Model support classification generation method and apparatus, and related device

Abstract

The application belongs to the technical field of 3D printing, and provides a model support classification generation method, device and related equipment; the method comprises the following steps: traversing a model triangular mesh; detecting and acquiring all suspended vertices on the model; generating a first type of support unit from the suspended vertices downward according to first type support parameters; determining all supportable regions of the model from the plane angle of the triangular mesh; acquiring all candidate support points on the supportable regions of the model; determining the candidate support points within a preset radius L1 millimeters in the horizontal direction as non-candidate support points with each suspended vertex as the center; selecting points with a horizontal interval distance of L2+△X millimeters from each other from the remaining all candidate support points and determining the points as surface support vertices; generating a second type of support unit from the surface support vertices downward according to second type support parameters; and storing model data. The method can automatically generate different support units suitable for different characteristics of the suspended regions of the model with different types of support parameters.

Description

Technical Field

[0001] This invention belongs to the field of photopolymer 3D printing technology, specifically involving a model support classification generation method, device, and related equipment. Background Technology

[0002] In the field of photopolymer 3D printing technology, during model preprocessing, it is necessary to add support units to the suspended parts of the model to enhance the bottom support and ensure successful printing after model slicing. However, there are currently two main methods for generating model supports. The first method involves manually adding support units one by one, which is inefficient and labor-intensive. The second method involves automatically generating support units according to uniform parameters, followed by manual addition of support units or modification or deletion of unsuitable units. This automatic support unit generation method cannot automatically select strong support parameters to thicken and strengthen specific areas such as suspension points, nor can it automatically select weak support parameters to thin and weaken delicate parts such as puppet fingers, requiring manual intervention and thus remaining inefficient. Therefore, a model support classification and generation method is needed to address these issues. Summary of the Invention

[0003] This application provides a model support classification generation method, device, electronic device, and storage medium. During model preprocessing, it is used to select strong support parameters for specific areas such as suspension points to automatically thicken and strengthen the support, thereby enhancing printing strength. It is also used to select weak support parameters for delicate parts such as puppet fingers to automatically thin and weaken the support, so as to facilitate the removal of the support after printing and prevent the model from being torn. This makes the automatic support generation method more efficient and more adaptable.

[0004] The first aspect of this application provides a model-supported classification generation method, including:

[0005] Traverse all the triangular meshes that make up the model;

[0006] Detect and acquire all suspended vertices on the model;

[0007] Based on the first type of support parameters, first type of support units are generated downwards from the suspension vertex and connected to the zero-plane platform or model;

[0008] The entire area of ​​the model to be supported is determined by the planar angles of the triangular mesh;

[0009] Obtain all candidate support points on the area to be supported in the model;

[0010] Using each suspension vertex as the center, the candidate support points within a preset radius L1 mm in the horizontal direction are determined as non-candidate support points;

[0011] Select points with a horizontal spacing of L2 + ΔX mm from all remaining candidate support points and determine them as the face support vertices;

[0012] According to the second type of support parameters, second type of support units are generated downwards from the surface support vertex and connected to the zero plane platform;

[0013] Store the overall three-dimensional data of the model and support units.

[0014] Specifically, L1 and L2 are positive integers or decimals; △X is a set error value.

[0015] Preferably, the first type of support parameters and the second type of support parameters include: support contact point shape, support contact point size, support column shape, support column size, support column bending angle, support column connection structure, support base shape, and support base size.

[0016] Furthermore, the model supporting the classification generation method also includes:

[0017] Obtain the inner cantilever surface as the candidate support point from all the candidate support points in the model;

[0018] Select points with a horizontal spacing of L3+△X mm from all the candidate support points of the inner suspended surface and determine them as the support vertices of the inner suspended surface;

[0019] According to the third type of support parameters, the third type of support unit is generated downward from the support vertex of the inner cantilever surface and connected to the model.

[0020] Specifically, L3 is a positive integer or a decimal; △X is a set error value.

[0021] Preferably, the third type of support parameters includes: support contact point shape, support contact point size, support column shape, support column size, support column bending angle, support column connection structure, support base shape, and support base size.

[0022] Furthermore, the detection and acquisition of all suspended vertices on the model includes:

[0023] The model's triangular mesh is extracted layer by layer using multiple cross-sectional planes with preset layer thicknesses;

[0024] Obtain M on each cross-sectional plane i An independent closed path;

[0025] M in the Nth layer n An independent closed path and M in the N+1th layer n+1 Perform overlapping matching on each independent closed path;

[0026] The lowest point of the triangular mesh edge containing the independent closed paths that have not achieved overlapping and pairing in the N+1th layer is determined as the suspension vertex.

[0027] Specifically, Mi, Mn, and Mn+1 are positive integers; N is a positive integer layer number that increments from 1; and n and i are positive integers.

[0028] Furthermore, the determination of the entire area to be supported by the planar angles of the triangular mesh includes:

[0029] Obtain the normal vector angles of all triangular mesh planes in the model;

[0030] The triangular mesh plane whose normal vector angle is within the set range is defined as the area to be supported.

[0031] Furthermore, obtaining all candidate support points on the model's support region includes:

[0032] Divide the zero-plane platform into a preset grid with a side length of Y millimeters, centered on the origin;

[0033] Obtain the vertical projection range of the area to be supported on the zero-plane platform;

[0034] A straight line is projected upward from the preset square feature points within the vertical projection range and intersects with the triangular grid plane within the area to be supported, and the intersection point is determined as the candidate support point.

[0035] Specifically, Y is a positive integer or a decimal.

[0036] Specifically, the preset grid feature point is the center point of the preset grid, or a grid point of the preset grid.

[0037] A second aspect of this application provides a model-supported classification generation apparatus, comprising:

[0038] The model mesh traversal module is used to traverse all the triangular meshes that make up the model;

[0039] The suspended vertex acquisition model is used to detect and acquire all suspended vertices on the model.

[0040] The first type of support unit generation module is used to generate first type of support units from the suspension vertex downwards according to the first type of support parameters and connect them to the zero plane platform or model;

[0041] The module for determining the area to be supported is used to determine the entire area to be supported of the model based on the planar angles of the triangular mesh.

[0042] The candidate support point acquisition module is used to acquire all candidate support points on the area to be supported in the model;

[0043] The non-selective support point determination module is used to determine the candidate support points within a preset radius L1 mm in the horizontal direction as non-selective support points, with each suspension vertex as the center.

[0044] The surface support vertex determination module is used to select points with a horizontal spacing of L2+△X millimeters from all remaining candidate support points and determine them as surface support vertices.

[0045] The second type of support unit generation module is used to generate second type of support units from the surface support vertex downwards according to the second type of support parameters and connect them to the zero plane platform.

[0046] Storage unit, used to store the overall three-dimensional data of the model and support unit.

[0047] Furthermore, the model-supporting classification generation device further includes:

[0048] The module for obtaining candidate support points for the inner suspended surface is used to obtain candidate support points for the inner suspended surface from all candidate support points in the model.

[0049] The inner suspended surface support vertex determination module is used to select points with a horizontal spacing of L3+△X millimeters from all the inner suspended surface candidate support points and determine them as the inner suspended surface support vertex.

[0050] The third type of support unit generation module is used to generate third type of support units from the inner cantilever surface support vertex according to the third type of support parameters and connect them to the model.

[0051] A third aspect of this application provides an electronic device, including:

[0052] At least one processing unit; and a storage unit communicatively connected to the at least one processing unit; wherein,

[0053] The storage unit stores instructions that can be executed by the at least one processing unit, and when the at least one processing unit executes the instructions, it implements the steps of the model support classification generation method as described in the first aspect above.

[0054] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processing unit, implements the steps of the model-supported classification generation method described in the first aspect above.

[0055] A fifth aspect of this application provides a computer program product comprising computer instructions that, when executed by a computer, implement the steps of the model-supported classification generation method described in the first aspect above.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. Using the method provided in the embodiments of this application, during the model preprocessing process, it is possible to automatically generate medium-density or medium-thickness support units for the general bottom overhanging surface of the model, and to automatically generate a small number of thickened support units for specific areas such as the bottom suspension point, so as to enhance the printing strength of specific areas.

[0058] 2. Using the method provided in the embodiments of this application, during the model preprocessing process, medium-density or medium-thickness support units can be automatically generated for the general bottom suspended surface of the model, and finer support units can be automatically generated for fragile suspension points such as fingers or clothing corners of the doll model, which facilitates the removal of support after the model is printed. Removing finer support units can easily prevent the fragile parts of the model from being torn.

[0059] 3. Using the method provided in the embodiments of this application, during the model preprocessing process, it is possible to automatically generate medium-density or medium-thickness support units on the general bottom overhanging surface of the model, and also generate support units with lower density on the inner cavity or the concave overhanging surface between layers of the model. Generating support units with lower density in the inner cavity or the concave part between layers of the model is necessary to ensure successful printing, and also reduces the workload and difficulty of dismantling in such inconvenient locations.

[0060] 4. Using the method provided in the embodiments of this application, during the model preprocessing process, it is possible to automatically generate support units with the first type of parameters for specific areas such as bottom suspension points, generate support units with the second type of parameters with corresponding density for general bottom suspended surfaces, and generate support units with the third type of parameters with corresponding density for the inner cavity or interlayer concave suspended surfaces of the model, thereby improving the adaptability of support generation. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the classification generation method supporting the model in the embodiments of this application;

[0062] Figure 2 This is a structural diagram of the model-supporting classification generation device in an embodiment of this application;

[0063] Figure 3 This is a flowchart illustrating the method for obtaining suspended vertices of a model according to an embodiment of this application.

[0064] Figure 4 A flowchart illustrating the method for determining the region to be supported in an embodiment of this application;

[0065] Figure 5 This is a flowchart illustrating the method for obtaining candidate support points in an embodiment of this application.

[0066] Figure 6 This is an example of an interlayer concave structure model in an embodiment of this application;

[0067] Figure 7 This is a schematic diagram of cross-sectional layering of the interlayer concave structure model in an embodiment of this application. Figure 1 ;

[0068] Figure 8 This is a schematic diagram of cross-sectional layering of the interlayer concave structure model in an embodiment of this application. Figure 2 ;

[0069] Figure 9 This is a schematic diagram illustrating the determination of the suspension position by overlapping and matching independent closed paths on a layered cross section according to an embodiment of this application;

[0070] Figure 10 This is a schematic diagram illustrating the acquisition of mesh plane normal vectors in an embodiment of this application.

[0071] Figure 11 This is a schematic diagram illustrating the determination of the region to be supported based on the normal vector angle in an embodiment of this application;

[0072] Figure 12 This is a schematic diagram illustrating the determination of non-selectable support points in an embodiment of this application.

[0073] Figure 13 This is a schematic diagram illustrating the determination of face support vertices in an embodiment of this application;

[0074] Figure 14 A schematic diagram of the first and second type of support units generated for embodiments of this application;

[0075] Figure 15 A schematic diagram showing the generation of the first, second, and third types of support units for embodiments of this application;

[0076] Figure 16 The software processing in this application generates the effect diagrams of the first and second types of support units;

[0077] Figure 17 The software processing in this application generates the effect diagrams of the first, second, and third types of support units.

[0078] Figure 18 The electronic device structure diagram for implementing the model-supported classification generation method in the embodiments of this application;

[0079] Figure 19 This is a schematic diagram illustrating the slicing process of the model by the electronic device in an embodiment of this application;

[0080] Figure 20 The structural block diagram of the 3D printing equipment supporting the classification generation method of this application is shown below;

[0081] Figure 21 This is a schematic diagram illustrating the import of image data obtained by slicing after implementation of the method described in this application into a 3D printing device.

[0082] Label Explanation:

[0083] Electronic device 10; 3D printing equipment 11; mobile storage device 12; processing unit 101; storage unit 102; computer program 103; controller 111; memory 112; printing control program 113;

[0084] Model mesh traversal module 100; Suspension vertex acquisition model 200; First type of support unit generation module 300; Area to be supported determination module 400; Candidate support point acquisition module 500; Non-candidate support point determination module 600; Surface support vertex determination module 700; Second type of support unit generation module 800; Storage module 900;

[0085] Interlayer concave structure model 401; Suspended cone 402; Interlayer concave structure 403; Inner top surface 404; Inner bottom surface 405; Independent closed path 501; Hemispherical model 601; Zero plane platform 602; Area to be supported 603; Vertical projection range 604; Preset grid 701; Feature point 702; Mapping point of suspension vertex in XY plane 703; Mapping point of candidate support point in XY plane 704; Mapping point of non-candidate support point in XY plane 705; Surface support vertex 706; First type of support unit 801; Second type of support unit 802; Third type of support unit 803. Detailed Implementation

[0086] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0087] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application.

[0088] Figure 1This is a flowchart of the model-supported classification generation method according to an embodiment of this application. As shown in the figure, the model-supported classification generation method of this application includes the following steps:

[0089] S100. Traverse all the triangular meshes that make up the model;

[0090] S200. Detect and acquire all suspended vertices on the model;

[0091] S300. Generate first-type support units downward from the suspension vertex according to the first-type support parameters and connect them to the zero-plane platform or model;

[0092] S400. The entire area of ​​the model to be supported is determined by the planar angles of the triangular mesh;

[0093] S500. Obtain all candidate support points on the area to be supported in the model;

[0094] S600. Using each suspension vertex as the center, determine the candidate support points within a preset radius L1 mm in the horizontal direction as non-candidate support points;

[0095] S700. Select points with a horizontal spacing of L2 + ΔX mm from all remaining candidate support points and determine them as the face support vertices;

[0096] S800. According to the second type of support parameters, a second type of support unit is generated downward from the surface support vertex and connected to the zero plane platform;

[0097] S900. Store the overall three-dimensional data of the model and support units.

[0098] In addition, the diagram includes optional steps:

[0099] S620. Obtain the candidate support points for the inner cantilever surface from all the candidate support points in the model;

[0100] S640. Select points with a horizontal spacing of L3+△X mm from all the candidate support points of the inner suspended surface and determine them as the support vertices of the inner suspended surface;

[0101] S660. Generate third-type support units downward from the inner cantilever surface support vertex according to the third-type support parameters and connect them to the model.

[0102] Specifically, L1, L2, and L3 are positive integers or decimals; △X is a set error value.

[0103] Furthermore, the first type of support parameters and the second type of support parameters include: support contact point shape, support contact point size, support column shape, support column size, support column bending angle, support column connection structure, support base shape, and support base size.

[0104] Figure 2 This is a structural diagram of the model support classification generation device according to an embodiment of this application. As shown in the figure, the model support classification generation device of this application includes:

[0105] Model mesh traversal module 100 is used to traverse all the triangular meshes that make up the model;

[0106] The Suspended Vertex Acquisition Model 200 is used to detect and acquire all suspended vertices on the model.

[0107] The first type of support unit generation module 300 is used to generate first type of support units from the suspension vertex downwards according to the first type of support parameters and connect them to the zero plane platform or model.

[0108] The module 400 for determining the area to be supported is used to determine the entire area to be supported of the model by the planar angles of the triangular mesh.

[0109] The candidate support point acquisition module 500 is used to acquire all candidate support points on the area to be supported in the model;

[0110] The non-selective support point determination module 600 is used to determine the candidate support points within a preset radius L1 mm in the horizontal direction as non-selective support points with each suspension vertex as the center.

[0111] The surface support vertex determination module 700 is used to select points with a horizontal spacing of L2+△X millimeters from all remaining candidate support points and determine them as surface support vertices.

[0112] The second type of support unit generation module 800 is used to generate second type of support units from the surface support vertex downwards according to the second type of support parameters and connect them to the zero plane platform.

[0113] Storage module 900 is used to store the overall three-dimensional data of the model and support units.

[0114] In addition, the diagram also includes the following modules:

[0115] The module 620 for obtaining candidate support points for the inner suspended surface is used to obtain candidate support points for the inner suspended surface from all candidate support points in the model.

[0116] The inner suspended surface support vertex determination module 640 is used to select points with a horizontal spacing of L3+△X millimeters from all the inner suspended surface candidate support points and determine them as the inner suspended surface support vertex.

[0117] The third type of support unit generation module 660 is used to generate third type of support units from the inner cantilever surface support vertex according to the third type of support parameters and connect them to the model.

[0118] Figure 3 This is a flowchart illustrating the method for obtaining suspended vertices of a model according to an embodiment of this application. As shown in the figure, this figure corresponds to... Figure 1 The sub-step of step S200, namely detecting and acquiring all suspended vertices on the model, includes the following sub-steps:

[0119] S210. The model triangular mesh is extracted by multiple cross-sectional planes with preset layer thicknesses;

[0120] S220. Obtain M on each cross-sectional plane. i An independent closed path;

[0121] S230. Transfer M from the Nth layer... n An independent closed path and M in the N+1th layer n+1 Perform overlapping matching on each independent closed path;

[0122] S240. Determine the lowest point of the triangular mesh edge containing the independent closed paths that have not achieved overlapping and pairing in the N+1th layer as the suspension vertex.

[0123] Specifically, Mi, Mn, and Mn+1 are positive integers; N is a positive integer layer number that increments from 1; and n and i are positive integers.

[0124] Figure 4 This is a flowchart illustrating a method for determining the region to be supported in an embodiment of this application. As shown in the figure, this figure corresponds to... Figure 1 The sub-step of step S400, which involves determining the entire area to be supported by the planar angles of the triangular mesh, includes the following sub-steps:

[0125] S410. Obtain the normal vector angles of all triangular mesh planes in the model;

[0126] S420. Define the triangular mesh plane whose normal vector angle is within the set range as the area to be supported.

[0127] Figure 5 This is a flowchart illustrating a method for obtaining candidate support points according to an embodiment of this application. As shown in the figure, this figure corresponds to... Figure 1 The sub-step of step S500, namely obtaining all the candidate support points on the model's support area, includes the following sub-steps:

[0128] S510. Divide the zero-plane platform into a preset grid with a side length of Y millimeters, centered on the origin;

[0129] S520. Obtain the vertical projection range of the area to be supported on the zero-plane platform;

[0130] S530. Project a straight line upward from the preset square feature points within the vertical projection range and intersect it with the triangular grid plane within the area to be supported, and determine the intersection point as the candidate support point.

[0131] Specifically, Y is a positive integer or a decimal.

[0132] Specifically, the preset grid feature point is the center point of the preset grid, or a grid point of the preset grid.

[0133] Figure 6 This is an example of an interlayer concave structure model in an embodiment of this application. As shown in the figure, this figure illustrates an interlayer concave structure model 401; the model has an interlayer concave structure 403, whose inner top surface 404 and inner bottom surface 405 are both horizontal surfaces, and a suspended cone 402 is provided on the inner top surface 404 at the concave location.

[0134] Figure 7 This is a schematic diagram of cross-sectional layering of the interlayer concave structure model in an embodiment of this application. Figure 1 As shown in the figure, this figure obtains multiple cross-sectional polygons by layering the concave interlayer structure model 401 according to multiple cross-sectional planes J1-J5 with equal height H mm.

[0135] Figure 8 This is a schematic diagram of cross-sectional layering of the interlayer concave structure model in an embodiment of this application. Figure 2 As shown in the figure, the cross-sectional planes J1-J5 cut through the concave structural model 401.

[0136] Figure 9 This is a schematic diagram illustrating the determination of the suspension position by overlapping and matching independent closed paths on a layered cross section, according to an embodiment of this application. As shown in the figure, in... Figure 8 Based on this, each of the cross-sectional planes J1-J5 has an independent closed path 501; among them, cross-sectional planes J1, J2, J3, and J4 each have only one independent closed path 501; while cross-sectional plane J4 has two independent closed paths 501.

[0137] Based on this, the independent closed paths 501 on J1 and J2 are overlapped and matched. Since there is an overlap and pairing relationship between the two, they are excluded from detection.

[0138] Next, the independent closed paths 501 on J2 and J3 are overlapped and matched. Since they have an overlapping pairing relationship, they are excluded from detection.

[0139] Next, overlapping and matching the independent closed paths 501 on J3 and J4, it was found that there are areas where the three do not overlap and match; therefore, the independent closed path 501 in the circular shaded area is an isolated independent closed path, so the lowest point of the triangular mesh edge where this isolated independent closed path is located can be determined as the suspension vertex; corresponding to Figure 6 The coordinates of the lowest suspension vertex of suspension cone 402 can be found; therefore, it is possible to determine the coordinates based on this. Figure 1 In step S300, according to the first type of support parameters, a first type of support unit is generated downwards from the suspension vertex and connected to the zero-plane platform or model; corresponding to Figure 6 The first type of support unit in the interlayer concave structure model 401 shown should be connected to the inner bottom surface 405 of the lower part of the suspension cone 402;

[0140] Next, the independent closed paths 501 on J4 and J5 are overlapped and matched. Since the three have an overlapping and pairing relationship, they are excluded from detection.

[0141] It is particularly important to note that during the suspended vertex detection process described above in this embodiment, overlapping matching must be performed sequentially in a specific order, such as J1-J5. Only when the number of independent closed paths 501 increases (e.g., the number of independent closed paths 501 on J3 is 1, while the number of independent closed paths 501 on J4 is 2) can the lowest point of the triangular mesh edge containing the isolated independent closed path be determined as the suspended vertex. Otherwise, if... Figure 6 When the inner bottom surface 405 also has an upward conical feature, it is easy to judge the upward conical feature as a suspension feature.

[0142] Figure 10 This is a schematic diagram illustrating the acquisition of the normal vectors of the mesh planes in an embodiment of this application. As shown in the figure, this figure illustrates a hemispherical model 601; the model is composed of triangular meshes, wherein the triangular meshes on the upper plane are omitted, and only the triangular meshes on the lower hemisphere are shown; the four triangular mesh planes M1, M2, M3, and M4 selected have plane normal vectors n1, n2, n3, and n4 respectively; with the normal vector direction of the zero plane platform 602 as a reference, the normal vector angles of the triangular mesh planes M1, M2, M3, and M4 can be obtained.

[0143] Figure 11 This is a schematic diagram illustrating the determination of the region to be supported based on the normal vector angle in an embodiment of this application. As shown in the figure, in... Figure 10 Based on this, after obtaining the normal vector angles of triangular mesh planes M1, M2, M3, and M4, triangular mesh planes with normal vector angles within a set range can be identified as the areas to be supported. Specifically, when the angle setting range is 0-45 degrees, all triangular mesh planes with normal vector angles less than or equal to 45 degrees can be selected; that is, the lower triangular mesh area of ​​the hemispherical model 601 in the figure is identified as the area to be supported 603. In particular, since the normal vector of the top plane of the model is from the inside upward, its normal vector is 180 degrees, so it will not be selected. Correspondingly, the area to be supported 603 can form a vertical projection range 604 on the zero plane platform 602.

[0144] In combination with the above Figure 9 The process shown reveals that there is also a suspended vertex at the lowest point of the bottom of the hemispherical model 601; therefore, it can be determined based on... Figure 1 In step S300, according to the first type of support parameters, a first type of support unit is generated downwards from the suspension vertex and connected to the zero-plane platform or model; corresponding to the hemispherical model 601 in this figure, the first type of support unit is as follows: Figure 14 It should be connected to the zero-plane platform 602 as shown.

[0145] Figure 12 This is a schematic diagram illustrating the determination of non-selectable support points in an embodiment of this application. As shown in the figure, firstly, a preset grid 701 with a side length of Y millimeters is divided on the zero-plane platform 602, centered on the origin; then... Figure 11 Based on this, the vertical projection range 604 of the area to be supported 603 on the zero plane platform 602 is obtained; a straight line can be projected upward from the feature points 702 of the preset square 701 within the vertical projection range 604, intersecting with the triangular mesh plane within the area to be supported 603 at the bottom of the hemispherical model 601, and the intersection point is determined as the candidate support point.

[0146] Specifically, feature point 702 in this figure is selected from the grid points of the preset square 701; correspondingly, the center point of the preset square 701 can also be selected as the feature point as needed.

[0147] Specifically, since the lowest point of the hemispherical model 601 is the suspension vertex, and since a first type of support unit needs to be added at the suspension vertex, the area within the radius L1 of the suspension vertex needs to be set as a prohibited support area. Therefore, the candidate support points within a preset radius L1 mm in the horizontal direction, centered on each suspension vertex, need to be determined as non-candidate support points. Correspondingly, in this figure, the horizontal plane where the suspension vertex is located can be equivalent to the plane where the zero-plane platform 602 is located. Therefore, the mapping point 705 of the non-candidate support points in the XY plane can be determined through the mapping point 703 of the suspension vertex in the XY plane. The remaining feature point 702 is the mapping point 704 of the candidate support points in the XY plane. Based on this, points with a horizontal spacing of L2 + ΔX mm are selected from the mapping points 704 of the candidate support points in the XY plane and determined as follows: Figure 13 The face supports vertex 706 as shown; in this figure, when the length of L2 is less than the side length X of the preset square 701, the mapping point 704 of the candidate support point in the XY plane is exactly on the grid point.

[0148] The above description may seem complicated to understand, but in actual data processing, it is only necessary to extract the plane coordinates (X, Y) of each point from the three-axis coordinates (X, Y, Z) of the candidate support points to easily obtain the horizontal interval distance between each candidate support point. Therefore, the implementation process is actually very simple.

[0149] Figure 13 This is a schematic diagram illustrating the determination of face support vertices in an embodiment of this application. As shown in the figure, in Figure 12 Based on this, points with a horizontal spacing of L2+△X millimeters can be selected from all remaining candidate support points and determined as surface support vertices 706; for the sake of simplicity, not all surface support vertices 706 are shown in the figure.

[0150] Figure 14 A schematic diagram illustrating the generation of the first and second type of support units for embodiments of this application is shown. As illustrated, according to this application... Figure 1 The basic steps and methods shown in the figure are as follows: At the lowest point of the hemispherical model 601, a first-type support unit 801 with a larger diameter is generated according to the first-type support parameters and connected to the zero-plane platform 602; Figure 13 At the vertex 706 of the surface support shown, a second type of support unit 802 with a medium diameter is generated according to the second type of support parameters and connected to the zero plane platform 602.

[0151] Figure 15 A schematic diagram illustrating the generation of first, second, and third type support units for embodiments of this application is shown. As illustrated, according to this application... Figure 1 The basic steps and methods shown are as follows: at the suspension cone 402 of the concave feature part of the concave structure model 401 in the figure, a first type of support unit 801 with a larger diameter is generated according to the first type of support parameters and connected downward to the inner bottom surface 405; then, combined with optional steps S620, S640, and S660, at the inner top surface 404 in the figure, a third type of support unit 803 with a smaller diameter is generated according to the third type of support parameters and connected downward to the inner bottom surface 405; finally, at the bottom plane of the concave structure model 401, a second type of support unit 802 with a medium diameter is generated according to the second type of support parameters and connected downward to the inner bottom surface 405.

[0152] Figure 16 The figure shows the effect diagram of the software processing generating the first and second types of support units in the embodiments of this application. As shown in the figure, this figure is the effect diagram actually generated by the software, and... Figure 14 Similarly, at the bottom of the hemispherical model 601, there are two types of support units: a first type of support unit 801 with a larger diameter generated according to the first type of support parameters, and a second type of support unit 802 with a medium diameter generated according to the second type of support parameters.

[0153] Figure 17 The figure shows the effect diagram of the software processing generating the first, second, and third types of support units according to the embodiments of this application. As shown in the figure, this figure is the effect diagram actually generated by the software, and... Figure 15 Similarly, a first type of support unit 801 with a relatively large diameter, generated according to the first type of support parameters, is generated at the suspension cone 402 of the interlayer concave structure model 401; a third type of support unit 803 with a relatively small diameter, generated according to the third type of support parameters, is generated at the inner top surface 404; and a second type of support unit 802 with a medium diameter, generated according to the second type of support parameters, is generated at the bottom plane of the model.

[0154] Figure 18 The electronic device structure diagram for implementing the model-supported classification generation method of this application is shown in the figure. As shown, the electronic device 10 in this figure is exemplified by having a processing unit 101. As shown, an electronic device 10 includes a processing unit 101 and a storage unit 102; wherein the storage unit 102 stores a computer program 103 or instructions executable by the processing unit 101, and the computer program 103 or instructions are executed by the processing unit 101 to enable the processing unit 101 to perform, for example... Figure 1 , Figure 3 , Figure 4 , Figure 5 The steps in the process.

[0155] Storage unit 102, which is the third aspect of this application, provides a non-transitory computer-readable storage medium. Storage unit 102 stores instructions executable by at least one processing unit 101, causing the at least one processing unit 101 to perform, as follows: Figure 1 , Figure 3 , Figure 4 , Figure 5 The steps in the process.

[0156] Storage unit 102 is defined as a non-transitory computer-readable storage medium, which can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as those implemented during execution. Figure 1 , Figure 3 , Figure 4 , Figure 5 The processing unit 101 executes various server functions and data processing by running the non-transient computer program 103, instructions, and modules stored in the storage unit 102, thereby achieving the above-mentioned functions. Figure 1 , Figure 3 , Figure 4 , Figure 5 The corresponding embodiments involve steps involving a computer and a processing unit.

[0157] Storage unit 102 may include a stored program area and a stored data area. The stored program area may store the operating system and applications required for at least one function; the stored data area may store data created when the electronic device 10 is used. Furthermore, storage unit 102 may include a high-speed random access memory unit and may also include non-transient storage units, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage devices. In some embodiments, storage unit 102 may optionally include storage units remotely located relative to processing unit 101. These remote storage units can be connected via a network to the electronic device performing model-supported classification generation. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0158] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processing unit, which may be a dedicated or general-purpose programmable processing unit, capable of receiving data and instructions from a storage system, at least one input unit, and at least one output device, and transmitting data and instructions to the storage system, the at least one input unit, and the at least one output device.

[0159] These computer programs 103 (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processing unit and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, storage unit, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processing unit, including machine-readable media that receive machine instructions determined to be machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processing unit.

[0160] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0161] Figure 19This is a schematic diagram illustrating the slicing process of a model by an electronic device according to an embodiment of this application. As shown in the figure, the user runs a 3D slicing program through the electronic device 10 to slice the stored overall three-dimensional data of the model and support units to obtain sliced ​​image data of the model and support units.

[0162] Figure 20 The structural block diagram of the 3D printing device supporting the classification generation method of this application is shown in the figure. As shown, a 3D printing device 11 includes a controller 111 and a memory 112; wherein the memory 112 stores a printing control program 113 or instructions that can be executed by the controller 111. The printing control program 113 or instructions are executed by the controller 111 to enable the controller 111 to perform actions such as... Figure 1 , Figure 3 , Figure 4 , Figure 5 The steps in this process are because the 3D printing device 11 can also embed the program functions of the classification generation method supported by the model of this application as needed.

[0163] Figure 21 This diagram illustrates the import of image data obtained by slicing after implementation of the method described in this application into a 3D printing device. As shown, the user uses a mobile storage device 12 to import the sliced ​​image data of the model and support unit into the 3D printing device 11 for exposure printing, thereby obtaining the overall printed parts of different types of supports and models generated in this application.

[0164] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A model-supported classification generation method, characterized in that, include: Traverse all the triangular meshes that make up the model; Detect and acquire all suspended vertices on the model; Based on the first type of support parameters, first type of support units are generated downwards from the suspension vertex and connected to the zero-plane platform or model; The entire area of ​​the model to be supported is determined by the planar angles of the triangular mesh; Obtain all candidate support points on the area to be supported in the model; Using each suspension vertex as the center, the candidate support points within a preset radius L1 mm in the horizontal direction are determined as non-candidate support points; Select points with a horizontal spacing of L2 + ΔX mm from all remaining candidate support points and determine them as the face support vertices; According to the second type of support parameters, second type of support units are generated downwards from the surface support vertex and connected to the zero plane platform; Store the overall three-dimensional data of the model and support units.

2. The model-supported classification generation method according to claim 1, characterized in that, The first type of support parameters and the second type of support parameters include: support contact point shape, support contact point size, support column shape, support column size, support column bending angle, support column connection structure, support base shape, and support base size.

3. The model-supported classification generation method according to claim 1, characterized in that, Also includes: Obtain the inner cantilever surface as the candidate support point from all the candidate support points in the model; Select points with a horizontal spacing of L3+△X mm from all the candidate support points of the inner suspended surface and determine them as the support vertices of the inner suspended surface; According to the third type of support parameters, the third type of support unit is generated downward from the support vertex of the inner cantilever surface and connected to the model.

4. The model-supported classification generation method according to claim 3, characterized in that, The third type of support parameters includes: support contact point shape, support contact point size, support column shape, support column size, support column bending angle, support column connection structure, support base shape, and support base size.

5. The model-supported classification generation method according to claim 1, characterized in that, The detection and acquisition of all suspended vertices on the model includes: The model's triangular mesh is extracted layer by layer using multiple cross-sectional planes with preset layer thicknesses; Obtain M on each cross-sectional plane i An independent closed path; M in the Nth layer n An independent closed path and M in the N+1th layer n+1 Perform overlapping matching on each independent closed path; The lowest point of the triangular mesh edge containing the independent closed paths that have not achieved overlapping and pairing in the N+1th layer is determined as the suspension vertex.

6. The model-supported classification generation method according to claim 1, characterized in that, The determination of the entire area to be supported by the planar angles of the triangular mesh includes: Obtain the normal vector angles of all triangular mesh planes in the model; The triangular mesh plane whose normal vector angle is within the set range is defined as the area to be supported.

7. The model-supported classification generation method according to claim 1, characterized in that, The process of obtaining all candidate support points on the region to be supported in the model includes: Divide the zero-plane platform into a preset grid with a side length of Y millimeters, centered on the origin; Obtain the vertical projection range of the area to be supported on the zero-plane platform; A straight line is projected upward from the preset square feature points within the vertical projection range and intersects with the triangular grid plane within the area to be supported, and the intersection point is determined as the candidate support point.

8. A model-supported classification generation device, characterized in that, include: The model mesh traversal module is used to traverse all the triangular meshes that make up the model; The suspended vertex acquisition model is used to detect and acquire all suspended vertices on the model. The first type of support unit generation module is used to generate first type of support units from the suspension vertex downwards according to the first type of support parameters and connect them to the zero plane platform or model; The module for determining the area to be supported is used to determine the entire area to be supported of the model based on the planar angles of the triangular mesh. The candidate support point acquisition module is used to acquire all candidate support points on the area to be supported in the model; The non-selective support point determination module is used to determine the candidate support points within a preset radius L1 mm in the horizontal direction as non-selective support points, with each suspension vertex as the center. The surface support vertex determination module is used to select points with a horizontal spacing of L2+△X millimeters from all remaining candidate support points and determine them as surface support vertices. The second type of support unit generation module is used to generate second type of support units from the surface support vertex downwards according to the second type of support parameters and connect them to the zero plane platform. The storage module is used to store the overall three-dimensional data of the model and support units.

9. The model-supported classification generation device according to claim 8, characterized in that, Also includes: The module for obtaining candidate support points for the inner suspended surface is used to obtain candidate support points for the inner suspended surface from all candidate support points in the model. The inner suspended surface support vertex determination module is used to select points with a horizontal spacing of L3+△X millimeters from all the inner suspended surface candidate support points and determine them as the inner suspended surface support vertex. The third type of support unit generation module is used to generate third type of support units from the inner cantilever surface support vertex according to the third type of support parameters and connect them to the model.

10. An electronic device, characterized in that, include: At least one processing unit; and a storage unit communicatively connected to the at least one processing unit; wherein, The storage unit stores instructions that can be executed by the at least one processing unit, and when the at least one processing unit executes the instructions, it implements the steps of the model support classification generation method as described in any one of claims 1-7.

11. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores a computer program that, when executed by the processing unit, implements the steps of the model-supported classification generation method as described in any one of claims 1-7.

12. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a computer, implement the steps of the model-supported classification generation method as described in any one of claims 1-7.

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