0020] Specifically, obtain the vibration and noise data of the transmission gear system under different working conditions, including vibration and noise signals under different speeds, different loads, different temperatures, etc. Conduct spectral analysis on the obtained vibration and noise data under multiple working conditions, and convert the time-domain signal into a frequency-domain signal to better analyze the vibration characteristics of the gear system. According to the characteristics of the gear system, decompose the spectrum and separate the frequency components related to gear meshing. Perform optimization processing for different frequency bands and extract the band information that can best represent the vibration and noise characteristics of the gear system. Based on the optimized band information, construct a vibration and noise characteristic sequence of the gear system to provide basic data for subsequent analysis.

[0021] Step S2: Conduct modal characteristic analysis on the vibration and noise characteristic sequence of the gear system to generate a multi-dimensional vibration modal characteristic matrix; perform optimization identification of the transmission path on the multi-dimensional vibration modal characteristic matrix to generate a vibration transmission path model of the gear system; Specifically, conduct modal analysis on the vibration and noise characteristic sequence of the gear system to identify modal parameters such as the natural frequency, vibration mode, and damping ratio of the system. Through modal analysis, decompose the complex vibration response into multiple independent vibration modes. Conduct characteristic analysis on each vibration mode and extract characteristic parameters such as modal frequency, modal damping, and modal vibration mode. Combine the characteristic parameters of each mode into a multi-dimensional vibration modal characteristic matrix to comprehensively characterize the vibration characteristics of the gear system. Based on the multi-dimensional vibration modal characteristic matrix, calculate the transfer function of the system to describe the relationship between the input excitation and the output response. By analyzing the transfer function, identify the transmission path of the vibration energy in the system. Optimize the identified transmission path and screen out the key path that contributes the most to the vibration and noise. According to the key transmission path, construct a vibration transmission path model of the gear system to provide a theoretical basis for subsequent noise control.

[0022] Step S3: Conduct microscopic contact dynamics modeling on the vibration transmission path model of the gear system to obtain a dynamic contact model of gear meshing; perform non-linear parameterization on the dynamic contact model of gear meshing to obtain a non-linear parameterized model of gear meshing; Specifically, based on the vibration transmission path model, deeply analyze the microscopic contact characteristics during the gear meshing process. Consider factors such as the microscopic topography, material properties, and lubrication conditions of the gear surface, and establish a microscopic contact dynamics model of the gear meshing process. This model can describe microscopic phenomena such as contact stress, deformation, and sliding during the gear meshing process. Based on the microscopic contact dynamics model, establish a dynamic contact model of gear meshing to describe the dynamic contact characteristics during the meshing process. Analyze the non-linear factors during the meshing process, including gear clearance, friction non-linearity, and material non-linearity, etc. Perform parameterization on the dynamic contact model of gear meshing to convert the complex non-linear characteristics into a controllable parameter model. Through parameterization, obtain a non-linear parameterized model of gear meshing to provide a basis for subsequent optimization control.

[0023] Step S4: Obtain gear manufacturing process parameter data; mine the precision influencing factors of the gear manufacturing process parameter data, and construct a gear manufacturing precision profile; Specifically, collect the process parameter data in the gear manufacturing process, including information such as machining methods, cutting parameters, heat treatment processes, and surface treatment processes. Analyze the influence of different process parameters on the gear manufacturing precision, and identify the key precision influencing factors. Through data mining techniques, quantify the influence degree of each process parameter on the gear precision. Based on the precision influencing factors, construct a gear manufacturing precision profile to comprehensively describe the manufacturing precision characteristics of the gear. This precision profile contains information on multiple dimensions such as the geometric precision, surface quality, and material properties of the gear.

[0024] Step S5: Analyze the key influencing factors of NVH for the gear manufacturing precision profile, and make an adaptive optimization decision to construct a gear manufacturing process optimization strategy; Specifically, based on the gear manufacturing precision profile, analyze the influence of different precision characteristics on the gear NVH performance. Identify the key factors that have the most significant influence on the NVH performance, such as tooth profile error, tooth direction error, and surface roughness. Quantify the contribution degree of each key factor to the NVH performance, and establish a mapping relationship between the precision characteristics and the NVH performance. According to the analysis results of the key influencing factors of NVH, formulate an adaptive optimization decision strategy. This strategy can automatically adjust the optimization objectives and optimization methods according to different NVH requirements. Based on the adaptive optimization decision, construct a gear manufacturing process optimization strategy to guide the improvement and optimization of the gear manufacturing process.

[0025] Step S6: Based on the gear manufacturing process optimization strategy, perform dynamic parameter adjustment on the gear meshing non-linear parametric model to construct a transmission gear NVH optimization control model.

[0026] Specifically, according to the gear manufacturing process optimization strategy, determine the key parameters that need to be adjusted in the gear meshing non-linear parametric model. Conduct a sensitivity analysis of the key parameters to evaluate the influence degree of parameter changes on the NVH performance. Based on the sensitivity analysis results, perform dynamic adjustment on the parameters to optimize the NVH performance of the gear system. Through parameter adjustment, construct a transmission gear NVH optimization control model to achieve precise control of the gear NVH performance. This control model can dynamically adjust the control parameters according to the actual operating conditions to maintain the best NVH performance of the gear system.

[0027] In the embodiments of the present application, through in-depth analysis of the vibration and noise data of the transmission gear system under multiple working conditions, a vibration and noise model based on modal characteristics and transmission paths is established, realizing the accurate characterization of the NVH characteristics of the gear system. Through microscopic contact dynamics modeling and non-linear parameterization processing, the dynamic contact characteristics during gear meshing are analyzed in depth, providing a theoretical basis for NVH optimization. Combining the analysis of gear manufacturing process parameters, a gear manufacturing precision profile is constructed, revealing the internal relationship between manufacturing precision and NVH performance. Through the analysis of key NVH influencing factors and adaptive optimization decision-making, a targeted gear manufacturing process optimization strategy is formulated. Finally, based on the gear manufacturing process optimization strategy, the dynamic parameters of the non-linear parameterization model of gear meshing are adjusted, and an NVH optimization control model for the transmission gear is constructed, realizing the accurate control of the NVH performance of the gear system.

[0028] In a specific embodiment, the process of executing step S1 specifically includes the following steps: Obtain the vibration and noise data of the transmission gear system under multiple working conditions; perform time-domain signal and frequency-domain signal separation processing on the vibration and noise data under multiple working conditions to obtain a time-domain data set and a frequency-domain data set; Extract instantaneous features from the time-domain data set to identify transient impact feature points; perform time-domain envelope analysis on the time-domain data set according to the transient impact feature points to obtain the time-domain characteristic data of the gear system; Perform harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized characteristic data; Perform time-frequency joint analysis processing on the time-domain characteristic data and the frequency-domain optimized characteristic data of the gear system to obtain the vibration and noise characteristic sequence of the gear system.

[0029] Specifically, vibration and noise signals of the transmission gear system under various working conditions such as different speeds, different loads, and different temperatures are collected by vibration acceleration sensors and sound sensors to obtain multi-condition vibration and noise data of the transmission gear system. During the collection process, ensure the accuracy and integrity of the data. The sampling frequency should be high enough to capture the high-frequency vibration characteristics, usually more than 10 times the meshing frequency. The obtained multi-condition vibration and noise data are preliminarily processed, including removing outliers, filtering background noise, and signal smoothing, etc., to improve the data quality. Then, the time-domain signal and the frequency-domain signal of the processed data are separated, and the original vibration and noise data are converted into a time-domain data set and a frequency-domain data set. The time-domain data set retains the time-varying characteristics of the signal, while the frequency-domain data set shows the frequency distribution characteristics of the signal. Instantaneous features are extracted from the time-domain data set to identify transient impact feature points in the signal. Transient impact feature points are usually related to gear meshing impact, defect collision, or abnormal operating conditions, and are important diagnostic information. By calculating parameters such as the kurtosis, impulse index, and peak factor of the signal, combined with threshold detection or adaptive threshold methods, transient impact feature points are identified. According to the identified transient impact feature points, time-domain envelope analysis is performed on the time-domain data set. Time-domain envelope analysis can extract the envelope of the modulated signal and reveal potential modulation characteristics, which is particularly effective for diagnosing early gear faults. The signal envelope is obtained by methods such as Hilbert transform or wavelet transform, and the envelope is subjected to spectral analysis to obtain the time-domain characteristic data of the gear system. Harmonic separation and frequency band optimization processing are performed on the frequency-domain data set. First, the gear meshing frequency and its harmonic components are identified, and then harmonic separation is performed to decompose the signal into harmonic components and non-harmonic components. Optimization processing such as energy focusing, frequency band screening, and feature enhancement is performed on the separated frequency bands to generate frequency-domain optimized feature data. The time-domain characteristic data and the frequency-domain optimized feature data of the gear system are subjected to time-frequency joint analysis processing. Time-frequency joint analysis can analyze the signal characteristics in both the time domain and the frequency domain simultaneously, providing more comprehensive vibration and noise characteristics. Through methods such as short-time Fourier transform, Wigner-Ville distribution, or wavelet transform, a vibration and noise characteristic sequence of the gear system is obtained, laying a foundation for subsequent modal characteristic analysis.

[0030] In a specific embodiment, the process of performing harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized feature data specifically includes the following steps: Perform meshing frequency feature analysis on the frequency-domain data set to obtain gear meshing frequency characteristics; Extract harmonic components according to the gear meshing frequency characteristics to obtain a gear system harmonic sequence; Perform frequency distribution analysis on the gear system harmonic sequence to generate harmonic distribution data; Based on the harmonic distribution data, perform parametric frequency band energy statistics on the frequency-domain data set to generate a parametric frequency band energy distribution; Identify abnormal frequency bands in the frequency-domain dataset based on parametric frequency-band energy distribution, and mark the abnormal frequency-band points; Perform frequency-band optimization processing on the abnormal frequency-band points to generate frequency-domain optimized feature data.

[0031] Specifically, first perform meshing frequency feature analysis on the frequency-domain dataset. By analyzing the structural parameters of the gear system, such as the number of teeth, module, and rotational speed, etc., calculate the theoretical meshing frequency fmesh = n×Z / 60, where n is the rotational speed (rpm) and Z is the number of teeth. Then identify the actual meshing frequency peak close to the theoretical meshing frequency in the frequency spectrum to obtain the gear meshing frequency feature. According to the identified meshing frequency feature, extract the harmonic components in the frequency spectrum. The harmonic components are usually located at integer multiples of the meshing frequency, that is, k×fmesh, where k is the harmonic order. By setting a frequency-band window or a frequency filter, extract each order of harmonic components to form a gear system harmonic sequence. Perform frequency distribution analysis on the extracted gear system harmonic sequence to study the variation law of the harmonic amplitude with frequency. By statistically analyzing the amplitude, phase, and energy distribution of each order of harmonic, generate harmonic distribution data. The harmonic distribution data can reflect the vibration characteristics and potential problems of the gear system. Based on the harmonic distribution data, perform parametric frequency-band energy statistics on the frequency-domain dataset. Divide the frequency spectrum into multiple frequency bands, such as a low-frequency band (0 - 0.5fmesh), a meshing frequency band (0.5fmesh - 1.5fmesh), a medium-frequency band (1.5fmesh - 3fmesh), and a high-frequency band (>3fmesh), etc., calculate the energy distribution and energy proportion within each frequency band to generate parametric frequency-band energy distribution. Based on the parametric frequency-band energy distribution, identify abnormal frequency bands in the frequency-domain dataset. By comparing the frequency-band energy distributions under different working conditions, or comparing with the standard distribution in the normal state, identify the frequency bands with abnormal energy. Use statistical methods or machine learning algorithms, such as anomaly detection or clustering analysis, to mark the abnormal frequency-band points. Perform frequency-band optimization processing on the marked abnormal frequency-band points. The optimization processing includes frequency-band enhancement, noise suppression, and feature extraction, etc., to improve the signal-to-noise ratio and feature prominence of the abnormal frequency band. Through methods such as adaptive filtering, wavelet threshold processing, or empirical mode decomposition, optimize the feature performance of the abnormal frequency band to generate frequency-domain optimized feature data.

[0032] In a specific embodiment, the process of executing step S2 specifically includes the following steps: Perform multi-dimensional modal decomposition on the gear system vibration noise feature sequence to generate vibration sequences of different modes; Perform modal characteristic analysis on the vibration sequences of different modes to obtain multiple vibration modal characteristics; Based on multiple vibration modal characteristics, perform modal superposition synthesis to generate a multi-dimensional vibration modal characteristic matrix; Perform transfer function calculation on the multi-dimensional vibration modal characteristic matrix to obtain vibration transfer function data; Perform a structural response sensitivity analysis on the multi-dimensional vibration mode feature matrix to generate a structural response sensitivity matrix; Based on the vibration transfer function data, perform a transfer path optimization identification on the structural response sensitivity matrix to generate a vibration transfer path model of the gear system.

[0033] Specifically, first perform a multi-dimensional modal decomposition on the vibration and noise characteristic sequence of the gear system. Using methods such as proper orthogonal decomposition (POD), empirical mode decomposition (EMD), or variational mode decomposition (VMD), decompose the vibration and noise characteristic sequence into multiple modal components. Each modal component represents a vibration mode of the system, generating vibration sequences of different modes. Perform a modal characteristic analysis on the decomposed vibration sequences of different modes. By calculating parameters such as the natural frequency, damping ratio, and modal shape of each mode, obtain multiple vibration mode characteristics. The modal characteristic analysis can adopt methods such as the frequency response function method, subspace iteration method, or stochastic subspace method. Based on the obtained multiple vibration mode characteristics, perform a modal superposition synthesis. Modal superposition is to linearly combine each mode according to a certain weight to generate the overall response of the system. By calculating the participation factor and weight coefficient of each mode, construct a modal superposition equation to generate a multi-dimensional vibration mode feature matrix. This matrix contains information such as the eigenvalues, eigenvectors, and participation factors of each order mode of the system. Perform a transfer function calculation on the multi-dimensional vibration mode feature matrix. The transfer function describes the relationship between the input and output of the system and can reflect the transfer characteristics of vibration from the excitation source to the response point. Calculate the transfer characteristics of the system at different frequencies through the frequency response function (FRF) or impulse response function to obtain vibration transfer function data. Perform a structural response sensitivity analysis on the multi-dimensional vibration mode feature matrix. Sensitivity analysis can evaluate the influence degree of system parameter changes on the response and help identify key transfer paths. By calculating the partial derivatives of the structural parameters with respect to the mode characteristics or using the finite difference method, evaluate the sensitivity of each parameter to generate a structural response sensitivity matrix. Based on the vibration transfer function data, perform a transfer path optimization identification on the structural response sensitivity matrix. The transfer path optimization identification aims to identify the main several paths from numerous possible transfer paths to simplify the system analysis. By comprehensively considering the transfer function characteristics and structural response sensitivity, using methods such as path contribution analysis, principal component analysis, or genetic algorithm, optimize and identify the main transfer paths to generate a vibration transfer path model of the gear system.

[0034] In a specific embodiment, the process of executing step S3 may specifically include the following steps: Calculate the tooth surface contact stiffness of the vibration transfer path model of the gear system to obtain the tooth surface contact stiffness distribution; Perform a microscopic contact dynamics modeling on the tooth surface contact stiffness distribution to obtain a gear meshing dynamic contact model; Perform a sensitivity analysis of the meshing parameters of the gear meshing dynamic contact model to obtain a sensitivity network of the meshing parameters; Perform a non-linear parameterization of the sensitivity network of the meshing parameters to obtain a non-linear parameterization model of gear meshing.

[0035] Specifically, first calculate the tooth surface contact stiffness of the gear system vibration transmission path model. The tooth surface contact stiffness is a key parameter affecting the dynamic characteristics of gears and is directly related to the vibration and noise level of gears. Based on the Hertz contact theory or the LTCA (Load Tooth Contact Analysis) method, considering the macroscopic geometric parameters of the gear (such as modulus, pressure angle, and tooth width, etc.) and microscopic parameters (such as surface roughness and surface hardness, etc.), calculate the change in the contact stiffness of the tooth surface during the meshing process to obtain the tooth surface contact stiffness distribution. Conduct a microscopic contact dynamics modeling of the tooth surface contact stiffness distribution. The microscopic contact dynamics modeling takes into account the influence of the tooth surface micro-topography on the contact behavior and can more accurately describe the dynamic contact characteristics during gear meshing. By establishing a dynamic equation considering factors such as tooth surface micro-topography, surface roughness, and lubricating oil film thickness, simulate the dynamic contact behavior during gear meshing, including normal contact force, tangential friction force, and micro-sliding and other phenomena, to obtain a gear meshing dynamic contact model. Perform a sensitivity analysis of the meshing parameters of the gear meshing dynamic contact model. The sensitivity analysis aims to evaluate the degree of influence of model parameter changes on the system dynamic response and helps to identify key parameters. By changing the meshing parameters (such as contact stiffness, meshing damping, meshing friction coefficient, and meshing error, etc.), observe the changes in the system dynamic response, calculate the sensitivity coefficients of each parameter, and construct a sensitivity network of the meshing parameters. This network describes the mutual relationship between the meshing parameters and the degree of influence on the system response. Perform a non-linear parameterization of the sensitivity network of the meshing parameters. The non-linear parameterization aims to establish a parameterization model that can describe the non-linear characteristics of the system for subsequent optimal control. Considering factors such as the time-varying characteristics of the meshing stiffness, the non-linear characteristics of the friction force, and the clearance non-linearity of the system, establish a mathematical model containing multiple non-linear parameters. Through techniques such as curve fitting, response surface method, or neural network, parameterize the complex non-linear relationship to obtain a non-linear parameterization model of gear meshing. This model can adapt to the gear meshing state under different working conditions by adjusting the parameters and provides a basis for subsequent optimal control.

[0036] In a specific embodiment, the process of performing step S4 may specifically include the following steps: Monitor the entire gear manufacturing process flow to obtain gear manufacturing process parameter data; Perform a process correlation analysis on the gear manufacturing process parameter data to generate process correlation data; Mine the accuracy influencing factors from the process correlation data to generate manufacturing accuracy influencing factors; Perform global portrait fitting on the influencing factors of manufacturing precision to construct a gear manufacturing precision portrait.

[0037] Specifically, first monitor the entire gear manufacturing process flow to obtain various process parameter data during gear manufacturing. The monitoring scope covers the entire process from raw material processing to final inspection, including processes such as blank manufacturing, heat treatment, rough machining, finish machining, and surface treatment. Through sensor networks, data acquisition systems, and manufacturing execution systems (MES), the process parameters of each process are monitored and recorded in real time, such as material hardness, heat treatment temperature, cutting speed, cutting depth, grinding parameters, and clamping force, etc., to obtain gear manufacturing process parameter data. Conduct process correlation analysis on the obtained gear manufacturing process parameter data. Process correlation analysis aims to reveal the mutual influence and correlation laws between different processes, providing a basis for subsequent precision influencing factor mining. By methods such as correlation analysis, regression analysis, and causal analysis, study the parameter transfer relationship between upstream and downstream processes, identify strong correlation factors and weak correlation factors between processes, and generate process correlation data. Mine precision influencing factors from the process correlation data. Precision influencing factor mining aims to identify the key factors that have a significant impact on gear manufacturing precision from numerous process parameters. By methods such as variance analysis, principal component analysis, and random forest, evaluate the influence degree of each process parameter on gear precision, identify the main influencing factors and secondary influencing factors, and generate manufacturing precision influencing factors. Perform global portrait fitting on the manufacturing precision influencing factors to construct a gear manufacturing precision portrait. The precision portrait is a comprehensive description of the relationship between various influencing factors in the gear manufacturing process and the final precision, which can guide subsequent process optimization. By establishing a mathematical model or machine learning model, such as a multiple regression model, support vector machine, or neural network, etc., fit the mapping relationship between the manufacturing precision influencing factors and gear precision, and construct a gear manufacturing precision portrait. This portrait can predict the gear precision level under different process parameter combinations, providing a decision-making basis for process optimization.

[0038] In a specific embodiment, the process of mining precision influencing factors from the process correlation data to generate manufacturing precision influencing factors specifically includes the following steps: Extract multiple key process parameters based on the process correlation data; Calculate the parameter sensitivity of the gear manufacturing process parameter data according to the multiple key process parameters to obtain the process parameter sensitivity; Statistically calculate the precision contribution degree of each process to the process correlation data to obtain the precision contribution value of each process; Conduct precision transfer chain analysis on the precision contribution value of each process according to the process parameter sensitivity to obtain precision transfer characteristic data; Identify the key control points of the manufacturing process based on the process correlation data; Perform process parameter distribution analysis based on the key control points of the manufacturing process to obtain process parameter distribution data; Conduct comprehensive influencing factor analysis on the process parameter distribution data and accuracy transfer characteristic data to generate manufacturing accuracy influencing factors.

[0039] Specifically, first, multiple key process parameters are extracted based on process correlation data. Through data mining and feature selection methods, such as correlation analysis, information gain, and chi-square test, etc., the key process parameters that have a significant impact on gear accuracy are screened out from the process correlation data. These parameters may include heat treatment temperature, heat treatment time, cutting speed, cutting feed rate, grinding grit size, and grinding depth, etc. According to the multiple key process parameters extracted, the parameter sensitivity of the gear manufacturing process parameter data is calculated. The parameter sensitivity describes the degree of influence of the process parameter change on the gear accuracy and is an important indicator for evaluating the importance of the parameter. Through designing experiments or numerical simulations, while keeping other parameters unchanged, the value of a single parameter is changed, and the change of the gear accuracy is observed. The parameter sensitivity S = ΔP / Δx is calculated, where ΔP is the accuracy change amount and Δx is the parameter change amount, to obtain the process parameter sensitivity. The accuracy contribution of each process to the process correlation data is statistically analyzed. The accuracy contribution describes the influence ratio of each process on the final gear accuracy and helps to identify the key processes. Through statistical analysis or machine learning methods, such as variance decomposition or random forest feature importance, etc., the accuracy contribution of each process to the gear accuracy is calculated to obtain the accuracy contribution value of each process. According to the process parameter sensitivity, the accuracy transfer chain analysis of the accuracy contribution value of each process is carried out. The accuracy transfer chain describes the transfer law of the accuracy error between processes and is an important tool for understanding the accuracy formation mechanism. By establishing an accuracy transfer model, analyzing how the accuracy error of the upstream process affects the machining accuracy of the downstream process, identifying the key links and bottlenecks of the accuracy transfer, the accuracy transfer characteristic data is obtained. Based on the process correlation data, the key control points of the manufacturing process are identified. The key control points are the processes or parameters that have a decisive impact on the product quality during the manufacturing process and are the focus of process control. By analyzing methods such as process flowcharts, fault tree analysis, or critical to quality (CTQ) analysis, etc., the key control points in the manufacturing process are identified. Based on the identified key control points of the manufacturing process, the process parameter distribution analysis is carried out. The parameter distribution analysis aims to study the variation law and distribution characteristics of the process parameters and provides a basis for the subsequent analysis of influencing factors. Through statistical analysis methods, such as histogram analysis, probability density function estimation, and box plot analysis, etc., the distribution characteristics of the process parameters at the key control points are studied, including statistical quantities such as mean, variance, skewness, and kurtosis, to obtain the process parameter distribution data. The comprehensive influence factor analysis of the process parameter distribution data and the accuracy transfer characteristic data is carried out. The comprehensive influence factor analysis aims to comprehensively evaluate the influence of each factor on the gear accuracy from multiple dimensions to obtain a comprehensive list of influence factors. Through methods such as multivariate statistical analysis, machine learning, or expert systems, etc., considering factors such as parameter sensitivity, process contribution, accuracy transfer characteristics, and parameter distribution characteristics, etc., the manufacturing accuracy influence factors are generated.

[0040] In a specific embodiment, the process of executing step S5 specifically includes the following steps: Extract key precision features from the gear manufacturing precision image to obtain key precision feature data; Conduct a full-spectrum analysis of the NVH characteristics of the gear system vibration noise feature sequence to obtain full-spectrum NVH characteristic data; Conduct a correlation analysis of the key precision feature data based on the full-spectrum NVH characteristic data to obtain NVH key influencing factor data; Quantify the contribution degree of the NVH key influencing factor data to generate the NVH influencing factor contribution degree value; Based on the NVH influencing factor contribution degree value, conduct an adaptive optimization decision to construct an optimization strategy for the gear manufacturing process.

[0041] Specifically, first extract key precision features from the gear manufacturing precision image. Key precision features are precision parameters that have a significant impact on the NVH characteristics of gears, such as tooth profile error, helix error, base pitch error, and radial runout. Through methods such as feature importance analysis or principal component analysis, extract key precision features from the gear manufacturing precision image to obtain key precision feature data. Conduct a full-spectrum analysis of the NVH characteristics of the gear system vibration noise feature sequence. The full-spectrum analysis of NVH characteristics aims to comprehensively analyze the vibration noise characteristics of the gear system from multiple dimensions such as frequency, amplitude, and phase. Through methods such as Fourier analysis, wavelet analysis, or Hilbert-Huang transform, process the vibration noise feature sequence to obtain full-spectrum NVH characteristic data, including spectrogram, order spectrum diagram, and time-frequency spectrum diagram. Conduct a correlation analysis of the key precision feature data based on the full-spectrum NVH characteristic data. The correlation analysis aims to explore the mapping relationship between precision features and NVH characteristics and provide a basis for subsequent optimization decisions. Through methods such as correlation analysis, regression analysis, or machine learning models, study the correlation law between key precision features and NVH characteristics, identify precision factors that have a significant impact on NVH characteristics, and obtain NVH key influencing factor data. Quantify the contribution degree of the NVH key influencing factor data. The quantification of the contribution degree aims to quantitatively evaluate the contribution of each influencing factor to the NVH characteristics and provide a quantitative basis for optimization decisions. Through methods such as variance analysis, sensitivity analysis, or feature importance of machine learning models, calculate the contribution degree of each influencing factor to the NVH characteristics to generate the NVH influencing factor contribution degree value. Conduct an adaptive optimization decision based on the NVH influencing factor contribution degree value. The adaptive optimization decision can dynamically adjust the optimization strategy according to specific situations to achieve precise control of the gear NVH characteristics. Through methods such as multi-objective optimization, genetic algorithm, or reinforcement learning, comprehensively consider factors such as NVH target requirements, manufacturing cost, and production efficiency to construct an optimization strategy for the gear manufacturing process. This strategy can adaptively adjust process parameters according to the NVH target requirements of different products to achieve optimized control of the gear NVH characteristics.

[0042] In a specific embodiment, the process of executing step S6 specifically includes the following steps: Perform a parameter sensitivity analysis on the gear meshing non-linear parametric model to generate a parameter sensitivity matrix; Based on the gear manufacturing process optimization strategy, perform an optimization objective mapping on the parameter sensitivity matrix to construct a parameter optimization framework; Perform dynamic parameter adjustment on the parameter optimization framework to construct a transmission gear NVH optimization control model; The dynamic parameter adjustment is specifically: based on the gear manufacturing process optimization strategy, identify the NVH risk level of the current gear system; The NVH risk levels include: high-risk area, medium-risk area, and low-risk area; When the NVH risk level of the gear system is in the high-risk area, the parameter optimization framework performs a tooth surface micro-topography reconstruction process; When the NVH risk level of the gear system is in the medium-risk area, the parameter optimization framework performs a meshing parameter dynamic compensation process; When the NVH risk level of the gear system is in the low-risk area, the parameter optimization framework performs a conventional meshing parameter optimization process.

[0043] Specifically, first, a parameter sensitivity analysis is performed on the non-linear parametric model of gear meshing. The parameter sensitivity analysis aims to evaluate the degree of influence of model parameter changes on NVH characteristics and helps to identify key parameters. By changing the parameter values in the model, such as contact stiffness, damping coefficient, friction coefficient, and meshing error, etc., observe the changes in the NVH characteristics of the system, calculate the sensitivity coefficients of each parameter, and generate a parameter sensitivity matrix. Based on the gear manufacturing process optimization strategy, an optimization target mapping is performed on the parameter sensitivity matrix. The optimization target mapping aims to transform the process optimization strategy into specific parameter optimization targets to provide guidance for subsequent parameter adjustment. By establishing the mapping relationship between process parameters and model parameters, the process optimization strategy is transformed into the optimization target of model parameters, and a parameter optimization framework is constructed. Dynamic parameter adjustment is performed on the parameter optimization framework to construct an NVH optimization control model for transmission gears. The dynamic parameter adjustment can adaptively adjust the parameters according to the real-time state of the system and the NVH risk level to achieve precise control of NVH characteristics. Based on the gear manufacturing process optimization strategy, considering factors such as vibration and noise levels, spectral characteristics, and transmission paths, etc., identify the NVH risk level of the current gear system. The NVH risk level is divided into high-risk areas, medium-risk areas, and low-risk areas, and different parameter adjustment strategies are adopted for different risk levels. When it is identified that the gear system is in the high-risk area, the parameter optimization framework performs a reconstruction process of the tooth surface micro-topography. The high-risk area usually shows serious vibration and noise problems, and it is necessary to fundamentally adjust the tooth surface micro-topography to solve them. The reconstruction process of the tooth surface micro-topography adjusts the micro-geometry of the tooth surface through precision machining technology, changes the contact stress distribution, and reduces the vibration and noise levels. When it is identified that the gear system is in the medium-risk area, the parameter optimization framework performs a dynamic compensation process for meshing parameters. The medium-risk area usually shows medium-level vibration and noise problems, which can be improved through the dynamic compensation of meshing parameters. The dynamic compensation process for meshing parameters compensates for errors and uncertainties in the system by adjusting meshing parameters in real time, such as modifying the tooth profile or tooth direction, and reduces the vibration and noise levels. When it is identified that the gear system is in the low-risk area, the parameter optimization framework performs a conventional optimization process for meshing parameters. The low-risk area usually shows minor vibration and noise problems, which can be solved through conventional parameter optimization. The conventional optimization process for meshing parameters fine-tunes the NVH characteristics of the system by optimizing basic meshing parameters, such as pressure angle, helix angle, and addendum coefficient, etc., and further reduces the vibration and noise levels.

[0044] In a specific embodiment, the reconstruction process of the tooth surface micro-topography includes: Measure the micro-topography of the tooth surface to obtain micro-topography data; Construct a three-dimensional micro-model of the tooth surface based on the micro-topography data; Calculate the contact stress distribution of the three-dimensional micro-model of the tooth surface to obtain a contact stress distribution map; Identify the stress concentration area according to the contact stress distribution map; Conduct micro-topography reconstruction design for the stress concentration area to generate an optimized micro-topography model; Based on the optimized micro-topography model, perform precision machining on the tooth surface to achieve tooth surface micro-topography reconstruction; The dynamic compensation process of meshing parameters includes: Obtain the real-time operation data of the gear system; Analyze the meshing state of the real-time operation data of the gear system to identify meshing deviations; Calculate the meshing parameter compensation amount based on the meshing deviation; Dynamically correct the meshing parameter compensation amount to generate dynamic compensation parameters; Feed the dynamic compensation parameters back to the gear manufacturing process to achieve dynamic compensation of meshing parameters; The conventional meshing parameter optimization process includes: Analyze the mapping relationship between the meshing parameters of the gear system and the NVH characteristics; Build a meshing parameter optimization model based on the mapping relationship; Perform multi-objective optimization calculation on the meshing parameter optimization model to obtain an optimized meshing parameter combination; Apply the optimized meshing parameter combination to the gear manufacturing process to achieve meshing parameter optimization.

[0045] Specifically, for the reconstruction of the tooth surface micro-topography, first use a high-precision measurement device (such as a white light interferometer, a confocal microscope, or an atomic force microscope, etc.) to measure the micro-topography of the tooth surface and obtain the micro-topography data of the tooth surface. The micro-topography data contains the micro-topography features of the tooth surface, such as surface roughness, waviness, and micro-profile, etc. Based on the micro-topography data, use 3D modeling technology to construct a 3D micro-model of the tooth surface. This model can accurately represent the micro-topography features of the tooth surface and provide a basis for subsequent stress analysis. Calculate the contact stress distribution of the 3D micro-model of the tooth surface, simulate the contact state during the gear meshing process, and obtain the contact stress distribution map. The calculation of the contact stress distribution adopts numerical calculation methods such as finite element analysis or boundary element analysis, considering factors such as the tooth surface micro-topography, material properties, and meshing conditions. According to the contact stress distribution map, identify the stress concentration areas, which are usually the main sources of vibration and noise. The identification of the stress concentration areas is based on the stress gradient analysis and threshold judgment method to determine the location and range of abnormal stress concentration. Conduct micro-topography reconstruction design for the stress concentration areas, optimize the contact stress distribution and reduce the stress concentration degree by adjusting the micro-topography features. The micro-topography reconstruction design adopts methods such as topology optimization, parametric design, and intelligent algorithms to generate an optimized micro-topography model. Based on the optimized micro-topography model, conduct precision machining treatment on the tooth surface to achieve the reconstruction of the tooth surface micro-topography. The precision machining treatment can adopt methods such as precision grinding, ultra-precision machining, laser surface treatment, or chemical mechanical polishing, and accurately control the micro-topography features of the tooth surface according to the requirements of the optimized micro-topography model.

[0046] For the dynamic compensation processing of meshing parameters, first, the real-time operation data of the gear system are obtained through sensors and monitoring systems, including vibration signals, noise signals, rotational speed signals, load signals, etc. The real-time operation data reflect the current operation state and meshing condition of the gear system. Analyze the meshing state of the real-time operation data of the gear system, and identify the deviations and abnormalities during the meshing process through signal processing and feature extraction methods. Meshing deviations include meshing angle deviation, meshing line deviation, meshing clearance deviation, etc., and these deviations will cause an increase in the vibration and noise of the gear system. Based on the identified meshing deviations, calculate the compensation amount of the meshing parameters to offset or reduce the influence of the deviations. The calculation of the meshing parameter compensation amount is based on meshing theory and dynamic models, considering the type, magnitude, and influence degree of the deviations, and accurately calculating the parameter values that need to be compensated. Dynamically correct the meshing parameter compensation amount, and dynamically adjust the compensation strategy and compensation amount according to the real-time operation state and environmental conditions to adapt to changing working conditions and requirements. Dynamic correction uses methods such as adaptive control, fuzzy control, or intelligent optimization to generate dynamic compensation parameters and achieve real-time adjustment of meshing parameters. Feed the dynamic compensation parameters back into the gear manufacturing process, and achieve dynamic compensation of meshing parameters through process parameter adjustment, machining path optimization, or post-processing techniques. The implementation of dynamic compensation can be achieved through parametric machining, on-line monitoring and control, or intelligent manufacturing systems, etc., to ensure the accuracy and stability of the compensation effect.

[0047] For the conventional optimization processing of meshing parameters, first analyze the mapping relationship between the meshing parameters of the gear system and NVH characteristics, and study the influence mechanism and laws of different meshing parameters on NVH performance. Meshing parameters include parameters such as module, pressure angle, addendum coefficient, tooth profile modification, and helix modification, and NVH characteristics include indicators such as vibration amplitude, noise level, and spectral characteristics. Through theoretical analysis, experimental research, and numerical simulation methods, establish the mapping relationship between meshing parameters and NVH characteristics to provide a theoretical basis for subsequent optimization. Based on the established mapping relationship, construct an optimization model of meshing parameters to describe the mathematical relationship between meshing parameters and NVH performance. The optimization model can adopt forms such as analytical models, numerical models, or data-driven models, and select appropriate modeling methods according to the complexity and accuracy requirements of the problem. Conduct multi-objective optimization calculations on the meshing parameter optimization model, considering multiple objectives such as NVH performance, transmission efficiency, load-carrying capacity, and service life, and find the optimal parameter combination. Multi-objective optimization calculations use methods such as genetic algorithms, particle swarm algorithms, or multi-objective evolutionary algorithms to obtain the optimized meshing parameter combination and achieve the balance and coordination between multiple objectives. Apply the optimized meshing parameter combination to the gear manufacturing process, and achieve the optimization of meshing parameters through process parameter adjustment, machining path optimization, or post-processing techniques. The implementation of optimization can be achieved through parametric design, precision machining, or intelligent manufacturing systems, etc., to ensure the practical feasibility and stability of the optimization effect.

[0048] The above describes a method for optimizing the NVH characteristics and noise control of a transmission gear in an embodiment of the present application. Next, a system for optimizing the NVH characteristics of a transmission gear in an embodiment of the present application will be described. Please refer to Figure 2 , an embodiment of a system for optimizing the NVH characteristics and noise control of a transmission gear in an embodiment of the present application includes: An acquisition and feature extraction module, configured to obtain multi-condition vibration and noise data of the transmission gear system; perform spectral decomposition and frequency band optimization processing on the multi-condition vibration and noise data to obtain a vibration and noise feature sequence of the gear system; An analysis and path identification module, configured to perform modal characteristic analysis on the vibration and noise feature sequence of the gear system to generate a multi-dimensional vibration modal feature matrix; perform transfer path optimization identification on the multi-dimensional vibration modal feature matrix to generate a vibration transfer path model of the gear system; A modeling and parameterization module, configured to perform microscopic contact dynamics modeling on the vibration transfer path model of the gear system to obtain a gear meshing dynamic contact model; perform non-linear parameterization processing on the gear meshing dynamic contact model to obtain a gear meshing non-linear parameterization model; An accuracy portrait construction module, configured to obtain gear manufacturing process parameter data; mine accuracy influencing factors from the gear manufacturing process parameter data to construct a gear manufacturing accuracy portrait; An analysis and optimization decision module, configured to perform NVH key influencing factor analysis on the gear manufacturing accuracy portrait and perform adaptive optimization decision-making to construct a gear manufacturing process optimization strategy; A regulation strategy implementation module, based on the gear manufacturing process optimization strategy, dynamically adjusts the parameters of the gear meshing non-linear parameterization model to construct an NVH optimization control model for the transmission gear; each module is connected by wired and / or wireless means to achieve data transmission between modules.

[0049] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0050] In the embodiments of the present application, by acquiring the vibration and noise data of the transmission gear system under multiple working conditions, performing spectrum decomposition and frequency band optimization processing on the data, the vibration and noise characteristic sequence of the gear system is obtained, providing basic data for subsequent analysis; by performing modal characteristic analysis and transfer path optimization identification on the characteristic sequence, a vibration transfer model of the gear system is established, revealing the vibration energy transfer law; by establishing a microscopic contact dynamics model and performing non-linear parameterization processing, a dynamic contact model of gear meshing is established, deeply understanding the dynamic characteristics during gear meshing; by acquiring the manufacturing process parameter data, excavating the accuracy influencing factors, a gear manufacturing accuracy portrait is constructed, revealing the relationship between process parameters and accuracy; by analyzing the key influencing factors of NVH and making an adaptive optimization decision, an optimization strategy for the gear manufacturing process is constructed, providing guidance for NVH optimization; by dynamically adjusting the dynamic parameters of the non-linear parameterization model of gear meshing, an NVH optimization control model of the transmission gear is constructed, realizing precise control of the NVH characteristics of the gear. This method forms a complete technical system from data acquisition, feature extraction, model establishment to optimization control, which can effectively solve the vibration and noise problems of the transmission gear and improve the product quality and use comfort.

[0051] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent substitution on some of the technical features. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0052] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0053] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0054] In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0055] In the description of the present invention, the meaning of "several" is one or more, and the meaning of "a large number" is two or more.

[0056] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0057] For the formulas in this specification, the dimensional quantities are removed and only the numerical values are calculated. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0058] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for optimizing NVH characteristics and controlling noise of a transmission gear, characterized in that: include: Step S1: Acquire multi-operating condition vibration and noise data of the transmission gear system; Perform spectrum decomposition and frequency band optimization on vibration and noise data of multiple working conditions to obtain the vibration and noise characteristic sequence of the gear system; Step S2: performing modal characteristic analysis on the vibration noise characteristic sequence of the gear system to generate a multi-dimensional vibration modal characteristic matrix; performing transmission path optimization identification on the multi-dimensional vibration modal characteristic matrix to generate a gear system vibration transmission path model; Step S3: performing micro-contact dynamics modeling on the gear system vibration transmission path model to obtain a gear meshing dynamic contact model; performing nonlinear parameterization processing on the gear meshing dynamic contact model to obtain a gear meshing nonlinear parameterized model; Step S4: Acquire gear manufacturing process parameter data; Mining the factors affecting the accuracy of gear manufacturing process parameter data and constructing a gear manufacturing accuracy portrait; Step S5: Analyze the key influencing factors of NVH on the gear manufacturing accuracy portrait, make adaptive optimization decisions, and build a gear manufacturing process optimization strategy; Step S6: Based on the gear manufacturing process optimization strategy, the gear meshing nonlinear parameterized model is dynamically adjusted to construct a transmission gear NVH optimization control model.

2. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S1 are: Acquire multi-operating condition vibration and noise data of the transmission gear system; perform time domain signal and frequency domain signal separation processing on the multi-operating condition vibration and noise data to obtain a time domain data set and a frequency domain data set; Perform instantaneous feature extraction on the time domain data set to identify transient impact feature points; perform time domain envelope analysis on the time domain data set based on the transient impact feature points to obtain time domain feature data of the gear system; Performing harmonic separation and frequency band optimization processing on the frequency domain data set to generate frequency domain optimized feature data; The gear system's time-domain characteristic data and frequency-domain optimized characteristic data are processed by time-frequency joint analysis to obtain the gear system's vibration noise characteristic sequence.

3. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 2, characterized in that: The performing harmonic separation and frequency band optimization processing on the frequency domain data set to generate frequency domain optimized feature data includes: The meshing frequency characteristics of the frequency domain data set are analyzed to obtain the gear meshing frequency characteristics; Extract the harmonic components according to the gear meshing frequency characteristics and obtain the gear system harmonic sequence; Conduct frequency distribution analysis on the harmonic sequence of the gear system and generate harmonic distribution data; performing parameterized frequency band energy statistics on the frequency domain data set based on the harmonic distribution data to generate parameterized frequency band energy distribution; Based on the parameterized frequency band energy distribution, the abnormal frequency bands of the frequency domain data set are identified and the abnormal frequency band points are marked; The abnormal frequency band points are subjected to frequency band optimization processing to generate frequency domain optimized feature data.

4. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S2 are: Perform multi-dimensional modal decomposition on the gear system vibration noise characteristic sequence to generate vibration sequences of different modes; Perform modal characteristic analysis on vibration sequences of different modes to obtain multiple vibration modal characteristics; Perform modal superposition synthesis based on multiple vibration modal features to generate a multi-dimensional vibration modal feature matrix; Perform transfer function calculation on the multi-dimensional vibration modal characteristic matrix to obtain vibration transfer function data; Perform structural response sensitivity analysis on the multi-dimensional vibration modal characteristic matrix to generate a structural response sensitivity matrix; Based on the vibration transfer function data, the transmission path of the structural response sensitivity matrix is ​​optimized and identified to generate a vibration transfer path model of the gear system.

5. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S3 are: Calculate the tooth surface contact stiffness of the gear system vibration transmission path model and obtain the tooth surface contact stiffness distribution; The micro-contact dynamics modeling is carried out on the tooth surface contact stiffness distribution to obtain the gear meshing dynamic contact model; the meshing parameter sensitivity analysis is carried out on the gear meshing dynamic contact model to obtain the meshing parameter sensitivity network; The meshing parameter sensitivity network is processed nonlinearly and parameterized to obtain a nonlinear parameterized model of gear meshing.

6. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S4 are: Monitor the entire gear manufacturing process and obtain gear manufacturing process parameter data; Conduct process correlation analysis on gear manufacturing process parameter data to generate process correlation data; Mining the factors affecting precision on process correlation data to generate factors affecting manufacturing precision; Perform global portrait fitting of factors affecting manufacturing accuracy and construct a gear manufacturing accuracy portrait.

7. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 6, characterized in that: The process of mining the precision influencing factors on the process correlation data to generate the manufacturing precision influencing factors includes: Extract multiple key process parameters based on process correlation data; Calculate the parameter sensitivity of gear manufacturing process parameter data according to multiple key process parameters to obtain process parameter sensitivity; Perform statistics on the accuracy contribution of each process on the process correlation data to obtain the accuracy contribution value of each process; According to the sensitivity of process parameters, the precision transfer chain analysis is performed on the precision contribution value of each process to obtain the precision transfer characteristic data; Identify critical control points of manufacturing processes based on process correlation data; Performing process parameter distribution analysis based on the critical control points of the manufacturing process to obtain process parameter distribution data; A comprehensive influencing factor analysis is performed on the process parameter distribution data and precision transfer characteristic data to generate manufacturing precision influencing factors.

8. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S5 are: Extract key precision features from gear manufacturing precision portraits to obtain key precision feature data; Perform NVH characteristic full spectrum analysis on the gear system vibration and noise characteristic sequence to obtain NVH characteristic full spectrum data; Conduct correlation analysis on key precision feature data based on the full spectrum of NVH characteristics to obtain NVH key influencing factor data; Quantify the contribution of key NVH influencing factor data and generate NVH influencing factor contribution values; Adaptive optimization decisions are made based on the contribution values ​​of NVH influencing factors, and a gear manufacturing process optimization strategy is constructed.

9. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 1, characterized in that: The specific steps of step S6 are: Perform parameter sensitivity analysis on the nonlinear parameterized model of gear meshing and generate a parameter sensitivity matrix; perform optimization target mapping on the parameter sensitivity matrix based on the gear manufacturing process optimization strategy and build a parameter optimization framework; Dynamically adjust the parameters of the parameter optimization framework and build a transmission gear NVH optimization control model; The dynamic parameter adjustment is specifically as follows: identifying the NVH risk level of the current gear system based on the gear manufacturing process optimization strategy; The NVH risk levels include: high risk area, medium risk area and low risk area; When the NVH risk level of the gear system is in the high-risk area, the parameter optimization framework performs a micro-morphology reconstruction process on the tooth surface; When the NVH risk level of the gear system is in the medium risk area, the parameter optimization framework performs dynamic compensation processing of meshing parameters; When the NVH risk level of the gear system is in the low risk area, the parameter optimization framework performs conventional meshing parameter optimization processing.

10. The method for optimizing NVH characteristics and controlling noise of transmission gears according to claim 9, characterized in that: The tooth surface micro-morphology reconstruction process comprises: Conduct micro-topological morphology measurement on the tooth surface to obtain micro-topological morphology data; Construct a three-dimensional microscopic model of the tooth surface based on microscopic topological data; Calculate the contact stress distribution on the three-dimensional microscopic model of the tooth surface and obtain the contact stress distribution diagram; Identify stress concentration areas based on contact stress distribution diagrams; Reconstruct the micro-morphology of the stress concentration area and generate an optimized micro-morphology model; Based on the optimized micro-morphology model, the tooth surface is precisely processed to achieve micro-morphology reconstruction of the tooth surface; The meshing parameter dynamic compensation process includes: Obtain real-time operation data of the gear system; Analyze the meshing state of the gear system's real-time operating data and identify meshing deviations; Calculate meshing parameter compensation based on meshing deviation; Dynamically correct the meshing parameter compensation amount to generate dynamic compensation parameters; Feedback dynamic compensation parameters to the gear manufacturing process to achieve dynamic compensation of meshing parameters; The conventional meshing parameter optimization process includes: Analyze the mapping relationship between the meshing parameters of the gear system and the NVH characteristics; Construct meshing parameter optimization model based on mapping relationship; Perform multi-objective optimization calculation on the meshing parameter optimization model to obtain the optimized meshing parameter combination; The optimized meshing parameter combination is applied to the gear manufacturing process to achieve meshing parameter optimization.

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