A method and system for evaluating fatigue resistance of gear tooth surfaces

Abstract

The application discloses a gear tooth surface fatigue resistance performance evaluation method and an evaluation system. The method calculates the maximum Mises stress according to historical topography height matrix and deep residual stress data at different depths; a first expression is constructed based on an initial expression; the coefficient of the first expression is inversely determined based on the correlation between the first expression and the maximum Mises stress, so as to obtain a second expression; the initial topography volume parameter is calculated according to the initial topography height matrix and the second expression; the initial topography height matrix is adjusted based on the initial topography volume parameter and a target topography volume parameter, so as to obtain a target surface topography height matrix; and the fatigue resistance performance evaluation result is obtained according to the target surface topography height matrix. The new topography volume parameter which is strongly related to the Mises stress is constructed, the parameter coefficient is inversely optimized, the accurate correlation between the topography parameter and the fatigue resistance performance is realized, and then the fatigue resistance performance is evaluated, so that the accuracy and the scientificity of the evaluation are improved.

Description

Technical Field

[0001] This application relates to the fields of gear surface engineering and fatigue resistance design technology, and in particular to a method and system for evaluating the fatigue resistance performance of gear tooth surfaces. Background Technology

[0002] Gears are core transmission components in equipment such as aero engines, wind power generation, and high-end automobiles. Their contact fatigue life directly determines the operational reliability of the entire machine, and surface morphology is a key factor affecting the distribution of contact stress, lubrication status, and fatigue crack initiation on the gear surface. Therefore, accurately characterizing and effectively controlling the surface morphology of gears is crucial for improving their fatigue resistance. Currently, the international standard ISO 25178 defines a series of surface morphology characterization parameters, including height and volume parameters, providing a unified basis for the quantitative evaluation of surface quality.

[0003] However, in engineering applications aimed at fatigue resistance design, existing parameters are mostly based on overall surface statistics or calculations within a fixed height range. This results in a generally weak correlation between these parameters and the maximum Mises stress (a key criterion for fatigue failure) on the tooth surface calculated using a thermo-elastic-plastic-hydrodynamic lubrication model. Consequently, the evaluation performance of the fatigue resistance of gear tooth surfaces is poor, making it difficult to provide a clear direction for morphological optimization to reduce contact stress and extend fatigue life. This, in turn, restricts the design of fatigue resistance performance of gear tooth surfaces. Summary of the Invention

[0004] The main objective of this disclosure is to propose a method and system for evaluating the fatigue resistance of gear tooth surfaces. This method can accurately correlate the morphological parameters with fatigue resistance by constructing new morphological volume parameters that are strongly correlated with Mises stress and optimizing the parameter coefficients through inversion, thereby evaluating fatigue resistance and improving the accuracy and scientific nature of the evaluation.

[0005] A first aspect of this application provides a method for evaluating the fatigue resistance of gear tooth surfaces, the method comprising:

[0006] Based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths, the maximum Mises stress on the tooth surface is calculated.

[0007] Based on the initial morphology-sensitive volume parameter expression, construct the first morphology-sensitive volume parameter expression;

[0008] Based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface, the coefficients of the first shape-sensitive volume parameter expression are determined by inversion using a genetic algorithm, so as to update the coefficients of the initial shape-sensitive volume parameter expression and obtain the second shape-sensitive volume parameter expression.

[0009] Obtain the initial three-dimensional surface topography height matrix and target topography sensitive volume parameters of the target grinding part;

[0010] The initial shape-sensitive volume parameters of the target grinding part are calculated based on the initial three-dimensional surface topography height matrix and the expression for the second topography-sensitive volume parameter.

[0011] Based on the initial morphology-sensitive volume parameters and the target morphology-sensitive volume parameters of the target grinding part, the initial three-dimensional surface morphology height matrix is ​​adjusted to obtain the target surface morphology height matrix;

[0012] The fatigue resistance evaluation result of the target grinding part is obtained based on the target surface morphology height matrix.

[0013] In some embodiments of this application, the initial topography-sensitive volume parameter expression is calculated based on the Abbott-Firstone curve integral, and the construction of the first topography-sensitive volume parameter expression based on the initial topography-sensitive volume parameter expression includes:

[0014] A first load area ratio coefficient and a second load area ratio coefficient are introduced into the integral upper and lower limits of the initial shape-sensitive volume parameter expression to extract the core height range associated with the Hertzian contact stress region of the tooth surface, thereby constructing the first shape-sensitive volume parameter expression, wherein the first load area ratio coefficient is smaller than the second load area ratio coefficient.

[0015] In some embodiments of this application, the first shape-sensitive volume parameter expression is obtained by multiplying a preset scaling constant by the integral operation result; wherein, the integral operation result is the difference between the height value of the Abbott-Firstone curve at the integral variable and the height value of the Abbott-Firstone curve at the second load area ratio coefficient, the integral variable is the load area ratio, the lower limit of the integral variable is the first load area ratio coefficient, and the upper limit of the integral variable is the second load area ratio coefficient.

[0016] In some embodiments of this application, the step of determining the coefficients of the first topography-sensitive volume parameter expression based on the correlation between the first topography-sensitive volume parameter expression and the maximum Mises stress on the tooth surface using a genetic algorithm to update the coefficients of the initial topography-sensitive volume parameter expression and obtain the second topography-sensitive volume parameter expression includes:

[0017] Construct an optimization variable set, which includes the first load area ratio coefficient, the second load area ratio coefficient, and position coordinate parameters for identifying the Hertzian contact stress region on the tooth surface.

[0018] A first optimization objective function is established, which is used to maximize the Pearson correlation coefficient between the calculated value of the first morphology-sensitive volume parameter expression and the maximum Mises stress on the tooth surface;

[0019] A second optimization objective function is established, which is used to characterize the matching degree between the coverage range of the core height interval and the Hertzian contact stress region of the tooth surface;

[0020] The first and second optimization objective functions are solved using a non-dominated sorting genetic algorithm to obtain a Pareto solution set. The first and second load area ratio coefficients are updated by extracting solutions from the Pareto solution set whose Pearson correlation coefficients meet preset conditions, thereby obtaining the third and fourth load area ratio coefficients.

[0021] Based on the third load area ratio coefficient and the fourth load area ratio coefficient, the coefficients of the initial topography-sensitive volume parameter expression are updated to obtain the second topography-sensitive volume parameter expression.

[0022] In some embodiments of this application, calculating the initial shape-sensitive volume parameter of the target grinding part based on the initial three-dimensional surface topography height matrix and the expression for the second shape-sensitive volume parameter includes:

[0023] Determine the position coordinate parameters used to identify key morphology regions in the second morphology-sensitive volume parameter expression. The position coordinate parameters are used to extract local morphology regions corresponding to the Hertzian contact stress region of the tooth surface from the initial three-dimensional surface morphology height matrix.

[0024] Based on the third and fourth load area ratio coefficients contained in the second morphology-sensitive volume parameter expression, the core height integral interval of the target grinding part is determined on the Albert-Firstone curve corresponding to the local morphology region.

[0025] The initial morphology-sensitive volume parameters of the target grinding part are obtained by integrating the curve within the core height integration interval of the target grinding part.

[0026] In some embodiments of this application, adjusting the initial three-dimensional surface topography height matrix based on the initial topography-sensitive volume parameters and the target topography-sensitive volume parameters of the target grinding part to obtain the target surface topography height matrix includes:

[0027] Based on the initial topography-sensitive volume parameters and the target topography-sensitive volume parameters, the adjustment mode of the initial three-dimensional surface topography height matrix is ​​determined, and the adjustment mode includes a height increase mode or a height decrease mode.

[0028] The initial three-dimensional surface topography height matrix is ​​expanded by boundary extension to obtain the expanded topography height matrix;

[0029] Traverse all non-boundary points in the expanded topographic height matrix, extract the height information of each point and its eight neighboring points to construct a feature information matrix. The feature information matrix includes the height value of each point, the height sorting in the neighborhood, the upper and lower limits of the height adjustment range, the spatial location index, and the preset adjustable height margin.

[0030] The number of height points to be adjusted is calculated based on the fourth load area ratio coefficient in the expression of the second morphology-sensitive volume parameter and the total number of points in the initial three-dimensional surface morphology height matrix.

[0031] Based on the adjustment mode and the number of height points to be adjusted, the height points in the initial three-dimensional surface topography height matrix are iteratively updated using the feature information matrix, and the feature information of the eight adjacent points of the point to be adjusted is updated synchronously during the update process until the updated topography sensitive volume parameters meet the preset error conditions, thereby obtaining the target surface topography height matrix.

[0032] In some embodiments of this application, obtaining the fatigue resistance evaluation result of the target ground part based on the target surface topography height matrix includes:

[0033] The initial three-dimensional surface topography height matrix and the target surface topography height matrix of the target grinding part are calculated with the deep residual stress data of the target grinding part at different depths to obtain the initial maximum Mises stress and the target maximum Mises stress of the tooth surface of the target grinding part.

[0034] Based on the initial maximum Mises stress on the tooth surface of the target grinding part and the maximum Mises stress on the target tooth surface, the fatigue resistance grade of the tooth surface of the target grinding part is determined to obtain the fatigue resistance evaluation result of the target grinding part.

[0035] The first aspect of this application provides a method for evaluating the fatigue resistance of gear tooth surfaces. This method calculates the maximum Mises stress based on a historical topographic height matrix and deep residual stress data at different depths; constructs a first expression based on an initial expression; determines the coefficients of the first expression based on the correlation between the first expression and the maximum Mises stress to obtain a second expression; calculates initial topographic volume parameters based on the initial topographic volume parameters and the second expression; adjusts the initial topographic height matrix based on the initial topographic volume parameters and the target topographic volume parameters to obtain the target surface topographic height matrix; and obtains the fatigue resistance evaluation result based on the target surface topographic height matrix. This method can achieve a precise correlation between topographic parameters and fatigue resistance by constructing new topographic volume parameters strongly correlated with Mises stress and optimizing the parameter coefficients through inversion, thereby improving the accuracy and scientific rigor of the evaluation.

[0036] To achieve the above objectives, a second aspect of the present invention provides a gear tooth surface fatigue resistance evaluation system, the system comprising:

[0037] The first module is used to calculate the maximum Mises stress on the tooth surface based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths.

[0038] The module is used to construct the first morphology-sensitive volume parameter expression based on the initial morphology-sensitive volume parameter expression;

[0039] The update module is used to determine the coefficients of the first shape-sensitive volume parameter expression based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface using a genetic algorithm, so as to update the coefficients of the initial shape-sensitive volume parameter expression and obtain the second shape-sensitive volume parameter expression.

[0040] The acquisition module is used to acquire the initial three-dimensional surface topography height matrix and the target topography sensitive volume parameters of the target grinding part;

[0041] The calculation module is used to calculate the initial shape-sensitive volume parameters of the target grinding part based on the initial three-dimensional surface topography height matrix and the second shape-sensitive volume parameter expression.

[0042] The adjustment module is used to adjust the initial three-dimensional surface topography height matrix based on the initial topography sensitive volume parameters and the target topography sensitive volume parameters of the target grinding part, so as to obtain the target surface topography height matrix.

[0043] The evaluation module is used to obtain the fatigue resistance evaluation result of the target grinding part based on the target surface topography height matrix.

[0044] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the above-described method for evaluating the fatigue resistance of gear tooth surfaces.

[0045] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for evaluating the fatigue resistance of gear tooth surfaces.

[0046] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0048] Figure 1 This is a schematic flowchart of a method for evaluating the fatigue resistance of gear tooth surfaces provided in an embodiment of this application;

[0049] Figure 2 The Abbott-Firestone curves and morphology provided in the embodiments of this application are... Association diagram;

[0050] Figure 3 This is a schematic diagram of the Pareto solution searched by NSGA-II provided in the embodiments of this application;

[0051] Figure 4 This is a schematic diagram of the initial grinding morphology sample provided in the embodiments of this application;

[0052] Figure 5 This is a schematic diagram of the final adjusted grinding morphology sample provided in the embodiments of this application;

[0053] Figure 6 This is a grinding morphology sample provided in the embodiments of this application. A comparison of Abbott-Firestone curves before and after value adjustment;

[0054] Figure 7 This is a grinding morphology sample provided in the embodiments of this application. A comparison of Abbott-Firestone curves before and after value adjustment;

[0055] Figure 8 This is the maximum Mises stress section cloud diagram obtained from the initial morphology calculation provided in the embodiments of this application;

[0056] Figure 9 This is the maximum Mises stress section cloud map obtained based on the controlled morphology calculation provided in the embodiments of this application.

[0057] Figure 10 This is a schematic diagram of the structure of a gear tooth surface fatigue resistance evaluation system provided in an embodiment of this application;

[0058] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0059] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0060] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0061] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0062] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0063] Gears, as core transmission components in equipment such as aero engines, wind power generation, and high-end automobiles, directly determine the reliability of the entire system through their contact fatigue life. Surface morphology is a key factor affecting the distribution of contact stress, lubrication state, and fatigue crack initiation on the tooth surface. International standard ISO 25178 defines a large number of morphology characterization parameters, such as height parameters Sa, Sq, Ssk, and Sku, and volume parameters Vmp and Vmc, providing a basis for surface quality evaluation. However, existing standard parameters have the following limitations when designing for tooth surface fatigue resistance:

[0064] (1) Weak correlation: Existing parameters are mostly based on overall surface statistics or calculations within a fixed height range, failing to accurately focus on the key local areas bearing the maximum Hertzian contact stress during tooth surface contact. This results in a weak physical correlation between the parameters and the maximum Mises stress (a key indicator of fatigue failure) on the tooth surface. Calculation results show that the ISO 25178 parameters are generally weakly correlated with the maximum Mises stress calculated by the thermo-elastic-plastic-hydrodynamic lubrication model for rough tooth surfaces. Due to the weak correlation, the existing parameter system is difficult to directly guide morphology design aimed at reducing contact stress and improving fatigue life. Traditional morphology optimization often simply pursues extremely low overall roughness (such as Sa, Sq), which is not only costly to process but may not be the most effective way to reduce the maximum local stress.

[0065] (2) Lack of precise control methods: Even if potential key morphological features are identified, there is a lack of digital tools that can precisely control the volume of a specific height range while maintaining the original surface texture and spatial characteristics. This is also a key aspect of verifying the fatigue resistance design of tooth surfaces.

[0066] Therefore, accurately characterizing and effectively controlling the surface morphology of gears is crucial for improving their fatigue resistance. Currently, the international standard ISO 25178 defines a series of surface morphology characterization parameters, including height and volume parameters, providing a unified basis for the quantitative evaluation of surface quality.

[0067] However, in engineering applications aimed at fatigue resistance design, existing parameters are mostly based on overall surface statistics or calculations within a fixed height range. This results in a generally weak correlation between these parameters and the maximum Mises stress (a key criterion for fatigue failure) on the tooth surface calculated using a thermo-elastic-plastic-hydrodynamic lubrication model. Consequently, the evaluation performance of the fatigue resistance of gear tooth surfaces is poor, making it difficult to provide a clear direction for morphological optimization to reduce contact stress and extend fatigue life. This, in turn, restricts the design of fatigue resistance performance of gear tooth surfaces.

[0068] Based on this, embodiments of this application provide a method and system for evaluating the fatigue resistance of gear tooth surfaces. The aim is to construct new morphological volume parameters that are strongly correlated with Mises stress and to perform inverse optimization of the parameter coefficients to achieve a precise correlation between morphological parameters and fatigue resistance, thereby evaluating fatigue resistance and improving the accuracy and scientific nature of the evaluation.

[0069] The gear tooth surface fatigue performance evaluation method and evaluation system provided in this application are specifically described through the following embodiments. First, the gear tooth surface fatigue performance evaluation method in this application embodiment is described.

[0070] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0071] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0072] The gear tooth surface fatigue performance evaluation method provided in this application relates to the field of gear surface engineering and fatigue resistance design technology. The gear tooth surface fatigue performance evaluation method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the gear tooth surface fatigue performance evaluation method, but is not limited to the above forms.

[0073] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0074] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0075] Therefore, referring to Figure 1 This application provides a method for evaluating the fatigue resistance of gear tooth surfaces. This method is applied to a central controller, which can be a server, an electronic device, or a mobile terminal, etc. There are no specific limitations here. The method includes the following steps S110 to S170.

[0076] Step S110: Calculate the maximum Mises stress on the tooth surface based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths;

[0077] Step S120: Based on the initial morphology-sensitive volume parameter expression, construct the first morphology-sensitive volume parameter expression;

[0078] Step S130: Based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface, the coefficients of the first shape-sensitive volume parameter expression are determined by inversion using a genetic algorithm, so as to update the coefficients of the initial shape-sensitive volume parameter expression and obtain the second shape-sensitive volume parameter expression.

[0079] Step S140: Obtain the initial three-dimensional surface topography height matrix and target topography sensitive volume parameters of the target grinding part;

[0080] Step S150: Calculate the initial shape-sensitive volume parameters of the target grinding part based on the initial three-dimensional surface topography height matrix and the expression for the second shape-sensitive volume parameter.

[0081] Step S160: Based on the initial morphology-sensitive volume parameters and the target morphology-sensitive volume parameters of the target grinding part, adjust the initial three-dimensional surface morphology height matrix to obtain the target surface morphology height matrix;

[0082] Step S170: Based on the target surface morphology height matrix, obtain the fatigue resistance evaluation results of the target grinding part.

[0083] First, let's clarify some terms used in this application: The historical three-dimensional surface morphology height matrix refers to the three-dimensional surface morphology height data of multiple sets of gear grinding samples with different processing techniques. It is presented in matrix form, with rows and columns defining planar positions, and the value of each cell being the height of the corresponding point. The sampling interval is fixed and it is measured by a white light interferometer.

[0084] The data on deep residual stress at different depths refers to the residual stress of gear grinding parts at different depths. It is preferably obtained by measuring the subsurface residual stress of the sample after electropolishing by X-ray.

[0085] The maximum Mises stress on the tooth surface is a key indicator for judging tooth surface fatigue failure. Specifically, it is the maximum contact stress on the tooth surface calculated by the rough tooth surface thermo-elastic-plastic hydrodynamic lubrication model.

[0086] The initial shape-sensitive volume parameter expression is preferably constructed based on the shape volume parameter expression in ISO 25178, which is the basic expression for the logic of the associated Abbott-Firestone curve.

[0087] In this step, the key indicator of tooth surface fatigue failure, namely the maximum Mises stress, is first calculated based on the historical three-dimensional surface topography height matrix and deep residual stress data of the ground sample. This provides a data foundation for subsequent parameter construction. Preferably, the maximum Mises stress can be calculated by inputting the historical three-dimensional surface topography height matrix and deep residual stress data of the ground sample into the rough tooth surface thermo-elastic-plastic-hydrodynamic lubrication model (TPEHL model). Then, based on the initial topography-sensitive volume parameter expression, the first topography-sensitive volume parameter expression is constructed to complete the preliminary optimization of the parameter expression. The initial topography-sensitive volume parameter expression is as follows:

[0088] ;

[0089] in, Here is the expression for the initial morphology-sensitive volume parameter. The preset scaling constant, To preset the initial load area ratio coefficient, Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value;

[0090] The expression for the first morphology-sensitive volume parameter is as follows:

[0091] ;

[0092] in, The first morphology-sensitive volume parameter, The preset scaling constant, The first load area ratio coefficient, This is the second load area ratio coefficient. Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value.

[0093] Furthermore, the above-mentioned method of determining the coefficients of the first shape-sensitive volume parameter expression based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface using a genetic algorithm can be understood as generating an optimization objective corresponding to the aforementioned correlation, and then using a genetic algorithm to determine the coefficients of the first shape-sensitive volume parameter expression based on this optimization objective. Specifically, this optimization objective could be aimed at improving the aforementioned correlation, and further, at improving the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface. The coefficients of this expression are determined through a genetic algorithm (non-dominated sorting genetic algorithm), and the coefficients are updated to the initial expression to obtain the second shape-sensitive volume parameter expression, thus making the expression strongly correlated with the tooth surface fatigue resistance.

[0094] Furthermore, based on the initial three-dimensional surface morphology height matrix and the expression for the second morphology-sensitive volume parameter of the target grinding part to be evaluated, the initial morphology-sensitive volume parameter of the target grinding part to be evaluated is calculated, and the correlation between the parameter and the original morphology of the target grinding part is established. Then, based on the difference between the initial morphology-sensitive volume parameter and the target morphology-sensitive volume parameter, the initial matrix is ​​adjusted to obtain the target surface morphology height matrix, thus completing the morphology control of the target grinding part. Finally, based on the adjusted target surface morphology height matrix, the evaluation result of the tooth surface fatigue resistance of the target grinding part is obtained, thus completing the entire evaluation process.

[0095] In one embodiment, a method for evaluating the fatigue resistance of gear tooth surfaces is specifically applied. The steps include: first, acquiring a dataset containing the morphology data of the ground sample and the measurement of deep residual stress; then, inputting the morphology and residual stress data into the rough tooth surface thermo-elastic-plastic-hydrodynamic lubrication model (TPEHL model) to calculate the maximum Mies stress. Here, the morphology data refers to the three-dimensional surface morphology height matrix, and the deep residual stress refers to the residual stress at different depths.

[0096] Specifically, 48 sets of ground planar part samples with different processing techniques were processed to approximately characterize the surface morphology after gear forming. Then, a white light interferometer was used to measure the surface morphology of the planar parts. Two sets of morphology were measured for each planar part, with a morphology matrix size of 42×243 and a sampling interval of 5. The topographic height matrix defines planar positions using rows and columns. Each cell value represents the height of that point. By using a fixed point spacing (resolution), the data index is converted into real spatial coordinates, thus fully recording the three-dimensional shape of the surface.

[0097] For example, the topography matrix is ​​in The number of data points in the direction (grinding direction) is 42, and the topography matrix is ​​in The number of data points in the direction (perpendicular to the grinding direction) is 243, and then using... Electrolytic polishing of planar parts using X-rays. X-ray residual stress meter measures the residual stress of the subsurface layer.

[0098] Furthermore, according to ISO 25178 The expression constructs new parameters. Used to enhance The morphology of the region with the maximum Hertzian contact stress on the tooth surface is highly characterized. Furthermore, based on a dataset containing morphology data of ground samples and measurements of deep residual stress, improvements are made. Using the correlation with the maximum Mies stress as the objective, the non-dominated sorting genetic algorithm (NSGA-II) was used to invert and determine the correlation. The coefficient was thus determined. The coefficient. Additionally. Includes Information, and reduces morphology The value is more in line with the grinding process.

[0099] like Figure 2 shown, specifically, The expression is as follows:

[0100] ;

[0101] in, Here is the expression for the initial morphology-sensitive volume parameter. This is a preset scaling constant, typically 1 / 100, used to convert percentages to decimals. To preset the initial load area ratio coefficient, The value ranges from 0 to 1, indicating The load-bearing area ratio, Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value, ;

[0102] The expression is as follows:

[0103] ;

[0104] in, The first morphology-sensitive volume parameter, The preset scaling constant, The first load area ratio coefficient, The value ranges from 0 to 1, indicating The load-bearing area ratio, It is the second load area ratio coefficient, and , and This is used to extract the core height range associated with the region of maximum Hertzian contact stress on the tooth surface from the Abbott-Fels curve, thereby eliminating redundant peak volumes. Figure 2 Interference from the volume of dark peaks in the middle. Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value, Figure 2 The calculated volume of the light-colored peak in the middle is The sum of the calculated volumes of the light-colored peak and the dark-colored peak is .

[0105] Specifically, the non-dominated sorting genetic algorithm (NSGA-II) is used to reverse the process. The coefficients are as follows: First, construct the optimization parameters. The specific optimization objectives are as follows:

[0106] ;

[0107] ;

[0108] ;

[0109] in, This is the Hertzian contact region. The third load area ratio coefficient, It is the fourth load area ratio coefficient, and , Indicates the Hertzian contact area in The range of coordinates in the direction, taking values ​​of , Indicates the maximum Mies stress. This indicates that parameters are calculated across all samples. and Pearson correlation coefficient, parameters , The settings are intended to identify key topographic regions related to the maximum mises stress, and are used to construct parameters. A crucial link, the goal Indicates parameters Correlation with maximum Mies stress, target Characterized Figure 2 The core height range in the middle.

[0110] Furthermore, while maintaining the spatial characteristics constraints of the grinding morphology, the morphology is precisely controlled. Value. Specifically, for the topography height matrix. The topography height matrix refers to any topography height matrix. These represent the rows and columns of the height matrix, respectively, first obtained using NSGA-II. coefficient and Substitution The expression will Updated to and will Updated to Then, by updating the coefficients Expression calculation initial Value .

[0111] Furthermore, based on the target parameters Value ,Compare and To select the adjustment mode based on size, if If you need to reduce, then select... Conversely, choose Specifically, first set the parameters. For 600,000 For 500~2000 0.01 and initialization Set it to 1, then... Pad the boundary with zeros to get Its dimensions are Its expression is as follows:

[0112] ;

[0113] Further calculation The height feature information of the edge points in the middle and lower reaches is stored in a matrix. In the middle. For example, point. For matrix One point in the middle, and , , Point The height matrix formed by the 8 adjacent points, assuming ,Right now The expression is as follows:

[0114] ;

[0115] Will Convert to dimension vector If we arrange them in ascending order, then there are three possibilities:

[0116] ;

[0117] Among them, point Height range Set to the following order , and Then point In the matrix The information line that needs to be saved as follows:

[0118] ;

[0119] In the formula, The height value of the index. R Point In the matrix The ranking, if points If the height value is the smallest, then , , , , Points In the matrix Location index, Point The adjustable height margin is set to 0.1~0.8μm in this embodiment and can be adjusted according to computational efficiency. Height feature extraction requires traversing... All non-edge points in the middle, i.e. It stores Line height information.

[0120] Furthermore, according to The magnitude of the value, for the matrix Sort all rows in descending order, if If the process ends, the final matrix is ​​output. With the final ( );like Then in the interval Generate random integers And select the adjustment mode according to the size, specifically performing the following logical operations:

[0121] ;

[0122] ;

[0123] In the formula, This represents logical AND. , All are in the matrix The index value. This includes calculating the height value to be reduced or increased. See the following formula:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] In the formula, This means generating random numbers between 0 and 1. This indicates finding the maximum value between two numbers. Then, it determines... Is it less than ,like Less than Then update the matrix. and To perform the following calculations based on different modes:

[0129] ;

[0130] ;

[0131] ;

[0132] ;

[0133] When the height value of the control position changes, the height information of the other 8 points within the associated area also changes. Therefore, it is necessary to... Updating the height information of these points internally only requires considering middle α , β The change.

[0134] Furthermore, if Greater than Then right If the remainder is found, right If the remainder is 0, then the matrix is ​​calculated. of value , and display, and then calculate and The absolute difference. If and The absolute difference is less than If the process ends, the final matrix is ​​output. With the final ( ).

[0135] like right If the result of the remainder is not 0, then And then continue to judge and ,like Then, starting from the above "if" Then in the interval Generate random integers And select the adjustment mode according to the size, and execute the following logical operations to begin execution; if If the process ends, the final matrix is ​​output. With the final ( ).

[0136] Furthermore, from the dataset containing morphology data and deep residual stress measurements of the ground samples, one group of samples with a large maximum Mies stress was randomly selected. This was achieved by precisely controlling the morphology while maintaining the spatial constraints of the grinding morphology. The "value" method reduces the morphology The value is then used to re-input the adjusted morphology into the rough tooth surface TPEHL model to calculate the maximum mises stress.

[0137] On the other hand, after morphology adjustment, the morphology with the minimum maximum Mies stress is selected, and its calculation is performed. Values, where the initial morphology is... The value is adjusted using the following formula:

[0138] ;

[0139] In the formula, Indicates the specified value, These represent the morphology before and after regulation, respectively. This indicates the calculation of standard deviation.

[0140] Further, will The adjusted morphology is input into the rough tooth surface TPEHL model, its maximum Mies stress is calculated, and finally the adjusted morphology can be visually compared. and The effect of the value on the maximum Mises stress.

[0141] In this first embodiment, a grinding experiment was first conducted on a VMC850 CNC machine tool to grind a planar part (machined surface size of 8×8mm) of AISI 9310 aerospace gear steel, which is used to approximate the forming method of spur gear machining. The grinding parameters are shown in Table 1, with the grinding wheel depth of cut, rotational speed, and feed rate being orthogonal, i.e., a total of 48 sets of parameters.

[0142] Table 1

[0143]

[0144] Input these parameters into the rough tooth surface TPEHL modeling tool. The spur gear pair and lubricating oil modeling parameters are shown in Table 2.

[0145] Table 2

[0146]

[0147] Furthermore, the results of the Pareto solution searched through NSGA-II are as follows: Figure 3 As shown, it exhibits two distinct distribution patterns. Two representative solutions were extracted from it based on the maximum Pearson correlation coefficient, denoted as... and The Pearson correlation coefficients between these values ​​and the maximum Mises stress reached 0.632 and 0.655, respectively, indicating a strong correlation (0.6–0.8). of The values ​​were 42, 186, 10.2%, and 26.7% respectively. of The values ​​were 60, 192, 0.6%, and 81.7% respectively. The final morphology adjustment area needed to cover the Hertz contact region. and The values ​​are 30 and 213. In summary, the final morphology adjustment area is: in The orientation was selected from the 0.915mm morphological region in the middle. The direction selects all the shapes.

[0148] Furthermore, a group of morphology samples with a large maximum mises stress was randomly selected, and the morphology was precisely controlled under the premise of maintaining the spatial characteristic constraints of the grinding morphology. The method of "value" reduces respectively and Values. The changes in morphology before and after morphology adjustment, and the Abbott-Firestone curve results are shown below. Figures 4-7 As shown, Figure 4 This represents the initial grinding morphology. Figure 5 The final morphology after adjustment. Figure 6 , Figure 7 These respectively demonstrate that the morphology was modulated , The Abbott-Firestone curve changes after value adjustment are shown in Table 3, which compares the key ISO 25178 parameters before and after morphology sample adjustment.

[0149] Table 3

[0150]

[0151] Furthermore, the adjusted morphology was input into the rough tooth surface TPEHL model, and the maximum Mises stress was output. The corresponding results are shown in Table 4. Figure 8 , Figure 9As shown. Then, from Table 4, the controlled morphology with the minimum maximum mises stress was selected, and its... The maximum Mies stress of the rough tooth surface was then calculated using the TPEHL model. Table 4 shows a comparison of the maximum Mies stress of the samples before and after morphology adjustment in the TPEHL model simulation.

[0152] Table 4

[0153]

[0154] Therefore, in comparison Figure 4 , Figure 5 As can be visually observed, the surface texture and spatial structure show no significant deformation after morphology adjustment. Table 3 also quantitatively confirms that the spatial parameters Sal and Str do not change significantly. The Sal and Str values ​​for the morphology samples are 8.542 and 0.257, respectively. After morphology adjustment, the fluctuation ranges of Sal and Str are 7.406–8.616 and 0.223–0.262, respectively. This indicates that the adjustment system is effective in adjusting... At the same time, it effectively maintains the spatial topological characteristics of the surface morphology, which is crucial for maintaining the consistency of lubrication performance and material contact behavior in the contact area.

[0155] Furthermore, as can be clearly seen from Table 3, the method of this embodiment can arbitrarily adjust the peak volume of the morphology, thereby reducing the peak volume and improving the morphology. The values ​​will decrease, and the Ssk and Sku values ​​will fluctuate. After the morphology samples are adjusted, The value decreased from 0.563 to 0.447. The fluctuation range of Ssk was -1.324 to -0.544, all of which were negatively skewed. The fluctuation range of Sku was 3.047 to 3.742, all of which were positively peaked, and the computation time of the method in this embodiment was only a few tens of seconds at most. As shown in Table 4, the computation time of the method in this embodiment was 3.0 to 35.8 seconds.

[0156] Therefore, reduce The improvement in fatigue resistance is more significant for rough tooth surfaces. Morphology samples After optimization, the maximum Mies stress on the tooth surface under load was significantly reduced from 1282.1 to 1150.6. In contrast, by adjusting... The value can only reduce the maximum Mises stress to 1196.1 MPa, and the optimization effect is weaker than... This indicates It is more targeted and effective in optimizing the maximum mises stress on rough tooth surfaces.

[0157] In conclusion, with Optimize in a guiding way The coefficient significantly improved the correlation between morphological parameters and the maximum mises stress on the tooth surface. More importantly, while maintaining a reasonable surface roughness, a significant reduction of over 10% in the maximum tooth surface stress was achieved. This result indicates that the fatigue-resistant design of high-performance tooth surfaces no longer necessarily relies on extremely low-roughness surface machining, but can achieve excellent contact performance over a wide roughness range by controlling the morphological volume parameters closely related to the contact stress response. Therefore, the method in this embodiment has the potential to expand the "economical machining range" of high-performance tooth surfaces, allowing reliable fatigue life and transmission performance to be achieved under more relaxed machining accuracy and lower manufacturing costs. This patent application provides a new method for gear surface engineering to transform from "experience-based design" to "physical data-driven precision design," offering new ideas for the fatigue-resistant design of high-performance rough tooth surfaces.

[0158] In some embodiments, in step S120, a first shape-sensitive volume parameter expression is constructed based on the initial shape-sensitive volume parameter expression, including the following step S210:

[0159] Step S210: Introduce a first load area ratio coefficient and a second load area ratio coefficient into the integral upper and lower limits of the initial shape-sensitive volume parameter expression to extract the core height range associated with the Hertzian contact stress region of the tooth surface, and construct the first shape-sensitive volume parameter expression, wherein the first load area ratio coefficient is smaller than the second load area ratio coefficient.

[0160] In this embodiment, the initial shape-sensitive volume parameter expression is obtained based on the integral calculation of the Albert-Firstone curve. The first shape-sensitive volume parameter expression is obtained by multiplying a preset scaling constant by the integral result. The integral result is the difference between the height value of the Albert-Firstone curve at the integral variable and the height value of the Albert-Firstone curve at the second load area ratio coefficient. The integral variable is the load area ratio, the lower limit of the integral variable is the first load area ratio coefficient, and the upper limit of the integral variable is the second load area ratio coefficient. Its expression is as follows:

[0161] ;

[0162] in, The first morphology-sensitive volume parameter, The preset scaling constant, The first load area ratio coefficient, This is the second load area ratio coefficient. Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value.

[0163] Specifically, the Abbott-Firestone curve, also known as the TP curve, describes the relationship between surface morphology height distribution and load area ratio. The horizontal axis represents the load area ratio (0-100%), and the vertical axis represents the height value. The first and second load area ratio coefficients are expressed as follows: and All are percentages between 0 and 1, and It is used to extract a specific height range from the Abbott-Firestone curve; the Hertz contact stress region refers to the local normal stress peak region that is generated in the theoretical contact elliptical region during gear meshing and is distributed in a semi-elliptical manner.

[0164] Therefore, this embodiment introduces the upper and lower limits of integration into the expression for the initial peak volume parameter. and Two coefficients are used to extract the core height range associated with the Hertzian contact stress region of the tooth surface (e.g., Figure 2 The light-colored peak volume region in the image eliminates the interference of redundant peak volumes, thus constructing a first morphology-sensitive volume parameter expression that is more relevant to the maximum Mises stress on the tooth surface.

[0165] In some embodiments, in step S130, based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface, the coefficients of the first shape-sensitive volume parameter expression are determined by inversion using a genetic algorithm to update the coefficients of the initial shape-sensitive volume parameter expression, thereby obtaining the second shape-sensitive volume parameter expression, including the following steps S310 to S350:

[0166] Step S310: Construct an optimization variable set, which includes a first load area ratio coefficient, a second load area ratio coefficient, and position coordinate parameters used to identify the Hertzian contact stress region on the tooth surface.

[0167] Step S320: Establish the first optimization objective function. The first optimization objective function is used to maximize the Pearson correlation coefficient between the calculated value of the first morphology-sensitive volume parameter expression and the maximum Mises stress on the tooth surface.

[0168] Step S330: Establish a second optimization objective function. The second optimization objective function is used to characterize the matching degree between the coverage range of the core height range and the Hertzian contact stress region of the tooth surface.

[0169] Step S340: Use the non-dominated sorting genetic algorithm to solve the first and second optimization objective functions in a multi-objective manner to obtain the Pareto solution set. Then, extract the solutions whose Pearson correlation coefficients meet the preset conditions from the Pareto solution set to update the first and second load area ratio coefficients, and obtain the third and fourth load area ratio coefficients.

[0170] Step S350: Update the coefficients of the initial morphology-sensitive volume parameter expression based on the third and fourth load area ratio coefficients to obtain the second morphology-sensitive volume parameter expression.

[0171] Specifically, the position coordinate parameters are expressed as and Used to identify key topographic regions associated with the Hertzian contact region in the topographic matrix (in (Coordinate range in the direction); Pearson correlation coefficient is used to measure the linear correlation between the first morphology-sensitive volume parameter and the maximum Mises stress. The numerical range is 0-1. The closer it is to 1, the stronger the correlation. In this embodiment, the preset condition for Pearson correlation coefficient is preferably to reach a strong correlation level; Pareto solution set refers to a set of optimal solutions in multi-objective optimization where no objective can be further optimized without harming other objectives.

[0172] In this embodiment, we first describe the construction of an optimization variable set including first and second load area ratio coefficients and position coordinate parameters to determine the solution object of the algorithm. The position coordinate parameters are used to accurately identify the Hertzian contact stress region of the tooth surface, so that the coefficient solution is more in line with the actual contact situation of the tooth surface. Then, we establish two optimization objective functions. The first function is used to improve the correlation between the parameters and the maximum Mises stress of the tooth surface, and the second function is used to ensure the matching degree between the core height range and the key area of ​​the tooth surface, so as to achieve multi-objective constraint optimization.

[0173] Furthermore, a non-dominated sorting genetic algorithm is used to solve the two objective functions in a multi-objective manner to obtain a Pareto solution set. The solution from the Pareto solution set with a Pearson correlation coefficient reaching a strong correlation level is selected as the optimal solution, and the first and second load area ratio coefficients are updated based on this optimal solution (i.e., the first load area ratio coefficient is updated). Second load area ratio coefficient ), thus obtaining the third and fourth load area ratio coefficients (third load area ratio coefficient) and the fourth load area ratio coefficient The criteria for extracting the optimal solution and the coefficient update results were defined. Finally, based on the updated third and fourth load area ratio coefficients, the integral upper and lower limit coefficients of the initial morphology-sensitive volume parameter expression were adjusted. Specifically, the integral lower limit coefficient of 0 in the initial morphology-sensitive volume parameter expression was updated to the third load area ratio coefficient. And preset the initial load area ratio coefficient in the integral upper limit coefficient of the initial morphology-sensitive volume parameter expression. Updated to the fourth load area ratio factor Finally, the expression for the second morphology-sensitive volume parameter, which is strongly correlated with fatigue failure, is obtained.

[0174] The expression for the second morphology-sensitive volume parameter is as follows:

[0175] ;

[0176] in, This is the second morphology-sensitive volume parameter. This is a preset scaling constant, typically 1 / 100, used to convert percentages to decimals. The third load area ratio coefficient represents The load-bearing area ratio, It is the fourth load area ratio coefficient, and , Represents the variable Accumulate points. This indicates that the Abbott-Firstone curve is in The corresponding height value, This indicates that the Abbott-Firstone curve is in The corresponding height value.

[0177] In some embodiments, in step S150, the initial shape-sensitive volume parameter of the target grinding part is calculated based on the initial three-dimensional surface topography height matrix and the expression for the second shape-sensitive volume parameter, including the following steps S410 to S430:

[0178] Step S410: Determine the position coordinate parameters used to identify key morphology regions in the expression of the second morphology-sensitive volume parameter. The position coordinate parameters are used to extract the local morphology region corresponding to the Hertzian contact stress region of the tooth surface from the initial three-dimensional surface morphology height matrix.

[0179] Step S420: Based on the third and fourth load area ratio coefficients contained in the expression of the second morphology-sensitive volume parameter, determine the core height integral interval of the target grinding part on the Albert-Firstone curve corresponding to the local morphology region.

[0180] Step S430: Perform integration calculation on the curve within the core height integration interval of the target grinding part to obtain the initial morphology sensitive volume parameters of the target grinding part.

[0181] Specifically, the local morphology region refers to the part corresponding to the Hertz contact region that is extracted from the complete initial three-dimensional surface morphology height matrix, and is determined according to the optimized position coordinate parameters; the core height integration interval refers to the integration range determined on the Abbott-Firstone curve of the local morphology region based on the optimized third and fourth load area ratio coefficients.

[0182] In this embodiment, firstly, the position coordinate parameters in the expression for the second morphology-sensitive volume parameter are determined, and based on these parameters, a local morphology region is extracted from the initial three-dimensional surface morphology height matrix of the target grinding part. The purpose is to focus on the key morphology region corresponding to the Hertzian contact stress on the tooth surface, eliminate the interference of redundant morphology data, and make the parameter calculation more accurate. Then, based on the third and fourth load area ratio coefficients in the second expression, the upper and lower limits of integration are determined on the Albert-Firstone curve corresponding to the local morphology region, forming the core height integration interval of the target grinding part. This defines the integration range for the parameter calculation of the target grinding part and matches the coefficients of the expression. Finally, according to the calculation logic of the expression for the second morphology-sensitive volume parameter, the Albert-Firstone curve within the core height integration interval is integrated to obtain the initial morphology-sensitive volume parameter of the target grinding part. This ensures that the parameter calculation logic of the target grinding part is consistent with the expression, establishing the correlation between the original morphology of the target grinding part and the morphology-sensitive volume parameter.

[0183] In some embodiments, in step S160, based on the initial topography-sensitive volume parameters and the target topography-sensitive volume parameters of the target grinding part, the initial three-dimensional surface topography height matrix is ​​adjusted to obtain the target surface topography height matrix, including the following steps S510 to S550:

[0184] Step S510: Based on the initial topography sensitive volume parameters and the target topography sensitive volume parameters, determine the adjustment mode of the initial three-dimensional surface topography height matrix. The adjustment mode includes a height increase mode or a height decrease mode.

[0185] Step S520: Expand the boundary of the initial three-dimensional surface topography height matrix to obtain the expanded topography height matrix;

[0186] Step S530: Traverse all non-boundary points in the expanded topographic height matrix, extract the height information of each point and its eight neighboring points to construct a feature information matrix. The feature information matrix includes the height value of each point, the height sorting in the neighborhood, the upper and lower limits of the height adjustment range, the spatial location index, and the preset adjustable height margin.

[0187] Step S540: Calculate the number of height points to be adjusted based on the fourth load area ratio coefficient in the expression of the second morphology-sensitive volume parameter and the total number of points in the initial three-dimensional surface morphology height matrix;

[0188] Step S550: Based on the adjustment mode and the number of height points to be adjusted, the height points in the initial three-dimensional surface topography height matrix are iteratively updated using the feature information matrix. During the update process, the feature information of the eight adjacent points of the point to be adjusted is updated synchronously until the updated topography sensitive volume parameters meet the preset error conditions, and the target surface topography height matrix is ​​obtained.

[0189] Specifically, the adjustment mode refers to the height adjustment method of the initial 3D surface topography height matrix, which is divided into height increase mode and height decrease mode, determined by the numerical relationship between the initial topography sensitive volume parameter and the target topography sensitive volume parameter; boundary zeroing refers to the processing method of expanding the initial 3D surface topography height matrix by one unit in both the row and column directions and setting the values ​​of the expanded edge points to 0; the expanded topography height matrix is ​​the matrix obtained after zeroing the boundaries of the initial matrix; non-boundary points are the points in the expanded topography height matrix excluding the edges; neighborhood points refer to the eight points adjacent to a non-boundary point in the expanded matrix, which together constitute the height feature matrix of that point; the feature information matrix is ​​a matrix that stores the height feature information of all non-boundary points in the expanded matrix, including key information such as the height value of each point and the neighborhood height sorting; adjustable height margin refers to the adjustable height range of each non-boundary point.

[0190] In this embodiment, the initial shape-sensitive volume parameter and the target shape-sensitive volume parameter are first compared to determine the adjustment mode of the initial matrix, thus defining the direction for shape adjustment. If the initial shape-sensitive volume parameter is less than the target value, the adjustment mode is height increase mode. If the initial shape-sensitive volume parameter is greater than the target value, the adjustment mode is height decrease mode. Then, the initial matrix is ​​expanded by padding the boundaries with zeros to obtain an expanded shape height matrix that is expanded by one unit in both the row and column directions, preparing for subsequent height feature information extraction.

[0191] Furthermore, all non-boundary points of the expanded matrix are traversed, and the height information of each point and its eight neighboring points is extracted. A feature information matrix containing information such as the height value of each point and the order of neighboring heights is constructed, and the range of adjustable height margin is determined to complete the basic data preparation for shape adjustment. Then, based on the fourth load area ratio coefficient and the total number of points in the initial matrix, the number of height points to be adjusted is calculated, and the number of core objects for shape adjustment is determined.

[0192] Furthermore, based on the adjustment mode and the number of points to be adjusted, the height points of the initial matrix are iteratively updated using the feature information matrix. During the update process, the feature information of the eight neighboring points of the point to be adjusted is updated synchronously. After each iteration, the current topography sensitive volume parameter is calculated. When the absolute difference between this parameter and the target parameter is less than the preset error threshold, the iteration stops, and the target surface topography height matrix is ​​obtained. The entire adjustment process must maintain the original spatial features and surface texture of the grinding topography.

[0193] In some embodiments, the fatigue resistance evaluation result of the target ground part is obtained in step S170 based on the target surface topography height matrix, including the following steps S610 to S620:

[0194] Step S610: Calculate the initial three-dimensional surface topography height matrix and the target surface topography height matrix of the target grinding part with the deep residual stress data at different depths of the target grinding part to obtain the initial maximum Mises stress on the tooth surface and the target maximum Mises stress on the tooth surface of the target grinding part.

[0195] Step S620: Determine the fatigue resistance grade of the tooth surface of the target grinding part based on the initial maximum Mises stress and the target maximum Mises stress of the tooth surface, so as to obtain the fatigue resistance evaluation result of the target grinding part.

[0196] In this embodiment, the initial three-dimensional surface topography height matrix and the target surface topography height matrix of the target grinding part are first calculated with the deep residual stress data at different depths of the target grinding part to obtain the initial maximum Mises stress and the target maximum Mises stress of the tooth surface. Specifically, the initial and target three-dimensional surface topography height matrices of the target grinding part are matched one by one with the deep residual stress data, and the two sets of matched data are input into the rough tooth surface thermo-elastic-plastic hydrodynamic lubrication model. The initial and target maximum Mises stresses of the tooth surface are obtained through model simulation calculation, providing core data for fatigue resistance performance evaluation.

[0197] Furthermore, the change in target stress relative to initial stress is calculated, and the fatigue resistance level of the tooth surface is determined according to preset rules. The criteria for determining the performance level are defined, and then, based on the determined fatigue resistance level of the tooth surface, a fatigue resistance performance evaluation result containing stress change data and performance level is generated. This makes the evaluation result more intuitive and complete. By comparing the stress changes before and after adjustment, the improvement in fatigue resistance performance brought about by morphology optimization is intuitively quantified. By determining the performance level, a clear basis for evaluating machining quality is provided to engineers, verifying the effectiveness of the entire evaluation and optimization method.

[0198] like Figure 10As shown in some embodiments of this application, a gear tooth surface fatigue resistance evaluation system is provided. The system includes a first module 1010, a construction module 1020, an update module 1030, an acquisition module 1040, a calculation module 1050, an adjustment module 1060, and an evaluation module 1070. Specifically:

[0199] The first module 1010 is used to calculate the maximum Mises stress on the tooth surface based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths.

[0200] Module 1020 is used to construct the first morphology-sensitive volume parameter expression based on the initial morphology-sensitive volume parameter expression;

[0201] The update module 1030 is used to determine the coefficients of the first morphology-sensitive volume parameter expression based on the correlation between the first morphology-sensitive volume parameter expression and the maximum Mises stress on the tooth surface using a genetic algorithm, so as to update the coefficients of the initial morphology-sensitive volume parameter expression and obtain the second morphology-sensitive volume parameter expression.

[0202] The acquisition module 1040 is used to acquire the initial three-dimensional surface topography height matrix and the target topography sensitive volume parameters of the target grinding part;

[0203] The calculation module 1050 is used to calculate the initial shape-sensitive volume parameters of the target grinding part based on the initial three-dimensional surface topography height matrix and the expression for the second shape-sensitive volume parameter.

[0204] The adjustment module 1060 is used to adjust the initial three-dimensional surface topography height matrix based on the initial topography sensitive volume parameters and the target topography sensitive volume parameters of the target grinding part, so as to obtain the target surface topography height matrix.

[0205] Evaluation module 1070 is used to obtain the fatigue resistance evaluation results of the target grinding part based on the target surface topography height matrix.

[0206] It should be noted that the gear tooth surface fatigue performance evaluation system provided in this embodiment is based on the same inventive concept as the gear tooth surface fatigue performance evaluation method described above. Therefore, the relevant content of the gear tooth surface fatigue performance evaluation method described above also applies to the content of the gear tooth surface fatigue performance evaluation system. Therefore, it will not be repeated here.

[0207] The system calculates the maximum Mises stress based on historical topographic height matrices and deep residual stress data at different depths; constructs a first expression based on an initial expression; determines the coefficients of the first expression by inversion based on the correlation between the first expression and the maximum Mises stress, thus obtaining a second expression; calculates initial topographic volume parameters based on the initial topographic height matrix and the second expression; adjusts the initial topographic height matrix based on the initial topographic volume parameters and the target topographic volume parameters to obtain the target surface topographic height matrix; and obtains the fatigue performance evaluation result based on the target surface topographic height matrix. In this way, by constructing new topographic volume parameters strongly correlated with Mises stress and optimizing the parameter coefficients by inversion, a precise correlation between topographic parameters and fatigue performance can be achieved, thereby improving the accuracy and scientific rigor of the fatigue performance evaluation.

[0208] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for evaluating the fatigue resistance of gear tooth surfaces.

[0209] like Figure 11 , Figure 11 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes:

[0210] At least one battery;

[0211] At least one memory;

[0212] At least one processor;

[0213] At least one program;

[0214] The program is stored in memory, and the processor executes at least one program to implement the above-described method for evaluating the fatigue resistance of gear tooth surfaces.

[0215] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0216] The electronic devices according to embodiments of this application will now be described in detail.

[0217] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0218] The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to perform a method for evaluating the fatigue resistance of gear tooth surfaces according to an embodiment of this disclosure.

[0219] The input / output interface 1800 is used to implement information input and output.

[0220] The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0221] Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900);

[0222] The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0223] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for evaluating the fatigue resistance of gear tooth surfaces.

[0224] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0225] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0226] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0227] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0228] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0229] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any related variations, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0230] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0231] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0232] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0233] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0234] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0235] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

[0236] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method for evaluating the fatigue resistance of gear tooth surfaces, characterized in that, The method includes: Based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths, the maximum Mises stress on the tooth surface is calculated. Based on the initial morphology-sensitive volume parameter expression, construct the first morphology-sensitive volume parameter expression; Based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface, the coefficients of the first shape-sensitive volume parameter expression are determined by inversion using a genetic algorithm, so as to update the coefficients of the initial shape-sensitive volume parameter expression and obtain the second shape-sensitive volume parameter expression. Obtain the initial three-dimensional surface topography height matrix and target topography sensitive volume parameters of the target grinding part; The initial shape-sensitive volume parameters of the target grinding part are calculated based on the initial three-dimensional surface topography height matrix and the expression for the second topography-sensitive volume parameter. Based on the initial morphology-sensitive volume parameters and the target morphology-sensitive volume parameters of the target grinding part, the initial three-dimensional surface morphology height matrix is ​​adjusted to obtain the target surface morphology height matrix; The fatigue resistance evaluation result of the target grinding part is obtained based on the target surface morphology height matrix.

2. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 1, characterized in that, The initial shape-sensitive volume parameter expression is obtained based on the Abbott-Firstone curve integral. The construction of the first shape-sensitive volume parameter expression based on the initial shape-sensitive volume parameter expression includes: A first load area ratio coefficient and a second load area ratio coefficient are introduced into the integral upper and lower limits of the initial shape-sensitive volume parameter expression to extract the core height range associated with the Hertzian contact stress region of the tooth surface, thereby constructing the first shape-sensitive volume parameter expression, wherein the first load area ratio coefficient is smaller than the second load area ratio coefficient.

3. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 2, characterized in that, The first shape-sensitive volume parameter expression is obtained by multiplying a preset scaling constant by the integral operation result; wherein, the integral operation result is the difference between the height value of the Abbott-Firstone curve at the integral variable and the height value of the Abbott-Firstone curve at the second load area ratio coefficient, the integral variable is the load area ratio, the lower limit of the integral variable is the first load area ratio coefficient, and the upper limit of the integral variable is the second load area ratio coefficient.

4. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 2, characterized in that, The first topography-sensitive volume parameter expression is determined by using a genetic algorithm to invert and update the coefficients of the initial topography-sensitive volume parameter expression based on the correlation between the first topography-sensitive volume parameter expression and the maximum Mises stress on the tooth surface, thereby obtaining the second topography-sensitive volume parameter expression, including: Construct an optimization variable set, which includes the first load area ratio coefficient, the second load area ratio coefficient, and position coordinate parameters for identifying the Hertzian contact stress region on the tooth surface. A first optimization objective function is established, which is used to maximize the Pearson correlation coefficient between the calculated value of the first morphology-sensitive volume parameter expression and the maximum Mises stress on the tooth surface; A second optimization objective function is established, which is used to characterize the matching degree between the coverage range of the core height interval and the Hertzian contact stress region of the tooth surface; The first and second optimization objective functions are solved using a non-dominated sorting genetic algorithm to obtain a Pareto solution set. The first and second load area ratio coefficients are updated by extracting solutions from the Pareto solution set whose Pearson correlation coefficients meet preset conditions, thereby obtaining the third and fourth load area ratio coefficients. Based on the third load area ratio coefficient and the fourth load area ratio coefficient, the coefficients of the initial topography-sensitive volume parameter expression are updated to obtain the second topography-sensitive volume parameter expression.

5. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 1, characterized in that, The step of calculating the initial shape-sensitive volume parameters of the target grinding part based on the initial three-dimensional surface topography height matrix and the second shape-sensitive volume parameter expression includes: Determine the position coordinate parameters used to identify key morphology regions in the second morphology-sensitive volume parameter expression. The position coordinate parameters are used to extract local morphology regions corresponding to the Hertzian contact stress region of the tooth surface from the initial three-dimensional surface morphology height matrix. Based on the third and fourth load area ratio coefficients contained in the second morphology-sensitive volume parameter expression, the core height integral interval of the target grinding part is determined on the Albert-Firstone curve corresponding to the local morphology region. The initial morphology-sensitive volume parameters of the target grinding part are obtained by integrating the curve within the core height integration interval of the target grinding part.

6. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 1, characterized in that, The process of adjusting the initial three-dimensional surface topography height matrix based on the initial topography-sensitive volume parameters and the target topography-sensitive volume parameters of the target grinding workpiece to obtain the target surface topography height matrix includes: Based on the initial topography-sensitive volume parameters and the target topography-sensitive volume parameters, the adjustment mode of the initial three-dimensional surface topography height matrix is ​​determined, and the adjustment mode includes a height increase mode or a height decrease mode. The initial three-dimensional surface topography height matrix is ​​expanded by boundary extension to obtain the expanded topography height matrix; Traverse all non-boundary points in the expanded topographic height matrix, extract the height information of each point and its eight neighboring points to construct a feature information matrix. The feature information matrix includes the height value of each point, the height sorting in the neighborhood, the upper and lower limits of the height adjustment range, the spatial location index, and the preset adjustable height margin. The number of height points to be adjusted is calculated based on the fourth load area ratio coefficient in the expression of the second morphology-sensitive volume parameter and the total number of points in the initial three-dimensional surface morphology height matrix. Based on the adjustment mode and the number of height points to be adjusted, the height points in the initial three-dimensional surface topography height matrix are iteratively updated using the feature information matrix, and the feature information of the eight adjacent points of the point to be adjusted is updated synchronously during the update process until the updated topography sensitive volume parameters meet the preset error conditions, thereby obtaining the target surface topography height matrix.

7. The method for evaluating the fatigue resistance of gear tooth surfaces according to claim 1, characterized in that, The step of obtaining the fatigue resistance evaluation result of the target ground part based on the target surface topography height matrix includes: The initial three-dimensional surface topography height matrix and the target surface topography height matrix of the target grinding part are calculated with the deep residual stress data of the target grinding part at different depths to obtain the initial maximum Mises stress and the target maximum Mises stress of the tooth surface of the target grinding part. Based on the initial maximum Mises stress on the tooth surface of the target grinding part and the maximum Mises stress on the target tooth surface, the fatigue resistance grade of the tooth surface of the target grinding part is determined to obtain the fatigue resistance evaluation result of the target grinding part.

8. A gear tooth surface fatigue resistance evaluation system, characterized in that, The system includes: The first module is used to calculate the maximum Mises stress on the tooth surface based on the historical three-dimensional surface topography height matrix and deep residual stress data at different depths. The module is used to construct the first morphology-sensitive volume parameter expression based on the initial morphology-sensitive volume parameter expression; The update module is used to determine the coefficients of the first shape-sensitive volume parameter expression based on the correlation between the first shape-sensitive volume parameter expression and the maximum Mises stress on the tooth surface using a genetic algorithm, so as to update the coefficients of the initial shape-sensitive volume parameter expression and obtain the second shape-sensitive volume parameter expression. The acquisition module is used to acquire the initial three-dimensional surface topography height matrix and the target topography sensitive volume parameters of the target grinding part; The calculation module is used to calculate the initial shape-sensitive volume parameters of the target grinding part based on the initial three-dimensional surface topography height matrix and the second shape-sensitive volume parameter expression. The adjustment module is used to adjust the initial three-dimensional surface topography height matrix based on the initial topography sensitive volume parameters and the target topography sensitive volume parameters of the target grinding part, so as to obtain the target surface topography height matrix. The evaluation module is used to obtain the fatigue resistance evaluation result of the target grinding part based on the target surface topography height matrix.

9. An electronic device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a method for evaluating the fatigue resistance of gear tooth surfaces as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method for evaluating the fatigue resistance of gear tooth surfaces as described in any one of claims 1 to 7.

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