Structural hole programming method

Abstract

The invention discloses a structural hole programming method, and relates to the technical field of machining. According to the method, the characteristics that most structural holes are high in similarity and easy to summarize are utilized, the complex structural holes are created into a module, an identification fitting method is researched, and subsequent modular integration is facilitated through feature fitting and fusion of contents such as tool information, machining parameter information and tool path information of machining in subsequent machining. The module can be applied to programming of multi-part shell structure holes, the programming speed and the machining speed are increased, the machining efficiency is improved, and the module can be vigorously popularized in other typical structures in the direction.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical processing, and in particular relates to a method for programming a structural hole. Background Art

[0002] Complex structure, many processing contents and long processing cycle are typical problems in shell processing. In shell processing, the processing of structural holes is the most typical structure that tests processing capabilities and technologies. Structural holes are often composed of a series of complex and extremely high-precision stepped holes. At present, the processing of structural holes is done by manual programming and computer programming. However, there are difficulties such as many processing contents, various tools required, and long programming time. Once negligence and errors occur during processing, it will cause troubles such as all previous efforts being wasted. In the current processing method, the program is reprogrammed once a new part is processed, which greatly reduces the efficiency of processing. The processing of structural holes is time-consuming and labor-intensive, which has always been a major problem in the field of mechanical processing. Therefore, modular rapid programming is introduced. Through feature recognition, feature fitting is performed on structural holes with high similarity, and the module programming information is integrated to speed up the programming progress, reduce the error rate, and achieve real cost reduction and efficiency improvement. Shell models with similar structural holes such as Figure 1 shown. Summary of the invention

[0003] Based on the above background, the present invention creates complex structural holes into modules, and transplants them to the structural holes of other shell parts through feature fitting, thereby solving the problem of repetitive work required for structural holes due to manual programming, computer programming, etc., improving work efficiency, reducing the error rate, and can be applied to other suitable structures for promotion.

[0004] In view of this, according to one aspect of an embodiment of the present invention, a method for structural hole programming is provided, comprising:

[0005] Step 1: Identify the structural hole feature module, where the structural hole feature module includes tool, fixture, programming origin, tool processing parameters, and processing tool path parameter features;

[0006] Step 2: Analyze the 3D process parts to be compiled, complete the model parameterization setting of the parts and perform feature recognition;

[0007] Step 3: Perform parameter feature fitting on the structural hole feature module and the three-dimensional process part model to be compiled, generate a modular model, and establish an equation through the parameter points of the modular model;

[0008] Step 4: Identify the modular model, complete the parameterization of the modular model and perform feature recognition;

[0009] Step 5: Remove the data set with gross errors and then perform fitting;

[0010] Step 6: Adapt the model settings and fit the model accurately;

[0011] Step 7: Identify similar structural holes to achieve the best fit between the part and the feature module.

[0012] Step 8: Generate programming origin, tool processing parameters and other information.

[0013] Step 9: Inherit the information in the module to the new parts.

[0014] Optionally, in step 3, the least squares method is used for fitting.

[0015] Optionally, step 5: according to a preset threshold, completing the feature fitting of the similarity model to the greatest extent.

[0016] Optionally, in step 2, parameterizing the model for feature recognition further includes: converting model points and lines in the original space into lines in the parameter space, or converting lines in the parameter space into points in the original space.

[0017] Optionally, a grid is created with the parameter resolution as the smallest unit.

[0018] Optionally, parameters are determined by voting to complete the model parameterization setting.

[0019] Optionally, in step 5, the gross errors are removed by setting thresholds for different modules to determine convergence through the iteration number change curve.

[0020] Optionally, in step 7, a human-computer interaction method is used to identify the partial structure that cannot be identified.

[0021] The beneficial effects of the present invention are:

[0022] 1. The traditional structural hole fitting method has limited effect when processing complex structural holes. The present invention proposes a method of fitting through hough transform feature recognition, least squares parameter feature fitting, random sampling consistency and other methods to achieve high-precision hole structure fitting.

[0023] 2. In processing and production, feature hole fitting can be achieved by aligning the known 3D model with the actual part, and the programming process can be realized as modular assembly, thereby improving work efficiency and reducing the risk of product scrapping.

[0024] 3. Highly unified, modular rapid programming has independent directionality, and is easy to maintain after promotion. In the later expansion, when adding programs, since the information such as tools and processing parameters is fixed, the later expansion and addition can reduce information asymmetry and be more accurate and convenient.

[0025] 4. The identification of complex structure holes is more accurate, the identification error is reduced, and higher reusability is achieved. Modular rapid programming makes the program more reusable and can be reused in different products. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a diagram of shell parts with similar structural holes;

[0027] Figure 2 It is a diagram of the structural hole module;

[0028] Figure 3 This is a comparison diagram of the import of similar structure hole modules;

[0029] Figure 4 It is a diagram of module feature fitting;

[0030] Figure 5 It is a diagram of module information inheritance;

[0031] Figure 6 It is a flowchart of modular programming. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] According to one aspect of an embodiment of the present invention, a method for structural hole programming is provided, comprising:

[0034] Step 1: Identify the structural hole feature module, which includes the features of tools, fixtures, programming origins, tool processing parameters, and tool path parameters. Edit and establish the structural hole feature module, and input information such as tools, fixtures, programming origins, tool processing parameters, and tool paths through human-computer interaction in the form of dialog boxes and process wizards, and store them in the structural hole feature module. The processing parameters can be fixed and optimized according to the machine tool model, and the modified content during the processing can be continuously optimized into the processing template to unify and standardize the information.

[0035] Step 2: Analyze the 3D process parts to be compiled, complete the model parameterization setting of the parts and perform feature recognition. Model parameterization setting is used for feature recognition. The idea of ​​hough transform is adopted. The shape boundary is assumed to be a straight line. This line is a set of many points. A line (boundary) is processed with points (convert y=mx+c to the point (m, c)) to extract the boundary.

[0036] The hough transform is extended from two dimensions to three dimensions. The three-dimensional model is represented by Z=Sx+Sy+D, where Sx and Sy represent the slopes in the x-axis and y-axis directions respectively, and D is the vertical distance from the plane to the coordinate. Each point in the parameter space (Sx, Sy, D) corresponds to a surface in the image space, and any point in the image space is also defined as a surface in the parameter space. The surfaces in the parameter space corresponding to the points in the data set are intersected at a certain point. Generally, according to the voting principle, the parameter set with the most votes is used as the final parameter.

[0037] Step 3: Perform parameter feature fitting on the structural hole feature module and the 3D process part model to be compiled, generate a modular model, and establish equations through the parameter points of the modular model. List observation equations (one can be listed for each point, and the most important content must be informed of the model first), adjust the observation equations, and obtain the optimal model parameters.

[0038] Step 4: Identify the modular model, complete the parameterization of the modular model and perform feature recognition;

[0039] Step 5: Remove the data set with gross errors and then perform fitting. Error analysis can usually be divided into two categories: systematic error and random error. Systematic error refers to the predictable error caused by imperfect deviations in system measurements, while random error refers to the unpredictable error caused by random factors. Extract the points of the composite model from the data set containing a large number of gross errors, and then use these correct points to perform least squares again to obtain the best model parameters.

[0040] Step 6: Adapt the model to fit the model accurately. To solve the problem that the feature module cannot match the target model 100%, this study sets the RANSAC method by adapting the model. This method estimates the parameters of the mathematical model from a set of observation data containing outliers, inputs a set of observation data, and fits the model to the observation value.

[0041] Step 7: Identify similar structural holes to achieve the best fit between the part and the feature module. Establish equations through the parameter points of the modular model to identify the parameters and similarities in the target model. A threshold can be set manually to maximize the feature fitting of similar models. Then, the module model and the part model are automatically compared and adjusted to accurately identify similar structural holes, so that the new part and the feature module can achieve the best fit.

[0042] Step 8: Generate programming origin, tool processing parameters and other information;

[0043] Step 9: Inherit the information in the module to the new part. Set the WorkPlace in the processing module to the new part, so that it is completely merged with the target module, and then generate the tool path, and inherit all the information in the module to the new part. Post-process the program, generate the G code program, and output it to the machine tool for processing and production.

[0044] Furthermore, in step 3, the least square method is used for fitting. The characteristic fitting of parameters is performed, and the principle of the least square method is used to identify the modular model and the target model. The equation is established through the parameter points of the modular model to identify the parameters and similarity in the target model. A threshold can be set manually, which can complete the characteristic fitting of similar models to the greatest extent. Then, the module model and the part model are automatically compared and adjusted, and similar structural holes are accurately identified, so that the new part and the characteristic module can achieve the best fit.

[0045] Furthermore, step 5: according to a preset threshold, the feature fitting of the similar model is completed to the greatest extent.

[0046] Furthermore, in step 2, parameterizing the model for feature recognition further includes: converting model points and lines in the original space into lines in the parameter space, or converting lines in the parameter space into points in the original space.

[0047] Furthermore, a grid is established with the parameter resolution as the minimum unit.

[0048] Furthermore, the parameters are determined by voting to complete the model parameterization setting.

[0049] Furthermore, in step 5, through the curve of the number of iterations, the thresholds are set for different modules to determine the convergence, so as to remove the gross errors. When the model shape is more complex, the parameterization of the model may cause insensitivity to noise, incomplete shape of the required data points, etc., but for the current regular structure holes, the system error has basically no effect, and only the random error may have a slight effect. Through the curve of the number of iterations, the thresholds are set for different modules to determine the convergence, so as to reduce the error. At present, the recognition rate of the feature can reach more than 90%.

[0050] Furthermore, in step 7, for the unrecognizable partial structures, human-computer interaction methods are used for recognition. For the unrecognizable partial structures (partial similarity is too poor), such as new structures and new content, the system accurately recognizes them through color difference, and subsequently uses human-computer interaction methods to adjust and change them.

[0051] The above method can quickly, accurately and efficiently complete the programming of the structural holes of shell parts. If changes occur in cutting methods or cutting parameters during the production process, it is only necessary to solidify and maintain the changes in the structural hole module.

[0052] Example 1

[0053] The present invention is limited to the implementation of G code programs for machining programming by UG, and can be implemented as long as the structure and program language are similar to those in this description.

[0054] Some program segments are as follows:

[0055]

[0056]

[0057] Example 2

[0058] A method for fast programming of structural holes, such as Figure 2-6 As shown, the following steps are included:

[0059] Step 1: Establish a structural hole feature module through human-computer interaction, such as Figure 2 As shown, the information such as the tool, fixture, programming origin, tool processing parameters, and processing tool path is input in the form of a dialog box and a process wizard, and stored in the structural hole feature module.

[0060] Step 2: Identify the structural hole model, complete the model parameterization setting and perform feature recognition.

[0061] Step 3: Analyze the 3D process parts to be compiled, complete the model parameterization settings and perform feature recognition.

[0062] Step 4: Use the principle of least squares method to perform characteristic fitting of parameters and establish equations through parameter points of modular model.

[0063] Step 5: Identify the structural hole model, complete the model parameterization setting and perform feature recognition.

[0064] Step 6: Set a threshold that can be set manually to maximize the feature fitting of similar models.

[0065] Step 7: Remove the data set with gross errors and then perform least squares fitting.

[0066] Step 8: Adapt the model settings and accurately fit the model through the RANSAC method.

[0067] Step 9: Use human-computer interaction methods to make adjustments and changes, accurately identify similar structural holes, and achieve the best fit between new parts and feature modules.

[0068] Step 10: Automatically generate programming origin, tool processing parameters and other information.

[0069] Step 11: Figure 5 As shown, set the WorkPlace in the processing module to the new part, then generate the tool path, and inherit the information in the module to the new part.

[0070] Step 12: Dynamic simulation of tool path and post-processing output to machine tool for processing.

[0071] The above method can quickly, accurately and efficiently complete the programming of the structural holes of shell parts. If changes occur in cutting methods or cutting parameters during the production process, it is only necessary to solidify and maintain the changes in the structural hole module.

[0072] The present invention is limited to the implementation of G code programs for machining programming by UG, and can be implemented as long as the structure and program language are similar to those in this description.

[0073] The above is only a specific embodiment of the present invention, and the present invention is described in detail. The unexplained part is a conventional technology. However, the protection scope of the present invention is not limited to this. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for programming a structural hole, characterized in that: include: Step 1: Identify the structural hole feature module, where the structural hole feature module includes tool, fixture, programming origin, tool processing parameters, and processing tool path parameter features; Step 2: Analyze the 3D process parts to be compiled, complete the model parameterization setting of the parts and perform feature recognition; Step 3: Perform parameter feature fitting on the structural hole feature module and the three-dimensional process part model to be compiled, generate a modular model, and establish an equation through the parameter points of the modular model; Step 4: Identify the modular model, complete the parameterization of the modular model and perform feature recognition; Step 5: Remove the data set with gross errors and then perform fitting; Step 6: Adapt the model settings and fit the model accurately; Step 7: Identify similar structural holes to achieve the best fit between the part and the feature module; Step 8: Generate programming origin, tool processing parameters and other information; Step 9: Inherit the information in the module to the new parts.

2. The method according to claim 1, characterized in that In step 3, the least squares method is used for fitting.

3. The method according to claim 1, characterized in that Step 5: According to the preset threshold, complete the feature fitting of the similar model to the greatest extent possible.

4. The method according to claim 1, characterized in that: In step 2, parameterizing the model for feature recognition further includes: converting model points and lines in the original space into lines in the parameter space, or converting lines in the parameter space into points in the original space.

5. The method according to claim 4, characterized in that Create a mesh with the parameter resolution as the smallest unit.

6. The method according to claim 5, characterized in that The parameters are determined by voting to complete the model parameterization setting.

7. The method according to claim 1, characterized in that In step 5, the gross errors are removed by setting thresholds for different modules to determine convergence through the iteration number change curve.

8. The method according to claim 1, characterized in that In step 7, for some structures that cannot be identified, a human-computer interaction method is used to identify them.

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