CN110795797A - MBD model processing feature recognition and information extraction method - Google Patents
MBD model processing feature recognition and information extraction method Download PDFInfo
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Abstract
The invention provides a processing feature recognition and information extraction method of an MBD model, which comprises the following steps: firstly, the method comprises the following steps: resolving the MBD model; II, secondly: traversing the cylindrical surface and identifying hole characteristics; thirdly, the method comprises the following steps: initializing an attribute adjacency graph storage matrix and constructing an attribute adjacency graph; fourthly, the method comprises the following steps: traversing the predefined processing characteristics and characteristic subgraphs thereof; fifthly: identifying all the processing feature surface sets of the type by using a subgraph isomorphic method; sixthly, the method comprises the following steps: calculating the key geometric dimension of the workpiece according to the machining feature type; seventhly, the method comprises the following steps: extracting three-dimensional labeling information of the model; eighthly: extracting part annotation information; nine: interactively identifying the custom processing characteristics; ten: visualizing a machining feature recognition result; eleven: outputting a feature recognition and information extraction result in a structured mode; through the steps, the processing characteristic surface set in the MBD model can be identified, the key geometric dimension of the MBD model can be automatically calculated, the three-dimensional marking and annotation information of the part can be extracted, the final result is output in an XML form, and a good data base is laid for the subsequent process design.
Description
Technical Field
The invention provides a method for identifying MBD Model processing characteristics and extracting information, which is a method for identifying the processing characteristics and extracting information based on a Model-based Definition (MBD) Model. The method can automatically decompose an input MBD model into a series of processing features and extract geometric information and process requirements of the processing features, thereby providing data support for subsequent process decision, and belongs to the field of computer aided process design (CAPP).
Background
Computer aided design (CAPP) is a bridge and a link for connecting design and manufacture, plays a role in starting and starting in the digital design and manufacture process of products, and is a key for improving the overall level of the digital manufacture technology. CAPP is a decision-making process that assists process designers in planning and determining processing routes and process parameters from blank to product, driven by certain objectives (time, cost, quality), under the constraints of actual production resources (machine tool equipment, tool holders, personnel configuration).
A good CAPP system must first solve the description and input problem of good part information. The simple and accurate description and acquisition of the part information are the preconditions for realizing the CAPP system and the reliable guarantee for the process decision analysis, are the basis and the basis for the operation of the CAPP system, and have decisive influence on the output quality and the operation efficiency of the CAPP system. The information of the part includes two aspects: geometric information and process information of the part. The geometric information of the part, namely the graphic information of the part, comprises the geometric shape, the size and the like of the part; the process information of the part comprises various information such as the precision, the roughness, the heat treatment requirement, the material and the blank type of each surface of the part. The part information description method based on machining characteristics is the most common method at present, and the method divides a part into a series of machining characteristic units such as various holes, surfaces, grooves, cavities and the like according to the structure of the part. Because a certain relation exists between the processing characteristic unit and the processing method, the description method provides a good data base for subsequent process design.
Under the background, the machining feature recognition and information extraction technology can automatically analyze an MBD model of a part from a Computer Aided Design (CAD) system and automatically disassemble the MBD model into a series of machining feature units, and has an important meaning for improving the automation level of CAPP, so that extensive research is performed. However, in the existing research, the operating efficiency of the feature recognition algorithm is low, meanwhile, the existing results are mostly directed at a CAD geometric model, and the research on the MBD model is less.
Disclosure of Invention
The invention aims to provide an MBD model processing feature recognition and information extraction method for machining process design, which aims to overcome the defects in the prior art, improve the recognition efficiency of processing features, extract the geometric information of the processing features, extract the three-dimensional labeling information attached to the surfaces of the processing features and the engineering annotation information attached to a model structure tree, and add the extracted information to a processing feature unit of a model. Therefore, manual participation is reduced, and the efficiency of reading and inputting part information in the CAPP system is improved.
(II) technical scheme
The method mainly comprises two parts, wherein the first part is the recognition of a processing feature surface set, and the second part is the extraction of processing feature geometric information and non-geometric information. In the processing feature recognition part, the invention adopts a feature recognition algorithm based on sub-graph isomorphism, and the core theory is that a boundary model of a part is represented by a data structure of 'attribute adjacency graph'. The attribute adjacency graph has uniqueness for different part models, wherein each surface of the part corresponds to one vertex in the graph, edges of the part correspond to arcs between the vertices in the graph, and the attributes of the arcs in the graph are assigned according to the concave-convex property of the edges in the part. Similarly, the predefined machining characteristics can also be represented by an "attribute adjacency graph" according to the rule. Thus, the recognition problem of the machined feature translates into a process for searching for a "small" of machined features in a "large" of parts. This problem is a classical sub-graph isomorphism problem that can be solved by graph search. Based on a backtracking algorithm, the invention realizes the rapid identification of processing characteristics by carrying out depth-first search on the 'big picture' and simultaneously adopting a series of pruning operations to reduce the search space.
In the second part, the size information of the processing characteristic comprises key data such as the diameter and the depth of the hole characteristic, the length, the width, the height, the chamfer radius and the like of the groove characteristic, and the size information is obtained by calculating a processing characteristic surface set; the non-geometric information comprises precision requirement data such as dimensional tolerance, roughness and the like, and is obtained by extracting three-dimensional labels attached to a machined feature surface and engineering annotation information on a part structure information tree by means of an API (application programming interface) provided by CAD (computer aided design).
The invention relates to a processing feature recognition and information extraction method of an MBD model, which comprises the following steps:
the method comprises the following steps: analyzing the MBD model to obtain a surface list set (Listface) and an edge list set (Listedge) of the MBD model;
step two: traversing the cylindrical surface in the Listface, and identifying the hole characteristics by judging whether a closed inner circle is formed or not; removing the identified hole feature surface in ListFace to obtain a surface list (ListAAGFace) forming an attribute adjacency graph;
step three: initializing a storage matrix (AAGMatrix) of the attribute adjacency graph, traversing ListEdge, calculating the concavity and convexity of the attribute adjacency graph, and writing the convexity and convexity into the AAGMatrix to finish the construction of the attribute adjacency graph;
step four: loading the predefined processing features and the feature sub-graphs thereof, traversing each predefined processing feature, if the traversal is finished, turning to the step six, otherwise, executing the step five;
step five: calling a sub-graph isomorphic method, identifying a face list of all machining features of the type on the part, and storing an identification structure into an identified feature container (vectorrecognized feeds);
step six: traversing vector recognized features, acquiring a face list of each processing feature, and calculating the key geometric dimension of the processing feature according to the type of the processing feature and the face list;
step seven: obtaining a label set list of the MBD model, reading the type and data of each label, obtaining the dimensional tolerance grade and the surface roughness data, finding the attached part element, and associating the attached part element to the corresponding processing characteristic according to the attached element;
step eight: acquiring an engineering annotation list of the MBD model, and extracting material information and product information of the part;
step nine: performing user-defined machining feature interactive identification; the predefined features can contain most processing features, but some designers have special purposes or less frequent processing feature programs cannot automatically identify the processing features, and the processing features which cannot be automatically identified by the programs are called user-defined processing features; user-defined machining features need interactive identification;
step ten: the processing characteristic recognition result is visualized; in order to more intuitively display the part machining feature recognition result, the program paints the recognized machining feature;
step eleven: and outputting the product information and the material information of the vectorecognizable feeds and the parts in a structured mode by adopting an extensible markup language (XML) format.
The "MBD model" in step one refers to a model set that is added with rich manufacturing semantic information on the basis of a traditional CAD geometric solid model, and a typical MBD model example and its information structure are shown in fig. 1.
The specific method of "analyzing the MBD model" in step one is as follows: acquiring an MBD model pointer pDOc in an editor, acquiring a part topology Body through the part pointer, and calling an acquiring topology element function GetAllCells () provided by a system twice to respectively acquire a topology surface list and an edge list of the part and respectively store the topology surface list and the edge list in a ListfaceListEdge;
wherein, the step two of judging whether to form a closed inner circle comprises the following specific steps: traversing the cylindrical surface in ListFace, finding out the center of the cylindrical surface and the external normal vectors (the cylindrical surface in CATIA is spliced by two semicylindrical surfaces), and if the external normal vectors of the two semicylindrical surfaces are opposite, determining that a closed inner circle is formed.
In step three, the "initializing the storage matrix (AAGMatrix) of the attribute adjacency graph" is initialized as follows: the attribute adjacency graph matrix is an n multiplied by n square matrix, all the surfaces are not adjacent by initialization default, and each element is initialized to-1.
The term "concave-convex" in step three means a concave-convex condition in which two adjacent surfaces form an included angle, and when the included angle formed by the adjacent surfaces (facing the outside of the body) is less than 180 degrees, the edge is called a concave edge, whereas when the included angle formed by the adjacent surfaces facing the outside of the body is greater than 180 degrees, the edge is called a convex edge;
wherein, the method for calculating the concave-convex of the edge in the step three "calculating the concave-convex and writing the concave-convex into the adjacency matrix AAGMatrix" is as follows:
for a straight line formed by intersecting two planes, as shown in FIG. 2, let the two adjacent surfaces of the edge e be F1 and F2, respectively, and find the external normal vectors of the two adjacent surfaces, which are denoted as n1、n2(ii) a Selecting a certain adjacent surface (in principle, it can be arbitrarily selected, the invention takes F1 as an example) as a base surface, and solving a tangent vector n of the edge eeGuarantee neOuter normal vector n to the base plane1Satisfying a right-handed relationship; calculating and obtaining an intermediate conversion vector n ═ n1×n2Calculating the outer normal vector n of n and the base plane1The included angle α, if α is more than 90 degrees, e is a concave edge, if α is less than 90 degrees, e is a convex edge, and for the concave-convex property of other types of edges, different attribute values are given according to the type shown in the figure 3;
wherein, the 'attribute adjacency graph' in the third step is constructed on the basis of judging the unevenness of the edge, and the attribute adjacency graph of the part is stored by using an adjacency matrix; the adjacency matrix is a square matrix, rows and columns correspond to the surface of the part, the element value reflects the adjacency attribute between two surfaces in the model, if the intersected edge of the two surfaces is a convex edge, the attribute of the corresponding edge is 1, and if the intersected edge of the two surfaces is a concave edge, the attribute of the corresponding edge is 0; the general flow of attribute map construction is shown in fig. 4.
Wherein, the "predefined processing feature and feature sub-graph" in the fourth step is shown in fig. 8; the method for loading the predefined processing features and the feature sub-graph thereof and traversing each predefined processing feature comprises the following steps: and acquiring a predefined processing feature list from a predefined processing feature library, acquiring a type feature sub-graph each time, identifying all isomorphic sub-graphs corresponding to the sub-graphs in the attribute adjacency graph constructed in the third step by using the sub-graph isomorphism method in the fifth step, and storing the isomorphic sub-graphs as the processing features identified for the type predefined features. And ending the operation until all the processing features in the predefined feature library are traversed.
The method for calling the subgraph isomorphic method in the step five comprises the following specific steps: the realization of a sub-graph isomorphic method is the key and difficult point of the whole processing characteristic identification, the invention records the isomorphic relation between the vertexes of a large graph and a small graph by constructing a mapping matrix, and converts the graph matching problem into the depth-first search problem of the mapping matrix; the specific process is as follows:
characteristic diagram GαIs MαPart attribute map GβIs MβWherein the small graph GαNumber of vertices m, big graph GβThe number of vertexes n; constructing an M multiplied by n mapping matrix M, wherein the rows of the M correspond to small graph vertexes, the columns correspond to large graph vertexes, each element records the isomorphic relation between the two vertexes, 1 represents matching, and 0 represents mismatching; feature sub-graph G is if and only if M satisfies that there is one and only one element 1 in each row, and all elements 1 are not in the same columnαAnd attribute map GβIsomorphism of subgraphs; thus, in the large graph GβIn which all isomorphic panels G are foundαOnly all mapping matrixes M meeting the isomorphic relation need to be found; in view of the big picture GβIn which there may be a plurality of isomorphic minimaps GαThe invention realizes the global search of the big picture recursively based on a backtracking method, and the whole method comprises the following steps:
inputting: mα，MβM, n, the initialized mapping matrix M, and the row space occupying array VH(represents the column number of each row with 1 element in the current matrix M, the number of the elements is M and V is more than or equal to 0H[i]N-1 or less, initialized to-1), column space occupying array VF(indicating whether each column in the current matrix M has an element with a value of 1, the number of the elements is n, VF[i]Initialized to false), and G is storedα、GβSet of vertices V already matched1And V2Row number row, column number col (all initialized to 0);
and (3) outputting: part attribute map GβMedian signature subgraph GαVertex sets meeting the isomorphic relation of the subgraph and vertex matching corresponding relation;
step1, judging whether the row number row is more than or equal to the characteristic subgraph GαThe number m of the vertexes is satisfied, if the number m of the vertexes is satisfied, the characteristic is found in the part, and a matching vertex set V is output and identified1And V2The method ends; otherwise, turning to Step 2;
step2, whether the column number col is less than n, if so, col + +, and turning to Step 3; otherwise the method ends;
step3. determination of VH[row]If the value is-1, the line is a returned line, if yes, the occupation is set before, and the occupation needs to be cleared, V1And V2All pop-up stack top elements until the number of the top points is equal to the current row number row, VF[VH[row]Is set as false, VH[row]Setting to be-1, turning to Step 4; otherwise, directly turning to Step 4;
step4. determination of VF[col]If false and M (row, col) is 1, if so, the vertex set V is1Add row, set V at vertex2Adding col, and turning to Step 5; otherwise the method ends;
step5. judging the matched vertex set V1And V2Whether the sub-images formed respectively are isomorphic, if isomorphic, order VH[row]＝ col，VF[col]Let row + +, go to Step1, recursively call the method; if they are not isomorphic, then V1And V2Popping up the elements at the top of the stack, and ending the method;
the subgraph isomorphism problem belongs to the graph search problem in graph theory, is NP complete, the complexity of the method is higher, in order to improve the execution efficiency of the method, the invention adopts the following method to carry out fast pruning operation, thus effectively reducing the global search space of the graph;
(1) when initializing the mapping matrix M, instead of setting each element to 1, the attribute map G is calculated firstβNeutral subfigure GαWhen the degree of the vertex of the sub-graph is larger than that of the vertex of the large graph, the element of the corresponding mapping matrix M is initialized to 0;
(2) by judging matched vertex set V in real time1And V2Whether the separately constructed subgraphs are isomorphic[i]To quickly determine whether the current mapping matrix meets the requirements; judgment V1And V2When the new vertex is isomorphic, only the newly added vertex and the existing vertex in the set need to be judged whether to be isomorphic.
Wherein, the method for "calculating the critical geometrical dimension of the processing feature" in the sixth step is as follows: the processing characteristic key geometric dimension calculation comprises the key geometric parameter extraction of each molded surface in the characteristic surface list and the processing characteristic bounding box dimension calculation; the parameters of various profiles of the characteristic surface mainly comprise: the key geometrical parameters of the plane are as follows: plane center coordinates and plane bounding box coordinates; the key geometric parameters of the cylindrical surface are as follows: the coordinate of the axis, the radius, the height of the cylindrical surface, the initial and final angles of the cylindrical surface and the coordinate of the cylindrical surface bounding box; the key geometric parameters of the conical surface are as follows: axis coordinates, conical angles and conical surface bounding box coordinates; the key geometrical parameters of the torus are as follows: the axis coordinate, the major and minor circumference radius parameters, the major and minor circumference initial angle and the torus bounding box coordinate; the characteristic critical dimension of the feature is determined by the position of the surface in the feature and the critical geometric parameter of the surface, and the dimension of the feature bounding box is obtained by the bounding box coordinate operation of each surface in the processing feature surface linked list; the characteristic bounding box size calculation flow is shown in fig. 5.
The specific implementation of the "acquiring the label set list of the MBD model" in the step seven is shown in fig. 6; the label set list refers to non-geometric information of the part, and the non-geometric information of the part can be obtained by obtaining the MBD model label set list.
The "acquiring the engineering annotation list of the MBD model" in step eight is implemented as follows: and acquiring a file in the current editor, acquiring a current MBD model pointer pDOc, and calling a function interface provided by the system to acquire a part annotation list and annotation content thereof.
The specific implementation of the "interactive identification of user-defined processing features" in the ninth step is as follows: the part processing characteristic is a group of faces, and when a program identifies a group of faces, an element is newly added in vector recogninized features; the user self-defines the characteristics of the part, adopts a screen selection agent, sets a selection object of the selection object as a Surface of the part, and clicks the Surface of the part on a screen by the user to obtain a Surface path; and obtaining a surface object through the obtained surface path; storing all the face objects selected by the screen in a face list, setting the face objects as user-defined features, and storing the face objects in vectorrecognitedFeataurs; modifying the name of the user-defined feature and setting the RGB value of the user-defined feature; and repeating the operation and interactively identifying all the custom machining features, and calling a function of calculating the key geometric dimension of the machining feature in the step 6 to calculate the key geometric dimension of the custom machining feature.
Wherein, the processing feature recognition result visualization in the step ten is specifically as follows: starting a characteristic coloring function to traverse vectorrecognized feeds, acquiring a face list (listofFeatFaces) of each characteristic element, traversing the listofFeatFaces, acquiring brep elements, and adding the brep elements into a to-be-colored brep list; and calling a painting function provided by the system, and reading the RGB values corresponding to the processing characteristics to paint each element in the brep list.
Wherein, the step eleven of outputting the product information and the material information of the vector recognitedfeatures and the parts in a structured manner by adopting an extensible markup language (XML) format specifically comprises the following steps: calling an XML document creating function to generate an XML document; generating two primary sub-node processing feature recognition results and part annotations; generating second-level sub-nodes under the first-level sub-node, wherein each processing feature corresponds to one second-level sub-node, and storing the key size information of the feature under the second-level sub-nodes; acquiring a geometric element attached to each label, and adding the geometric elements into the corresponding processing characteristics according to the geometric elements to form child nodes of the processing characteristics; storing the extraction result in the step eight under the second and child nodes, annotating a secondary child node for each item, and storing the type and content of the annotation under the secondary child node; and saving the XML document after the generation is finished.
Through the steps, the processing feature surface set in the MBD model can be successfully and effectively identified, after the feature identification is finished, the geometric information of each processing feature can be automatically calculated, the three-dimensional labeling information attached to the processing feature surface is extracted, a complete processing feature information unit is constructed, and finally the processing feature identification and information extraction results are automatically output in an XML form, so that a good data base is laid for the subsequent process design.
(III) advantages and benefits
The invention provides an MBD model processing feature recognition and information extraction method for machining process design. Compared with the prior art, the effect is positive and obvious. Firstly, when the processing characteristic surface set is identified, although the traditional subgraph isomorphism method is adopted, the realization efficiency of the method is improved through a backtracking method and a series of rapid pruning operations, and the global search space of the graph is effectively reduced. Meanwhile, the method has good expansibility and supports the expansion of processing characteristics. Compared with the traditional feature identification method only aiming at the CAD model, the method increases the identification and extraction of the MBD model three-dimensional labeling information and the engineering annotation information, realizes the complete extraction of the geometric information and the non-geometric information of the processing feature, and finally constructs a complete processing feature unit. Finally, the whole process of the method is realized automatically by a program, the operation is simple and convenient, the characteristic identification and the information extraction can be realized conveniently, and the operation time is saved.
Drawings
FIG. 1(a) is an example of a typical MBD model.
Fig. 1(b) is an MBD model information structure.
Fig. 2 is a schematic view for judging the unevenness of the edge.
FIG. 3(a) shows the intersection of a cylindrical surface and a plane and the roughness value thereof.
FIG. 3(b) is the intersection of the cylindrical surface and the plane and the roughness value thereof.
FIG. 3(c) shows the intersection of the cylindrical surface and the plane and the roughness value thereof.
Fig. 4 is a flow of attribute adjacency graph construction.
FIG. 5 is a flow chart of the process feature bounding box calculation.
Fig. 6 is a flow of non-geometric information extraction.
Fig. 7 is a flow chart of the method of the present invention.
Fig. 8 is a processing feature and its sub-graph information.
The numbers, symbols and codes in the figures are explained as follows:
wherein the symbols in fig. 2 have the following meanings: f1 and F2 are two part adjacent surfaces; e is the intersection of the two abutment faces F1 and F2; n is1、n2The outer normal vectors of the two abutment faces F1 and F2, respectively; n iseIs a tangent vector of e, and neOuter normal vector n to base plane F11Satisfying a right-handed relationship; n is an intermediate conversion vector, n is equal to n1×n2。
Wherein the symbols in fig. 5 have the following meanings: cell is a topological surface element obtained after the part is analyzed; xmin is the minimum value of the X-direction coordinate of each surface surrounding box of the characteristic surface; xmax is the maximum value of the X-direction coordinate of each surface surrounding box of the characteristic surface; ymin is the minimum value of the coordinate of each surface surrounding box in the Y direction of the characteristic surface; ymax is the maximum value of the coordinate in the Y direction of each surface surrounding box of the characteristic surface; zmin is the minimum value of Z-direction coordinates of the bounding boxes of the faces on the characteristic surface; zmax is the maximum value of Z-direction coordinates of bounding boxes of all surfaces of the characteristic surface; x is the length of the characteristic bounding box; y is the width of the feature bounding box; z is the height of the characteristic bounding box.
Wherein the symbols in fig. 8 have the following meanings: f1，F2,，……，FnRefers to the concave-convex relation between the surface and the number on the connecting line of the surface and the surface in the surface list of the part.
Detailed Description
The present invention is further illustrated by, but is not limited to, the following examples.
The embodiment is an actual machined part of an aviation enterprise, and the machining characteristics of the MBD model of the actual machined part are identified. The embodiment is implemented by taking Microsoft Visual Studio 2005 and RADEV5R18 as development platforms based on the CATIA CAA secondary development technology, and the following steps are specific steps of the embodiment of the invention:
the invention discloses a processing feature recognition and information extraction method of an MBD model, which is shown in the attached figure 7 and comprises the following specific implementation steps:
the method comprises the following steps: the MBD model (see fig. 1(a) for an example of the model, but not limiting the invention) is parsed to obtain ListFace, ListEdge. The specific implementation process comprises the following steps: and opening the MBD model of the part, clicking a part feature recognition toolbar and starting a part feature recognition dialog box. And clicking a part feature identification key, automatically acquiring a part pointer in the current editor by the program, and analyzing the MBD model corresponding to the part pointer to acquire a surface list set (Listface) and an edge list set (Listedge) of the MBD model. Compared with the traditional CAD geometric solid model, the MBD model adds a model set of rich manufacturing semantic information, and a typical MBD model comprises a solid model, a design reference, a three-dimensional label, other information and engineering annotation, which is shown in the attached figure 1 (b). The abundant information contained in the MBD model is the source of information for all subsequent steps.
Step two: part hole characteristics are identified and ListAAGFace is obtained. The specific implementation process comprises the following steps: and traversing Listface to obtain the cylindrical surface, and finding the center and the outer normal vector of each cylindrical surface. In the CATIA, the cylindrical surfaces are spliced by two semicylindrical surfaces, the external normal vectors of the cylindrical surfaces are back to back, and the external normal vectors of the circular hole surfaces are opposite. The center of the cylindrical surface and the outer normal vector can be judged to judge whether the cylindrical surfaces form hole characteristics. The identified hole feature faces are removed in ListFace to obtain a list of faces, ListAAGFace, that constitute the attribute adjacency graph.
Step three: the attribute adjacency graph matrix AAGMatrix is assigned. The specific implementation process comprises the following steps: the attribute adjacency graph matrix AAGMatrix is initialized. AAGMatrix is an n × n square matrix, each original value of which is-1 after initialization. Traversing Listedge, calculating the concave-convex property and writing the concave-convex property into AAGMatrix to complete the attribute adjacency graph GβThe structure of (1). The unevenness of each element in ListEdge is the unevenness of an included angle formed by two adjacent surfaces, and when the included angle formed by the unevenness is smaller than 180 degrees, the edge is called a concave edge, otherwise, the edge is called a convex edge. The relief of the edge is calculated from the outer normal vector of its abutment surface. The schematic diagram of the judgment of the unevenness of the edge is shown in the attached figure 2. For a straight line edge formed by intersecting two planes, respectively calculating the external normal vectors of the two adjacent surfaces, and recording the external normal vectors as n1、 n2. Selecting the front adjacent surface in the list as a base surface, and solving the tangent vector n of the edge eeGuarantee neThe outer normal vector n to the base plane satisfies the right hand relationship. Intermediate conversion vector n ═ n1×n2Calculating the outer normal vector n of n and the base plane1The angle α of the AAGMatrix matrix is 0 if α > 90 DEG, e is a concave side, the corresponding element value of the AAGMatrix matrix is 0, if α < 90 DEG, e is a convex side, the corresponding element value of the AAGMatrix matrix is 1, the type of the side is not only related to the concavity and convexity but also related to the type of the two intersecting curved surfaces, three regular curved surfaces commonly used in CAD models are a plane, a cylindrical surface and a torus, when the cylindrical surface is tangent to the plane, see FIG. 3(a), the tangent line of which is concave, the corresponding element value of the AAGMatrix matrix is-2, when the tangent line of which is convex, the corresponding element value of the AAGMatrix matrix is 2, when the plane is tangent to the torus, there is only one condition, see FIG. 3(b), if it is determined that a certain side is obtained by tangent to the plane and the torus, the corresponding element of the AAGMatrix matrix is directly assigned a value of 3, when the tangent to the torus, see FIG. 3(c), the AAGMatrix matrix is directly assigned a value of 4.
The construction of the attribute adjacency graph is completed on the basis of judging the edge concavity and convexity, and the invention uses AAGMatrix to store the attribute adjacency graph of the part. The method utilizes the concave-convex judgment principle explained by the invention to form a part attribute adjacency graph and then store the part attribute adjacency graph as AAGMatrix. The general flow of attribute map construction is shown in fig. 4.
Step four: predefined machining features are loaded one by one. The specific implementation process comprises the following steps: and traversing each predefined processing feature, loading the predefined features and the feature sub-graphs thereof one by one, if all the predefined features are traversed, turning to the step six, and if not, executing the step five.
The pre-defined feature library mainly comprises twelve features and feature subgraphs, namely a step groove, a circular truncated cone step groove, a slot, a round slot, a blind step groove, a round blind step groove, a blind slot, a round blind slot, a through groove, a round through groove, a blind groove, a round blind groove and the like. The corresponding relationship is shown in fig. 8.
Step five: a sub-graph isomorphism method is invoked to identify the current predefined features. The specific implementation process comprises the following steps: and calling a sub-graph isomorphic method, and identifying a face list of all processing features matched with the current predefined processing features. Reading the predefined characteristic subgraph and assigning to GαAnd further calculates its adjacency matrix MαLet the adjacency matrix be MβAAGMatrix, panel GαThe number of vertexes is m, the big picture GβThe number of vertices is n. An M x n mapping matrix M is constructed. Compute attribute graph GβNeutral subfigure GαWhen the degree of the vertex of the sub-graph is greater than that of the vertex of the large graph, the element of the corresponding mapping matrix M is initialized to 0, and the rest elements are initialized to 1. Initializing row placeholder array VHColumn space occupying array VFSet of vertices V1And V2. The row number row and column number col (both initialized to 0) are initialized. After the initialization is finished, the following steps are executed:
(1) judging whether the row number row is more than or equal to the characteristic subgraph GαThe number m of the vertexes is satisfied, if the number m of the vertexes is satisfied, the characteristic is found in the part, and a matching vertex set V is output and identified1And V2The method ends; otherwise, turning to (2);
(2) if the column number col is less than n, if so, col + +, and then (3); otherwise the method ends;
(3) judgment VH[row]If the value is-1, the line is a returned line, if yes, the occupation is set before, and the occupation needs to be cleared, V1And V2All pop-up stack top elements until the number of the top points is equal to the current row number row, VF[VH[row]Is set as false, VH[row]Setting as-1, turning to (4); otherwise, directly turning to (4);
(4) judgment VF[col]If false and M (row, col) is 1, if so, the vertex set V is1Add row, set V at vertex2Adding col and turning to (5); otherwise the method ends;
(5) judging matched vertex set V1And V2Whether the sub-images formed respectively are isomorphic, if isomorphic, order VH[row]＝col， VF[col]Let row + +, go to (1), recursively call the method; if they are not isomorphic, then V1And V2The top of stack element is popped and the method ends.
Outputting a part attribute graph G after the algorithm is finishedβMedian signature subgraph GαAnd vertex sets and vertex matching corresponding relations meeting the isomorphic relations of the subgraph. At the moment, the part predefined machining feature recognition is completed, and the recognition result is stored in vectorrecognized features.
Step six: the critical geometry of the identified features is calculated. The specific implementation process comprises the following steps: traversing the part identified machining feature list vectorecognizadfeedfeeds, reading the machining feature surface list of the part, and calculating the key geometric dimension of the machining feature according to the type of the machining feature; the key geometrical parameters of the plane are as follows: a plane central coordinate and a plane bounding box coordinate bounding box; the key geometric parameters of the cylindrical surface are as follows: the coordinate of the axis, the radius, the height of the cylindrical surface, the starting and ending angle of the cylindrical surface and the coordinate bounding box of the cylindrical surface; the key geometric parameters of the conical surface are as follows: an axis coordinate, a conical angle and a conical surface bounding box coordinate bounding box; the key geometrical parameters of the torus are as follows: the axis coordinate, the major and minor circumference radius parameters, the major and minor circumference starting angle and the torus bounding box coordinate bounding box; the characteristic critical dimension of the feature is determined by the position of the surface in the feature and the critical geometric parameters of the surface, and the dimension of the feature bounding box is obtained by the bounding box coordinate operation of each surface in the processing feature surface linked list. The characteristic minimum bounding box size calculation method is shown in fig. 5.
Step seven: and extracting part marking information. The specific implementation process comprises the following steps: obtaining a label set list of the current MBD model, reading the type and data of each label, obtaining the dimension tolerance grade by inquiring a tolerance grade database, labeling the part elements to which the information is attached by a recursive method, and associating the part elements to the corresponding processing characteristics according to the attached elements. The part labeling information extraction flow chart is shown in figure 6.
Step eight: and extracting the part engineering annotation. The specific implementation process comprises the following steps: and acquiring an engineering annotation list of the MBD model, and extracting material information and product information of the part.
Step nine: and (4) user-defined machining feature interactive recognition. The specific implementation process comprises the following steps: and picking up the surface of the part by using a selection agent, calculating the key geometric parameters of the surface, finding all screen click surface pointers in Listface according to the key geometric parameters, and adding the screen click surface pointers into the self-defined feature surface linked list. The program sets four keys, and the functions are respectively: setting a characteristic button, finding a corresponding surface pointer of the current screen selection surface linked list in the Listface, and adding the corresponding surface pointer to a user-defined characteristic surface list; deleting a selection key, and deleting the surface selected by the screen from the selected list; saving the editing key, and storing the modified feature name and feature color in a multiple list (multilinest) in a user-defined feature; removing the characteristic key, and removing the selected self-defined characteristic in vectorRecognized features; and determining keys and calculating key size parameters of the custom features.
Step ten: and visualizing the machining feature recognition result. The specific implementation process comprises the following steps: according to the RGB value of the feature surface list in the feature library, traversing the identified machining feature vectorRecognized features, obtaining the surface list (listofFeatFaces) of each feature element, traversing the listofFeatFaces, obtaining the deep element, and adding the deep element into the deep list to be painted. And calling a painting function provided by the system, and reading the RGB values corresponding to the processing characteristics to paint each element in the brep list. And updating the part model, namely displaying the identified part machining characteristics on a screen.
Step eleven: and saving the characteristic identification result in the XML document. And setting a storage path, and storing the part machining feature recognition result, the machining feature key geometric dimension, each machining feature RGB value and the non-geometric information in an XML document.
Through the steps, the processing feature surface set in the MBD model can be successfully and effectively identified, after the feature identification is finished, the geometric information of each processing feature can be automatically calculated, the three-dimensional labeling information attached to the processing feature surface is extracted, a complete processing feature information unit is constructed, and finally the processing feature identification and information extraction results are automatically output in an XML form, so that a good data base is laid for the subsequent process design.
Claims (11)
1. A processing feature recognition and information extraction method for an MBD model is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: analyzing the MBD model to obtain a surface list set namely ListFace and an edge list set namely Listedge;
step two: traversing the cylindrical surface in the Listface, and identifying the hole characteristics by judging whether a closed inner circle is formed or not; removing the identified hole characteristic surface from the ListFace to obtain a surface list, namely ListAAGFace, forming the attribute adjacency graph;
step three: initializing a storage matrix of the attribute adjacency graph, namely AAGMatrix, traversing ListEdge, calculating the concavity and convexity of the attribute adjacency graph and writing the convexity and convexity into the AAGMatrix to finish the construction of the attribute adjacency graph;
step four: loading the predefined processing features and the feature sub-graphs thereof, traversing each predefined processing feature, if the traversal is finished, turning to the step six, otherwise, executing the step five;
step five: calling a sub-graph isomorphic method, recognizing a surface list of all machining features of the type on the part, and storing a recognition structure into a recognized feature container, namely vectorrecognized feeds;
step six: traversing vector recognized features, acquiring a face list of each processing feature, and calculating the key geometric dimension of the processing feature according to the type of the processing feature and the face list;
step seven: obtaining a label set list of the MBD model, reading the type and data of each label, obtaining the dimensional tolerance grade and the surface roughness data, finding the attached part element, and associating the attached part element to the corresponding processing characteristic according to the attached element;
step eight: acquiring an engineering annotation list of the MBD model, and extracting material information and product information of the part;
step nine: performing user-defined machining feature interactive identification; the predefined features can contain most of the processing features, but some designers have special purposes and rare processing feature programs cannot automatically identify the processing features, and the processing features which cannot be automatically identified by the programs are called user-defined processing features; user-defined machining features need interactive identification;
step ten: the processing characteristic recognition result is visualized; in order to more intuitively display the part machining feature recognition result, the program paints the recognized machining feature;
step eleven: and outputting the product information and the material information of the vectorecognizable feeds and parts in a structured mode by adopting extensible markup language (XML).
2. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the MBD model in the step one is a model set which is added with abundant manufacturing semantic information on the basis of the traditional CAD geometric solid model;
the "analytic MBD model" described in the step one is specifically as follows: the method comprises the steps of obtaining an MBD model pointer pDOc in an editor, obtaining a part topology Body through the part pointer, calling a topological element obtaining function GetAllCells () provided by a system twice to obtain a topological surface list and an edge list of a part, and storing the topological surface list and the edge list in a ListfaceListEdge respectively.
3. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the step two of judging whether to form a closed inner circle comprises the following specific steps: and traversing the cylindrical surface in the Listface to find the center and the outer normal vectors of the cylindrical surface, wherein if the outer normal vectors of the two semi-cylindrical surfaces are opposite, the closed inner circle can be determined to be formed.
4. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein: the initialization of the storage matrix of the attribute adjacency graph, namely AAGMatrix, described in step three is as follows: the attribute adjacency graph matrix is an n multiplied by n square matrix, all the default surfaces are not adjacent by initialization, and each element is initialized to-1;
the term "concave-convex" in step three means a concave-convex condition in which two adjacent surfaces form an included angle, and when the included angle formed by the adjacent surfaces (facing the outside of the body) is less than 180 degrees, the edge is called a concave edge, whereas when the included angle formed by the adjacent surfaces facing the outside of the body is greater than 180 degrees, the edge is called a convex edge;
the method for calculating the concavity and convexity of the edge of the adjacency matrix AAGMatrix described in step three is as follows:
for a straight line edge formed by intersecting two planes, assuming that two adjacent surfaces of the edge e are respectively F1 and F2, the external normal vectors of the two adjacent surfaces are respectively obtained and are recorded as n1、n2(ii) a Selecting one of the adjacent surfaces as a base surface, and calculating a tangent vector n of the edge eeGuarantee neOuter normal vector n to the base plane1Satisfying a right-handed relationship; calculating and obtaining an intermediate conversion vector n ═ n1×n2Calculating the outer normal vector n of n and the base plane1The included angle α, e is concave if α > 90 °, e is convex if α < 90 °;
the construction of the attribute adjacency graph in the step three is completed on the basis of judging the unevenness of the edge, and the attribute adjacency graph of the part is stored by using an adjacency matrix; the adjacency matrix is a square matrix, rows and columns correspond to the surface of the part, the element value reflects the adjacency attribute between two surfaces in the model, if the intersected edge of the two surfaces is a convex edge, the attribute of the corresponding edge is 1, and if the intersected edge of the two surfaces is a concave edge, the attribute of the corresponding edge is 0.
5. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the process of "loading the predefined processing features and their feature sub-graph, and traversing each predefined processing feature" described in step four is as follows: and acquiring a predefined processing feature list from a predefined processing feature library, acquiring a type feature sub-graph each time, identifying all isomorphic sub-graphs corresponding to the sub-graphs in the attribute adjacency graph constructed in the third step by using a sub-graph isomorphic method in the fifth step, storing the processing features identified for the type predefined features until the processing features in all the predefined feature library are traversed, and finishing the operation.
6. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the "call subgraph isomorphic method" described in step five is specifically as follows: the realization of a sub-graph isomorphic method is the key and difficult point of the whole processing characteristic identification, the invention records the isomorphic relation between the vertexes of a large graph and a small graph by constructing a mapping matrix, and converts the graph matching problem into the depth-first search problem of the mapping matrix; the specific process is as follows:
characteristic diagram GαIs MαPart attribute map GβIs MβWherein the small graph GαNumber of vertices m, big graph GβThe number of vertexes n; constructing an M multiplied by n mapping matrix M, wherein the rows of the M correspond to small graph vertexes, the columns correspond to large graph vertexes, each element records the isomorphic relation between the two vertexes, 1 represents matching, and 0 represents mismatching; if and only if M satisfies that each row has and only one element is 1, andfeature subgraph G with 1 elements not in the same columnαAnd attribute map GβIsomorphism of subgraphs; thus, in the large graph GβIn which all isomorphic panels G are foundαOnly all mapping matrixes M meeting the isomorphic relation need to be found; in view of the big picture GβIn which there may be a plurality of isomorphic minimaps GαThe invention realizes the global search of the big picture recursively based on a backtracking method, and the whole method comprises the following steps:
inputting: mα，MβM, n, the initialized mapping matrix M, and the row space occupying array VH(represents the column number of each row with 1 element in the current matrix M, the number of the elements is M and V is more than or equal to 0H[i]N-1 or less, initialized to-1), column space occupying array VF(indicating whether each column in the current matrix M has an element with a value of 1, the number of the elements is n, VF[i]Initialized to false), and G is storedα、GβSet of vertices V already matched1And V2Row number row, column number col (all initialized to 0);
and (3) outputting: part attribute map GβMedian signature subgraph GαVertex sets meeting the isomorphic relation of the subgraph and vertex matching corresponding relation;
step1, judging whether the row number row is more than or equal to the characteristic subgraph GαThe number m of the vertexes is satisfied, if the number m of the vertexes is satisfied, the characteristic is found in the part, and a matching vertex set V is output and identified1And V2The method ends; otherwise, turning to Step 2;
step2, whether the column number col is less than n, if so, col + +, and turning to Step 3; otherwise the method ends;
step3. determination of VH[row]If the value is-1, the line is a returned line, if yes, the occupation is set before, and the occupation needs to be cleared, V1And V2All pop-up stack top elements until the number of the top points is equal to the current row number row, VF[VH[row]]Is set as false, VH[row]Setting to be-1, turning to Step 4; otherwise, directly turning to Step 4;
step4. determination of VF[col]Whether false and M (row, col) is 1, ifIf so, then set V of vertices1Add row, set V at vertex2Adding col, and turning to Step 5; otherwise the method ends;
step5. judging the matched vertex set V1And V2Whether the sub-images formed respectively are isomorphic, if isomorphic, order VH[row]＝col，VF[col]Let row + +, go to Step1, recursively call the method; if they are not isomorphic, then V1And V2Popping up the elements at the top of the stack, and ending the method;
the subgraph isomorphism problem belongs to the graph search problem in graph theory, is NP complete, the complexity of the method is higher, in order to improve the execution efficiency of the method, the invention adopts the following method to carry out fast pruning operation, thus effectively reducing the global search space of the graph;
(1) when initializing the mapping matrix M, instead of setting each element to 1, the attribute map G is calculated firstβNeutral subfigure GαWhen the degree of the vertex of the sub-graph is larger than that of the vertex of the large graph, the element of the corresponding mapping matrix M is initialized to 0;
(2) by judging matched vertex set V in real time1And V2Whether the separately constructed subgraphs are isomorphic[i]To quickly determine whether the current mapping matrix meets the requirements; judgment V1And V2When the new vertex is isomorphic, only the newly added vertex and the existing vertex in the set need to be judged whether to be isomorphic.
7. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the method for calculating the critical geometrical dimension of the machined feature described in step six is as follows: the processing characteristic key geometric dimension calculation comprises the key geometric parameter extraction of each molded surface in the characteristic surface list and the processing characteristic bounding box dimension calculation; the parameters of various profiles of the characteristic surface mainly comprise: the key geometrical parameters of the plane are as follows: plane center coordinates and plane bounding box coordinates; the key geometric parameters of the cylindrical surface are as follows: the coordinate of the axis, the radius, the height of the cylindrical surface, the initial and final angles of the cylindrical surface and the coordinate of the cylindrical surface bounding box; the key geometric parameters of the conical surface are as follows: axis coordinates, conical angles and conical surface bounding box coordinates; the key geometrical parameters of the torus are as follows: the axis coordinate, the major and minor circumference radius parameters, the major and minor circumference initial angle and the torus bounding box coordinate; the characteristic critical dimension of the feature is determined by the position of the surface in the feature and the critical geometric parameters of the surface, and the dimension of the feature bounding box is obtained by the bounding box coordinate operation of each surface in the processing feature surface linked list.
8. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the "obtaining the engineering annotation list of the MBD model" described in step eight is implemented as follows: and acquiring a file in the current editor, acquiring a current MBD model pointer pDOc, and calling a function interface provided by the system to acquire a part annotation list and annotation content thereof.
9. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the "interactive recognition of user-defined processing features" described in the ninth step is implemented as follows: the part processing characteristic is a group of faces, and when a program identifies a group of faces, an element is newly added in vector recogninized features; the user self-defines the characteristics of the part, adopts a screen selection agent, sets a selection object of the selection object as a Surface of the part, and clicks the Surface of the part on a screen by the user to obtain a Surface path; and obtaining a surface object through the obtained surface path; storing all the face objects selected by the screen in a face list, setting the face objects as user-defined features, and storing the face objects in vectorrecognitedFeataurs; modifying the name of the user-defined feature and setting the RGB value of the user-defined feature; and repeating the operation and interactively identifying all the custom machining features, and calling a function of calculating the key geometric dimension of the machining feature in the step 6 to calculate the key geometric dimension of the custom machining feature.
10. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
the "visualization of the processing feature recognition result" described in the step ten is implemented as follows: starting a characteristic coloring function to traverse vectorecognizable features, acquiring a surface list of each characteristic element, namely listofFeatFaces, traversing the listofFeatFaces, acquiring brep elements, and adding the brep elements into a to-be-colored brep list; and calling a painting function provided by the system, and reading the RGB values corresponding to the processing characteristics to paint each element in the brep list.
11. The method for processing feature recognition and information extraction of an MBD model according to claim 1, wherein:
in step eleven, "structured output of the product information and material information of the vector recognitedfeatures and parts in the XML format using extensible markup language" is specifically as follows: calling an XML document creating function to generate an XML document; generating two primary sub-node processing feature recognition results and part annotations; generating second-level sub-nodes under the first-level sub-node, wherein each processing feature corresponds to one second-level sub-node, and storing the key size information of the feature under the second-level sub-nodes; acquiring a geometric element attached to each label, and adding the geometric elements into the corresponding processing characteristics according to the geometric elements to form child nodes of the processing characteristics; storing the extraction result in the step eight under the second and child nodes, annotating a secondary child node for each item, and storing the type and content of the annotation under the secondary child node; and saving the XML document after the generation is finished.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111666674A (en) * | 2020-06-01 | 2020-09-15 | 西北工业大学 | Method for calculating minimum size of cavity channel |
| CN111783217A (en) * | 2020-05-15 | 2020-10-16 | 成都飞机工业（集团）有限责任公司 | Automatic acquisition method for size of machined part blank based on CATIA VBA |
| CN112149611A (en) * | 2020-10-10 | 2020-12-29 | 山东科技大学 | MBD-based Creo model feature recognition and similarity evaluation method and storage medium |
| CN112232182A (en) * | 2020-10-14 | 2021-01-15 | 山东科技大学 | Method for judging and removing isolated face in process of identifying local features of three-dimensional model and storage medium |
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| CN113744417A (en) * | 2021-11-08 | 2021-12-03 | 山东捷瑞数字科技股份有限公司 | Dimension marking method of complex node model |
| CN113901256A (en) * | 2021-09-22 | 2022-01-07 | 成都飞机工业（集团）有限责任公司 | Similarity detection method and device for CAD model, terminal device and storage medium |
| CN114067231A (en) * | 2022-01-14 | 2022-02-18 | 成都飞机工业（集团）有限责任公司 | Part machining feature identification method based on machine vision learning identification |
| CN114219959A (en) * | 2021-11-27 | 2022-03-22 | 西北工业大学 | Rotary part feature recognition method |
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| CN114896671A (en) * | 2022-06-09 | 2022-08-12 | 中国电建集团成都勘测设计研究院有限公司 | Method for deriving BIM geometric attribute information based on CATIA software enveloping body model |
| CN115391932A (en) * | 2022-08-10 | 2022-11-25 | 中国航空综合技术研究所 | Sheet metal part characteristic interval judgment method based on three-dimensional model |
| WO2023103415A1 (en) * | 2021-12-09 | 2023-06-15 | 上海望友信息科技有限公司 | Component modeling and parameterization method and system, electronic device, and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164582A (en) * | 2013-03-21 | 2013-06-19 | 西北工业大学 | Identification method for three-dimensional computer-aided design (CAD) model intersection manufacture characteristics |
| CN105242538A (en) * | 2015-10-12 | 2016-01-13 | 南京航空航天大学 | Layer-based part multi-processing procedure MBD model and realization method thereof |
| CN105354353A (en) * | 2015-09-28 | 2016-02-24 | 武汉开目信息技术有限责任公司 | MBD model based processing feature identification and modeling method |
| CN105718653A (en) * | 2016-01-20 | 2016-06-29 | 西北工业大学 | Automatic checking method of annotated information completeness for MBD (Model Based Definition) process model |
| CN107180138A (en) * | 2017-06-05 | 2017-09-19 | 武汉开目信息技术股份有限公司 | A kind of part machinability analysis method and system based on MBD models |
| CN107944015A (en) * | 2017-12-11 | 2018-04-20 | 山东农业大学 | Threedimensional model typical structure based on simulated annealing excavates and method for evaluating similarity |
| US20180268724A1 (en) * | 2010-06-30 | 2018-09-20 | Primal Space Systems, Inc. | Visibility event navigation method and system |
| CN109117560A (en) * | 2018-08-17 | 2019-01-01 | 武汉理工大学 | Automobile typical machined part three-dimensional process design method and platform based on MBD |
-
2019
- 2019-09-26 CN CN201910915952.9A patent/CN110795797B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180268724A1 (en) * | 2010-06-30 | 2018-09-20 | Primal Space Systems, Inc. | Visibility event navigation method and system |
| CN103164582A (en) * | 2013-03-21 | 2013-06-19 | 西北工业大学 | Identification method for three-dimensional computer-aided design (CAD) model intersection manufacture characteristics |
| CN105354353A (en) * | 2015-09-28 | 2016-02-24 | 武汉开目信息技术有限责任公司 | MBD model based processing feature identification and modeling method |
| CN105242538A (en) * | 2015-10-12 | 2016-01-13 | 南京航空航天大学 | Layer-based part multi-processing procedure MBD model and realization method thereof |
| CN105718653A (en) * | 2016-01-20 | 2016-06-29 | 西北工业大学 | Automatic checking method of annotated information completeness for MBD (Model Based Definition) process model |
| CN107180138A (en) * | 2017-06-05 | 2017-09-19 | 武汉开目信息技术股份有限公司 | A kind of part machinability analysis method and system based on MBD models |
| CN107944015A (en) * | 2017-12-11 | 2018-04-20 | 山东农业大学 | Threedimensional model typical structure based on simulated annealing excavates and method for evaluating similarity |
| CN109117560A (en) * | 2018-08-17 | 2019-01-01 | 武汉理工大学 | Automobile typical machined part three-dimensional process design method and platform based on MBD |
Non-Patent Citations (3)
| Title |
|---|
| 万能等: ""机械加工MBD毛坯模型的特征识别设计方法"", 《计算机辅助设计与图形学学报》 * |
| 樊锡鹏: ""基于模型定义的制造特征识别方法研究"", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅱ辑》 * |
| 白茜: "基于MBD的特征识别方法研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 * |
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| CN114067231B (en) * | 2022-01-14 | 2022-05-10 | 成都飞机工业（集团）有限责任公司 | Part machining feature recognition method based on machine vision learning recognition |
| CN114067231A (en) * | 2022-01-14 | 2022-02-18 | 成都飞机工业（集团）有限责任公司 | Part machining feature identification method based on machine vision learning identification |
| CN114398691B (en) * | 2022-03-25 | 2022-06-10 | 山东豪迈机械科技股份有限公司 | Intelligent design method and equipment for two-dimensional view of tire mold |
| CN114398691A (en) * | 2022-03-25 | 2022-04-26 | 山东豪迈机械科技股份有限公司 | Intelligent design method and equipment for two-dimensional view of tire mold |
| CN114896671A (en) * | 2022-06-09 | 2022-08-12 | 中国电建集团成都勘测设计研究院有限公司 | Method for deriving BIM geometric attribute information based on CATIA software enveloping body model |
| CN115391932A (en) * | 2022-08-10 | 2022-11-25 | 中国航空综合技术研究所 | Sheet metal part characteristic interval judgment method based on three-dimensional model |
| CN115391932B (en) * | 2022-08-10 | 2024-06-04 | 中国航空综合技术研究所 | Sheet metal part characteristic distance judging method based on three-dimensional model |
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