A laser galvanometer scanning partitioning method based on isoparametric line spirals

Abstract

The present application belongs to the technical field of curved surface laser subtractive machining, and particularly relates to a complex curved surface partitioning method based on laser etching machining, which can optimize partitioning strategy and improve machining efficiency and quality. The specific process of the method is as follows: the coordinates and normal vectors of discrete points of a curved surface to be machined are extracted; a specified point in a machining area is selected as an iteration starting point, and a spiral scanning method based on an equal parameter line is used for iteration; a mirror machining range, a laser focal depth and a laser incidence angle are selected as constraint conditions, and once any of the above conditions is not satisfied, the iteration of the partitioning is ended; a seed point is continuously selected, and the above steps are repeated until the partitioning of all machining areas is completed.

Description

Technical Field

[0001] This invention belongs to the field of curved surface laser subtractive processing technology, specifically relating to a laser galvanometer scanning partitioning method based on an isoparametric linear spiral. Background Technology

[0002] Complex surfaces typically possess numerous curvature characteristics, and the curvature at each point on the surface changes continuously. Machining complex surfaces has always been a challenging and crucial aspect of modern manufacturing, and its processing technology has attracted considerable attention from scholars both domestically and internationally. Partitioning the machining process for complex surfaces is one of the effective methods for achieving high-precision and high-efficiency machining.

[0003] Surface partitioning algorithms are techniques used to divide complex surfaces into smaller, more manageable regions. These algorithms have applications in various fields, such as CNC machining in manufacturing, rendering optimization in computer graphics, and region analysis in scientific research. The key to surface partitioning is how to divide a continuous surface into finite, individually processable segments while minimizing transition errors between these segments. The choice of surface partitioning typically depends on the specific application requirements, such as machining accuracy, computational efficiency, or specific geometric constraints.

[0004] The first category is surface partitioning methods based on the shape and specific meaning of the surface. Typical surface algorithms include region growing and cluster analysis. Yang Yongtao et al. proposed an improved K-means 3D point cloud data segmentation method using curvature constraints. This method segmentes the point cloud based on the curvature information and the distance between points, following the K-means approach, solving problems such as large variations in surface features and sensitivity to local information in reverse reconstruction of 3D point clouds. Jia Hui et al. proposed a 3D mesh segmentation method based on regional discrete curvature. This method automatically acquires and pre-segments the feature points of the mesh model, and uses geodesic distance and curvature extrema to perform watershed segmentation of the 3D model, achieving high segmentation edge accuracy and fast segmentation speed. Katz et al. proposed a hierarchical mesh decomposition algorithm. This algorithm segments the given mesh into meaningful parts in deeply concave regions, avoiding over-segmentation between components and jagged boundaries.

[0005] The second category is methods that partition surfaces based on their geometric features. Chen et al., based on surface information such as concavity / convexity and curvature, used a combination of fuzzy clustering and Voronoi diagrams to divide complex surfaces into a series of easily machinable surfaces, which can reduce machining costs. Zhu et al. proposed a surface partitioning method based on a combination of clustering and normal vector direction cones. This method uses binary space to partition the parameter domain into regular partitioned surfaces, which can achieve surface machining for 3+2 axis equipment. Giri et al. divided surfaces into different partitions according to the principle of curvature similarity and used the edge of each surface as the main tool path. Lin Jieqiong proposed a complex surface partitioning algorithm based on curvature and a combination of fuzzy C-median partitioning. This algorithm can classify surfaces according to similar curvature, and experiments have shown that the algorithm can significantly improve polishing efficiency.

[0006] The third category is clustering methods for laser processing partitions. Wang Fuyang designed a region model segmentation algorithm that meets the needs of laser processing. This algorithm is based on the idea of ​​region expansion and is the core of the laser 3D model processing. Dang Yao proposed using bounding box technology to automatically segment different models and designed a simplification method for 3D industrial models, which can effectively improve the movement efficiency of the laser head.

[0007] The above-mentioned surface partitioning methods can process large and complex curved surfaces, and each method has its own unique advantages. However, these partitioning methods cannot meet the needs of large-format complex curved surfaces for galvanometer laser processing. Galvanometer processing requires each partition to meet the requirements of processing area, laser focal depth, and laser incident angle, and requires that the dividing lines between each partition be relatively regular, and also requires to reduce splicing errors caused by irregular surface partitioning. Summary of the Invention

[0008] In view of this, the present invention provides a method for partitioning complex curved surfaces based on laser etching. This method, based on the specific limitations and process requirements of galvanometer processing, can optimize the partitioning strategy and improve processing efficiency and quality.

[0009] The technical solution for implementing the present invention is as follows:

[0010] A method for partitioning complex curved surfaces based on laser etching is described below:

[0011] (1) Extract the surface to be machined from the workpiece and represent it discretely, then extract the characteristic parameters of the discrete points;

[0012] (2) Set the galvanometer machining constraints and select the center point of the surface as the initial point;

[0013] (3) Starting from the initial point, the region expansion iteration is carried out using the spiral scanning method based on isoparameter lines. Each time the region expansion iteration is carried out, it is determined whether the current partition meets the constraint conditions. If it does, the current partition is further expanded and the constraint conditions are judged. Otherwise, the iteration is terminated, and the current partition is returned to the region obtained by the previous iteration expansion. That is, the partition region of the previous step of this expansion iteration is recorded as this partition, and then step (4) is entered.

[0014] (4) Determine whether the surface to be processed has been partitioned. If yes, proceed to step (5). Otherwise, update the seed point candidate library: select the point on the boundary of the current partition as the seed point, assign weights to the newly added seed points, and add them to the seed point candidate library; select the point with the highest priority in the seed point candidate library as the initial point of the new partition, and return to step (3).

[0015] (5) Determine whether the partitioning result meets the processing requirements. If it does, the partitioning ends and the surface partitioning result is output. Otherwise, update the seed point allocation weight, select the point with the highest priority as the initial point, and return to step (3).

[0016] Furthermore, in step (3) of the present invention, the region expansion iteration is performed starting from the initial point using a spiral scanning method based on isoparameter lines. The specific process is as follows: when performing the region expansion iteration in the horizontal direction, the row above or below the partition is merged into this partition; when performing the region expansion iteration in the vertical direction, the column to the left or right of the partition is merged into this partition. The partition obtained in each iteration is a rectangular region.

[0017] Furthermore, in step (3) of the present invention, the region expansion iteration is performed using a spiral scanning method based on isoparameter lines in a counterclockwise manner. If the iteration reaches the partitioned part, the iteration position is changed according to the principle of edge transfer.

[0018] Furthermore, in step (4) of the present invention, the midpoints of the four edges of the current partition and the vertices of the four edges are selected as seed points, and the weight of the midpoints of the four edges is higher than that of the vertices of the four edges.

[0019] Furthermore, in step (4) of the present invention, the product of the distance between the seed point and the center point and the corresponding weight of the seed point is defined as the priority.

[0020] Furthermore, in step (1) of the present invention, non-uniform rational B-splines (NURBS) are used to represent the surface to be processed, thereby realizing the discrete representation of the surface to be processed.

[0021] Furthermore, the constraints in step (2) of the present invention are: setting the galvanometer processing area, laser focal depth and laser incident angle as constraints.

[0022] Furthermore, this invention utilizes Siemens NX software to model the workpiece with curved surfaces to be machined.

[0023] Furthermore, the characteristic parameters of the discrete point described in this invention include the discrete point coordinates, normal vector, and principal curvature.

[0024] Furthermore, the present invention calculates and extracts point coordinates, normal vectors, and principal curvature information using UG / Open.

[0025] Beneficial effects

[0026] First, this invention proposes a laser galvanometer scanning partitioning method based on an isoparametric linear spiral for curved surface partitioning in galvanometer laser processing, which can effectively solve the requirements of galvanometer processing range and processing incident angle for each partition.

[0027] Secondly, the partition lines of this invention are relatively regular, and all partitions are rectangular, which reduces splicing errors caused by irregular boundaries of curved partitions.

[0028] Third, all partitions in this invention are approximately square rectangles, which match the processing range of conventional galvanometers, thus maximizing the processing range of each partition and improving processing efficiency.

[0029] Fourth, the present invention meets all processing requirements from the initial design stage, eliminating the trouble of secondary division. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of a workpiece with a curved surface where laser light is incident.

[0032] Figure 2 This is a flowchart of the partitioning method in an embodiment of this application;

[0033] Figure 3 It is a large-format curved surface structure model;

[0034] Figure 4 This is a schematic diagram of the discretization of the UV direction of the curved surface;

[0035] Figure 5 This is a schematic diagram of the counterclockwise spiral scanning algorithm for isoparameter lines;

[0036] Figure 6 A diagram showing the partitioning results for a spiral scan with equal parameters for partition number N=40.

[0037] Figure 7 The results of spiral scanning partitioning with parameters N=20, 60, 100, 140, etc.

[0038] Figure 8 The algorithm partitioning results are shown for different curved surfaces. Detailed Implementation

[0039] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0040] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0041] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0042] The above process is described in detail below:

[0043] The main equipment limitation affecting surface partitioning is the size of the X and Y processing areas of the galvanometer. Generally speaking, the processing area of ​​the galvanometer is in the range of 40mm-100mm. The complex surface partitioning processed in this embodiment has a processing area between 1mm and 300mm, therefore, due to the processing range of the laser galvanometer, it is necessary to limit the processing range of a single operation.

[0044] The main factors affecting the processing effect of curved surface partitioning are the laser incident angle and the depth of focus. The laser incident angle refers to the angle between the laser beam and the normal to the material surface, such as... Figure 1 As shown, the focusing and energy distribution of the laser beam change with the incident angle. When the laser is incident perpendicularly to the material surface, the energy concentration is highest, resulting in the best processing effect. When the laser is not incident perpendicularly, the shape of the spot on the material surface changes from circular to elliptical, which alters the energy distribution in the processing area and may lead to uneven processing results. To optimize the laser processing effect on curved materials, it is usually necessary to limit the range of a single laser processing pass and ensure that the laser is as perpendicular as possible to the surface of each processing point.

[0045] The depth of focus characteristic of a laser refers to the range within which the laser spot size and energy distribution can be considered constant. This range is called the laser depth of focus. Figure 1 As shown. The theoretical formula for calculating the focused spot is:

[0046]

[0047] Where 2ω0 is the size of the focused spot, M 2 λ is the beam quality factor, λ is the laser wavelength, f is the focal length of the lens, and D is the laser spot size at incident.

[0048] The theoretical formula for calculating depth of focus is:

[0049]

[0050] Where Z represents the laser focal depth.

[0051] This can be understood as follows: the focusing depth is directly proportional to the square of the laser wavelength λ and the lens focal length f, and inversely proportional to the square of the diameter of the spot incident on the focusing lens surface. To ensure processing consistency, the curved workpiece must be within the laser's focal depth range during laser processing; therefore, the processing range of a single laser operation needs to be limited. Thus, it is necessary to further partition complex curved surfaces, and a partitioning method should be established to simultaneously satisfy constraints such as the galvanometer's processing area, laser incident angle, and focal depth during a single laser operation.

[0052] The design concept of this invention is as follows: The coordinates and normal vectors of discrete points on the surface to be processed are extracted along the directions of isoparametric lines u and v; a specified point within the processing area is selected as the starting point for iteration, and an iterative process is performed using a spiral scanning method based on isoparametric lines; the galvanometer processing area, laser focal depth, and laser incident angle are selected as constraints, and the iteration of that partition ends if any of these conditions are not met; seed points are then selected, and the above steps are repeated until the partitioning of all processing areas is completed. Therefore, this invention, based on the specific limitations and process requirements of galvanometer processing, can optimize the partitioning strategy and improve processing efficiency and quality.

[0053] This application provides an embodiment of a method for partitioning complex curved surfaces based on laser etching, such as... Figure 2 As shown, the specific process is as follows:

[0054] (1) Extract the surface to be processed from the workpiece and express it using non-uniform rational B-spline (NURBS). Then extract the surface features such as the coordinates of discrete points and normal vectors of the NURBS surface according to the direction of parametric lines such as u and v.

[0055] (2) Set the galvanometer processing area, laser focal depth and laser incident angle as constraints, and select the center point of the NURBS surface as the initial point.

[0056] (3) Starting from the initial point, the region expansion iteration is carried out using the spiral scanning method based on isoparameter lines. Each time the region expansion iteration is carried out, it is determined whether the current partition meets the constraint conditions. If it does, the current partition is further expanded and the constraint conditions are judged. Otherwise, the iteration is terminated and the current partition is returned to the region obtained by the previous iteration expansion. That is, the partition region of the previous step of this expansion iteration is recorded as this partition, and then step (4) is entered.

[0057] (4) Has the NURBS surface been partitioned? If yes, proceed to step (5); otherwise, update the seed point candidate library: select the point on the boundary of the current partition as the seed point, assign priority weight to the newly added seed point, and add it to the seed point candidate library; select the point with the highest priority in the seed point candidate library as the initial point of the new partition, and return to step (3).

[0058] (5) Determine whether the partitioning result meets the processing requirements. If it does, the partitioning ends and the surface partitioning result is output. Otherwise, update the seed point allocation weight, select the point with the highest priority as the initial point, and return to step (3).

[0059] The above process will be explained in detail below:

[0060] To implement the surface partitioning processing strategy, it is necessary to obtain the feature information of the surface to be processed. This embodiment uses the commercial modeling software Siemens NX to model the workpiece and extract surface information. The large-format curved surface structure model designed in this embodiment is as follows: Figure 3 As shown, the concave surface of this surface is the machining section. Therefore, the surface to be machined can be extracted from the upper surface of the workpiece, resulting in a surface to be machined expressed as a non-uniform rational B-spline (NURBS). Then, based on the geometric properties of the NURBS surface, the surface to be machined is discretized along the u and v directions in the parameter domain, as shown... Figure 4 As shown, the density of discrete points only needs to meet the requirements of machining accuracy and the partitioning algorithm. This embodiment uses UG / Open for secondary development to calculate and extract information such as point coordinates, normal vectors, and principal curvatures.

[0061] Surface partitioning needs to simultaneously satisfy constraints such as the galvanometer's processing area, laser incident angle, and focal depth. Due to the u and v parameter domain characteristics of NURBS surfaces, this embodiment proposes a spiral scanning partitioning method based on isoparametric lines. This algorithm takes the processing range X and Y, laser focal depth Z, allowable incident angle θ, and discrete point information of the freeform surface as input, and outputs isoparametric dividing lines. It can achieve partitioning of arbitrarily complex surfaces, with the dividing lines parallel or perpendicular to the isoparametric lines.

[0062] Using the NURBS surface parameter domain as the iteration domain, a point on the surface to be processed is selected as the starting point, serving as the starting point for partitioning iterations. At this point, there is only one point within the partition. Iterations are expanded counter-clockwise from this point outwards. The resulting partitions are approximately square rectangles within the NURBS surface parameter domain. A schematic diagram of the counter-clockwise scan is shown below. Figure 5 As shown. For example, when expanding upwards, the row number of the existing partition is incremented by one, and the row above the partition is merged into this partition. When expanding to the left, the column number of the existing partition is incremented by one, and the column to the left of the partition is merged into this partition. Each expansion counts as one iteration. Each iteration checks whether the processing area of ​​the galvanometer, laser incident angle, and focal depth are satisfied. If satisfied, the iteration continues; otherwise, the iteration terminates, and the partitioned area of ​​the previous step is recorded as this new partition. Figure 6 The image shown is a diagram of the partitioning results using a spiral scan with a parameter N=40.

[0063] After completing the iteration of one partition, the next partition iteration center point, i.e., the seed point, needs to be selected. The midpoints and vertices of the four edges of each partition are added to the seed point candidate pool. The weights of the midpoints and vertices of the four edges are defined by the user; generally, the weight of the midpoint is higher than that of the vertices. The product of the distance to the center point and the corresponding weight is defined as the selection priority. The points are sorted according to priority, and the point with the highest priority is selected as the next partition iteration center point. Then, the selection proceeds according to priority... Figure 5 Iterative scanning is performed using a counter-clockwise spiral scanning method.

[0064] When iterating outwards in a counter-clockwise direction, if the iteration reaches an already partitioned area, the iteration position is changed according to the principle of edge transfer. For example, when expanding upwards, if the current row is a region of another partition, the iteration is changed to expanding downwards. If the partition does not meet the processing conditions or accuracy requirements, the priority weight of the seed point is continuously adjusted until all the surface areas are partitioned.

[0065] This application addresses the zoning problem in laser processing of curved galvanometers by establishing a spiral scanning method based on NURBS surfaces, which effectively solves the zoning requirements. It allows for flexible adjustment of seed point weights, optimizing unsatisfactory zoning results. The zoning achieves relatively regular dividing lines, maximizes the zoning processing area, and eliminates the need for secondary zoning. Figure 7-8 As shown, Figure 7 The results of spiral scanning partitioning with parameters N=20, 60, 100, 140, etc. Figure 8 The algorithm partitioning results are shown for different curved surfaces.

[0066] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for partitioning complex curved surfaces based on laser etching, characterized in that, The specific process is as follows: (1) Extract the surface to be machined from the workpiece and represent it discretely, then extract the characteristic parameters of the discrete points; (2) Set the galvanometer machining constraints and select the center point of the surface as the initial point; (3) Starting from the initial point, the region expansion iteration is carried out using the spiral scanning method based on isoparameter lines. Each time the region expansion iteration is carried out, it is determined whether the current partition meets the constraint conditions. If it does, the current partition is further expanded and the constraint conditions are judged. Otherwise, the iteration is terminated, and the current partition is returned to the region obtained by the previous iteration expansion. That is, the partition region of the previous step of this expansion iteration is recorded as this partition, and then step (4) is entered. (4) Determine whether the surface to be processed has been partitioned. If yes, proceed to step (5). Otherwise, update the seed point candidate library: select the point on the boundary of the current partition as the seed point, assign weights to the newly added seed points, and add them to the seed point candidate library; select the point with the highest priority in the seed point candidate library as the initial point of the new partition, and return to step (3). (5) Determine whether the partitioning result meets the processing requirements. If it does, the partitioning ends and the surface partitioning result is output. Otherwise, update the seed point allocation weight, select the point with the highest priority as the initial point, and return to step (3).

2. The method for partitioning complex curved surfaces based on laser etching as described in claim 1, characterized in that, Step (3) begins with the initial point and uses a spiral scanning method based on isoparameter lines to perform region expansion iteration. The specific process is as follows: when performing region expansion iteration in the horizontal direction, the row above or below the partition is merged into this partition; when performing region expansion iteration in the vertical direction, the column to the left or right of the partition is merged into this partition. Each iteration results in a rectangular region.

3. The method for partitioning complex curved surfaces based on laser etching as described in claim 2, characterized in that, Step (3) Use the spiral scanning method based on isoparameter lines to expand the region in a counterclockwise direction. If the iteration reaches the partitioned part, change the iteration position according to the principle of edge transfer.

4. The method for partitioning complex curved surfaces based on laser etching according to claim 2, characterized in that, Step (4) involves selecting the midpoints and vertices of the four edges of the current partition as seed points, with the weight of the midpoints of the four edges being higher than that of the vertices of the four edges.

5. The method for partitioning complex curved surfaces based on laser etching as described in claim 2, characterized in that, In step (4), the product of the distance between the seed point and the center point and the corresponding weight of the seed point is defined as the priority.

6. The method for partitioning complex curved surfaces based on laser etching as described in claim 1, characterized in that, Step (1) Use non-uniform rational B-splines to represent the surface to be processed, thereby realizing the discrete representation of the surface to be processed.

7. The method for partitioning complex curved surfaces based on laser etching according to claim 2, characterized in that, The constraints in step (2) are: setting the galvanometer processing area, laser focal depth and laser incident angle as constraints.

8. The method for partitioning complex curved surfaces based on laser etching according to claim 1, characterized in that, In step (1), Siemens NX software is used to model the workpiece with curved surface to be machined.

9. The method for partitioning complex curved surfaces based on laser etching according to claim 1, characterized in that, The characteristic parameters of the discrete point include the discrete point coordinates, normal vector, and principal curvature.

10. The method for partitioning complex curved surfaces based on laser etching according to claim 9, characterized in that, The discrete point coordinates, normal vectors, and principal curvature information are calculated and extracted using UG / Open.

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