A three-dimensional geometric feature automatic identification method based on topological rules

Abstract

The present application relates to the technical field of computer-aided design, in particular to a three-dimensional geometric feature automatic identification method based on topological rules. It comprises the following steps: obtaining a surface list and an edge list of a part according to a B-Rep model; constructing attribute description data of the part according to the surface list and the edge list in combination with model topological information; cyclically traversing the surface list; matching the traversal result of the surface list with a feature rule library according to the attribute description data to identify three-dimensional geometric features; and calculating basic dimensions of the three-dimensional geometric features. The purpose of the three-dimensional geometric feature automatic identification method based on topological rules is to solve the problems of complicated operation and easy errors of traditional machining feature identification algorithms.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, and more specifically to a method for automatic recognition of three-dimensional geometric features based on topological rules. Background Technology

[0002] In computer-aided technology, CAD (Computer-Aided Drafting) systems complete the part's shape design, CAM (Computer-Aided Manufacturing) systems generate machining instructions, and CAPP (Computer-Aided Process Planning) systems connect the CAD and CAM systems to realize the machining process planning for the part. In current computer-aided systems, CAD systems often only contain the geometric information of the part, i.e., points, lines, surfaces, and their topological relationships. However, CAPP and CAM systems require feature information with machining semantics. The information contained in the CAD system cannot be directly used by CAPP and CAM systems, thus requiring manual intervention to convert the geometric model in the CAD system into a part feature model. This process not only increases the workload but also hinders fully automated process planning for parts and affects the integration of CAD and CAPP systems.

[0003] Feature recognition technology can extract geometric entities with machining semantics from the design model of a part, thereby transforming the design model into a feature model with machining semantics. The extracted geometric entities can be used for subsequent machining process planning and machining programming, completing the conversion of design information in the CAD system to process information in the CAPP system, which is the key to realizing the integration of CAD and CAPP systems. Automatic machining feature recognition technology can automatically extract machining features from the part model, avoiding manual marking by process engineers, saving time and avoiding omissions caused by manual marking.

[0004] Currently, commonly used feature recognition methods include graph-based feature recognition algorithms. These methods require building an attribute adjacency graph (AAG) of the model and using subgraph isomorphism algorithms to find subgraphs with the same topological structure as predefined features from the part's attribute adjacency graph. However, subgraph isomorphism is an NP-complete problem (Non-deterministic Polynomial), making direct searching within the part inefficient. Neural network-based feature recognition requires constructing a large training dataset, converting the part's topological and geometric information into numerical information needed by the neural network, and overcoming the influence of feature space pose in its feature representation method. Furthermore, it needs to be able to extract feature parameters after feature recognition. Considering these factors, the application of such algorithms in practical engineering remains to be seen.

[0005] Therefore, the inventors provide an automatic identification method for three-dimensional geometric features based on topological rules. Summary of the Invention

[0006] (1) Technical problems to be solved

[0007] This invention provides an automatic three-dimensional geometric feature recognition method based on topological rules, which solves the technical problems of traditional processing feature recognition algorithms being cumbersome and prone to errors.

[0008] (2) Technical solution

[0009] The first aspect of this invention provides a method for automatic recognition of three-dimensional geometric features based on topological rules, comprising the following steps:

[0010] Based on the B-Rep model, obtain the list of faces and the list of edges of the part;

[0011] Based on the face list and the edge list, and combined with the model topology information, construct the attribute description data of the part;

[0012] Iterate through the list of faces in a loop;

[0013] Based on the attribute description data, the traversal results are matched with the feature rule library to identify three-dimensional geometric features;

[0014] Calculate the basic dimensions of the three-dimensional geometric feature.

[0015] Furthermore, the process of obtaining the face list and edge list of the part based on the B-Rep model specifically involves:

[0016] The B-Rep model is parsed, the topology of the parts is analyzed, and the face list and edge list are obtained based on the topology.

[0017] Furthermore, the step of constructing the attribute description data of the part based on the face list and the edge list, combined with the model topology information, specifically includes the following steps:

[0018] Based on the B-Rep model, the types of faces and edges are obtained by analyzing geometric information; the concavity and convexity of faces and edges are calculated by analyzing topological information, and the set of inner / outer boundary curves of the faces is obtained.

[0019] Obtain the adjacent faces of the inner / outer boundary and their connection attributes, and construct the attribute description data.

[0020] Furthermore, the concavity or convexity of a surface is determined by the sign of its radius of curvature. If the vector from the center of curvature of a point on the surface to that point is in the same direction as the normal vector of that point, then the surface is considered to have a negative radius of curvature and is a convex surface; otherwise, it is a concave surface.

[0021] Furthermore, the concavity or convexity of an edge is determined by the concavity or convexity of the angle formed by its adjacent surfaces. When the angle is less than 180 degrees, the edge is a concave edge; otherwise, it is a convex edge.

[0022] Furthermore, the attribute description data includes surface type, surface concavity / convexity, number of adjacent surfaces in the outer ring, number of adjacent surfaces in the inner ring, number of concave / convex / smooth edges in the outer ring, and number of concave / convex / smooth edges in the inner ring.

[0023] Furthermore, the step of matching the traversal results of the face list with the feature rule base to identify 3D geometric features specifically includes:

[0024] The face list is traversed and matched against the feature rule base. If a match is successful, the adjacent faces of the outer ring are found, and the hole-type features are obtained; and...

[0025] Traverse the list of faces and match it with the feature rule base. If a match is successful, store the base face and its adjacent faces to obtain the slot feature.

[0026] Furthermore, the calculation of the basic dimensions of the three-dimensional geometric features specifically includes:

[0027] Calculate the hole depth, radius, and axial direction of the hole feature; and,

[0028] Calculate the length, width, height, and bottom normal of the groove for the groove type feature.

[0029] Furthermore, the feature rule base includes geometric features and their corresponding topological rules.

[0030] (3) Beneficial effects

[0031] In summary, this invention analyzes the topological structure of the B-Rep model, calculates the concavity and convexity of edges and faces, establishes attribute description data for parts, and achieves automatic feature recognition by matching a feature rule base, and calculates feature parameters. This recognition method features a simple algorithm, high recognition efficiency, and strong scalability. It avoids the massive matrix calculations required in attribute adjacency graph-based matching methods and eliminates the need for the large training dataset required for neural network recognition. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating an automatic three-dimensional geometric feature recognition method based on topological rules provided in an embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram illustrating the calculation of the concavity and convexity of an edge according to an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram illustrating the calculation of the concavity and convexity of a surface provided in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram of a hole-like feature provided in an embodiment of the present invention;

[0037] Figure 5 This is a structural schematic diagram of a groove-type feature provided in an embodiment of the present invention.

[0038] In the picture:

[0039] 101 - Hole top surface; 102 - Hole bottom surface; 103 - Hole side surface; 201 - Groove top surface; 202 - Groove bottom surface; 203 - Groove side surface. Detailed Implementation

[0040] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present invention by way of example, but should not be used to limit the scope of the present invention, that is, the present invention is not limited to the described embodiments.

[0041] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0042] In the description of this invention, it should be understood that the terms "upper," "lower," "front," "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that is commonly placed when the product of this invention is used, or the orientation or positional relationship that is commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the model referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0043] Figure 1 This is a flowchart illustrating an automatic 3D geometric feature recognition method based on topological rules provided in an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0044] S100. Based on the B-Rep model, obtain the face list and edge list of the part;

[0045] S200. Based on the face list and edge list, and combined with the model topology information, construct the attribute description data of the part;

[0046] S300, Loop through the face list;

[0047] S400. Based on the attribute description data, match the traversal results with the feature rule library to identify three-dimensional geometric features.

[0048] S500: Calculates the basic dimensions of three-dimensional geometric features.

[0049] In the above implementation, by analyzing the topological structure in the B-Rep (Boundary Representation) model, calculating the concavity and convexity of edges and faces, and establishing attribute description data for the part (i.e., attribute adjacency relationships, not attribute adjacency graphs), automatic feature recognition is achieved by matching a feature rule base, and feature parameters are calculated. This recognition method directly identifies features through the matching results between the face list and the feature rule base, without requiring a large training dataset. The algorithm is simple, highly efficient, and scalable. It avoids the massive matrix calculations required in attribute adjacency graph-based matching methods and eliminates the need for the large training datasets required for neural network recognition.

[0050] Step S100 specifically involves: parsing the B-Rep model, analyzing the topology of the parts, and obtaining a face list and an edge list based on the topology. The specific method for obtaining the face and edge lists is not limited; they can be obtained directly from the B-Rep model, or a ring list can be obtained first by parsing the B-Rep model, and then the face and edge lists can be parsed from the ring list.

[0051] Step S200 specifically includes the following steps:

[0052] S201. Based on the B-Rep model, the types of faces and edges are obtained by analyzing geometric information; the concavity and convexity of faces and edges are calculated by analyzing topological information, and the set of inner / outer boundary curves of the faces is obtained.

[0053] S202. Obtain the adjacent faces of the inner / outer boundary and their connection attributes with the adjacent faces, and construct attribute description data.

[0054] In step S201, the concavity and convexity of faces and edges are calculated. First, the pointers of the faces are obtained, and the sets of inner / outer boundary curves of the faces are analyzed. The concavity and convexity of an edge are determined by the concavity and convexity of the angle formed by its adjacent faces. When the angle is less than 180 degrees, the edge is called a concave edge; otherwise, it is called a convex edge. A schematic diagram for determining the concavity and convexity of an edge is attached. Figure 2 Let plane A intersect plane B at edge E, and let P be a point on E. The normal vector of plane A at point P is... The normal vector of surface B at point P is Based on the direction of the directed edge at point P on face A, determine the tangent vector of edge E at point P as follows: When calculating the concavity and convexity of an edge, first calculate... and If the dot product is 1, then proceed to the analysis of the concavity / convexity of the smooth edge; otherwise, calculate... and cross product Recalculate tangent vector If the dot product d is greater than 0, then edge E is a convex edge; otherwise, it is a concave edge.

[0055] Methods for determining the concavity and convexity of a surface, such as Figure 3 As shown, for a point on the surface, if the vector from the center of curvature 01 of point P1 to that point is equal to the normal vector of that point... If the directions are the same, then the surface is considered to have a negative radius of curvature, and the surface is considered convex; if the vector from the center of curvature O2 of point P2 to point P2 is the same as the normal vector of point P2... If the direction is opposite, the surface is considered to have a positive radius of curvature and is considered concave. For freeform surfaces, there may be different points where the radius of curvature has different signs. Based on the sign of the radius of curvature at each point on the surface, the surfaces in the part can be divided into three types: concave, convex, and flat.

[0056] As an optional implementation, as shown in Table 1 below, the attribute description data includes, but is not limited to: face type, face concavity / convexity, number of adjacent faces in the outer ring, number of adjacent faces in the inner ring, number of concave / convex / smooth edges in the outer ring, and number of concave / convex / smooth edges in the inner ring.

[0057] Table 1

[0058]

[0059] As an optional implementation, the traversal results of the face list are matched with a feature rule base to identify 3D geometric features, specifically including:

[0060] As shown in Table 2 below, the face list is traversed and matched against the feature rule base. Successful matches identify the adjacent faces of the outer ring, thus obtaining the pore-type features; and...

[0061] As shown in Table 2 below, the surface list is traversed and matched with the feature rule base. If a match is successful, the base surface and its adjacent surfaces are stored to obtain the slot feature.

[0062] Table 2

[0063]

[0064]

[0065] It should be noted that the three-dimensional geometric features involved in this invention include, but are not limited to, hole features, groove features, openings and bosses. The following description will only use hole features and groove features as examples.

[0066] As an optional implementation method, calculating the basic dimensions of the three-dimensional geometric features specifically includes:

[0067] like Figure 4 As shown, the hole features include a top surface 101, a bottom surface 102, and a side surface 103. The hole depth, radius, and axial direction of the hole features are calculated; and...

[0068] like Figure 5 As shown, the groove-type features include the top surface 201, the bottom surface 202, and the side surface 203. The length, width, height, and bottom surface normal of the groove-type features are calculated.

[0069] As an optional implementation, the feature rule base includes geometric features and their corresponding topological rules.

[0070] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The present invention is not limited to the specific steps and structures described above and shown in the figures. Furthermore, for the sake of brevity, detailed descriptions of known methods and techniques are omitted here.

[0071] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art without departing from the scope of the invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for automatic recognition of three-dimensional geometric features based on topological rules, characterized in that, The method includes the following steps: Based on the B-Rep model, obtain the list of faces and the list of edges of the part; Based on the face list and the edge list, and combined with the model topology information, construct the attribute description data of the part; Iterate through the list of faces in a loop; Based on the attribute description data, the traversal results are matched with the feature rule library to identify three-dimensional geometric features; Calculate the basic dimensions of the three-dimensional geometric features; The attribute description data includes the face type, the concavity / convexity of the face, the number of adjacent faces in the outer ring, the number of adjacent faces in the inner ring, the number of concave / convex / smooth edges in the outer ring, and the number of concave / convex / smooth edges in the inner ring.

2. The automatic identification method for three-dimensional geometric features based on topological rules according to claim 1, characterized in that, The process of obtaining the face list and edge list of the part based on the B-Rep model is as follows: The B-Rep model is parsed, the topology of the parts is analyzed, and the face list and edge list are obtained based on the topology.

3. The automatic recognition method for three-dimensional geometric features based on topological rules according to claim 1, characterized in that, The step of constructing the attribute description data of the part based on the face list and the edge list, combined with the model topology information, specifically includes the following steps: Based on the B-Rep model, the types of faces and edges are obtained by analyzing geometric information; the concavity and convexity of faces and edges are calculated by analyzing topological information, and the set of inner / outer boundary curves of the faces is obtained. Obtain the adjacent faces of the inner / outer boundary and their connection attributes, and construct the attribute description data.

4. The automatic identification method for three-dimensional geometric features based on topological rules according to claim 3, characterized in that, The concavity or convexity of a surface is determined by the sign of its radius of curvature. If the vector from the center of curvature of a point on the surface to that point is in the same direction as the normal vector of that point, then the surface is considered to have a negative radius of curvature and is a convex surface; otherwise, it is a concave surface.

5. The automatic identification method for three-dimensional geometric features based on topological rules according to claim 3, characterized in that, The concavity or convexity of an edge is determined by the concavity or convexity of the angle formed by its adjacent surfaces. When the angle is less than 180 degrees, the edge is a concave edge; otherwise, it is a convex edge.

6. The automatic recognition method for three-dimensional geometric features based on topological rules according to claim 1, characterized in that, The step of matching the traversal results with the feature rule base based on the attribute description data to identify three-dimensional geometric features specifically includes: The face list is traversed and matched against the feature rule base. If a match is successful, the adjacent faces of the outer ring are found, and the hole-type features are obtained; and... Traverse the list of faces and match it with the feature rule base. If a match is successful, store the base face and its adjacent faces to obtain the slot feature.

7. The automatic recognition method for three-dimensional geometric features based on topological rules according to claim 6, characterized in that, The calculation of the basic dimensions of the three-dimensional geometric features specifically includes: Calculate the hole depth, radius, and axial direction of the hole feature; and, Calculate the length, width, height, and bottom normal of the groove for the groove type feature.

8. The automatic recognition method for three-dimensional geometric features based on topological rules according to claim 6, characterized in that, The feature rule base includes geometric features and their corresponding topological rules.

9. The automatic identification method for three-dimensional geometric features based on topological rules according to any one of claims 1-8, characterized in that, The three-dimensional geometric features include hole-type features and groove-type features.

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