Method for laser processing of discontinuous patterns on curved surfaces and laser processing method

Abstract

The application discloses a laser processing fragmentation method for a discontinuous pattern on a curved surface, comprising: pretreating a curved surface to be processed to obtain a UV development diagram of the curved surface to be processed; performing contour extraction on the UV development diagram based on pixel-level precision to obtain a UV contour diagram; mapping pixel points on the contour in the UV contour diagram back to the curved surface to be processed; clustering the contour in the envelope of the processing range of each preset galvanometer pose into a corresponding fragment, and recording each preset galvanometer pose; and converting the contour in each fragment from a workpiece coordinate system to a galvanometer coordinate system based on the corresponding preset galvanometer pose to complete fragmentation. The application extracts a contour based on pixel-level precision first, and then fragments based on the contour as the minimum unit, so that the fragmentation edge can reach pixel-level precision, thereby eliminating the seams while improving the effective processing range in the fragments and ensuring the processing efficiency.

Description

Technical Field

[0001] This application relates to the field of laser processing technology, and more specifically, to a laser processing method for segmenting discontinuous patterns on curved surfaces and a laser processing method. Background Technology

[0002] Laser galvanometers (hereinafter referred to as galvanometers) combined with five-axis machine tools are an important means of achieving laser etching of large and complex curved surfaces. The galvanometer is used for localized pattern processing, while the five-axis mechanism moves the galvanometer to process patterns in various localized areas of the curved surface, thereby achieving overall pattern processing on a large curved surface. Each localized curved surface processed by the galvanometer is called a processing segment. Due to limitations in the precision of the five-axis machine tool, the actual pose of the galvanometer often deviates from its theoretical pose, leading to seams between two adjacent processed segments and affecting the pattern quality of the curved surface.

[0003] To eliminate seams, there are currently two main methods for surface segmentation. Method 1: During texture segmentation design, a certain overlapping area is set, and specific processes (such as roughening, overlapping, and misalignment) are used to ensure a smooth transition at the seams. However, for different materials and laser etching depths, the process parameters for the overlapping areas vary, requiring extensive testing to avoid seams, thus reducing overall processing efficiency. Method 2: The texture is divided into several layers with smaller thicknesses. The seams of each layer are staggered on the surface, and each layer is processed sequentially to avoid excessively deep seams at any point on the surface, thereby reducing or even eliminating seams. However, since each layer requires a galvanometer at a different segmentation position, and the layer thickness cannot be too large, the number of galvanometer movements and the distance traveled on the five-axis machine tool will increase exponentially, further reducing overall processing efficiency.

[0004] For discontinuous etched patterns, due to the inherent gaps, existing technology has proposed a method: if the slicing method allows the seams of the slices to occur between discontinuous patterns, then visually, there will be no seams. However, this existing technology still performs slicing based on triangular facets, which leads to new problems: slicing uses triangular facets as the smallest unit. If the gaps between independent contours are small, excessively fine triangular facet division results in excessive computational load; conversely, insufficient division leads to triangular facets spanning multiple contours, requiring additional rejection within the slice. Rejection operations introduce additional computational burden and reduce the effective processing range of the galvanometer, preventing the slices from maximizing the utilization of the galvanometer's effective processing range, increasing the number of galvanometer movements required, and thus reducing overall processing efficiency to some extent. Summary of the Invention

[0005] To address at least one deficiency or improvement requirement of the prior art, this application provides a laser processing segmentation method and a laser processing method for non-continuous patterns on curved surfaces. This method enables the elimination of seams and the improvement of the effective processing range within the segmentation while ensuring processing efficiency during the laser etching of non-continuous patterns on large curved surfaces.

[0006] To achieve the above objectives, in a first aspect, this application provides a laser processing method for segmenting discontinuous patterns on a curved surface, comprising:

[0007] The surface to be processed is preprocessed to obtain the UV unfolded pattern of the surface to be processed;

[0008] The UV unfolded map is extracted with pixel-level precision to obtain a UV contour map.

[0009] The pixels on the contours in the UV contour map are mapped back to the surface to be processed; each contour in the UV contour map is composed of a set of points formed by sequentially connecting the pixels on that contour.

[0010] The contours within the galvanometer processing range envelope under each preset galvanometer pose are clustered into corresponding segments, and each preset galvanometer pose is recorded.

[0011] Based on the corresponding preset galvanometer pose, the contours in each segment are transformed from the workpiece coordinate system to the galvanometer coordinate system to complete the segmentation.

[0012] Furthermore, the contours within the galvanometer processing range envelope under each preset galvanometer pose are clustered into corresponding segments, including:

[0013] Step 41: Set all unclassified contours as set A;

[0014] Step 42: Take a profile from set A and mark it as an unused category m;

[0015] Step 43: Set all remaining unclassified contours as set B;

[0016] Step 44: Take a contour from set B and denote it as contour j;

[0017] Step 45: Based on contour j and all contours labeled as category m, solve for the pose of the galvanometer.

[0018] Step 46: Determine whether all pixels on contour j and contours of all categories m are within the envelope of the galvanometer processing range; if so, mark contour j as category m, and use the current galvanometer pose as the galvanometer pose of the slice of category m.

[0019] Step 47: Repeat steps 44 to 46 until all contours in set B have been traversed, completing the contour clustering of category m.

[0020] Step 48: Repeat steps 42 to 47 to complete the clustering and segmentation of all contours.

[0021] Furthermore, solving for the pose of the galvanometer includes:

[0022] The mean coordinates of all pixels on the contour involved in the calculation are calculated and used as the reference tool tip coordinates in the galvanometer coordinate system.

[0023] The mean normal coordinates of all pixels on the contour involved in the calculation are calculated and normalized to serve as the reference axial coordinates of the galvanometer coordinate system.

[0024] Solve for the angles between the two rotation axes of the galvanometer from the initial axial coordinate to the reference axial coordinate;

[0025] Based on the angles of the two rotation axes, the width and height directions of the galvanometer are solved, thus completing the pose calculation of the galvanometer.

[0026] Furthermore, the criteria for determining whether all pixels on contour j and contours of all categories m are within the envelope of the galvanometer processing range include:

[0027] The pixels on the outline are within the envelope formed by the width, height, and depth of focus of the horizontal plane of the galvanometer;

[0028] The angle between the normal of a pixel on the contour and the axis of the galvanometer is no greater than the angle range of the normal cone.

[0029] Furthermore, the preprocessing includes:

[0030] The surface to be processed is divided into triangular meshes to obtain sorted triangular facets and the three-dimensional coordinates of the vertices of the triangular facets;

[0031] The triangular meshing is non-uniformly divided with the maximum allowable linear deviation and the maximum allowable normal angle deviation as constraints.

[0032] Furthermore, based on pixel-level precision, contour extraction is performed on the UV unfolded map to obtain a UV contour map, including:

[0033] UV texture mapping is performed on the UV unfolded map, and the textured map is binarized and the outline of the binary map is extracted to obtain the UV outline map.

[0034] The grayscale threshold for binarization is set to the grayscale value that minimizes the difference between the grayscale values ​​of the area to be processed and the area not to be processed.

[0035] Furthermore, it also includes:

[0036] After extracting the contours, if a contour is completely surrounded by another contour, then that contour is merged into the other contour and combined into a single contour for subsequent processing.

[0037] Furthermore, mapping the pixels on the contour in the UV contour map back to the surface to be processed includes:

[0038] Based on the mapping relationship between the vertices of the triangular facets in the UV unwrapping diagram, obtain the index of the triangular facet to which the pixel on the contour in the UV unwrapping diagram belongs.

[0039] Obtain vertex information of a triangle based on its index;

[0040] By using the vertex information of the triangular facets, the centroid projection parameters of the pixels on the contour in the UV unfolded diagram are solved, thus obtaining the coordinates and normals of the pixels on the contour on the surface to be processed.

[0041] Secondly, this application provides a laser processing method for discontinuous patterns on curved surfaces. After slicing using any of the aforementioned methods, the contours in each slice are filled with scanning trajectories in the galvanometer coordinate system, and the corresponding slice contour surfaces are etched using the galvanometer at each galvanometer pose.

[0042] Thirdly, this application provides an apparatus including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, enables the processing unit to perform the steps of any of the aforementioned sharding methods.

[0043] In summary, compared with the prior art, the above-described technical solutions conceived in this application can achieve the following beneficial effects:

[0044] This application first extracts the contour with pixel-level precision, and then segments the pieces with the contour as the smallest unit. This allows the edges of the segments to achieve pixel-level precision, thereby eliminating seams while increasing the effective processing range within the segments and ensuring processing efficiency. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A core flowchart of a laser processing method for segmenting discontinuous patterns on a curved surface, provided in an embodiment of this application;

[0047] Figure 2 This application provides a core flowchart illustrating a laser processing method for segmenting discontinuous patterns on a curved surface, as illustrated in an embodiment of the present application.

[0048] Figure 3 A flowchart of contour clustering provided for embodiments of this application;

[0049] Figure 4 A schematic diagram of the coordinate system and processing range of the laser galvanometer provided in the embodiments of this application;

[0050] exist Figure 4 In this context, 1-laser galvanometer; 1.1-axis of the laser galvanometer; 1.2-tool tip of the laser galvanometer; 1.3-height of the horizontal plane of the laser galvanometer; 1.4-coordinate system of the laser galvanometer; 1.5-depth of focus of the laser galvanometer; 1.6-width of the horizontal plane of the laser galvanometer; 1.7-envelope of the laser galvanometer's processing range; 1.8-focal plane of the laser galvanometer;

[0051] Figure 5 A block diagram illustrating an apparatus suitable for implementing the slicing method described above, provided as an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Furthermore, the technical features involved in the various embodiments described below can be combined with each other as long as they do not conflict with each other.

[0053] The terms "comprising" or "having," and any variations thereof, in the specification, claims, or drawings of this application are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0054] As described in the background section of this specification, existing technologies often suffer from low overall processing efficiency when eliminating seams. Therefore, this application provides a laser processing method for segmenting discontinuous patterns on curved surfaces, and a laser processing method thereof, which enables the elimination of seams while increasing the effective processing area within the segment and ensuring processing efficiency during laser etching of discontinuous patterns on large curved surfaces.

[0055] refer to Figure 1 and Figure 2 One embodiment of this application proposes a laser processing method for segmenting discontinuous patterns on large, complex curved surfaces. This segmentation method may specifically include the following steps.

[0056] Step 1: Preprocess the surface to be processed to obtain the UV unfolded image of the surface to be processed.

[0057] In some embodiments, specifically, the curved surface of the car body requires laser etching of multiple independent logos (non-continuous patterns) to extract the 3D model of the car body. UV unwrapping is then performed to generate a 2D planar image. The UV unwrapped image uses a U (horizontal axis) and V (vertical axis) coordinate system to map each vertex of the 3D model to a 2D texture space, ensuring that the texture pixels accurately correspond to the model surface. This process is similar to disassembling a 3D packaging box into a planar design.

[0058] More specifically, the following steps are performed sequentially: extracting the surface to be processed from the 3D model of the car body, dividing the triangle mesh (to obtain the sorted triangular facets and the 3D coordinates of the vertices of the triangular facets), solving the vertex normal (to obtain the normal coordinates of the vertices of the triangular facets), and UV unwrapping, to obtain the 2D image of the surface to be processed after UV unwrapping (i.e., the UV unwrapped image), and obtaining the 2D coordinates of the vertices of the sorted triangular facets in the UV unwrapped image.

[0059] Preferably, the triangular mesh is non-uniformly divided under constraints of the maximum allowable linear deviation and the maximum allowable normal angle deviation. That is, a finer mesh is used for areas with significant surface variations, while a coarser mesh is used for areas with less surface variation. This non-uniform division reduces computational load while maintaining accuracy, thus balancing accuracy and computational efficiency and improving overall processing efficiency.

[0060] Step 2: Extract the contour of the UV unfolded map with pixel-level precision to obtain the UV contour map.

[0061] In some embodiments, specifically, the UV unfolded map is sequentially subjected to UV texture mapping, the textured map is binarized, and the contours are extracted from the binary map (for example, all logo contours are extracted with pixel-level precision) to obtain a UV contour map. Each contour in the UV contour map can be described as a set of points formed by sequentially connecting the pixels on the contour.

[0062] Preferably, the grayscale threshold for binarization is set to the grayscale value that minimizes the difference between the grayscale values ​​of the area to be processed and the unprocessed area. When processing the curved surface of a car body with subtle pattern differences, accurately setting the grayscale threshold for binarization ensures accurate differentiation between the area to be processed and the unprocessed area, improves the accuracy of contour extraction, and provides a more reliable basis for subsequent segmentation.

[0063] Preferably, after contour extraction, if a contour is completely surrounded by another contour, then that contour is merged into the other contour, becoming a single contour for subsequent processing. This operation halves the number of nested pattern segments, thus reducing invalid segments. The inner circle also does not require separate positioning, preventing the galvanometer from running idle and further improving overall processing efficiency.

[0064] Step 3: Map the pixels on the contours in the UV contour map back to the surface to be processed; each contour in the UV contour map is composed of a set of points formed by sequentially connecting the pixels on that contour. In some embodiments, this includes the following sub-steps.

[0065] Step 31: Based on the mapping relationship of each vertex of the 3D triangular facet (the triangular facet is derived from the triangular mesh division) in the UV unfolding diagram (this mapping relationship is derived from the UV unfolding in Step 1), obtain the index number (i.e. the sequence number of the triangular facet) to which each contour pixel point (pixel point on the contour) belongs in the UV unfolding diagram.

[0066] Step 32: Obtain the coordinates of the three vertices of the triangle in the UV unfolded map based on the triangle's index number. , and 3D coordinates , , and legal direction , , .

[0067] Step 33: In the UV unwrapping diagram, solve for the centroid projection parameters α, β, and γ of the pixels on the contour in the UV unwrapping diagram. The solution formula is as follows:

[0068]

[0069] in, This represents the pixel coordinates of the outline pixels in the UV unfolded map.

[0070] Step 34: Based on parameters α, β, and γ, solve for the coordinates and normals of the contour pixels on the 3D surface. The solution formula is as follows:

[0071]

[0072] in, and These represent the coordinates and normals of the contour pixels on the triangular mesh, respectively, which are approximated as the coordinates and normals on the 3D surface.

[0073] When mapping pixels on the contour in the UV profile map back to the surface to be processed, sub-pixel precision is used, and the 3D coordinate mapping error can be less than 0.005 mm. The introduction of normal information preserves the surface characteristics, ensures perpendicular laser incidence, and makes the etching depth uniform.

[0074] Step 4: Cluster the contours within the galvanometer processing range envelope under each preset galvanometer pose into a corresponding segment, and record each preset galvanometer pose. (Reference) Figure 3 and Figure 4 In some embodiments, the logo outlines that can be covered by the same galvanometer pose are clustered into a segment (e.g., all small logos on the left side of the vehicle are clustered into segment A), which specifically includes the following sub-steps.

[0075] Step 41: Set all uncategorized outlines to {A}. For example, the set of uncategorized outlines is A = {Logo1, Logo2, Logo3}.

[0076] Step 42: Take one outline from {A} and mark it as an unused category tag m. For example, mark Logo1 as category m.

[0077] Step 43: Set all remaining unclassified contours to {B}.

[0078] Step 44: Take one contour from {B} and set it as contour j.

[0079] Step 45: Based on contour j and all contours labeled as category m, solve for the pose of the galvanometer. The specific process for solving the galvanometer pose includes the following steps:

[0080] Step 451: Calculate the mean coordinates of all pixels on the contour involved in the calculation, and use them as the reference tool tip point (i.e., the position of the galvanometer) coordinates of the galvanometer coordinate system.

[0081] Step 452: Calculate the mean normal coordinates of all pixels on the contour involved in the calculation, and normalize them to serve as the reference axis coordinates (i.e., the orientation of the galvanometer) of the galvanometer coordinate system.

[0082] Step 453: Based on the standard inverse kinematics method, solve for the angles of the two rotation axes of the galvanometer from the initial axial coordinate to the reference axial coordinate (there is one and only one solution).

[0083] Step 454: Based on the standard forward kinematics method, solve for the width and height directions of the galvanometer based on the angles of the two rotation axes, and complete the pose solution of the galvanometer.

[0084] When dealing with large molds with complex curved surfaces, this method is used to solve the galvanometer pose, ensuring that each piece can be effectively processed under the appropriate galvanometer pose. This improves the accuracy and efficiency of galvanometer pose solving and provides a better foundation for subsequent processing.

[0085] Step 46: Determine whether all pixels on contour j and the contours of all categories m are within the envelope of the galvanometer processing range; if so, mark contour j as category m, and record the current galvanometer pose as M, using M as the galvanometer pose of the slice of category m. For example, determine whether Logo2 can share the galvanometer pose with Logo1 (calculate the envelope of the galvanometer processing range); if they can share, merge it into category m.

[0086] Step 47: Repeat steps 44-46 until all contours in {B} have been traversed, completing the contour clustering of category m. For example, traversing Logo3, if the galvanometer pose cannot be shared, a new category n is created.

[0087] Step 48: Repeat steps 42 to 47 to complete the clustering and segmentation of all contours.

[0088] This embodiment employs dynamic optimization segmentation, adaptively merging contours that can be processed in co-pose, thereby reducing the number of segments. Furthermore, it avoids redundant computation, requiring only the traversal of unclassified contours, thus improving clustering efficiency.

[0089] The constraints on the galvanometer's processing range include the width and height range of the galvanometer's horizontal plane, the depth of focus range along the axial direction, and the range of the angle between the galvanometer and the galvanometer's axial direction. These can be referenced. Figure 4 This embodiment takes into account the processing range constraints of the galvanometer and rationally divides the curved surface into segments, ensuring that each segment can be effectively processed within the processing range of the galvanometer. This improves the rationality of segmentation and the stability of the processing process, thereby ensuring processing quality.

[0090] The criteria for determining whether all pixels on contour j and contours of all categories m are within the envelope of the galvanometer processing range include:

[0091] Criterion 1: The pixels on the contour are within the envelope formed by the width, height, and depth of focus of the horizontal plane of the galvanometer.

[0092] Criterion 2: The angle between the normal of a pixel on the contour and the axis of the galvanometer is not greater than the angle range of the normal cone.

[0093] For example, determine whether Logo2 can be clustered with Logo1. If they can be clustered, the following two conditions must be met.

[0094] 1. Envelope box detection: All pixels of Logo2 must be within the range of the galvanometer aperture (200 mm wide × 150 mm high) and the depth of focus (±5 mm).

[0095] 2. Normal angle detection: The angle between the normal at each point and the galvanometer axis is ≤30° (within the normal cone range).

[0096] This configuration effectively eliminates contours that extend beyond the envelope, preventing uneven etching depth caused by galvanometer defocusing and ensuring processing quality. It also automatically avoids invalid clusters, further reducing process setup time, thereby minimizing trial-and-error costs and improving processing efficiency.

[0097] Step 5: Based on the corresponding preset galvanometer pose, transform the contours in each segment from the workpiece coordinate system to the galvanometer coordinate system to complete the segmentation.

[0098] Existing technologies typically use triangular facets as the unit of segmentation, segmenting first and then processing the contour (segmentation accuracy is limited by the accuracy of the triangular mesh). This application, however, first extracts the contour point set, then segments, with segmentation accuracy constrained by pixel precision. That is, it first extracts the contour with pixel-level precision, and then segments using the contour as the smallest unit, enabling pixel-level precision at the segment edges. This eliminates seams while increasing the effective processing range within each segment and ensuring processing efficiency.

[0099] Another embodiment of this application provides a laser processing method for discontinuous patterns on curved surfaces. This method employs the segmentation method described in the previous embodiment to complete the segmentation, then fills the contour of each segment with a scanning trajectory in the galvanometer coordinate system, and etches the corresponding segment contour surfaces at various galvanometer poses. This eliminates seams while improving overall processing efficiency.

[0100] Figure 5 A block diagram schematically illustrates an apparatus suitable for implementing the slicing method described above, according to an embodiment of this application. Figure 5 The device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 5 As shown, the device 1000 described in this embodiment includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage portion 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the sliced ​​method flow according to embodiments of this application.

[0102] RAM 1003 stores various programs and data required for the operation of device 1000. Processor 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Processor 1001 executes various operations of the fragmentation method flow according to embodiments of this application by executing programs in ROM 1002 and / or RAM 1003. It should be noted that the programs may also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 may also execute various operations of the fragmentation method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0103] According to embodiments of this application, device 1000 may further include an input / output (I / O) interface 1005, which is also connected to bus 1004. Device 1000 may also include one or more of the following components connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0104] The fragmentation method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the fragmentation method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by processor 1001, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, and / or units described above can be implemented using computer program modules.

[0105] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or it may exist independently without being assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the steps of the fragmentation method according to the embodiments of this application can be implemented.

[0106] According to embodiments of this application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include one or more memories other than the ROM 1002 and / or RAM 1003 described above.

[0107] It should be noted that the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product.

[0108] The flowcharts and / or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowcharts and / or block diagrams may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. Furthermore, it should be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0109] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the technical features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, and all such combinations and / or combinations fall within the scope of this application.

[0110] Although this application has been shown and described with reference to specific exemplary embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made to this application without departing from the spirit and scope of the application as defined by the appended claims and their equivalents. Therefore, the scope of this application should not be limited to the above embodiments, but should be determined not only by the appended claims, but also by their equivalents.

Claims

1. A method for laser processing and slicing discontinuous patterns on curved surfaces, characterized in that, include: The surface to be processed is preprocessed to obtain the UV unfolded pattern of the surface to be processed; The UV unfolded map is extracted with pixel-level precision to obtain a UV contour map. The pixels on the contours in the UV contour map are mapped back to the surface to be processed; each contour in the UV contour map is composed of a set of points formed by sequentially connecting the pixels on that contour. The contours within the galvanometer processing range envelope under each preset galvanometer pose are clustered into corresponding segments, and each preset galvanometer pose is recorded. Based on the corresponding preset galvanometer pose, the contours in each segment are transformed from the workpiece coordinate system to the galvanometer coordinate system to complete the segmentation.

2. The segmentation method as described in claim 1, characterized in that, The contours within the galvanometer processing range envelope under each preset galvanometer pose are clustered into corresponding segments, including: Step 41: Set all unclassified contours as set A; Step 42: Take a profile from set A and mark it as an unused category m; Step 43: Set all remaining unclassified contours as set B; Step 44: Take a contour from set B and denote it as contour j; Step 45: Based on contour j and all contours labeled as category m, solve for the pose of the galvanometer. Step 46: Determine whether all pixels on contour j and contours of all categories m are within the envelope of the galvanometer processing range; if so, mark contour j as category m, and use the current galvanometer pose as the galvanometer pose of the slice of category m. Step 47: Repeat steps 44 to 46 until all contours in set B have been traversed, completing the contour clustering of category m. Step 48: Repeat steps 42 to 47 to complete the clustering and segmentation of all contours.

3. The segmentation method as described in claim 2, characterized in that, Solving for the pose of the galvanometer includes: The mean coordinates of all pixels on the contour involved in the calculation are calculated and used as the reference tool tip coordinates in the galvanometer coordinate system. The mean normal coordinates of all pixels on the contour involved in the calculation are calculated and normalized to serve as the reference axial coordinates of the galvanometer coordinate system. Solve for the angles between the two rotation axes of the galvanometer from the initial axial coordinate to the reference axial coordinate; Based on the angles of the two rotation axes, the width and height directions of the galvanometer are solved, thus completing the pose calculation of the galvanometer.

4. The segmentation method as described in claim 2, characterized in that, The criteria for determining whether all pixels on contour j and contours of all categories m are within the envelope of the galvanometer processing range include: The pixels on the outline are within the envelope formed by the width, height, and depth of focus of the horizontal plane of the galvanometer; The angle between the normal of a pixel on the contour and the axis of the galvanometer is no greater than the angle range of the normal cone.

5. The segmentation method as described in claim 1, characterized in that, The preprocessing includes: The surface to be processed is divided into triangular meshes to obtain sorted triangular facets and the three-dimensional coordinates of the vertices of the triangular facets; The triangular meshing is non-uniformly divided with the maximum allowable linear deviation and the maximum allowable normal angle deviation as constraints.

6. The segmentation method as described in claim 1, characterized in that, Based on pixel-level precision, the UV unfolded map is subjected to contour extraction to obtain a UV contour map including: UV texture mapping is performed on the UV unfolded map, and the textured map is binarized and the outline of the binary map is extracted to obtain the UV outline map. The grayscale threshold for binarization is set to the grayscale value that minimizes the difference between the grayscale values ​​of the area to be processed and the area not to be processed.

7. The segmentation method as described in claim 1, characterized in that, Also includes: After extracting the contours, if a contour is completely surrounded by another contour, then that contour is merged into the other contour and combined into a single contour for subsequent processing.

8. The segmentation method as described in claim 5, characterized in that, Mapping the pixels on the contour in the UV contour map back to the surface to be processed includes: Based on the mapping relationship between the vertices of the triangular facets in the UV unwrapping diagram, obtain the index of the triangular facet to which the pixel on the contour in the UV unwrapping diagram belongs. Obtain vertex information of a triangle based on its index; By using the vertex information of the triangular facets, the centroid projection parameters of the pixels on the contour in the UV unfolded diagram are solved, thus obtaining the coordinates and normals of the pixels on the contour on the surface to be processed.

9. A laser processing method for discontinuous patterns on a curved surface, characterized in that, After segmentation is completed using the segmentation method described in any one of claims 1-8, the contour of each segment is filled with a scanning trajectory in the galvanometer coordinate system, and the corresponding segment contour surface is etched by the galvanometer at each galvanometer pose.

10. A device, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, enables the processing unit to perform the steps of the sharding method according to any one of claims 1-8.

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