Impeller machining tool automatic selection method and system based on feature recognition

Abstract

The invention provides an impeller machining tool automatic selection method and system based on feature recognition, and belongs to the technical field of aero-engines. The method comprises the steps that feature recognition is conducted on an impeller three-dimensional model through a graph feature recognition method, and then the diameter of an impeller machining tool is determined; according to the diameter of a current impeller machining tool, the surface of the impeller is biased, feature recognition is conducted on the biased impeller three-dimensional model again through a graph feature recognition method, and blade feature points of the biased impeller are divided into a blade root line point set and a blade surface point set; and if all the blade feature points in the blade root line point set have reachability and all the blade feature points in the surface point set have reachability, selecting the maximum value in all the shortest cutter lengths as the final shortest cutter length. According to the method, the impeller machining tool can be automatically selected, the time complexity is reduced, and the calculation speed is increased.

Description

Technical Field

[0001] The invention belongs to the technical field of aero-engines, and in particular relates to a method and system for automatically selecting an impeller machining tool based on feature recognition. Background Art

[0002] The integral impeller is one of the key parts in the aircraft engine, and it is also a typical difficult-to-process part. The main reason is that the strong interference structure and difficult-to-process materials of the integral impeller make it difficult to improve the processing efficiency. Without changing the material, choosing a processing tool of the right size can not only ensure the feasibility of processing, but also effectively improve the processing efficiency. Therefore, in the processing of the integral impeller, it is very important to effectively and automatically select the appropriate processing tool size to improve processing efficiency and ensure product quality.

[0003] Generally speaking, the size of a tool mainly includes two aspects: diameter and length. As the diameter of the tool increases, the processing efficiency is improved accordingly, but at the same time, the probability of interference will also increase. The length of the tool affects the rigidity of the tool. As the length of the tool decreases, the rigidity of the tool will increase, but it also increases the probability of collision between the fixture and the workpiece.

[0004] In order to solve this contradiction, there are some solutions in the prior art, such as: Chinese patent "CN116150994A A method for selecting tool for cavity feature processing based on process information mining". Literature "Tool selection method for multi-axis processing of complex surface channels, Journal of Mechanical Engineering, Vol.54, No.3, 117-124". The above method will cause the maximum tool diameter obtained by a large number of feature points to be larger than the final tool diameter, resulting in a large number of redundant calculations, which greatly increases the time complexity of the algorithm. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present application proposes a method and system for automatically selecting impeller machining tools based on feature recognition.

[0006] In a first aspect, the present application proposes a method for automatically selecting an impeller machining tool based on feature recognition, comprising:

[0007] Step S1: obtaining a three-dimensional model of an impeller;

[0008] Step S2: using a feature recognition method of a graph to perform feature recognition on the three-dimensional model of the impeller to obtain blade feature points of the impeller;

[0009] Step S3: according to the blade feature points of the impeller, obtaining the shortest spacing between blades and the minimum curvature radius on the blades;

[0010] Step S4: determining the diameter of the impeller machining tool according to the shortest distance between the blades and the minimum radius of curvature on the blades;

[0011] Step S5: offset the surface of the impeller according to the diameter of the current impeller machining tool, and perform feature recognition again on the offset impeller three-dimensional model using a graph feature recognition method to obtain blade feature points of the offset impeller;

[0012] Step S6: dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set;

[0013] Step S7: If all blade feature points in the blade root line point set are reachable, go to step S8; otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset;

[0014] Step S8: If all blade feature points in the blade surface point set are reachable, compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool. Otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset.

[0015] The feature recognition method using a graph is used to perform feature recognition on the three-dimensional model of the impeller to obtain the blade feature points of the impeller, including:

[0016] Step S2.1: Obtain the topological tree of the impeller three-dimensional model, the attributes of all faces, and the concavity and convexity of the connecting edges;

[0017] Step S2.2: Obtain the adjacent relationship of the faces in the impeller three-dimensional model according to the topological tree, and construct an initial adjacency matrix A, where A ij is the adjacency attribute between the i-th face and the j-th face, A ji is the adjacency attribute between the jth face and the ith face;

[0018] Step S2.3: Traverse the initial adjacency matrix A. If face i is connected to face j, let A ij =A ji = 1, if face i and face j are not connected, then let A ij =A ji =0, and the adjacency matrix A after traversal is obtained;

[0019] Step S2.4: According to the attributes of the face and the concavity of the connecting edge, the traversed adjacency matrix A is updated to the attribute adjacency matrix B, where B ij is the concavity of the connecting edge between the i-th and j-th surfaces, Bii is the attribute of the i-th face;

[0020] Step S2.5: Traverse the attribute adjacency matrix B. If the connected faces i and j need to be split, let B ij =B ji =0, and the attribute adjacency matrix B after segmentation is obtained;

[0021] Step S2.6: Use a depth-first traversal algorithm to extract a submatrix from the segmented attribute adjacency matrix B, and encode it to compress the multi-dimensional information into one-dimensional information to obtain an encoded submatrix;

[0022] Step S2.7: Compare the encoded sub-matrix with the predefined feature library one by one. If the encoded sub-matrix is ​​consistent with the features in the predefined feature library, the impeller blade feature points corresponding to the encoded sub-matrix are obtained.

[0023] Determining the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum radius of curvature on the blades includes: selecting the minimum value between the shortest spacing between the blades and the minimum radius of curvature on the blades as the diameter of the impeller machining tool.

[0024] The accessibility includes: simplifying the reachable direction cone on the Gaussian sphere by using the proportion of the reachable direction cone in each spherical zone to obtain the simplified reachability.

[0025] The simplified reachability is calculated as follows:

[0026]

[0027] in, i is the serial number of the current ball zone, C i is the proportion of the accessible direction cone in each spherical zone of the Gaussian sphere, T is the step distance, R is the diameter of the current impeller machining tool, θ i is the angle between the i-th ray and the Z axis on the surface YOZ, wherein a three-dimensional Cartesian coordinate system is established in the hemispherical space of the Gaussian sphere with the center of the Gaussian sphere as the origin O, and the surface YOZ is a plane in the three-dimensional Cartesian coordinate system.

[0028] The shortest tool length is calculated as follows:

[0029]

[0030] Among them, L is the shortest tool length, R is the diameter of the current impeller machining tool, (x p ,y p ,z p ) is the coordinate of the impeller blade feature point after offset, (x p′ ,y p′,z p′ ) is the coordinate of the intersection of the tool axis vector and the outer surface of the blank.

[0031] In the second aspect, the present application proposes an automatic selection system for impeller machining tools based on feature recognition, comprising:

[0032] A model acquisition module, used to acquire a three-dimensional model of an impeller;

[0033] The first feature recognition module is used to perform feature recognition on the three-dimensional model of the impeller using a feature recognition method of a graph to obtain blade feature points of the impeller;

[0034] A data calculation module, used for obtaining the shortest spacing between blades and the minimum curvature radius on the blades according to the blade feature points of the impeller;

[0035] A diameter determination module, used to determine the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum curvature radius on the blades;

[0036] The second feature recognition module is used to offset the surface of the impeller according to the diameter of the current impeller processing tool, and use the feature recognition method of the graph to perform feature recognition on the offset impeller three-dimensional model again to obtain the blade feature points of the offset impeller;

[0037] A point set division module, used for dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set;

[0038] The first reachability judgment module is used to switch to the second reachability judgment module if all blade feature points in the blade root line point set are reachable, otherwise, the diameter of the current impeller machining tool is subtracted by a preset offset, and the module is returned to the second feature recognition module to re-offset;

[0039] The second reachability judgment module is used to compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set if all blade feature points in the blade surface point set are reachable, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool; otherwise, subtract a preset offset from the current impeller machining tool diameter, return to the second feature recognition module, and re-offset.

[0040] In a third aspect, the present application proposes an electronic device comprising: one or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the automatic selection method for impeller machining tools based on feature recognition.

[0041] In a fourth aspect, the present application proposes a computer-readable storage medium storing executable instructions, which, when executed, enable a processor to execute the method for automatic selection of impeller machining tools based on feature recognition.

[0042] In a fifth aspect, the present application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the automatic selection method for impeller machining tools based on feature recognition.

[0043] Beneficial effects:

[0044] This application proposes a method and system for automatically selecting impeller machining tools based on feature recognition. The feature recognition technology is introduced, and the target surface, interference surface and maximum optional diameter no longer need to be manually selected. In combination with the feature recognition results, the discrete feature point set is divided into the blade root line point set and the blade surface point set, and the blade root line point set with generally poor accessibility is judged first, which reduces the time complexity and speeds up the calculation speed. At the same time, the maximum diameter and shortest length of the tool are taken into account, and the proportion of the minimum accessible direction cone can be adjusted to more finely adjust the accessibility of the machining tool size. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Flow chart of the method for automatically selecting impeller machining tools based on feature recognition according to an embodiment of the present application;

[0046] Figure 2 A schematic flow chart of a method for automatically selecting a tool for machining an impeller based on feature recognition according to an embodiment of the present application;

[0047] Figure 3 Schematic diagram of the feature recognition principle of the embodiment of the present application;

[0048] Figure 4 Schematic diagram of discrete blade surface in an embodiment of the present application;

[0049] Figure 5 A simplified directional cone ratio distribution diagram of an embodiment of the present application;

[0050] Figure 6 The shortest tool length distribution diagram of the embodiment of the present application;

[0051] Figure 7 Schematic diagram of an actual reachable direction cone and a simplified reachable direction cone according to an embodiment of the present application;

[0052] Figure 8 Schematic diagram of step distance and relative error in an embodiment of the present application;

[0053] Fig. 9 Schematic diagram of error calculation evaluation value in the embodiment of the present application;

[0054] Fig.10 Schematic diagram of the ball zone in the embodiment of the present application;

[0055] Fig.11 Schematic diagram of the theoretical shortest tool length of an embodiment of the present application;

[0056] Fig.12 The embodiment of the present application provides an automatic selection system for impeller machining tools based on feature recognition. DETAILED DESCRIPTION

[0057] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.

[0058] Embodiment 1:

[0059] This embodiment proposes a method for automatically selecting impeller machining tools based on feature recognition, such as Figure 1 , Figure 2 As shown, including:

[0060] Step S1: obtaining a three-dimensional model of an impeller;

[0061] Step S2: Using the feature recognition method of the graph to perform feature recognition on the impeller three-dimensional model to obtain the blade feature points of the impeller, including:

[0062] Step S2.1: Obtain the topological tree of the impeller three-dimensional model, the attributes of all faces, and the concavity and convexity of the connecting edges;

[0063] Step S2.2: Obtain the adjacent relationship of the faces in the impeller three-dimensional model according to the topological tree, and construct an initial adjacency matrix A, where A ij is the adjacency attribute between the i-th face and the j-th face, A ji is the adjacency attribute between the jth face and the ith face;

[0064] Step S2.3: Traverse the initial adjacency matrix A. If face i is connected to face j, let A ij =A ji = 1, if face i and face j are not connected, then let A ij =A ji =0, and the adjacency matrix A after traversal is obtained;

[0065] Step S2.4: According to the attributes of the face and the concavity of the connecting edge, the traversed adjacency matrix A is updated to the attribute adjacency matrix B, where B ij is the concavity of the connecting edge between the i-th and j-th surfaces, B ii is the attribute of the i-th face;

[0066] Step S2.5: Traverse the attribute adjacency matrix B. If the connected faces i and j need to be split, let B ij =B ji=0, and the attribute adjacency matrix B after segmentation is obtained;

[0067] Step S2.6: Use a depth-first traversal algorithm to extract a submatrix from the segmented attribute adjacency matrix B, and encode it to compress the multi-dimensional information into one-dimensional information to obtain an encoded submatrix;

[0068] Step S2.7: Compare the encoded sub-matrix with the predefined feature library one by one. If the encoded sub-matrix is ​​consistent with the features in the predefined feature library, the impeller blade feature points corresponding to the encoded sub-matrix are obtained.

[0069] In this embodiment, the image feature recognition algorithm includes three main steps: extracting feature information, constructing and decomposing the attribute adjacency matrix, and identifying features and their sub-features. In the feature information extraction stage, the topology, geometry and concave-convex edge information of the model are focused on. Topological information describes the hierarchical relationship between bodies, faces, edges and points, geometric information indicates the type of face, and concave-convex edge information describes the connection mode between faces, providing a basis for the subsequent decomposition of the attribute adjacency matrix. The attribute adjacency matrix is ​​another representation of feature information. It stores the acquired feature information in a matrix and splits the attribute adjacency matrix according to the structural characteristics of the impeller. After decomposition, the original attribute adjacency matrix is ​​divided into multiple sub-matrices, each of which corresponds to a feature. After completing feature recognition, some sub-matrices will further identify sub-features, such as the sub-matrix of the blade feature will be subdivided into blade wall surface, blade fillet surface and blade cover surface. The recognition of features and sub-features depends on the predefined feature library. Only features and sub-features that already exist in the predefined feature library will be recognized. Accordingly, adding new features to the predefined feature library can also increase new identifiable features.

[0070] Step S3: according to the blade feature points of the impeller, obtaining the shortest spacing between blades and the minimum curvature radius on the blades;

[0071] Step S4: determining the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum radius of curvature on the blades, including: selecting the minimum value between the shortest spacing between the blades and the minimum radius of curvature on the blades as the diameter of the impeller machining tool.

[0072] In this embodiment, a method for automatically selecting impeller machining tools based on feature recognition includes three major steps. Step 1 includes: Step S1 to Step S4, firstly, the impeller model is imported into UG software, and the impeller is abstracted into an attribute adjacency graph using a graph-based feature recognition method, and then the impeller is segmented according to the structural characteristics, and compared with a predefined feature library to identify the blade features of the impeller, such as Figure 3Finally, on this basis, the shortest distance H between the blades and the minimum radius of curvature ρ on the blades are obtained, the two are compared, the minimum value of the two quality inspections is taken, and the initial diameter d0 of the tool is determined.

[0073] Step S5: offset the surface of the impeller according to the diameter of the current impeller machining tool, and perform feature recognition again on the offset impeller three-dimensional model using a graph feature recognition method to obtain blade feature points of the offset impeller;

[0074] Step S6: dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set;

[0075] Step S7: If all blade feature points in the blade root line point set are reachable, go to step S8; otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset;

[0076] Step S8: If all blade feature points in the blade surface point set are reachable, compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool. Otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset.

[0077] In this embodiment, step 2 includes: steps S5 to S8, offsetting the impeller surface according to the current tool diameter, and then using the feature recognition method again to identify the offset blade features, and discretizing the blade into a number of feature points along the U and V directions, such as Figure 4 As shown. And according to the distribution diagram of the simplified reachable direction cone ratio and the theoretical shortest tool length on the blade, it can be found that the maximum tool diameter and the shortest tool length both appear at the blade root, as shown in Figure 5 , Figure 6 As shown. Therefore, the feature point set is further divided into the blade root line point set and the blade surface point set. When judging the feasibility of the tool diameter, the blade root line point set is traversed first. At each feature point, a number of tool axis vectors are emitted with a certain step distance to judge the interference between these tool axis vectors and the offset surface, so as to obtain the reachable direction cone according to the tool axis vector without interference, thereby judging the reachability of this feature point. However, considering that the actual size of the reachable direction cone is not easy to obtain accurately, it is simplified to a simplified reachable direction cone, as shown in Figure 7 shown.

[0078] In the absence of any interference, the reachable direction cone of the tool axis vector will map a complete sphere on the Gaussian sphere. However, in actual situations, the tool axis vector is limited by the range of the machine tool, and the actual reachable range is the upper hemisphere of the original sphere. Considering the interference of the workpiece, the actual reachable direction cone can only map part of the sphere on the Gaussian sphere, such as Figure 7 As shown in the area surrounded by irregular curves in the figure. However, the shape of the impeller is generally complex, and the actual accessible direction cone obtained is mostly an irregular geometric body, and it is difficult to obtain the accurate size. Therefore, it is necessary to simplify it to quickly obtain the approximate value of the accessible direction cone. On this basis, the proportion of the accessible direction cone in the total is further obtained to make the accessibility more intuitive. For the convenience of expression, it is referred to as the proportion of the accessible direction cone in the following.

[0079] Several rays emitted from the center of the sphere will interfere with the workpiece. After traversing all tool axis vectors, the boundary of the non-interference tool axis vector set is obtained. Then, a simplified reachable direction cone can be constructed through the boundary of the non-interference tool axis vector set, such as Figure 7 As shown on the right. There will inevitably be an error between the simplified feasible direction cone and the actual feasible direction cone, but the error value will decrease as the step size decreases. Therefore, it is necessary to study the relationship between the error value and the step size and select an appropriate step size. In this article, the focus is on the minimum value of the reachable direction cone, because the size of the minimum value determines whether the tool can successfully reach the blade surface. Therefore, the point with the smallest reachable direction cone is specially selected as the research object. After selecting the ray source point, different step sizes are set in turn. The proportion of the reachable direction cone and the relative error obtained by each step size can be seen. Figure 8 In the test, a total of 7 different step lengths were set from small to large. The proportion of the reachable direction cone in the hemispherical space increased with the increase of the step length, and began to converge when the step length was 0.5°. Therefore, it is possible to approximate the result when the step length is 0.3° as the actual reachable direction cone proportion of the current point, and use this as a standard to obtain the relative errors of other step lengths. The specific results are as follows: Figure 8 As shown by the broken line in the middle. Figure 8 The data in prove that reducing the step size can indeed reduce the error between the simplified direction cone and the actual direction cone, but at the same time it will cause the calculation to increase rapidly. Therefore, it is necessary to minimize the amount of calculation while maintaining a small error. To this end, the following error calculation evaluation formula is proposed:

[0080]

[0081] Among them, T is the step size, S T is the proportion of the reachable direction cone with step size T. The above formula consists of the relative error of the reachable direction cone proportion and the inverse of the order of magnitude of the calculation amount. The relative error and the calculation amount are considered at the same time. The smaller the calculation result is, the more appropriate the current step size is. According to the above formula, Figure 8 Evaluate each step length and obtain Fig. 9 .according to Fig. 9 Based on the evaluation value in , T = 5° step length is selected as the step length for obtaining the simplified reachable direction cone in the subsequent step.

[0082] The simplified expression of the reachable direction cone is as follows:

[0083]

[0084] in, i is the serial number of the current ball zone, such as Fig.10 As shown. i is the proportion of the accessible direction cone in each spherical zone of the Gaussian sphere, T is the step distance, R is the diameter of the current impeller machining tool, θ i is the angle between the i-th ray on the surface YOZ and the Z axis, θ is the angle between the ray on the surface YOZ and the Z axis when a number of rays are emitted from the center of the Gaussian sphere with a step distance of T in the hemispherical space of the Gaussian sphere, wherein a three-dimensional Cartesian coordinate system is established in the hemispherical space of the Gaussian sphere with the center of the Gaussian sphere as the origin O, and the surface YOZ is a plane in the three-dimensional Cartesian coordinate system.

[0085] Count the sizes of reachable direction cones of all feature points in the leaf root line point set. If the minimum value reaches the predetermined lower limit, perform the same reachability analysis on the feature points in the leaf surface point set; if it is not feasible, reduce the tool diameter and repeat step two. Count the sizes of reachable direction cones of all feature points. If the minimum value reaches the predetermined lower limit, proceed to the next step; if it is not feasible, reduce the tool diameter and repeat step two. Dividing the point set into the leaf root line point set and the leaf surface point set helps to reduce time complexity. If the blade wall is discretized into m scattered points, of which the number of scattered points of the leaf root line is k, the actual number of offsets is n, and the number of times the leaf root line point set is satisfied is i, then the time complexity before segmentation is as follows:

[0086]

[0087] The time complexity after segmentation is as follows:

[0088]

[0089] Among them, p is the pth scattered point, and q is the qth bias.

[0090] By comparing the time complexity expressions before and after segmentation, it can be found that segmenting the point set (i.e. dividing the set into leaf root line point set and leaf surface point set) can effectively reduce the time complexity.

[0091] In this embodiment, step three includes the shortest tool length obtained by comparing the blade root line point set and the blade surface point set in step 8. Based on step two, step three traverses all feature points and obtains the theoretical shortest tool length of a feature point according to the intersection of its non-interference tool axis vector and the blank surface. The calculation formula is as follows:

[0092]

[0093] Among them, L is the shortest tool length, R is the diameter of the current impeller machining tool, (x p ,y p ,z p ) is the coordinate of the impeller blade feature point after offset, (x p′ ,y p′ ,z p′ ) is the coordinate of the intersection of the tool axis vector and the outer surface of the blank, such as Fig.11 Then find the longest theoretical shortest tool length among all feature points as the theoretical shortest tool length of the tool. So far, the maximum diameter and shortest length of the tool are obtained.

[0094] This application proposes an automatic selection method for impeller machining tools based on feature recognition. By introducing feature recognition technology, the various features of the impeller are automatically identified, and the target surface and interference surface no longer need to be manually selected. The maximum optional diameter of the tool size is obtained while obtaining the target surface. In order to further reduce the time complexity of the algorithm, the discrete feature point set is divided into a blade root line point set and a blade surface point set in combination with the feature recognition results, and the blade root line point set with generally poor accessibility is prioritized to reduce the time complexity.

[0095] Embodiment 2:

[0096] This embodiment proposes an automatic selection system for impeller machining tools based on feature recognition, such as Fig.12 As shown, including:

[0097] A model acquisition module, used to acquire a three-dimensional model of an impeller;

[0098] The first feature recognition module is used to perform feature recognition on the three-dimensional model of the impeller using a feature recognition method of a graph to obtain blade feature points of the impeller;

[0099] A data calculation module, used for obtaining the shortest spacing between blades and the minimum curvature radius on the blades according to the blade feature points of the impeller;

[0100] A diameter determination module, used to determine the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum curvature radius on the blades;

[0101] The second feature recognition module is used to offset the surface of the impeller according to the diameter of the current impeller processing tool, and use the feature recognition method of the graph to perform feature recognition on the offset impeller three-dimensional model again to obtain the blade feature points of the offset impeller;

[0102] A point set division module, used for dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set;

[0103] The first reachability judgment module is used to switch to the second reachability judgment module if all blade feature points in the blade root line point set are reachable, otherwise, the diameter of the current impeller machining tool is subtracted by a preset offset, and the module is returned to the second feature recognition module to re-offset;

[0104] The second reachability judgment module is used to compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set if all blade feature points in the blade surface point set are reachable, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool; otherwise, subtract a preset offset from the current impeller machining tool diameter, return to the second feature recognition module, and re-offset.

[0105] Embodiment 3:

[0106] This embodiment proposes an electronic device, including: one or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the automatic selection method for impeller machining tools based on feature recognition.

[0107] The electronic device may be a mobile phone, a computer or a tablet computer, etc., including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the automatic selection method for impeller machining tools based on feature recognition as described in the embodiment is implemented. It is understood that the electronic device may also include an input / output (I / O) interface and a communication component.

[0108] The processor is used to execute all or part of the steps in the method for automatically selecting an impeller machining tool based on feature recognition as described in the above embodiment. The memory is used to store various types of data, which may include instructions of any application or method in the electronic device, as well as data related to the application.

[0109] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the automatic selection method of impeller machining tools based on feature recognition described in the above embodiments.

[0110] Embodiment 4:

[0111] This embodiment provides a computer-readable storage medium storing executable instructions. When the instructions are executed and implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0112] The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method for automatic selection of impeller machining tools based on feature recognition described in each embodiment of the present application.

[0113] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (for example, SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR abbreviation, memory data register) memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, CD, server, APP (Application, abbreviation of application software) application mall and other media that can store program verification codes, on which computer programs are stored. When the computer program is executed by the processor, the various steps of the above-mentioned method for automatic selection of impeller machining tools based on feature recognition can be implemented.

[0114] Embodiment 5:

[0115] This embodiment provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the automatic selection method for impeller machining tools based on feature recognition.

[0116] Based on such understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a computer program product.

[0117] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0118] The protection scope of the present application is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A method for automatic selection of impeller machining tools based on feature recognition, characterized in that: include: Step S1: obtaining a three-dimensional model of an impeller; Step S2: using a feature recognition method of a graph to perform feature recognition on the three-dimensional model of the impeller to obtain blade feature points of the impeller; Step S3: according to the blade feature points of the impeller, obtaining the shortest spacing between blades and the minimum curvature radius on the blades; Step S4: determining the diameter of the impeller machining tool according to the shortest distance between the blades and the minimum radius of curvature on the blades; Step S5: offset the surface of the impeller according to the diameter of the current impeller machining tool, and perform feature recognition again on the offset impeller three-dimensional model using a graph feature recognition method to obtain blade feature points of the offset impeller; Step S6: dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set; Step S7: If all blade feature points in the blade root line point set are reachable, go to step S8; otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset; Step S8: If all blade feature points in the blade surface point set are reachable, compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool. Otherwise, subtract the preset offset from the current impeller machining tool diameter, return to step S5, and re-offset.

2. The method for automatically selecting impeller machining tools based on feature recognition according to claim 1, characterized in that: The feature recognition method using a graph is used to perform feature recognition on the three-dimensional model of the impeller to obtain the blade feature points of the impeller, including: Step S2.1: Obtain the topological tree of the impeller three-dimensional model, the attributes of all faces, and the concavity and convexity of the connecting edges; Step S2.2: Obtain the adjacent relationship of the faces in the impeller three-dimensional model according to the topological tree, and construct an initial adjacency matrix A, where A ij is the adjacency attribute between the i-th face and the j-th face, A ji is the adjacency attribute between the jth face and the ith face; Step S2.3: Traverse the initial adjacency matrix A. If face i is connected to face j, let A ij =A ji = 1, if face i and face j are not connected, then let A ij =A ji =0, and the adjacency matrix A after traversal is obtained; Step S2.4: According to the attributes of the face and the concavity of the connecting edge, the traversed adjacency matrix A is updated to the attribute adjacency matrix B, where B ij is the concavity of the connecting edge between the i-th and j-th surfaces, B ii is the attribute of the i-th face; Step S2.5: Traverse the attribute adjacency matrix B. If the connected faces i and j need to be split, let B ij =B ji =0, and the attribute adjacency matrix B after segmentation is obtained; Step S2.6: Use a depth-first traversal algorithm to extract a submatrix from the segmented attribute adjacency matrix B, and encode it to compress the multi-dimensional information into one-dimensional information to obtain an encoded submatrix; Step S2.7: Compare the encoded sub-matrix with the predefined feature library one by one. If the encoded sub-matrix is ​​consistent with the features in the predefined feature library, the impeller blade feature points corresponding to the encoded sub-matrix are obtained.

3. The automatic selection method for impeller machining tools based on feature recognition according to claim 1 is characterized in that: Determining the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum radius of curvature on the blades includes: selecting the minimum value between the shortest spacing between the blades and the minimum radius of curvature on the blades as the diameter of the impeller machining tool.

4. The method for automatic selection of impeller machining tools based on feature recognition according to claim 1, characterized in that: The accessibility includes: simplifying the reachable direction cone on the Gaussian sphere by using the proportion of the reachable direction cone in each spherical zone to obtain the simplified reachability.

5. The method for automatically selecting impeller machining tools based on feature recognition according to claim 4, characterized in that: The simplified reachability is calculated as follows: in, i is the serial number of the current ball zone, C i is the proportion of the accessible direction cone in each spherical zone of the Gaussian sphere, T is the step distance, R is the diameter of the current impeller machining tool, θ i is the angle between the i-th ray and the Z axis on the surface YOZ, wherein a three-dimensional Cartesian coordinate system is established in the hemispherical space of the Gaussian sphere with the center of the Gaussian sphere as the origin O, and the surface YOZ is a plane in the three-dimensional Cartesian coordinate system.

6. The method for automatically selecting impeller machining tools based on feature recognition according to claim 1, characterized in that: The shortest tool length is calculated as follows: Among them, L is the shortest tool length, R is the diameter of the current impeller machining tool, (x p ,y p ,z p ) is the coordinate of the impeller blade feature point after offset, (x p′ ,y p′ ,z p′ ) is the coordinate of the intersection of the tool axis vector and the outer surface of the blank.

7. An automatic selection system for impeller machining tools based on feature recognition, characterized in that: include: A model acquisition module, used to acquire a three-dimensional model of an impeller; The first feature recognition module is used to perform feature recognition on the three-dimensional model of the impeller using a feature recognition method of a graph to obtain blade feature points of the impeller; A data calculation module, used for obtaining the shortest spacing between blades and the minimum curvature radius on the blades according to the blade feature points of the impeller; A diameter determination module, used for determining the diameter of the impeller machining tool according to the shortest spacing between the blades and the minimum curvature radius on the blades; The second feature recognition module is used to offset the surface of the impeller according to the diameter of the current impeller processing tool, and use the feature recognition method of the graph to perform feature recognition on the offset impeller three-dimensional model again to obtain the blade feature points of the offset impeller; A point set division module, used for dividing the offset impeller blade feature points into a blade root line point set and a blade surface point set; The first reachability judgment module is used to switch to the second reachability judgment module if all blade feature points in the blade root line point set are reachable, otherwise, the diameter of the current impeller machining tool is subtracted by a preset offset, and the module is returned to the second feature recognition module to re-offset; The second reachability judgment module is used to compare the shortest tool lengths obtained from the blade root line point set and the blade surface point set if all blade feature points in the blade surface point set are reachable, select the maximum value of all the shortest tool lengths as the final shortest tool length, and use the final shortest tool length and the current impeller machining tool diameter as the result of automatic selection of the impeller machining tool; otherwise, subtract a preset offset from the current impeller machining tool diameter, return to the second feature recognition module, and re-offset.

8. An electronic device, characterized in that: include: One or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the method for automatically selecting an impeller machining tool based on feature recognition as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The device stores executable instructions, which, when executed, enable a processor to execute the method for automatically selecting an impeller machining tool based on feature recognition as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method for automatically selecting an impeller machining tool based on feature recognition as described in any one of claims 1 to 6 is implemented.

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