A micro-drill edge trimming method and device, electronic equipment and storage medium

Abstract

This application provides a method, apparatus, electronic device, and storage medium for dressing micro-drill cutting edges, including: determining the feature to be dressed based on a three-dimensional model of the surface of the micro-drill to be dressed; wherein the feature to be dressed is a cutting edge line and / or a groove surface; filtering the three-dimensional geometric data of the feature to be dressed to generate smooth target geometric elements; determining the material removal allowance distribution based on the deviation between the target geometric elements and the original geometric elements of the feature to be dressed; and controlling a laser processing device to perform laser dressing processing on the feature to be dressed based on the material removal allowance distribution. This solution solves the problems of cumbersome process, poor consistency, and poor accuracy of traditional micro-drill cutting edge dressing methods, and improves the accuracy, consistency, and overall automation level of the micro-drill cutting edge dressing process.

Description

Technical Field

[0001] The present invention relates to the field of digital control technology, and in particular to a micro-drill cutting edge dressing method, apparatus, electronic device, and storage medium. Background Technology

[0002] In micro-drill manufacturing, limitations in machining precision often lead to microscopic wavy defects on the cutting edge. At the same time, the surface texture of the spiral groove is difficult to polish, affecting the cutting performance and life of the tool.

[0003] Currently, laser trimming technology is typically used to acquire the tool's morphology through 3D scanning, followed by manual calibration and path planning, and then processing using a single laser source to achieve digital trimming. However, this method is highly dependent on manual operation. Not only do the 3D imaging require manual pasting of identification points, but the trimming path also relies on experience for manual selection, resulting in a cumbersome process with poor consistency, making it impossible to achieve high-precision automated trimming. Summary of the Invention

[0004] This application provides a micro-drill cutting edge dressing method, apparatus, electronic device, and storage medium to solve the problems of cumbersome process, poor consistency, and poor precision in traditional micro-drill cutting edge dressing methods, thereby improving the precision, consistency, and overall automation level of the micro-drill cutting edge dressing process.

[0005] In a first aspect, embodiments of this application provide a method for trimming the cutting edge of a micro drill, the method comprising:

[0006] Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be dressed, the features to be dressed are determined; wherein, the features to be dressed are the cutting edge line and / or the groove surface.

[0007] The three-dimensional geometric data of the feature to be modified is filtered to generate a smooth target geometric element.

[0008] The material removal allowance distribution is determined based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0009] Based on the material removal allowance distribution, the laser processing device is controlled to perform laser trimming on the feature to be trimmed.

[0010] Secondly, embodiments of this application also provide a micro-drill edge dressing device, the device comprising:

[0011] The feature to be repaired module is used to determine the features to be repaired based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired; wherein, the features to be repaired are cutting edge lines and / or groove surfaces;

[0012] The target feature determination module is used to filter the three-dimensional geometric data of the feature to be modified to generate smooth target geometric features.

[0013] The material removal allowance determination module is used to determine the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0014] The micro-drilling trimming module is used to control the laser processing device to perform laser trimming on the feature to be trimmed based on the material removal allowance distribution.

[0015] Thirdly, embodiments of this application also provide an electronic device, which includes:

[0016] One or more processors;

[0017] Storage device for storing one or more programs.

[0018] When one or more programs are executed by one or more processors, the one or more processors implement a micro-drill cutting edge dressing method as described in any of the embodiments of this application.

[0019] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a micro-drill cutting edge dressing method as described in any of the embodiments of this application.

[0020] This application provides a method for dressing micro-drill cutting edges. The method includes: determining the features to be dressed, including cutting edge lines and / or groove surfaces, based on a three-dimensional model of the surface of the micro-drill to be dressed; filtering the three-dimensional geometric data of the features to be dressed to generate smooth target geometric elements; determining the material removal allowance distribution based on the deviation between the target geometric elements and the original geometric elements of the features to be dressed; and finally, controlling a laser processing device to perform laser dressing on the features to be dressed based on the material removal allowance distribution. The technical solution of this application automatically identifies the features to be dressed based on the three-dimensional model of the micro-drill surface, generates target geometric elements using filtering, accurately calculates the material removal allowance distribution, and ultimately drives the laser processing device to perform dressing. This achieves full automation from feature recognition and path planning to processing execution, solving the problems of cumbersome process, poor consistency, and low accuracy in traditional micro-drill cutting edge dressing methods, and improving the accuracy, consistency, and overall automation level of the micro-drill cutting edge dressing process. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the embodiments to be described in this application, and not all of them. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0022] Figure 1 A schematic flowchart illustrating a micro-drill cutting edge trimming method provided in an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the five-axis linkage platform involved in this embodiment;

[0024] Figure 3 This is a schematic front view of the three-dimensional model of the micro-drill surface involved in this embodiment;

[0025] Figure 4 This is a side view of the three-dimensional model of the micro-drill surface involved in this embodiment;

[0026] Figure 5 This is a flowchart illustrating another micro-drill edge dressing method provided in an embodiment of this application;

[0027] Figure 6 This is a schematic diagram illustrating the determination of the feature to be trimmed, including the cutting edge line, in this embodiment.

[0028] Figure 7 A schematic flowchart illustrating another micro-drill cutting edge dressing method provided in an embodiment of this application;

[0029] Figure 8 A schematic flowchart illustrating another micro-drill cutting edge dressing method provided in an embodiment of this application;

[0030] Figure 9 This is a schematic diagram of a micro-drill cutting edge dressing device provided in an embodiment of this application;

[0031] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0033] Before introducing the technical solution provided in the embodiments of this application, the application scenarios of the solution can be explained first. This embodiment is applicable to various scenarios that require high-precision dressing of micro-drill cutting edges. Currently, although laser dressing methods based on manual intervention are widely used in tool repair, traditional methods have obvious limitations. In practical applications, due to the small size of micro-drills, the complex morphology of cutting edges and grooves, and the high requirements for machining accuracy, and the need to simultaneously improve geometric accuracy and surface quality during the dressing process, traditional dressing methods heavily rely on operator experience for calibration and path planning, making it difficult to achieve full-process automation and intelligent optimization. This easily leads to problems such as poor dressing consistency, low efficiency, and difficulty in guaranteeing accuracy. Therefore, there is an urgent need for a dressing method that can automatically identify the features to be dressed, intelligently generate dressing paths, and precisely control the machining process to improve the accuracy, efficiency, and process consistency of micro-drill cutting edge dressing. This embodiment focuses on generating a precise material removal allowance distribution. Under the premise of strictly adhering to the original morphology and target geometry matching relationship, the laser processing device is driven to perform trimming processing, thereby ensuring the automation and intelligence of the trimming process and effectively improving the trimming quality and process stability of the micro-drill edge.

[0034] Example 1

[0035] Figure 1 This is a flowchart illustrating a micro-drill cutting edge dressing method provided in an embodiment of this application. This embodiment is applicable to various situations requiring high-precision dressing of micro-drill cutting edges. The method can be executed by a micro-drill cutting edge dressing device, which can be implemented in the form of software and / or hardware. The hardware can be a controller, such as a mobile terminal, PC, or server.

[0036] like Figure 1 As shown, the micro-drill cutting edge dressing method provided in this embodiment of the invention includes the following steps:

[0037] S110. Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired, determine the features to be repaired.

[0038] Among them, a micro-drill to be repaired refers to a miniature drill bit entity that requires geometric accuracy or surface quality repair. A micro-drill is a cutting tool with a tiny diameter used for high-precision drilling, and its cutting edge and spiral groove surface and other key geometric features require extremely high dimensional and morphological accuracy. A micro-drill surface 3D model refers to a set of digital 3D data that characterizes the actual surface morphology of the micro-drill, obtained through 3D scanning.

[0039] In this embodiment, a three-dimensional scanning data of the micro-drill to be repaired can be jointly acquired by a five-axis linkage platform and a high-precision line laser scanner, thereby constructing a three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired based on the three-dimensional scanning data.

[0040] Figure 2This is a schematic diagram of a five-axis linkage platform. The following section will use this platform to explain how to acquire 3D scanning data. For example... Figure 2 As shown, the X, Y, and Z axes of the five-axis linkage platform are standard spatial coordinate axes, the A-axis is the rotation axis for clamping the workpiece (i.e., the micro-drill to be dressed in this embodiment), and the C-axis is the rotation axis for clamping the mounting base. In practical applications, the micro-drill to be dressed is first firmly clamped in the three-jaw chuck of the five-axis linkage platform, and its overall tilt angle is adjusted using the C-axis to optimize the scanning angle. Subsequently, by coordinating the control of the A-axis (driving the micro-drill to rotate continuously around its own axis) and the Z-axis (performing precise axial micro-movements), the high-precision line laser scanner can completely scan the surface of the micro-drill along a preset path, ensuring that all parts of the cutting edge line and the surface of the spiral groove are captured with high density and high precision, ultimately generating comprehensive three-dimensional point cloud data, which is the three-dimensional scanning data.

[0041] After obtaining the 3D point cloud data, a 3D model of the micro-drill surface can be constructed using the following processing flow:

[0042] First, the raw 3D point cloud data is preprocessed, including removing background noise and outliers generated during the scanning process, and smoothing the point cloud data to suppress high-frequency measurement errors. Then, using coordinate registration technology, multiple point clouds obtained from different perspectives are unified into the same micro-drill workpiece coordinate system, forming a complete and seamless point cloud dataset.

[0043] Next, a point cloud simplification algorithm is used to reduce the amount of data while maintaining feature accuracy, thereby improving computational efficiency. Based on this, a surface reconstruction algorithm (such as Poisson reconstruction or triangulation) is employed to convert the discrete point cloud data into a continuous, closed 3D mesh model composed of triangular facets. This mesh model constitutes the preliminary 3D model of the micro-drill surface.

[0044] Finally, the reconstructed model undergoes repair and optimization, including filling any potential holes, smoothing irregular meshes, and ensuring the correctness of the model's manifold structure. The resulting 3D mesh model serves as the precise digital base for subsequent intelligent feature recognition, baseline path generation, and machining allowance calculation; it is essentially the 3D model of the micro-drill surface.

[0045] Understandably, the process of constructing a 3D model of the micro-drill surface is not time-bound. This model can be pre-built and stored, or it can be scanned and constructed in real-time when the micro-drill needs to be trimmed. This solution does not impose such limitations; its core requirement is to obtain an accurate 3D model of the micro-drill surface corresponding to the micro-drill to be trimmed, serving as the data foundation for subsequent intelligent recognition and path planning.

[0046] The feature to be repaired refers to the specific geometric structure or region identified from the 3D model of the micro-drill surface that needs to be repaired. The feature to be repaired is the cutting edge line and / or the groove surface. It can be understood that the feature to be repaired in this embodiment can be one or a combination of the cutting edge line and the groove surface. The cutting edge line refers to the spatial curve edge of the main cutting edge. The groove surface refers to the inner surface of the helical groove. For example, when the central axis of the 3D model of the micro-drill surface is placed parallel to the horizontal plane... Figure 3 This is a schematic front view of the 3D model of the micro-drill surface. Figure 3 The marked area in the middle groove is the surface of the groove. Figure 3 The curve marked by the dashed line is the cutting edge line. Figure 4 This is a side view of a 3D model of the micro-drill surface. Similarly, Figure 4 The curve marked by the dashed line is the cutting edge line.

[0047] Specifically, the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired is used as the processing object. The specific geometric structures that need to be repaired or optimized are identified and extracted from the three-dimensional model of the micro-drill surface. These identified structures are uniformly defined as features to be repaired, and their specific contents are cutting edge lines and / or groove surfaces.

[0048] For example, after loading an acquired 3D model of a micro-drill surface, by analyzing the model data, it can be identified that the curve of the main cutting edge has wavy undulations, which can be identified as the cutting edge line that needs to be repaired; if obvious vibration marks are also identified on the inner surface of the spiral groove, it can be identified as the groove surface that needs to be repaired; finally, these identified specific problem structures are determined as the features to be repaired in this repair operation.

[0049] S120. Filter the three-dimensional geometric data of the features to be modified to generate smooth target geometric elements.

[0050] Among them, three-dimensional geometric data refers to the original three-dimensional data extracted from the three-dimensional model of the micro-drill surface, which is used to accurately characterize the actual spatial shape and position of the feature to be repaired.

[0051] Among them, the target geometric element refers to a digital model generated after applying a filtering algorithm to the three-dimensional geometric data, which represents the ideal smooth geometric shape (such as a smooth curve or surface) that the feature should achieve after repair.

[0052] In this embodiment, a smoothing filtering algorithm can be applied to the original three-dimensional geometric data extracted from the three-dimensional model of the micro-drill surface, which characterizes the actual morphology of the cutting edge line or groove surface. This algorithm outputs a theoretically smooth and continuous idealized geometric shape, which serves as the smooth target geometric element to guide subsequent laser trimming. The core function of the smoothing filtering algorithm is to suppress or eliminate high-frequency components or noise representing microscopic defects, waviness, or roughness in the data, while preserving its macroscopic reference geometric profile.

[0053] For example, for the features to be trimmed on the cutting edge line, a set of three-dimensional coordinate points representing a cutting edge line with wavy undulations can be obtained, which is the three-dimensional geometric data. Then, a Gaussian low-pass filtering algorithm is applied to the three-dimensional coordinate point set to filter out the high-frequency fluctuation components representing the wavy undulations. Finally, a smooth and continuous ideal curve is calculated and generated. This generated ideal curve is the smooth target geometric element used to guide laser trimming.

[0054] For example, for the features to be repaired on the surface of a groove, three-dimensional point cloud or mesh data representing a groove surface with vibration marks can be obtained, which is three-dimensional geometric data. Then, a Gaussian low-pass filtering algorithm is applied to the surface data to filter out the high-frequency fluctuation components representing vibration marks and micro-roughness. Finally, an ideal smooth surface is calculated and generated. This generated ideal smooth surface is the smooth target geometric element used to guide the laser polishing process.

[0055] S130. Determine the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0056] Here, the original geometric element refers to the unfiltered 3D geometric data directly extracted from the 3D model of the micro-drill surface, representing the actual shape of the feature to be repaired. The deviation refers to the vertical or normal distance between each point on the original geometric element and the corresponding smoothed target geometric element in 3D space.

[0057] Among them, the material removal allowance distribution is a quantitative mapping relationship formed on the spatial location of the feature to be repaired, based on the calculated deviation of all points, describing the thickness or depth of the material to be removed at each point.

[0058] Specifically, the spatial distance between the smoothed target geometry and the corresponding original geometry can be calculated point by point. This distance reflects the scale of the original surface's outward bulge or inward depression relative to the ideal shape. All calculation results are arranged according to spatial position to form a continuous distribution map, which is the material removal allowance distribution. The material removal allowance distribution can be used to guide the thickness of material to be removed and the energy input required by the laser processing device at each position.

[0059] For example, for a cutting edge line, the vertical distance from each point on its original wavy curve (original geometry) to the corresponding smooth target curve (target geometry) can be calculated. This series of distance values ​​constitutes the material removal allowance distribution along the length of the cutting edge line. For a groove surface, the normal distance from each point on its original rough surface (original geometry) to the corresponding smooth target surface (target geometry) can be calculated. The resulting full-field distance data constitutes the three-dimensional material removal allowance distribution covering the groove surface.

[0060] S140. Based on the material removal allowance distribution, control the laser processing device to perform laser trimming processing on the feature to be trimmed.

[0061] Laser processing equipment refers to physical equipment systems used to perform final material removal and finishing tasks.

[0062] In practical applications, the material thickness or depth information that needs to be removed at each point in the three-dimensional space of the feature to be repaired can be used as the direct processing basis, which is quantitatively defined by the material removal allowance distribution. The CNC system drives the multi-axis motion platform of the laser processing device and the laser output parameters, so that the laser focus can accurately scan the surface of the feature to be repaired along a specific path, and the processing energy or scanning strategy can be adjusted in real time. This achieves the point-by-point and layer-by-layer removal of excess material on the original geometric elements until its shape matches the smooth target geometric elements, thus completing the repair.

[0063] This application provides a method for dressing micro-drill cutting edges. The method includes: determining the features to be dressed, including cutting edge lines and / or groove surfaces, based on a three-dimensional model of the surface of the micro-drill to be dressed; filtering the three-dimensional geometric data of the features to be dressed to generate smooth target geometric elements; determining the material removal allowance distribution based on the deviation between the target geometric elements and the original geometric elements of the features to be dressed; and finally, controlling a laser processing device to perform laser dressing on the features to be dressed based on the material removal allowance distribution. The technical solution of this application automatically identifies the features to be dressed based on the three-dimensional model of the micro-drill surface, generates target geometric elements using filtering, accurately calculates the material removal allowance distribution, and ultimately drives the laser processing device to perform dressing. This achieves full automation from feature recognition and path planning to processing execution, solving the problems of cumbersome process, poor consistency, and low accuracy in traditional micro-drill cutting edge dressing methods, and improving the accuracy, consistency, and overall automation level of the micro-drill cutting edge dressing process.

[0064] Based on the above embodiments, the cutting edge trimming method further includes: when it is determined from the three-dimensional model of the micro-drill surface that there are geometric fluctuations in the cutting edge line that need trimming, the cutting edge line is determined as a first priority feature to be trimmed; when it is determined from the three-dimensional model of the micro-drill surface that there are surface defects in the groove surface that need trimming, the groove surface is determined as a second priority feature to be trimmed; wherein, the second priority is lower than the first priority.

[0065] Based on the above embodiments, optionally, the specific implementation method of controlling the laser processing device to perform laser trimming on the features to be trimmed may include: performing laser trimming on the cutting edge line and the groove surface in sequence according to the priority order of the features to be trimmed.

[0066] In this embodiment, after analyzing the 3D model of the micro-drill surface, if geometric fluctuations in the cutting edge line and surface defects in the groove surface are detected simultaneously, the cutting edge line is set as the first feature to be processed, and the groove surface is set as the feature to be processed subsequently, based on the logic of correcting macroscopic geometric errors first and then optimizing surface quality. This ensures that laser processing is performed sequentially with the first priority preceding the second priority, and that the processing is not performed in parallel or reversed order. In this way, by establishing a processing sequence rule based on the first priority of the cutting edge line and the second priority of the groove surface, the macroscopic geometric accuracy (cutting edge waviness), which has the most direct impact on the key cutting performance of the micro-drill, is ensured to be corrected preferentially and independently. This provides a more stable and accurate geometric reference for subsequent surface polishing (for groove ripples), thereby optimizing the logic and efficiency of the processing process as a whole, avoiding problems such as repeated processing, reference loss, or a decrease in final overall accuracy due to improper processing sequence, and improving the reliability of the method and the predictability of the processing quality.

[0067] Example 2

[0068] Figure 5 This is a schematic diagram of a micro-drill cutting edge dressing method provided in an embodiment of this application. Based on the foregoing embodiments, this embodiment provides a more detailed description of steps S110 and S120. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0069] like Figure 5 As shown, the method specifically includes the following steps:

[0070] S210. Based on the three-dimensional model of the micro-drill surface, extract the cutting edge line and the inner surface area of ​​the groove of the micro-drill.

[0071] The cutting edge line refers to the three-dimensional continuous curve extracted from the three-dimensional model of the micro-drill surface, representing the spatial orientation and sharp edge of the main cutting edge. The inner surface region of the groove refers to the three-dimensional closed region extracted from the three-dimensional model of the micro-drill surface, representing the complete curved surface of the inner wall of the helical groove.

[0072] Specifically, the extraction of the cutting edge line of a micro-drill can be achieved by: performing point cloud processing on the 3D model of the micro-drill surface to separate the region representing the cutting edge; and identifying and extracting continuous cutting edge lines based on the 3D data of the region representing the cutting edge. Similarly, the extraction of the inner surface region of the groove of a micro-drill can be achieved by: performing point cloud processing on the 3D model of the micro-drill surface to separate the region representing the helical groove; and reconstructing a continuous inner surface model of the groove based on the 3D data of the region representing the helical groove.

[0073] S220. Based on the three-dimensional spatial curve of the cutting edge line, determine the geometric fluctuation of the cutting edge line.

[0074] Among them, geometric fluctuation refers to a scalar value used to quantitatively describe the degree of macroscopic fluctuation of the cutting edge line deviating from an ideal smooth straight line or curve within its preset evaluation length range.

[0075] Specifically, the overall undulation of the three-dimensional spatial curve of the cutting edge line within a preset length range can be calculated and evaluated, thereby outputting a specific value to quantify its macroscopic unevenness, which is the geometric fluctuation amount.

[0076] In this embodiment, optionally, the specific steps for determining the geometric fluctuation of the cutting edge line based on the three-dimensional spatial curve of the cutting edge line may include:

[0077] (1) Based on the three-dimensional space curve, determine the highest and lowest points of the contour within the preset evaluation length.

[0078] The preset evaluation length refers to a specific length range of the curve selected in advance for quantitative calculation and evaluation of the geometric fluctuation of the cutting edge line.

[0079] Specifically, on the three-dimensional spatial curve of the cutting edge line, a section of the curve corresponding to the preset evaluation length is intercepted. By comparing the spatial height coordinates of all points on this section of the curve, the point with the maximum height value (i.e., the highest point) and the point with the minimum height value (i.e., the lowest point) are found.

[0080] (2) Determine the geometric fluctuation of the cutting edge line based on the height difference between the highest and lowest points.

[0081] Specifically, the spatial height coordinates of the highest point and the lowest point of the contour within the preset evaluation length can be subtracted, and the absolute difference can be defined as the geometric fluctuation of the cutting edge line within the evaluation range.

[0082] S230. Based on the three-dimensional point cloud data of the inner surface region of the trench, determine the surface roughness parameters of the inner surface region of the trench.

[0083] Among them, the surface roughness parameter refers to a scalar index used to quantitatively describe the degree of undulation of the microscopic surface profile in the inner surface region of the trench.

[0084] Specifically, the three-dimensional point cloud data that represents the complete morphology of the inner surface of the trench can be extracted from the three-dimensional model of the micro-drill surface. These data can be calculated and analyzed to output a scalar value that can quantitatively represent the micro-roughness of the surface in that area. This value is defined as the surface roughness parameter.

[0085] In this embodiment, optionally, the specific implementation method for determining the surface roughness parameter of the inner surface region of the trench based on the three-dimensional point cloud data of the inner surface region of the trench may include: determining the arithmetic mean deviation used to characterize the overall undulation of the surface based on the three-dimensional point cloud data of the inner surface region of the trench, and determining the arithmetic mean deviation as the surface roughness parameter.

[0086] Among them, the arithmetic mean deviation is a parameter used to quantitatively describe the overall surface roughness of the inner surface area of ​​the trench. It is calculated by taking the arithmetic mean of the absolute values ​​of the vertical distances from each sampling point on the surface to the evaluation reference surface of the area.

[0087] Specifically, based on the three-dimensional point cloud data of the inner surface area of ​​the trench, the arithmetic mean of the absolute values ​​of the vertical distances from all sampling points in the area to their evaluation reference surface can be calculated, and the calculated arithmetic mean deviation value can be used as a surface roughness parameter to quantitatively evaluate the overall level of the surface micro-irregularity.

[0088] S240. If the geometric fluctuation exceeds the first preset threshold, then the feature to be repaired is determined to include the cutting edge line.

[0089] The first preset threshold refers to a pre-set critical value used to determine whether the geometric fluctuation amount constitutes a defect that needs to be repaired.

[0090] In this embodiment, when the calculated geometric fluctuation value of the cutting edge line is greater than the first preset threshold, it is determined that the cutting edge line has an unqualified macroscopic geometric defect and is included in the range of features to be repaired in this repair operation.

[0091] For example, the first preset threshold can be set to 0.3 μm. Figure 6 To determine the feature to be trimmed, including the cutting edge line, a schematic diagram is shown, such as... Figure 6 As shown below, the geometric fluctuation is 1.1, which exceeds the first preset threshold of 0.3, so the feature to be trimmed includes the cutting edge line.

[0092] S250. If the surface roughness parameter exceeds the second preset threshold, then the feature to be repaired is determined to include the groove surface.

[0093] The second preset threshold is a pre-set critical value used to determine whether the surface roughness parameter constitutes a defect that needs to be repaired.

[0094] In this embodiment, when the calculated surface roughness parameter value of the inner surface area of ​​the trench is greater than the preset second threshold, it is automatically determined that there are unqualified micro-surface defects on the surface of the trench, and it is included in the range of features to be repaired in this repair operation.

[0095] S260. Filter the three-dimensional geometric data of the features to be modified to generate smooth target geometric elements.

[0096] In this embodiment, optionally, when the feature to be trimmed is a cutting edge line, the target geometric element includes a target three-dimensional trimming path. The target three-dimensional trimming path refers to a smooth, continuous curve in three-dimensional space generated by filtering and reconstructing the three-dimensional spatial data of the cutting edge line. This curve defines the spatial motion trajectory that the focus of the laser processing device needs to precisely follow during the trimming process.

[0097] Based on this, the specific steps for filtering the 3D geometric data of the features to be modified to generate smooth target geometric elements may include:

[0098] (1) Based on the three-dimensional spatial data of the cutting edge line, determine the projection profile of the cutting edge line on the theoretical cylindrical surface.

[0099] Specifically, based on the original three-dimensional spatial coordinate data of the cutting edge line, it can be mapped onto an ideal cylindrical surface with the theoretical rotation axis of the micro-drill as its center through geometric projection. This results in a two-dimensional unfolded curve on this cylindrical surface, which is the projected profile. In this way, the complex cutting edge line fluctuation problem in three-dimensional space is simplified into a profile filtering problem on a two-dimensional plane through projection transformation, which greatly reduces the complexity of data processing and the amount of computation.

[0100] (2) Perform low-pass digital filtering on the projected contour to obtain a smooth two-dimensional contour curve.

[0101] Specifically, the two-dimensional curve data of the projected profile can be processed by a low-pass digital filter. The function of this filter is to attenuate or filter out the components representing high-frequency geometric fluctuations and noise in the curve, while retaining its low-frequency macroscopic trend, thereby outputting an idealized smooth two-dimensional profile curve that eliminates micro-ripples.

[0102] (3) Based on the smooth two-dimensional contour curve, determine the target three-dimensional maintenance path that matches the spatial position of the cutting edge line.

[0103] Specifically, the smooth two-dimensional contour curve obtained after filtering can be transformed back into the real three-dimensional space from the theoretical cylindrical surface through inverse geometric mapping, forming a smooth three-dimensional curve that is consistent with the spatial direction of the original cutting edge line but eliminates waviness defects and can be used to directly drive the laser focus motion. This curve is the target three-dimensional trimming path. By generating the three-dimensional trimming path through inverse mapping, it is ensured that the final smooth target path is perfectly matched with the original cutting edge in spatial position, thus providing a spatial motion trajectory that is both geometrically accurate and efficiently calculable for laser trimming, effectively improving trimming accuracy and system processing efficiency.

[0104] In this embodiment, optionally, when the feature to be repaired is a groove surface, the target geometric element includes a target polishing reference surface. The target polishing reference surface refers to a three-dimensional surface generated by performing low-pass digital filtering on the original three-dimensional geometric model of the groove surface. This surface defines the ideal geometric shape that the groove surface should achieve after polishing and serves as the geometric reference for calculating the material removal allowance and planning the laser polishing path.

[0105] Based on this, the specific steps for filtering the 3D geometric data of the features to be modified to generate smooth target geometric elements include:

[0106] (1) The three-dimensional geometric model of the groove surface is subjected to low-pass digital filtering to obtain a smooth three-dimensional reference surface.

[0107] Specifically, the original three-dimensional geometric model representing the actual morphology of the groove surface can be filtered by a low-pass digital filter. This filter works in the three-dimensional spatial domain and can filter out high-frequency fluctuations in the model surface data, such as micro-ripples and roughness, while retaining its macroscopic surface shape, thereby outputting a smooth and ideal three-dimensional surface model that eliminates micro-irregularities. This model is the smooth three-dimensional reference surface.

[0108] (2) Based on the smooth three-dimensional reference surface, determine the target polishing reference surface corresponding to the shape of the groove surface.

[0109] In this embodiment, the smooth three-dimensional reference surface generated after low-pass digital filtering can be used as the geometric target for laser polishing. This surface is the target polishing reference surface. The target polishing reference surface maintains the same macroscopic shape as the original groove surface, but achieves an ideal smoothness at the microscopic scale. This provides a precise, uniform, and calculable geometric ideal target for the laser polishing process, allowing subsequent material removal allowance calculations and polishing path planning to be based on a clear and stable digital reference. This fundamentally ensures the consistency and controllability of the surface quality after polishing and improves the accuracy and efficiency of the entire finishing process.

[0110] S270. Based on the deviation between the target geometric element and the original geometric element of the feature to be repaired, determine the distribution of material removal allowance.

[0111] S280. Based on the material removal allowance distribution, control the laser processing device to perform laser trimming processing on the feature to be trimmed.

[0112] The technical solution of this application, when determining the features to be repaired based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired, extracts the cutting edge line and the inner surface area of ​​the groove from the three-dimensional model of the micro-drill surface, and uses geometric fluctuation to evaluate the defects of the cutting edge line and surface roughness parameters to evaluate the defects of the inner surface area of ​​the groove. This establishes an objective and accurate basis for defect identification and judgment. Furthermore, by comparing the calculated index values ​​with preset thresholds, it can automatically and reliably determine what type of defects exist on the micro-drill, thereby realizing intelligent identification and decision-making of the features to be repaired. This provides accurate data input and logical starting point for subsequent targeted repair processes, improving the automation level and processing accuracy of the entire method.

[0113] Example 3

[0114] Figure 7 This is a schematic diagram of a micro-drill cutting edge dressing method provided in an embodiment of this application. Based on the foregoing embodiments, this embodiment provides a more detailed description of steps S130 and S140. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0115] like Figure 7 As shown, the method specifically includes the following steps:

[0116] S310. Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired, determine the features to be repaired.

[0117] Among them, the features to be repaired are the cutting edge line and / or the groove surface.

[0118] S320. Filter the three-dimensional geometric data of the features to be modified to generate smooth target geometric elements.

[0119] S330. Based on the target geometric elements, determine the target position corresponding to each sampling point on the feature to be modified.

[0120] The target position corresponding to each sampling point refers to the theoretical ideal space coordinates that are pre-set on the smooth target geometric elements and correspond one-to-one with each sampling point on the feature to be repaired.

[0121] Specifically, on the smooth target geometric features (smooth curves or surfaces), a series of location points can be selected according to the spatial distribution rules that match the original geometric data of the features to be repaired. The three-dimensional spatial coordinates of these location points are defined as the theoretical ideal position that each original sampling point should reach after repair, that is, the target position corresponding to each sampling point.

[0122] S340. Based on the original geometric elements of the features to be modified, determine the original actual position of each sampling point.

[0123] The original actual position of each sampling point refers to the actual measured spatial coordinates of the sampling point on the original geometric element of the feature to be repaired, obtained from the three-dimensional model of the micro-drill surface.

[0124] Specifically, a series of spatial coordinate points corresponding to the target location point can be read from the original geometric elements that characterize the actual shape of the feature to be repaired, or obtained through interpolation calculation. These coordinate points are defined as the original actual position of each sampling point before repair.

[0125] S350. Based on the deviation distance between the original actual position of each sampling point and its corresponding target position, determine the material removal allowance at each sampling point.

[0126] Here, deviation distance refers to the straight-line distance in space between the original actual position of the same sampling point and its corresponding target position. Material removal allowance refers to the thickness or depth of the material layer that needs to be removed at the sampling point to achieve its actual shape at the target position.

[0127] In this embodiment, the straight-line distance in space between the original actual position coordinates of each sampling point and its corresponding target position coordinates can be calculated, which is the deviation distance. This distance value is then converted into the thickness of the material layer that needs to be removed at that point to achieve the transformation from the actual morphology to the target morphology according to certain rules. This thickness value is the material removal allowance at that sampling point.

[0128] S360. Based on the material removal allowance at all sampling points, determine the overall material removal allowance distribution covering the feature to be repaired.

[0129] Specifically, the independent material removal allowance data calculated at all sampling points can be integrated and spatially interpolated according to their corresponding spatial locations to form a continuous, complete, and quantified three-dimensional data field within the entire geometric region of the feature to be repaired. This data field can comprehensively describe the thickness or depth of the material to be removed at any point on the feature surface, thus obtaining the material removal allowance distribution.

[0130] S370. Based on the distribution of material removal allowance, identify the primary repair area on the feature to be repaired where the material removal requirement is greater than the first preset value.

[0131] Material removal requirement refers to the amount of material that needs to be removed at any point on the feature to be trimmed, as indicated by the overall material removal allowance distribution. The first preset value is a pre-defined allowance threshold used to prioritize trimming. The primary trimming area refers to the critical parts identified as requiring priority processing.

[0132] Specifically, by comparing the material removal demand value at each point in the material removal margin distribution with a pre-set first preset value, all spatial points whose material removal demand value exceeds the threshold value are screened and marked. The continuous or discrete local spatial range formed by these marked points is identified as the primary trimming area that needs to be prioritized.

[0133] S380, Control the first scanning device in the laser processing apparatus to perform a first laser scan on the primary trimming area to complete contour trimming or major undulation removal.

[0134] The first scanning device refers to a laser scanning processing unit equipped in the laser processing apparatus, used to perform high-intensity or high-efficiency material removal tasks. It is specifically configured to rapidly and extensively trim the contours or remove major surface undulations in the identified primary trimming areas. Optionally, the specific parameters of the first scanning device include: a nanosecond laser head; a wavelength of 355nm (shorter wavelengths are better absorbed by hard alloys, resulting in high processing efficiency); a pulse width of ~15ns; an average power of 5W-15W (for micro-drilling with small removal volumes, excessively high power is unnecessary); a repetition frequency of 10-100kHz; and a maximum scanning speed of 2000mm / s.

[0135] Optionally, when performing the first laser scan on the primary trimming area, the scanning parameters of the first scanning device can be configured as follows: power of 8W; frequency of 50kHz; scanning speed of 300mm / s; processing actions and switching logic.

[0136] Set the roughing pass to 1 pass.

[0137] Specifically, after configuring the scanning parameters of the first scanning device, the first scanning device in the laser processing device can be driven to make its laser focus accurately scan and cover the primary trimming area according to the planned path, quickly remove most of the excess material in the area, thereby achieving preliminary correction of the macroscopic contour of the area or significant reduction of its main surface undulations.

[0138] S390. After completing the first laser scan, control the second scanning device in the laser processing device to perform a second laser scan on the entire area of ​​the feature to be repaired, so as to complete the surface micro-smoothing process.

[0139] The second scanning device refers to another laser scanning processing unit equipped in the laser processing apparatus, used to perform high-precision or high-quality surface treatment tasks. It is specifically configured to perform a fine scan of the entire area of ​​the feature to be repaired after the first scanning device has completed rough processing, in order to achieve microscopic smoothing and final finishing of the surface. Optionally, the specific parameters of the second scanning device include: a picosecond laser head; a wavelength of 532nm; a pulse width of <15ps; an average power of 2W-8W; a repetition frequency of 100kHz-2MHz; and a scanning speed of up to 5000mm / s.

[0140] Optionally, when performing a second laser scan on the entire area, the scanning parameters of the second scanning device can be configured as follows: power of 3W; frequency of 800kHz; scanning speed of 2000mm / s; processing actions and switching logic.

[0141] Set the finishing pass to 1 pass.

[0142] Specifically, after the primary trimming area has been preliminarily trimmed by the first scanning device, the second scanning device in the laser processing unit is then driven to perform a full-coverage fine scan of the entire feature surface to be trimmed, including the primary trimming area and the remaining areas, with its laser focus following an optimized path. By setting a lower laser energy and a denser scanning strategy, residual trace materials are uniformly removed and the surface texture is improved, thereby achieving the final polishing effect of improving the overall smoothness and consistency of the surface.

[0143] The technical solution of this application, when determining the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be trimmed, establishes a precise positional mapping from the target geometric element to the original geometric element, and directly quantifies the material removal allowance of each local point based on the spatial deviation distance, ultimately constructing a continuous and complete overall material removal allowance distribution. This process achieves the refinement and digitization of the trimming amount, providing high-precision and quantifiable direct data drive for subsequent partitioned and layered laser processing strategies, thereby ensuring precise control of material removal and a high degree of conformity between the trimmed morphology and the target geometry, effectively improving the consistency of processing and the final quality.

[0144] The technical solution of this application embodiment, when controlling the laser processing device to perform laser trimming on the feature to be trimmed based on the material removal allowance distribution, achieves optimized energy allocation and improved processing efficiency by identifying and prioritizing the primary trimming area based on the material removal allowance distribution. First, a first scanning device is used to quickly remove the large allowance portion, creating a uniform approximate benchmark for subsequent fine processing; then, a second scanning device is used to perform global fine processing on the entire feature, ensuring the overall consistency and microscopic flatness of the surface quality. This collaborative strategy of first performing rough processing in sections and then overall fine processing effectively avoids unnecessary repeated processing or energy waste in areas with small allowances while ensuring the final trimming accuracy, thereby optimizing the overall processing efficiency, accuracy, and surface finish.

[0145] Example 4

[0146] Figure 8 This is a schematic diagram of a micro-drill edge dressing method provided in an embodiment of this application. Based on the aforementioned embodiments, this embodiment, after controlling the laser processing device to perform laser dressing on the feature to be dressed, can also obtain a reprocessing 3D model corresponding to the dressed feature; based on the reprocessing 3D model, determine the final quality parameters of the dressed feature; based on the comparison result of the final quality parameters and a preset quality threshold, determine whether the dressing result is qualified. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0147] like Figure 8 As shown, the method specifically includes the following steps:

[0148] S410. Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired, determine the features to be repaired.

[0149] Among them, the features to be repaired are the cutting edge line and / or the groove surface.

[0150] S420: Filter the three-dimensional geometric data of the features to be modified to generate smooth target geometric elements.

[0151] S430. Determine the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0152] S440. Based on the material removal allowance distribution, control the laser processing device to perform laser trimming processing on the feature to be trimmed.

[0153] S450. Obtain the reprocessing 3D model corresponding to the features to be repaired after the repair.

[0154] Among them, the reprocessed 3D model refers to the updated 3D model of the micro-drill surface obtained again through 3D scanning technology after the laser finishing process is completed, which is used to characterize the current actual shape of the feature to be finished.

[0155] Specifically, after the laser finishing process is completed, a 3D scanning device can be used to rescan and collect data on the features to be finished on the micro-drill (i.e. the processed area), and a digital 3D model reflecting its current surface state can be reconstructed through data processing, which is the reprocessing 3D model.

[0156] S460. Based on the reprocessing 3D model, determine the final quality parameters of the features to be repaired after the repair process.

[0157] Among them, the final quality parameter refers to the index value used to quantitatively evaluate the actual processing quality of the feature to be repaired after repair.

[0158] Specifically, after the laser finishing process is completed, the corresponding geometric data of the cutting edge line and / or groove surface in the reprocessed 3D model are extracted. The spatial shape error or surface roughness index is calculated by the built-in algorithm. The calculation result is used as the final quality parameter to characterize the finishing effect and is compared with the preset quality threshold to determine the pass rate.

[0159] S470. Based on the comparison results between the final quality parameters and the preset quality threshold, determine whether the trimming result is qualified.

[0160] Among them, the preset quality threshold refers to one or more pre-set quantitative indicator thresholds used to determine whether the finishing process has reached the qualified standard.

[0161] In this embodiment, the final quality parameters extracted from the reprocessed 3D model can be compared with a preset quality threshold item by item. If all the final quality parameters are within the range allowed by the preset quality threshold, the repair result is deemed qualified and the process ends. If any final quality parameter exceeds the preset quality threshold, the repair result is deemed unqualified and the subsequent compensation repair processing step is triggered.

[0162] S480. If the final quality parameters do not reach the preset quality threshold, the allowance distribution for compensation processing shall be redetermined based on the reprocessed 3D model and the target geometric elements.

[0163] Specifically, when it is determined that the final quality parameters fail to meet the preset quality threshold, the current actual geometric state presented by the reprocessed 3D model can be used as a new starting point to calculate the corresponding positional deviation between it and the target geometric elements that remain unchanged. This generates a set of local material removal distribution maps that only target the remaining defect area. This distribution map is the allowance distribution for compensation processing, which is used to guide the subsequent laser processing device to carry out additional removal, so that the actual shape is closer to the target geometric elements.

[0164] S490. Based on the redetermined allowance distribution of the compensation processing, control the laser processing device to perform compensation processing on the feature to be processed.

[0165] Specifically, when the repair result is deemed unqualified, a new material removal allowance distribution can be recalculated based on the difference between the reprocessed 3D model and the target geometric elements. Using this distribution map as a precise guide, the laser processing device can be driven again to perform an additional round of laser processing on the same feature to be repaired, aimed at eliminating residual errors.

[0166] For example, for a trimmed cutting edge, the final peak-to-valley value of its profile can be measured. If this value is less than or equal to a set first quality threshold (e.g., between 0.5 and 0.8 micrometers), the trimming result is deemed acceptable, and the process ends. If the value is greater than the first quality threshold, a compensation machining process is automatically initiated, controlling the picosecond laser to perform finishing on the cutting edge again until the measured value meets the standard. Similarly, for a polished groove surface, the final arithmetic mean deviation of its surface roughness can be measured. If this value is less than or equal to a set second quality threshold (e.g., 0.5 micrometers), the surface polishing result is deemed acceptable, and the process ends. If the value is greater than the second quality threshold, a compensation polishing process is automatically initiated, controlling the picosecond laser to perform finishing polishing on the groove surface again until the measured value meets the standard.

[0167] The technical solution of this application embodiment, after controlling the laser processing device to perform laser trimming on the feature to be trimmed, can also obtain a reprocessing 3D model corresponding to the trimmed feature; based on the reprocessing 3D model, determine the final quality parameters of the trimmed feature; based on the comparison result of the final quality parameters and the preset quality threshold, determine whether the trimming result is qualified. In this way, by introducing an online detection closed loop of scanning, evaluation, and judgment after performing the main trimming, the processing result is verified in real time, objectively, and quantitatively. It can accurately identify the trimming result that does not meet the standard, provide a decision basis for possible compensation processing, thereby ensuring that the trimming quality of each micro-drill reliably and consistently meets the preset standard, and also accumulates feedback data for the optimization of process parameters, improving the reliability, adaptability, and finished product qualification rate of the entire trimming method.

[0168] Example 5

[0169] Figure 9 This is a schematic diagram of a micro-drill cutting edge dressing device provided in an embodiment of this application. The device includes:

[0170] The feature to be repaired module 510 is used to determine the feature to be repaired based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired; wherein, the feature to be repaired is the cutting edge line and / or the groove surface.

[0171] The target element determination module 520 is used to filter the three-dimensional geometric data of the feature to be modified to generate smooth target geometric elements.

[0172] The material removal allowance determination module 530 is used to determine the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0173] The micro-drilling trimming module 540 is used to control the laser processing device to perform laser trimming on the feature to be trimmed based on the material removal allowance distribution.

[0174] This application provides a micro-drill edge dressing device. In application, based on a 3D model of the micro-drill surface to be dressed, the device identifies the features to be dressed, including the cutting edge line and / or groove surface. Then, it filters the 3D geometric data of the features to be dressed to generate smooth target geometric elements. Based on the deviation between the target geometric elements and the original geometric elements of the features to be dressed, the material removal allowance distribution is determined. Finally, based on the material removal allowance distribution, the laser processing device is controlled to perform laser dressing on the features to be dressed. The technical solution of this application automatically identifies the features to be dressed based on the 3D model of the micro-drill surface, generates target geometric elements using filtering, accurately calculates the material removal allowance distribution, and ultimately drives the laser processing device to perform dressing. This achieves full automation from feature recognition and path planning to processing execution, solving the problems of cumbersome processes, poor consistency, and low accuracy in traditional micro-drill edge dressing methods, and improving the accuracy, consistency, and overall automation level of the micro-drill edge dressing process.

[0175] Based on the above-mentioned device, optionally, the feature-to-be-repaired module 510 includes:

[0176] The feature extraction unit is used to extract the cutting edge line and the inner surface area of ​​the groove of the micro-drill based on the three-dimensional model of the micro-drill surface.

[0177] A geometric fluctuation determination unit is used to determine the geometric fluctuation amount of the cutting edge line based on the three-dimensional spatial curve of the cutting edge line;

[0178] The roughness parameter determination unit is used to determine the surface roughness parameter of the inner surface region of the trench based on the three-dimensional point cloud data of the inner surface region of the trench.

[0179] The cutting edge line determination unit is used to determine that the feature to be repaired includes the cutting edge line if the geometric fluctuation exceeds a first preset threshold.

[0180] The groove surface determination unit is used to determine that the feature to be repaired includes the groove surface if the surface roughness parameter exceeds a second preset threshold.

[0181] Based on the above device, optionally, the feature to be repaired determination module 510 and the geometric fluctuation determination unit are used to determine the highest and lowest points of the contour within the preset evaluation length based on the three-dimensional space curve.

[0182] The geometric fluctuation of the cutting edge line is determined based on the height difference between the highest point and the lowest point.

[0183] Based on the above-mentioned device, optionally, a trench surface determination unit is used to determine an arithmetic mean deviation for characterizing the overall undulation of the surface based on the three-dimensional point cloud data of the inner surface region of the trench, and to determine the arithmetic mean deviation as the surface roughness parameter.

[0184] Based on the above-mentioned device, optionally, when the feature to be repaired is a cutting edge line, the target geometric element includes a target three-dimensional repair path. The target element determination module 520 is specifically used to determine the projection profile of the cutting edge line on the theoretical cylindrical surface based on the three-dimensional spatial data of the cutting edge line; perform low-pass digital filtering on the projection profile to obtain a smooth two-dimensional profile curve; and determine a target three-dimensional repair path that matches the spatial position of the cutting edge line based on the smooth two-dimensional profile curve.

[0185] Based on the above-mentioned device, optionally, when the feature to be repaired is a groove surface, the target geometric element includes a target polishing reference surface. The target element determination module 520 is specifically used to perform low-pass digital filtering on the three-dimensional geometric model of the groove surface to obtain a smooth three-dimensional reference surface; based on the smooth three-dimensional reference surface, a target polishing reference surface corresponding to the shape of the groove surface is determined.

[0186] Based on the above-mentioned device, optionally, the residual amount determination module 530 includes:

[0187] The target location determination unit is used to determine the target location corresponding to each sampling point on the feature to be modified based on the target geometric elements.

[0188] The actual position determination unit is used to determine the original actual position of each sampling point based on the original geometric elements of the feature to be modified.

[0189] The material removal allowance determination unit is used to determine the material removal allowance at each sampling point based on the deviation distance between the original actual position of each sampling point and its corresponding target position.

[0190] The material removal amount distribution determination unit is used to determine the overall material removal amount distribution covering the feature to be repaired based on the material removal amount at all sampling points.

[0191] Based on the above-mentioned device, the optional micro-drill dressing module 540 includes:

[0192] The primary repair area determination unit is used to identify, based on the material removal allowance distribution, the primary repair area on the feature to be repaired where the material removal requirement is greater than a first preset value.

[0193] The primary repair area trimming unit is used to control the first scanning device in the laser processing device to perform a first laser scan on the primary repair area in order to complete contour trimming or removal of major undulations.

[0194] The overall area trimming unit is used to control the second scanning device in the laser processing device to perform a second laser scan on the overall area of ​​the feature to be trimmed after the first laser scan is completed, so as to complete the surface micro-smoothing process.

[0195] Based on the above-mentioned device, optionally, the micro-drill cutting edge dressing device further includes: a dressing result verification module, used to obtain a reprocessing three-dimensional model corresponding to the feature to be dressed after dressing; based on the reprocessing three-dimensional model, determine the final quality parameters of the feature to be dressed after dressing; and based on the comparison result of the final quality parameters and a preset quality threshold, determine whether the dressing result is qualified.

[0196] Based on the above-mentioned device, optionally, the micro-drill cutting edge dressing device further includes: a compensation dressing module, used to redetermine the allowance distribution of compensation processing based on the reprocessed three-dimensional model and the target geometric elements if the final quality parameter does not reach the preset quality threshold; and to control the laser processing device to perform compensation dressing processing on the feature to be dressed based on the redetermined allowance distribution of compensation processing.

[0197] Optionally, based on the above-described device, the micro-drill cutting edge dressing device further includes: a dressing priority determination module, used to determine the cutting edge line as a first-priority feature to be dressed when the three-dimensional model of the micro-drill surface determines that the cutting edge line has geometric fluctuations requiring dressing; and to determine the groove surface as a second-priority feature to be dressed when the three-dimensional model of the micro-drill surface determines that the groove surface has surface defects requiring dressing; wherein the second priority is lower than the first priority.

[0198] Based on the above-mentioned device, optionally, the micro-drill dressing module 540 is also used to perform laser dressing on the cutting edge line and the groove surface in sequence according to the priority order of the features to be dressed.

[0199] The micro-drill cutting edge dressing device provided in this application embodiment can perform the micro-drill cutting edge dressing method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method.

[0200] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0201] Example 6

[0202] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 10 A block diagram is shown of an exemplary electronic device 60 suitable for implementing embodiments of the present application. Figure 10 The electronic device 60 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0203] like Figure 10 As shown, the electronic device 60 is presented in the form of a general-purpose computing device. The components of the electronic device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).

[0204] Bus 603 represents one or more of several bus architectures, including memory buses or memory electronics, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0205] Electronic device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 60, including volatile and non-volatile media, removable and non-removable media.

[0206] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 604 and / or cache memory 605. Electronic device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 606 may be used to read and write non-removable, non-volatile magnetic media (… Figure 10 Not shown; usually referred to as a "hard drive"). Although Figure 10As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 603 via one or more data media interfaces. Memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0207] A program / utility 608 having a set (at least one) of program modules 607 may be stored, for example, in memory 602. Such program modules 607 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 607 typically perform the functions and / or methods described in the embodiments of this application.

[0208] Electronic device 60 can also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 610, etc.), and with one or more devices that enable a user to interact with the electronic device 60, and / or with any device that enables the electronic device 60 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 611. Furthermore, electronic device 60 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 612. As shown, network adapter 612 communicates with other modules of electronic device 60 via bus 603. It should be understood that, although... Figure 10 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 60, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0209] The processing unit 601 executes various functional applications and page processing by running programs stored in the system memory 602, such as implementing the micro-drill cutting edge dressing method provided in the embodiments of this application.

[0210] Example 7

[0211] This application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a micro-drill edge dressing method, the method comprising:

[0212] Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be dressed, the features to be dressed are determined; wherein, the features to be dressed are the cutting edge line and / or the groove surface.

[0213] The three-dimensional geometric data of the feature to be modified is filtered to generate a smooth target geometric element.

[0214] The material removal allowance distribution is determined based on the deviation between the target geometric element and the original geometric element of the feature to be repaired.

[0215] Based on the material removal allowance distribution, the laser processing device is controlled to perform laser trimming on the feature to be trimmed.

[0216] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0217] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0218] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0219] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0220] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method of micro-drill land finishing, characterized by, include: Based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be dressed, the features to be dressed are determined; wherein, the features to be dressed are the cutting edge line and / or the groove surface. The three-dimensional geometric data of the feature to be modified is filtered to generate a smooth target geometric element. The material removal allowance distribution is determined based on the deviation between the target geometric element and the original geometric element of the feature to be repaired. Based on the material removal allowance distribution, the laser processing device is controlled to perform laser trimming on the feature to be trimmed. The step of determining the features to be repaired based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired includes: Based on the 3D model of the micro-drill surface, the cutting edge line and the inner surface area of ​​the groove are extracted; based on the 3D spatial curve of the cutting edge line, the geometric fluctuation of the cutting edge line is determined; based on the 3D point cloud data of the inner surface area of ​​the groove, the surface roughness parameter of the inner surface area of ​​the groove is determined; if the geometric fluctuation exceeds a first preset threshold, the feature to be repaired is determined to include the cutting edge line; if the surface roughness parameter exceeds a second preset threshold, the feature to be repaired is determined to include the groove surface. Wherein, when the feature to be repaired is a cutting edge line, the target geometric element includes a target three-dimensional repair path. The step of filtering the three-dimensional geometric data of the feature to be repaired to generate a smooth target geometric element includes: determining the projection profile of the cutting edge line on a theoretical cylindrical surface based on the three-dimensional spatial data of the cutting edge line; performing low-pass digital filtering on the projection profile to obtain a smooth two-dimensional profile curve; and determining a target three-dimensional repair path that matches the spatial position of the cutting edge line based on the smooth two-dimensional profile curve. When the feature to be repaired is a groove surface, the target geometric element includes a target polishing reference surface. The step of filtering the three-dimensional geometric data of the feature to be repaired to generate a smooth target geometric element includes: performing low-pass digital filtering on the three-dimensional geometric model of the groove surface to obtain a smooth three-dimensional reference surface; and determining a target polishing reference surface corresponding to the shape of the groove surface based on the smooth three-dimensional reference surface. The step of determining the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired includes: determining the target position corresponding to each sampling point on the feature to be repaired based on the target geometric element; determining the original actual position of each sampling point based on the original geometric element of the feature to be repaired; determining the material removal allowance at each sampling point based on the deviation distance between the original actual position of each sampling point and its corresponding target position; and determining the overall material removal allowance distribution covering the feature to be repaired based on the material removal allowance at all sampling points. The step of controlling the laser processing device to perform laser trimming on the feature to be trimmed based on the material removal allowance distribution includes: identifying a primary trimming area on the feature to be trimmed where the material removal requirement is greater than a first preset value based on the material removal allowance distribution; controlling a first scanning device in the laser processing device to perform a first laser scan on the primary trimming area to complete contour trimming or removal of major undulations; and after completing the first laser scan, controlling a second scanning device in the laser processing device to perform a second laser scan on the entire area of ​​the feature to be trimmed to complete surface micro-smoothing.

2. The method of claim 1, wherein, The determination of the geometric fluctuation of the cutting edge line based on the three-dimensional spatial curve of the cutting edge line includes: Based on the three-dimensional spatial curve, determine the highest and lowest points of the contour within the preset evaluation length; The geometric fluctuation of the cutting edge line is determined based on the height difference between the highest point and the lowest point.

3. The method of claim 1, wherein, The determination of surface roughness parameters of the inner surface region of the trench based on the three-dimensional point cloud data of the inner surface region includes: Based on the three-dimensional point cloud data of the inner surface region of the trench, the arithmetic mean deviation used to characterize the overall surface undulation is determined, and the arithmetic mean deviation is determined as the surface roughness parameter.

4. The method of claim 1, wherein, After the laser processing device performs laser trimming on the feature to be trimmed, the process further includes: Obtain the reprocessed 3D model corresponding to the feature to be repaired after the repair process; Based on the reprocessed 3D model, the final quality parameters of the feature to be repaired after repair are determined; Based on the comparison results between the final quality parameters and the preset quality threshold, it is determined whether the trimming result is qualified.

5. The method of claim 4, wherein, The step of determining whether the trimming result is qualified based on the comparison result between the final quality parameter and the preset quality threshold includes: If the final quality parameter does not reach the preset quality threshold, the compensation processing allowance distribution is redetermined based on the reprocessed 3D model and the target geometric elements. Based on the redetermined allowance distribution of the compensation processing, the laser processing device is controlled to perform compensation processing on the feature to be repaired.

6. The method of claim 1, wherein, The method further includes: When it is determined from the three-dimensional model of the micro-drill surface that there are geometric fluctuations in the cutting edge line that need to be repaired, the cutting edge line is identified as a feature to be repaired with the first priority. When it is determined from the three-dimensional model of the micro-drill surface that there are surface defects on the trench surface that need to be repaired, the trench surface is identified as a second priority feature to be repaired. The second priority is lower than the first priority.

7. The method of claim 6, wherein, The laser processing control device performs laser trimming on the feature to be trimmed, including: Laser finishing processes are performed on the cutting edge line and the groove surface in sequence according to the priority order of the features to be finished.

8. A micro drill land finishing apparatus, characterized by, The device includes: The feature to be repaired module is used to determine the features to be repaired based on the three-dimensional model of the micro-drill surface corresponding to the micro-drill to be repaired; wherein, the features to be repaired are cutting edge lines and / or groove surfaces; The target feature determination module is used to filter the three-dimensional geometric data of the feature to be modified to generate smooth target geometric features. The material removal allowance determination module is used to determine the material removal allowance distribution based on the deviation between the target geometric element and the original geometric element of the feature to be repaired. The micro-drilling trimming module is used to control the laser processing device to perform laser trimming on the feature to be trimmed based on the material removal allowance distribution. Specifically, the feature to be repaired determination module is used to extract the cutting edge line and the inner surface area of ​​the groove of the micro-drill based on the three-dimensional model of the micro-drill surface; determine the geometric fluctuation of the cutting edge line based on the three-dimensional spatial curve of the cutting edge line; determine the surface roughness parameter of the inner surface area of ​​the groove based on the three-dimensional point cloud data of the inner surface area of ​​the groove; if the geometric fluctuation exceeds a first preset threshold, the feature to be repaired is determined to include the cutting edge line; if the surface roughness parameter exceeds a second preset threshold, the feature to be repaired is determined to include the groove surface. The target element determination module is used to determine the projection profile of the cutting edge line on a theoretical cylindrical surface based on the three-dimensional spatial data of the cutting edge line; perform low-pass digital filtering on the projection profile to obtain a smooth two-dimensional profile curve; determine a target three-dimensional maintenance path matching the spatial position of the cutting edge line based on the smooth two-dimensional profile curve; and perform low-pass digital filtering on the three-dimensional geometric model of the groove surface to obtain a smooth three-dimensional reference surface; and determine a target polishing reference surface corresponding to the shape of the groove surface based on the smooth three-dimensional reference surface. The material removal allowance determination module is specifically used to: determine the target position corresponding to each sampling point on the feature to be repaired based on the target geometric elements; determine the original actual position of each sampling point based on the original geometric elements of the feature to be repaired; determine the material removal allowance at each sampling point based on the deviation distance between the original actual position of each sampling point and its corresponding target position; and determine the overall material removal allowance distribution covering the feature to be repaired based on the material removal allowance at all sampling points. The micro-drilling trimming module is specifically used to identify, based on the material removal allowance distribution, the primary trimming area on the feature to be trimmed where the material removal requirement is greater than a first preset value; control the first scanning device in the laser processing device to perform a first laser scan on the primary trimming area to complete contour trimming or removal of major undulations; after completing the first laser scan, control the second scanning device in the laser processing device to perform a second laser scan on the entire area of ​​the feature to be trimmed to complete surface micro-smoothing.

9. An electronic device, comprising: The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the micro-drill cutting edge dressing method as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, wherein: The computer-executable instructions, when executed by a computer processor, are used to perform the micro-drill cutting edge dressing method as described in any one of claims 1-7.

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