A simulation experiment method and system for surface heat treatment strengthening of a steel gear

Abstract

The application relates to the field of data processing, in particular to a simulation experiment method and system for surface heat treatment strengthening of a steel gear. The application comprises the following steps: acquiring a grid model; applying a target working condition to the grid model to obtain a stress value change sequence; calculating the convergence speed of each finite element in the grid model; clustering the convergence speed set through an iterative self-organizing clustering algorithm to obtain a clustering result; acquiring the convergence speed average of each grid cluster in the clustering result to determine a target cluster; acquiring the pseudo-stable quantity of each target cluster, and calculating the harmonic speed value of each target cluster according to the pseudo-stable quantity of each target cluster; determining the grid size of the finite element corresponding to each target cluster according to the harmonic speed value of each target cluster; acquiring an updated grid model, and using the updated grid model to perform simulation experiment on the steel gear. The application can improve the fineness of the gear finite element model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a simulation experiment method and system for surface heat treatment strengthening of steel gear. BACKGROUND

[0002] Surface heat treatment strengthening of steel gear refers to changing the physical and mechanical properties of the gear surface and its near-surface layer region through heat treatment process to enhance its wear resistance, fatigue resistance, corrosion resistance, etc. Since gears often bear large mechanical loads and operate in harsh working environments, surface heat treatment has become an important means to improve the performance of gears. Specifically, the surface heat treatment strengthening of steel gear usually includes the following main purposes: 1. Improve hardness: change the hardness of the gear surface and its near-surface layer region through heat treatment to enhance the wear resistance of the gear and reduce the gear surface wear; 2. Enhance fatigue resistance: improve the fatigue resistance of the gear surface to extend the service life of the gear, especially in high load, impact load working environment; 3. Increase corrosion resistance: improve the resistance of the gear surface to corrosion, especially in humid or corrosive environments.

[0003] Common methods for surface heat treatment strengthening of steel gear include carburizing, exposing the gear surface to a carbon-containing gas to heat and form a carbon-containing layer, and then quenching to obtain a hardened layer; induction hardening, electromagnetic induction heating of the gear surface to rapidly heat it to the austenite region, then quenching to form a hardened layer; laser hardening, using a laser beam to locally heat the gear surface to rapidly reach the required temperature, then quenching; nitriding, introducing nitrogen into the gas to form a nitrided layer on the gear surface. The nitrided layer has high hardness, good wear resistance, and strong corrosion resistance; high-frequency quenching, i.e. induction hardening, heating the gear surface by high-frequency current to rapidly raise the surface temperature and then quenching for strengthening, mainly used for surface hardening of gears. In order to simulate the experiment, the finite element analysis method is used, which is a numerical calculation method that discretizes complex physical problems (such as heat conduction, mechanical deformation, thermal stress coupling, etc.) into a number of small regions (finite elements), then uses mathematical models to simulate the behavior of these regions to estimate the response of the entire structure under various working conditions. Griding (discretization) is the process of dividing a continuous geometric body (such as a gear model) into a set of finite, computable small regions. Through this process, complex physical problems can be converted into discrete problems that can be solved mathematically. Specifically, in heat treatment simulation, griding is the refinement of the three-dimensional space of the gear CAD model, dividing the entire region into a series of small elements (finite elements), such as simulating the process of gear induction quenching.

[0004] The complexity of the gear structure, especially the non-uniformity of the main areas such as the tooth surface and the tooth root, determines that the mesh needs to be divided non-uniformly, in order to ensure that the calculation complexity is low and the accuracy of the grid calculation is improved, the sensitivity of the network is analyzed to determine the fineness of the grid division.

[0005] However, in the scenario of gear induction quenching, due to the complexity of the gear structure, the induction method of feedback stability cannot accurately measure the grid fineness of the main structural areas such as the tooth surface and the tooth root, because the stress field, temperature and hardness distribution corresponding to such areas will be different from other regular areas of the gear due to the influence of the structure position, and then the sensitivity of the gear parameters under different grid conditions will cause the physical influence between the parameters due to the inconsistency of the neighborhood grid parameters (for example, the temperature of the tooth root area is different from that of the tooth surface area), which will cause the existence of the false "stability" with errors, and cannot accurately judge the stability trend. SUMMARY

[0006] The present application provides a kind of steel gear surface heat treatment strengthening simulation experiment method and system to solve the existing problems.

[0007] The steel gear surface heat treatment strengthening simulation experiment method of the present application adopts the following technical scheme:

[0008] One embodiment of the present application provides a kind of steel gear surface heat treatment strengthening simulation experiment method, which comprises the following steps:

[0009] The finite element model of the steel gear is divided according to the target parameters to obtain the grid model, wherein the target parameters are obtained by determining each grid parameter in the grid parameter set as the target parameter, and the grid parameter set is obtained by determining each grid interval in the grid interval set as the grid parameter set;

[0010] The target working condition is applied to the grid model to obtain the stress value change sequence;

[0011] According to the stress value change sequence, the convergence speed of each finite element in the grid model is calculated to obtain the convergence speed set;

[0012] The convergence speed set is clustered by an iterative self-organizing clustering algorithm to obtain a clustering result;

[0013] The average convergence speed of each grid cluster in the clustering result is obtained, and the grid cluster with an average convergence speed greater than a preset convergence speed threshold is determined as a target cluster;

[0014] The false stability of each target cluster is obtained, and the harmonic speed value of each target cluster is calculated according to the false stability of each target cluster;

[0015] determine the grid size of the finite element corresponding to each target cluster according to the harmonic speed value of each target cluster;

[0016] divide the finite element model according to the grid size of the finite element corresponding to each target cluster to obtain an updated grid model, and use the updated grid model to perform simulation experiment on the steel gear.

[0017] Optionally, the convergence speed of each finite element in the grid model is calculated according to the stress value change sequence, specifically including:

[0018] According to the stress value change sequence, the stress value difference of each finite element in the grid model is calculated;

[0019] According to the stress value difference of each finite element, the stress value stability of each finite element is calculated;

[0020] According to the stress value stability of each finite element, the convergence speed of each finite element is calculated.

[0021] Optionally, the grid size of the finite element corresponding to each target cluster is determined according to the harmonic speed value of each target cluster, specifically including:

[0022] The target cluster with a harmonic speed value less than a preset harmonic speed threshold is determined as a first target cluster;

[0023] The smallest grid parameter in the grid parameter set is determined as the grid size of the finite element corresponding to the first target cluster;

[0024] The finite element corresponding to the first target cluster is removed from the grid model to obtain a first updated grid model;

[0025] The preset grid size interval is divided according to a preset length to obtain a grid interval set, wherein the grid interval set includes at least two grid intervals;

[0026] The first updated grid model is divided according to the a th grid parameter set to determine the a th target cluster and the grid size of the finite element corresponding to the a th target cluster, and to obtain an a th updated grid model;

[0027] The a+n th target cluster, the grid size of the finite element corresponding to the a+n th target cluster and the a+n th updated grid model are determined until the harmonic speed value of the target cluster is greater than or equal to the preset harmonic speed threshold, and the division of the a+n th updated grid model is stopped, wherein a and n are positive integers, and a is greater than the serial number of the grid parameter set in the grid interval set.

[0028] Optionally, according to the stress value change sequence, the stress value difference of each finite element in the grid model is calculated, specifically including:

[0029] The stress value of the mth finite element under the nth target parameter and the stress value of the mth finite element under the (n-1)th target parameter are obtained from the stress value change sequence;

[0030] The absolute value of the difference between the stress value of the mth finite element under the nth target parameter and the stress value of the mth finite element under the (n-1)th target parameter is calculated to obtain the stress value difference of the mth finite element under the nth target parameter;

[0031] The stress value difference of the mth finite element under each target parameter is obtained;

[0032] The stress value difference of each finite element under each target parameter is obtained.

[0033] Optionally, according to the stress value difference of each finite element, the stress value stability of each finite element is calculated, specifically including:

[0034] The stress value difference of the mth finite element under the nth target parameter is added by one to obtain an increment correction value;

[0035] The reciprocal of the increment correction value is obtained to obtain the stress value stability of the mth finite element under the nth target parameter;

[0036] The mean value of the stress value stability of the mth finite element under each target parameter is calculated to obtain the stress value stability of the mth finite element;

[0037] The stress value stability of each finite element is obtained.

[0038] Optionally, according to the stress value stability of each finite element, the convergence speed of each finite element is calculated, specifically including:

[0039] The ath grid parameter set and the (a+1)th grid parameter set in the grid interval set are obtained respectively;

[0040] The stress value stability of the mth finite element under the ath grid parameter set and the stress value stability of the mth finite element under the (a+1)th grid parameter set are obtained respectively;

[0041] The difference between the stress value stability of the mth finite element under the ath grid parameter set and the stress value stability of the mth finite element under the (a+1)th grid parameter set is calculated to obtain a stress value stability difference;

[0042] The ratio of the stress value stability difference to the number of grid parameters in the grid parameter set is calculated, and the absolute value is taken to obtain the convergence speed of the mth finite element under the ath grid parameter set;

[0043] Sum the convergence speed of the mth finite element under each grid parameter set to obtain the convergence speed of the mth finite element;

[0044] Obtain the convergence speed of each finite element.

[0045] Optionally, the false stability quantity of each target class cluster is obtained, and specifically includes:

[0046] Obtain the convergence speed variance of the cth target class cluster and the geometric center of each target class cluster;

[0047] Determine the neighborhood class cluster of the cth target class cluster;

[0048] Calculate the difference between the convergence speed of the cth target class cluster and the convergence speed of the neighborhood class cluster, and take the absolute value to obtain the convergence speed difference of the cth target class cluster;

[0049] The product of the convergence speed variance of the cth target class cluster and the convergence speed difference of the cth target class cluster is normalized, and the normalized result is determined as the false stability quantity of the cth target class cluster;

[0050] Obtain the false stability quantity of each target class cluster.

[0051] Optionally, the neighborhood class cluster of the cth target class cluster is determined, and specifically includes:

[0052] Calculate the distance between the geometric center of the cth target class cluster and the geometric center of other target class clusters, respectively;

[0053] The target class cluster corresponding to the minimum distance is determined as the neighborhood class cluster of the cth target class cluster.

[0054] Optionally, the harmonic speed value of each target class cluster is calculated, and specifically includes:

[0055] The product of the false stability quantity of the cth target class cluster and the mean value of the convergence speed of the cth target class cluster is determined as the harmonic speed value of the cth target class cluster;

[0056] Obtain the harmonic speed value of each target class cluster.

[0057] The present application provides a kind of steel gear surface heat treatment strengthening simulation experiment system, including memory, processor and the computer program stored in memory and can be run on processor, the computer program is implemented when processor executes like the step of the method for the simulation experiment of the steel gear surface heat treatment strengthening of one kind.

[0058] The technical scheme of the present application has the following advantages:

[0059] In the embodiment of the present application, the fine simulation of the gear induction quenching process is realized by the finite element analysis method, and the error problem caused by the complex structure of the gear in the simulation process is excluded. The corresponding convergence speed is determined according to the trend change of the physical parameters (such as temperature stress value) in the simulation process, the rationality of the current grid fineness value is judged through feedback, and the problem of unreasonable distribution of the grid density in some areas in the process of non-uniform grid arrangement is eliminated. By analyzing the change characteristics of the low convergence speed area in the horizontal (time) and vertical (space) directions, the pseudo-stable quantity is determined, and then the grid dynamic adjustment result for the important area is obtained. The grid arrangement accuracy is improved, and the error in the finite element simulation is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0061] Figure 1 The flow chart of the simulation experiment method for the surface heat treatment strengthening of the steel gear provided by an embodiment of the present application;

[0062] Figure 2 The structural diagram of the simulation experiment system for the surface heat treatment strengthening of the steel gear provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will combine the drawings and the preferred embodiments to specifically describe the simulation experiment method for the surface heat treatment strengthening of the steel gear according to the present application, the specific implementation, structure, features and effects thereof, as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0065] The specific scheme of the simulation experiment method for the surface heat treatment strengthening of the steel gear provided by the present application will be specifically described below with reference to the drawings.

[0066] The embodiment of the present application provides a simulation experiment method and system for the surface heat treatment strengthening of the steel gear. Please refer to Figure 1Fig. 1 shows a flowchart of a simulation experiment method for surface heat treatment strengthening of a steel gear according to an embodiment of the present application, which comprises the following steps:

[0067] In S101, the finite element model of the steel gear is divided according to the target parameter to obtain a grid model, wherein the target parameter is obtained by determining each grid parameter in a grid parameter set as the target parameter, and the grid parameter set is obtained by determining each grid interval in a grid interval set as the grid parameter set.

[0068] For example, the finite element model of the steel gear can be obtained by using CAD software (SolidWorks) to draw a detailed geometric model of the gear, including regular regions and irregular regions (such as tooth surfaces, tooth roots, inner holes, etc.) of the gear. After the model is drawn in the CAD software, it is exported in a format suitable for finite element analysis software (STEP). The exported model does not damage or lose key geometric information when imported into the finite element software. Open the finite element analysis software (ANSYS) and import the CAD model into the software. The imported model maintains the original geometric shape and undergoes necessary checks to ensure the model's closure and integrity. In the finite element software, according to the geometric shape of the gear, define the regular regions (such as tooth surfaces, circular regions of the gear body) and irregular regions (such as tooth roots, contact surfaces, etc.) of the gear to obtain the finite element model of the steel gear.

[0069] The software can distinguish between regular and irregular regions through geometric recognition or user manual selection. In the grid division module, the grid type (hexahedron), grid parameters (size of hexahedron), and other related parameters can be set, and the finite element model of the steel gear can be divided using hexahedrons to obtain the grid model.

[0070] In this embodiment, the grid interval set can be obtained by dividing a preset grid size interval according to a preset length, wherein the preset grid size interval and the preset length can be set according to actual conditions. In a preferred embodiment, the preset grid size interval can be 1%-10% of the gear height, and the preset length can be 1% of the gear height.

[0071] In S102, the grid model is subjected to a target working condition to obtain a stress value change sequence.

[0072] For example, after the finite element model is divided using the target parameter (which can determine the grid type, such as hexahedron), the grid model can be obtained. And by applying the same working condition (which can be set according to actual conditions, generally selecting the working condition parameters frequently used by the steel gear during quenching) to the grid model under different target parameters, a stress value change sequence can be obtained.

[0073] For example, under the same working condition, the finite element analysis software ANSYS yields the result of the first... The first target parameter Stress values ​​of a finite element This leads to the stress value variation sequence of the m-th finite element under each target parameter, and further to the stress value variation sequence of each finite element under each target parameter. The stress value variation sequence of the m-th finite element can be expressed as: Furthermore, when the mesh is coarse (i.e., the target parameter is large), the physical quantities within each mesh cell (i.e., finite element) are calculated by averaging or approximating the values ​​within that cell.

[0074] S103. Based on the stress value change sequence, calculate the convergence rate of each finite element in the mesh model to obtain the convergence rate set.

[0075] In this embodiment, the convergence rate of each finite element in the mesh model is calculated based on the stress value change sequence, specifically including:

[0076] Based on the stress value change sequence, calculate the stress value difference of each finite element in the mesh model;

[0077] The stress stability of each finite element is calculated based on the stress value difference of each finite element.

[0078] The convergence rate of each finite element is calculated based on the stress stability of each finite element.

[0079] Based on the stress value change sequence, the stress value difference of each finite element in the mesh model is calculated, specifically including:

[0080] Obtain the stress values ​​of the m-th finite element under the n-th and (n-1)-th target parameters from the stress value variation sequence;

[0081] Calculate the absolute value of the difference between the stress value of the m-th finite element under the n-th target parameter and the stress value of the m-th finite element under the (n-1)-th target parameter to obtain the stress value difference of the m-th finite element under the n-th target parameter;

[0082] Obtain the stress difference of the m-th finite element under each target parameter;

[0083] Obtain the stress difference for each finite element under each target parameter.

[0084] The stress stability of each finite element is calculated based on the stress value difference of each finite element, specifically including:

[0085] The incremental correction value is obtained by adding one to the stress difference of the m-th finite element under the n-th target parameter.

[0086] Taking the reciprocal of the incremental correction value, we obtain the stress stability of the m-th finite element under the n-th objective parameter;

[0087] Calculate the mean value of the stress stability of the m-th finite element under each objective parameter to obtain the stress stability of the m-th finite element;

[0088] Obtain the stress stability of each finite element.

[0089] Based on the stress stability of each finite element, the convergence rate of each finite element is calculated, specifically including:

[0090] Obtain the parameter set of the a-th grid and the parameter set of the (a+1)-th grid in the grid interval set respectively;

[0091] Obtain the stress stability of the m-th finite element under the a-th and (a+1)-th mesh parameter sets, respectively;

[0092] Calculate the stress stability of the m-th finite element under the a-th mesh parameter set and the difference between the stress stability of the m-th finite element under the (a+1)-th mesh parameter set to obtain the stress stability difference.

[0093] The ratio of the stress stability difference to the number of mesh parameters in the mesh parameter set is calculated, and the absolute value is taken to obtain the convergence rate of the m-th finite element under the a-th mesh parameter set;

[0094] The convergence rate of the m-th finite element is obtained by summing the convergence rates of the m-th finite element under each set of mesh parameters.

[0095] Obtain the convergence rate for each finite element.

[0096] For example, in coarse meshes (where each parameter in the mesh parameter set is large), the stress distribution tends to be relatively smooth, without subtle local fluctuations. This is because larger mesh units cannot capture local stress concentrations or changes, resulting in calculated stress values ​​that are lower than expected, and the transitions between regions are relatively smooth. As the mesh becomes finer, each mesh cell can capture more local physical changes. With fine meshes, stress values ​​exhibit more complex variations.

[0097] For example, stress gradients and variations become more pronounced at the tooth surface or root of a gear. Stress concentration areas (such as the tooth root junction or contact point) are revealed in fine meshes, exhibiting abrupt stress changes, while these phenomena are smoothed out in coarse meshes.

[0098] In summary, the characteristics above indicate that during gear quenching, different cooling zones have different cooling rates, resulting in varying rates of change in internal tensile stress. Furthermore, as mesh parameters become more refined, the variation range is smaller and the tendency to stabilize is faster for general gear regions (regular areas); however, for areas such as the tooth surface and tooth root, the rate of change in thermal stress during cooling is faster due to the influence of heat, leading to a larger variation range and a slower tendency to stabilize.

[0099] Therefore, for each mesh element stress value sequence, its corresponding stability is first calculated. Based on the stress value change sequence, the formula for calculating the stress value difference of each finite element in the mesh model can be:

[0100]

[0101] in, This represents the stress difference of the m-th finite element under the n-th objective parameter. This represents the stress value of the m-th finite element under the n-th objective parameter. This represents the stress value of the m-th finite element under the (n-1)-th objective parameter.

[0102] The stress stability of each finite element is calculated based on the stress difference between each element. The calculation formula can be:

[0103]

[0104] in, Indicates the first Stress stability of a mesh (i.e., a finite element) under the a-th mesh parameter set. This indicates the number of target parameters in the mesh parameter set. This represents the stress difference of the m-th finite element under the n-th target parameter.

[0105] By summing the differences, we can obtain the gear's position during the quenching process in the [number]th [period]. Stress value variation stability of each grid. Difference The smaller the value, the smaller the stress difference between different grids. During the quenching process, the change in internal tensile stress slows down and tends to stabilize. Therefore, when The closer the value is to 1, the higher the stability of the corresponding mesh stress value.

[0106] When the stability of mesh stress values ​​is closer to being equal across different finite elements, the higher the convergence speed of its sensitivity. For the general region of gear quenching, the internal tensile stress changes faster and stabilizes faster than in regions such as the gear tooth root. Consequently, the stability of mesh stress values ​​in the general region of gear quenching is more similar across different finite elements, and the higher the convergence speed.

[0107] For example, for finite element A, the first 5 changes (the number of target parameters in the mesh parameter set is 5) are considered as a mesh stress stability interval, and the calculated stress value stability is 0.85; the last 5 changes are considered as a new mesh stress stability interval, and the calculated stress value stability is 0.88. The difference between the two is 0.03, so it can be considered that finite element A has a high sensitivity convergence speed in the gear quenching process.

[0108] Therefore, based on the stress stability of each finite element, the convergence rate of each finite element can be calculated using the following formula:

[0109]

[0110] in, Indicates the first The convergence speed of each grid (i.e., finite element). This indicates the number of grid parameter sets in the grid interval set. Represents the set of grid parameters in the grid interval set. Each grid parameter.

[0111] The closer the convergence rate is to 0, the more drastic the convergence rate of the corresponding finite element, and the slower the change rate of tensile stress inside the corresponding gear region, the closer it is to the limit stability.

[0112] By obtaining the convergence rate of each finite element, the set of convergence rates can be represented as: .

[0113] S104. Cluster the convergence velocity set using an iterative self-organizing clustering algorithm to obtain the clustering results.

[0114] For example, an iterative self-organizing clustering algorithm is used to cluster the convergence rate set. The clustering parameters are the spatial distance between grid cells and the convergence rate. The clustering results after the algorithm's iterations are obtained. Each cluster shares a similar characteristic; clusters with similar convergence rates are more likely to be grouped together.

[0115] S105. Obtain the average convergence speed of each grid cluster in the clustering results, and determine the grid clusters with an average convergence speed greater than the preset convergence speed threshold as the target clusters.

[0116] For example, obtain the first Average convergence speed of each grid cluster This mean refers to the average convergence speed of different grid types within that cluster. The average cluster convergence speed for different grid types is obtained. Afterwards, the closer the convergence rate is to 0, the higher the corresponding mesh parameter fit value.

[0117] Mesh clusters with convergence rates greater than a preset convergence rate threshold after normalization can be identified as fine regions of the gear. In other words, these target clusters are considered critical areas for gear quenching, such as the tooth root, where internal tensile stress changes are significant and are not easily stabilized. Therefore, further dynamic adjustments are needed.

[0118] Optionally, in this embodiment, the preset convergence speed threshold can be set to 0.7. The preset convergence speed threshold can be set according to the actual situation or adjusted according to historical experience values. Here, there is no specific limitation on the specific value of the preset convergence speed threshold.

[0119] S106. Obtain the pseudo-stability value of each target cluster, and calculate the harmonic velocity value of each target cluster based on the pseudo-stability value of each target cluster.

[0120] Obtain the pseudo-stable value for each target cluster, specifically including:

[0121] Obtain the convergence rate variance of the c-th target cluster and the geometric center of each target cluster;

[0122] Determine the neighboring clusters of the c-th target cluster;

[0123] Calculate the difference between the convergence speed of the c-th target cluster and the convergence speed of the neighboring clusters, and take the absolute value to obtain the convergence speed difference of the c-th target cluster.

[0124] The product of the convergence rate variance of the c-th target cluster and the convergence rate difference of the c-th target cluster is normalized, and the normalization result is determined as the pseudo-stability of the c-th target cluster.

[0125] Obtain the pseudo-stable value for each target cluster.

[0126] Determining the neighborhood clusters of the c-th target cluster specifically includes:

[0127] Calculate the distances between the geometric center of the c-th target cluster and the geometric centers of other target clusters;

[0128] The target cluster corresponding to the minimum distance is determined as the neighbor cluster of the c-th target cluster.

[0129] Calculate the harmonic velocity value for each target cluster, specifically including:

[0130] The product of the pseudo-stability of the c-th target cluster and the average convergence rate of the c-th target cluster is determined as the harmonic velocity value of the c-th target cluster.

[0131] Obtain the harmonic velocity value for each target cluster.

[0132] For example, the internal tensile stress in the critical region of a gear is affected by the surrounding tensile stress, which is generated by the gear portion with a lower average cluster convergence rate. The critical region of the gear is also affected by other normal gear tensile stress convergence regions, similar to the heat transfer effect between temperatures. Therefore, after obtaining the convergence rate value of the fine region (i.e., the target cluster), to prevent the mesh parameters from iterating infinitely due to the persistently high convergence rate, leading to a continuous decrease in mesh cells and thus increasing unnecessary computation, the horizontal and vertical convergence rates of the fine region can be calculated to obtain "pseudo-stable" values, which can then be used to dynamically adjust the mesh refinement value.

[0133] The formula for calculating the pseudo-steady quantity can be:

[0134]

[0135] in, Indicates the first The pseudo-stable quantity of each target cluster, Represents the normalization function. Indicates the first The convergence rate variance of each target cluster Indicates the first The convergence speed of the target clusters differs.

[0136] convergence rate variance of target clusters The larger the sum of the convergence rates, the greater the difference in convergence rates between meshes within the cluster, the more complex the interaction of stresses within the meshes, and the higher the pseudostability. Similarly, the greater the difference in the neighborhood convergence rates of the target cluster, the higher the sum of the convergence rates. The higher the value, the greater the influence between internal tensile stresses, and the higher the pseudo-stability.

[0137] The formula for calculating the harmonic velocity value can be:

[0138]

[0139] in, Indicates the first The harmonic velocity value of each target cluster. Indicates the first The pseudo-stable quantity of each target cluster, Indicates the first The average convergence rate of each target cluster.

[0140] S107. Determine the mesh size of the finite element corresponding to each target cluster based on the harmonic velocity value of each target cluster.

[0141] In this embodiment, the mesh size of the finite element corresponding to each target cluster is determined based on the harmonic velocity value of each target cluster, specifically including:

[0142] The target cluster whose harmonic velocity value is less than the preset harmonic velocity threshold is identified as the first target cluster;

[0143] The smallest mesh parameter in the set of mesh parameters is determined as the mesh size of the finite element corresponding to the first target cluster;

[0144] The first updated mesh model is obtained by removing the finite elements corresponding to the first target cluster from the mesh model.

[0145] Divide the preset grid size range according to the preset length to obtain a set of grid intervals, wherein the set of grid intervals includes at least two grid intervals;

[0146] The mesh model after the first update is divided according to the a-th mesh parameter set, the mesh size of the a-th target cluster and the corresponding finite element is determined, and the mesh model after the a-th update is obtained.

[0147] Determine the mesh size of the (a+n)th target cluster, the finite element corresponding to the (a+n)th target cluster, and the mesh model after the (a+n)th update, until the harmonic velocity value of the target cluster is greater than or equal to the preset harmonic velocity threshold, and stop dividing the mesh model after the (a+n)th update, where a and n are positive integers, and a is greater than the index of the mesh parameter set in the mesh interval set.

[0148] For example, after calculating the harmonic velocity value of each target cluster, the smallest mesh parameter in the set of mesh parameters (denoted as the a-th set of mesh parameters) can be determined as the mesh size of the finite element corresponding to the first target cluster. At this point, the size of some finite elements in the key region of the gear has been determined, and the mesh size of the remaining finite elements needs to be determined. The finite elements corresponding to the first target cluster are removed from the mesh model to obtain the mesh model after the first update. Then, the model is divided according to the (a+1)-th set of mesh parameters, and the mesh size of some finite elements is determined from the mesh model after the first update. This yields the mesh size of the (a+1)-th target cluster, the finite element corresponding to the (a+1)-th target cluster, and the mesh model after the (a+1)-th update. This process is iterated until the mesh size of the finite elements for all clusters in the target cluster has been determined, completing the iteration and yielding the mesh size of the (a+n)-th target cluster, the finite element corresponding to the (a+n)-th target cluster, and the mesh model after the (a+n)-th update.

[0149] Optionally, in a preferred embodiment, the preset harmonic speed threshold can be 0.4. The preset harmonic speed threshold can be set and changed according to the actual situation of the actual iteration, or set according to historical experience values. Here, there is no specific limitation on the value of the preset harmonic speed threshold.

[0150] S108. Divide the finite element model according to the mesh size of the finite element corresponding to each target cluster to obtain the updated mesh model, and use the updated mesh model to conduct a simulation experiment of steel gears.

[0151] For example, for mesh clusters with an average convergence rate greater than a preset convergence rate threshold (i.e., target clusters, where the region corresponding to the target cluster is considered to be the fine region of the gear), the mesh can be generated using the calculated finite element size of each target cluster. For mesh clusters with an average convergence rate less than or equal to the preset convergence rate threshold (i.e., non-target clusters, where the region corresponding to the non-target cluster is considered to be the regular region of the gear), conventional mesh generation methods can be used, and the finite element mesh generation method for regular objects can be referenced to generate the updated mesh model.

[0152] After mesh generation, check the mesh quality (e.g., shape, size, and distribution of mesh elements) to ensure there are no distorted or excessively large mesh elements. Save the mesh model and prepare it for subsequent finite element analyses (e.g., stress analysis, thermal analysis). Based on the generated mesh, perform physical simulations and analyses to obtain the stress, temperature, and hardness distribution of the gear under different operating conditions.

[0153] In summary, this invention achieves a refined simulation of the gear induction hardening process using the finite element method, eliminating errors caused by the complex structure of the gear during simulation. By analyzing the stability of the trend changes in physical parameters (such as temperature and stress values) during the simulation, the corresponding convergence rate is determined, providing feedback to judge the rationality of the current mesh refinement value and eliminating the problem of unreasonable mesh density distribution in some areas during non-uniform mesh layout. By analyzing the variation characteristics of low convergence rate regions in the horizontal (time) and vertical (space) directions, pseudo-stability quantities are determined, thus obtaining the dynamic adjustment results of the mesh for important regions. This improves the accuracy of mesh layout and reduces errors in finite element simulation.

[0154] This invention also proposes a simulation experimental system for strengthening the surface of steel gears through heat treatment; please refer to [link to relevant documentation]. Figure 2 The diagram shows a structural diagram of a simulation experimental system for strengthening the surface of steel gears by heat treatment according to an embodiment of the present invention. The system includes: a model acquisition module 101, a data processing module 102, and a model output module 103.

[0155] The model acquisition module 101 is used to divide the finite element model of the steel gear according to the target parameters to obtain a mesh model. The target parameters are obtained by determining each mesh parameter in the mesh parameter set as the target parameters, and the mesh parameter set is obtained by determining each mesh interval in the mesh interval set as the mesh parameter set.

[0156] The data processing module 102 is used to apply the target working condition to the mesh model to obtain a stress value change sequence; based on the stress value change sequence, calculate the convergence rate of each finite element in the mesh model to obtain a convergence rate set; cluster the convergence rate set using an iterative self-organizing clustering algorithm to obtain clustering results; obtain the mean convergence rate of each mesh cluster in the clustering results, and determine the mesh clusters with a mean convergence rate greater than a preset convergence rate threshold as target clusters; obtain the pseudo-stability value of each target cluster, and calculate the harmonic velocity value of each target cluster based on the pseudo-stability value of each target cluster; and determine the mesh size of the finite element corresponding to each target cluster based on the harmonic velocity value of each target cluster.

[0157] The model output module 103 is used to divide the finite element model according to the mesh size of the finite element corresponding to each target cluster, obtain the updated mesh model, and use the updated mesh model to conduct simulation experiments on steel gears.

[0158] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the simulation experimental system for strengthening the surface of steel gears by heat treatment provided in the above embodiments and the simulation experimental method for strengthening the surface of steel gears by heat treatment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0159] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0160] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of simulating an experiment for surface heat treatment strengthening of a steel gear, characterized by, The application relates to a method for updating a grid model of a finite element model of a steel gear. The method comprises the following steps: dividing the finite element model of the steel gear according to target parameters to obtain a grid model, wherein the target parameters are obtained by determining each grid parameter in a grid parameter set as a target parameter, and the grid parameter set is obtained by determining each grid interval in a grid interval set as the grid parameter set; applying a target working condition to the grid model to obtain a stress value change sequence; calculating a stress value difference of each finite element in the grid model according to the stress value change sequence; calculating a stress value stability of each finite element according to the stress value difference of each finite element; calculating a convergence speed of each finite element according to the stress value stability of each finite element to obtain a convergence speed set; clustering the convergence speed set by using an iterative self-organizing clustering algorithm to obtain a clustering result; obtaining a convergence speed average of each grid cluster in the clustering result, and determining a grid cluster with a convergence speed average greater than a preset convergence speed threshold as a target cluster; obtaining a convergence speed variance of the cth target cluster and a geometric center of each target cluster; calculating distances between the geometric center of the cth target cluster and geometric centers of other target clusters; determining a target cluster corresponding to the minimum distance as a neighborhood cluster of the cth target cluster; calculating a difference between the convergence speed of the cth target cluster and the convergence speed of the neighborhood cluster, and taking an absolute value to obtain a convergence speed difference of the cth target cluster; normalizing a product of the convergence speed variance of the cth target cluster and the convergence speed difference of the cth target cluster, and determining a normalization result as a pseudo-stability quantity of the cth target cluster; obtaining the pseudo-stability quantity of each target cluster; determining a product of the pseudo-stability quantity of the cth target cluster and the convergence speed average of the cth target cluster as a harmonic speed value of the cth target cluster according to the pseudo-stability quantity of each target cluster; obtaining the harmonic speed value of each target cluster; determining a target cluster with a harmonic speed value less than a preset harmonic speed threshold as a first target cluster; determining a minimum grid parameter in the grid parameter set as a grid size of a finite element corresponding to the first target cluster; eliminating the finite element corresponding to the first target cluster from the grid model to obtain a first updated grid model; dividing a preset grid size interval according to a preset length to obtain a grid interval set, wherein the grid interval set comprises at least two grid intervals; dividing the first updated grid model according to an ath grid parameter set to determine an ath target cluster, a grid size of a finite element corresponding to the ath target cluster, and an ath updated grid model; determining an a+nth target cluster, a grid size of a finite element corresponding to the a+nth target cluster, and an a+nth updated grid model until the harmonic speed value of the target cluster is greater than or equal to the preset harmonic speed threshold, and stopping the division of the a+nth updated grid model, wherein a and n are positive integers, and a is greater than a serial number of the grid parameter set in the grid interval set. The finite element model is divided according to the grid size of the finite element corresponding to each target cluster, to obtain an updated grid model, and the simulation experiment of the steel gear is performed using the updated grid model.

2. The method of simulating the surface heat treatment strengthening of a steel gear according to claim 1, characterized in that, The stress value difference of each finite element in the grid model is calculated according to the stress value change sequence, and specifically includes: The stress values of the mth finite element under the nth target parameter and the (n-1)th target parameter are obtained from the stress value change sequence; The absolute value of the difference between the stress value of the mth finite element under the nth target parameter and the stress value of the mth finite element under the (n-1)th target parameter is calculated to obtain the stress value difference of the mth finite element under the nth target parameter; The stress value difference of the mth finite element under each target parameter is obtained; The stress value difference of each finite element under each target parameter is obtained.

3. The method of simulating the surface heat treatment strengthening of a steel gear according to claim 1, characterized in that, The stress value stability of each finite element is calculated according to the stress value difference of each finite element, and specifically includes: The stress value difference of the mth finite element under the nth target parameter is added by one to obtain an incremental correction value; The reciprocal of the incremental correction value is obtained to obtain the stress value stability of the mth finite element under the nth target parameter; The mean value of the stress value stability of the mth finite element under each target parameter is calculated to obtain the stress value stability of the mth finite element; The stress value stability of each finite element is obtained.

4. The method of simulating the surface heat treatment strengthening of a steel gear according to claim 1, wherein The convergence speed of each finite element is calculated according to the stress value stability of each finite element, and specifically includes: The ath grid parameter set and the (a+1)th grid parameter set in the grid interval set are obtained respectively; The stress value stability of the mth finite element under the ath grid parameter set and the (a+1)th grid parameter set is obtained respectively; The difference between the stress value stability of the mth finite element under the ath grid parameter set and the stress value stability of the mth finite element under the (a+1)th grid parameter set is calculated to obtain a stress value stability difference value; The ratio of the stress value stability difference value to the number of grid parameters in the grid parameter set is calculated, and the absolute value is taken to obtain the convergence speed of the mth finite element under the ath grid parameter set; The convergence speeds of the mth finite element under each grid parameter set are summed to obtain the convergence speed of the mth finite element; The convergence speed of each finite element is obtained.

5. A simulation experiment system for surface heat treatment strengthening of a steel gear, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the simulation experiment method of the steel gear surface heat treatment strengthening according to any one of claims 1-4.

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