Numerical control machining path planning method, device and equipment based on MBD model

Abstract

The embodiment of the invention discloses a numerical control machining path planning method, device and equipment based on an MBD model. The method comprises the steps that a three-dimensional model and machining labeling information of a to-be-machined part in an MBD model are obtained; performing feature recognition on the three-dimensional model part to obtain to-be-processed features; fusing each to-be-processed feature and the processing annotation information to generate to-be-processed feature data; constructing constraint conditions according to the to-be-processed feature data and preset path planning parameters; determining a numerical control machining path planning problem as a combined problem of solving machining sequence planning, machining path optimization and feed point and feed direction optimization among to-be-machined features; according to the to-be-machined feature data and the numerical control machining path planning problem, the machining time of the to-be-machined part is determined to serve as an optimization target; according to the constraint condition, the numerical control machining path planning problem and the optimization target, the optimal machining path is calculated, machining path optimization can be carried out according to the process information in the MBD model, and the numerical control machining efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided process planning, and in particular to a numerical control machining path planning method, device and equipment based on an MBD model. BACKGROUND

[0002] In numerical control machining, due to the large size of structural parts and the multiple machining features, the numerical control machining path is usually optimized.

[0003] In the prior art, the programming of numerical control programs is mostly realized by using a man-machine interactive mode. The numerical control machining path planning in the man-machine interactive mode is usually manually formulated based on experience, which wastes manpower and prolongs the production preparation cycle. In addition, if the path planning is improper, the numerical control machining efficiency will be affected.

[0004] In practical applications, with the rapid development of the manufacturing industry, high-speed and high-quality numerical control path optimization has become an inevitable trend. Therefore, the path optimization by using the traditional man-machine interactive mode cannot meet the actual production requirements. SUMMARY

[0005] The present application provides a numerical control machining path planning method, device and equipment based on an MBD model, which optimizes the machining path of a part according to the process information in the MBD model, and improves the numerical control machining efficiency.

[0006] According to an aspect of the present application, a numerical control machining path planning method based on an MBD model is provided, which comprises the following steps:

[0007] obtaining a three-dimensional model of a part to be machined in an MBD model and machining annotation information; and performing part feature recognition on the three-dimensional model to obtain a plurality of machining features to be machined;

[0008] fusing each machining feature to be machined and the machining annotation information to generate a plurality of machining feature data;

[0009] constructing constraint conditions in a numerical control machining path planning model according to each machining feature data to be machined and a preset path planning parameter;

[0010] determining the numerical control machining path planning problem as a combination problem of solving the machining sequence planning between each machining feature to be machined, the machining path optimization in each machining feature to be machined, and the optimization of the feed point and the feed direction in each machining feature to be machined during numerical control machining;

[0011] determining the machining time of the part to be machined according to each machining feature data to be machined and the numerical control machining path planning problem; and determining the machining time as an optimization target in the numerical control machining path planning model;

[0012] According to the constraint condition, the numerical control machining path planning problem and the optimization target, an optimal machining path in the numerical control machining path planning is calculated.

[0013] According to another aspect of the present application, there is provided a numerical control machining path planning device based on an MBD model, comprising:

[0014] A to-be-machined feature identification module is configured to acquire a three-dimensional model of a to-be-machined part in an MBD model and machining annotation information, and perform part feature identification on the three-dimensional model to obtain a plurality of to-be-machined features corresponding thereto;

[0015] A to-be-machined feature data generation module is configured to fuse each to-be-machined feature and machining annotation information to generate a plurality of to-be-machined feature data;

[0016] A constraint condition determination module is configured to construct constraint conditions in a numerical control machining path planning model according to each to-be-machined feature data and a preset path planning parameter;

[0017] A numerical control machining path planning problem determination module is configured to determine the numerical control machining path planning problem as a combination problem of solving machining sequence planning between each to-be-machined feature, machining path optimization in each to-be-machined feature and tool feed point and tool feed direction optimization in each to-be-machined feature during numerical control machining;

[0018] An optimization target determination module is configured to determine machining time of the to-be-machined part according to each to-be-machined feature data and the numerical control machining path planning problem, and determine the machining time as an optimization target in the numerical control machining path planning model;

[0019] An optimal machining path calculation module is configured to calculate an optimal machining path in the numerical control machining path planning according to the constraint condition, the numerical control machining path planning problem and the optimization target.

[0020] According to another aspect of the present application, there is provided an electronic device, comprising:

[0021] at least one processor; and

[0022] a memory connected with the at least one processor in communication; wherein,

[0023] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the numerical control machining path planning method based on an MBD model according to any one of the embodiments of the present application.

[0024] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the MBD model based NC machining path planning method according to any of the embodiments of the present application when executed.

[0025] According to another aspect of the present application, there is provided a computer program product comprising a computer program for implementing the MBD model based NC machining path planning method according to any of the embodiments of the present application when executed by a processor.

[0026] The technical solution of the embodiments of the present application comprises: obtaining a three-dimensional model of a part to be machined in an MBD model and machining annotation information; performing part feature recognition on the three-dimensional model to obtain a plurality of features to be machined; fusing each feature to be machined and the machining annotation information to generate a plurality of feature to be machined data; constructing a constraint condition in a NC machining path planning model according to each feature to be machined data and a preset path planning parameter; determining a NC machining path planning problem as a combination problem of machining sequence planning between each feature to be machined, machining path optimization in each feature to be machined, and optimization of a feed point and a feed direction in each feature to be machined when solving the NC machining; determining a machining time of the part to be machined according to each feature to be machined data and the NC machining path planning problem; determining the machining time as an optimization target in the NC machining path planning model; and calculating an optimal machining path in the NC machining path planning according to the constraint condition, the NC machining path planning problem, and the optimization target.

[0027] Through the above technical solution, the path optimization problem in NC machining is solved, and the part machining path can be optimized according to the process information in the MBD model, thereby improving the NC machining efficiency.

[0028] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0030] Figure 1 is a flowchart of an MBD model based NC machining path planning method according to an embodiment of the present application;

[0031] Figure 2 is a schematic diagram of obtaining machining marking information according to an embodiment of the present application;

[0032] Figure 3 is a flowchart of generating feature data to be machined according to an embodiment of the present application;

[0033] Figure 4 is a schematic diagram of a part to be machined according to an embodiment of the present application;

[0034] Figure 5 is a schematic diagram of path planning according to an embodiment of the present application;

[0035] Figure 6 is a structural schematic diagram of a numerical control machining path planning device based on an MBD model according to an embodiment of the present application;

[0036] Figure 7 is a structural schematic diagram of an electronic device for implementing a numerical control machining path planning method based on an MBD model according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the personnel in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0038] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0039] Figure 1is a flow chart of a numerical control machining path planning method based on an MBD model according to an embodiment of the present application. The embodiment can be applied to the numerical control path optimization of part machining using information in an MBD model. The method can be executed by an MBD model based numerical control machining path planning device. The MBD model based numerical control machining path planning device can be realized in the form of hardware and / or software. The MBD model based numerical control machining path planning device can be configured in an electronic device such as a computer. As shown in Figure 1 , the method comprises the following steps.

[0040] In step 110, a three-dimensional model of a part to be machined in an MBD model and machining annotation information are obtained. Part feature recognition is performed on the three-dimensional model to obtain a plurality of to-be-machined features.

[0041] Model Based Definition (MBD) is a product description method. The MBD model contains annotation information, process information and product attribute information, etc. The inventors have found in actual research that, since there is a large amount of process-related information in the MBD model, if the information in the MBD model can be extracted and effectively utilized, the part machining production process can be optimized. Specifically, by integrating, organizing and expressing product information on a three-dimensional entity model, the consistency and flow of design and manufacturing information can be ensured, which has a good guiding significance for the manufacturing process. The method provided in the embodiment is particularly suitable for the machining of parts with large dimensions and many features, such as the production and machining of aircraft structural parts.

[0042] The three-dimensional model in the MBD model can be a three-dimensional rendering of the part to be machined, which has geometric data such as size information and position information of the part to be machined. The machining annotation information can be the tolerance data allowed to exist in the machining of the part to be machined. Specifically, the machining annotation information can include size tolerance, roughness, design reference label and form and position tolerance of the feature.

[0043] An exemplary Figure 2 is a schematic diagram of obtaining machining annotation information according to an embodiment of the present application. As shown in Figure 2As shown, the annotation information of the part to be processed in the MBD model can be traversed first, and the annotation information can be selectively classified; the classification results include: dimensional tolerance, roughness, design reference label, and geometric and position tolerance of the feature. By establishing a machining annotation information container library, machining annotation information of a single category is classified and stored. Specifically, the dimensional tolerance can include the extracted size, size type, size deviation, and extraction reference. The roughness can include the extracted roughness value and the extraction reference. The reference label can include the extraction reference geometry element address. The geometric and position tolerance can include the extracted tolerance value, tolerance type, reference datum, and extraction reference. The extracted machining annotation information can be stored to generate an annotation information library.

[0044] The features to be processed can include, but are not limited to, geometric features such as holes, grooves, cavities, edges, and contours. The part feature recognition of the three-dimensional model can be matching the dimensional information and the specific model in the three-dimensional model with the preset part feature. When the matching is passed, part of the model in the three-dimensional model can be determined as the corresponding part feature, that is, the corresponding features to be processed are obtained. The preset part feature can be generated by pre-defining and classifying the machining features.

[0045] In an optional embodiment of the present application, part feature recognition is performed on a three-dimensional model to obtain a plurality of corresponding features to be processed, including: obtaining a part feature library, the part feature library including: feature subgraphs of each part feature; generating a feature adjacency graph corresponding to the part to be processed according to the three-dimensional model; and performing feature matching between the feature adjacency graph and each feature subgraph in the part feature library to identify a plurality of part features corresponding to the part to be processed.

[0046] The feature subgraph can be a graph generated according to the geometric data of the part feature. For example, when the part feature is a groove, the nodes, edges, and relationships between the nodes and edges can be determined according to the constituent elements in the groove to generate the feature subgraph. For example, the feature subgraph or the feature adjacency graph can be generated by the screw theory in machinery. The feature adjacency graph can be a feature subgraph of a plurality of part features corresponding to the three-dimensional model, which is formed by splicing according to the adjacent position relationship. If there is a target feature subgraph in the feature adjacency graph, the target part feature corresponding to the target feature subgraph is taken as the part feature to be processed corresponding to the part to be processed.

[0047] For a three-dimensional model that cannot be recognized by the part feature library, the three-dimensional model can be transmitted to a preset feature recognition platform, and a feature recognition result fed back by the preset feature recognition platform can be obtained. The three-dimensional model and the corresponding feature recognition result can be stored in the part feature library to update the part feature library. The technician can perform feature recognition on the three-dimensional model in the preset feature recognition platform.

[0048] Step 120: Fusing the features to be processed and the processing annotation information to generate a plurality of feature data to be processed.

[0049] In the embodiment of the present invention, there is a mapping relationship between the features to be processed and the processing annotation information based on the parts to be processed. Therefore, based on the mapping relationship between the features to be processed and the processing annotation information, the two can be associated to form structured data of the features to be processed.

[0050] In an optional implementation of an embodiment of the present invention, each feature to be processed and the processing annotation information are fused to generate multiple feature data to be processed, including: encoding and calibrating each feature to be processed according to a preset encoding rule; according to the position of each feature to be processed and the processing annotation information in the three-dimensional model, and the encoding calibration result, each feature to be processed and the processing annotation information are fused to generate multiple feature data to be processed.

[0051] Among them, through coding calibration, the features to be processed can be linked to the information in the MBD model. Through the dual mapping of position and coding calibration results, the features to be processed and the processing annotation information can be accurately integrated. For example, the position of the features to be processed in the three-dimensional model can be determined by the position information, and the part features represented by the features to be processed can be determined by the coding calibration results. Based on the position of the processing annotation information in the three-dimensional model, the features to be processed corresponding to the processing annotation information can be determined. Based on the specific content of the processing annotation information, such as the coding information contained therein, it can be further determined whether the matched features to be processed are correct.

[0052] For example, Figure 3 This is a flow chart of generating feature data to be processed according to an embodiment of the present invention. Figure 3 As shown, the features to be processed can be obtained one by one from multiple features to be processed, and the feature type can be identified, such as slots, holes, edges, etc. In the MBD model, the three-dimensional annotation table can be obtained first, and traversed to obtain the three-dimensional processing annotation information. The three-dimensional processing annotation information is classified. According to the position and encoding of the features to be processed and the processing annotation information in the three-dimensional model, the type of the features to be processed is calibrated, and structured expression is performed with the processing annotation information to obtain the information fusion result. The features to be processed containing the processing annotation information, that is, the feature data to be processed, are stored. A feature database can be generated based on the feature data to be processed. In order to enhance the correlation between the feature data to be processed, the knowledge graph technology can be used to save the data model of each processing feature and display it visually.

[0053] Step 130: Constructing the constraint conditions in the NC machining path planning model according to the feature data to be machined and the preset path planning parameters.

[0054] The preset path planning parameters can include but are not limited to tool size, tool type, tool cutting angle and tool cutting mode. According to the to-be-processed feature data and the preset path planning parameters, path constraints, feed points and feed direction constraints in the numerical control machining path planning model can be generated as constraint conditions in the numerical control machining path planning model.

[0055] In the embodiment of the application, by means of the to-be-processed feature data in the MBD model, the positional relationship between the to-be-processed features and the preset path planning parameters, the combination constraints of tool path trajectories and machining feed points and feed directions in the numerical control machining path planning model can be established.

[0056] According to the to-be-processed features, sizes and tolerance data in the to-be-processed feature data, the cutting time when different tools are used for numerical control machining with different cutting depths can be determined. Thus, the optimal path, feed point and feed direction are searched in the numerical control machining of the to-be-processed features with the cutting time as the target.

[0057] In step 140, the numerical control machining path planning problem is determined as a combination problem of solving the machining sequence planning between the to-be-processed features, the machining path optimization in each to-be-processed feature and the feed point and feed direction optimization in each to-be-processed feature in numerical control machining.

[0058] The numerical control machining path planning problem is reduced to a combination optimization problem of machining sequence planning and machining feed point and feed direction of each cavity in numerical control machining. The cavity with a combination of multiple feed points and cutting directions is analogous to a city group, the cutting time and idle cutting time are the cost of traveling between cities, and then the model of the partition machining path planning problem can be abstracted as a generalized traveling salesman (TSP) problem, and the mathematical description of the problem is G=(T,E,W).

[0059] Wherein, T represents the set of feed points and exit points (cities), E is the set of edges (routes between cities), and W is the set of weights (traveling costs between cities). For a pair of cavities, if the feed point and the cutting direction are fixed and the machining sequence of the cavities is reversed, the idle cutting path between the cavities will change, so the weight matrix described above is asymmetric. Considering the constraint of the machining area, the numerical control machining path planning problem can be defined as a constrained asymmetric TSP problem.

[0060] By creating constraint dynamic parameters of tool tip points and machining errors, the constraint conditions of the feed point and the feed direction are determined by the to-be-processed feature data.

[0061] In step 150, the machining time of the to-be-processed part is determined according to the to-be-processed feature data and the numerical control machining path planning problem, and the machining time is determined as the optimization target in the numerical control machining path planning model.

[0062] Estimate the time required to cut the entire feature to be machined. Assume that there is no idle travel (e.g., tool jumping) when machining a single cavity feature, and that the difference in idle travel between different retract points to the retract plane after machining each cavity is negligible. Optimize only the idle travel of the tool between features.

[0063] For example, the tool path of each part to be processed can be regarded as a Hamiltonian cycle, which contains J processing areas, and the corresponding area contains M J The schematic diagram of path planning is obtained by combining the area to be processed of the part. Figure 5 The machining process of each part to be machined consists of three parts: the cutting process C of all cavities in all machining areas, the idle stroke L1 between cavities, and the idle stroke L2 between machining areas.

[0064] The total cutting time is Where A j,i , R j,i They represent the entry point and exit point of the i-th feature to be processed in the j-th processing area. c , w l They are the time required for the cutting process and the air cutting stroke between the entry point and the exit point during the machining process. c (A j,i ,R j,i ) represents the total cutting time required from the entry point to the exit point of the i-th feature to be processed in the j-th processing area. l (R j,i-1 ,A j,i ) represents the total air cutting time (cavity switching) required between the retract point of the i-1th feature to be processed in the jth processing area and the feed point of the i-th feature to be processed. Indicates the j-1th processing area M J The total time required for air cutting between the retract point of the first feature to be machined and the first entry point of the jth feature to be machined in the jth machining area (machining area switching). The entry point and feed direction of the feature to be machined are constrained by the feature database to be machined and the preset path planning parameters.

[0065] The cutting time for a single cavity feature is determined by the cavity's entry point and cutting direction, while the air cutting time is determined by the machining sequence and the entry and exit points of each cavity. Cavity cutting uses a zigzag cutting pattern by default.

[0066] Step 160 : Calculate the optimal machining path in the NC machining path planning according to the constraint conditions, the NC machining path planning problem, and the optimization goal.

[0067] In the calculation of the optimal machining path, an ant colony algorithm can be used for path search.

[0068] Figure 4 is a schematic diagram of a part to be machined according to an embodiment of the present application. Figure 5 is a path planning schematic diagram according to an embodiment of the present application. As shown in Figure 4 The part to be machined can be identified according to the part features to generate Figure 5 The machining path optimization can be performed for each of the features to be machined in Figure 5

[0069] In an optional embodiment of the present application, the optimal machining path in the numerical control machining path planning is calculated, including: determining the optimal machining sequence between each of the features to be machined, the optimal machining path in each of the features to be machined, and the optimal feed point and the optimal feed direction in each of the features to be machined corresponding to the optimal machining time by using the ant colony algorithm.

[0070] In the improved ant colony algorithm, the global optimal solution can be obtained at a faster speed, the machining path after time optimization can be obtained, the machining tool path trajectory can be generated through the machining sequence arrangement, and the simulation generation of the machining tool path and the automatic generation of the numerical control programming code can be guided. In the improved ant colony algorithm, the MMAS algorithm with better optimization effect can be selected.

[0071] In an optional embodiment of the present application, the optimal machining path in the numerical control machining path planning is calculated, including: determining at least one same-type feature group in the plurality of features to be machined corresponding to the part to be machined; and after the path search in the current same-type feature group is completed by using the ant colony algorithm, switching to the path search in the next same-type feature group.

[0072] In the improved ant colony algorithm, the global optimal solution can be obtained at a faster speed, the machining path after time optimization can be obtained, the machining tool path trajectory can be generated through the machining sequence arrangement, and the simulation generation of the machining tool path and the automatic generation of the numerical control programming code can be guided. In the improved ant colony algorithm, the MMAS algorithm with better optimization effect can be selected.

[0073] In an optional embodiment of the present application, the part to be machined includes: a plurality of machining regions, and each machining region includes a plurality of cavities; and the optimal machining path in the numerical control machining path planning is calculated, including: when the features to be machined are transferred by using the ant colony algorithm, the nearest neighbor principle is used when the machining regions are switched, and the roulette method is used when the cavities are switched. ​

[0074] The machining features in each machining area are sequentially accessed by selecting the nearest neighbor machining cavity of the adjacent machining area, so that the air cutting time can be reduced.

[0075] Based on the above-mentioned embodiments, optionally, when the path is searched by the MMAS algorithm, the pheromone update only retains the pheromone left by the global optimal ant, so the pheromone calculation formula is In the formula, denotes the reciprocal of the path length walked by the global optimal ant, and p is the evaporation rate of the pheromone. In order to avoid the algorithm converging to a local optimal solution too early, the MMAS algorithm limits the pheromone concentration on each path to the interval [τ min ,τ max ], and initializes the pheromone concentration to the maximum value to increase the search ability in the initial stage of the algorithm.

[0076] In the ant colony iterative search, if the current optimal solution does not improve the optimal solution of the last time, local search can be performed according to the current optimal solution to avoid the current optimal solution being actually a local optimal solution. The local search mode can be that the partial solution is free from the feed point and feed direction combination constraint in the candidate machining scheme. Through the local search, further optimization can be performed to improve the solving performance of the MMAS algorithm.

[0077] When the optimal machining time corresponding to the optimal machining sequence between the machining features, the optimal machining path in each machining feature, and the optimal feed point and optimal feed direction in each machining feature are obtained by the ant colony algorithm, the tool path trajectory of the machining part can be generated according to the above optimal results to guide the simulation generation of the machining tool path and the automatic generation of the numerical control programming code.

[0078] The technical scheme of the embodiment is characterized in that the three-dimensional model of the part to be machined in the MBD model and machining annotation information are acquired, part feature recognition is performed on the three-dimensional model, and a plurality of machining features to be machined are obtained; the machining features to be machined and the machining annotation information are fused to generate a plurality of machining feature data; constraint conditions in a numerical control machining path planning model are constructed according to the machining feature data and preset path planning parameters; the numerical control machining path planning problem is determined as a combination problem of machining sequence planning among the machining features to be machined, machining path optimization in the machining features to be machined, and feed point and feed direction optimization in the machining features to be machined during numerical control machining; the machining time of the part to be machined is determined according to the machining feature data and the numerical control machining path planning problem; and the machining time is determined as an optimization target in the numerical control machining path planning model; and the optimal machining path in the numerical control machining path planning is calculated according to the constraint conditions, the numerical control machining path planning problem, and the optimization target, thereby solving the path optimization problem in numerical control machining, optimizing the machining path of the part according to the process information in the MBD model, effectively utilizing the information in the MBD model, and improving the numerical control machining efficiency.

[0079] Figure 6 It is a structure schematic diagram of a numerical control machining path planning device based on an MBD model according to the embodiment of the application. Figure 6 As shown in the figure, the device comprises a machining feature to be machined recognition module 610, a machining feature data to be machined generation module 620, a constraint condition determination module 630, a numerical control machining path planning problem determination module 640, an optimization target determination module 650, and an optimal machining path calculation module 660.

[0080] Among them:

[0081] The machining feature to be machined recognition module 610 is configured to acquire the three-dimensional model of the part to be machined in the MBD model and machining annotation information, and perform part feature recognition on the three-dimensional model to obtain a plurality of machining features to be machined.

[0082] The machining feature data to be machined generation module 620 is configured to fuse the machining features to be machined and the machining annotation information to generate a plurality of machining feature data.

[0083] The constraint condition determination module 630 is configured to construct constraint conditions in a numerical control machining path planning model according to the machining feature data and preset path planning parameters.

[0084] The numerical control machining path planning problem determination module 640 is configured to determine the numerical control machining path planning problem as a combination problem of machining sequence planning among the machining features to be machined, machining path optimization in the machining features to be machined, and feed point and feed direction optimization in the machining features to be machined during numerical control machining.

[0085] The optimization target determination module 650 is configured to determine a machining time of the part to be machined according to the data of the features to be machined and the NC machining path planning problem, and determine the machining time as an optimization target in the NC machining path planning model.

[0086] The optimal machining path calculation module 660 is configured to calculate an optimal machining path in the NC machining path planning according to the constraint condition, the NC machining path planning problem, and the optimization target.

[0087] Optionally, the feature to be machined recognition module 610 comprises:

[0088] The part feature library acquisition unit is configured to acquire a part feature library, and the part feature library comprises feature subgraphs of part features.

[0089] The feature adjacency graph generation unit is configured to generate a feature adjacency graph corresponding to the part to be machined according to the three-dimensional model.

[0090] The feature to be machined recognition unit is configured to perform feature matching between the feature adjacency graph and the feature subgraphs in the part feature library, and recognize a plurality of features to be machined corresponding to the part to be machined.

[0091] Optionally, the feature to be machined data generation module 620 comprises:

[0092] The coding calibration unit is configured to code and calibrate the features to be machined according to a preset coding rule.

[0093] The feature to be machined data generation unit is configured to fuse the features to be machined and the machining annotation information according to positions of the features to be machined and the machining annotation information in the three-dimensional model and the coding calibration result, and generate a plurality of feature to be machined data.

[0094] Optionally, the optimal machining path calculation module 660 comprises:

[0095] The optimal machining path determination unit is configured to determine, by using an ant colony algorithm, an optimal machining sequence between the features to be machined, an optimal machining path in each feature to be machined, and an optimal feed-in point and an optimal feed-in direction in each feature to be machined corresponding to the optimal machining time.

[0096] Optionally, the optimal machining path calculation module 660 comprises:

[0097] The same-type feature group determination unit is configured to determine at least one same-type feature group from the plurality of features to be machined corresponding to the part to be machined.

[0098] The path searching unit is configured to switch to path searching of the next same type feature group after path searching of the current same type feature group is completed by using the ant colony algorithm when the optimal machining path in the numerical control machining path planning is calculated.

[0099] Optionally, the part to be machined comprises a plurality of machining regions, and each machining region comprises a plurality of cavities.

[0100] The optimal machining path calculation module 660 comprises:

[0101] The path switching unit is configured to use the nearest neighbor principle when switching between machining regions and use the roulette method when switching between cavities when transferring the feature to be machined by using the ant colony algorithm.

[0102] The numerical control machining path planning device based on the MBD model provided in the embodiments of the present application can execute the numerical control machining path planning method based on the MBD model provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0103] Figure 7 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0104] As shown in Figure 7 The electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0106] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the MBD model based NC machining path planning method.

[0107] In some embodiments, the MBD model based NC machining path planning method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the MBD model based NC machining path planning method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the MBD model based NC machining path planning method by any other appropriate means, such as by means of firmware.

[0108] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0109] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0110] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0112] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0114] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0115] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A CNC machining path planning method based on MBD model, characterized in that: include: Obtain the 3D model and machining annotation information of the part to be machined in the MBD model; and performing part feature recognition on the three-dimensional model to obtain a corresponding plurality of features to be processed; Fusing each feature to be processed and the processing annotation information to generate multiple feature data to be processed; Constructing the constraint conditions in the CNC machining path planning model according to each of the feature data to be processed and the preset path planning parameters; The NC machining path planning problem is defined as: solving the combined problem of machining sequence planning between various features to be machined, optimizing the machining path in each feature to be machined, and optimizing the feed point and feed direction in each feature to be machined during NC machining. Determining a processing time of the part to be processed based on each of the feature data to be processed and the NC machining path planning problem; and determining the processing time as an optimization target in a NC machining path planning model; According to the constraint conditions, the numerical control machining path planning problem and the optimization goal, the optimal machining path in the numerical control machining path planning is calculated.

2. The method according to claim 1, characterized in that Calculate the optimal machining path in CNC machining path planning, including: The ant colony algorithm is used to determine the optimal processing sequence between the features to be processed, the optimal processing path in each feature to be processed, and the optimal feed point and feed direction in each feature to be processed corresponding to the optimal processing time.

3. The method according to claim 2, characterized in that Calculate the optimal machining path in CNC machining path planning, including: Determining at least one feature group of the same type among a plurality of features to be processed corresponding to the part to be processed; When calculating the optimal machining path in CNC machining path planning, after the path search is completed in the current feature group of the same type through the ant colony algorithm, the next feature group of the same type is switched to perform path search.

4. The method according to claim 2, characterized in that The part to be processed includes: a plurality of processing areas, and one processing area includes a plurality of cavities; Calculate the optimal machining path in CNC machining path planning, including: When transferring features to be processed through the ant colony algorithm, the nearest neighbor principle is adopted when switching the processing area, and the roulette method is used when switching the cavity.

5. The method according to claim 1, wherein Perform part feature recognition on the three-dimensional model to obtain corresponding multiple features to be processed, including: Acquire a part feature library, wherein the part feature library includes: a feature subgraph of each part feature; generating a feature adjacency graph corresponding to the part to be processed based on the three-dimensional model; The feature adjacency graph is matched with each feature subgraph in the part feature library to identify a plurality of to-be-processed features corresponding to the to-be-processed part.

6. The method according to claim 1, characterized in that The features to be processed and the processing annotation information are integrated to generate multiple feature data to be processed, including: According to the preset coding rules, each feature to be processed is coded and calibrated; According to the positions of the features to be processed and the processing annotation information in the three-dimensional model, and the coding calibration result, the features to be processed and the processing annotation information are fused to generate a plurality of feature data to be processed.

7. A CNC machining path planning device based on MBD model, characterized in that: include: The feature recognition module to be processed is used to obtain the 3D model of the part to be processed in the MBD model and the processing annotation information; and performing part feature recognition on the three-dimensional model to obtain a corresponding plurality of features to be processed; A module for generating feature data to be processed, which is used to fuse the features to be processed and the processing annotation information to generate multiple feature data to be processed; A constraint condition determination module is used to construct the constraint conditions in the CNC machining path planning model according to each of the feature data to be processed and the preset path planning parameters; A CNC machining path planning problem determination module is used to define the CNC machining path planning problem as: solving the combined problem of machining sequence planning between various features to be machined, optimizing the machining path in each feature to be machined, and optimizing the feed point and feed direction in each feature to be machined during CNC machining; An optimization target determination module is used to determine the processing time of the part to be processed based on each of the feature data to be processed and the NC machining path planning problem; and determine the processing time as the optimization target in the NC machining path planning model; The optimal machining path calculation module is used to calculate the optimal machining path in the numerical control machining path planning according to the constraint conditions, the numerical control machining path planning problem and the optimization target.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the MBD model-based NC machining path planning method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the MBD model-based numerical control machining path planning method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when executed by a processor, the computer program implements the numerical control machining path planning method based on the MBD model according to any one of claims 1 to 6.

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