A Method for Optimizing the NVH Characteristics and Controlling the Noise of a Transmission Gear

Abstract

The present invention belongs to the technical field of transmission gears, and discloses a method for optimizing the NVH characteristics and controlling the noise of transmission gears; it includes: obtaining the vibration and noise data of the transmission gear system under multiple working conditions, performing spectrum decomposition and frequency band optimization processing to obtain the vibration and noise characteristic sequence of the gear system; then analyzing the modal characteristics of the characteristic sequence, constructing a vibration transmission path model, further modeling and processing to establish a gear meshing dynamic contact model; at the same time, obtaining the gear manufacturing process parameter data and constructing a manufacturing accuracy profile; on this basis, performing NVH key influencing factor analysis and adaptive optimization decision-making to construct a process optimization strategy; finally, implementing dynamic parameter adjustment according to the NVH risk level. This method combines multi-condition data analysis with micro-contact dynamics, establishes an association mechanism, and realizes a complete technical system from data acquisition to optimization control.

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] Currently, for the research on the NVH characteristics of gear transmission systems, there are studies on the vibration characteristics during gear meshing by establishing gear dynamics models, 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 effects are 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 are generally problems with unsatisfactory vibration and noise control effects. 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 costs are 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 and severely restricting the technological progress and product upgrading 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 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:

[0007] 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;

[0008] 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 transfer path optimization identification on the multi-dimensional vibration modal characteristic matrix to generate a vibration transfer path model of the gear system;

[0009] Step S3: 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;

[0010] 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 portrait;

[0011] Step S5: 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;

[0012] 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.

[0013] Preferably, the specific steps of Step S1 are:

[0014] 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;

[0015] Extract instantaneous characteristics from the time-domain data set to identify transient impact characteristic points; perform time-domain envelope analysis on the time-domain data set according to the transient impact characteristic points to obtain the time-domain characteristic data of the gear system;

[0016] Perform harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized characteristic data;

[0017] 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.

[0018] Preferably, the harmonic separation and frequency band optimization processing of the frequency domain data set to generate frequency domain optimized feature data includes:

[0019] Analyze the meshing frequency characteristics of the frequency domain data set to obtain the gear meshing frequency characteristics;

[0020] Extract harmonic components according to the gear meshing frequency characteristics to obtain the gear system harmonic sequence;

[0021] Analyze the frequency distribution of the gear system harmonic sequence to generate harmonic distribution data;

[0022] 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;

[0023] Based on the parametric frequency band energy distribution, identify abnormal frequency bands in the frequency domain data set and mark abnormal frequency band points;

[0024] Perform frequency band optimization processing on the abnormal frequency band points to generate frequency domain optimized feature data.

[0025] Preferably, the specific steps of step S2 are:

[0026] Perform multi-dimensional modal decomposition on the gear system vibration and noise characteristic sequence to generate vibration sequences of different modes;

[0027] Analyze the modal characteristics of the vibration sequences of different modes to obtain multiple vibration modal characteristics;

[0028] Based on multiple vibration modal characteristics, perform modal superposition synthesis to generate a multi-dimensional vibration modal characteristic matrix;

[0029] Calculate the transfer function of the multi-dimensional vibration modal characteristic matrix to obtain vibration transfer function data;

[0030] Perform structural response sensitivity analysis on the multi-dimensional vibration modal characteristic matrix to generate a structural response sensitivity matrix;

[0031] Based on the vibration transfer function data, perform transfer path optimization identification on the structural response sensitivity matrix to generate a gear system vibration transfer path model.

[0032] Preferably, the specific steps of step S3 are:

[0033] Calculate the tooth surface contact stiffness of the gear system vibration transfer path model to obtain the tooth surface contact stiffness distribution;

[0034] 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;

[0035] Perform non - linear parameterization on the meshing parameter sensitivity network to obtain a non - linear parameterized model of gear meshing.

[0036] Preferably, the specific steps of step S4 are as follows:

[0037] Monitor the entire gear manufacturing process flow to obtain gear manufacturing process parameter data;

[0038] Perform process - relatedness analysis on the gear manufacturing process parameter data to generate process - relatedness data;

[0039] Mine the accuracy - influencing factors from the process - relatedness data to generate manufacturing accuracy - influencing factors;

[0040] Perform global portrait fitting on the manufacturing accuracy - influencing factors to construct a gear manufacturing accuracy portrait.

[0041] Preferably, the mining of the accuracy - influencing factors from the process - relatedness data to generate manufacturing accuracy - influencing factors includes:

[0042] Extract multiple key process parameters based on the process - relatedness data;

[0043] Calculate the parameter sensitivity of the gear manufacturing process parameter data according to the multiple key process parameters to obtain the process parameter sensitivity;

[0044] Statistically analyze the accuracy contribution of each process to the process - relatedness data to obtain the accuracy contribution value of each process;

[0045] Perform accuracy transfer chain analysis on the accuracy contribution value of each process according to the process parameter sensitivity to obtain accuracy transfer characteristic data;

[0046] Identify the key control points of the manufacturing process based on the process - relatedness data;

[0047] Perform process parameter distribution analysis based on the key control points of the manufacturing process to obtain process parameter distribution data;

[0048] Perform comprehensive influence factor analysis on the process parameter distribution data and the accuracy transfer characteristic data to generate manufacturing accuracy - influencing factors.

[0049] Preferably, the specific steps of step S5 are as follows:

[0050] Extract key accuracy features from the gear manufacturing accuracy portrait to obtain key accuracy feature data;

[0051] Perform full - spectrum analysis of the NVH characteristics on the gear system vibration and noise characteristic sequence to obtain NVH characteristic full - spectrum data;

[0052] Perform a correlation analysis on the key precision feature data based on the full-spectrum data of NVH characteristics to obtain the NVH key influencing factor data;

[0053] Quantify the contribution degree of the NVH key influencing factor data to generate the NVH influencing factor contribution degree value;

[0054] Based on the NVH influencing factor contribution degree value, perform an adaptive optimization decision to construct an optimization strategy for the gear manufacturing process.

[0055] Preferably, the specific steps of step S6 are as follows:

[0056] 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 target mapping on the parameter sensitivity matrix to construct a parameter optimization framework;

[0057] Perform dynamic parameter adjustment on the parameter optimization framework to construct an NVH optimization control model for the transmission gear;

[0058] The specific dynamic parameter adjustment is: based on the gear manufacturing process optimization strategy, identify the NVH risk level of the current gear system;

[0059] The NVH risk levels include: high-risk area, medium-risk area, and low-risk area;

[0060] 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;

[0061] 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;

[0062] 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.

[0063] Preferably, the tooth surface micro-topography reconstruction process includes:

[0064] Measure the micro-topography of the tooth surface to obtain micro-topography data;

[0065] Based on the micro-topography data, construct a three-dimensional micro-model of the tooth surface;

[0066] Calculate the contact stress distribution of the three-dimensional micro-model of the tooth surface to obtain a contact stress distribution map;

[0067] Identify the stress concentration area according to the contact stress distribution map;

[0068] Perform a micro-topography reconstruction design on the stress concentration area to generate an optimized micro-topography model;

[0069] Precisely process the tooth surface based on an optimized micro-topography model to achieve the reconstruction of the tooth surface micro-topography;

[0070] The dynamic compensation process of the meshing parameters includes:

[0071] Obtain the real-time operation data of the gear system;

[0072] Analyze the meshing state of the real-time operation data of the gear system to identify meshing deviations;

[0073] Calculate the compensation amount of the meshing parameters based on the meshing deviations;

[0074] Dynamically correct the compensation amount of the meshing parameters to generate dynamic compensation parameters;

[0075] Feed the dynamic compensation parameters back to the gear manufacturing process to achieve the dynamic compensation of the meshing parameters;

[0076] The conventional optimization process of the meshing parameters includes:

[0077] Analyze the mapping relationship between the meshing parameters of the gear system and the NVH characteristics;

[0078] Build an optimization model of the meshing parameters based on the mapping relationship;

[0079] Perform multi-objective optimization calculations on the optimization model of the meshing parameters to obtain an optimized combination of meshing parameters;

[0080] Apply the optimized combination of meshing parameters to the gear manufacturing process to achieve the optimization of the meshing parameters.

[0081] The technical effects and advantages of a method for optimizing the NVH characteristics and controlling the noise of a transmission gear according to the present invention:

[0082] 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 improve 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 the manufacturing process and performance indicators is achieved, and the manufacturing accuracy and quality stability of gear products are greatly improved. It can not only reduce the energy loss of the gear transmission system, extend the 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 at the same time reduce the maintenance cost. Brief Description of the Drawings

[0083] Figure 1 It 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;

[0084] Figure 2 Schematic diagram of an NVH characteristic optimization and noise control system for a transmission gear of the present invention. Specific implementation manners

[0085] 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.

[0086] For easy understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 One embodiment of the NVH characteristic optimization and noise control method for the transmission gear in the embodiments of the present application includes:

[0087] Step S1: Obtain the vibration and noise data of the transmission gear system under multiple working conditions; perform spectral decomposition and frequency band optimization processing on the vibration and noise data under multiple working conditions to obtain the vibration and noise characteristic sequence of the gear system;

[0088] It can be understood that the execution subject of the present application can be an NVH characteristic optimization and noise control system for a transmission gear, or a terminal or a server. Specifically, no limitation is made here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0089] Specifically, obtain the vibration and noise data of the transmission gear system under different working conditions, including vibration and noise signals under different rotational speeds, different loads, different temperatures, etc. Perform spectral analysis on the obtained vibration and noise data under multiple working conditions to convert the time-domain signal into a frequency-domain signal for better analysis of the vibration characteristics of the gear system. According to the characteristics of the gear system, decompose the spectrum to separate the frequency components related to gear meshing. Perform optimization processing for different frequency bands to extract the frequency band information that can best represent the vibration and noise characteristics of the gear system. Based on the optimized frequency band information, construct the vibration and noise characteristic sequence of the gear system to provide basic data for subsequent analysis.

[0090] 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 transfer path optimization identification on the multi-dimensional vibration modal characteristic matrix to generate a vibration transfer path model of the gear system;

[0091] Specifically, perform 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. Analyze the characteristics of 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 mode characteristic matrix to comprehensively characterize the vibration characteristics of the gear system. Based on the multi-dimensional vibration mode 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.

[0092] Step S3: Conduct microscopic contact dynamics modeling on the gear system vibration transmission path model to obtain a gear meshing dynamic contact model; perform non-linear parameterization on the gear meshing dynamic contact model to obtain a gear meshing non-linear parameterized model;

[0093] 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 for 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 gear meshing dynamic contact model 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 gear meshing dynamic contact model to transform the complex non-linear characteristics into a controllable parameter model. Through parameterization, obtain a gear meshing non-linear parameterized model to provide a basis for subsequent optimization control.

[0094] Step S4: Obtain gear manufacturing process parameter data; mine the accuracy influencing factors of the gear manufacturing process parameter data to construct a gear manufacturing accuracy profile;

[0095] Specifically, collect the process parameter data during the gear manufacturing process, including information such as processing methods, cutting parameters, heat treatment processes, and surface treatment processes. Analyze the influence of different process parameters on the gear manufacturing accuracy and identify the key accuracy influencing factors. Through data mining techniques, quantify the influence degree of each process parameter on the gear accuracy. Based on the accuracy influencing factors, construct a gear manufacturing accuracy profile to comprehensively describe the manufacturing accuracy characteristics of the gear. This accuracy profile contains information in multiple dimensions such as the geometric accuracy, surface quality, and material properties of the gear.

[0096] Step S5: Analyze the key NVH influencing factors of the gear manufacturing precision profile, make an adaptive optimization decision, and construct an optimization strategy for the gear manufacturing process;

[0097] Specifically, based on the gear manufacturing precision profile, analyze the influence of different precision characteristics on the NVH performance of the gear. 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 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 NVH influencing factors, formulate an adaptive optimization decision-making strategy. This strategy can automatically adjust the optimization objectives and optimization methods according to different NVH requirements. Based on the adaptive optimization decision, construct an optimization strategy for the gear manufacturing process to guide the improvement and optimization of the gear manufacturing process.

[0098] Step S6: Dynamically adjust the parameters of the gear meshing non-linear parametric model based on the gear manufacturing process optimization strategy, and construct an NVH optimization control model for the transmission gear.

[0099] 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 degree of influence of parameter changes on the NVH performance. Based on the sensitivity analysis results, dynamically adjust the parameters to optimize the NVH performance of the gear system. Through parameter adjustment, construct an NVH optimization control model for the transmission gear to achieve precise control of the NVH performance of the gear. 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.

[0100] In the embodiment of the present application, through in-depth analysis of the multi-condition vibration and noise data of the transmission gear system, a vibration and noise model based on modal characteristics and transmission paths is established to achieve precise characterization of the NVH characteristics of the gear system. Through microscopic contact dynamics modeling and non-linear parametric processing, the dynamic contact characteristics during gear meshing are deeply analyzed, providing a theoretical basis for NVH optimization. Combining the analysis of gear manufacturing process parameters, a gear manufacturing precision profile is constructed to reveal the internal relationship between manufacturing precision and NVH performance. Through the analysis of key NVH influencing factors and adaptive optimization decision-making, a targeted optimization strategy for the gear manufacturing process is formulated. Finally, based on the gear manufacturing process optimization strategy, the parameters of the gear meshing non-linear parametric model are dynamically adjusted to construct an NVH optimization control model for the transmission gear, achieving precise control of the NVH performance of the gear system.

[0101] In a specific embodiment, the process of executing step S1 specifically includes the following steps:

[0102] 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;

[0103] 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;

[0104] Perform harmonic separation and frequency band optimization processing on the frequency-domain data set to generate frequency-domain optimized characteristic data;

[0105] 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.

[0106] Specifically, vibration and noise signals of the transmission gear system under various working conditions such as different rotational speeds, different loads, and different temperatures are collected through 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 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 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 impacts, defect collisions, or abnormal operating states, 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 through methods such as Hilbert transform or wavelet transform, and the envelope is spectrally analyzed 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 frequency-domain optimized feature data of the gear system are subjected to time-frequency joint analysis processing. Time-frequency joint analysis can analyze 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.

[0107] 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:

[0108] Perform meshing frequency feature analysis on the frequency-domain data set to obtain gear meshing frequency features;

[0109] Extract harmonic components according to the gear meshing frequency features to obtain a gear system harmonic sequence;

[0110] Perform frequency distribution analysis on the gear system harmonic sequence to generate harmonic distribution data;

[0111] Perform parametric band energy statistics on the frequency domain data set based on the harmonic distribution data to generate a parametric band energy distribution;

[0112] Identify abnormal frequency bands in the frequency domain data set based on the parametric band energy distribution, and mark the abnormal frequency band points;

[0113] Perform frequency band optimization processing on the abnormal frequency band points to generate frequency domain optimized feature data.

[0114] Specifically, first perform meshing frequency feature analysis on the frequency domain data set. 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 spectrum to obtain the gear meshing frequency feature. According to the identified meshing frequency feature, extract the harmonic components in the 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 harmonics, 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 band energy statistics on the frequency domain data set. Divide the 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 a parametric band energy distribution. Based on the parametric band energy distribution, identify abnormal frequency bands in the frequency domain data set. 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.

[0115] In a specific embodiment, the process of executing step S2 specifically includes the following steps:

[0116] Perform multi-dimensional modal decomposition on the gear system vibration noise feature sequence to generate vibration sequences of different modes;

[0117] Perform modal characteristic analysis on vibration sequences of different modes to obtain multiple vibration modal characteristics;

[0118] Based on multiple vibration modal characteristics, perform modal superposition synthesis to generate a multi-dimensional vibration modal characteristic matrix;

[0119] Calculate the transfer function of the multi-dimensional vibration modal characteristic matrix to obtain vibration transfer function data;

[0120] Perform structural response sensitivity analysis on the multi-dimensional vibration modal characteristic matrix to generate a structural response sensitivity matrix;

[0121] Based on the vibration transfer function data, perform transfer path optimization identification on the structural response sensitivity matrix to generate a vibration transfer path model of the gear system.

[0122] Specifically, first, perform 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. Conduct 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 modal characteristics. 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 modal characteristics, perform 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 modal characteristic matrix. This matrix contains information such as the eigenvalues, eigenvectors, and participation factors of each order mode of the system. Calculate the transfer function for the multi-dimensional vibration modal characteristic 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. Conduct structural response sensitivity analysis on the multi-dimensional vibration modal characteristic matrix. Sensitivity analysis can evaluate the degree of influence of system parameter changes on the response and helps to identify key transfer paths. By calculating the partial derivatives of the structural parameters with respect to the modal 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, optimize and identify the transfer paths for the structural response sensitivity matrix. The purpose of optimizing and identifying the transfer paths is to identify the main several paths from numerous possible transfer paths to simplify 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 for the gear system.

[0123] In a specific embodiment, the process of executing step S3 may specifically include the following steps:

[0124] Calculate the tooth surface contact stiffness for the vibration transfer path model of the gear system to obtain the tooth surface contact stiffness distribution;

[0125] Conduct microscopic contact dynamics modeling on the tooth surface contact stiffness distribution to obtain a gear meshing dynamic contact model;

[0126] Conduct meshing parameter sensitivity analysis on the gear meshing dynamic contact model to obtain a meshing parameter sensitivity network;

[0127] Perform non-linear parameterization processing on the meshing parameter sensitivity network to obtain a non-linear parameterized model of gear meshing.

[0128] 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 gears (such as module, pressure angle, and tooth width, etc.) and microscopic parameters (such as surface roughness and surface hardness, etc.), calculate the change in the contact stiffness during the meshing process of the tooth surface to obtain the tooth surface contact stiffness distribution. Conduct microscopic contact dynamics modeling on 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 the gear meshing process. 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 the gear meshing process, including normal contact force, tangential friction force, and micro-sliding and other phenomena, to obtain the gear meshing dynamic contact model. Conduct sensitivity analysis of the meshing parameters for 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 change in the system dynamic response, calculate the sensitivity coefficients of each parameter, and construct a meshing parameter sensitivity network. This network describes the mutual relationship between each meshing parameter and the degree of influence on the system response. Conduct non-linear parameterization processing on the meshing parameter sensitivity network. The non-linear parameterization processing aims to establish a parameterized 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 the gear meshing non-linear parameterized model. This model can adapt to the gear meshing state under different working conditions by adjusting parameters and provides a basis for subsequent optimal control.

[0129] In a specific embodiment, the process of executing step S4 may specifically include the following steps:

[0130] Monitor the entire gear manufacturing process flow to obtain gear manufacturing process parameter data;

[0131] Conduct process relevance analysis on the gear manufacturing process parameter data to generate process relevance data;

[0132] Mine the accuracy influencing factors from the process relevance data to generate manufacturing accuracy influencing factors;

[0133] Conduct global portrait fitting on the manufacturing accuracy influencing factors to construct a gear manufacturing accuracy portrait.

[0134] Specifically, first, monitor the entire process of gear manufacturing 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), monitor and record the process parameters of each process in real time, such as material hardness, heat treatment temperature, cutting speed, cutting depth, grinding parameters, and clamping force, 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 exploration of accuracy influencing factors. Through 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 accuracy influencing factors from the process correlation data. Accuracy influencing factor mining aims to identify key factors that have a significant impact on gear manufacturing accuracy from numerous process parameters. Through methods such as variance analysis, principal component analysis, and random forest, evaluate the influence degree of each process parameter on gear accuracy, identify the main influencing factors and secondary influencing factors, and generate manufacturing accuracy influencing factors. Perform global portrait fitting on the manufacturing accuracy influencing factors to construct a gear manufacturing accuracy portrait. The accuracy portrait is a comprehensive description of the relationship between various influencing factors and the final accuracy during gear manufacturing, which can guide subsequent process optimization. By establishing mathematical models or machine learning models, such as multiple regression models, support vector machines, or neural networks, fit the mapping relationship between manufacturing accuracy influencing factors and gear accuracy, and construct a gear manufacturing accuracy portrait. This portrait can predict the gear accuracy level under different process parameter combinations, providing a decision-making basis for process optimization.

[0135] In a specific embodiment, the process of mining accuracy influencing factors from the process correlation data to generate manufacturing accuracy influencing factors specifically includes the following steps:

[0136] Extract multiple key process parameters based on the process correlation data;

[0137] Calculate the parameter sensitivity of the gear manufacturing process parameter data according to the multiple key process parameters to obtain the process parameter sensitivity;

[0138] Statistically analyze the accuracy contribution of each process to the process correlation data to obtain the accuracy contribution value of each process;

[0139] Conduct accuracy transfer chain analysis on the accuracy contribution value of each process according to the process parameter sensitivity to obtain accuracy transfer characteristic data;

[0140] Identify key control points of the manufacturing process based on the process correlation data;

[0141] Based on the key control points of the manufacturing process, analyze the distribution of process parameters to obtain the process parameter distribution data;

[0142] Conduct a comprehensive influence factor analysis on the process parameter distribution data and the accuracy transfer characteristic data to generate the manufacturing accuracy influence factors.

[0143] 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., 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 extracted multiple key process parameters, the parameter sensitivity of the gear manufacturing process parameter data is calculated. Parameter sensitivity describes the degree of influence of process parameter changes on gear accuracy and is an important indicator for evaluating parameter importance. By designing experiments or numerical simulations, while keeping other parameters unchanged, the value of a single parameter is changed, and the change in gear accuracy is observed. The parameter sensitivity S = ΔP / Δx is calculated, where ΔP is the change in accuracy and Δx is the change in the parameter, 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 key processes. Through statistical analysis or machine learning methods, such as variance decomposition or random forest feature importance, etc., the contribution of each process to gear accuracy is calculated to obtain the accuracy contribution value of each process. According to the process parameter sensitivity, an accuracy transfer chain analysis is performed on the accuracy contribution value of each process. The accuracy transfer chain describes the transfer law of accuracy errors between processes and is an important tool for understanding the accuracy formation mechanism. By establishing an accuracy transfer model, analyzing how the accuracy errors of upstream processes affect the machining accuracy of downstream processes, identifying the key links and bottlenecks of 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 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, a process parameter distribution analysis is performed. The parameter distribution analysis aims to study the variation law and distribution characteristics of process parameters and provides a basis for subsequent factor analysis. Through statistical analysis methods, such as histogram analysis, probability density function estimation, and box plot analysis, etc., the distribution characteristics of 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. A comprehensive influence factor analysis is performed on the process parameter distribution data and the accuracy transfer characteristic data. The comprehensive influence factor analysis aims to comprehensively evaluate the influence of various factors on 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, the manufacturing accuracy influence factors are generated.

[0144] In a specific embodiment, the process of executing step S5 specifically includes the following steps:

[0145] Extract key precision features from the gear manufacturing precision image to obtain key precision feature data;

[0146] Conduct a full-spectrum analysis of the NVH characteristics of the gear system vibration noise feature sequence to obtain full-spectrum NVH characteristic data;

[0147] 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;

[0148] Quantify the contribution degree of the NVH key influencing factor data to generate the NVH influencing factor contribution degree value;

[0149] Make an adaptive optimization decision based on the NVH influencing factor contribution degree value and construct an optimization strategy for the gear manufacturing process.

[0150] Specifically, first, key precision features are extracted from the gear manufacturing precision portrait. Key precision features are precision parameters that have a significant impact on the NVH characteristics of gears, such as tooth profile error, helix error, pitch error, and radial runout. Through methods such as feature importance analysis or principal component analysis, key precision features are extracted from the gear manufacturing precision portrait to obtain key precision feature data. Perform a full-spectrum analysis of the NVH characteristics on the vibration and noise feature sequence of the gear system. The full-spectrum analysis of NVH characteristics aims to comprehensively analyze the vibration and 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, the vibration and noise feature sequence is processed to obtain full-spectrum data of NVH characteristics, including spectrograms, order spectra, and time-frequency spectrograms. Conduct a correlation analysis on the key precision feature data based on the full-spectrum data of NVH characteristics. 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 size 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 the feature importance of machine learning models, calculate the contribution degree of each influencing factor to the NVH characteristics and generate NVH influencing factor contribution degree values. Make an adaptive optimization decision based on the NVH influencing factor contribution degree values. The adaptive optimization decision can dynamically adjust the optimization strategy according to specific situations to achieve precise control of the NVH characteristics of gears. Through methods such as multi-objective optimization, genetic algorithms, or reinforcement learning, comprehensively consider factors such as NVH target requirements, manufacturing costs, and production efficiency, and construct an optimization strategy for gear manufacturing processes. This strategy can adaptively adjust process parameters according to the NVH target requirements of different products to achieve optimized control of the NVH characteristics of gears.

[0151] In a specific embodiment, the process of executing step S6 specifically includes the following steps:

[0152] Perform a parameter sensitivity analysis on the gear meshing non-linear parametric model to generate a parameter sensitivity matrix;

[0153] Based on the gear manufacturing process optimization strategy, perform an optimization target mapping on the parameter sensitivity matrix to construct a parameter optimization framework;

[0154] Perform dynamic parameter adjustment on the parameter optimization framework to construct a transmission gear NVH optimization control model;

[0155] The dynamic parameter adjustment is specifically: based on the gear manufacturing process optimization strategy, identify the NVH risk level of the current gear system;

[0156] The NVH risk levels include: high-risk areas, medium-risk areas, and low-risk areas;

[0157] 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;

[0158] When the NVH risk level of the gear system is in the medium-risk area, the parameter optimization framework performs dynamic compensation of meshing parameters;

[0159] When the NVH risk level of the gear system is in the low-risk area, the parameter optimization framework performs conventional meshing parameter optimization.

[0160] Specifically, first, a parameter sensitivity analysis is conducted on the non-linear parametric model of gear meshing. The parameter sensitivity analysis aims to evaluate the influence degree 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 targets of model parameters, and a parameter optimization framework is constructed. Dynamic parameter adjustment is carried out 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, factors such as vibration and noise levels, spectral characteristics, and transmission paths are comprehensively considered to 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 the errors and uncertainties in the system by real-time adjusting meshing parameters, 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.

[0161] In a specific embodiment, the reconstruction process of the tooth surface micro-topography includes:

[0162] Measure the micro-topography of the tooth surface to obtain micro-topography data;

[0163] Construct a three-dimensional micro-model of the tooth surface based on the micro-topography data;

[0164] Calculate the contact stress distribution of the three-dimensional microscopic model of the tooth surface to obtain the contact stress distribution map;

[0165] Identify the stress concentration area according to the contact stress distribution map;

[0166] Carry out microscopic topography reconstruction design for the stress concentration area to generate an optimized microscopic topography model;

[0167] Based on the optimized microscopic topography model, carry out precision machining treatment on the tooth surface to realize the reconstruction of the tooth surface microscopic topography;

[0168] The dynamic compensation process of the meshing parameters includes:

[0169] Obtain the real-time operation data of the gear system;

[0170] Analyze the meshing state of the real-time operation data of the gear system to identify the meshing deviation;

[0171] Calculate the meshing parameter compensation amount based on the meshing deviation;

[0172] Dynamically correct the meshing parameter compensation amount to generate dynamic compensation parameters;

[0173] Feed back the dynamic compensation parameters to the gear manufacturing process to realize the dynamic compensation of the meshing parameters;

[0174] The conventional meshing parameter optimization process includes:

[0175] Analyze the mapping relationship between the meshing parameters of the gear system and the NVH characteristics;

[0176] Build a meshing parameter optimization model based on the mapping relationship;

[0177] Carry out multi-objective optimization calculation on the meshing parameter optimization model to obtain an optimized meshing parameter combination;

[0178] Apply the optimized meshing parameter combination to the gear manufacturing process to realize the optimization of the meshing parameters.

[0179] Specifically, for the reconstruction of the tooth surface micro-topography, first, a high-precision measurement device (such as a white light interferometer, a confocal microscope, or an atomic force microscope, etc.) is used to measure the micro-topography of the tooth surface to 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, three-dimensional modeling technology is used to construct a three-dimensional 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 three-dimensional 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 uses 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 degree of stress concentration by adjusting the micro-topography features. The micro-topography reconstruction design uses methods such as topology optimization, parametric design, and intelligent algorithms to generate an optimized micro-topography model. Based on the optimized micro-topography model, perform precision machining on the tooth surface to achieve the reconstruction of the tooth surface micro-topography. Precision machining can use 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.

[0180] For the dynamic compensation processing of meshing parameters, first, real-time operating data of the gear system is obtained through sensors and monitoring systems, including vibration signals, noise signals, rotational speed signals, load signals, etc. The real-time operating data reflects the current operating state and meshing condition of the gear system. Analyze the meshing state of the real-time operating data of the gear system, and identify 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 operating 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 realization 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.

[0181] 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, helix modification, and profile 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, providing 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 at the same time, and find the best 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.

[0182] 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 and noise control 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:

[0183] An acquisition and feature extraction module, configured to obtain multi-condition vibration and noise data of the transmission gear system; perform spectrum 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;

[0184] 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 transmission path optimization identification on the multi-dimensional vibration modal feature matrix to generate a vibration transmission path model of the gear system;

[0185] A modeling and parameterization module, configured to 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;

[0186] 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;

[0187] 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 to construct a gear manufacturing process optimization strategy;

[0188] 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 realize data transmission between modules.

[0189] 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.

[0190] In the embodiments of the present application, by acquiring the vibration and noise data of the transmission gear system under multiple working conditions, performing spectral 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, mining the accuracy influencing factors, a gear manufacturing accuracy profile 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 gear manufacturing process is constructed, providing guidance for NVH optimization; by dynamically adjusting the non-linear parameters of the gear meshing model, an NVH optimization control model of the transmission gear is constructed, achieving 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 usage comfort.

[0191] The above are only the preferred embodiments of the present invention and are not used 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 recorded 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.

[0192] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover a 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 a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0193] 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.

[0194] In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0195] 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.

[0196] 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 expressions 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.

[0197] 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.

[0198] 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 the NVH characteristics and controlling the noise of a transmission gear, characterized in that Including: Step S1: Obtain the vibration and noise data of the transmission gear system under multiple working conditions; Perform spectral decomposition and frequency band optimization processing on the vibration and noise data under multiple working conditions to obtain the vibration and noise characteristic sequence of the gear system; 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 transfer path optimization identification on the multi-dimensional vibration modal characteristic matrix to generate a vibration transfer path model of the gear system; Step S3: Conduct 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 non-linear parameterized model of gear meshing; Step S4: Obtain the gear manufacturing process parameter data; Mine the accuracy influencing factors from 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 make an adaptive optimization decision to construct a gear manufacturing process optimization strategy; The specific steps of Step S5 are: Extract the key accuracy features from the gear manufacturing accuracy profile to obtain the key accuracy feature data; Conduct a full-spectrum analysis of the NVH characteristics on the vibration and noise characteristic sequence of the gear system to obtain the full-spectrum data of the NVH characteristics; Conduct a correlation analysis on the key accuracy feature data based on the full-spectrum data of the NVH characteristics to obtain the 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; Make an adaptive optimization decision based on the NVH influencing factor contribution degree value to construct a gear manufacturing process optimization strategy; Step S6: Dynamically adjust the parameters of the non-linear parameterized model of gear meshing based on the gear manufacturing process optimization strategy to construct an NVH optimization control model for the transmission gear.

2. The NVH characteristic optimization and noise control method for the transmission gear according to claim 1, wherein The specific steps of Step S1 are: 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; conduct time-domain envelope analysis on the time-domain data set based on 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; Conduct 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.

3. The NVH characteristic optimization and noise control method for the transmission gear according to claim 2, characterized in that, The harmonic separation and frequency band optimization processing of the frequency-domain data set to generate frequency-domain optimized feature data includes: Conduct meshing frequency characteristic analysis on the frequency-domain data set to obtain the gear meshing frequency characteristic; Extract harmonic components based on the gear meshing frequency characteristic to obtain the gear system harmonic sequence; Conduct frequency distribution analysis on the gear system harmonic sequence to generate harmonic distribution data; Conduct parameterized frequency band energy statistics on the frequency-domain data set based on the harmonic distribution data to generate a parameterized frequency band energy distribution; Identify abnormal frequency bands in the frequency-domain data set based on the parameterized frequency band energy distribution and mark the abnormal frequency band points; Perform band optimization processing on abnormal band points to generate frequency-domain optimized feature data.

4. The method for optimizing the NVH characteristics and controlling the noise of the transmission gear according to claim 1, wherein The specific steps of step S2 are as follows: Perform multi-dimensional modal decomposition on the vibration 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; 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 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, perform transfer path optimization identification on the structural response sensitivity matrix to generate a vibration transfer path model of the gear system.

5. The NVH characteristic optimization and noise control method for the transmission gear according to claim 1, characterized in that, The specific steps of step S3 are as follows: Perform tooth surface contact stiffness calculation on 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 processing on the meshing parameter sensitivity network to obtain a gear meshing non-linear parameterization model.

6. The method for optimizing the NVH characteristics and controlling the noise of the transmission gear according to claim 1, characterized in that 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.

7. The method for optimizing the NVH characteristics and controlling the noise of the transmission gear according to claim 6, characterized in that, 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 according to the multiple key process parameters to obtain the process parameter sensitivity; Perform statistics on the accuracy contribution of each process to the process correlation data to obtain the accuracy contribution value of each process; Perform accuracy transfer chain analysis on the accuracy contribution value of each process according to the process parameter sensitivity to obtain accuracy 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; Perform comprehensive influencing factor analysis on the process parameter distribution data and the accuracy transfer characteristic data to generate manufacturing accuracy influencing factors.

8. The method for optimizing the NVH characteristics and controlling the noise of the transmission gear according to claim 1, wherein, The specific steps of step S6 are as follows: Perform parameter sensitivity analysis on the gear meshing non-linear parameterization model to generate a parameter sensitivity matrix; based on the gear manufacturing process optimization strategy, perform optimization target 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: 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 processing.

9. The method for optimizing the NVH characteristics and controlling the noise of the transmission gear according to claim 8, wherein, The tooth surface micro-topography reconstruction processing 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; Perform micro-topography reconstruction design on 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 realize tooth surface micro-topography reconstruction; The meshing parameter dynamic compensation processing 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 deviation; 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 realize meshing parameter dynamic compensation; The conventional meshing parameter optimization processing 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 realize meshing parameter optimization.

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