Bionic tooth surface microstructure concrete design method

Abstract

The invention relates to the field of mechanical surface engineering and bionic design, in particular to a bionic tooth surface microstructure concrete design method. The method comprises the following steps: acquiring point cloud data of a shell body surface, and constructing a shell body surface three-dimensional model by using the point cloud data; constructing a bionic coupling element matrix by using the shell body surface three-dimensional model; constructing an equal-arc-length parameter sequence and a gray mapping matrix based on the bionic coupling element matrix, and constructing a structural feature matrix by using the equal-arc-length parameter sequence and the gray mapping matrix; and establishing a tooth surface geometric model of the target gear, and mapping the structural feature matrix to a parameter domain of the tooth surface geometric model to obtain initial micro-texture geometric distribution. And constructing a morphological design parameter matrix, and regulating and controlling the initial micro-texture geometric distribution by using the morphological design parameter matrix to obtain a bionic tooth surface micro-texture model. The problem that in the prior art, a quantitative conversion method from biological structure geometric characteristics to engineering tooth surface micro-texture design parameters is lacked is solved.

Description

A biomimetic design method for microtextured tooth surfaces Technical Field

[0001] This invention relates to the fields of mechanical surface engineering and biomimetic design, specifically a biomimetic tooth surface microtexture figurative design method. Background Technology

[0002] The surface features of gear meshing pairs have a significant impact on their load-carrying capacity, lubrication condition, and meshing stability. The design of tooth surface microtextures can improve gear transmission performance by introducing specific geometric features into the meshing surface, thereby adjusting the contact stress distribution and fluid lubrication condition.

[0003] Current methods for designing tooth surface microtextures are mostly based on empirical parameters or two-dimensional morphological settings, lacking a systematic quantitative description and controllable mapping mechanism for surface geometry. This results in a lack of clear functional relationship between texture distribution and tooth surface contact performance, making it difficult to achieve precise performance-oriented design. On the other hand, biological surface structures have developed multi-scale coupled surface morphologies over long-term evolution, exhibiting significant natural adaptability in mechanical response, lubrication retention, and wear control. Shell surfaces possess distinct composite structural features such as radial ribs, nodules, and grooves, which coordinate stress dispersion and fluid regulation at the local scale. However, existing research largely remains at the level of biomimetic inspiration, lacking quantitative methods for converting biological structural geometry features into engineering tooth surface microtexture design parameters. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a biomimetic design method for tooth surface microtextures, solving the problem of the lack of a quantitative conversion method from biological structural geometric features to engineering tooth surface microtexture design parameters in existing technologies.

[0005] To achieve the above objectives, this invention provides a method for the figurative design of biomimetic tooth surface microtexture. The method includes: acquiring point cloud data of a seashell surface and constructing a three-dimensional model of the seashell surface using the point cloud data; constructing a biomimetic coupling element matrix using the three-dimensional model of the seashell surface; constructing an equal arc length parameter sequence and a grayscale mapping matrix based on the biomimetic coupling element matrix, and constructing a structural feature matrix using the equal arc length parameter sequence and the grayscale mapping matrix; establishing a tooth surface geometric model of the target gear, mapping the structural feature matrix to the parameter domain of the tooth surface geometric model to obtain an initial microtexture geometric distribution; constructing a morphological design parameter matrix, and using the morphological design parameter matrix to regulate the initial microtexture geometric distribution to obtain a biomimetic tooth surface microtexture model.

[0006] This invention constructs a three-dimensional model by extracting point cloud data from the surface of a seashell, integrates biological features through a biomimetic coupling matrix, and constructs a structural feature matrix by combining an equal arc length parameter sequence and a grayscale mapping matrix. This matrix is ​​then accurately mapped onto the geometric model of a gear tooth surface. Furthermore, the initial microtexture distribution is controlled by a morphological design parameter matrix, thus completely replicating the multi-scale coupling features of the biological surface. This improves the concreteness of the microtexture design and the adaptability of the tooth surface, achieving quantitative control of the microtexture geometry. Simultaneously, it enhances the lubrication, friction reduction, fatigue resistance, and load-bearing performance of the gear tooth surface, thereby improving the reliability and engineering application value of the biomimetic tooth surface design.

[0007] Optionally, acquiring point cloud data of the shell surface and constructing a three-dimensional model of the shell surface using the point cloud data includes: collecting raw point cloud data of the shell surface; performing point cloud registration, coordinate unification, and noise removal on the raw point cloud data to form a point cloud model of the shell surface; and performing mesh repair, hole filling, and smooth reconstruction on the point cloud model of the shell surface to generate a three-dimensional model of the shell surface.

[0008] This invention collects raw point cloud data of the shell surface, then performs registration, coordinate unification, and noise removal on the raw point cloud to form a point cloud model of the shell surface. Finally, through mesh repair, hole filling, and smooth reconstruction, a three-dimensional model of the shell surface is generated. This effectively eliminates coordinate deviation and noise interference from multi-station scanning, avoids defects such as mesh holes and distortion from affecting the extraction of biological features, and improves the restoration degree of geometric features of the shell surface and the accuracy of the three-dimensional model.

[0009] Optionally, the step of constructing a biomimetic coupling element matrix using the three-dimensional model of the shell surface includes: constructing a point cloud feature vector set by calculating the geometric feature parameters in the three-dimensional model of the shell surface; classifying the point cloud feature vector set using the K-nearest neighbor algorithm to obtain structural units of ridges, grooves and nodules on the shell surface; and constructing a biomimetic coupling element matrix based on the geometric feature parameters of the structural units.

[0010] This invention constructs a point cloud feature vector set by calculating the geometric feature parameters of the three-dimensional model of the shell's body surface, uses the K-nearest neighbor algorithm to classify ridge, groove, and nodule structural units, and then constructs a biomimetic coupling element matrix based on the unit geometric parameters. This achieves accurate division and parameter quantification of the core structure of the shell's body surface, avoids confusion or omission of biological features, and improves the accuracy of the biomimetic coupling element matrix.

[0011] Optionally, constructing the equal arc length parameter sequence and grayscale mapping matrix based on the biomimetic coupling element matrix includes: constructing the equal arc length parameter sequence based on the biomimetic coupling element matrix; and constructing the grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix.

[0012] This invention first constructs an equal arc length parameter sequence based on a biomimetic coupled matrix, and then combines this sequence with the biomimetic coupled matrix to construct a grayscale mapping matrix. This not only maintains the relative geometric relationship of the shell surface feature points by using the equal arc length parameter sequence, avoiding morphological distortion during feature mapping, but also quantifies biological depth information through the grayscale mapping matrix, achieving accurate conversion of the concave and convex features of the biological surface, thus improving the accuracy and consistency of converting multi-scale features of the shell surface to the engineering parameter domain.

[0013] Optionally, the step of constructing an equal arc length parameter sequence based on the biomimetic coupling element matrix includes: extracting the shell surface feature point cloud from the biomimetic coupling element matrix; projecting the shell surface feature point cloud onto a pre-set intermediate reference surface to obtain a two-dimensional feature point distribution; calculating the arc length between adjacent points in the two-dimensional feature point distribution, and using the arc length to construct an equal arc length parameter sequence.

[0014] This invention extracts the feature point cloud of the shell surface from the biomimetic coupling element matrix, projects it onto an intermediate reference surface to obtain a two-dimensional feature point distribution, calculates the arc length of adjacent points and constructs an equal arc length parameter sequence, accurately selects the core morphological feature points of the shell surface, avoids redundant point interference, simplifies the difficulty of three-dimensional feature processing through projection, and maintains the relative positional relationship of feature points by relying on arc length calculation and sequence construction, preventing biological morphology distortion during the mapping process and improving the accuracy of the equal arc length parameter sequence construction.

[0015] This invention improves the standardization of the equal arc length parameter sequence and the accuracy of feature mapping by defining an equal arc length parameter sequence formula, calculating the arc length of adjacent points based on the coordinate difference of two-dimensional feature points, and then quantifying the equal arc length parameter of the first feature point by the ratio of the cumulative arc length to the total arc length.

[0016] Optionally, constructing a grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix includes: performing equal arc length discretization on the equal arc length parameter sequence to obtain ordered feature point position indices; extracting depth information from the biomimetic coupling element matrix and converting the depth information into grayscale values; and constructing a grayscale mapping matrix based on the ordered feature point position indices and the grayscale values.

[0017] This invention obtains ordered feature point position indices by discretizing an equal arc length parameter sequence, extracts depth information from the biomimetic coupling element matrix and converts it into grayscale values, and then constructs a grayscale mapping matrix based on the index using the grayscale values. This achieves the quantization and ordered mapping of the depth features of the shell surface, avoids misalignment or distortion of depth information during the conversion process, and improves the accuracy of the grayscale mapping matrix.

[0018] Optionally, the step of controlling the initial microtexture geometric distribution using the morphology design parameter matrix to obtain a biomimetic tooth surface microtexture model includes: extracting the initial three-dimensional coordinates of the texture units in the initial microtexture geometric distribution; multiplying the morphology design parameter matrix with the initial three-dimensional coordinates of the texture to obtain the controlled texture three-dimensional coordinates; constructing a microtexture three-dimensional geometric model using the controlled texture three-dimensional coordinates and the tooth surface geometric model; and fusing the microtexture three-dimensional geometric model and the tooth surface geometric model to obtain a biomimetic tooth surface microtexture model.

[0019] This invention extracts the initial three-dimensional coordinates of the texture unit from the initial microtexture geometric distribution, multiplies the morphological design parameter matrix with these coordinates to obtain the three-dimensional coordinates of the adjustable texture, and then uses the adjustable coordinates and the tooth surface geometric model to construct a three-dimensional geometric model of the microtexture and merge it with the tooth surface model. This achieves quantitative control of the microtexture morphology, and allows for flexible adjustment of texture size, distribution and other characteristics according to engineering needs, avoiding the uniformity of microtexture morphology and improving the design flexibility and engineering adaptability of biomimetic tooth surface microtextures.

[0020] Optionally, the biomimetic tooth surface microtexture design method further includes: performing finite element analysis on the biomimetic tooth surface microtexture model and evaluating its performance.

[0021] This invention improves the reliability of biomimetic tooth surface microtexture model design by performing finite element analysis and evaluating the performance of the biomimetic tooth surface microtexture model.

[0022] Optionally, the finite element analysis and performance evaluation of the biomimetic tooth surface microtexture model includes: establishing an untextured tooth surface model and constructing a gear meshing contact simulation comparison model based on the untextured tooth surface model; establishing a gear meshing contact simulation model based on the biomimetic tooth surface microtexture model; performing finite element analysis on the gear meshing contact simulation comparison model and the gear meshing contact simulation model under pre-set working conditions; and evaluating the performance of the biomimetic tooth surface microtexture model based on the results of the finite element analysis.

[0023] This invention constructs a simulation comparison model by establishing an untextured tooth surface model, and then establishes a meshing simulation model based on the biomimetic tooth surface microtextured model. Under the same working conditions, finite element analysis is performed on both models and their performance is evaluated. This achieves a quantitative comparison of the performance differences between the biomimetic microtexture and the original tooth surface, avoids performance judgment bias caused by the lack of reference, and further improves the reliability of the biomimetic tooth surface microtexture model design. Attached Figure Description

[0024] Figure 1 is a flowchart of a biomimetic tooth surface microtexture figurative design method according to an embodiment of the present invention; Figure 2 is a schematic diagram of the original point cloud data of the shell according to an embodiment of the present invention; Figure 3 is a schematic diagram of the three-dimensional geometric model of the target gear according to an embodiment of the present invention; Figure 4 is a schematic diagram of the equal arc length geometric features of the gear tooth profile according to an embodiment of the present invention; Figure 5 is a schematic diagram of the biomimetic tooth surface microtexture model according to an embodiment of the present invention. Detailed Implementation

[0025] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0026] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0027] Please refer to Figure 1. In order to solve the problems in the prior art, in an optional embodiment, a biomimetic tooth surface microtexture figurative design method as shown in the figure includes the following steps: Step S1, acquire point cloud data of the shell surface, and use the point cloud data to construct a three-dimensional model of the shell surface.

[0028] The process of acquiring point cloud data of the shell surface and constructing a three-dimensional model of the shell surface using the point cloud data specifically includes the following sub-steps: Step S101, acquiring the original point cloud data of the shell surface.

[0029] First, healthy shells with intact shells, no damage or deformation, and clear ridge-groove-nodule features were selected as samples. The shell surface was cleaned to remove impurities such as mud and attached substances. After drying, the samples were fixed to the scanning platform using a non-obstructive clamp to prevent sample displacement or surface damage during collection. Raw point cloud data of the shell surface of the shells (such as blood clams, bay scallops, and mud clams) were acquired using industrial CT scanning or 3D laser scanning technology. The nominal point spacing for laser scanning was controlled at 0.01-0.1 mm, and the spatial resolution for industrial CT was no less than 5-50 μm to ensure that the point cloud density met the requirements for subsequent extraction of fine geometric features such as ridge curvature and nodule depth. To meet the requirements, a 360° surround multi-station scanning method was adopted during the data collection process to cover the entire surface of the shell (including the front, sides, and edge transition areas) to avoid data loss caused by feature occlusion. The scanning data from each station were unified to the same coordinate system using the bundle method global registration technique to ensure global consistency of the point cloud. After the data collection was completed, the original point cloud data was checked immediately to confirm that there were no continuous data holes, no obvious noise interference, and that the core biomimetic features of the shell, such as ridges, grooves, and nodules, were fully presented. The final schematic diagram of the original point cloud data of the shell is shown in Figure 2, which clearly presents the biological prototype features such as ridges, grooves, and nodules, providing a data foundation for subsequent biomimetic design.

[0030] Step S102: Perform point cloud registration, coordinate unification and noise removal processing on the original point cloud data to form a point cloud model of the shell surface.

[0031] In this embodiment, the original point cloud data of the collected seashells is first registered. The discrete point clouds obtained from multi-station scanning are imported into 3D engineering software such as nTop and Geomagic Wrap. Using significant feature points such as typical nodal vertices and ridge intersections on the surface of the seashell as registration benchmarks, the Iterative Closest Point (ICP) global registration algorithm or feature point matching algorithm is used to gradually optimize the spatial positional relationship of the point clouds at each station, ensuring that there is no significant misalignment of the point clouds at different stations after registration, and the registration error is controlled within 0.02mm to meet the requirements of subsequent high-precision modeling. Next, coordinate unification processing is performed. A right-handed Cartesian coordinate system is established with the center of the largest circumcircle of the seashell as the origin, the axis of symmetry of the shell (the central axis running through the front and rear ends) as the Z-axis, and the direction passing through the origin and parallel to the longest ridge as the X-axis. All registered point cloud data are uniformly transformed to this coordinate system to eliminate coordinate offsets from different station scans and achieve the unification of the spatial position of the point clouds. Finally, noise removal is performed using a statistical filtering algorithm. A threshold for the number of neighboring points (e.g., 8) and a standard deviation multiple threshold (e.g., 1.8 times) are set. The average distance between each point and its neighboring points is calculated. Isolated noise points that deviate from the average distance and exceed the threshold are identified and removed. At the same time, a radius filtering algorithm is used to set the search radius (e.g., 0.08 mm) to delete pseudo-points with fewer than 3 neighboring points within the search range, thus avoiding noise interference with subsequent feature extraction. After the above processing, a point cloud model of the shell surface with unified coordinates, no redundant noise, and complete features is formed.

[0032] Step S103: Perform mesh repair, hole filling and smooth reconstruction on the point cloud model of the shell surface to generate a three-dimensional model of the shell surface.

[0033] In this embodiment, the point cloud model of the shell surface is first imported into 3D engineering software such as nTop, Geomagic Wrap, or CloudCompare. The processing is performed in the order of mesh repair, hole filling, and smooth reconstruction. The mesh repair function is activated, and an adaptive triangular mesh algorithm is used to traverse the initial mesh, automatically identifying and deleting isolated points not connected to the main mesh and distorted triangles (pseudo-triangles) with side lengths exceeding four times the average point spacing of the point cloud. Simultaneously, the mesh topology is optimized, adjusting the angular deviation between adjacent triangular units to within 20° to reduce the impact of distorted meshes on subsequent modeling accuracy. Next, holes on the mesh surface are filled. For tiny holes with a diameter less than 0.3 mm, the software's automatic hole filling module is directly invoked, generating a transition mesh based on the curvature trend of the hole boundary points. For larger holes with a diameter greater than 0.3 mm formed due to scan occlusion, the hole boundary is first manually extracted and a smooth boundary curve is fitted. Then, the mesh is reconstructed based on the boundary curve and the normal distribution of the surrounding surface, ensuring that the filled area is consistent with the geometry and topological relationship of the main surface. After restoration, a smooth reconstruction was performed. Quadrilateral mesh fitting or NURBS surface fitting techniques were used to ensure the curvature of the restored mesh was continuous. By iteratively adjusting the vertex coordinates, the transition areas of the ridges, grooves, and nodules on the shell surface were smoothed without sharp edges. Finally, a software deviation analysis tool was used to compare the reconstructed model with the original point cloud data, ensuring that the surface continuity error was less than 0.02 mm. This resulted in a feature-accurate and smooth 3D model of the shell surface. This 3D model was then voxelized using the binvox tool into a 256×256×256 dimensional voxel mesh (each voxel is 0.005 mm in size), preserving the core geometric features of the ridges, grooves, and nodules for accurate feature parameter extraction later.

[0034] Step S2: Construct a biomimetic coupling element matrix using the three-dimensional model of the shell surface.

[0035] Specifically, the process of constructing a biomimetic coupled element matrix using the three-dimensional model of the shell surface includes the following sub-steps: Step S201, constructing a point cloud feature vector set by calculating the geometric feature parameters in the three-dimensional model of the shell surface.

[0036] In this embodiment, the geometric feature parameters include principal curvature, radius of curvature, normal vector, and depth. The three-dimensional model of the shell surface is exported as a standard point cloud data format (such as PLY or STL format). The point cloud data is read by a MATLAB program, and geometric feature calculations are performed using each discrete point in the point cloud model as an independent analysis unit. First, the unit normal vector of each point is calculated. 15-25 neighboring points around the point are selected, and the three-dimensional plane is fitted using the least squares method. The normal vector direction of the plane is further normalized to obtain the unit normal vector, and the normal vector direction is uniformly pointed to the outside of the shell to avoid affecting the subsequent clustering accuracy due to direction confusion. Next, the principal curvature and radius of curvature are calculated. Based on the obtained normal vector and the coordinates of the neighboring points, the local curvature tensor of the point is constructed. By solving the eigenvalues ​​of the curvature tensor, two principal curvatures are obtained (corresponding to the maximum and minimum curvature, respectively). The corresponding radius of curvature is calculated according to the principal curvature to distinguish the geometric differences between the ridge region (large radius of curvature), the nodular region (small radius of curvature), and the groove region (negative curvature). Subsequently, the plane containing the lowest z-coordinate at the bottom of the shell is defined as the depth reference plane. The difference between the z-coordinate of each point and the z-coordinate of the reference plane is calculated as the depth value of that point, used to quantify the concavity and convexity amplitude of the point cloud in the vertical direction. Finally, the three-dimensional coordinates, principal curvature, unit normal vector, and depth value of each point are integrated in sequence to form the point cloud feature vector of a single point. The feature vectors of all points are then summed to form a complete feature vector set of the point cloud on the shell surface.

[0037] Step S202: The K-nearest neighbor algorithm is used to classify the point cloud feature vector set to obtain the structural units of ridges, grooves and nodules on the shell surface.

[0038] In this embodiment, firstly, labeled samples required for KNN supervised classification are prepared. From the point cloud model of the shell surface, feature points of three typical regions are selected as labeled samples: points with continuous surface convexities and large principal curvature radii are labeled as ridges; points with surface depressions and negative principal curvature are labeled as grooves; and points with isolated surface convexities and small principal curvature radii are labeled as nodules. Each group of labeled samples contains no fewer than 50 samples to cover feature differences. Next, the point cloud feature vector set (including parameters such as principal curvature, normal vector, and depth) is subjected to min-max normalization, mapping all parameters to... The interval was used to eliminate the influence of dimensions and avoid a single parameter dominating the classification. Then, KNN parameters were set, and for each feature vector to be classified, the K nearest labeled samples were selected. The number of samples in each category was counted, and the vector to be classified was assigned to the category with the most samples. Finally, the classification results were checked using MATLAB visualization, and misclassification points with a percentage below 0.5% were corrected. The final structural units of the shell surface ridges, grooves, and nodules were obtained, as shown in Figure 2.

[0039] Step S203: Construct a biomimetic coupling element matrix based on the geometric feature parameters of the structural unit.

[0040] In this embodiment, the geometric parameters of each of the three structural units—ridges, grooves, and nodules—are extracted first. The radius of curvature corresponds to the first column element of the matrix in the formula (e.g., the radius of curvature of the first structural unit is denoted as...). The radius of curvature of the second structural unit is denoted as... , No. The radius of curvature of each structural unit is denoted as . The normal distribution is decomposed into components in the x, y, and z directions, corresponding to the elements in the second to fourth columns of the matrix (e.g., the normal x component of the first structural unit is...). y-components are z-components are , No. The normal components of each structural unit are , , The surface depth corresponds to the fifth column element of the matrix (e.g., the surface depth of the first structural unit is...). , No. The surface depth of each structural unit is Subsequently, the parameter data was organized according to the structural unit type and geometric parameters to ensure that each parameter of each type of unit has a clear quantified value or statistical range: the row index of the matrix corresponds to different structural units (from the first structural unit to the second). Each structural unit has a column index corresponding to a geometric parameter (radius of curvature, x-component of normal, y-component of normal, z-component of normal, and surface depth, in that order). Finally, the corresponding geometric parameter values ​​of the structural units are filled into the corresponding positions in the matrix to form a biomimetic coupling element matrix (CE matrix).

[0041] The biomimetic coupling element matrix satisfies the following formula: Step S3: Construct an equal arc length parameter sequence and a grayscale mapping matrix based on the biomimetic coupling element matrix, and construct a structural feature matrix using the equal arc length parameter sequence and the grayscale mapping matrix.

[0042] Specifically, the process of constructing the equal arc length parameter sequence and grayscale mapping matrix based on the biomimetic coupling element matrix includes the following sub-steps: Step S301, constructing the equal arc length parameter sequence based on the biomimetic coupling element matrix.

[0043] The construction of the equal arc length parameter sequence based on the biomimetic coupling element matrix specifically includes the following sub-steps: Step S30101, extracting the feature point cloud of the shell surface from the biomimetic coupling element matrix.

[0044] In this embodiment, based on the structural unit classification and parameters of the biomimetic coupling matrix, feature points that can characterize the core morphology of the shell surface are selected. For ridge units, combining their characteristic orientation angle (characterizing the extension direction of the ridge) and regional scale (characterizing the length and width of the ridge), continuous points distributed along the extension direction of the ridge and whose depth values ​​conform to the average depth of the ridge units are extracted from the corresponding original point cloud to form the centerline feature points of the ridge. For nodular units, based on their surface depth (characterizing the height of the protrusion) and regional scale (characterizing the size of the nodule), the point with the largest depth value (i.e., the apex of the protrusion) in each nodular unit is extracted as the nodular feature point. For groove units, based on their surface depth (characterizing the depth of the depression) and characteristic orientation angle (characterizing the direction of the groove), the point with the smallest depth value (i.e., the bottom line of the depression) in the groove unit is extracted as the bottom line feature point of the groove. Finally, the extracted ridge centerline points, nodule apex points, and groove bottom line points are summarized and duplicate points are removed (for cases where feature points of different units overlap), forming a shell surface feature point cloud that can completely reflect the key geometric features of the shell surface. This feature point cloud not only retains the morphological information of the three core structures, but also ensures the representativeness of the features through the selection of biomimetic coupling element matrix parameters, avoiding redundant points from interfering with subsequent projection processing.

[0045] Step S30102: Project the feature point cloud of the shell surface onto a pre-set intermediate reference surface to obtain a two-dimensional feature point distribution.

[0046] In this embodiment, the intermediate reference plane is an orthogonal reference plane (i.e., a top-view plane) perpendicular to the overall normal direction of the shell surface feature point cloud. The origin is the projection point of the geometric center of the shell surface feature point cloud onto this plane, ensuring the consistency of the coordinate system. Next, the projection direction is set to the overall normal direction of the shell surface feature point cloud (perpendicular to the intermediate reference plane, ensuring that the depth information of the feature points during projection can be indirectly reflected through subsequent grayscale values). Then, a projection operation is performed on each point in the feature point cloud, projecting the ridge centerline point, nodule apex, and groove bottom line point onto the intermediate reference plane along the set direction. The coordinates of each point on the reference plane are recorded, and the original structural unit category label (ridge, nodule, groove) is retained to avoid feature confusion after projection. Finally, a two-dimensional feature point distribution that reflects the relative geometric relationships of the three core structures on the shell surface is formed.

[0047] Step S30103: Calculate the arc length between adjacent points in the two-dimensional feature point distribution, and use the arc length to construct an equal arc length parameter sequence.

[0048] In this embodiment, based on the two-dimensional feature point distribution formed by the projection of shell surface features, the geometric arc length between adjacent feature points is calculated sequentially. These feature points cover the core projection positions corresponding to ridges, grooves, and nodules on the shell surface, ensuring that the arc length calculation can fully reflect the morphological distribution pattern of the organism's surface. Subsequently, the arc lengths of all adjacent points are accumulated to obtain the total arc length of the feature point sequence. Then, the accumulated arc length corresponding to each feature point is normalized to map all arc length parameters to a unified interval, thereby maintaining the inherent relative positional relationship and spatial distribution logic between feature points, and finally forming an equal arc length parameter sequence that can be directly used for subsequent tooth surface mapping.

[0049] Step S302: Construct a grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix.

[0050] The construction of the grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix specifically includes the following sub-steps: Step S30201, the equal arc length parameter sequence is subjected to equal arc length discretization processing to obtain ordered feature point position indexes.

[0051] In this embodiment, firstly, based on the meshing accuracy requirements of the target gear tooth surface parameter domain, the number of discrete segments is set, and the [0,1] interval of the equal arc length parameter sequence is evenly divided into several equal arc length discrete segments. The number of discrete segments must be consistent with the number of discrete nodes on the tooth surface in the corresponding direction to ensure the integrity of the feature mapping. Then, discrete processing is performed. Starting from the initial parameter of the equal arc length parameter sequence, discrete parameter values ​​are extracted sequentially according to the set discrete interval. For each discrete parameter value, the feature point closest to the value is found in the original equal arc length parameter sequence. The original position number of the feature point in the two-dimensional feature point distribution after the feature point cloud projection on the shell surface is determined. Finally, the original position numbers of these feature points are arranged sequentially according to the discrete parameter values ​​from 0 to 1 to form an ordered feature point position index. This index not only preserves the relative arc length relationship of the feature points on the shell surface, but also establishes the positional association between the discrete parameters and the original feature points.

[0052] Step S30202: Extract depth information from the biomimetic coupling element matrix and convert the depth information into grayscale values.

[0053] In this embodiment, the depth corresponding to each feature point is first retrieved from the biomimetic coupling element matrix. Next, iterate through the depth of all feature points. Determine the minimum depth With the maximum value Then, the depth was converted into grayscale values ​​according to the linear normalization formula in the disclosure document. At the same time ensure The range of values ​​is limited to [0,1].

[0054] Step S30203: Construct a grayscale mapping matrix based on the ordered feature point position index and the grayscale value.

[0055] In this embodiment, the gray values ​​of all feature points are first arranged in the order of their ordered position indices to form a one-dimensional gray-scale mapping array. Then, based on the spatial range of the two-dimensional feature point distribution, the row and column dimensions of the grayscale mapping matrix are determined, where rows correspond to the X-axis direction of the projection plane and columns correspond to the Y-axis direction of the projection plane, ensuring that the matrix dimensions accurately match the distribution range of the two-dimensional feature points. Finally, the array is indexed according to the ordered feature point positions. The gray value corresponding to each feature point in the image The row and column coordinates corresponding to the projection position of the feature point are filled into the matrix one by one, and finally an m×n dimension grayscale mapping matrix is ​​formed, where m is the number of discrete points in the X-axis direction and n is the number of discrete points in the Y-axis direction.

[0056] Based on the order of feature points and their corresponding two-dimensional coordinates in the equal arc length parameter sequence, and combined with the normalized gray values ​​of the corresponding feature points provided by the gray-level mapping matrix, a structural feature matrix is ​​constructed. Structural feature matrix It is an m×4 dimensional data matrix, where each row corresponds to a feature point, which contains the x-coordinate, y-coordinate, a depth reference coefficient set to 1, and its mapped gray value. The depth reference coefficient provides a standardized dimension for the subsequent unified control of the microtexture depth through the depth scaling coefficient in the morphology design parameter matrix, thereby integrating the two-dimensional morphological distribution and depth gray information of the shell surface into a unified data structure.

[0057] Step S4: Establish the tooth surface geometric model of the target gear, and map the structural feature matrix to the parameter domain of the tooth surface geometric model to obtain the initial microtexture geometric distribution.

[0058] In this embodiment, firstly, based on the basic parameters of the target gear (accuracy level, center distance, tooth width, number of teeth, pressure angle, displacement coefficient, etc.), as shown in Figure 3, a three-dimensional geometric model and core parameter annotations of the target gear are presented, clearly showing the overall structure and key design parameters of the spur gear (number of teeth 16, module 4.5, displacement coefficient 0.8635), providing basic geometric model support for subsequent equal arc length processing of tooth surfaces.

[0059] The involute tooth profile equation is used to construct the two-dimensional curve of the tooth surface. The involute tooth profile equation is as follows: in, It is the angle parameter for generating the involute curve. It is the involute base circle radius, which defines the two-dimensional curve shape of the tooth profile through this equation, and at the same time limits... The maximum value is: in, This refers to the addendum circle radius, ensuring that the tooth profile does not exceed the actual size range of the gear; next, to match the equal arc length sequence of shell feature points, the total arc length of the tooth profile is first calculated using the arc length formula. The formula for arc length is as follows: Then discretize it uniformly into If there are 1 unit arc segment, then the unit arc length satisfies: Solving the system of equations simultaneously yields the angular parameters for each discrete point: in, It is a discrete sequence number.

[0060] As shown in Figure 4, the geometric features of the tooth profile after equal arc length processing are displayed through three coordinate views (ZY direction, ZX direction, and distribution of discrete nodes in the tooth profile): the discrete nodes are evenly distributed along the tooth profile, and the coordinate values ​​conform to the derivation results of the involute equation, which verifies the accuracy of equal arc length discretization and ensures accurate matching with the equal arc length parameter sequence of shell feature points in the subsequent process.

[0061] Substituting this into the involute tooth profile equation yields the tooth profile coordinates after equal arc length. The first derivative of the tooth profile curve , and second derivative , Substitute into the radius of curvature formula to calculate the actual radius of curvature at each discrete point. ,Will By comparing the curvature radius with that of the involute theory, we ensure that the deviation meets the gear accuracy requirements and verify that the tooth profile after equal arc length discretization conforms to the true involute shape.

[0062] The formula for radius of curvature is as follows: Will Substituting into the basic tooth profile equation, we obtain the tooth profile equation after equal arc length: The tooth profile nodes are distributed according to a uniform arc length rule. Subsequently, based on the tooth width... Along the Z-axis (tooth width direction, range) ,Right now By extending the tooth profile to the constant arc length, a three-dimensional tooth surface geometric model is constructed using tooth surface parametric equations. The tooth surface parametric equations are as follows: in, The first involute curve is formed by sweeping along the Z-axis direction. Line and number Point cloud data of tooth surface, For the point cloud of involute tooth surface in position index Below coordinate, For the point cloud of involute tooth surface in position index Below coordinate, For the point cloud of involute tooth surface in position index Below coordinate, To form position number Line and number The angular parameters of the involute curve of the column. For along Number of points in a row The number, ranging from 50 to 400, is not limited to. For along Number of points in the column The number of items is within the range of 50 to 400. It is the point cloud of the involute tooth surface. Line and number Z-coordinate information of the column.

[0063] The number of discrete nodes in the model is matched to the number of feature points in the structural feature matrix. Finally, the ordered two-dimensional coordinates and grayscale mapping array in the structural feature matrix are read, and a one-to-one correspondence between the matrix feature points and the discrete nodes on the tooth surface is established according to the equal arc length matching principle. At the same time, the grayscale values ​​are converted into microtexture depth parameters and applied along the tooth surface normal (from the tooth profile tangent vector). Derived tooth profile normal vector By assigning a value to the direction, the initial microtexture geometric distribution that preserves the morphological characteristics of the shell surface is obtained.

[0064] Tooth profile tangent vector and mold length : unit normal vector of tooth profile as follows: in, The position of the involute curve at the th Line number The normal vector of the column. The position of the involute curve at the th Line number The unit normal vector of the column. The position of the involute curve at the th Line number Along the column The three-dimensional (3D) normal vector of the direction. The position of the involute curve at the th Line number Along the column The three-dimensional (3D) normal vector of the direction. The position of the involute curve at the th Line number Along the column The three-dimensional (3D) normal vector of the direction.

[0065] Step S5: Construct a morphological design parameter matrix, and use the morphological design parameter matrix to control the geometric distribution of the initial microtexture to obtain a biomimetic tooth surface microtexture model.

[0066] In this embodiment, the morphological design parameter matrix is ​​first constructed. The shape design parameter matrix is ​​composed of the translation matrix. Scale factor matrix and rotation transformation matrix The structure consists of a translation matrix used to fine-tune the spatial position of micro-texture units on the tooth surface (e.g., translating texture units corresponding to shell features to non-stress concentration areas on the tooth surface to avoid affecting gear load-bearing capacity); a scaling factor matrix used to control the scaling of texture units in the tooth width, tooth height, and depth directions (e.g., adjusting texture dimensions to adapt to the geometric characteristics of different regions based on the differences between the meshing and non-meshing areas of the tooth surface); and a rotation transformation matrix around an arbitrary unit vector axis. Rotate, where The spatial orientation of the rotation axis can be defined based on the relative motion direction during tooth meshing (such as the rolling-sliding direction of gear meshing). The direction of the texture units is flexibly adjusted to optimize their distribution angle in the tooth surface space, making the texture direction more suitable for the actual working motion of the gear and improving lubrication or friction reduction. Next, the three-dimensional coordinates of each texture unit in the initial microtexture geometry are extracted. Translation, scaling factor, and rotation transformation matrices are applied sequentially through matrix multiplication to achieve precise control of the texture units' position, scale, and direction. Then, considering the performance requirements of different areas of the tooth surface (e.g., appropriately reducing the texture depth in the meshing area to ensure load-bearing capacity, and maintaining or increasing the texture depth in the non-meshing area to enhance lubrication), the adjusted texture depth is optimized for adaptability. Finally, the adjusted microtexture is fused with the tooth surface geometry model of the target gear (a tooth surface mesh model discretized with equal arc lengths). During the fusion process, the basic geometric accuracy of the tooth surface (such as tooth profile dimensions and tooth pitch) is ensured to remain unaffected, ultimately resulting in a biomimetic tooth surface microtexture model that combines the morphological characteristics of a seashell with the engineering adaptability of a gear.

[0067] The final biomimetic tooth surface microtexture model is shown in Figure 5. Figure 5(a) is a two-dimensional view of the original tooth surface without microtexture, showing the basic planar morphology of the tooth surface. Figure 5(b) is a two-dimensional distribution view of the fused microtextured tooth surface. It can be seen that the microtexture units corresponding to the ridges, grooves, and nodules on the shell surface have uniformly covered the tooth surface, and the distribution pattern is consistent with the biological prototype. Figure 5(c) is a three-dimensional model view of the biomimetic tooth surface microtexture from different perspectives. It clearly shows the concave-convex amplitude and spatial distribution of the microtexture along the tooth surface normal, verifying the adaptability of the microtexture morphology and tooth surface geometry, and providing an intuitive model basis for subsequent performance evaluation.

[0068] Form design parameter matrix Satisfy the following formula: in, For along Offset distance in the axial direction, For along Offset distance in the axial direction, if there is no offset and , For along Scaling factor of the axis For along Scaling factor of the axis For along The scaling factor for the axis, without changing the actual magnification ratio, defaults to 1. , , The unit axis of rotation at Projection components along the axial direction, The unit axis of rotation at Projection components along the axial direction, The unit axis of rotation at Projection components along the axial direction.

[0069] Given the unit vector of the rotation axis (e.g., the unit vector of the rotation axis about the Z-axis) and , Rotation angle .

[0070] Specifically, the process of using the morphological design parameter matrix to control the initial microtexture geometric distribution to obtain a biomimetic tooth surface microtexture model includes the following sub-steps: Step S501, extracting the initial three-dimensional coordinates of the texture unit from the initial microtexture geometric distribution.

[0071] In this embodiment, the initial microtexture geometric distribution is utilized to extract the spatial three-dimensional coordinates of discrete nodes contained in each texture unit one by one according to the correspondence of texture units such as ridges, grooves, and nodules on the shell surface using three-dimensional data extraction tools (such as the MATLAB three-dimensional data processing module or the point cloud extraction function of professional CAD software), forming a set of initial texture three-dimensional coordinates.

[0072] Step S502: Multiply the morphology design parameter matrix with the initial texture three-dimensional coordinates to obtain the controlled texture three-dimensional coordinates.

[0073] In this embodiment, the translation matrix, scaling factor matrix, and rotation transformation matrix in the morphology design parameter matrix are sequentially multiplied with the initial texture three-dimensional coordinates. Essentially, the translation matrix is ​​used to fine-tune the texture position, the scaling factor matrix is ​​used to scale the texture scale, and the rotation transformation matrix is ​​used to adjust the texture direction. This allows for precise control of the texture morphology in the three dimensions of position, scale, and direction, resulting in three-dimensional coordinates of the controlled texture that are adapted to the tooth surface characteristics.

[0074] Step S503: Construct a microtexture three-dimensional geometric model using the controlled texture three-dimensional coordinates and the tooth surface geometric model.

[0075] In this embodiment, the three-dimensional coordinates of the texture and the tooth surface geometry model are first imported into three-dimensional engineering software such as nTop and GeomagicWrap. The software coordinate alignment function is used to ensure that the two are unified to the same coordinate system to avoid the deviation of the texture and tooth surface due to coordinate misalignment. Then, according to the three-dimensional coordinates of the texture, the three-dimensional morphology of the microtexture is constructed along the tooth surface normal (the direction of the normal vector derived from the tooth profile tangent vector) on the corresponding discrete nodes of the tooth surface geometry model. The texture units corresponding to the shell ridge form a continuous convex structure according to the control coordinates, the texture units corresponding to the groove form a concave structure, and the texture units corresponding to the nodules form an isolated convex structure, thus completely preserving the morphological logic of the shell surface features. Subsequently, the three-dimensional morphology of the microtexture is optimized using the software's mesh reconstruction function (e.g., deleting distorted meshes and ensuring mesh continuity) to ensure that the microtexture and the tooth surface mesh model are meshed and adapted to each other. Finally, the consistency between the three-dimensional morphology of the microtexture and the three-dimensional coordinates of the texture is checked using the software's deviation analysis tool (e.g., the deviation is controlled within 0.002mm). After confirming that there is no morphological distortion, an independent three-dimensional geometric model of the microtexture is generated.

[0076] Step S504: The microtexture three-dimensional geometric model and the tooth surface geometric model are fused to obtain a biomimetic tooth surface microtexture model.

[0077] In this embodiment, the microtexture 3D geometric model and the tooth surface geometric model are fused using 3D Boolean operations to obtain a biomimetic tooth surface microtexture model. Specifically, the microtexture 3D geometric model and the tooth surface geometric model are imported into 3D engineering software such as nTop and Geomagic Wrap. First, the software coordinate calibration function is used to ensure that the two are unified to the gear design coordinate system to avoid fusion misalignment due to coordinate offset. Then, 3D Boolean operations (union operation) are used to fuse the models. During the fusion process, the core accuracy of the tooth surface geometric model is strictly preserved to ensure that the tooth profile size, tooth pitch, pressure angle, etc. meet the accuracy requirements. At the same time, the morphological features of the corresponding shell ridges, grooves, and nodules in the microtexture model are completely preserved. After fusion, the distorted mesh is deleted and the tiny gaps are filled using the software mesh repair tool. Then, surface smoothing is performed. Finally, it is verified that the fusion deviation is less than 0.002mm. After confirming that there is no morphological distortion, the biomimetic tooth surface microtexture model is obtained.

[0078] in, For location index The biomimetic tooth surface microtexture dot cloud, The point cloud of the involute tooth surface is formed by sweeping the involute curve along the Z-axis direction. The microtexture depth increment is obtained by inverse normalizing the normalized gray values ​​in the structural feature matrix. It is the unit normal vector of the tooth profile (pointing outward along the normal to the tooth surface).

[0079] Tooth surface microtexture point cloud collection : in, Dot cloud edge for biomimetic tooth surface microtexture The number of rows, ranging from 50 to 400. Dot cloud edge for biomimetic tooth surface microtexture The number of columns, with values ​​ranging from 50 to 400, but not limited to.

[0080] Step S6: Perform finite element analysis on the biomimetic tooth surface microtexture model and evaluate its performance.

[0081] The finite element analysis and performance evaluation of the biomimetic tooth surface microtexture model specifically includes the following sub-steps: Step S601, establish an untextured tooth surface model, and construct a gear meshing contact simulation comparison model based on the untextured tooth surface model.

[0082] In this embodiment, an untextured tooth surface model is first established. The basic geometric and physical parameters of this model, such as tooth profile (module, number of teeth, pressure angle, etc.), tooth width, and material properties, are consistent with the tooth surface geometry model of the biomimetic microtextured tooth surface model used in the next step. Only all microtextured features are removed to ensure fairness in subsequent comparisons. Subsequently, based on this untextured tooth surface model, professional simulation software such as ANSYS or ADAMS is used to construct a gear meshing contact simulation comparison model, setting identical gear parameters and meshing conditions (rated load, operating speed, lubricant type and viscosity, etc.) according to the actual gear transmission system configuration. During construction, fine meshing is required to ensure calculation accuracy, and contact pairs and boundary conditions (constraints and load application methods) are defined.

[0083] Step S602: Establish a gear meshing contact simulation model based on the biomimetic tooth surface microtexture model.

[0084] In this embodiment, the biomimetic tooth surface microtexture model is imported into the simulation software. During the import process, it is ensured that the model coordinate system and mesh topology are completely matched with the untextured model to avoid import errors affecting the simulation comparability.

[0085] Step S603: Under the pre-set working conditions, perform finite element analysis on the gear meshing contact simulation comparison model and the gear meshing contact simulation model respectively.

[0086] In this embodiment, finite element analysis is performed on a gear meshing contact simulation comparison model (untextured) and a gear meshing contact simulation model (bionic textured) for pre-set operating conditions (such as rated speed and rated load of industrial gears, while considering different lubrication conditions such as oil lubrication and grease lubrication). During the analysis, key performance indicators such as contact stress distribution and peak value, oil film pressure characteristics, friction coefficient variation, and transmission power loss are collected simultaneously. Multiple simulation iterations are performed for each operating condition to ensure data stability, and the changes in indicators under different meshing positions, such as single-tooth meshing area and double-tooth meshing area, are recorded.

[0087] Step S604: Evaluate the performance of the biomimetic tooth surface microtexture model based on the results of the finite element analysis.

[0088] In this embodiment, the performance of the biomimetic tooth surface microtextured model is comprehensively evaluated based on multi-dimensional data from finite element analysis. First, the peak and distribution of contact stress are compared to determine whether it reduces stress concentration and improves load uniformity. Second, oil film pressure and friction coefficient are analyzed to assess the improvement effect on lubrication performance (such as oil film load capacity and frictional power consumption), and the impact on transmission efficiency is determined by combining power loss data. Simultaneously, from an engineering practicality perspective, the potential impact of microtexture on processing difficulty and cost is evaluated (based on manufacturability analysis). Finally, by comprehensively considering various indicators, it is determined whether the biomimetic model is superior to the untextured model in key performance aspects such as load-bearing capacity, lubrication, and efficiency, whether it meets industrial application requirements, and optimization directions are proposed.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A biomimetic dental surface microtexture figurative design method, characterized in that, The method includes: acquiring point cloud data of the shell surface and constructing a three-dimensional model of the shell surface using the point cloud data; constructing a biomimetic coupling element matrix using the three-dimensional model of the shell surface; constructing an equal arc length parameter sequence and a grayscale mapping matrix based on the biomimetic coupling element matrix, and constructing a structural feature matrix using the equal arc length parameter sequence and the grayscale mapping matrix; establishing a tooth surface geometric model of the target gear, mapping the structural feature matrix to the parameter domain of the tooth surface geometric model to obtain an initial microtexture geometric distribution; constructing a morphology design parameter matrix, and using the morphology design parameter matrix to regulate the initial microtexture geometric distribution to obtain a biomimetic tooth surface microtexture model.

2. The biomimetic tooth surface microtexture figurative design method according to claim 1, characterized in that, The process of acquiring point cloud data of the shell surface and constructing a three-dimensional model of the shell surface using the point cloud data includes: collecting raw point cloud data of the shell surface; performing point cloud registration, coordinate unification, and noise removal on the raw point cloud data to form a point cloud model of the shell surface; and performing mesh repair, hole filling, and smooth reconstruction on the point cloud model of the shell surface to generate a three-dimensional model of the shell surface.

3. The biomimetic dental surface microtexture figurative design method according to claim 1, characterized in that, The process of constructing a biomimetic coupling matrix using the three-dimensional model of the shell surface includes: constructing a point cloud feature vector set by calculating the geometric feature parameters in the three-dimensional model of the shell surface; classifying the point cloud feature vector set using the K-nearest neighbor algorithm to obtain structural units of ridges, grooves and nodules on the shell surface; and constructing a biomimetic coupling matrix based on the geometric feature parameters of the structural units.

4. The biomimetic dental surface microtexture figurative design method according to claim 1, characterized in that, The construction of the equal arc length parameter sequence and grayscale mapping matrix based on the biomimetic coupling element matrix includes: constructing the equal arc length parameter sequence based on the biomimetic coupling element matrix; and constructing the grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix.

5. The biomimetic tooth surface microtexture figurative design method according to claim 4, characterized in that, The construction of the equal arc length parameter sequence based on the biomimetic coupled matrix includes: extracting the shell surface feature point cloud from the biomimetic coupled matrix; projecting the shell surface feature point cloud onto a pre-set intermediate reference surface to obtain a two-dimensional feature point distribution; calculating the arc length between adjacent points in the two-dimensional feature point distribution, and using the arc length to construct the equal arc length parameter sequence.

6. The biomimetic tooth surface microtexture figurative design method according to claim 4, characterized in that, The construction of a grayscale mapping matrix based on the equal arc length parameter sequence and the biomimetic coupling element matrix includes: performing equal arc length discretization on the equal arc length parameter sequence to obtain ordered feature point position indices; extracting depth information from the biomimetic coupling element matrix and converting the depth information into grayscale values; and constructing a grayscale mapping matrix based on the ordered feature point position indices and the grayscale values.

7. The biomimetic dental surface microtexture figurative design method according to claim 1, characterized in that, The process of controlling the initial microtexture geometric distribution using the morphological design parameter matrix to obtain a biomimetic tooth surface microtexture model includes: extracting the initial three-dimensional coordinates of the texture units from the initial microtexture geometric distribution; multiplying the morphological design parameter matrix with the initial three-dimensional coordinates of the texture to obtain the controlled three-dimensional coordinates of the texture; constructing a microtexture three-dimensional geometric model using the controlled three-dimensional coordinates of the texture and the tooth surface geometric model; and fusing the microtexture three-dimensional geometric model and the tooth surface geometric model to obtain a biomimetic tooth surface microtexture model.

8. The biomimetic tooth surface microtexture figurative design method according to claim 1, characterized in that, The biomimetic tooth surface microtexture design method further includes: performing finite element analysis on the biomimetic tooth surface microtexture model and evaluating its performance.

9. The biomimetic tooth surface microtexture figurative design method according to claim 8, characterized in that, The finite element analysis and performance evaluation of the biomimetic tooth surface microtexture model includes: establishing an untextured tooth surface model and constructing a gear meshing contact simulation comparison model based on the untextured tooth surface model; establishing a gear meshing contact simulation model based on the biomimetic tooth surface microtexture model; performing finite element analysis on the gear meshing contact simulation comparison model and the gear meshing contact simulation model under pre-set working conditions; and evaluating the performance of the biomimetic tooth surface microtexture model based on the results of the finite element analysis.

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