A method for calculating the hot wear of a hypoid gear tooth surface

Abstract

This invention discloses a method for calculating the thermal wear of quasi-hyperboloid gear tooth surfaces, relating to the field of engineering data processing technology. The method includes: generating spatial meshing point position data based on the position data of the primary gear and the secondary gear using a spatial meshing point position algorithm; generating spatial relative motion quantities using a three-dimensional relative motion quantitative formula based on the spatial meshing point position data; generating spatial sliding distances based on the spatial meshing point position data, spatial relative motion quantities, and a predetermined sliding amount construction logic; and generating the sliding velocity of the meshing area by collecting local velocity changes in the meshing zone and the spatial sliding distance. Under the condition of time-varying migration of the contact point of the quasi-hyperboloid gear along a three-dimensional curved surface, this invention enables the dynamic wear coefficient and interface shear heat to synchronously reflect the time-varying contact stress field and sliding path characteristics, thereby ensuring the spatial consistency and temporal continuity of the wear driving quantity calculation in the thermal wear model.

Description

Technical Field

[0001] This invention relates to the field of engineering data processing technology, specifically to a method for calculating the thermal wear of quasi-hyperboloid gear tooth surfaces. Background Technology

[0002] In the field of quasi-hyperboloid gear transmission, tooth surface thermal wear calculation is an important foundation for evaluating gear service life and transmission reliability. Its core lies in the quantitative description of the sliding behavior, contact stress distribution, and resulting thermal effects of the tooth surface under complex spatial meshing conditions. The relevant calculation process usually revolves around the gear geometric model, meshing motion relationship, and frictional heat generation mechanism. By introducing sliding distance, contact stress, and wear correlation coefficient, the energy dissipation and material damage of the tooth surface during the transmission process are characterized, thus providing a basis for gear design optimization, working condition evaluation, and operational status analysis.

[0003] Currently, in the calculation of thermal wear of quasi-hyperboloid gear tooth surfaces, the sliding behavior is simultaneously affected by changes in spatial motion trajectory and local velocity fluctuations due to the continuous migration of the contact point along the three-dimensional surface. Related calculations often rely on the assumption of fixed contact positions or simplified meshing paths, making it difficult for the spatial sliding distance to be consistent with the actual sliding process in the meshing zone. Under these circumstances, there is no stable spatial correspondence between the sliding distance and the sliding rate, which makes it easy for the thermal wear model to deviate when describing the sliding input under different meshing positions, thus affecting the continuity and consistency of subsequent wear calculation results.

[0004] Secondly, in the process of constructing a thermal wear model, the wear driving quantity usually needs to reflect the changes in energy input and contact state on the sliding path simultaneously. However, the relevant calculations often fail to unify the correlation between the time-varying contact stress field, heat input intensity, and spatial sliding distance, resulting in a lack of a basis for the coordinated change of dynamic wear coefficient and interfacial shear heat in the spatial and temporal dimensions. Under these conditions, the wear driving quantity cannot accurately reflect the real wear trend of the tooth surface in different meshing sections and different operating stages, thus limiting the adaptability of the thermal wear model to complex meshing conditions of hypoid gears. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for calculating the thermal wear of quasi-hyperboloid gear tooth surfaces, the method comprising: Based on the position data of the main wheel and the position data of the driven wheel, spatial meshing point position data is generated through a spatial meshing point position algorithm, and spatial relative motion is generated through a three-dimensional relative motion quantitative formula based on the spatial meshing point position data. Based on the spatial meshing point position data, spatial relative motion, and predetermined slip volume, a logic is constructed to generate the spatial slip distance. By collecting the local velocity change in the meshing area and the spatial slip distance, the sliding velocity in the meshing area is generated. A dynamic wear coefficient is generated by using tooth surface roughness data, meshing zone temperature rise data, and meshing zone sliding speed. Based on the dynamic wear coefficient, the change value of roughness peak shear heat is collected to generate interfacial shear heat. Based on the spatial sliding distance and interface shear heat, the wear driving force is generated according to the predetermined wear driving force iteration logic.

[0006] Furthermore, the steps for generating spatial relative motion are as follows: Based on the position data of the master wheel and the slave wheel, the node coordinate values ​​of the master wheel tooth surface and the slave wheel tooth surface are extracted by node coordinate parsing to generate node coordinate data; Based on the node coordinate data, the minimum three-dimensional distance between the master wheel tooth surface node and the slave wheel tooth surface node is calculated using three-dimensional distance calculation, and three-dimensional minimum distance data is generated. Based on node coordinate data and three-dimensional minimum distance data, spatial normal vector and nodal surface direction quantity are obtained by performing spatial meshing point position algorithm to generate spatial meshing point position data; Based on the spatial meshing point position data, the tangential velocity component and the normal velocity component of the tooth surface are calculated using a three-dimensional relative motion quantitative formula to generate the spatial relative motion quantity.

[0007] Furthermore, the steps for generating 3D minimum distance data are as follows: Based on node coordinate data, the three-dimensional distance between any master gear tooth surface node and all slave gear tooth surface nodes is calculated by scanning node pairs to generate an initial node distance set; Based on the initial node distance set, the minimum distance value corresponding to each main gear tooth surface node is extracted by distance filtering to generate node minimum distance data; Based on the minimum node distance data, the global convergence distance value is calculated from the minimum node distance values ​​of all main gear tooth surfaces through spatial convergence to generate convergence distance data; Based on the convergence distance data, the minimum three-dimensional distance value for subsequent meshing point calculation is obtained through three-dimensional geometric synthesis operation to generate the minimum three-dimensional distance data.

[0008] Furthermore, the steps for generating the spatial sliding distance are as follows: Based on spatial meshing point position data, the displacement of the meshing point along the three-dimensional surface is calculated by comparing adjacent meshing positions to generate three-dimensional displacement data. Based on the three-dimensional displacement data and spatial relative motion, the displacement component value of the meshing point along the tangential direction is extracted by decomposing the velocity direction component to generate tangential displacement data. Based on tangential displacement data, the cumulative tangential distance along the meshing path at the meshing point is calculated by spatial step accumulation to generate the spatial sliding distance.

[0009] Furthermore, the steps for generating tangential displacement data are as follows: Based on three-dimensional displacement data, the velocity direction of the meshing point in the local meshing plane is extracted by spatial direction quantity analysis to generate local direction quantity data; Based on the local directional data and the spatial relative motion, the velocity component value of the meshing point along the tangential direction is calculated by directional projection to generate tangential velocity component data; Based on the tangential velocity component data, the tangential displacement value of the meshing point per unit time is calculated by instantaneous displacement conversion to generate tangential displacement data.

[0010] Furthermore, the steps for generating the dynamic wear coefficient are as follows: Based on tooth surface roughness data, the average protrusion height value of the tooth surface roughness peaks is extracted by comparing the roughness peak heights to generate roughness peak height data. Based on the temperature rise data and sliding speed in the meshing zone, the local temperature change rate of the tooth surface is calculated by comparing the temperature rise and speed together, so as to generate local temperature rise change rate data. Based on rough peak height data and local temperature rise rate data, parameter values ​​for characterizing tooth surface wear sensitivity are generated through thermal-velocity joint quantization processing to generate dynamic wear coefficient.

[0011] Furthermore, the logic for generating local temperature rise rate data is as follows: Based on the temperature rise data in the meshing zone, the temperature difference value at consecutive moments in the meshing zone is extracted by comparing the temperature at adjacent time points to generate temperature difference data. Based on the temperature difference data and the sliding speed in the meshing area, the temperature rise conversion value per unit time is calculated through time step conversion to generate temperature rise conversion data. Based on the temperature rise conversion data, the local temperature change rate of the meshing zone is generated through velocity correlation quantification to generate local temperature rise change rate data.

[0012] Furthermore, the steps for generating temperature rise conversion data are as follows: Based on temperature difference data, the time interval values ​​corresponding to the temperature difference in the meshing zone are extracted through time interval quantization to generate temperature time interval data. Based on the temperature time interval data and the sliding speed in the meshing area, the temperature change per unit length of the sliding path is calculated by converting the temperature change rate to generate path temperature rise component data. Based on the path temperature rise component data, the temperature rise conversion value per unit time in the meshing zone is calculated through path cumulative quantization to generate temperature rise conversion data.

[0013] Furthermore, the steps for generating wear-driving quantities are as follows: Based on the spatial sliding distance, the sliding path increment value of the meshing point per unit time is extracted through sliding path quantization to generate sliding path increment data. Based on the interface shear heat and sliding path increment data, the unit energy output value of the meshing interface on the sliding path is calculated through ratio conversion logic to generate unit energy output data. Based on the unit energy output data, the energy driving quantity within the meshing cycle is generated through path integral logic to generate the wear driving quantity.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention establishes a consistent spatial reference relationship between the spatial meshing point position data and the spatial relative motion amount under the working condition of time-varying migration of the contact point of the quasi-hyperboloid gear along a three-dimensional curved surface. This allows the spatial sliding distance to correspond with the sliding velocity of the meshing area within the same spatiotemporal framework. On this basis, the sliding input can accurately reflect the real relative sliding state of the tooth surface, avoiding the sliding amount mismatch problem introduced by the migration of the contact point, thereby providing continuous and traceable sliding input conditions for the thermal wear model. Furthermore, this invention unifies the correlation between tooth surface roughness data, meshing zone temperature rise data, and meshing zone sliding speed, enabling the dynamic wear coefficient to change synchronously with spatial sliding distance and interfacial shear heat. It further incorporates interfacial shear heat and spatial sliding distance into the generation process of wear driving force, allowing the wear driving force to simultaneously reflect the coupling effect of time-varying contact stress field and heat input intensity. This ensures that the thermal wear model has a consistent physical constraint basis under different meshing positions and time periods, and maintains its adaptability to complex contact states of aligned hypoid gears. In summary, under the condition that the contact point of the quasi-hyperboloid gear migrates along the three-dimensional curved surface in a time-varying manner, this invention, based on the unified correlation between the spatial sliding distance and the sliding speed in the meshing zone, enables the dynamic wear coefficient and the interfacial shear heat to synchronously reflect the time-varying contact stress field and sliding path characteristics, thereby ensuring the spatial consistency and temporal continuity of the wear driving quantity calculation in the thermal wear model. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 A flowchart illustrating a method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces, provided as an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown in the figure, this embodiment discloses a method for calculating the thermal wear of quasi-hyperboloid gear tooth surfaces, the method comprising: S11: Based on the position data of the main wheel and the position data of the driven wheel, spatial meshing point position data is generated through a spatial meshing point position algorithm, and spatial relative motion is generated through a three-dimensional relative motion quantitative formula based on the spatial meshing point position data. In a quasi-hyperboloid gear, the meshing point between the primary gear and the secondary gear continuously migrates on a three-dimensional curved surface, and its contact posture changes in real time with the meshing angle. In order to obtain the true motion trend of the corresponding meshing position, it is necessary to deduce the meshing point position, normal direction and instantaneous relative motion components from the spatial distribution of the tooth surface nodes. Step S11 is used to construct all the basic geometric quantities and motion quantities required for subsequent sliding distance, contact stress, and dynamic wear coefficient, and is the basic input source for the entire thermal wear model. This step sequentially realizes the extraction of tooth surface nodes, the calculation of the three-dimensional minimum distance between nodes, the determination of the spatial meshing point position, and the generation of relative motion quantities at the meshing point.

[0019] Specifically, the steps for generating spatial relative motion are as follows: S111: Based on the position data of the main wheel and the position data of the driven wheel, the node coordinate values ​​of the main wheel tooth surface and the driven wheel tooth surface are extracted through node coordinate parsing to generate node coordinate data; In this embodiment, the node coordinates of each tooth surface are extracted based on the three-dimensional position data of the primary and secondary gears. The tooth surface nodes are discrete points used to construct the quasi-hyperboloid tooth surface geometry model, and are typically stored as grid points or a set of points fitted from measurement data.

[0020] In practical implementation, the 3D mesh of the tooth surfaces of the primary and secondary gears can be obtained from CAD models or measurement data. Node coordinate analysis is then performed on both to obtain a set of nodes containing the spatial coordinates of the tooth surfaces. For example, each node can be recorded as... , For the first Each node.

[0021] It should be noted that the position data of the main wheel and the position data of the driven wheel can be obtained through a 3D scanning device. This device can collect the spatial coordinate values ​​of the tooth surface point by point. The purpose of generating node coordinate data in this step is to provide the spatial geometric basis required for subsequent 3D minimum distance calculation. Both the master gear position data and the slave gear position data include the spatial attitude information of the corresponding gear at the current meshing moment. The spatial attitude information includes at least the position parameters of the gear center in a unified spatial reference coordinate system and the spatial orientation parameters of the gear itself. In this step, the node coordinate data are uniformly represented as three-dimensional coordinate values ​​under the same spatial reference coordinate system. Before entering the subsequent three-dimensional distance calculation, the node coordinate values ​​of the main gear tooth surface and the slave gear tooth surface have completed coordinate transformation through the corresponding position data, so that the node coordinates of different gear tooth surfaces have a consistent spatial reference benchmark. By using the above method, it is ensured that the spatial distance calculation between the tooth surface node of the master gear and the tooth surface node of the slave gear is not affected by the difference in coordinate system, thus providing a unified spatial geometric basis for subsequent three-dimensional minimum distance calculation and meshing point position determination.

[0022] S112: Based on the node coordinate data, calculate the minimum three-dimensional distance between the main gear tooth surface node and the slave gear tooth surface node using three-dimensional distance, and generate three-dimensional minimum distance data; In this embodiment, in order to obtain the spatial contact relationship between the tooth surfaces of the master wheel and the slave wheel, the nodal coordinate data of the two can be used to perform three-dimensional distance calculation to form the minimum distance data that can reflect the local clearance state of the tooth surface. By establishing a set of spatial distances between nodes, it is possible to identify the proximity region of the master and slave tooth surfaces during meshing and provide a geometric basis for determining the subsequent meshing point position.

[0023] Specifically, the steps for generating 3D minimum distance data are as follows: S112.1: Based on node coordinate data, calculate the three-dimensional distance between any master gear tooth surface node and all slave gear tooth surface nodes by scanning node pairs to generate an initial node distance set; In this embodiment, each node on the main wheel tooth surface can be used sequentially as a reference point, and a three-dimensional distance comparison can be performed with all nodes on the slave wheel tooth surface. By scanning the two sets of nodes pairwise, an initial distance set containing a large amount of spatial distance data can be formed. These distance values ​​can describe the spatial proximity of each discrete position on the main wheel to the entire slave wheel tooth surface.

[0024] It should be noted that node scanning can be performed by traversing point by point or by using a spatial index structure, such as a KD tree, to accelerate the process and ensure computational efficiency for large-scale node sets. The distance value can be calculated based on the three-dimensional Euclidean distance formula, so that each main wheel node has a complete alignment distance sequence.

[0025] S112.2: Based on the initial node distance set, extract the minimum distance value corresponding to each main gear tooth surface node through distance filtering to generate node minimum distance data; After obtaining the initial set of node distances, the distance sequence of each master wheel node can be filtered to extract the minimum distance value. The minimum distance represents the closest contact gap between the master wheel node and the tooth surface of the slave wheel in the current meshing state, which can be used to reflect the local contact trend of the tooth surface.

[0026] It should be noted that the minimum distance data can reflect the contact point transfer phenomenon caused by the change in tooth surface curvature, so that the geometric relationship of each node in discrete space can be quantified, thus laying the foundation for spatial convergence and meshing point positioning.

[0027] S112.3: Based on the minimum node distance data, calculate the global convergence distance value among the minimum node distance values ​​of all main gear tooth surfaces through spatial convergence to generate convergence distance data; In this embodiment, spatial convergence calculation is performed according to a fixed neighborhood weighted fusion rule: With each main gear tooth surface node as the center, a set of neighboring nodes within a preset spatial radius is constructed in the node coordinate data; Read the minimum distance value of each node in the neighborhood node set, and assign a corresponding weight value to each minimum distance value based on the spatial distance between nodes. Specifically, let the main gear tooth surface node, which is currently the computing center, be... The first in the neighborhood node set The node of the main gear tooth surface is ,Will and The Euclidean distance between them is denoted as Then the first The formula for calculating the weight of each node on the tooth surface of the main gear is as follows: ; In the formula, For the first The weight values ​​of each main gear tooth surface node. The total number of nodes in the neighborhood node set is the minimum distance between all nodes in the neighborhood node set. With the corresponding weight value Perform a weighted fusion operation to obtain the candidate convergence distance values ​​corresponding to the current main gear tooth surface nodes. ; Represented as: ; All candidate convergence distance values ​​of the main gear tooth surface nodes are uniformly summarized, and the above neighborhood weighted fusion process is repeated iteratively until the change in convergence distance between two adjacent iterations is lower than the preset change threshold. Then the iteration is stopped, and the final result is used as the convergence distance data. It should be noted that the convergence distance can describe the positional relationship of the tooth surface during the overall meshing process, and is more suitable as a basic constraint for the positioning of the meshing point compared with the node-level distance.

[0028] S112.4: Based on the convergence distance data, the minimum three-dimensional distance value for subsequent meshing point calculation is obtained through three-dimensional geometric synthesis operation to generate the minimum three-dimensional distance data.

[0029] In this embodiment, the three-dimensional geometry synthesis operation is performed according to the following determination rules: Read the convergence distance data as the global reference distance value in the current engagement state; Read the spatial normal direction information recorded in the node coordinate data of the corresponding main gear tooth surface node, and determine the local normal correction direction based on the normal direction; Using the minimum node distance data as the local distance input and the converged distance data as the global distance constraint, the two are proportionally fused to generate a corrected distance value for calculating the meshing point position. In the proportional fusion process, global distance constraints are used to limit the variation of local distances within the overall meshing trend range, while local distances are used to preserve tooth surface curvature and node geometric features. The corrected distance value is written into the data structure as the three-dimensional minimum distance data for subsequent execution of the spatial meshing point position algorithm; Three-dimensional geometric synthesis can simultaneously absorb local geometric features and global meshing trends, making the minimum distance more physically plausible in curved space.

[0030] S113: Based on node coordinate data and three-dimensional minimum distance data, spatial normal vector and nodal surface direction quantity are obtained by performing spatial meshing point position algorithm to generate spatial meshing point position data; In this embodiment, the spatial engagement point position algorithm is executed according to the following fixed process: Based on the three-dimensional minimum distance data, the nodes on the main wheel tooth surface are sorted, and a number of candidate nodes whose three-dimensional minimum distance values ​​are within the preset distance threshold range are selected. The candidate nodes constitute the initial contact area. Within the initial contact area, local geometric fitting is performed on the neighborhood of the candidate node based on the node coordinate data, and the local normal direction of the tooth surface in this area is extracted to describe the contact orientation at this location. Based on the spatial position of the candidate node on the main gear tooth surface, and combined with the node coordinate data on the gear tooth surface, a matching node with the smallest spatial distance and the same normal direction as the candidate node is searched in the gear tooth surface. Based on the spatial positional relationship between the candidate node of the master wheel and the matching node of the slave wheel, the nearest projection position along the normal direction between the two is taken as the spatial meshing point position at the current moment. The position of the spatial engagement point is recorded as spatial engagement point position data and written into the data structure along with the corresponding normal direction information for subsequent calculation of relative motion.

[0031] It should be noted that the contact point of the quasi-hyperboloid gear exhibits a time-varying trajectory along the three-dimensional surface. Accurate determination of the meshing point position is of great significance for subsequent analysis of sliding amount, velocity components, and wear.

[0032] S114: Based on the spatial meshing point position data, the tangential velocity component and the normal velocity component of the tooth surface are calculated using the three-dimensional relative motion quantitative formula to generate the spatial relative motion quantity.

[0033] In this embodiment, the instantaneous relative motion relationship between the master and slave wheels at the contact point can be obtained using spatial meshing point position data; In this embodiment, the relative velocity vector between the primary wheel and the driven wheel is first calculated using the vector difference principle based on the instantaneous velocity vectors of the primary wheel and the driven wheel at the meshing point. The instantaneous velocity vector of the main gear and the driven gear at the meshing point is determined by the rotation state of the corresponding gear and the spatial position of the meshing point. Specifically, the instantaneous velocity of the meshing point at the current moment is determined by the rotation direction and rotation speed of the gear, combined with the positional relationship of the meshing point relative to the center of the gear. By performing vector difference processing on the instantaneous velocity at the meshing point of the main wheel and the instantaneous velocity at the meshing point of the driven wheel, a spatial relative velocity vector at the meshing point is generated; In the local coordinate system, the component of the spatial relative velocity vector in the normal direction is taken as the normal velocity component, and its component in the tangential plane is taken as the tangential velocity component. The two types of velocity components are used to characterize the squeezing separation tendency and the relative sliding tendency between the tooth surfaces, respectively. The projection modulus of the relative velocity vector in the tangential plane is defined as the tangential velocity component, which is used to characterize the actual sliding rate between the tooth surfaces.

[0034] It should be noted that, through the above vector projection and decomposition steps, the complex spatial motion is decoupled into an impact component perpendicular to the tooth surface and a sliding component parallel to the tooth surface. The tangential velocity component is the direct numerical basis for subsequent calculation of the sliding distance.

[0035] S12: Based on the spatial meshing point position data, spatial relative motion, and predetermined sliding amount, construct logic to generate spatial sliding distance. By collecting the local velocity change in the meshing area and the spatial sliding distance, generate the sliding speed in the meshing area. During meshing, the meshing point migrates over time on the three-dimensional surface, and its trajectory and local velocity changes jointly affect the degree of sliding. By collecting the local velocity changes within the meshing zone and combining them with the generated spatial sliding distance, the sliding velocity in the meshing zone can be further obtained, providing a complete velocity input for subsequent wear analysis.

[0036] Specifically, the steps for generating the spatial sliding distance are as follows: S121.1: Based on spatial meshing point position data, the displacement of the meshing point along the three-dimensional surface is calculated by comparing adjacent meshing positions to generate three-dimensional displacement data; In the embodiment, the spatial positions of the meshing points at continuous meshing moments can be compared to extract the displacement of the meshing points along the three-dimensional surface. Since the trajectory of the meshing points of the quasi-hyperboloid gear in space changes continuously, the coordinates of the meshing points at two adjacent moments can be differentially processed to obtain the corresponding displacement vector, thereby forming three-dimensional displacement data. It should be noted that three-dimensional displacement data can reflect the actual migration trend of the meshing point on the curved surface, and is an important basis for constructing tangential displacement and sliding amount, so that the construction of the sliding path has spatial continuity and physical rationality.

[0037] S121.2: Based on the three-dimensional displacement data and spatial relative motion, the displacement component value of the meshing point along the tangential direction is extracted by decomposing the velocity direction component to generate tangential displacement data; In this embodiment, by combining the three-dimensional displacement vector with the spatial relative motion at the meshing point, the displacement can be decomposed in the local coordinate system. Since tooth surface wear mainly occurs along the tangential direction, the tangential component needs to be extracted from the displacement as the basis for subsequent sliding distance accumulation.

[0038] Specifically, the steps for generating tangential displacement data are as follows: S121.2.1: Based on three-dimensional displacement data, the velocity direction of the meshing point in the local meshing plane is extracted by spatial direction quantity analysis to generate local direction quantity data; In this embodiment, the direction quantity of the local meshing plane can be constructed based on the spatial geometry of the tooth surface near the meshing point. By mapping the three-dimensional displacement vector onto this local plane, local direction quantity data for tangential direction identification can be obtained. It should be noted that the construction of local directional quantities can accurately describe the meshing tangential direction that changes with the curvature and avoid directional deviations caused by curvature changes.

[0039] S121.2.2: Based on the local directional data and the spatial relative motion, calculate the velocity component value of the meshing point along the tangential direction through directional projection to generate tangential velocity component data; In the embodiment, the spatial relative motion can be projected onto the local direction to obtain the velocity component of the meshing point along the tangential direction. The projected value can reflect the relative sliding trend of the tooth surface at the current meshing position, providing a velocity basis for displacement conversion. It should be noted that the larger the tangential velocity component, the stronger the sliding in the meshing area, and the more significant the contribution to interface wear.

[0040] S121.2.3: Based on the tangential velocity component data, the tangential displacement value of the meshing point per unit time is calculated by instantaneous displacement conversion to generate tangential displacement data; In the embodiment, based on the tangential velocity component, the tangential displacement value per unit time can be obtained by using the conversion relationship between velocity and time. This displacement can characterize the local slippage of the meshing point in a short time, so that the calculation of the sliding distance has time resolution. It should be noted that the tangential displacement data and the three-dimensional displacement data together constitute a complete description of spatial motion, providing a data foundation for the subsequent accumulation of sliding distance.

[0041] S121.3: Based on tangential displacement data, the tangential cumulative distance value along the meshing path at the meshing point is calculated by spatial step accumulation to generate spatial sliding distance; In the embodiment, the tangential displacement at consecutive moments is accumulated step by step to obtain the cumulative tangential sliding distance of the meshing point along the entire meshing path. The sliding distance can reflect the relative sliding degree between the tooth surfaces and is an important input for calculating the wear driving amount and wear depth. It should be noted that the spatial sliding distance can describe the amount of slippage generated by the rolling and sliding combined motion at the meshing point on the curved surface, and can be used together with tangential velocity, normal load and dynamic wear coefficient to construct a wear model, providing a basic driving force for the analysis of thermal wear law.

[0042] In this embodiment, the logic for generating the sliding speed in the meshing zone is as follows: Based on the spatial relative motion, the tangential velocity component at the meshing point is extracted, and the tangential velocity components corresponding to each meshing moment are organized according to the time sequence of the meshing process to characterize the instantaneous sliding state of the tooth surface at different meshing positions. The tangential velocity components at adjacent meshing moments are compared moment by moment. The tangential velocity component at the current meshing moment is compared with the tangential velocity component at the previous meshing moment to obtain the change between the two. This change is used as the local velocity change in the meshing area to characterize the dynamic fluctuation characteristics of the sliding velocity in the meshing area during the meshing process. Based on this, the path association processing of the tangential velocity components corresponding to each meshing moment is performed by combining the spatial sliding distance, so that each tangential velocity component corresponds to its spatial sliding position in the meshing area. Furthermore, the tangential velocity components mentioned above are integrated based on the spatial sliding distance, so that the tangential velocity components corresponding to the meshing segment, which accounts for a larger proportion of the spatial sliding distance, have a higher contribution to the integration result, thereby obtaining a velocity value that can comprehensively reflect the overall sliding state of the meshing area, and this velocity value is used as the sliding velocity output of the meshing area.

[0043] S13: By using tooth surface roughness data, meshing zone temperature rise data and meshing zone sliding speed, a dynamic wear coefficient is generated. Based on the dynamic wear coefficient, the change value of roughness peak shear heat is collected to generate interfacial shear heat. In this embodiment, the interfacial shear heat is determined by the frictional energy generated by the shearing action of the tooth surface during sliding. Specifically, based on the tangential velocity at the meshing point, the tooth surface contact state, and the dynamic wear coefficient, the shear work output of the tooth surface per unit time is determined, and this shear work is regarded as the main source of heat energy conversion. The interfacial shear heat is obtained by accumulating the shear work output over time and corresponding it one-to-one with the sliding path, so that the interfacial shear heat can reflect the heat input intensity of the meshing zone at different positions and time periods. Furthermore, this step may include the following execution: The roughness height samples obtained from gear tooth surface roughness measurement are imported into the processing unit, and all roughness data are processed by peak-valley partitioning to identify the prominent height values ​​of roughness peaks. Then, the temperature rise sampling sequence and sliding speed sequence of the meshing area are read and synchronized in time so that each temperature rise sample corresponds to a sliding speed sample. Next, based on the synchronized temperature rise and sliding speed changes, the local temperature rise change rate is calculated for each meshing area sample point as a thermal correlation parameter of the dynamic wear coefficient. Finally, the roughness peak height data and local temperature rise change rate data are input into the thermal-velocity joint processing module to generate the dynamic wear coefficient. After the roughness data is imported into the processing module, the highest peak value in each local area can be extracted and its protrusion height recorded according to the set roughness peak identification threshold. In the temperature rise processing part, the local temperature rise change rate is obtained by means of temperature difference calculation, time step conversion, path temperature rise accumulation, etc. Finally, through parameter combination logic, the roughness peak height and the local temperature rise change rate are jointly quantified to form a dynamic wear coefficient. It should be noted that the dynamic wear coefficient generated in this step will be written into the dynamic wear coefficient data structure and used as input material for subsequent interfacial shear heat calculation.

[0044] Specifically, the steps for generating the dynamic wear coefficient are as follows: S131.1: Based on tooth surface roughness data, the average protrusion height value of tooth surface roughness peaks is extracted by comparing roughness peak heights to generate roughness peak height data; In one specific embodiment, the data obtained from the tooth surface roughness measurement can be input into the roughness peak processing module in a grid format; The roughness peak processing module reads the roughness height value at each grid point and performs peak and valley determination in sequence according to the coordinate order; First, the height value of each grid point is compared with the height value of its adjacent areas. If the height of the grid point is greater than the average height of the adjacent areas and exceeds the preset protrusion threshold, the grid point is recorded as a coarse peak point. After all rough peaks have been identified, the protrusion height of each rough peak can be read and written into the rough peak height set. Then, the data in this set is averaged, that is, the height values ​​of all rough peaks are summed and then divided by the number of rough peaks to obtain the average protrusion height of the rough peaks. The average protrusion height is written into the data structure as the rough peak height data for subsequent calculation of the dynamic wear coefficient. During the rough peak identification process, the system can scan the tooth surface area in a block manner, for example, using fixed-size grid blocks as units, and perform local comparison of the height data in each block. When the height of a certain point is located in the highest value area in that block, it can be recorded as a candidate point for rough peaks. Subsequently, noise peaks or isolated erroneous points are eliminated through further comparison, so that the final retained rough peaks are representative and stable.

[0045] It should be noted that all roughness peak height data are obtained directly from roughness measurement results without interpolation or expansion, so that they can faithfully reflect the micro-geometric characteristics of the tooth surface and provide reliable geometric input for the subsequent construction of dynamic wear coefficient; the roughness peak height data can reflect the actual micro-contact area of ​​the tooth surface and the degree of contact participation of the roughness peak. For example, when the roughness peak is high, the contact actually occurs at a few prominent peaks, which causes higher local contact pressure and thus accelerates the wear of the material at the peaks; while when the roughness peak is low and uniformly distributed, the tooth surface contact is smoother and the local wear rate is reduced. The extraction process based on the roughness peak height data can provide a microscopic geometric basis for the subsequent construction of dynamic wear coefficients, enabling the wear model to automatically adjust its sensitivity to temperature rise and sliding speed according to different roughness levels, thereby improving the adaptability of the wear model under different working conditions.

[0046] S131.2: Based on the temperature rise data and sliding speed in the meshing zone, calculate the local temperature change rate of the tooth surface by comparing the temperature rise and speed together, so as to generate local temperature rise change rate data; In one specific embodiment, the temperature rise data and sliding velocity data of the meshing zone are imported into the processing module according to the same sampling sequence, and a "temperature-velocity" synchronous data queue is established. During calculation, the processing module reads the first set of temperature rise and sliding velocity values ​​from the beginning of the queue, using it as the current reference sample; then it reads the next set of temperature rise and sliding velocity values ​​immediately following it, for joint comparison with the reference sample. By comparing the temperature change and corresponding velocity at two adjacent moments, the thermal change trend of the current segment is calculated, and this trend is written into the local temperature rise rate data structure. Before generating local temperature rise rate data, intermediate data needs to be generated sequentially, including temperature difference data, temperature rise conversion data, temperature time interval data, and path temperature rise component data. Then, based on these data, the temperature change per unit time and per unit path is calculated, and finally, local temperature rise rate data is generated.

[0047] Specifically, the logic for generating local temperature rise rate data is as follows: a1: Based on the temperature rise data of the meshing zone, the temperature difference value of consecutive moments in the meshing zone is extracted by comparing the temperature of adjacent time points to generate temperature difference data; In one specific embodiment, the temperature rise data of the meshing zone can be loaded into a buffer queue in chronological order of sampling time. The processing module reads the first temperature rise record from the queue and stores it as the "previous temperature value" in the register unit; then it reads the next temperature rise record as the "current temperature value". The processing module compares the current temperature value with the previous temperature value and generates the temperature change of the segment based on the difference between the two. After generating the temperature change, the processing module writes the change into the temperature difference data structure, updates the "current temperature value" to the new "previous temperature value", and continues to read the next temperature rise record to repeat the comparison operation. This process is repeated until all temperature rise data is traversed, ensuring that the temperature change in each time period is recorded and forming complete temperature difference data. During the comparison process, if the temperature rise data sampling is discontinuous or interrupted, abnormal samples can be skipped, and the next effective temperature rise value can be compared with the most recent effective temperature rise value according to the set rules to ensure the continuity and availability of temperature difference data.

[0048] a2: Based on the temperature difference data and the sliding speed in the meshing area, the temperature rise conversion value per unit time is calculated through time step conversion to generate temperature rise conversion data; In one specific embodiment, the temperature difference data and the sliding speed data corresponding to the time stamp can be paired item by item. For each temperature difference record, the processing module reads its corresponding time length (i.e., the time interval between two temperature samples) and simultaneously reads the sliding speed data within that time interval; Subsequently, the processing module, based on the correspondence between temperature difference and time length, converts the temperature difference into the amount of temperature rise or fall per unit time. The converted temperature change, together with the current sliding speed, is processed as a single unit and written into the temperature rise conversion data structure. Furthermore, for cases where the sliding speed fluctuates significantly between segments, the processing module can establish a stable correspondence between the speed and the temperature rise conversion value by taking the average value of the speed list for that time period, thereby ensuring the stability and processability of the temperature rise conversion data.

[0049] Specifically, the steps for generating temperature rise conversion data are as follows: a21: Based on temperature difference data, the time interval value corresponding to the temperature difference in the meshing zone is extracted through time interval quantization to generate temperature time interval data; In one specific embodiment, the timestamps in the original temperature rise data records can be parsed to extract the time difference between each pair of temperature rise records. The processing module reads the timestamps of the temperature rise records one by one, compares the timetamp of the current record with the timetamp of the next record, and writes the time difference obtained from the comparison into the temperature time interval data structure; If the temperature rise data is provided by the device at a fixed sampling frequency, the time interval for each record may be the same; if there are actual fluctuations in temperature rise sampling, the time interval is written according to the actual record to ensure that the time basis for subsequent sliding path conversion is true and effective.

[0050] a22: Based on the temperature time interval data and the sliding speed in the meshing area, the temperature change rate is converted to calculate the temperature change per unit length of the sliding path to generate path temperature rise component data. In one specific embodiment, the processing module first reads each time interval from the temperature time interval data, and simultaneously reads the sliding speed corresponding to that time interval. Then, it multiplies the time interval by the sliding speed to obtain the sliding distance of the engagement point within that time interval.

[0051] The processing module then pairs the temperature difference within this time period with the sliding distance, distributing the temperature change according to the sliding distance to obtain the temperature rise contribution value on that sliding path segment. This temperature rise contribution value is written into the corresponding data structure as the path temperature rise component data.

[0052] After performing the above processing on all time slices in a loop, a complete path temperature rise component data sequence can be formed.

[0053] During the processing, if the sliding speed is zero or close to zero, the time period is regarded as an extremely low sliding condition. The temperature rise contribution value of the time period can be directly written into the path temperature rise component data without distance amortization, so as to maintain the continuity of the calculation link.

[0054] a23: Based on the path temperature rise component data, the temperature rise conversion value per unit time in the meshing area is calculated through path cumulative quantization to generate temperature rise conversion data; In one specific embodiment, the path temperature rise component data can be summarized and processed according to the sliding path order. The processing module reads the first path temperature rise component starting from the beginning of the path temperature rise data list and uses it as the current cumulative temperature rise value; then it reads the next path temperature rise component and adds it to the current cumulative temperature rise value.

[0055] As the path progresses, the processing module continuously receives the temperature rise components of the subsequent path and accumulates them to generate the temperature rise conversion value of the meshing area per unit time. Each accumulated result is written into the temperature rise conversion data structure, so that the temperature rise conversion data can reflect the temperature rise accumulation over different time periods.

[0056] Once this step is completed, complete temperature rise conversion data will be obtained, providing input for the next step of generating the local temperature rise rate of change.

[0057] a3: Based on temperature rise conversion data, the local temperature change rate of the meshing zone is generated through velocity correlation quantification to generate local temperature rise change rate data.

[0058] In one specific implementation, the temperature rise conversion data and the corresponding sliding speed data are subjected to speed correlation quantification to generate the local temperature change rate of the meshing area. The local temperature change rate reflects the transient thermal growth capability of the meshing point under the combined effects of thermal and sliding effects. The higher the speed, the stronger the frictional heating. Under the same temperature rise conversion value, speed correlation processing can make the temperature rise change rate higher, thus more accurately reflecting the wear sensitivity under thermo-mechanical coupling.

[0059] For example, in the high-speed meshing section, although the temperature difference may not change much, the local temperature rise rate will still increase significantly due to the increase in speed.

[0060] S131.3: Based on rough peak height data and local temperature rise rate data, parameter values ​​for characterizing tooth surface wear sensitivity are generated through thermal-velocity joint quantization processing to generate dynamic wear coefficient; In a specific embodiment, to address the inconsistency between the dimensions of roughness and temperature rise, a benchmark correction method is used to calculate the dynamic wear coefficient. The specific calculation logic is as follows: The first step is to perform normalization: divide the current roughness peak height value at the meshing point by the average height value of the roughness peak on the entire tooth surface to obtain the roughness influence factor. At the same time, divide the local temperature rise rate at the current meshing point by the preset reference temperature rise rate to obtain the thermal effect influence factor. The second step is to perform weighted correction: set corresponding weight coefficients for the roughness influence factor and the thermal effect influence factor, multiply the two by their respective weight coefficients and sum them to obtain the comprehensive correction coefficient; The third step is to generate the final coefficient: multiply or add the basic wear coefficient with the comprehensive correction coefficient, depending on the specific model, to obtain the dynamic wear coefficient at the current position.

[0061] It should be noted that if rough peak height data or temperature rise rate data are missing at a certain meshing position, interpolation can be generated based on the changing trends of the two sets of data before and after that position to keep the dynamic wear coefficient sequence continuous and avoid breakpoints in wear sensitivity calculation due to data interruption.

[0062] In a specific embodiment, the calculation process for generating interfacial shear heat is as follows: After determining the dynamic wear coefficient and the sliding speed in the meshing zone, for each meshing zone location, the corresponding rough peak height data is read to identify the rough peak area that participates in actual contact at that location. At each engagement moment, the relative slip process of the rough peak at that moment is determined by combining the sliding velocity of the meshing zone, and this slip process is regarded as the process of the rough peak shearing action. Using the dynamic wear coefficient as the quantitative basis for the shear energy conversion intensity at the meshing position, the shear work output corresponding to the sliding speed in the meshing area per unit time is weighted according to the dynamic wear coefficient to obtain the rough peak shear heat change value at the meshing position at that moment. The change in shear heat of the rough peak represents the heat increment generated by shear slip of the rough peak within the current time step, and corresponds one-to-one with the sliding speed of the meshing zone corresponding to that time step. The changes in rough peak shear heat obtained during continuous meshing in the meshing zone are accumulated in chronological order to obtain the interfacial shear heat of the meshing zone throughout the entire meshing process. The interfacial shear heat is used to characterize the total heat input intensity of the tooth surface due to the rough peak shearing action in the meshing zone, and is used as the energy input data for subsequent wear drive calculation.

[0063] S14: Based on the spatial sliding distance and interface shear heat, the wear driving amount is generated according to the predetermined wear driving amount iterative logic. Specifically, the steps for generating wear-driving quantities are as follows: S141.1: Based on the spatial sliding distance, the sliding path increment value of the meshing point per unit time is extracted through sliding path quantization to generate sliding path increment data; In one specific embodiment, the spatial sliding distance is compared point by point in chronological order, and the difference in sliding distance between two consecutive moments is taken as the sliding path increment for that time period. By continuously reading the sliding distance at all times, complete incremental data of the sliding path can be formed.

[0064] It should be noted that the incremental sliding path data is used for subsequent energy output conversion and is the most basic path input for calculating wear-driven quantities.

[0065] S141.2: Based on the interface shear heat and sliding path increment data, calculate the unit energy output value of the meshing interface on the sliding path through the ratio conversion logic to generate unit energy output data; In a specific embodiment, in order to quantify the distribution density of heat energy along the path, the system performs the following calculation: First, read the interface shear heat value and the corresponding sliding path increment value within the current time step; then, use the interface shear heat value as the dividend and the sliding path increment value as the divisor to perform a division operation. The quotient obtained from the calculation is defined as the linear energy density, i.e., the output data per unit energy. It should be noted that the significance of this step lies in spreading the instantaneously generated heat energy evenly across the tiny slip path where it occurs, thereby obtaining the heat load intensity carried per unit length of the path. This solves the problem that the heat value alone cannot reflect path correlation, and gives the energy distribution spatial resolution.

[0066] S141.3: Based on the unit energy output data, the energy driving amount within the meshing cycle is generated through path integral logic to generate the wear driving amount; In a specific embodiment, the wear-driving quantity is generated using a discrete integral summation method: For each discrete meshing time step, the linear energy density (unit energy output data) calculated for that time step is multiplied again by the corresponding sliding path increment data, and a preset energy conversion efficiency factor is introduced for correction to obtain the effective wear work within that micro-segment. The energy conversion efficiency factor is determined based on the gear material properties and experimental calibration results, and is used to characterize the conversion ratio of shear energy to effective wear energy. The energy conversion efficiency factor remains constant throughout the entire meshing cycle, or is updated in segments based on temperature rise data, to ensure that the wear drive calculation process has a consistent physical constraint basis. Subsequently, the effective wear work of all micro-segments during the entire meshing cycle is summed up, and the final sum is the wear driving amount. It should be noted that this step uses the numerical processing logic of "differentiating to calculate density and then integrating to calculate total amount" to accurately capture the non-uniform wear potential of the tooth surface caused by speed and heat flow fluctuations. The resulting wear driving amount can truly reflect the cumulative damage degree throughout the entire meshing process.

[0067] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces, characterized in that, The method includes: Based on the position data of the main wheel and the position data of the driven wheel, spatial meshing point position data is generated through a spatial meshing point position algorithm, and spatial relative motion is generated through a three-dimensional relative motion quantitative formula based on the spatial meshing point position data. Based on the spatial meshing point position data, spatial relative motion, and predetermined slip volume, a logic is constructed to generate the spatial slip distance. By collecting the local velocity change in the meshing area and the spatial slip distance, the sliding velocity in the meshing area is generated. A dynamic wear coefficient is generated by using tooth surface roughness data, meshing zone temperature rise data, and meshing zone sliding speed. Based on the dynamic wear coefficient, the change value of roughness peak shear heat is collected to generate interfacial shear heat. Based on the spatial sliding distance and interface shear heat, the wear driving force is generated according to the predetermined wear driving force iteration logic.

2. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 1, characterized in that, The steps for generating spatial relative motion are as follows: Based on the position data of the master wheel and the slave wheel, the node coordinate values ​​of the master wheel tooth surface and the slave wheel tooth surface are extracted by node coordinate parsing to generate node coordinate data; Based on the node coordinate data, the minimum three-dimensional distance between the master wheel tooth surface node and the slave wheel tooth surface node is calculated using three-dimensional distance calculation, and three-dimensional minimum distance data is generated. Based on node coordinate data and three-dimensional minimum distance data, spatial normal vector and nodal surface direction quantity are obtained by performing spatial meshing point position algorithm to generate spatial meshing point position data; Based on the spatial meshing point position data, the tangential velocity component and the normal velocity component of the tooth surface are calculated using a three-dimensional relative motion quantitative formula to generate the spatial relative motion quantity.

3. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 2, characterized in that, The steps to generate 3D minimum distance data are as follows: Based on node coordinate data, the three-dimensional distance between any master gear tooth surface node and all slave gear tooth surface nodes is calculated by scanning node pairs to generate an initial node distance set; Based on the initial node distance set, the minimum distance value corresponding to each main gear tooth surface node is extracted by distance filtering to generate node minimum distance data; Based on the minimum node distance data, the global convergence distance value is calculated from the minimum node distance values ​​of all main gear tooth surfaces through spatial convergence to generate convergence distance data; Based on the convergence distance data, the minimum three-dimensional distance value for subsequent meshing point calculation is obtained through three-dimensional geometric synthesis operation to generate the minimum three-dimensional distance data.

4. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 3, characterized in that, The steps to generate the spatial sliding distance are as follows: Based on spatial meshing point position data, the displacement of the meshing point along the three-dimensional surface is calculated by comparing adjacent meshing positions to generate three-dimensional displacement data. Based on the three-dimensional displacement data and spatial relative motion, the displacement component value of the meshing point along the tangential direction is extracted by decomposing the velocity direction component to generate tangential displacement data. Based on tangential displacement data, the cumulative tangential distance along the meshing path at the meshing point is calculated by spatial step accumulation to generate the spatial sliding distance.

5. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 4, characterized in that, The steps to generate tangential displacement data are as follows: Based on three-dimensional displacement data, the velocity direction of the meshing point in the local meshing plane is extracted by spatial direction quantity analysis to generate local direction quantity data; Based on the local directional data and the spatial relative motion, the velocity component value of the meshing point along the tangential direction is calculated by directional projection to generate tangential velocity component data; Based on the tangential velocity component data, the tangential displacement value of the meshing point per unit time is calculated by instantaneous displacement conversion to generate tangential displacement data.

6. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 5, characterized in that, The steps for generating the dynamic wear coefficient are as follows: Based on tooth surface roughness data, the average protrusion height value of the tooth surface roughness peaks is extracted by comparing the roughness peak heights to generate roughness peak height data. Based on the temperature rise data and sliding speed in the meshing zone, the local temperature change rate of the tooth surface is calculated by comparing the temperature rise and speed together, so as to generate local temperature rise change rate data. Based on rough peak height data and local temperature rise rate data, parameter values ​​for characterizing tooth surface wear sensitivity are generated through thermal-velocity joint quantization processing to generate dynamic wear coefficient.

7. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 6, characterized in that, The logic for generating local temperature rise rate data is as follows: Based on the temperature rise data in the meshing zone, the temperature difference value at consecutive moments in the meshing zone is extracted by comparing the temperature at adjacent time points to generate temperature difference data. Based on the temperature difference data and the sliding speed in the meshing area, the temperature rise conversion value per unit time is calculated through time step conversion to generate temperature rise conversion data. Based on the temperature rise conversion data, the local temperature change rate of the meshing zone is generated through velocity correlation quantification to generate local temperature rise change rate data.

8. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 7, characterized in that, The steps to generate temperature rise conversion data are as follows: Based on temperature difference data, the time interval values ​​corresponding to the temperature difference in the meshing zone are extracted through time interval quantization to generate temperature time interval data. Based on the temperature time interval data and the sliding speed in the meshing area, the temperature change per unit length of the sliding path is calculated by converting the temperature change rate to generate path temperature rise component data. Based on the path temperature rise component data, the temperature rise conversion value per unit time in the meshing zone is calculated through path cumulative quantization to generate temperature rise conversion data.

9. The method for calculating thermal wear of quasi-hyperboloid gear tooth surfaces according to claim 8, characterized in that, The steps for generating wear driving forces are as follows: Based on the spatial sliding distance, the sliding path increment value of the meshing point per unit time is extracted through sliding path quantization to generate sliding path increment data. Based on the interface shear heat and sliding path increment data, the unit energy output value of the meshing interface on the sliding path is calculated through ratio conversion logic to generate unit energy output data. Based on the unit energy output data, the energy driving quantity within the meshing cycle is generated through path integral logic to generate the wear driving quantity.

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