Gear squeal optimization method and system based on contact spot unbalance loading quantization

Abstract

The invention discloses a gear squeal optimization method and system based on contact spot unbalance loading quantification, and relates to the technical field of NVH performance optimization of a vehicle transmission system. The method aims at solving the problems that in the prior art, gear microscopic modification optimization lacks quantitative standards, parameter and target correlation is fuzzy, and low-torque working condition howling control is insufficient. The method provided by the invention comprises the following steps: defining an unbalance loading quantitative index system comprising offset, divergence and all-condition comprehensive evaluation parameters (R); collecting data through a variable control simulation experiment; constructing a quantitative mathematical correlation model between the microscopic modification parameters (fH [alpha], fH [beta], C [alpha], C [beta]) and quantitative indexes; and performing multi-target optimization by taking R value minimization as a target and combining contact stress constraint. By establishing parameter-index quantitative mapping, accurate prediction and optimization of the gear whistling performance are realized, the development period is greatly shortened, and the NVH performance, durability and reliability are effectively considered.

Description

Technical Field

[0001] This invention belongs to the field of NVH (noise, vibration, and harshness) performance optimization and gear design technology of gear transmission systems, specifically involving a method and system for optimizing the microscopic parameters of gears in core transmission components such as vehicle gearboxes and drive axles. Background Technology

[0002] Gear squealing is one of the core sources of NVH complaints in vehicles. Its essence is the localized stress concentration and periodic vibration caused by uneven loading of the tooth surface (such as vertical offset, horizontal offset, and divergence). Especially under low-speed and low-torque conditions, due to the weak background noise and insufficient masking effect, gear squealing noise is easily perceived by users, becoming a core pain point that the industry urgently needs to address.

[0003] Gear micro-modification parameters are key to controlling the characteristics of contact spots. Among them, the profile modification amount (fHα) mainly affects the vertical offset of the contact spot, the tooth direction modification amount (fHβ) mainly affects the horizontal offset, while the profile bulging amount (Cα) and the tooth direction bulging amount (Cβ) jointly determine the degree of divergence or focusing of the spot.

[0004] However, the current industry faces significant technical bottlenecks in optimizing gear micro-parameters:

[0005] 1. Lack of quantitative assessment: Existing optimizations often rely on qualitative judgments such as "centering of spots" and "regularity of shape," lacking unified quantitative indicators. It is impossible to accurately define the "optimization compliance threshold," often resulting in the contradiction that "the shape appears to meet the standard, but the actual squealing exceeds the standard."

[0006] 2. Qualitative Analysis of Parameter-Indicator Correlation: Existing technologies can only describe the qualitative trend of microscopic parameters on spots (e.g., "increasing fHα can cause the spot to move upward"), but cannot quantify "the specific change in the off-load index when the parameter changes by 1 μm". In addition, the synergistic nonlinear effect of Cα and Cβ on divergence is often ignored, leading to optimization relying on repeated trial and error, and a development cycle of 3 to 6 months.

[0007] 3. Imbalance in weighting sensitive areas: Existing optimizations focus on medium and high torque conditions (emphasizing strength and durability), neglecting the whistling sensitivity in the medium and low torque range, resulting in "meeting standards in non-sensitive areas but exceeding whistling standards in sensitive areas".

[0008] 4. Disconnect between assessment and optimization: There is a lack of optimization methods that are linked to a unified quantitative assessment system, making it difficult to form a closed loop.

[0009] Therefore, there is an urgent need for an optimization method that can achieve a quantitative mapping between microscopic parameters and contact spot performance, while also taking into account the performance balance under all operating conditions. Summary of the Invention

[0010] To address the problems in existing technologies such as the lack of quantitative standards for gear micro-modification optimization, the ambiguity between parameters and objectives, the long trial-and-error cycle, and the insufficient control of squealing under low torque conditions, this invention provides a gear squealing optimization method and system based on contact spot off-center loading quantification, which achieves precise design of micro-parameters by constructing a quantitative correlation model.

[0011] To address the aforementioned technical problems, in a first aspect, the present invention provides a gear squeal optimization method based on contact spot off-center loading quantization, comprising the following steps:

[0012] Step S1: Define the off-center load quantification index system for gear contact pattern. The off-center load quantification index system includes left and right offset index, up and down offset index, divergence index, and comprehensive evaluation parameters for all working conditions, calculated based on the effective contact area.

[0013] Step S2: Based on the preset gear micro-modification parameter variables, sample data is obtained by multi-condition simulation experiments. The sample data includes the values ​​of the off-center load quantification index corresponding to different combinations of micro-modification parameters under different torque conditions.

[0014] Step S3: Based on the sample data, construct quantitative mathematical models for the relationship between the gear micro-modification parameters and the off-center load quantification index under all working conditions;

[0015] Step S4: Taking the minimization of the comprehensive evaluation parameters under all working conditions as the core optimization objective, and combining the preset gear contact stress constraints, the optimal combination of gear micro-modification parameters is solved using the quantitative correlation mathematical model.

[0016] Step S5: Substitute the optimal gear micro-modification parameter combination into the simulation model for verification. If the preset threshold is met, output the result.

[0017] Through the above approach, this invention transforms traditional qualitative trial and error into quantitative optimization based on mathematical models, thus clarifying the optimization objective.

[0018] As a preferred embodiment of the present invention, the effective contact area S0 is defined as a rectangular area excluding the tooth tip transition area, tooth root transition area and left and right end chamfers, and its geometric parameters are width W and height H, and the center coordinates are O (W0, H0).

[0019] The left and right offset index ΔW i Defined as the center of the contact spot O i (W) i H i The tooth width direction normalized distance that deviates from the center O (W0, H0) of the effective contact area;

[0020] The vertical offset index ΔH i Defined as the center of the contact spot Oi (W) i H i The normalized distance in the tooth height direction that deviates from the center O (W0, H0) of the effective contact area;

[0021] The divergence index ΔDi is defined as the percentage of the area of ​​the contact spot within the effective contact area S0.

[0022] As a preferred embodiment of the present invention, the formula for calculating the comprehensive evaluation parameter R under all operating conditions is as follows:

[0023]

[0024] Where, ω i Let ΔP be the weighting coefficient for the i-th working condition. i Let ΔP be the single-condition comprehensive off-center load index for the i-th working condition; the single-condition comprehensive off-center load index ΔP i The calculation formula is:

[0025]

[0026] Where α, β, and γ are the weight coefficients of each sub-item, and α + β + γ = 1.

[0027] As a preferred embodiment of the present invention, the weighting coefficient ω i The allocation principle follows: the weight value of the whistling sensitive operating condition area is greater than the weight value of the secondary sensitive operating condition area, and the weight value of the secondary sensitive operating condition area is greater than the weight value of the non-sensitive operating condition area; the whistling sensitive operating condition area corresponds to the low torque operating condition range.

[0028] As a preferred embodiment of the present invention, the quantitative correlation mathematical model includes: a linear model of fHα and ΔH, a linear model of fHβ and ΔW, and a synergistic bivariate quadratic polynomial model of Cα, Cβ, and ΔD. This scheme accurately captures the different physical influences of various microscopic parameters on the characteristics of contact spots, especially capturing the synergistic effect of bulging.

[0029] To address the aforementioned technical problems, in a second aspect, the present invention provides a gear squeal optimization system based on contact spot off-center loading quantization, comprising:

[0030] The index definition module is used to store and define the off-center load quantification index system for gear contact patterns;

[0031] The data acquisition module is used to perform multi-condition variable control simulation and collect off-center load quantization index data under different combinations of micro-modification parameters.

[0032] The modeling module is used to construct a quantitative mathematical model of the relationship between gear micro-modification parameters and off-center load quantification index based on the collected data.

[0033] The optimization solution module is used to calculate the optimal combination of micro-modification parameters by means of the quantitative correlation mathematical model, with the goal of minimizing the comprehensive evaluation parameters under all working conditions and under the premise of satisfying the contact stress constraint.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Optimize target precision: By introducing a unified off-center load quantification index R and combining it with a high-weight design for sensitive areas, the problem of "the shape meets the standard but the howling exceeds the standard" is solved, which can significantly reduce the howling sound pressure level in the vehicle (for example, from 35dB to 28dB).

[0036] 2. Significantly improved development efficiency: A quantitative mathematical model of parameters and indicators has been established, making the "parameter adjustment-indicator prediction" process calculable and replacing the traditional trial-and-error method. The development cycle can be shortened by more than 60% (e.g., from 4 months to 1.5 months), and the number of trials and errors is significantly reduced.

[0037] 3. Multi-performance synergistic balance: By quantifying the synergistic effect of drum shape (Cα, Cβ) and introducing stress constraints, it is possible to maximize NVH performance while meeting durability requirements (such as stress ≤1800MPa), thus resolving the contradiction between NVH and strength reliability.

[0038] 4. Technology closed loop and standardization: A complete process of "standard definition - experimental design - correlation modeling - multi-objective optimization - verification closed loop" has been formed, which reduces the dependence on the personal experience of engineers. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments disclosed in this invention, the accompanying drawings of the embodiments will be briefly described below. These drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0040] Figure 1 In this embodiment of the invention, the tooth profile modification amount fHα and the vertical offset index ΔH under all working conditions are... total A schematic diagram of the linear fitting relationship.

[0041] Figure 2 In this embodiment of the invention, the tooth profile modification amount fHβ and the left-right offset index ΔW under all working conditions are... total A schematic diagram of the linear fitting relationship.

[0042] Figure 3 The tooth profile bulge amount in the embodiment of the present invention

[0043] Cα, tooth bulging Cβ, and the divergence index ΔD under all operating conditions total A schematic diagram of the fitting relationship surface.

[0044] Figure 4 This is a flowchart of the gear squeal optimization method provided by the present invention. Detailed Implementation

[0045] The technical solutions (including preferred technical solutions) of the present invention will be further described in detail below with reference to the accompanying drawings and by way of listing some optional embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] Example 1

[0047] This embodiment provides a gear squeal optimization method based on contact spot off-center loading quantization, such as... Figure 4 As shown, this method mainly focuses on the micro-parameter design of gear pairs in vehicle transmissions or drive axles.

[0048] Step S1: Define the quantitative index system for gear contact pattern off-center loading

[0049] To overcome the ambiguity of traditional qualitative evaluation, this embodiment first clarifies the definitions of core terms:

[0050] Effective contact area (S0): Defined as a rectangular area excluding the tooth tip transition area, tooth root transition area, and left and right end chamfers. Its geometric parameters are width W, height H, and center coordinates O (W0, H0).

[0051] Quantitative indicators of contact spot off-center loading:

[0052] Left and right offset index (ΔW) i ): Contact spot center O i (W) i H i The normalized distance in the tooth width direction deviating from the center O of S0. Calculation formula:

[0053] The value ranges from -1 to 1. The larger the absolute value, the more severe the off-center loading.

[0054] Vertical offset index (ΔH) i ): Contact spot center O i Normalized distance in the tooth height direction deviating from the center O of S0. Calculation formula:

[0055] The value range is [-1, 1].

[0056] Divergence index (ΔDi): The degree of focusing of the contact spot within S0. Calculation formula:

[0057]

[0058] Among them, A in_i is the spot area within S0, and A total_i is the total spot area. The value range is [0, 1], and the closer to 1, the better the focusing performance.

[0059] Single-condition comprehensive off-load index (ΔP i ): Integrate the above three indexes:

[0060]

[0061] In the formula, α, β, and γ are the weight coefficients of each sub-item, satisfying α + β + γ = 1.

[0062] Preferably, considering that the "area exceeding the effective region" has a greater impact on local stress concentration, the weights are assigned as follows: α = 0.2, β = 0.2, γ = 0.6.

[0063] Full-condition comprehensive evaluation parameter (R):

[0064]

[0065] Evaluation criteria: R ≤ 0.3 is excellent (mass production possible); 0.3 < R ≤ 0.5 is good (minor adjustment required); R > 0.5 is poor (redesign required).

[0066] Operating condition weight (ω i ):

[0067] Divide into three intervals according to the whine sensitivity:

[0068] Area A (sensitive area, low torque): For example, 5 - 20 Nm, with a weight ratio of 50% (single-condition weight such as 0.125). Whine is most easily perceived here.

[0069] Area B (less sensitive area, medium torque): For example, 30 - 100 Nm, with a weight ratio of 30%.

[0070] Area C (non-sensitive area, high torque): For example, 120 - 200 Nm, with a weight ratio of 20%.

[0071] Step S2: Multi-condition variable control simulation experiment and data acquisition

[0072] Design three groups of independent simulation experiments to obtain the data relationship between the micro-profile modification parameters (optimization variables) and the above indexes.

[0073] The optimization variables include: tooth profile modification amount fHα (controlling ΔH); tooth direction modification amount fHβ (controlling ΔW); tooth profile crowning amount Cα and tooth direction crowning amount Cβ (cooperatively controlling ΔD).

[0074] Experimental design (taking the driven gear of an electric vehicle transmission as an example, a total of 35 sets of data were collected):

[0075] fHα-ΔH experiment: Fix fHβ, Cα, Cβ, and vary fHα (e.g., -20, -10, 0, 10, 20 μm), and collect the corresponding ΔH data.

[0076] fHβ-ΔW experiment: Fix fHα, Cα, Cβ, vary fHβ, and collect the corresponding ΔW data.

[0077] Cα-Cβ-ΔD experiment: A full factorial design was used, with Cα and Cβ varying synergistically (e.g., taking values ​​of 0, 5, 10, 15, and 20 μm respectively) to form 25 combinations, and the corresponding ΔD data were collected.

[0078] Data acquisition tools can employ advanced LTCA algorithm software such as MASTA.

[0079] Step S3: Construction of Parameter-Indicator Quantitative Correlation Model

[0080] A mathematical model is established based on the collected data.

[0081] The correlation model between fHα and ΔH: (e.g.) Figure 1 As shown, it exhibits a clear linear characteristic. Model establishment:

[0082] Δ

[0083] The correlation model between fHβ and ΔW: (e.g.) Figure 2 As shown, it exhibits linear characteristics. Model establishment:

[0084] Δ

[0085] Co-correlation model of Cα-Cβ and ΔD: such as Figure 3 As shown, due to the existence of synergistic effects, a bivariate quadratic polynomial model is adopted:

[0086]

[0087] Full-condition correlation model:

[0088] Based on the above model, the functional relationship between R and the four parameters is obtained as follows:

[0089]

[0090] Step S4: Multi-objective optimization solution

[0091] Define the optimization problem:

[0092] Core objective: minR (prioritize satisfying R≤0.3).

[0093] Constraints:

[0094] The maximum contact stress of the gear is σ≤1800MPa (based on the allowable value of the material 20CrMnTi).

[0095] Process constraints: fHα, fHβ∈[-25, 25] μm; Cα, Cβ∈[0, 20] μm.

[0096] The above function is optimized using conventional optimization algorithms (such as genetic algorithms or direct traversal solutions, which have fewer dependent variables and simpler models) to obtain the optimal parameter combination.

[0097] For example, in this embodiment, the optimal combination is (0, 0, 20, 20), and its predicted R value is 0.120.

[0098] Step S5: Full-condition verification of closed loop

[0099] The optimal parameter combination was substituted into the MASTA software to simulate 17 operating conditions.

[0100] Quantitative index verification: The simulation calculation yielded a full-condition R value of 0.132, which differed from the predicted value of 0.120 by only 9.09% (meeting the error requirement of ≤10%), and was much smaller than the initial scheme's 0.389.

[0101] Performance verification: The maximum contact stress is 1680MPa (≤1800MPa), which meets the strength requirements. On-vehicle testing shows that the in-vehicle whistling sound pressure level decreased from 35dB(A) to 28dB(A), and there were no whistling complaints under all operating conditions.

[0102] Example 2

[0103] This embodiment, based on a pure electric vehicle (EV) reducer R&D project, demonstrates the specific application of the invention's method in engineering practice using concrete data. In particular, this embodiment details the operating condition weight allocation strategy and DOE experimental data to showcase the invention's expertise in solving the "low torque whistling" problem.

[0104] 1. Basic Parameters and Problem Definition

[0105] The object is the driven gear of a certain EV single-stage reducer.

[0106] Macroscopic parameters: module m=3.0mm, number of teeth z=50, tooth width B=40mm, pressure angle α=20°, helix angle β=12°.

[0107] Material properties: 20CrMnTi (carburized and quenched), surface hardness 58-62HRC.

[0108] Effective contact area S0: Calculated width W=36mm, height H=6.75mm, center coordinates O(18, 3.375).

[0109] Initial state: Initial micro parameters are (0, 0, 5, 5), and the comprehensive evaluation parameter for all operating conditions is R=0.389 (rated "Good"). However, in real-vehicle testing, at speeds of 20–40 km / h (corresponding to low-torque motor operating conditions), the sound pressure level of the whistling noise inside the vehicle reaches as high as 35 dB(A), leading to strong user complaints. This exposes the deficiency of traditional optimization methods in neglecting low-torque operating conditions.

[0110] 2. Working condition division and weight allocation strategy

[0111] To address the aforementioned pain points, this embodiment employs a unique weight allocation strategy, namely an "inverted triangle" weight distribution, which focuses on strengthening the low-torque region. Specific operating conditions and weight allocation are shown in the table below:

[0112]

[0113] According to the strategy in the table above, region A (5-20 Nm) accounts for only 10% of the total torque range, but its weight is as high as 50%. This means that the optimization algorithm will sacrifice some of the performance in the high torque region (as long as it does not exceed the stress limit) in exchange for perfect centering and focusing of the contact spots in the low torque region.

[0114] For area A: This area has extremely low background noise and the weakest howling masking effect, so it needs to be controlled with the highest priority.

[0115] For Zone B: This area is a transition zone, taking into account both NVH and strength.

[0116] For region C: This region focuses primarily on intensity, and the NVH weight is reduced due to the high background noise.

[0117] 3. Design of Experiments for Multi-condition Variable Control (DOE)

[0118] To construct an accurate mathematical model, this embodiment performed rigorous DOE experiments. The specific parameter combinations are shown in the table below:

[0119]

[0120] fHα-ΔH experiment: capturing the independent effect of tooth profile modification on vertical offset.

[0121] fHβ-ΔW experiment: capturing the independent effect of tooth profile modification on left and right offset.

[0122] Cα-Cβ-ΔD experiment: capturing the nonlinear effect of the synergistic effect of drum-shaped quantities on divergence.

[0123] 4. Simulation data and correlation modeling results

[0124] Based on the above 35 sets of experimental data, the system calculated the weighted index under all operating conditions. The following are some key simulation data:

[0125] fHα data:

[0126]

[0127] A model is constructed using regression analysis: ΔH total =0.030×|fHα|+0.30, fitting accuracy R 2 ≥0.99.

[0128] fHβ data:

[0129]

[0130] Model building:

[0131] ΔW total =0.025×|fHβ|-0.35, fitting accuracy R 2 ≥0.99.

[0132] Cα and Cβ data (25 groups):

[0133]

[0134] Constructing a bivariate quadratic model:

[0135]

[0136] Fitting accuracy R 2 ≥0.99;

[0137] Based on the above model, the functional relationship between R and the four parameters is obtained as follows:

[0138]

[0139] 5. Optimize verification and compare results

[0140] The optimal solution output by the optimization algorithm is (0, 14, 20, 20). To verify the reliability of this result, three sets of parameters not involved in the modeling were selected for verification, and the performance before and after optimization was finally compared:

[0141]

[0142] The data in the table above shows that the relative error of all random verification points is controlled within 10%, proving that the correlation model constructed in this invention has extremely high prediction accuracy and can completely replace the cumbersome finite element simulation for optimization.

[0143]

[0144] As can be seen from the table above, after optimization by this invention, the comprehensive evaluation parameter R value under all operating conditions decreased significantly, directly leading to a 7dB reduction in the in-vehicle whistling sound pressure level, completely resolving the NVH complaints about this model. Simultaneously, due to the reasonable increase in drum shape (Cα, Cβ increased from 5 to 20), the edge contact condition was improved, resulting in a 4% reduction in maximum contact stress, achieving a win-win situation for both NVH and durability. This result fully demonstrates the effectiveness and advancement of the method of this invention in engineering applications.

[0145] In summary, this embodiment, by introducing a weight table, a DOE experimental table, and a verification comparison table, completely reproduces the entire engineering process from problem definition to final solution. The data is detailed, the logic is rigorous, and it has high feasibility.

[0146] Example 3

[0147] This embodiment provides a gear squeal optimization system based on contact spot off-center load quantization, including a processor, a memory, and a computer program stored in the memory.

[0148] The system is logically divided into:

[0149] Indicator definition module: used to preset the calculation logic of ΔW, ΔH, ΔD and R values ​​as described in Example 1.

[0150] Data interface module: Used to interact with gear simulation software (such as MASTA / Romax), automatically submit calculation tasks in batches and read the results.

[0151] Modeling and Analysis Module: Built-in regression analysis algorithm, used to automatically fit and generate the linear and nonlinear model parameters described in Example 1 based on the input data points.

[0152] Optimization Engine: Built-in nonlinear programming solver for finding the parametric solution that minimizes the R value under given stress constraints.

[0153] Those skilled in the art will understand that although this embodiment uses 20CrMnTi material as an example, the method of the present invention is also applicable to other gear materials, requiring only corresponding adjustment of the stress constraint threshold. Furthermore, the setting of the operating condition weights can be flexibly adjusted according to vehicle type (e.g., commercial vehicles prioritize low-speed, high-torque performance), all of which fall within the protection scope of this invention.

Claims

1. A gear squeal optimization method based on contact spot off-center loading quantization, characterized in that, Includes the following steps: Step S1: Define the off-center load quantification index system for gear contact pattern. The off-center load quantification index system includes left and right offset index, up and down offset index, divergence index, and comprehensive evaluation parameters for all working conditions, calculated based on the effective contact area. Step S2: Based on the preset gear micro-modification parameter variables, sample data is obtained by multi-condition simulation experiments. The sample data includes the values ​​of the off-center load quantification index corresponding to different combinations of micro-modification parameters under different torque conditions. Step S3: Based on the sample data, construct quantitative mathematical models for the relationship between the gear micro-modification parameters and the off-center load quantification index under all working conditions; Step S4: Taking the minimization of the comprehensive evaluation parameters under all working conditions as the core optimization objective, and combining the preset gear contact stress constraints, the optimal combination of gear micro-modification parameters is solved using the quantitative correlation mathematical model. Step S5: Substitute the optimal gear micro-modification parameter combination into the simulation model for verification. If the preset threshold is met, output the result.

2. The method according to claim 1, characterized in that, In step S1, the effective contact area S0 is defined as a rectangular area excluding the tooth tip transition area, tooth root transition area and left and right end chamfers, with geometric parameters being width W and height H, and center coordinates being O (W0, H0). The left and right offset index ΔW i Defined as the center of the contact spot O i (W) i H i The tooth width direction normalized distance that deviates from the center O (W0, H0) of the effective contact area; The vertical offset index ΔH i Defined as the center of the contact spot O i (W) i H i The normalized distance in the tooth height direction that deviates from the center O (W0, H0) of the effective contact area; The divergence index ΔDi is defined as the percentage of the area of ​​the contact spot within the effective contact area S0.

3. The method according to claim 2, characterized in that, In step S1, the formula for calculating the comprehensive evaluation parameter R under all working conditions is as follows: Where, ω i Let ΔP be the weighting coefficient for the i-th working condition. i The single-condition comprehensive off-center load index for the i-th working condition; The single-condition comprehensive off-center load index ΔP i The calculation formula is: Where α, β, and γ are the weight coefficients of each sub-item, and α + β + γ = 1.

4. The method according to claim 3, characterized in that, The weighting coefficient ω i The allocation principle follows: the weight value of the whistling sensitive operating condition area is greater than the weight value of the secondary sensitive operating condition area, and the weight value of the secondary sensitive operating condition area is greater than the weight value of the non-sensitive operating condition area; the whistling sensitive operating condition area corresponds to the low torque operating condition range.

5. The method according to claim 1, characterized in that, The gear micro-modification parameters include tooth profile modification amount fHα, tooth direction modification amount fHβ, tooth profile bulging amount Cα, and tooth direction bulging amount Cβ. In step S2, the multi-condition variable control simulation experiment adopts orthogonal experimental design or full factorial design, including: First group of experiments: Fix fHβ, Cα, Cβ, change fHα, and obtain data on the influence of fHα on the vertical offset index ΔH; The second set of experiments: fix fHα, Cα, Cβ, change fHβ, and obtain the data on the influence of fHβ on the left and right offset index ΔW; The third group of experiments: fix fHα and fHβ, and coordinately change Cα and Cβ to obtain data on the interaction effect of Cα and Cβ on the divergence index ΔD.

6. The method according to claim 5, characterized in that, In step S3, constructing the quantitative correlation mathematical model specifically includes: Establish fHα and the upper and lower offset index ΔH under all working conditions total Linear model in one variable: Establish fHβ and the left and right offset index ΔW under all working conditions total Linear model in one variable: Establish Cα, Cβ and the divergence index ΔD under all operating conditions. total The bivariate quadratic polynomial regression model: Among them, the k-series parameters are influence coefficients, and the b-series parameters are intercepts.

7. The method according to claim 3, characterized in that, In step S4, the core optimization objective satisfies: minR; The preset gear contact stress constraint condition is: σ≤σ limit Where σ is the maximum contact stress of the gear, σ limit This represents the allowable stress threshold for the gear material.

8. The method according to claim 1, characterized in that, In step S5, if the verification result does not meet the preset threshold, the coefficients of the quantitative correlation mathematical model are recalibrated or the constraints in step S4 are adjusted, and step S4 is repeated.

9. A gear squeal optimization system based on contact spot off-center loading quantization, characterized in that, include: The index definition module is used to store and define the off-load quantification index system of gear contact spots. The index system includes the geometric offset and divergence based on the effective contact area. The data acquisition module is used to perform multi-condition variable control simulation and collect off-center load quantization index data under different combinations of micro-modification parameters. The modeling module is used to construct a quantitative mathematical model of the relationship between gear micro-modification parameters and off-center load quantification index based on the collected data. The optimization solution module is used to calculate the optimal combination of micro-modification parameters by means of the quantitative correlation mathematical model, with the goal of minimizing the comprehensive evaluation parameters under all working conditions, while satisfying the contact stress constraints. The system includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. The system according to claim 9, characterized in that, The modeling module is configured to identify the whistling sensitivity in the low torque operating range and assign a higher weight coefficient to the low torque operating range when building the model.

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