A dental restoration with a microstructured bonding surface and a preparation method thereof

Abstract

The present invention provides a dental restoration with a microstructure bonding surface and a preparation method thereof, comprising: designing and establishing a preliminary dental restoration model, identifying and segmenting the bonding surface of the dental restoration; using a parameterized method to map the micropore generation area onto a two-dimensional plane and uniformly sampling; inversely mapping the sampling point array onto the dental restoration model by mapping the corresponding relationship and the sampling points; combining the micropore generation position to generate a micropore texture array of the required shape and size, and performing Boolean operations with the dental restoration model to obtain a dental restoration model with a microstructure bonding surface. The above method can be used to natively realize a micropore structure of a constant shape on the restoration surface, and obtain a dental restoration with a microstructure bonding surface, which can avoid corrosion of the tooth body during bonding, can greatly increase the bonding surface area, increase the mechanical interlocking force, and improve the bonding stability.

Description

Technical Field

[0001] The invention relates to the field of dental restoration preparation, in particular to a dental restoration with a microstructure bonding surface and a preparation method thereof. Background Art

[0002] Dental restorations are artificial devices used to repair broken or missing teeth and improve the appearance and function of teeth. They usually include inlays, veneers, single crowns, bridge crowns, etc. When bonding dental restorations, the teeth are often corroded to provide roughness to ensure the bonding effect; but the roughness and bonding area provided are limited, and it is difficult to significantly improve the bonding effect. Moreover, if the degree of corrosion on the tooth itself is increased, the roughness and bonding area can be increased, but it will greatly affect the health of the patient's own teeth. Summary of the invention

[0003] The object of the present invention is to provide a dental restoration with a microstructured bonding surface, which can greatly increase the bonding surface area, increase the mechanical interlocking force, and improve the bonding stability, and a preparation method thereof.

[0004] In order to solve the above technical problems, the present invention provides a method for preparing a dental restoration having a microstructure bonding surface, comprising the following steps:

[0005] S1, obtaining a three-dimensional digital image of the tooth, establishing a dental restoration model, identifying and segmenting the bonding surface of the dental restoration as a micropore generation area;

[0006] S2, mapping the micropore generation area onto a two-dimensional plane; performing sampling on the two-dimensional plane to obtain a sampling point array corresponding to the coordinates of the micropores;

[0007] S3, inversely mapping the sampling point array onto the dental restoration model as the micropore generation position through mapping the corresponding relationship and the centroid coordinates of the sampling point in the two-dimensional plane;

[0008] S4. On the bonding surface, combining the average normal vector corresponding to the micropore generation position and the curvature information, generate a micropore texture array and perform Boolean operation with the dental restoration model to obtain a dental restoration model with a microstructure bonding surface, and manufacture a dental restoration based on the dental restoration model with a microstructure bonding surface.

[0009] Furthermore, in S1, the dental restoration model is meshed so that the dental restoration model is divided into a number of triangular facets, and a region recognition algorithm is used to pre-process the triangular facets, and the sharp edges and tiny facets are diffused and deleted, and the inner and outer sides of the dental restoration model are judged. If the dental restoration model is a monomer type, the inner side of the dental restoration model is the bonding surface of the dental restoration, that is, the micropore generation area.

[0010] Furthermore, the region identification algorithm includes: calculating the dihedral angle for each edge of the mesh, when the dihedral angle is less than a set angle threshold, the corresponding edge is a sharp edge, the facet with an area less than a set area threshold is a tiny facet, deleting the triangular facets with sharp edges and the tiny facets, and deleting the facets adjacent to the triangular facets or the tiny facets, the positions of the deleted facets form a sharp edge area, which is the edge of the dental restoration; determining the center point of the plane where the edge is located in the z-axis direction, recorded as point M, for each triangular facet on the dental restoration model, if the angle between the line connecting the center of the triangular facet and point M and the facet normal vector corresponding to the triangular facet does not exceed 90 degrees, then the triangular facet is the inner side of the model, and several inner side surfaces of the model form the inner side of the dental restoration model.

[0011] Further, for a bridge-type dental restoration model, the region recognition algorithm further includes: after determining the sharp edges in the mesh, dividing the mesh into a plurality of parts according to the sharp edges, deleting the part where the number of facets is less than a number threshold, and screening the faces in the retained part;

[0012] The screening conditions are a combination of the following conditions:

[0013] Tend to keep faces with downward normal direction;

[0014] The surface that tends to be retained as the inner side of the dental restoration model;

[0015] Tends to keep faces with "concave" shapes;

[0016] After screening, the portion that is finally retained is the bonding surface where microstructures need to be generated, serving as the micropore generation area of ​​the bridge-type dental restoration model.

[0017] Further, in S2, the micropore generation area is mapped onto a two-dimensional plane by a parameterization method, and the parameterization method includes:

[0018] Tutte parameterization is used for initialization, and the boundary of the mesh to be parameterized is fixed on a circle: first, the number of mesh vertices and faces is obtained, the area and center of mass of the mesh are calculated, and the radius of the circle is obtained. Then, the boundary of the mesh is found, the UV coordinates of the boundary vertices are calculated and placed on the circumference, and finally, a linear system is constructed: a sparse matrix is ​​used to represent the adjacency relationship of the graph, the internal vertices are calculated through the coordinates of the neighbor vertices, and the sparse LU decomposition is used to solve the linear system to obtain the initial UV coordinates of each vertex.

[0019] Furthermore, the initial UV coordinates are obtained by tutte parameterization. For each face, the local coordinates and Jacobian transformation matrix are calculated, and a linear system for energy minimization is constructed: the relationship between vertices is represented by a sparse matrix, the Jacobian transformation matrix R is calculated for each face and used to minimize energy, and the final UV coordinates of the vertices are obtained after iterative adjustment.

[0020] Furthermore, in S3, after obtaining the sampling point coordinates, find the face patch on the two-dimensional plane that is closest to each sampling point coordinate and the centroid coordinates of the sampling point on the face patch, obtain the mapping correspondence between the centroid coordinates and the two-dimensional plane face patch and the dental restoration model, and inversely map the sampling point array to the face patch of the dental restoration model, which are the coordinates of the micropores that need to be generated on the dental restoration.

[0021] Furthermore, in S4, when obtaining the average normal vector corresponding to the micropore generation position, the average normal vector of the grid coordinate point on the dental restoration model is first obtained by weighting the normal vectors of the facets adjacent to the grid coordinate point, and then the average normal vectors of the vertices of the facets where the micropore is located are weighted averaged according to the centroid coordinates of the micropore location to obtain the average normal vector of the micropore coordinates.

[0022] Furthermore, in S4, a micropore texture array is generated by a depth adjustment method, and the depth adjustment method includes: adjusting the depth of the micropores along the average normal vector direction of the micropore coordinates according to the curvature information of different micropore generation positions, and judging the surroundings of the micropores. If the surroundings of the micropores are convex, the micropore depth is increased, and if the surroundings of the micropores are concave, the depth is reduced.

[0023] The invention also discloses a dental restoration with a microstructure bonding surface, which is prepared according to the preparation method of the dental restoration with a microstructure bonding surface.

[0024] The beneficial effects of the present invention are:

[0025] The present invention realizes the preparation of a dental restoration with a microstructure bonding surface by combining a region recognition method based on sharp edge segmentation, a parameterization method, a sampling and inverse mapping method, and a depth adjustment method, wherein a microporous structure of a desired size, shape, and arrangement is distributed on the bonding surface. This technology involves multiple key technical points:

[0026] First, the present invention uses a region recognition method based on sharp edge segmentation to accurately and efficiently identify the bonding surface of the dental restoration, and uses it as the micropore generation area. ARAP parameterization is then used, which is a shape-preserving parameterization method that aims to unfold the three-dimensional surface onto a two-dimensional plane as non-deformed as possible, maintaining the rigidity of the local geometric shape. This provides a stable foundation for subsequent uniform sampling and ultimately forming a consistent micropore distribution on the bonding surface, ensuring that the sampling points have consistent distribution characteristics on the unfolded plane.

[0027] The location of the holes can then be precisely determined by uniformly sampling on the parameterized plane. This process ensures uniform distribution of holes while satisfying the texture arrangement method, while reducing errors caused by deformation. The inverse mapping process maps the sampling points from the two-dimensional plane back to the original three-dimensional mesh surface and generates holes at these locations. Due to the conformal properties of ARAP parameterization, this mapping process can maintain the integrity of the surface structure, thereby achieving uniform hole distribution on the complex three-dimensional surface inside the dental restoration, and combined with depth adjustment to obtain the desired microporous texture.

[0028] Therefore, the method of forming a microporous texture on a complex three-dimensional surface of the present invention can not only generate a controllable microstructure on the surface of a dental restoration, but is also suitable for application scenarios that require the generation of high-precision, uniformly distributed holes on complex curved surfaces, such as advanced 3D modeling, engineering design, and detail processing in computer graphics. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process of the present invention;

[0030] Figure 2 The effect diagram of the microporous texture formed by the method of the present invention on the veneer and crown monomer;

[0031] Figure 3 This is a microporous texture effect diagram formed by using the method of the present invention on a multi-bridge structure;

[0032] Figure 4 The following are some photos of actual products made using the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0034] Those skilled in the art should understand that, in the disclosure of the present invention, the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the above terms should not be understood as limiting the present invention.

[0035] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0036] like Figure 1-Figure 4 The present invention provides a method for preparing a dental restoration with a microstructure bonding surface, comprising: firstly scanning the oral cavity with an oral scanner to obtain a tooth point cloud image, then converting the image into an STL model file using common dental software such as iTero, and designing and establishing a preliminary dental restoration model; then performing the following processing on the established dental restoration model, such as Figure 1 As shown, the following steps are included:

[0037] 1) The dental restoration model is meshed so that it is composed of triangular facets. The input dental restoration model is preprocessed by the region recognition algorithm to identify and judge the specific bonding surface of the dental restoration, which is the micropore generation range of the microstructure to be designed. For a single-body dental restoration, the input dental restoration model is usually bowl-shaped. The area where micropores need to be generated is the inner side of the "bowl" and needs to be kept at a certain distance from the edge of the "bowl", which requires the method to first accurately identify the area. The region recognition algorithm makes a comprehensive judgment based on parameters such as sharp edges, the angle between the face normal vector and the center of gravity.

[0038] In the half-edge data structure, traverse each edge of the input mesh to find the edge where the dihedral angle between two adjacent faces is less than a certain threshold, which is called a "sharp edge", that is, the edge area corresponding to the dental restoration. In this example, the threshold can be 0.24π; the mesh is segmented at the sharp edge. In order to ensure that the mesh can be segmented completely, it is necessary to avoid the situation where the edge of the dental restoration is smoothly transitioned by a small area patch. For this, supplementary processing of small area patches is added, and patches with an area of ​​less than 0.00015 square millimeters are considered small patches. In order to ensure that the surface can be completely segmented, the searched "sharp edges" and small patches are deleted, and their adjacent patches are also deleted to achieve the segmentation of the input mesh. Among them, the inner side is the bonding surface area where micropores need to be generated. In order to identify it well, the center point of the plane where the edge of the dental restoration is located is determined in the z-axis direction, recorded as point M; for each triangular facet on the dental restoration, the facet on the inner side has the following characteristics: the angle between the line connecting the facet center and point M and the facet normal vector does not exceed 90 degrees, thereby distinguishing the micropore generation area on the inner side.

[0039] 2) After identifying and segmenting the micropore generation area, i.e., the bonding surface of the dental restoration, the bonding surface area is flattened onto a two-dimensional plane through a parametric method so that the micropore generation coordinates can be uniformly sampled in a square or honeycomb pattern on the plane. In this process, the correspondence between the original model facets and the mapped two-dimensional graphic facets is retained.

[0040] Among them, parameterization methods include:

[0041] Local rigidity: When deforming the mesh, we strive to maintain rigid transformation locally, that is, for each facet, and only allow rotation and translation in the local area, while trying to avoid stretching or shearing deformation;

[0042] Global optimization: Through a global optimization process, the current optimal vertex position is solved so that the difference between the deformed shape and the original shape in local rigidity is minimized;

[0043] Energy function: It relies on defining an energy function to measure the difference between the local deformation of the deformed mesh and the ideal rigid transformation. By minimizing this energy function, iteratively adjusting the vertex coordinates, gradually approaching the ideal rigid transformation, and obtaining the optimal vertex layout;

[0044] Among them, the energy function is the weighted sum of the difference values ​​of the Jacobian matrix and the rotation matrix before and after the triangle patch is deformed (weighted according to the triangle area). The rotation matrix represents that the transformation is ideal and has no deformation; that is, the lower the energy function, the smaller the overall deformation.

[0045] Establish mapping: Save the coordinates and numbering information of the pieces before and after mapping to achieve the inverse mapping of micropore coordinates from 2D to 3D.

[0046] The process from 3D to 2D uses the ARAP parameterization method, and its main steps are as follows:

[0047] First, use the tutte parameterization method to initialize and fix the boundary of the mesh to be parameterized on a circle. The specific operation is to first obtain the number of mesh vertices and faces, calculate the area and center of mass of the mesh, and then reversely calculate the radius of the circle to ensure the same area. Then traverse the half-edge data structure of the mesh, find the boundary of the mesh, calculate the UV coordinates of the boundary vertices, and place them on a circle. Finally, build a linear system: use a sparse matrix to represent the adjacency relationship of the graph, the UV coordinates of the boundary vertices are fixed, and the internal vertices are calculated using the coordinates of the neighboring vertices. Use sparse LU decomposition to solve the linear system and obtain the UV coordinates of each vertex.

[0048] Then, based on the tutte parameterization, the ARAP method is used for optimization, with the goal of maintaining better local rigidity. The main steps are as follows:

[0049] Initialization: Get the number of vertices and faces of the mesh, and calculate the normal vector of the face. Call the tutte parameterization method to get the initial UV coordinates, and iterate on this basis. For each face, calculate the local coordinates and Jacobian transformation matrix. Then build a linear system that minimizes energy: represent the relationship between vertices through a sparse matrix, calculate the transformation matrix R based on each face, and use it to minimize energy. Finally, iterate and adjust the UV coordinates of the vertices to gradually approach the rigid deformation of the target. This method has 100 iterations and can achieve very good results.

[0050] So far, the 3D mesh is parameterized, and the vertices of the 3D mesh are mapped to the 2D plane, so that the mesh expansion maintains as much local rigidity as possible. The tutte parameterization method provides the initial parameterization result, and the ARAP method combines the cotangent weight information of the edges in the original mesh to further optimize the parameterization result, so that the final result is more in line with the requirements of local rigidity.

[0051] 3) After obtaining the 2D parameterization results and the corresponding relationship of the facets, sampling is performed on the 2D plane according to the requirements of different arrangement methods. After obtaining the sampling point coordinates, traverse each point coordinate to find the corresponding 2D triangle facet with the nearest distance and the centroid coordinates of the sampling point on the facet. Obtaining the centroid coordinates and the facet information of the input mesh corresponding to the 2D facet can inversely map the sampling point array to the input model facet. The positions of these points are the positions where micropores are to be generated on the bonding surface of the dental restoration. Combined with the average normal vector and curvature information corresponding to the micropore generation position, the depth adjustment method is used to generate a micropore texture array of a specific shape, size, and depth according to the requirements, and a Boolean operation is performed with the original model to obtain the final result. The steps for calculating the average normal vector corresponding to the micropore coordinates are as follows: on the bonding surface, first calculate the average normal vector of the input mesh vertex, which is determined by the weighted normal vectors of the three triangles adjacent to the mesh vertex, and then perform a weighted average of the average normal vectors of the three vertices of the facet where the micropore is located according to the centroid coordinates of the micropore location to obtain the average normal vector of the micropore.

[0052] The depth adjustment method is as follows: according to the curvature information of different positions on the grid, the depth of the micropore is adjusted along the direction of the average normal vector of the micropore. The "topography" around the target point is judged. If it is convex, the depth of the micropore is increased, and if it is concave, the depth is reduced, in order to pursue the beauty of the final effect. The specific implementation method is to sample and analyze the points within the required micropore radius range with the target position point as the center, and obtain the average displacement distance of the surrounding points in the direction of the average normal vector of the micropore. A positive mean value means concave, and vice versa. In addition, the absolute value of the distance average value can also affect the micropore depth with a certain weight.

[0053] When adjusting the depth of the micropore, the points on the micropore radius circle are sampled, and the distance between the sampling point and the center point along the average normal vector direction is calculated, thereby reflecting the curvature of the sampling point, that is, the curvature information.

[0054] Compared with the traditional manual operation of the industry, which projects micropore arrays vertically in the three directions of xyz, the method of the present invention has the characteristics of high efficiency, automation, high precision, and uniform micropores. Especially when the grid normal vector is not parallel to the coordinate axis, the effect is greatly improved.

[0055] 4) The above method is applicable to a single dental restoration, including anterior crowns, posterior crowns and inlays. The microporous texture generation effect is as follows: Figure 2As shown; for more complex bridge crowns, including complex objects such as triple bridges, quadruple bridges and multi-bridges, the present invention also has a more targeted processing method. The specific difference lies in the region recognition step, using a sharp edge detection algorithm to find the sharp edges in the grid, and then segmenting the grid according to the sharp edges. After segmentation, the interference part with very few facets (interference items and scraps generated by cutting the uneven position) is deleted, and the remaining part is retained. Based on the connectivity of the smooth part (the remaining part after deleting the interference part), the grid is divided into multiple parts, and the faces in the retained part are screened. The screening conditions are comprehensively determined by the following points: tend to retain most of the parts with the normal direction of the facet downward, tend to retain the "inside" face, which is determined by calculating the angle between the normal vector of each face of the grid and the direction relative to the center point, and tend to retain the face with a "concave" shape (i.e., the face on the inside of the bowl mouth), which requires that the final facet curvature needs to vary within a certain range. After identifying the areas where micropores need to be generated, these areas can be processed multiple times by monomer generation to achieve controllable texture generation on the multi-link bridge. The micropore texture generation effect is as follows: Figure 3 shown.

[0056] Among them, when deleting the interference part with very few facets, the interference part is identified by the number of facets being less than the number threshold, such as the connection position between multiple "bowl mouths", the "scraps" cut out by the sharp edge detection algorithm due to unevenness, etc.

[0057] The above method can be used to generate a texture with controllable spacing, radius, style and arrangement on a given dental restoration model. After obtaining a model with the texture structure, a dental restoration with a microstructure bonding surface can be prepared by methods such as 3D printing or mechanical processing, thereby natively realizing a microporous structure with a constant shape on the restoration surface, which can avoid corrosion of the tooth body during bonding, greatly increase the bonding surface area, increase the mechanical interlocking force, and improve the bonding stability. Some dental restorations obtained by the method of the present invention are as follows: Figure 4 shown.

[0058] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other various forms of products under the inspiration of the present invention. However, no matter what changes are made in the shape or structure, all technical solutions that are the same or similar to those of the present application fall within the protection scope of the present invention.

Claims

1. A method for preparing a dental restoration having a microstructured bonding surface, characterized in that: The following steps are involved: S1, obtaining a three-dimensional digital image of the tooth, establishing a dental restoration model, identifying and segmenting the bonding surface of the dental restoration as a micropore generation area; S2, mapping the micropore generation area onto a two-dimensional plane; performing sampling on the two-dimensional plane to obtain a sampling point array corresponding to the coordinates of the micropores; S3, inversely mapping the sampling point array onto the dental restoration model as the micropore generation position through mapping the corresponding relationship and the centroid coordinates of the sampling point in the two-dimensional plane; S4. On the bonding surface, combining the average normal vector corresponding to the micropore generation position and the curvature information, generate a micropore texture array and perform Boolean operation with the dental restoration model to obtain a dental restoration model with a microstructure bonding surface, and manufacture a dental restoration based on the dental restoration model with a microstructure bonding surface.

2. The method for preparing a dental restoration having a microstructured bonding surface according to claim 1, characterized in that: In S1, the dental restoration model is meshed so that the dental restoration model is divided into a number of triangular facets, and a region recognition algorithm is used to pre-process the triangular facets, and the sharp edges and tiny facets are diffused and deleted, and the inner and outer sides of the dental restoration model are judged. If the dental restoration model is a monomer type, the inner side of the dental restoration model is the bonding surface of the dental restoration, that is, the micropore generation area.

3. The method for preparing a dental restoration having a microstructured bonding surface according to claim 2, characterized in that: The region identification algorithm includes: calculating the dihedral angle for each edge of the grid, when the dihedral angle is less than a set angle threshold, the corresponding edge is a sharp edge, the facet with an area less than the set area threshold is a tiny facet, deleting the triangular facets with sharp edges and the tiny facets, and deleting the facets adjacent to the triangular facets or the tiny facets, and the positions of the deleted facets form a sharp edge area, which is the edge of the dental restoration; determining the center point of the plane where the edge is located in the z-axis direction, recorded as point M, for each triangular facet on the dental restoration model, if the angle between the line connecting the center of the triangular facet and point M and the facet normal vector corresponding to the triangular facet does not exceed 90 degrees, then the triangular facet is the inner side surface of the model, and several inner side surfaces of the model form the inner side surface of the dental restoration model.

4. The method for preparing a dental restoration having a microstructured bonding surface according to claim 3, characterized in that: For a bridge-type dental restoration model, the region recognition algorithm further includes: after determining the sharp edges in the mesh, dividing the mesh into a plurality of parts according to the sharp edges, deleting the part where the number of facets is less than a threshold number, and screening the faces in the retained part; The screening conditions are a combination of the following conditions: Tend to keep faces with downward normal direction; The surface that tends to be retained as the inner side of the dental restoration model; Tends to keep faces with "concave" shapes; After screening, the portion that is finally retained is the bonding surface where microstructures need to be generated, serving as the micropore generation area of ​​the bridge-type dental restoration model.

5. The method for preparing a dental restoration having a microstructured bonding surface according to claim 1, characterized in that: In S2, the micropore generation area is mapped onto a two-dimensional plane by a parameterization method, wherein the parameterization method includes: Tutte parameterization is used for initialization, and the boundary of the mesh to be parameterized is fixed on a circle: first, the number of mesh vertices and faces is obtained, the area and center of mass of the mesh are calculated, and the radius of the circle is obtained. Then, the boundary of the mesh is found, the UV coordinates of the boundary vertices are calculated and placed on the circumference, and finally, a linear system is constructed: a sparse matrix is ​​used to represent the adjacency relationship of the graph, the internal vertices are calculated through the coordinates of the neighbor vertices, and the sparse LU decomposition is used to solve the linear system to obtain the initial UV coordinates of each vertex.

6. The method for preparing a dental restoration having a microstructured bonding surface according to claim 5, characterized in that: Iterate on the basis of obtaining the initial UV coordinates through tutte parameterization. For each face, calculate the local coordinates and Jacobian transformation matrix, and build a linear system for energy minimization: the relationship between vertices is represented by a sparse matrix, the Jacobian transformation matrix R is calculated based on each face and used for energy minimization, and the final UV coordinates of the vertex are obtained after iterative adjustment.

7. The method for preparing a dental restoration having a microstructured bonding surface according to claim 1, characterized in that: In S3, after obtaining the sampling point coordinates, find the face patch on the two-dimensional plane that is closest to each sampling point coordinate and the centroid coordinates of the sampling point on the face patch, obtain the mapping correspondence between the centroid coordinates and the two-dimensional plane face patch and the dental restoration model, and inversely map the sampling point array to the face patch of the dental restoration model, which are the coordinates of the micropores that need to be generated on the dental restoration.

8. The method for preparing a dental restoration having a microstructured bonding surface according to claim 1, characterized in that: In S4, when obtaining the average normal vector corresponding to the micropore generation position, the average normal vector of the grid coordinate point on the dental restoration model is first obtained by weighting the normal vectors of the facets adjacent to the grid coordinate point. Then, according to the centroid coordinates of the micropore location, the average normal vectors of the vertices of the facet where the micropore is located are weighted averaged to obtain the average normal vector of the micropore coordinates.

9. The method for preparing a dental restoration having a microstructured bonding surface according to claim 1, characterized in that: In S4, a micropore texture array is generated by a depth adjustment method, and the depth adjustment method includes: adjusting the depth of the micropores along the average normal vector direction of the micropore coordinates according to the curvature information of different micropore generation positions, and judging the surroundings of the micropores. If the surroundings of the micropores are convex, the micropore depth is increased, and if the surroundings of the micropores are concave, the depth is reduced.

10. A dental restoration having a microstructured bonding surface, characterized in that: The method for preparing a dental restoration with a microstructured bonding surface according to any one of claims 1 to 9 is used.

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