Laser etching adjustment method and system

Abstract

This application discloses a laser etching adjustment method and system, which includes: obtaining model data of a workpiece and constructing a normal vector distribution map of the workpiece surface to divide the workpiece into processing areas; in each processing area, adjusting the laser incident angle based on the normal vector data of the processing area; calculating the power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirements to obtain a preliminary power distribution plan. For each processing area, a control instruction sequence is generated and transmitted to the laser processing system. This method can achieve dynamic adjustment of the laser incident angle and power parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of laser etching processing, and in particular to a laser etching adjustment method and system. Background Art

[0002] As one of the core pillars of modern manufacturing, laser processing technology occupies a vital position in aerospace, medical equipment, microelectronics and other fields due to its high precision, high efficiency and flexibility. In particular, in the etching processing of three-dimensional complex curved surface structures, the application potential of laser technology is particularly significant, which directly affects the processing quality and product performance. However, when faced with complex geometric features, existing laser etching methods often expose obvious limitations. The traditional etching method with fixed angle and fixed power is difficult to adapt to the diversity of curved surface structures, resulting in uneven energy distribution, which in turn causes problems such as reduced surface quality, local overheating or inconsistent etching depth. These defects are particularly prominent in scenarios with high precision requirements, and have become a bottleneck restricting the further development of the technology. Summary of the Invention

[0003] The present application provides a laser etching adjustment method and system to achieve dynamic adjustment of laser incident angle and power parameters.

[0004] In a first aspect, in order to solve the above technical problems, the present application provides a laser etching adjustment method, comprising:

[0005] Acquiring model data of a workpiece and constructing a normal vector distribution map of the surface of the workpiece to divide the processing area of ​​the workpiece;

[0006] In each processing area, the laser incident angle is adjusted according to the normal vector data of the processing area;

[0007] Calculate the power parameter adjustment value for each processing area based on the laser incident angle, material properties of the workpiece, and processing depth requirements to obtain a preliminary power allocation plan;

[0008] Based on the laser incident angle and power parameters of each processing area, a control instruction sequence is generated and transmitted to the laser processing system.

[0009] In a second aspect, the present application provides a laser etching adjustment system, comprising:

[0010] a processing area division module, configured to obtain model data of a workpiece and construct a normal vector distribution map of a surface of the workpiece to divide the workpiece into processing areas;

[0011] An incident angle adjustment module, configured to adjust the laser incident angle in each processing area according to normal vector data of the processing area;

[0012] A power allocation module, configured to calculate a power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirement, so as to obtain a preliminary power allocation plan;

[0013] The instruction generation module is used to generate a control instruction sequence according to the laser incident angle and power parameters of each processing area and transmit the control instruction sequence to the laser processing system.

[0014] In a third aspect, the present application further provides a storage medium, which includes a computer program, and when the computer program is executed, it can implement any one of the laser etching adjustment methods described above.

[0015] Compared with the existing technology, the present application has at least the following beneficial effects: according to the normal vector characteristics of the surface of the workpiece, the laser incident angle and power parameters are adjusted dynamically in real time, so that laser processing can be applied to the etching processing of three-dimensional complex curved surface structures, avoiding uneven energy distribution of laser etching processing leading to local overheating or inconsistent etching depth, and improving the surface quality of the processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 1 is a flow chart of the laser etching adjustment method provided in the first embodiment of the present application;

[0017] Figure 2 It is a structural diagram of the laser etching adjustment system provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] Reference Figure 1 , the first embodiment of the present application provides a method, comprising the following steps:

[0020] S11: Acquire model data of the workpiece and construct a normal vector distribution map of the surface of the workpiece to divide the processing area of ​​the workpiece;

[0021] S12: In each processing area, adjusting the laser incident angle according to the normal vector data of the processing area;

[0022] S13: Calculate the power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirement to obtain a preliminary power allocation plan;

[0023] S14: Generate a control instruction sequence for the laser incident angle and power parameters of each processing area and transmit it to the laser processing system.

[0024] In step S11, the model data of the workpiece is acquired and a normal vector distribution map of the surface of the workpiece is constructed to divide the processing area of ​​the workpiece, including the following steps:

[0025] S111: collecting surface data of the workpiece by a scanning device, converting the surface data into a coordinate point cloud, and generating a geometric model of the workpiece by using a point cloud construction technology based on the coordinate point cloud;

[0026] Surface data is collected by scanning equipment and converted into a coordinate point cloud to obtain initial geometric information. From the coordinate point cloud, point cloud construction technology is used to generate a geometric model and determine the surface structure.

[0027] Specifically, surface data is collected by scanning equipment and converted into a coordinate point cloud to obtain initial geometric information. For example, a laser scanner can be used to scan the surface of a metal workpiece. The collected data are discrete points in three-dimensional space, each with x, y, and z coordinates, forming a point cloud. This point cloud reflects the preliminary geometric shape of the workpiece surface. For example, the point cloud of a cylindrical workpiece will show a circular cross-section and height features. The advantage of this is that it can quickly obtain digital information of complex surfaces, providing a basis for subsequent modeling. Point cloud construction technology is used from the coordinate point cloud to generate a geometric model and determine the surface structure.

[0028] In one possible implementation, Delaunay triangulation can be used to connect discrete points in a point cloud into triangular facets, ultimately generating a continuous surface model. For example, for the cylindrical workpiece described above, triangulation of the point cloud will produce a smooth cylindrical surface. This approach effectively preserves the geometric properties of the original data while providing a workable structured model for subsequent analysis.

[0029] S112: Calculating the normal vector of each point on the geometric model, and constructing a normal vector distribution map based on the normal vector to obtain the overall directional characteristics of the workpiece, so as to divide the processing area of ​​the workpiece and determine the boundary of the processing area.

[0030] Calculate the normal vector for the geometric model to obtain the directional information of each point. Construct a normal distribution map based on the normal vector to obtain the overall directional characteristics. Use the normal distribution map to divide the processing area and determine the area boundaries.

[0031] Specifically, for each triangular facet of a cylindrical model, the normal vector of the facet can be obtained by calculating the cross product of the vectors of adjacent vertices. For example, the normal vector of the side of a cylinder points radially outward, while the normal of the top surface points vertically upward. This directional information reflects the local orientation of the surface and helps to understand how the surface responds to external processing or forces. A normal distribution map is constructed based on the normal vectors to obtain the overall directional characteristics.

[0032] Ideally, all normal vectors can be projected onto a unit sphere to form a distribution map. For cylindrical workpieces, this map will show that side normals are concentrated near the equator and top normals are concentrated at the poles. This distribution map intuitively demonstrates the directional consistency of the workpiece, helps identify the regularity of surface features, and provides a basis for machining planning. The normal distribution map is used to divide the machining area and determine the area boundaries.

[0033] In one embodiment, the side and top surfaces of a cylindrical workpiece are divided into two processing zones based on the clustering of normal distribution, with the boundary defined by the point where the normal suddenly changes. For example, at the junction of the side and top surfaces, where the normal changes from radial to vertical, the boundary becomes apparent. This division effectively distinguishes areas with different processing requirements and improves processing efficiency.

[0034] In step S11, after obtaining the model data of the workpiece and constructing a normal vector distribution map of the surface of the workpiece to divide the processing area of ​​the workpiece, the following steps are further included:

[0035] S113: Analyze the normal vector distribution of the processing area, use a principal component analysis algorithm to extract the change characteristics of the normal vector, and adjust the division of the processing area according to the change characteristics to obtain an optimized processing area.

[0036] It is understandable that principal component analysis extracts the main direction of change by calculating the covariance matrix of the normal vector. For example, the normal direction of the side area of ​​the cylinder varies less, with the principal components concentrated in the radial direction, while the top surface tends to be single. This feature extraction can quantify the uniformity of directions within the region, providing data support for optimized partitioning. The regional division is adjusted according to the change characteristics to obtain the optimized processing partition. For example, if the normal distribution of the side area shows that a part deviates from the average direction due to surface defects, this part can be separated into a separate sub-region and the processing parameters can be adjusted to adapt to the local changes.

[0037] It should be noted that this optimization can improve processing accuracy and avoid ignoring detailed features due to overly coarse area division.

[0038] Preferably, the adjusted partitions can also reduce tool wear during machining and improve overall production efficiency.

[0039] Specifically, this process from point cloud to partition optimization forms a complete chain of geometric analysis and processing planning.

[0040] For example, for a cylindrical workpiece, the initial point cloud collection may contain 1 million points. After the model is generated, normal calculation takes only seconds, and the final partition optimization reduces the processing time by 20%. This technology significantly improves the machining adaptability of complex curved workpieces while ensuring high-quality surface consistency.

[0041] In step S12, in each processing area, the laser incident angle is adjusted according to the normal vector data of the processing area, including the following steps:

[0042] S121: In each of the processing areas, obtaining direction information of the normal vector according to the normal vector data of the processing area, and calculating an angle between the normal vector direction and the initial direction of the laser;

[0043] It can be understood that the normal vector essentially reflects the local orientation of the surface at each point.

[0044] For example, for a spherical workpiece, the normal vector in the center will point radially outward, while the edge areas will gradually deflect. This directional information is the basis for subsequent calculations.

[0045] Specifically, we can start from the relationship between the normal vector and the initial direction of the laser and calculate the angle between the two.

[0046] For example, assuming that the initial direction of the laser is vertically downward, and the angle between the normal vector of a point on the sphere and the vertical direction is 30 degrees, this angle value becomes the basis for adjustment.

[0047] S122: Determine whether the angle value exceeds a preset threshold; and S123: If the angle value exceeds the preset threshold, use a geometric transformation method to determine a new laser incident angle.

[0048] In one possible implementation, if the preset threshold is 45 degrees and 30 degrees does not exceed the threshold, no adjustment is required; however, if the angle reaches 60 degrees, further processing is required. For cases where the angle exceeds the threshold, a geometric transformation method is used to determine the new incident angle value.

[0049] Preferably, the laser direction can be adjusted to be closer to the normal vector by rotating the coordinate system.

[0050] For example, if the normal deflection of a certain area on a spherical workpiece is 60 degrees, the incident angle can be adjusted to within 15 degrees by rotating the laser head. This transformation ensures that the laser energy acts more evenly on the surface.

[0051] It should be noted that the adjusted incident angle requires calculation of corresponding angular parameters, such as rotation angle and offset, to form a new directional signature. This signature directly impacts machining accuracy. By obtaining distribution information about the changed features from the adjusted directional signature, the distribution of normal vectors in space can be statistically analyzed to determine whether machining requirements are met.

[0052] Specifically, for spherical workpieces, if the angle of most areas is controlled within 20 degrees after adjustment, the requirements are met; if some areas still exceed the standard, further optimization is required.

[0053] In step S12, after adjusting the laser incident angle in each processing area according to the normal vector data of the processing area, the following steps are performed:

[0054] S124: Calculating corresponding angle adjustment parameters according to the adjusted laser incident angle to obtain an adjusted directional feature;

[0055] S125: Obtaining distribution information of the change feature from the adjusted directional feature;

[0056] S126: extracting main direction components using a principal component analysis algorithm based on the distribution of the change characteristics to determine optimized angle parameters;

[0057] S127: adjusting the laser incident direction according to the optimized angle parameter to obtain the final processing area division;

[0058] For example, if the normal variation in a certain area of ​​a spherical surface is concentrated within a certain plane, principal component analysis can quantify this trend and derive optimized angle parameters, such as adjusting the laser inclination angle by 5 degrees. By adjusting the laser incident direction based on the optimized angle parameters, the final processing area division can be obtained.

[0059] S128: According to the final processing area division, obtain the normal vector distribution of each processing area to determine the direction sequence of the corresponding processing path.

[0060] For example, a spherical workpiece can be divided into a top low-angle zone and an edge high-angle zone, each with a different normal vector distribution. For the top zone, where normals are evenly distributed, the machining path can be designed as a sequence of concentric circles. However, the edge zone experiences significant normal variation, requiring a path adjustment along the curvature. This division and path design can improve machining consistency.

[0061] In one embodiment, after obtaining the normal vector distribution of each region, the normal vectors may be sorted according to their continuity when determining the direction sequence of the processing path.

[0062] For example, the normal vector direction in the top region changes smoothly, allowing the path to advance continuously in a clockwise direction. However, the edge regions require segmented processing to avoid sudden changes in direction that could cause machining defects. This approach effectively adapts to the surface characteristics and ensures the stability of the machining process.

[0063] In step S13, after calculating the power parameter adjustment value of each processing area according to the laser incident angle, the material properties of the workpiece, and the processing depth requirement to obtain a preliminary power allocation plan, the following steps are included:

[0064] S131: Obtaining energy distribution characteristics of each processing area from the preliminary power allocation plan;

[0065] It is understood that material properties such as thermal conductivity and absorptivity directly affect energy distribution.

[0066] For example, for metal materials with high absorptivity, the laser energy is more likely to be concentrated on the surface, and the power adjustment value may be too low.

[0067] For example, if the incident angle at the top of a spherical workpiece is adjusted to 10 degrees and the material absorptivity is 0.8, the energy distribution model might calculate a power adjustment value of 90% of the base value. However, at the edge of the workpiece, where the incident angle is 50 degrees, the adjustment value might increase to 110%. The energy distribution characteristics for each region are then derived from the preliminary power allocation results.

[0068] S132: Determine whether the energy distribution characteristic exceeds a preset threshold;

[0069] S133: If the energy distribution characteristic exceeds a preset threshold, modifying the power parameter by adjusting the value to obtain an optimized power distribution solution;

[0070] Specifically, the energy density diagram can be used to determine whether the processing depth is uniform.

[0071] In one possible implementation, the energy density in the top area is 5J / cm² and that in the edge area is 4J / cm². If the processing depth requirement is 0.5mm and the preset threshold is ±10%, the edge area is too low and needs to be adjusted.

[0072] It should be noted that this feature analysis can help identify areas with insufficient power. If the energy distribution feature exceeds a preset threshold, the power parameter is corrected by adjusting the value.

[0073] For example, the power in the edge area is increased from 110% to 120%, bringing the energy density to approximately 5J / cm². This adjustment ensures consistent processing depth.

[0074] S134: Determine power parameter distribution information of each processing area according to the optimized power allocation scheme to generate a corresponding parameter adjustment sequence;

[0075] Preferably, the thermal diffusion characteristics of the material can be taken into consideration during the correction to avoid local overheating. The power parameter distribution information of each area is determined based on the optimized allocation scheme.

[0076] In one embodiment, the power parameter of the top area is stabilized at 100W, and the power parameter of the edge area gradually changes to 120W. The generated parameter adjustment sequence can be arranged in order of the areas. This serialization design facilitates subsequent control.

[0077] S135: extracting main distribution trends using a support vector machine algorithm according to the parameter adjustment sequence and the laser incident angle to obtain an adjusted power feature;

[0078] It can be understood that the support vector machine algorithm can identify the correlation between power and angle.

[0079] For example, if the angle increases from 10 degrees to 50 degrees, the power demand increases nonlinearly. The adjusted power signature may indicate that the edge areas require additional attention. This trend analysis helps optimize resource allocation.

[0080] S136: Acquire boundary data of the processing area division according to the adjusted power characteristics to determine the final processing power distribution;

[0081] Specifically, a spherical workpiece can be divided into a low-power zone at the top and a high-power zone at the edge. The boundary may appear at a sudden angle change, such as around 30 degrees. This division can improve processing efficiency.

[0082] S137: Generate a control instruction sequence for each of the processing areas according to the final processing power distribution.

[0083] In one embodiment, the top region instruction is “power 100 W, last 2 seconds”, and the edge region instruction is “power 120 W, last 1.5 seconds”.

[0084] For example, command sequences can be arranged in the order of machining paths to ensure smooth transitions of the equipment. This approach can improve control accuracy and machining stability.

[0085] In step S13, after calculating the power parameter adjustment value of each processing area according to the laser incident angle, the material properties of the workpiece, and the processing depth requirement to obtain a preliminary power allocation solution, the following steps are also included:

[0086] S138: Obtain the preliminary power distribution plan and analyze the energy distribution uniformity of each processing area;

[0087] S139: Determine whether the energy density deviation of each processing area exceeds a preset threshold; if the energy density deviation exceeds the preset threshold, iteratively adjust the power parameter until the deviation converges.

[0088] Based on the preliminary power allocation plan, a simulation algorithm is used to calculate the energy distribution data for each region and obtain uniformity analysis results. From the uniformity analysis results, energy density distribution information for each region is obtained to determine whether any deviations exceed a preset threshold. If the energy density deviation in a region exceeds the preset threshold, the power allocation parameters are adjusted through an iterative process to obtain the distribution characteristics at the convergence state. Based on the distribution characteristics at the convergence state, the energy density adjustment value for each region is determined to generate the optimized power allocation data. For this optimized power allocation data, a region partitioning method is used to extract boundary information and obtain the final distribution characteristic description. Based on this final distribution characteristic description, a parameter adjustment sequence for each region is generated, completing the power allocation optimization process.

[0089] Specifically, through the preliminary power allocation plan, the simulation algorithm is used to calculate the energy distribution data of each area.

[0090] It can be understood that the simulation algorithm can simulate the energy transfer process of the laser on the workpiece surface.

[0091] For example, for a spherical workpiece, the energy distribution may be more concentrated in the top area due to the small incident angle, while the energy may be dispersed in the edge area due to the large angle.

[0092] For example, the simulation results may show that the energy density in the top area is 6J / cm² and that in the edge area is 3J / cm². This difference reflects the characteristics of the preliminary solution. The energy density distribution information of each area is obtained from the uniformity analysis results.

[0093] Specifically, the deviation can be determined by comparing the simulation data with the target value.

[0094] In one possible implementation, assuming a target energy density of 5 J / cm² and a preset threshold of ±15%, the top region has a deviation of +20% and the edge has a deviation of -40%, clearly exceeding the range. This analysis can intuitively reflect the imbalance in the distribution. If the energy density deviation in a particular region exceeds the preset threshold, the power allocation parameters are adjusted through an iterative process.

[0095] Preferably, the iteration can be performed based on a gradient adjustment strategy.

[0096] For example, the power in the edge area is gradually increased from an initial 100W by 10W each time, and the energy density is simulated and observed until it approaches 5J / cm².

[0097] It should be noted that the total energy input can be monitored during the iteration process to avoid over-adjustment and resource waste, and eventually converge to a stable state. The energy density adjustment value of each region is determined based on the distribution characteristics in the converged state.

[0098] In one embodiment, the power in the top region is adjusted down to 90W, reducing the energy density from 6J / cm² to 5J / cm², while the power in the edge region is adjusted up to 120W, bringing it to the target value. This adjustment provides a basis for subsequent optimization. For the optimized power distribution data, a region partitioning method is used to extract boundary information.

[0099] It can be understood that the boundary can be divided according to the energy density mutation point.

[0100] For example, the region where the energy density drops from 5J / cm² to 4.5J / cm² might correspond to an incident angle near 30 degrees, dividing it into a low-power zone and a high-power zone. This division provides a clear regional basis for parameter adjustment. The resulting distribution characteristics are then used to generate a parameter adjustment sequence for each region.

[0101] In one embodiment, the top region parameter is “power 90 W, duration 2 s”, and the edge region parameter is “power 120 W, duration 1.5 s”.

[0102] For example, the sequence is arranged according to the processing path, first the top and then the edge, to ensure smooth execution of the equipment. This sequential design facilitates practical operation and improves control accuracy.

[0103] It should be noted that the above process can effectively optimize power distribution through the combination of simulation, iteration and partitioning.

[0104] For example, increasing the energy density in the edge area ensures uniformity in machining depth, while decreasing it in the top area prevents local overheating. This approach optimizes energy consumption while ensuring quality, offering high practical value.

[0105] In step S14, after generating a control instruction sequence for the laser incident angle and power parameters of each processing area and transmitting it to the laser processing system, the following steps are included:

[0106] S141: When the laser processing system executes the control instruction sequence, collecting thermal imaging data of the surface of the workpiece;

[0107] Specifically, the temperature distribution data of the processed surface is collected by thermal imaging equipment.

[0108] It can be understood that this device can capture infrared radiation information on the surface of the workpiece in real time and convert it into temperature values.

[0109] S142: Determine whether there is a local overheating area; and S143: If there is a local overheating area, adjust the power parameters according to the temperature distribution feedback and update the control instruction sequence.

[0110] For example, when machining a metal disk, a thermal imaging device scans the surface and shows a temperature of 75°C in the center and 50°C at the edges, with a preset threshold of 70°C. The center area exceeds the threshold, indicating a risk of overheating.

[0111] Ideally, thermal imaging equipment should have a resolution of 1°C, enabling accurate identification of temperature anomalies. This acquisition method can quickly locate problem areas, determining if the temperature in a local area exceeds a preset threshold and pinpointing the location of the overheated area.

[0112] For example, overheating areas can be directly marked using a temperature distribution map generated by thermal imaging.

[0113] For example, the diameter of the circular range of the central area at 75°C is about 5 cm, and the position coordinates can be recorded as x=0, y=0 at the center of the workpiece.

[0114] It should be noted that this positioning relies on the spatial resolution of the thermal imaging device, which can effectively narrow the subsequent adjustment range. The boundary information of the overheating area is extracted based on the temperature distribution data.

[0115] In one possible implementation, the temperature gradient change may be identified through an image processing algorithm.

[0116] For example, the transition bandwidth from 75°C in the center to 50°C at the edge is 1 cm, and the boundary range data can be defined as a circular area with a diameter of 5 cm.

[0117] In one embodiment, boundary information can be further refined into multiple curve segments to more accurately describe irregular overheating areas. This extraction method provides a concrete basis for subsequent parameter adjustments. Power parameters are adjusted based on the boundary range data, and a control instruction sequence is generated.

[0118] For example, the original power of the center area is 100W, which needs to be reduced to 80W due to overheating, and the processing time is maintained at 2s. The generated instruction sequence is such as "center: 80W, 2s".

[0119] Specifically, the power in the edge area remains unchanged at 100W, and the sequence is "edge: 100W, 1.5s".

[0120] In one embodiment, the power adjustment range can be gradually reduced according to the degree of temperature exceeding the standard, so as to ensure that the processing efficiency is not excessively reduced. The control instruction sequence is encoded and processed by a digital signal processor.

[0121] It is understood that the processor converts the instructions into signals that can be recognized by the device.

[0122] For example, "80W" is encoded as "01010000", and "2s" is encoded as "00000010", and a check digit is added to ensure the data is correct.

[0123] Preferably, the encoding process can also optimize the bit length according to the transmission protocol to improve the transmission speed. When the encoded instruction data is used to update the processing path.

[0124] For example, the path in the central area is adjusted to a denser grid scan, covering a 5cm diameter area, and the time series is recorded as "Path Update: 14:30:02." The edge path remains the original straight-line scan. This update method can optimize the machining effect in overheated areas. The operating status of the machining system can be extracted from the time series to identify abnormalities.

[0125] In one possible implementation, if the temperature in the central area drops to 65°C after the update, the status is normal. If it remains at 72°C, it is considered abnormal and the information is returned.

[0126] It should be noted that this real-time monitoring can promptly identify potential problems and generate new control instruction sequences based on the returned operating status.

[0127] For example, if the center area is still abnormal, the power is reduced to 70 W, and a new sequence "Center: 70 W, 2 s" is generated with a timestamp of 14:30:04.

[0128] Specifically, this dynamic adjustment can quickly respond to system changes and ensure stable processing.

[0129] In one embodiment, cooling parameters may also be adjusted synchronously to further assist in cooling.

[0130] In step S14, after generating a control instruction sequence for the laser incident angle and power parameters of each processing area and transmitting it to the laser processing system, the following steps are also included:

[0131] S144: When the laser processing system executes the control instruction sequence and completes the first etching process on the workpiece, obtaining surface quality data of the workpiece;

[0132] The laser equipment is driven by a control instruction sequence to perform etching processing on complex surfaces and obtain surface quality data after processing. Feature information is extracted from the surface quality data and classified using a support vector machine algorithm to determine whether the consistency meets the preset target. If the consistency does not meet the standard, the abnormal areas of etching depth are determined through deviation analysis to obtain the distribution data of the abnormal areas. The control instruction sequence is adjusted based on the distribution data, and updated processing drive parameters are generated to determine the new etching processing path. The laser equipment is driven by the updated processing drive parameters to perform secondary etching processing on the complex surface and obtain adjusted surface quality data. By comparing the adjusted surface quality data with the preset target, it is determined whether the etching depth is consistent and the final processing status data is obtained.

[0133] Specifically, the laser device is driven by a control instruction sequence to perform etching processing on complex surfaces.

[0134] It is understood that the laser equipment accurately etches the curved surface according to pre-set parameters such as power, speed and path.

[0135] For example, a complex curved part is a spherical metal shell. The initial command sequence sets the laser power to 50W, the moving speed to 10mm / s, and the path to spiral scanning. After processing, the surface quality data is obtained.

[0136] For example, an optical microscope can be used to scan the surface to obtain a roughness distribution, such as a roughness of Ra 1.2 μm in the center area and Ra 1.5 μm at the edge. Feature information can be extracted from the surface quality data.

[0137] In one possible implementation, roughness, texture direction, and etching depth may be selected as features.

[0138] For example, the etching depth in the center area is 20 μm, the edge is 25 μm, and the texture direction deviates from the expected direction by 5 degrees. The support vector machine algorithm is used for classification.

[0139] Specifically, these feature values ​​were input, and the target consistency was set at a depth deviation of less than 3μm. The classification results showed a depth difference of 5μm between the center and the edge, which did not meet the target.

[0140] It should be noted that this algorithm can effectively distinguish whether the consistency meets the requirements. The abnormal area of ​​etching depth is determined through deviation analysis.

[0141] Preferably, a depth profile may be generated.

[0142] For example, the abnormal area is concentrated at the edge, ranging from a ring with a diameter of 10 cm, and the depth exceeds the standard by 5 μm. After obtaining the distribution data, the control instruction sequence is adjusted.

[0143] In one embodiment, the power in the edge region is reduced to 45W and the speed is increased to 12mm / s, generating a new sequence: "Edge: 45W, 12mm / s." The new etching path is adjusted to a localized, intensified scan to cover the abnormal area. A second etching is performed using the updated process drive parameters.

[0144] For example, the laser equipment is run in a new sequence, and surface quality data is collected again after processing. The adjusted data shows that the center depth remains at 20μm, while the edge depth drops to 22μm. This determines whether the etching depth is becoming consistent.

[0145] Specifically, the deviation was reduced to 2μm, meeting the target. The final processing status data was recorded as "Consistency achieved, time: 14:35:00."

[0146] In one embodiment, if the target is still not met, the path can be further refined or the cooling conditions can be adjusted. This approach can gradually optimize the processing accuracy.

[0147] For example, the feature extraction and classification process can also be combined with texture uniformity analysis to enhance the basis for judgment.

[0148] Preferably, visualization of the distribution data can help operators quickly locate problem areas.

[0149] It is understandable that the strategy of dynamically adjusting parameters and paths can significantly improve the processing quality and consistency of complex surfaces and has strong adaptability.

[0150] In one embodiment, the surface stress distribution can be verified after secondary processing to further ensure the reliability of the parts.

[0151] S145: extracting a deviation area from the surface quality data and determining whether a ratio of the deviation area exceeds a preset threshold; and S146: if the ratio of the deviation area exceeds the preset threshold, optimizing the control instruction sequence using an adaptive filtering algorithm to obtain an improved processing plan;

[0152] Extract the deviation area distribution information from the processed surface quality data, determine whether the deviation area ratio exceeds the preset threshold, and obtain a preliminary evaluation result. If the deviation area ratio exceeds the preset threshold, the control instruction sequence is processed through an adaptive filtering algorithm to generate optimized instruction data. Adjust the processing plan based on the optimized instruction data and determine the updated processing path parameters. Use the updated processing path parameters to perform local correction processing on the deviation area to obtain the corrected surface data. Extract the regional ratio change trend from the corrected surface data, determine whether the change trend meets the preset target, and obtain trend evaluation data. Update the processing plan based on the trend evaluation data and generate the final control instruction sequence. Use the final control instruction sequence to drive the equipment to perform processing and obtain the quality data after processing.

[0153] Specifically, deviation area distribution information is extracted from the processed surface quality data.

[0154] For example, a three-dimensional profilometer can be used to scan the surface of a complex curved part to generate a depth distribution cloud map.

[0155] For example, after machining a cylindrical metal shell, the center area is etched to a depth of 18μm, while the edge area is etched to a depth of 24μm. The deviation area is defined as the portion where the depth exceeds the expected value by 20μm ± 2μm. The distribution information shows that the deviation is significant in the annular area at the edge, accounting for approximately 30% of the total surface area.

[0156] In one possible implementation, the preset threshold is that the proportion of deviation areas does not exceed 20%. A preliminary assessment indicates that the deviation ratio exceeds the standard, reaching 30%, exceeding the expected 10%. A determination is made as to whether the proportion of deviation areas exceeds the preset threshold.

[0157] Specifically, statistical analysis tools can be used to calculate the pixel ratio of the deviation area.

[0158] For example, the depth distribution cloud map shows the exceeded area in red, and its proportion of the total area is calculated to obtain a result of 30%.

[0159] It is understandable that this method directly reflects the problem of machining consistency and provides a basis for subsequent optimization. The control instruction sequence is processed by an adaptive filtering algorithm.

[0160] In one embodiment, laser power and speed may be adjusted based on the deviation distribution.

[0161] For example, if the power in the edge area is reduced from 50W to 46W and the speed is increased from 10mm / s to 13mm / s, the adaptive filtering algorithm will smooth out the transition parameter changes and generate optimized command data such as "Edge: 46W, 13mm / s."

[0162] Preferably, such adjustment can effectively reduce the over-engraving phenomenon. Adjust the processing plan according to the optimized instruction data.

[0163] For example, the path can be changed from uniform spiral scanning to localized dense scanning to cover the edge deviation area. The updated processing path parameters are "edge dense spacing 0.5mm, center maintain 2mm".

[0164] In one possible implementation, this path adjustment can focus energy distribution and improve correction efficiency. The updated machining path parameters are used to perform local correction machining on the deviation area.

[0165] Specifically, the laser equipment runs along the new path and obtains correction data after processing.

[0166] For example, the edge depth is reduced from 24μm to 21μm, while the center remains at 18μm.

[0167] It should be noted that local correction can significantly reduce the deviation range. The regional proportion change trend is extracted from the corrected surface data.

[0168] For example, the depth cloud map can be regenerated, and the deviation area ratio is reduced from 30% to 15%. When judging whether the change trend meets the preset target.

[0169] In one embodiment, the target is a ratio below 20%, and the trend assessment data indicates that the target has been met.

[0170] It is understood that the downward trend reflects the effectiveness of the processing plan. The processing plan is updated based on the trend evaluation data.

[0171] For example, the path density can be further fine-tuned to generate a final control instruction sequence such as “edge: 46W, 13mm / s, spacing 0.4mm”.

[0172] Preferably, such refinement can ensure a more stable consistency. The final control instruction sequence is used to drive the equipment to perform the processing.

[0173] Specifically, the equipment is operated according to the new parameters and quality data is obtained after the processing is completed.

[0174] For example, the center depth is 19μm and the edge depth is 20μm, and the deviation area ratio is reduced to 8%.

[0175] In one embodiment, such results demonstrate significantly improved machining accuracy and better consistency.

[0176] S147: Re-execute the etching task on the workpiece according to the improved processing scheme, and monitor the surface roughness and processing depth distribution of the workpiece in real time to determine whether the final processing result meets the quality requirements.

[0177] The improved processing plan drives the etching task, and high-precision sensors are used to collect surface data in real time to obtain roughness and depth distribution information. Feature data is extracted from the collected roughness and depth distribution information to determine whether it meets the preset threshold and determine the preliminary evaluation result. If the preliminary evaluation result exceeds the preset threshold, the feature data is processed through a convolutional neural network to generate adjusted task parameters. The etching task execution sequence is updated based on the adjusted task parameters to obtain optimized processing data. The optimized processing data is used to drive the equipment operation and obtain the surface distribution information after processing. The change trend is extracted from the surface distribution information after processing to determine whether it meets quality standards and obtain the final result data. The processing plan is adjusted based on the final result data to generate a new task execution sequence.

[0178] Specifically, when the etching task is driven by the improved processing scheme, high-precision sensors can be used to collect surface data in real time.

[0179] For example, laser interferometry can be used to monitor the etching process of complex curved parts, generating roughness and depth distribution information.

[0180] For example, during machining of a cylindrical workpiece, the sensor collects 1,000 data points per second. The roughness Ra ranges from 0.2μm to 0.8μm, and the depth distribution gradually changes from 15μm at the center to 25μm at the edge. This real-time data acquisition fully records the surface condition, providing a basis for subsequent analysis. Feature data is extracted from the collected roughness and depth distribution information.

[0181] Specifically, key indicators can be separated through signal processing technology.

[0182] In a possible implementation, the extracted features include roughness peak value, depth gradient change rate, etc.

[0183] For example, the depth gradient of the edge area is 0.5 μm / mm, and the depth gradient of the center area is close to 0 μm / mm. When determining whether the preset threshold is met.

[0184] Preferably, the roughness Ra is set to no more than 0.5 μm, and the depth deviation range is within 20 μm ± 3 μm. Preliminary assessments indicate that the edge depth exceeds the upper threshold and requires further processing. If the preliminary assessment results exceed the preset threshold, the feature data can be processed through a convolutional neural network.

[0185] It is understood that convolutional neural networks can identify spatial distribution patterns of deviations.

[0186] In one embodiment, the network inputs a depth distribution matrix and outputs adjustment recommendations.

[0187] For example, if the etching intensity at the edge of the process is too high, the network recommends reducing the power to 42W and increasing the scanning speed to 15mm / s. This intelligent analysis quickly locates the problem and generates optimized parameters. The etching task execution sequence is then updated based on the adjusted task parameters.

[0188] For example, the uniform power distribution in the original sequence was changed to zone control. The parameters in the edge area were adjusted to 42W and 15mm / s, while the center area remained at 50W and 10mm / s.

[0189] It's important to note that this partitioning strategy balances machining efficiency and precision. After acquiring optimized machining data, the machine operates according to a new sequence, generating a more uniform surface distribution. Using this optimized machining data to drive the machine operation, the resulting surface distribution is obtained.

[0190] For example, the edge depth is reduced from 25 μm to 22 μm, and the roughness Ra value is stabilized within 0.4 μm. The change trend is extracted from the surface distribution information after processing.

[0191] In one possible implementation, a depth variation curve can be plotted to show that the edge deviation is reduced and the overall image is smoothed. This is used to determine whether the image meets quality standards.

[0192] Specifically, the target was a depth deviation of less than 3μm and a roughness of less than 0.5μm, which the results showed were met. The machining plan was adjusted based on the final result data.

[0193] For example, the scanning path is further refined, the edge area spacing is adjusted from 1mm to 0.6mm, and a new task execution sequence is generated, such as "edge: 42W, 15mm / s, spacing 0.6mm".

[0194] In one embodiment, this adjustment can improve machining consistency and ensure better surface quality. The advantage of this method is that the task parameters can be gradually optimized to meet the machining requirements of different areas.

[0195] It is understandable that the implementation of each of the above links is closely connected, forming a complete closed loop from data collection to parameter adjustment.

[0196] Preferably, this systematic approach can effectively improve machining accuracy and reduce rework rate, providing reliable support for the machining of complex curved parts.

[0197] Reference Figure 2 The second embodiment of the present application provides a laser etching adjustment system, comprising:

[0198] a processing area division module, configured to obtain model data of a workpiece and construct a normal vector distribution map of a surface of the workpiece to divide the workpiece into processing areas;

[0199] An incident angle adjustment module, configured to adjust the laser incident angle in each processing area according to normal vector data of the processing area;

[0200] A power allocation module, configured to calculate a power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirement, so as to obtain a preliminary power allocation plan;

[0201] The instruction generation module is used to generate a control instruction sequence according to the laser incident angle and power parameters of each processing area and transmit the control instruction sequence to the laser processing system.

[0202] It should be noted that a laser etching adjustment system provided in an embodiment of the present invention is used to execute all process steps of a laser etching adjustment method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0203] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for laser etching adjustment. When the processor executes the computer program, the steps in the above-mentioned characteristic analysis method for infrared thermal imagers are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the heat flow vector field module.

[0204] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0205] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0206] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0207] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0208] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0209] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0210] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application by those skilled in the art should be included within the scope of protection of this application.

Claims

1. A laser etching adjustment method, characterized in that: include: Acquiring model data of a workpiece and constructing a normal vector distribution map of a surface of the workpiece to divide a processing area of ​​the workpiece; In each of the processing areas, adjusting the laser incident angle according to the normal vector data of the processing area; Calculating a power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirement to obtain a preliminary power allocation plan; Obtaining energy distribution characteristics of each of the processing areas from the preliminary power allocation plan; Determining whether the energy distribution characteristic exceeds a preset threshold; If the energy distribution characteristic exceeds a preset threshold, the power parameter is corrected by adjusting the value to obtain an optimized power distribution scheme; Determining power parameter distribution information of each processing area according to the optimized power allocation scheme to generate a corresponding parameter adjustment sequence; According to the parameter adjustment sequence and the laser incident angle, a support vector machine algorithm is used to extract the main distribution trend to obtain an adjusted power feature; Obtaining boundary data of the processing area division according to the adjusted power characteristics to determine a final processing power distribution; generating a control instruction sequence for each of the processing areas according to the final processing power distribution; Alternatively, after calculating and obtaining the preliminary power allocation plan, the method further includes: obtaining the preliminary power allocation plan, analyzing the energy distribution uniformity of each processing area; determining whether the energy density deviation of each processing area exceeds a preset threshold; if the energy density deviation exceeds the preset threshold, iteratively adjusting the power parameter until the deviation converges; A control instruction sequence is generated for the laser incident angle and power parameters of each processing area and transmitted to the laser processing system.

2. The laser etching adjustment method according to claim 1, characterized in that: The acquiring model data of the workpiece and constructing a normal vector distribution map of the surface of the workpiece to divide the processing area of ​​the workpiece includes: Collecting surface data of the workpiece by a scanning device, converting the surface data into a coordinate point cloud, and generating a geometric model of the workpiece by using a point cloud construction technology based on the coordinate point cloud; The normal vector of each point of the geometric model is calculated, and a normal vector distribution map is constructed based on the normal vector to obtain the overall directional characteristics of the workpiece, so as to divide the processing area of ​​the workpiece and determine the boundary of the processing area.

3. The laser etching adjustment method according to claim 1, characterized in that: In each of the processing areas, adjusting the laser incident angle according to the normal vector data of the processing area includes: In each of the processing areas, obtaining direction information of the normal vector through the normal vector data of the processing area, and calculating the angle between the normal vector direction and the initial direction of the laser; Determining whether the angle value exceeds a preset threshold; If the angle value exceeds the preset threshold, a geometric transformation method is used to determine a new laser incident angle.

4. The laser etching adjustment method according to claim 1, characterized in that: After adjusting the laser incident angle in each processing area according to the normal vector data of the processing area, the method further comprises: According to the adjusted laser incident angle, the corresponding angle adjustment parameter is calculated to obtain the adjusted directional characteristics; Obtaining distribution information of the change feature from the adjusted directional feature; Based on the distribution of the change characteristics, a principal component analysis algorithm is used to extract the main direction components to determine the optimized angle parameters; Adjusting the laser incident direction according to the optimized angle parameters to obtain the final processing area division; According to the final processing area division, the normal vector distribution of each processing area is obtained to determine the direction sequence of the corresponding processing path.

5. The laser etching adjustment method according to claim 1, characterized in that: After generating a control instruction sequence for the laser incident angle and power parameters of each processing area and transmitting the sequence to the laser processing system, the process includes: When the laser processing system executes the control instruction sequence, collecting thermal imaging data of the surface of the processing workpiece; Determine whether there are areas of local overheating; If there is a local overheating area, the power parameters are adjusted according to the temperature distribution feedback and the control instruction sequence is updated.

6. The laser etching adjustment method according to claim 1, characterized in that: After generating a control instruction sequence for the laser incident angle and power parameters of each processing area and transmitting the sequence to the laser processing system, the process includes: When the laser processing system executes the control instruction sequence and completes the first etching process on the workpiece, obtaining surface quality data of the workpiece; extracting a deviation area from the surface quality data, and determining whether a ratio of the deviation area exceeds a preset threshold; If the ratio of the deviation area exceeds a preset threshold, an adaptive filtering algorithm is used to optimize the control instruction sequence to obtain an improved processing plan; The etching task is re-executed on the workpiece according to the improved processing scheme, and the surface roughness and processing depth distribution of the workpiece are monitored in real time to determine whether the final processing result meets the quality requirements.

7. A laser etching adjustment system, characterized in that: A method for adjusting laser etching according to any one of claims 1 to 6, comprising: a processing area division module, configured to obtain model data of a workpiece and construct a normal vector distribution map of a surface of the workpiece to divide the workpiece into processing areas; An incident angle adjustment module, configured to adjust the laser incident angle in each processing area according to normal vector data of the processing area; A power allocation module, configured to calculate a power parameter adjustment value for each processing area based on the laser incident angle, the material properties of the workpiece, and the processing depth requirement, so as to obtain a preliminary power allocation plan; The instruction generation module is used to generate a control instruction sequence according to the laser incident angle and power parameters of each processing area and transmit the control instruction sequence to the laser processing system.

8. A storage medium, characterized in that: The method comprises a computer program, which can implement the laser etching adjustment method according to any one of claims 1 to 6 when the computer program is executed.

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