Feature data acquisition and processing method and system for machinability analysis of firearm parts

Abstract

The invention discloses a characteristic data acquisition and processing method and system for machinability analysis of firearm parts, and belongs to the technical field of machine manufacturing. The method comprises the following steps: acquiring a machining feature adjacency matrix A of each machining part in the important firearm parts; matching the processing feature adjacency matrix A with a predefined processing feature library of the important firearm parts to determine general processing features of the important firearm parts; special machining feature recognition is carried out to determine special machining features of the important firearm parts; and performing hybrid reasoning on the general machining features and the special machining features of the important firearm parts by using the machining rule base of the important firearm part structure so as to obtain feature data of machinability analysis of the firearm parts. According to the method, the intersection features can be completely and accurately recognized, the recognition efficiency is high, meanwhile, it is guaranteed that the machining difficulty of the product is considered in the product design stage, the situation that a complex shape or an internal structure is designed is avoided, and the requirement for a special machining technology is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of mechanical manufacturing, and in particular relates to a method and system for acquiring and processing feature data for machinability analysis of firearm parts. Background Art

[0002] Machining feature recognition is one of the important technologies in the field of computer-aided manufacturing. It aims to automatically identify the machining feature information in the CAD model by analyzing and processing it, and provide data support for subsequent CNC machining, process planning and process optimization. Machining features refer to local areas with certain geometric shapes and sizes in the product model, such as holes, bosses, wedge grooves, etc., which are the basic units for machining operations. Feature recognition methods, including those based on geometric shape matching, feature template matching and machine learning, are used to extract and identify various machining features from CAD models. Machining feature recognition is an important step in converting CAD models into machinable feature information and is one of the key technologies for realizing intelligent manufacturing.

[0003] Existing general feature recognition methods for ordinary mechanical parts have the following shortcomings: intersecting features are common features in key firearms parts. Traditional feature recognition technology may have ambiguous interpretations of intersecting features, and even cause multiple combination explosion problems. Therefore, traditional feature recognition technology has low recognition efficiency for intersecting features and cannot fully and accurately identify intersecting features. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a feature data acquisition and processing method for the machinability analysis of firearm parts. The method can completely and accurately identify intersecting features with high recognition efficiency. At the same time, it ensures that the processing difficulty of the product is taken into account during the product design stage, avoids the design of complex shapes or internal structures, and reduces the need for special processing technology.

[0005] A second object of the present invention is to provide a feature data acquisition and processing system for machinability analysis of firearm parts.

[0006] In order to achieve one of the above purposes, the present invention adopts the following technical solutions:

[0007] A method for acquiring and processing feature data for machinability analysis of firearm parts, comprising:

[0008] Step S1, obtaining the processing feature adjacency matrix A of each processing component in the firearm key parts;

[0009] Step S2, matching the processing feature adjacency matrix A of each processing component in the firearm's critical parts with a predefined processing feature library of firearm's critical parts to determine the universal processing features of the firearm's critical parts;

[0010] Step S3: using the NX processing module to perform special processing feature recognition to determine the special processing features of the firearm's key components;

[0011] Step S4: using the gun key parts structure machining rule library, perform forward and reverse mixed reasoning on the general machining features and special machining features of the gun key parts to obtain feature data for gun part machinability analysis.

[0012] Furthermore, in step S1, the key parts of the firearm include a bolt carrier, a bolt body, a barrel, and a hammer.

[0013] Furthermore, in step S1, the specific process of obtaining the processing feature adjacency matrix A of each processing component in the firearm key parts includes:

[0014] Step S11, using the AP214 information model of firearm key components defined by ISO, determining each processed component in the firearm key components;

[0015] Step S12: obtaining a STEP file of the firearm's key components, and extracting geometric topological information of each processed component from the STEP file;

[0016] Step S13: constructing a topological structure of each processing component using the geometric topological information of each processing component;

[0017] Step S14: using the topological structure of each processing component, determining a basic attribute adjacency graph of each processing component;

[0018] Step S15: using the basic attribute adjacency graph of each processing component, determine the type and concavity of the intersection edge between any two faces on each processing component to form a processing feature adjacency matrix A of each processing component.

[0019] Furthermore, in the step S15, the types of the intersecting edges include straight line edges and arc edges;

[0020] The rows and columns of the processing feature adjacency matrix A are the faces included in the processing parts;

[0021] The element values in the processing feature adjacency matrix A are two-digit numbers;

[0022] The tens digit of the two-digit number is a value indicating the type of the intersecting edge, and the units digit is a value indicating the concavity and convexity of the intersecting edge.

[0023] Furthermore, in step S2, the specific matching process includes:

[0024] Step S21: selecting a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm critical parts to form a first processing feature adjacency matrix set M corresponding to each processing component;

[0025] Step S22: Set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1;

[0026] Step S23: Determine the number of rows of the processing feature adjacency matrix A and the number of rows of the i-th first processing feature adjacency matrix M i If the number of rows is the same, then go to step S24; if not, then go to step S27;

[0027] Step S24: Find the adjacency matrix M of the first processing feature from the processing feature adjacency matrix A. i The row with the same element value as the j-th row of j ;

[0028] Step S25: The first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped;

[0029] Step S26, let j=j+1, and determine whether j is N1, if yes, then end; if not, then return to step S23;

[0030] Where N1 is the number of faces included in the processed component;

[0031] Step S27, let i=i+1, and determine whether i is N2, if yes, then end; if not, then return to step S23;

[0032] Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

[0033] Furthermore, in step S3, the specific process of identifying the dedicated processing features includes:

[0034] Step S31, performing PMI marking on the geometric dimensions and processing information of the special processing features of each processing component of the firearm key parts;

[0035] Step S32: using the machining feature navigator in the NX machining module, select a teach feature corresponding to the geometric dimensions and machining information;

[0036] Step S33: Using the teaching features, perform feature mapping to obtain the dedicated processing features of each processing component in the firearm's key parts.

[0037] In order to achieve the second of the above objectives, the present invention adopts the following technical solutions:

[0038] A feature data acquisition and processing system for machinability analysis of firearm machining parts, the feature data acquisition and processing system comprising:

[0039] An acquisition module is used to obtain the processing feature adjacency matrix A of each processing component in the key parts of the firearm;

[0040] a matching module, configured to match the processing feature adjacency matrix A of each processing component in the firearm's critical parts with a predefined processing feature library of firearm's critical parts to determine the universal processing features of the firearm's critical parts;

[0041] A dedicated processing feature recognition module, configured to use an NX processing module to perform dedicated processing feature recognition to determine the dedicated processing features of the firearm's key components;

[0042] The reasoning module is used to use the machining rule base of the structure of key firearm parts to perform forward and reverse mixed reasoning on the general processing features and special processing features of the key firearm parts to obtain feature data for machinability analysis of firearm processing parts.

[0043] Furthermore, the acquisition module includes:

[0044] The first determination submodule is configured to determine each processing component in the firearm's key components by using the AP214 information model of firearm's key components defined by ISO;

[0045] An extraction submodule, for obtaining the STEP file of the firearm's key parts and extracting the geometric topology information of each processed part from the STEP file;

[0046] A construction submodule, configured to construct a topological structure of each processing component by using the geometric topological information of each processing component;

[0047] A second determining submodule is configured to determine a basic attribute adjacency graph of each processing component by using the topological structure of each processing component;

[0048] A submodule is formed, which is used to determine the type and concavity of the intersection edge between any two faces on each processing component by using the basic attribute adjacency graph of each processing component, so as to form the processing feature adjacency matrix A of each processing component.

[0049] Furthermore, the matching module includes:

[0050] A selection submodule is used to select a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm key parts, so as to form a first processing feature adjacency matrix set M corresponding to each processing component;

[0051] The setting submodule is used to set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1;

[0052] The first judgment submodule is used to judge the number of rows of the processing feature adjacency matrix A and the i-th first processing feature adjacency matrix M i Are the number of rows the same? If so, the adjacency matrix M of the first processing feature of the i-th i If not, i is transmitted to the third judgment submodule;

[0053] The search submodule is used to find the adjacent matrix M of the first processing feature from the processing feature adjacency matrix A. i The row with the same element value as the j-th row of j ;

[0054] The interchange submodule is used to convert the first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped;

[0055] The second judgment submodule is used to set j=j+1 and judge whether j is N1. If yes, the process ends; if not, j is transmitted to the first judgment submodule;

[0056] Among them, N1 is the number of surfaces included in the processing component; the third judgment submodule is used to set i=i+1 and judge whether i is N2. If so, end; if not, transmit i to the first judgment submodule.

[0057] Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

[0058] Furthermore, it is characterized in that the dedicated processing feature recognition module includes:

[0059] The PMI marking submodule is used to perform PMI marking on the geometric dimensions and processing information of the special processing features of each processing component in the key parts of the firearm;

[0060] A selection submodule for selecting a teach feature corresponding to the geometric dimensions and machining information using a machining feature navigator in an NX machining module;

[0061] The feature mapping submodule is used to perform feature mapping using the teaching features to obtain the dedicated processing features of each processing component in the key parts of the firearm.

[0062] In summary, the technical solution of the present invention has the following technical effects:

[0063] The present invention uses a pre-defined processing feature library for key firearm parts and a processing feature adjacency matrix of each processing component in the key firearm parts to realize the identification of general processing features of key firearm parts, avoid the ambiguity of intersection features, reduce the multiple combinations of general processing features, improve the recognition efficiency of intersection features in key firearm parts, and completely and accurately identify intersection features; adopts the NX processing module to realize the identification of special processing features of key firearm parts through part standards and feature teaching; uses the machining rule library of key firearm parts structure to realize forward and reverse mixed reasoning (i.e. forward reasoning and reverse reasoning) of general processing features and special processing features of key firearm parts, realizes part machinability analysis to judge whether the designed parts meet the machinability conditions, effectively solves the machinability problem of firearm parts in the design stage, shortens the design cycle of firearm parts, and greatly improves design efficiency. At the same time, it ensures that the processing difficulty of the product is taken into account in the product design stage, avoids the design of complex shapes or internal structures, and reduces the requirements for special processing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0065] Figure 1 Schematic diagram of the flow of the method for acquiring and processing characteristic data for machinability analysis of firearm parts according to the present invention;

[0066] Figure 2 The hammer boss machining feature model and machining feature attribute adjacency graph;

[0067] Figure 3 This is a schematic diagram of a hybrid reasoning process that combines forward reasoning and backward reasoning;

[0068] Figure 4 This is a schematic diagram of the forward reasoning process;

[0069] Figure 5 This is a schematic diagram of the reverse reasoning process. DETAILED DESCRIPTION

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0071] This embodiment provides a method for acquiring and processing feature data for machinability analysis of firearm parts. Figure 1 , the feature data acquisition and processing method includes:

[0072] Step S1: Obtain the processing feature adjacency matrix A of each processing component in the key parts of the firearm.

[0073] The key components of the firearm in this embodiment include the bolt carrier, bolt body, housing, barrel, and hammer. While constraints on upper-level elements in the topological information model directly affect lower-level elements, the definitions of each element themselves contain a certain degree of topological relationship, thus ensuring valid topological relationships between topological entities.

[0074] Obtaining B-rep information for critical firearm parts is crucial for constructing an adjacency graph for their basic attributes. Based on the relationships between geometric and topological entities in the ISO-defined AP214 information model, a C++ class is defined for each entity. By defining the classes of each entity in the file, the geometric and topological information in the STEP file is extracted, thereby establishing the complete topological structure of the part. Based on this extracted geometric topology, an adjacency graph for the basic attributes of critical firearm parts can be created. By analyzing the geometric topological structure of the part (i.e., the machined component), a basic attribute adjacency graph is constructed, and an adjacency matrix is established to match the machining features in the predefined machining feature library to obtain recognition results. However, different parts have different predefined machining feature libraries, and the number of identifiable machining features is also limited.

[0075] In summary, the specific process of obtaining the processing feature adjacency matrix A of each processing component in the firearm key parts in this embodiment includes:

[0076] Step S11, using the AP214 information model of firearm key components defined by ISO, determining each processed component in the firearm key components;

[0077] Step S12: obtaining a STEP file of the firearm's key components, and extracting geometric topological information of each processed component from the STEP file;

[0078] Step S13: constructing a topological structure of each processing component using the geometric topological information of each processing component;

[0079] Step S14: using the topological structure of each processing component, determining a basic attribute adjacency graph of each processing component;

[0080] Step S15: using the basic attribute adjacency graph of each processing component, determine the type and concavity of the intersection edge between any two faces on each processing component to form a processing feature adjacency matrix A of each processing component.

[0081] The types of intersecting edges in this embodiment include straight edges and arc edges. The concavity and convexity relationship of the intersecting edges of two surfaces in this embodiment is mainly the geometric relationship between the surfaces, which is divided into the concavity and convexity of straight edges and arc edges.

[0082] 1. Determine the convexity of straight line edges

[0083] When the intersection of two adjacent faces f1 and f2 is a straight line, the intersection is e. The rule for judging the concavity of the straight line e is:

[0084] First, select a surface from the surfaces f1 and f2 as the reference surface, and determine the vector n of the intersecting edge e according to the right-hand screw rule. e direction, and then calculate n=n2×n by cross product of the vector e , get the direction of n, let the angle between vectors n2 and n1 be α, α is:

[0085]

[0086] Wherein, the normal vector n1 of face f1 = (x1, y1, z1), and the normal vector n2 of face f2 = (x2, y2, z2). If |α| > π / 2, the edge e is convex; if |α| < π / 2, the edge e is concave.

[0087] When the intersection of two adjacent faces f1 and f2 is a straight line edge of a plane cylinder, the intersection edge is e. The normal vector of f1 is n1, P is an intersection point of edge e and the cylindrical circular curve, and the center of the cylindrical circular curve is P0. The rule for judging the concavity of the intersection edge e is:

[0088] First, determine the normal vector of the cylindrical curve and the vector P pointing from P to P0 v , calculate the tangent vector R through point P ν If it is the outer surface of f2, then n2=R ν ×V, V is the unit normal vector; if f2 is the inner surface, then n2=V×R ν Then calculate n=n2×n by vector cross product e Get the direction of n, let the angle between vectors n2 and n1 be α, and calculate α according to formula (1). If |α|>π / 2, the edge e is convex; if |α|<π / 2, the edge e is concave.

[0089] 2. Determination of the convexity of the arc edge

[0090] When the intersection formed by two adjacent faces is a plane curve, f1 is a plane, f2 is a cylindrical surface, and vector n2 is the normal vector of plane f2. Select any point P on the plane curve e formed by the two adjacent faces f1 and f2, and take the tangent vector of point P on e as n. e Then, based on the normal vector n1 of face f1 and the right-hand screw rule, calculate the annular direction of faces f1 and f2. Calculate n=n by vector cross product. e ×n2, let the angle between vectors n2 and n1 be α, and calculate α according to formula (1). If |α|>π / 2, the plane curve e is a convex edge; if |α|<π / 2, the plane curve e is a concave edge; if |α|=π, the plane curve e is a convex edge.

[0091] 3. Processing feature attribute adjacency graph

[0092] For example, the construction of the hammer processing feature attribute adjacency graph: using the basic attribute adjacency graph of a firearm hammer (i.e., the hammer processing feature model), the hammer processing feature attribute adjacency graph is constructed, such as Figure 2 The hammer boss machining feature model and machining feature attribute adjacency graph are shown. The specific process of constructing the machining feature attribute adjacency graph of the firearm's key parts is as follows:

[0093] 00. Get the information of all the hammer faces and form a face list;

[0094] 10. Create an identification chain object with a face as a node;

[0095] 20. Repeat steps 00 and 10 until a complete object identification chain is established and the basic attribute adjacency graph is created;

[0096] 30. Get the list of established face nodes, traverse the adjacent edges of all face nodes, and determine whether they are convex edges by edge concavity calculation method;

[0097] 40. If it is a convex edge, remove the specified object from the identifier list;

[0098] 50. The remaining nodes are the processing feature attribute adjacency graph.

[0099] This embodiment uses an extended matrix approach to store and express the adjacency graph of the processing feature attributes of key firearm parts. (In C++) it is expressed in the form of a two-dimensional array [i, j], which is specifically defined as:

[0100] (a) m represents the number of all faces that constitute the key parts of the firearm. 0 indicates that the intersection of two faces is concave or the two faces do not intersect, and 1 indicates that the intersection of two faces is convex.

[0101] (b) For the two-dimensional array [i, j] (in C++), if i is not equal to j, it means that face i intersects face j, and a two-digit number is used to represent the concavity of the intersecting edge. The units digit is 0 and 1 to indicate concavity; the tens digit is 1 and 2 to indicate whether the intersecting edge is a straight line or a circular arc. For example, 11 indicates that the intersecting edge is a convex straight line, 10 indicates that the intersecting edge is a concave straight line, 21 indicates that the intersecting edge is a convex circular arc, and 20 indicates that the intersecting edge is a concave circular arc.

[0102] In summary, the rows and columns of the machining feature adjacency matrix A in this embodiment represent the faces of the machined component. The element values in the machining feature adjacency matrix A are two-digit numbers, where the tens digit represents the type of intersecting edge, and the units digit represents the concavity of the intersecting edge. This embodiment improves the efficiency and accuracy of intersection feature recognition by using two-digit element values in the machining feature adjacency matrix A.

[0103] According to the hammer processing feature model and the processing feature attribute adjacency graph, combined with the matrix storage method, the hammer through hole processing feature adjacency matrix, the open slot processing feature adjacency matrix and the irregular boss processing feature adjacency matrix are established, as shown in Table 1:

[0104] Table 1 Adjacency matrix of hammer processing features

[0105]

[0106] Step S2: Match the processing feature adjacency matrix A of each processing component in the firearm's key components with a predefined processing feature library of firearm's key components to determine the universal processing features of the firearm's key components.

[0107] Processing feature adjacency matrix matching mainly refers to matching the processing feature adjacency matrix to be identified with the adjacency matrix of the predefined processing feature library of firearms key parts. The matching principle is:

[0108] First, a universal processing feature adjacency matrix is established, which is denoted as processing feature adjacency matrix A. The dimension of processing feature adjacency matrix A is calculated. Then, the first processing feature adjacency matrix with the same dimension as A is selected from the predefined processing feature library of firearms key parts. This matrix set is denoted as M = {M1, M2, ..., M i ,…,M N2}; After obtaining the matrix set M, select a matrix M1 from the matrix set M, and find out whether all rows of matrix A are "similar" to all rows of matrix M1. If not, match them with M2; if they are "similar", find the row that is "similar" to the first row in matrix M1 (denoted as row I1), swap the I1th row of A with the first row, and swap the J1th column with the first column; then find the row that is "similar" to the second row in matrix M1 (denoted as row I2), and do the same transformation; traverse all rows of matrix M1 in turn, and when the elements corresponding to all rows are "similar" to matrix A, the processing feature adjacency matrix is successfully matched, that is, the hammer universal processing feature recognition result is output.

[0109] Analysis of the hammer's 3D model reveals that its universal machining features include one through-hole, two slots, nine flat surfaces, seven fillets, and one boss. Using the aforementioned attribute adjacency graph-based machining feature recognition method to analyze the hammer model, all of the hammer's universal machining features were identified.

[0110] In summary, the specific matching process in this embodiment includes:

[0111] Step S21: selecting a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm critical parts to form a first processing feature adjacency matrix set M corresponding to each processing component;

[0112] Step S22: Set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1;

[0113] Step S23: Determine the number of rows of the processing feature adjacency matrix A and the number of rows of the i-th first processing feature adjacency matrix M i If the number of rows is the same, then go to step S24; if not, then go to step S27;

[0114] Step S24: Find the adjacency matrix M of the first processing feature from the processing feature adjacency matrix A.i The row with the same element value as the j-th row of j ;

[0115] Step S25: The first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped;

[0116] Step S26, let j=j+1, and determine whether j is N1, if yes, then end; if not, then return to step S23;

[0117] Where N1 is the number of faces included in the processed component;

[0118] Step S27, let i=i+1, and determine whether i is N2, if yes, then end; if not, then return to step S23;

[0119] Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

[0120] The predefined processing feature library for key firearm components in this embodiment serves as the data foundation for image matching in subsequent processing feature recognition. This library is established through parametric modeling. Parametric modeling uses parameters as constraints for geometric feature models. Prior to parametric modeling, the model of key firearm components must be analyzed to separate basic features into additional features. The size and shape of the model can then be modified by modifying the numerical values of the parameters within the model. The goal of parametric modeling is to parametrically model the component model and complete the creation of a predefined processing feature library for key firearm components. There are three main forms of feature mapping. First, the design feature is converted into a combination of multiple processing features. For example, the chamber and slope features in the gun are generally composed of multiple conical surfaces. During the processing, only one clamping is required to complete the processing task, so it is identified as a processing feature of a multi-cone combination; second, the design feature is the processing feature. For example, the pre-rotation surface in the gun body is an inclined surface. During the processing, it is processed as an inclined surface, so it is identified as an inclined surface; third, several design features are converted into a combination of processing features, such as the base mirror surface and the base circumferential groove in the gun frame. For the convenience of processing, it is identified as a blind hole + groove combination feature.

[0121] The predefined processing feature library for key firearm components in this embodiment includes model numbers, bolt carrier processing feature mapping tables, bolt body processing feature mapping tables, sleeve processing feature mapping tables, and barrel processing feature mapping tables. The processing feature mapping tables include design features and processing features. Design features include bolt carrier, bolt body, sleeve, and barrel design features, while processing features include bolt carrier, bolt body, sleeve, and barrel processing features. Bolt carrier design features include the opening and closing spiral groove, front collision surface, piston hole, bolt hole, recoil spring hole, and guide groove. Bolt carrier processing features include spatial curved grooves, planes, universal holes, universal holes, universal holes, and irregular grooves. Bolt body design features include the locking projection, guide post, locking support spiral surface, pre-rotation surface, ejector projection, extractor hook hole, firing pin hole, base recess, and ejector plunger clearance groove. The machining features of the bolt body include irregular bosses, irregular bosses, irregular surfaces, inclined surfaces, irregular bosses, blind holes, ultra-slender deep holes, through-hole + cone combinations, blind hole + groove combinations, and irregular grooves. The machining features in this embodiment include general machining features and special machining features. General machining features include body features, hole features, surface features, groove features, and spatial curve features. Special machining features include special features, combination features, and complex features. Special features include the machining features of ultra-slender deep holes and spatial curve grooves in key firearm components. Combination features are features composed of two or more features in key firearm components, including the machining features of multi-cone combinations, blind hole + groove combinations, and through-hole + cone combinations. Complex features include the machining features of irregular grooves and irregular bosses. Irregular grooves include ejector ejector clearance grooves and piston grooves, and irregular bosses include locking protrusions and guide posts.

[0122] In summary, the process of constructing the predefined processing feature library for firearm critical parts in this embodiment includes:

[0123] Step A: Modeling the key parts of the firearm to determine the key parts model of the firearm;

[0124] Step B, modifying the shape and size parameters of the firearm key component model to obtain the design features of each processed component in the firearm key component;

[0125] Step C: performing feature mapping on the design features of each processing component in the firearm's key components to determine all processing features corresponding to each processing component in the firearm's key components.

[0126] The specific process of feature mapping in this step includes:

[0127] Step C1: determine whether each design feature corresponds to multiple processing features. If yes, convert the design feature into the corresponding multiple processing features, and then end; if no, proceed to step C2;

[0128] Step C2: determine whether each design feature corresponds to only one processing feature. If so, directly map the design feature to the corresponding processing feature, and end; if not, proceed to step C3;

[0129] Step C3: Determine whether the multiple design features correspond to the multiple processing features. If so, convert the multiple design features into the corresponding multiple processing features, and end; if not, convert the multiple design features into the corresponding processing features, and end.

[0130] Step D: constructing a predefined processing feature library of firearms' key components using all processing features corresponding to each processing component in the firearms' key components.

[0131] Step S3: Using the NX processing module, special processing feature recognition is performed to determine the special processing features of the firearm's key parts.

[0132] The specialized feature recognition system for critical firearm parts is developed based on the NX processing module. This module is primarily implemented through the Mkeditor processing rule editor included in NX and the teach-in function in the NX processing module.

[0133] Taking the gun parts as an example, the specific process includes: PMI marking of the gun parts, focusing on the geometric dimensions and processing information of special features that need to be paid attention to during processing, such as tapered holes, irregular bosses, etc.; opening the NX processing module, right-clicking in the processing feature navigator task bar, selecting teach features, and inputting feature mapping based on the summarized special features of firearm parts. Special features require the establishment of corresponding feature libraries based on different parts. The specific process of special processing feature recognition includes:

[0134] Step S31, performing PMI marking on the geometric dimensions and processing information of the special processing features of each processing component of the firearm key parts;

[0135] Step S32: using the machining feature navigator in the NX machining module, select a teach feature corresponding to the geometric dimensions and machining information;

[0136] Step S33: Using the teaching features, perform feature mapping to obtain the dedicated processing features of each processing component in the firearm's key parts.

[0137] Step S4: using the gun key parts structure machining rule library, perform forward and reverse mixed reasoning on the general machining features and special machining features of the gun key parts to obtain feature data for gun part machinability analysis.

[0138] The acquisition of knowledge base of machining rule of key parts of firearms is a necessary condition for realizing the machinability analysis of key parts of firearms. The specific ways to obtain knowledge of machining rule of key parts of firearms include: (1) firearms machining instance knowledge: all the machining rule knowledge of existing key parts of firearms; (2) design principle: the field design knowledge formed by the long-term development of key parts of firearms; (3) design specification knowledge: standard design manuals and standard design specifications of key parts of firearms, such as the diameter of holes; (4) expert experience knowledge: expert experience knowledge is the knowledge of experience formulas, experience data and other knowledge accumulated by experts in the process of machining key parts of firearms; (5) structural graphics knowledge: structural graphics are the representation tools of engineering structure construction and design parameters.

[0139] According to the structural characteristics and machining process of key parts of firearms, the knowledge of machining rules for the structure of key parts of firearms is divided into feature-level machining rules and part-level machining rules, refer to Tables 2 and 3.

[0140] Table 2 Machining rules for some part layers

[0141] Rule Name Rule Explanation Tool accessibility The cutting tool can access machining features in the first machining direction Set process boss Parts with cones or arc surfaces should be provided with process bosses to facilitate clamping. Hole entry and exit surfaces The entry and exit surfaces of the hole should be perpendicular to the radial direction of the hole, otherwise it will be difficult to process Pore-cavity intersection The hole should not intersect the cavity, otherwise it will be difficult to process Partial hole The cross-sectional area of the hole should occupy more than 75% of the entire plane

[0142] Table 3 Machining rules for some feature layers

[0143]

[0144] Book

[0145] Knowledge representation can be understood as a mapping relationship, expressed as a one-to-one functional relationship, mapping the knowledge of the machining rules for key firearm components into computer language. Its data structure in SQL Server is shown in Table 4:

[0146] Table 4 Data structure of machining rules

[0147]

[0148] (1) Feature-level machining rule knowledge representation: The knowledge representation of the machining rule that the hole depth-to-diameter ratio must be greater than 50 has a unique ID of H_N_1, indicating a numerical rule for holes, numbered 1; the knowledge name is Hole; and the knowledge category is numerical. The parameter values of this attribute include hole depth, hole diameter, and the ratio of hole depth to hole diameter. If the ratio is greater than 50, the result is rejected; otherwise, the result is passed.

[0149] (2) Part (i.e., machined component) level machining rule knowledge representation: Taking the hole axis face perpendicularity as an example, the hole axis face perpendicularity machining rule representation has a unique ID number of H_A_F_NM_1, indicating a non-numeric rule for the hole axis face, numbered 1; the knowledge name is Hole_Axis_Face, and the knowledge category is non-numeric. This attribute represents the relationship between the hole axis direction vector and the face normal vector. The child attribute is the hole axis direction vector, and the parent attribute is the face normal vector. When the hole axis direction vector is parallel to the face normal vector, the pass is obtained; otherwise, the pass is rejected.

[0150] In summary, the construction process of the firearm key component structure machining rule library in this embodiment includes:

[0151] Set up design examples and processing examples for key firearm parts to determine the machining rule knowledge for key firearm parts;

[0152] Classifying the machining rule knowledge of the firearm's key parts to determine component-level machining rules and feature-level machining rules;

[0153] The component layer machining rules and feature layer machining rules are used to form a gun key parts structure machining rule library.

[0154] This embodiment uses a gun key component structure machining rule library and adopts a hybrid reasoning method that combines forward reasoning strategy with reverse reasoning strategy to perform forward and reverse hybrid reasoning (including forward reasoning and reverse reasoning) on the gun key component. Figure 3 As shown, this is to check whether the structural design of the key parts of the firearm meets the processing requirements.

[0155] 1. Forward reasoning

[0156] This embodiment performs forward reasoning on the machinability of feature layers (i.e., feature layers in general machining features and special machining features), such as Figure 4 As shown in Figure 2, the specific process of forward reasoning includes:

[0157] (1-a) obtaining feature layer processing features of the firearm's key components to determine search keywords in the feature layer processing features of the firearm's key components;

[0158] (1-b) using the search keyword, searching the firearm key component structure machining rule library to obtain firearm key component structure machining rule knowledge corresponding to the search keyword;

[0159] (1-c) Determining feature data for machinability analysis of firearm parts using the knowledge of structural machining rules for key firearm parts corresponding to the search keyword.

[0160] 2. Reverse reasoning

[0161] This embodiment uses reverse reasoning to analyze the machinability of the part layer (i.e., the part layer in the general processing features and the special processing features), such as Figure 5 As shown in Figure 2, the specific process of reverse reasoning includes:

[0162] (2-a) Acquiring knowledge of structural machining rules for key firearm parts to determine search keywords in the part layer machining features of key firearm parts;

[0163] (2-b) using the search keyword, searching the machining rule library of the firearm key component structure to obtain part layer machining features corresponding to the search keyword;

[0164] (2-c) Determining feature data for machinability analysis of firearm parts using the part layer processing features corresponding to the search keyword.

[0165] This embodiment uses a pre-defined processing feature library for key firearm parts and a processing feature adjacency matrix of each processing component in the key firearm parts to realize the identification of general processing features of key firearm parts, avoid the ambiguity of intersection features, reduce the multiple combinations of general processing features, improve the recognition efficiency of intersection features in key firearm parts, and completely and accurately identify intersection features; adopts the NX processing module to realize the identification of special processing features of key firearm parts through part standards and feature teaching; uses the structural machining rule library of key firearm parts to realize forward and reverse mixed reasoning (i.e. forward reasoning and reverse reasoning) of general processing features and special processing features of key firearm parts, realizes part machinability analysis to determine whether the designed parts meet the machinability conditions, effectively solves the machinability problem of firearm parts in the design stage, shortens the design cycle of firearm parts, and greatly improves design efficiency. At the same time, it ensures that the processing difficulty of the product is taken into account in the product design stage, avoids the design of complex shapes or internal structures, and reduces the requirements for special processing technology.

[0166] The above embodiment can be implemented by adopting the technical solutions given in the following embodiments:

[0167] Another embodiment provides a feature data acquisition and processing system for machinability analysis of firearm machining parts, the feature data acquisition and processing system comprising:

[0168] An acquisition module is used to obtain the processing feature adjacency matrix A of each processing component in the key parts of the firearm;

[0169] a matching module, configured to match the processing feature adjacency matrix A of each processing component in the firearm's critical parts with a predefined processing feature library of firearm's critical parts to determine the universal processing features of the firearm's critical parts;

[0170] A dedicated processing feature recognition module, configured to use an NX processing module to perform dedicated processing feature recognition to determine the dedicated processing features of the firearm's key components;

[0171] The reasoning module is used to use the machining rule base of the structure of key firearm parts to perform forward and reverse mixed reasoning on the general processing features and special processing features of the key firearm parts to obtain feature data for machinability analysis of firearm processing parts.

[0172] Furthermore, the acquisition module includes:

[0173] The first determination submodule is configured to determine each processing component in the firearm's key components by using the AP214 information model of firearm's key components defined by ISO;

[0174] An extraction submodule, for obtaining the STEP file of the firearm's key parts and extracting the geometric topology information of each processed part from the STEP file;

[0175] A construction submodule, configured to construct a topological structure of each processing component by using the geometric topological information of each processing component;

[0176] A second determining submodule is configured to determine a basic attribute adjacency graph of each processing component by using the topological structure of each processing component;

[0177] A submodule is formed, which is used to determine the type and concavity of the intersection edge between any two faces on each processing component by using the basic attribute adjacency graph of each processing component, so as to form the processing feature adjacency matrix A of each processing component.

[0178] Furthermore, the matching module includes:

[0179] A selection submodule is used to select a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm key parts, so as to form a first processing feature adjacency matrix set M corresponding to each processing component;

[0180] The setting submodule is used to set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1;

[0181] The first judgment submodule is used to judge the number of rows of the processing feature adjacency matrix A and the i-th first processing feature adjacency matrix M i Are the number of rows the same? If so, the adjacency matrix M of the first processing feature of the i-th i If not, i is transmitted to the third judgment submodule;

[0182] The search submodule is used to find the adjacent matrix M of the first processing feature from the processing feature adjacency matrix A. i The row with the same element value as the j-th row of j ;

[0183] The interchange submodule is used to convert the first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped;

[0184] The second judgment submodule is used to set j=j+1 and judge whether j is N1. If yes, the process ends; if not, j is transmitted to the first judgment submodule;

[0185] Among them, N1 is the number of surfaces included in the processing component; the third judgment submodule is used to set i=i+1 and judge whether i is N2. If so, end; if not, transmit i to the first judgment submodule.

[0186] Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

[0187] Furthermore, the dedicated processing feature recognition module includes:

[0188] The PMI marking submodule is used to perform PMI marking on the geometric dimensions and processing information of the special processing features of each processing component in the key parts of the firearm;

[0189] A selection submodule for selecting a teach feature corresponding to the geometric dimensions and machining information using a machining feature navigator in an NX machining module;

[0190] The feature mapping submodule is used to perform feature mapping using the teaching features to obtain the dedicated processing features of each processing component in the key parts of the firearm.

[0191] The principles, formulas and parameter definitions involved in the above embodiments are all applicable and will not be described in detail here.

[0192] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for acquiring and processing feature data for machinability analysis of firearm parts, characterized in that: The characteristic data acquisition and processing method includes: Step S1, obtaining the processing feature adjacency matrix A of each processing component in the firearm key parts; Step S2, matching the processing feature adjacency matrix A of each processing component in the firearm's critical parts with a predefined processing feature library of firearm's critical parts to determine the universal processing features of the firearm's critical parts; Step S3: using the NX processing module to perform special processing feature recognition to determine the special processing features of the firearm's key components; Step S4: using a firearms key component structure machining rule library, perform hybrid reasoning on the general machining features and special machining features of the firearms key component to obtain feature data for firearms part machinability analysis.

2. The method for acquiring and processing characteristic data according to claim 1, wherein: In step S1, the key parts of the firearm include a bolt carrier, a bolt body, a barrel, and a hammer.

3. The method for acquiring and processing characteristic data according to claim 2, wherein: In step S1, the specific process of obtaining the processing feature adjacency matrix A of each processing component in the firearm key parts includes: Step S11, using the AP214 information model of firearm key components defined by ISO, determining each processed component in the firearm key components; Step S12: obtaining a STEP file of the firearm's key components, and extracting geometric topological information of each processed component from the STEP file; Step S13: constructing a topological structure of each processing component using the geometric topological information of each processing component; Step S14: using the topological structure of each processing component, determining a basic attribute adjacency graph of each processing component; Step S15: using the basic attribute adjacency graph of each processing component, determine the type and concavity of the intersection edge between any two faces on each processing component to form a processing feature adjacency matrix A of each processing component.

4. The method for acquiring and processing characteristic data according to claim 3, wherein: In step S15, the types of the intersecting edges include straight lines and arcs; The rows and columns of the processing feature adjacency matrix A are the faces included in the processing parts; The element values in the processing feature adjacency matrix A are two-digit numbers; The tens digit of the two-digit number is a value indicating the type of the intersecting edge, and the units digit is a value indicating the concavity and convexity of the intersecting edge.

5. The method for acquiring and processing characteristic data according to any one of claims 1 to 4, characterized in that: In step S2, the specific matching process includes: Step S21: selecting a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm critical parts to form a first processing feature adjacency matrix set M corresponding to each processing component; Step S22: Set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1; Step S23: Determine the number of rows of the processing feature adjacency matrix A and the number of rows of the i-th first processing feature adjacency matrix M i If the number of rows is the same, then go to step S24; if not, then go to step S27; Step S24: Find the adjacency matrix M of the first processing feature from the processing feature adjacency matrix A. i The row with the same element value as the j-th row of j ; Step S25: The first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped; Step S26, let j=j+1, and determine whether j is N1, if yes, then end; if not, then return to step S23; Where N1 is the number of faces included in the processed component; Step S27, let i=i+1, and determine whether i is N2, if yes, then end; if not, then return to step S23; Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

6. The method for acquiring and processing characteristic data according to claim 5, wherein: In step S3, the specific process of identifying the special processing features includes: Step S31, performing PMI marking on the geometric dimensions and processing information of the special processing features of each processing component of the firearm key parts; Step S32: using the machining feature navigator in the NX machining module, select a teach feature corresponding to the geometric dimensions and machining information; Step S33: Using the teaching features, perform feature mapping to obtain the dedicated processing features of each processing component in the firearm's key parts.

7. A feature data acquisition and processing system for machinability analysis of firearm machining parts, characterized in that: The feature data acquisition and processing system includes: An acquisition module is used to obtain the processing feature adjacency matrix A of each processing component in the key parts of the firearm; a matching module, configured to match the processing feature adjacency matrix A of each processing component in the firearm's critical parts with a predefined processing feature library of firearm's critical parts to determine the universal processing features of the firearm's critical parts; A dedicated processing feature recognition module, configured to use an NX processing module to perform dedicated processing feature recognition to determine the dedicated processing features of the firearm's key components; The reasoning module is used to use the machining rule base of the structure of key firearm parts to perform mixed reasoning on the general machining features and special machining features of the key firearm parts to obtain feature data for machinability analysis of firearm machining parts.

8. The feature data acquisition and processing system according to claim 7, characterized in that: The acquisition module includes: The first determination submodule is configured to determine each processing component in the firearm's key components by using the AP214 information model of firearm's key components defined by ISO; An extraction submodule, for obtaining the STEP file of the firearm's key parts and extracting the geometric topology information of each processed part from the STEP file; A construction submodule, configured to construct a topological structure of each processing component by using the geometric topological information of each processing component; A second determining submodule is configured to determine a basic attribute adjacency graph of each processing component by using the topological structure of each processing component; A submodule is formed, which is used to determine the type and concavity of the intersection edge between any two faces on each processing component by using the basic attribute adjacency graph of each processing component, so as to form the processing feature adjacency matrix A of each processing component.

9. The feature data acquisition and processing system according to claim 8, characterized in that: The matching module includes: A selection submodule is used to select a first processing feature adjacency matrix having the same dimension as the processing feature adjacency matrix A of each processing component from a predefined processing feature library of firearm key parts, so as to form a first processing feature adjacency matrix set M corresponding to each processing component; The setting submodule is used to set the initial value of the sequence number of the first processing feature adjacency matrix in the first processing feature adjacency matrix set M to i=1, and the i-th first processing feature adjacency matrix M i The initial value of the row number is j=1; The first judgment submodule is used to judge the number of rows of the processing feature adjacency matrix A and the i-th first processing feature adjacency matrix M i Are the number of rows the same? If so, the adjacency matrix M of the first processing feature of the i-th i If not, i is transmitted to the third judgment submodule; The search submodule is used to find the adjacent matrix M of the first processing feature from the processing feature adjacency matrix A. i The row with the same element value as the j-th row of j ; The interchange submodule is used to convert the first j Row and I j The element values of the column are respectively related to the adjacency matrix M of the first processing feature of the i-th i The values of the elements in the j-th row and j-th column of are swapped; The second judgment submodule is used to set j=j+1 and judge whether j is N1. If yes, the process ends; if not, j is transmitted to the first judgment submodule; Among them, N1 is the number of surfaces included in the processing component; the third judgment submodule is used to set i=i+1 and judge whether i is N2. If so, end; if not, transmit i to the first judgment submodule. Wherein, N2 is the number of first processing feature adjacency matrices in the first processing feature adjacency matrix set M.

10. The feature data acquisition and processing system according to claim 9, characterized in that: The dedicated processing feature recognition module includes: The PMI marking submodule is used to perform PMI marking on the geometric dimensions and processing information of the special processing features of each processing component in the key parts of the firearm; A selection submodule for selecting a teach feature corresponding to the geometric dimensions and machining information using a machining feature navigator in an NX machining module; The feature mapping submodule is used to perform feature mapping using the teaching features to obtain the dedicated processing features of each processing component in the key parts of the firearm.

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