A laser cutting control system and method for a lens assembly

Abstract

The present invention relates to the field of laser cutting control technology, and particularly to a laser cutting control system and method for a lens assembly. The method includes the following steps: scanning the lens assembly in all directions to obtain point cloud data, then removing abnormal points, performing three-dimensional reconstruction to generate a lens assembly model, obtaining cutting task data and identifying cutting targets, based on which an optimal cutting time and space arrangement is carried out on the lens assembly model to generate an optimized cutting blueprint, planning a cutting path according to the optimized blueprint to obtain a candidate cutting path, and performing dynamic compensation on it to form a compensated cutting path, mapping control parameters according to the compensated cutting path to obtain variable control parameters, and then performing parameter replacement to achieve precise intelligent laser cutting control. The present invention realizes a more precise and efficient laser cutting control method for lens assemblies.

Description

Technical Field

[0001] The present invention relates to the technical field of laser cutting control, and particularly to a laser cutting control system and method for a lens assembly. Background Art

[0002] The processing precision and efficiency of precision optical components have increasingly become the focus of attention in the manufacturing industry. Especially in the fields of high-end optical instruments, precision instruments, and medical equipment, the processing quality of lens assemblies directly affects the performance and reliability of the final products. The traditional cutting methods for lens assemblies have prominent problems such as unstable processing precision, low cutting efficiency, and large damage to materials, which severely restrict the processing quality and production efficiency of optical components. The traditional cutting technology has problems such as difficulty in precisely controlling the cutting path, inability to dynamically adjust cutting parameters, lack of precise recognition and modeling capabilities for the complex shapes of lens assemblies, and often relies on empirical operations, unable to achieve comprehensive intelligent control of the cutting process of lens assemblies, resulting in fluctuations in cutting precision, poor processing consistency, low material utilization rate, increased production costs, and the risk of defective products. Summary of the Invention

[0003] Based on this, it is necessary to provide a laser cutting control system and method for a lens assembly to solve at least one of the above technical problems.

[0004] To achieve the above object, a laser cutting control method for a lens assembly includes the following steps:

[0005] Step S1: Scan the lens assembly in all directions to obtain the point cloud data of the lens assembly; eliminate abnormal points from the point cloud data of the lens assembly to obtain the filtered point cloud data of the lens assembly; perform three-dimensional reconstruction on the filtered point cloud data of the lens assembly to generate a lens assembly model;

[0006] Step S2: Obtain cutting task data; identify cutting targets from the cutting task data to obtain cutting target data; based on the cutting target data, perform an optimal cutting time and space arrangement on the lens assembly model to generate an optimal cutting blueprint;

[0007] Step S3: Plan a cutting path for the lens assembly model based on the optimal cutting blueprint to obtain a candidate cutting path; perform dynamic compensation on the candidate cutting path to obtain a compensated cutting path;

[0008] Step S4: Perform control parameter mapping according to the compensated cutting path to obtain variable control parameters; perform parameter replacement based on the variable control parameters to execute the intelligent laser cutting control method.

[0009] The present invention achieves precise digital capture of the surface geometric features of the lens assembly through an all-round high-precision scanning technology, introduces an intelligent recognition and elimination algorithm for abnormal points based on machine learning, significantly improves the quality and reliability of the point cloud data, and converts the discrete point cloud into a continuous three-dimensional geometric model through an advanced three-dimensional reconstruction technology, breaking through the limitations of traditional reconstruction methods, laying a precise data foundation for subsequent cutting and processing. At the same time, a deep learning object recognition model is constructed in the data analysis link of the cutting task to accurately understand the processing requirements of complex lens assemblies, introduces a multi-dimensional optimization algorithm for cutting space-time arrangement, comprehensively considers multiple constraint conditions such as material utilization rate, processing accuracy, and cutting efficiency, realizes intelligent planning and dynamic adjustment of the cutting path, further develops an intelligent path planning algorithm based on the optimized cutting blueprint, precisely controls the cutting path of the lens assembly by introducing multi-constraint conditions and dynamic path generation technology, constructs a machine learning dynamic compensation model, real-time senses the small deformations and stress distributions during the cutting process, adaptively adjusts the cutting path, effectively suppresses the accumulation of processing errors and material deformations, and finally realizes the precise parameter conversion of the compensation cutting path by constructing a control parameter mapping model based on machine learning, introduces an adaptive parameter replacement algorithm, dynamically adjusts the laser cutting control parameters according to the real-time processing environment, breaks through the traditional static parameter setting mode, significantly improves the intelligent level of the laser cutting process, realizes precise regulation of the processing process of the lens assembly, and comprehensively improves the cutting quality and production efficiency.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Perform an all-round laser scan on the lens assembly to obtain the original point cloud data; perform spatial registration on the original point cloud data to obtain the point cloud data of the lens assembly;

[0012] Step S12: Perform local density analysis on the point cloud data of the lens assembly to obtain the point cloud density distribution data; perform outlier detection based on the point cloud density distribution data to generate a set of abnormal points;

[0013] Step S13: Perform abnormal elimination processing on the set of abnormal points to generate filtered point cloud data of the lens assembly; perform normal vector estimation processing on the filtered point cloud data of the lens assembly to obtain a normal vector field;

[0014] Step S14: Perform three-dimensional reconstruction on the filtered point cloud data of the lens assembly based on the normal vector field to generate a lens assembly model.

[0015] Through the implementation of omnidirectional laser scanning of the lens assembly, the present invention can obtain comprehensive original point cloud data. The implementation of spatial registration can ensure data consistency under different perspectives. The generated point cloud data of the lens assembly provides a reliable basis for subsequent processing. The implementation of local density analysis of the point cloud data of the lens assembly can reveal the distribution characteristics of the point cloud, and the obtained point cloud density distribution data provides a basis for outlier detection. The implementation of outlier detection based on the point cloud density distribution data can accurately identify the outliers in the data. The generated set of outlier positions provides a target for rejection processing. The implementation of outlier rejection processing on the set of outlier positions can improve the data quality. The generated filtered point cloud data of the lens assembly provides a clear basis for subsequent normal vector estimation. The implementation of normal vector estimation processing on the filtered point cloud data of the lens assembly can provide direction information for 3D reconstruction. The obtained normal vector field provides support for effective reconstruction. The implementation of 3D reconstruction of the filtered point cloud data of the lens assembly based on the normal vector field can generate an accurate lens assembly model, ensuring the efficiency and accuracy of subsequent laser cutting, and improving the quality and reliability of the entire cutting process.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Obtain cutting task data; classify the cutting target of the cutting task data to obtain a target classification result;

[0018] Step S22: Identify the cutting target of the cutting task data based on the target classification result to generate cutting target data;

[0019] Step S23: Perform feature matching on the lens assembly model according to the cutting target data to obtain a candidate cutting target model;

[0020] Step S24: Perform arrangement constraint processing on the candidate cutting model to obtain constraint cutting conditions; perform optimized space-time arrangement processing on the candidate cutting model based on the constraint cutting conditions to generate an optimized cutting blueprint.

[0021] The implementation of obtaining cutting task data in the present invention can ensure the information integrity of the cutting process. The implementation of classifying the cutting tasks for the cutting targets can effectively distinguish different cutting requirements, and the obtained target classification results provide a basis for subsequent recognition. The implementation of identifying the cutting targets for the cutting task data based on the target classification results can accurately extract the targets to be cut, and the generated cutting target data provides a basis for feature matching. The implementation of performing feature matching on the lens component model according to the cutting target data can generate a candidate cutting target model, and the obtained candidate cutting target model provides a clear reference for subsequent layout processing. The implementation of performing layout constraint processing on the candidate cutting model can ensure the feasibility of the cutting process, and the generated constraint cutting conditions provide a direction for the optimal space-time layout. The implementation of performing optimal space-time layout processing on the candidate cutting model based on the constraint cutting conditions can form an efficient cutting plan, and the generated optimal cutting blueprint provides clear guidance for subsequent cutting path planning, improving the accuracy and efficiency of laser cutting control and ensuring the cutting quality and consistency of the lens components.

[0022] Preferably, step S24 includes the following steps:

[0023] Step S241: Apply boundary condition constraints to the candidate cutting model to obtain boundary condition constraint data; apply material utilization rate constraints to the candidate cutting model to generate material constraint data;

[0024] Step S242: Perform constraint integration processing on the boundary condition constraint data and the material constraint data based on preset constraint weight data to obtain constraint cutting conditions;

[0025] Step S243: Perform optimal cutting target matching on the candidate cutting model based on the constraint cutting conditions to obtain an optimal cutting sequence; calculate the adjacent distances for the optimal cutting sequence to generate a target spacing matrix;

[0026] Step S244: Conduct thermal influence analysis based on the optimal cutting sequence to obtain thermal accumulation distribution data; identify conflict regions for the thermal accumulation distribution data to generate a conflict time sequence point set; perform time reallocation on the optimal cutting sequence based on the conflict time sequence point set to generate time sequence optimization data;

[0027] Step S245: Calculate the safety distance for the target spacing matrix to obtain gap safety distance data; perform spacing adjustment processing on the target spacing matrix according to the gap safety distance to generate spacing optimization data;

[0028] Step S246: Integrate and map the time sequence optimization data and the spacing optimization data to generate an optimal cutting blueprint.

[0029] By implementing boundary condition constraints on the candidate cutting model, the present invention can ensure that the cutting operation is carried out within physical limits. The generated boundary condition constraint data provides a basis for subsequent processing. Implementing material utilization constraints on the candidate cutting model can optimize material usage, and the generated material constraint data provides a basis for constraint integration. Implementing constraint integration processing on the boundary condition constraint data and the material constraint data based on preset constraint weight data can form comprehensive constraint cutting conditions. The obtained constraint cutting conditions provide conditional support for matching the optimal cutting target. Implementing matching of the optimal cutting target for the candidate cutting model based on the constraint cutting conditions can identify the optimal cutting sequence. The generated optimal cutting sequence provides a basis for subsequent distance calculation. Implementing adjacent distance calculation on the optimal cutting sequence can identify the relative positional relationship between cutting targets. The generated target spacing matrix provides a reference for thermal impact analysis. Implementing thermal impact analysis according to the optimal cutting sequence can evaluate the thermal effect during the cutting process. The obtained thermal accumulation distribution data provides support for identifying conflict areas. Implementing identification of conflict areas on the thermal accumulation distribution data can identify potential cutting conflicts. The generated conflict time sequence point set provides a basis for time reassignment. Implementing time reassignment on the optimal cutting sequence based on the conflict time sequence point set can optimize the cutting time sequence, generating time sequence optimization data. Implementing safety distance calculation on the target spacing matrix can ensure the safety of the cutting process. The obtained gap safety distance data provides a basis for adjusting the target spacing. Implementing spacing adjustment processing on the target spacing matrix according to the gap safety distance can optimize the distance between cutting targets, generating spacing optimization data. Finally, implementing integration mapping on the time sequence optimization data and the spacing optimization data can form the final optimal cutting blueprint, ensuring the efficiency and safety of laser cutting and improving the precision and overall quality of lens component cutting.

[0030] Preferably, step S245 includes the following steps:

[0031] Perform spatial requirement calculation on the target spacing matrix to obtain a spatial requirement matrix; perform effective utilization analysis based on the spatial requirement matrix to obtain effective utilization space data;

[0032] Extract cutting gaps based on the effective utilization space data and the target spacing matrix to generate cutting gap data;

[0033] Perform safety distance calculation on the cutting gap data to obtain gap safety distance data;

[0034] Perform safety reserved space mapping on the target spacing matrix according to the gap safety distance data to generate safety required reserved space data;

[0035] Perform interval adjustment processing on the target spacing matrix based on the safety required reserved space data to generate spacing optimization data.

[0036] Through the implementation of calculating the space requirements for the target spacing matrix, the present invention can evaluate the space required during the cutting process. The generated space requirement matrix provides a basis for subsequent effective utilization analysis. Through the implementation of effective utilization analysis based on the space requirement matrix, resource allocation can be optimized. The obtained effective utilization space data provides a basis for cutting gap extraction. Through the implementation of cutting gap extraction based on the effective utilization space data and the target spacing matrix, the gaps suitable for cutting can be identified. The generated cutting gap data provides support for safety distance calculation. Through the implementation of safety distance calculation for the cutting gap data, the safety during the cutting process can be ensured. The obtained gap safety distance data provides a reference for the adjustment of the target spacing matrix. Through the implementation of safety reserved space mapping for the target spacing matrix according to the gap safety distance data, a reasonable reserved space strategy can be formulated. The generated safety required reserved space data provides a basis for interval adjustment. Through the implementation of interval adjustment processing for the target spacing matrix based on the safety required reserved space data, the spacing between cutting targets can be optimized. The generated spacing optimization data ensures the accuracy and safety of laser cutting, improving the overall effect and quality control of lens component cutting.

[0037] Preferably, step S3 includes the following steps:

[0038] Step S31: Perform path constraint mapping on the lens component model based on the preferred cutting blueprint to obtain path constraint data;

[0039] Step S32: Plan the cutting path for the lens component model according to the path constraint data to obtain candidate cutting paths;

[0040] Step S33: Perform cutting simulation based on the candidate cutting paths to obtain simulated component cutting data; perform deformation prediction on the simulated component cutting data to generate cutting deformation prediction data;

[0041] Step S34: Calculate the stress distribution based on the cutting deformation prediction data to generate stress distribution data; perform stress path compensation according to the stress distribution data to obtain the compensated cutting path.

[0042] The implementation of path constraint mapping for the lens component model based on the optimized cutting blueprint in the present invention can ensure that the cutting path complies with the design specifications. The generated path constraint data provides the necessary constraint basis for cutting path planning. The implementation of cutting path planning for the lens component model according to the path constraint data can identify effective cutting paths. The obtained candidate cutting paths provide the basis for subsequent simulation analysis. The implementation of cutting simulation based on the candidate cutting paths can evaluate the cutting effect before actual cutting. The generated simulated component cutting data provides an important basis for deformation prediction. The implementation of deformation prediction for the simulated component cutting data can identify the deformation during cutting. The generated cutting deformation prediction data supports stress analysis. The implementation of stress distribution calculation based on the cutting deformation prediction data can reveal the stress conditions inside the material during cutting. The generated stress distribution data provides a reference for path optimization. The implementation of stress path compensation according to the stress distribution data can adjust the cutting path to reduce the deformation risk. The obtained compensated cutting path ensures the precise control of laser cutting, improves the quality and consistency of lens component cutting, and ensures the controllability and safety of the final cutting result.

[0043] Preferably, step S33 includes the following steps:

[0044] Obtain laser cutting heat source data; perform cutting temperature field simulation on the laser cutting heat source data to obtain the simulated temperature field distribution;

[0045] Perform cutting cooling field simulation on the laser cutting heat source data to generate the simulated cooling field distribution; perform stress calculation on the candidate cutting paths to obtain stress field data;

[0046] Perform cutting simulation on the candidate cutting paths based on the simulated temperature field distribution, simulated cooling field distribution, and stress field data to obtain simulated component cutting data;

[0047] Perform local strain calculation on the simulated component cutting data to obtain the local strain field; perform thermal deformation evaluation based on the local strain field to generate local thermal deformation data;

[0048] Perform global deformation cumulative calculation on the simulated component cutting data according to the local thermal deformation data to obtain the cumulative deformation field;

[0049] Perform deformation prediction based on the cumulative deformation field to generate cutting deformation prediction data.

[0050] The implementation of obtaining the laser cutting heat source data in the present invention can accurately reflect the characteristics of the heat source during the cutting process. The implementation of simulating the cutting temperature field for the laser cutting heat source data can predict the changes in the temperature distribution during the cutting process. The generated simulated temperature field distribution provides a basis for the evaluation of the cutting effect. The implementation of simulating the cutting cooling field for the laser cutting heat source data can analyze the cooling effect. The generated simulated cooling field distribution provides a reference for thermal management. The implementation of calculating the stress for the candidate cutting path can reveal the stress distribution during the cutting process. The obtained stress field data provides support for subsequent cutting simulations. The implementation of performing cutting simulations on the candidate cutting path based on the simulated temperature field distribution, simulated cooling field distribution, and stress field data can comprehensively consider the thermal effects and stress influences. The obtained simulated component cutting data provides a basis for the optimization of the cutting process. The implementation of calculating the local strain for the simulated component cutting data can identify the local deformation of the material during the cutting process. The generated local strain field provides data support for the thermal deformation evaluation. The implementation of performing thermal deformation evaluation based on the local strain field can evaluate the thermal response of the material during the cutting process. The generated local thermal deformation data provides a basis for the global deformation accumulation calculation. The implementation of performing global deformation accumulation calculation on the simulated component cutting data according to the local thermal deformation data can obtain the overall deformation situation of the material. The obtained cumulative deformation field provides support for the optimization of the cutting path. The implementation of performing deformation prediction based on the cumulative deformation field can predict the final cutting effect. The generated cutting deformation prediction data provides an important reference for the adjustment of subsequent cutting strategies, ensuring the accuracy and quality control of the lens component cutting.

[0051] Preferably, step S34 includes the following steps:

[0052] Perform stress tensor analysis on the cutting deformation prediction data to obtain stress component data;

[0053] Perform distribution calculation on the stress component data to generate stress distribution data;

[0054] Perform compensation amount calculation on the stress distribution data to obtain path compensation data;

[0055] Perform compensation superposition processing on the simulated component cutting data according to the path compensation data to obtain a compensated cutting path.

[0056] The implementation of stress tensor analysis on the cutting deformation prediction data in the present invention can deeply understand the stress state inside the material during the cutting process. The obtained stress component data provides a basis for stress distribution calculation. The implementation of distribution calculation on the stress component data can reveal the change trend of stress in the material. The generated stress distribution data provides a basis for subsequent compensation processing. The implementation of compensation amount calculation on the stress distribution data can determine the correction amount required for the cutting path. The obtained path compensation data provides specific parameters for compensation superposition processing. The implementation of compensation superposition processing on the simulated component cutting data according to the path compensation data can adjust the cutting path to adapt to the deformation of the material. The finally generated compensated cutting path ensures the precise control of laser cutting, improves the quality and consistency of lens component cutting, ensures the controllability and safety of the final cutting result, and further optimizes the cutting efficiency and material utilization rate.

[0057] Preferably, step S4 includes the following steps:

[0058] Step S41: Analyze the control characteristics of the compensated cutting path to obtain compensated control characteristics; extract the curvature of the compensated cutting path to obtain path curvature data;

[0059] Step S42: Calculate the rate of change of speed for the path curvature data to obtain curvature speed change rate data; map the compensated control characteristics according to the curvature speed change rate data to obtain variable control parameters;

[0060] Step S43: Generate a multi-objective optimization model based on the variable control parameters; perform parameter replacement on the variable control parameters according to the multi-objective optimization model to obtain optimized control instructions;

[0061] Step S44: Perform laser cutting control according to the optimized control instructions.

[0062] Through the implementation of controlling feature analysis on the compensation cutting path, the present invention can identify the key control parameters during the cutting process. The obtained compensation control features provide a basis for path optimization. Through the implementation of curvature extraction on the compensation cutting path, the bending degree of the cutting path can be quantified. The generated path curvature data provides a basis for speed adjustment. Through the implementation of calculating the speed change rate for the path curvature data, the change characteristics of the speed during the cutting process can be evaluated. The obtained curvature speed change rate data provides support for control parameter mapping. Through the implementation of control parameter mapping for the compensation control features based on the curvature speed change rate data, variable control parameters adapted to the cutting requirements can be generated. Through the implementation of multi-objective optimization modeling based on the variable control parameters, multiple optimization objectives can be considered simultaneously. The generated multi-objective optimization model provides a framework for improving the cutting effect. Through the implementation of parameter replacement for the variable control parameters according to the multi-objective optimization model, targeted optimized control instructions can be generated. Finally, through the implementation of laser cutting control according to the optimized control instructions, precise cutting operations can be achieved, improving the efficiency and quality of lens component cutting, ensuring the stability and safety of the cutting process, and further enhancing the intelligent level and automation ability of the system.

[0063] The present invention also provides a laser cutting control system for lens components, which is used to execute the laser cutting control method for lens components as described above. The laser cutting control system for lens components includes:

[0064] A lens reconstruction module, which is used to azimuthally scan the lens component to obtain lens component point cloud data; remove abnormal point positions from the lens component point cloud data to obtain filtered lens component point cloud data; perform three-dimensional reconstruction on the filtered lens component point cloud data to generate a lens component model;

[0065] A space-time arrangement module, which is used to obtain cutting task data; identify cutting target data from the cutting task data; perform optimal cutting space-time arrangement on the lens component model based on the cutting target data to generate an optimal cutting blueprint;

[0066] A dynamic compensation module, which is used to plan a cutting path for the lens component model based on the optimal cutting blueprint to obtain a candidate cutting path; perform dynamic compensation on the candidate cutting path to obtain a compensated cutting path;

[0067] A parameter replacement module, which is used to perform control parameter mapping based on the compensated cutting path to obtain variable control parameters; perform parameter replacement based on the variable control parameters to execute the intelligent laser cutting control method.

[0068] Through the implementation of the lens reconstruction module, the present invention can achieve a comprehensive scan of the lens assembly, obtain detailed point cloud data. The implementation of abnormal point position elimination can improve the accuracy of the data. The generated filtered lens assembly point cloud data provides a reliable basis for subsequent 3D reconstruction. The implementation of 3D reconstruction can generate an accurate lens assembly model, providing a clear reference for the cutting process. The implementation of the space-time arrangement module can effectively obtain cutting task data and achieve accurate identification of the cutting target. The implementation of optimizing the cutting space-time arrangement of the lens assembly model based on the cutting target data can optimize the cutting plan. The generated optimal cutting blueprint provides a clear guidance for the cutting path planning. The implementation of the dynamic compensation module can accurately plan the cutting path. The generation of the candidate cutting path provides a basis for subsequent compensation. The implementation of dynamic compensation can ensure the accuracy and effectiveness of the cutting path. The implementation of the parameter replacement module can convert the compensated cutting path into operable control parameters. The generation of the variable control parameters lays a foundation for the precise control of laser cutting. The implementation of parameter replacement based on the variable control parameters can perform efficient intelligent laser cutting control, improving the precision and efficiency of lens assembly processing. Description of the Drawings

[0069] Figure 1 It is a schematic diagram of the step flow of a laser cutting control method for a lens assembly;

[0070] Figure 2 It is a schematic diagram of the detailed implementation step flow of step S2;

[0071] Figure 3 It is a schematic diagram of the detailed implementation step flow of step S3;

[0072] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment

[0073] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] To achieve this, please refer to Figures 1 to 3 , a laser cutting control method for a lens assembly, comprising the following steps:

[0077] Step S1: scanning the lens assembly in all directions to obtain lens assembly point cloud data; removing abnormal points from the lens assembly point cloud data to obtain filtered lens assembly point cloud data; performing three-dimensional reconstruction on the filtered lens assembly point cloud data to generate a lens assembly model;

[0078] Step S2: obtaining cutting task data; performing cutting target recognition on the cutting task data to obtain cutting target data; performing optimal cutting time-space arrangement on the lens component model based on the cutting target data to generate an optimal cutting blueprint;

[0079] Step S3: planning a cutting path for the lens component model based on the preferred cutting blueprint to obtain a candidate cutting path; dynamically compensating the candidate cutting path to obtain a compensated cutting path;

[0080] Step S4: mapping control parameters according to the compensation cutting path to obtain changed control parameters; performing parameter replacement based on the changed control parameters to execute the intelligent laser cutting control method.

[0081] The present invention realizes the accurate digital capture of the surface geometric features of the lens assembly through all-round high-precision scanning technology, introduces an intelligent recognition and elimination algorithm for abnormal points based on machine learning, significantly improves the quality and reliability of point cloud data, and converts the discrete point cloud into a continuous three-dimensional geometric model through advanced three-dimensional reconstruction technology, breaking through the limitations of traditional reconstruction methods, laying an accurate data foundation for subsequent cutting and processing. At the same time, a deep learning object recognition model is constructed in the data analysis link of the cutting task to accurately understand the processing requirements of complex lens assemblies, introduces a multi-dimensional optimization algorithm for cutting space-time arrangement, comprehensively considers multiple constraint conditions such as material utilization rate, processing accuracy, and cutting efficiency, realizes the intelligent planning and dynamic adjustment of the cutting path, further develops an intelligent path planning algorithm based on the optimized cutting blueprint, accurately controls the cutting path of the lens assembly by introducing multi-constraint conditions and dynamic path generation technology, constructs a machine learning dynamic compensation model, real-time senses the minute deformation and stress distribution during the cutting process, adaptively adjusts the cutting path, effectively suppresses the accumulation of processing errors and material deformation, and finally realizes the accurate parameter conversion of the compensation cutting path by constructing a control parameter mapping model based on machine learning, introduces an adaptive parameter replacement algorithm, dynamically adjusts the laser cutting control parameters according to the real-time processing environment, breaks through the traditional static parameter setting mode, significantly improves the intelligent level of the laser cutting process, realizes the precise control of the processing process of the lens assembly, and comprehensively improves the cutting quality and production efficiency.

[0082] In the embodiment of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of a laser cutting control method for a lens assembly according to the present invention. In this example, the laser cutting control method for a lens assembly includes the following steps:

[0083] Step S1: Scan the lens assembly all-round to obtain the point cloud data of the lens assembly; eliminate the abnormal points from the point cloud data of the lens assembly to obtain the filtered point cloud data of the lens assembly; perform three-dimensional reconstruction on the filtered point cloud data of the lens assembly to generate a lens assembly model;

[0084] In this embodiment, the lens assembly is scanned all-round by using a laser scanner or a structured light scanning device. The lens assembly is fixed by a high-speed rotating platform, and the point cloud data of the lens assembly is obtained through the scanning device. After obtaining the data, preliminary processing is performed, and the filter is used to denoise the point cloud data and eliminate the abnormal points therein. These abnormal points are generated due to factors such as equipment errors and surface unevenness. The filtered point cloud data is more accurate. Next, a three-dimensional reconstruction algorithm, such as the Delaunay Triangulation algorithm, is used to perform three-dimensional reconstruction on the filtered point cloud data to generate a three-dimensional model of the lens assembly. This model accurately reflects the shape, edge, thickness, and its minute details of the lens, facilitating subsequent cutting path planning and cutting quality control.

[0085] Step S2: Obtain cutting task data; perform cutting target recognition on the cutting task data to obtain cutting target data; based on the cutting target data, perform an optimal cutting time and space arrangement on the lens component model to generate an optimal cutting blueprint.

[0086] In this embodiment, obtain the current cutting task data from the production line database, which includes the cutting dimensions, shapes, and cutting surface types required by the user. After obtaining the task data, use a deep learning model or pattern recognition algorithm to recognize the cutting target, extract the key features of the cutting target, such as cutting area, cutting shape, angle, etc., and perform precise calibration of the target data according to the dimensions, thickness, etc. of the lens component. Then, based on these cutting target data, use a heuristic algorithm (such as Simulated Annealing) to perform an optimal cutting time and space arrangement on the lens component model. Through multiple iterations of optimization, ensure that the cutting area precisely matches the lens component. The generated optimal cutting blueprint provides the best cutting plan, maximizing the cutting efficiency and reducing waste.

[0087] Step S3: Based on the optimal cutting blueprint, perform cutting path planning on the lens component model to obtain candidate cutting paths; perform dynamic compensation on the candidate cutting paths to obtain compensated cutting paths.

[0088] In this embodiment, based on the generated optimal cutting blueprint, use a path planning algorithm, such as the A* algorithm (A-star Algorithm), to perform cutting path planning on the lens component model. By calculating the optimization method of the cutting path, obtain several candidate cutting paths. These paths will try to avoid damaging the lens component and improve the cutting speed. Next, perform dynamic compensation on these candidate cutting paths. The compensation parameters include laser focus position, cutting speed, cutting depth, etc. By real-time feedback data, combined with factors such as temperature and lens material properties during the cutting process, dynamically adjust the cutting path to generate the compensated cutting path, ensuring precise processing of the lens material during the cutting process and avoiding problems such as thermal expansion or focus shift.

[0089] Step S4: Perform control parameter mapping according to the compensated cutting path to obtain variable control parameters; perform parameter substitution based on the variable control parameters to execute the intelligent laser cutting control method.

[0090] In this embodiment, according to the compensated cutting path, the laser cutting control system is used to perform control parameter mapping. First, according to the compensated cutting path, the corresponding control parameters are obtained. The control parameters include laser power, pulse frequency, cutting speed, focus position, etc. These parameters will be adjusted according to the thickness, material of the lens and the real-time temperature change. Then, through the calculation formula, these control parameters are mapped into the control system of the cutting machine to generate variable control parameters. Then, according to these variable control parameters, parameter replacement is performed on the cutting equipment to ensure that the power of the laser, the cutting speed, the focus of the laser beam, etc. can be adjusted in real time to perform the cutting operation, and then precise laser cutting of the lens is carried out to achieve the predetermined cutting effect, ensuring that the cutting surface is smooth and the size is accurate.

[0091] Preferably, step S1 includes the following steps:

[0092] Step S11: Perform omnidirectional laser scanning on the lens assembly to obtain the original point cloud data; perform spatial registration on the original point cloud data to obtain the point cloud data of the lens assembly;

[0093] Step S12: Perform local density analysis on the point cloud data of the lens assembly to obtain the point cloud density distribution data; perform outlier detection based on the point cloud density distribution data to generate an outlier point set;

[0094] Step S13: Perform outlier removal processing on the outlier point set to generate filtered point cloud data of the lens assembly; perform normal vector estimation processing on the filtered point cloud data of the lens assembly to obtain the normal vector field;

[0095] Step S14: Perform three-dimensional reconstruction on the filtered point cloud data of the lens assembly based on the normal vector field to generate a lens assembly model.

[0096] In this embodiment, a laser scanning device is used to perform an omnidirectional scan on the lens assembly. The scanner performs high-frequency scans on each surface of the lens through a laser beam. The lens assembly is fixed on a rotating platform during the scan, and the rotation angle and speed of the platform are adjusted according to the size of the lens to ensure the integrity and accuracy of the scan data. After obtaining the original point cloud data by scanning, a spatial registration algorithm is used to process the point cloud. The Iterative Closest Point (ICP) algorithm is adopted in the registration process to accurately align the data scanned from different angles, ensuring that all scanned point clouds can be matched in the same coordinate system. By iteratively calculating and adjusting the relative positions of the point cloud data through the ICP algorithm, a unified point cloud data of the lens assembly is obtained. The registered data is a clear and accurate point cloud model. Based on the point cloud data of the lens assembly, local density analysis is carried out. First, the point cloud data is spatially segmented, and the Voxel Grid method is used to divide the point cloud data into multiple small grid cells. The number of point clouds in each grid is calculated to obtain the local point cloud density of each grid. Then, based on the local point cloud density data, the K-Nearest Neighbor algorithm is used to evaluate the local density of each point. By calculating the number of neighborhood points within a certain range around each point, the density distribution of the point cloud data is determined. Next, the density values are statistically analyzed, and regions with significantly high or low density values are marked to identify the existing abnormal points. On this basis, through an outlier detection method, such as the Z-score-based detection method, the points with abnormal density are determined and marked as an abnormal point set. The abnormal point set is removed. The Random Sample Consensus (RANSAC) algorithm is used to identify the points that do not conform to the surface flatness and edge characteristics of the lens from the abnormal point set. For the local anomalies in the point cloud data, the RANSAC algorithm is used for fitting, and the points with large fitting errors are removed to ensure that the remaining point cloud data conforms to the actual surface morphology. Next, normal vector estimation is performed on the filtered point cloud data. Using the Principal Component Analysis (PCA) method, the normal vector of each point is calculated. By calculating the covariance matrix of the local neighborhood of each point, the principal components are extracted as the normal vectors. The estimation of the normal vector can provide the surface normal direction of each point, further providing accurate geometric information for subsequent 3D reconstruction. The normal vector field is a set of normal vector data for each point. Based on the normal vector field, 3D reconstruction of the filtered point cloud data of the lens assembly is carried out. First, according to the spatial relationship between the normal vector of each point and the local point cloud, the Poisson Surface Reconstruction algorithm is used to interpolate the point cloud. A smooth 3D surface model is generated through Poisson reconstruction. The algorithm iteratively solves the optimal solution, combines the normal vectors with the point cloud data, and generates a 3D grid model that conforms to the actual surface characteristics.Next, the Multigrid Method is used to refine the grid to obtain a high-resolution three-dimensional lens model. The generated lens component model has high-precision surface features, can accurately reflect the geometric shape and details of the lens, and is convenient for subsequent cutting path planning and control.

[0097] Preferably, step S2 includes the following steps:

[0098] Step S21: Obtain cutting task data; classify the cutting target of the cutting task data to obtain a target classification result;

[0099] Step S22: Identify the cutting target of the cutting task data based on the target classification result to generate cutting target data;

[0100] Step S23: Perform feature matching on the lens component model according to the cutting target data to obtain a candidate cutting target model;

[0101] Step S24: Perform arrangement constraint processing on the candidate cutting model to obtain constraint cutting conditions; perform optimal space-time arrangement processing on the candidate cutting model based on the constraint cutting conditions to generate an optimal cutting blueprint.

[0102] In this embodiment, after obtaining the cutting task data, a high-precision sensor is used to collect the size, shape, and surface feature information of the lens assembly. The cutting task data contains the complete geometric data of the lens assembly, including information such as the thickness, curvature, and surface texture of the lens. The data is collected by a laser scanning device and stored in the point cloud data format. Subsequently, the data is preprocessed, and the Statistical Outlier Removal (SOR) filter is used to remove the noisy point cloud data to obtain more accurate cutting task data. Then, according to the specific requirements of the task, a classification algorithm in machine learning is used to classify the cutting target of the cutting task data. The classification basis includes the cutting area, the shape and position of the target. The Support Vector Machine (SVM) algorithm is used to determine the classification model through the training set data. The classification result includes the cutting area and the non-cutting area, and the target classification result is output. Based on the target classification result, the cutting target of the cutting task data is recognized. First, further data processing is performed on the classification result, and the geometric features corresponding to the cutting task data and the target area are matched. An algorithm based on edge detection (such as Canny Edge Detection) is used to identify the specific edge of the cutting target. The edge information is further associated with the cutting target area in the point cloud data. The Iterative Closest Point (ICP) algorithm is used to register the cutting target area to accurately determine the spatial position and orientation of the cutting target. Based on this recognition process, cutting target data is generated, which contains cutting feature information such as the coordinate information of the specific cutting area, the area shape, and the cutting depth. Based on the cutting target data, feature matching is performed on the lens assembly model. First, feature points are extracted from the cutting target data, and these feature points include the vertices of the target edge, curvature points, surface normal vectors, etc. The Fast Point Feature Histograms (FPFH) algorithm is used to match these feature points. During the matching process, the feature points in the lens assembly model and the cutting target data are compared one by one, and the Euclidean distance between each pair of points is calculated. Based on the matching result, the candidate target model that best meets the cutting requirements is selected. The candidate cutting target model is displayed graphically, and corresponding geometric dimensions, shapes, and positions are provided. The feature matching process is based on an iterative optimization algorithm, and finally, the candidate cutting target model is obtained. According to the candidate cutting target model, arrangement constraint processing is performed on it. First, the arrangement constraint conditions are defined, and these constraints include the spatial position of the cutting target area, the boundary of the lens assembly, the parallelism of the cutting path, and the cutting angle, etc. Based on these constraint conditions, the simulated annealing algorithm is used to optimize the arrangement of the candidate cutting target.The simulated annealing algorithm gradually approaches the global optimal solution by continuously adjusting the position and orientation of the cutting target on the lens assembly, and obtains the optimal arrangement plan of the candidate cutting models. Next, based on the optimized arrangement conditions, a spatio-temporal constraint algorithm (e.g., an arrangement algorithm based on dynamic programming) is used to perform spatio-temporal optimization on the cutting targets. During the processing, combined with the geometric features of the lens assembly and the requirements of the cutting path planning, dynamic adjustment of the spatial positions is carried out to ensure that there are no conflicts in the physical space for the arrangement of each cutting target. Finally, an optimized cutting blueprint is generated, and the blueprint includes the layout information of the final cutting path, meeting the requirements of cutting accuracy and timeliness.

[0103] Preferably, step S24 includes the following steps:

[0104] Step S241: Perform boundary condition constraints on the candidate cutting models to obtain boundary condition constraint data; perform material utilization rate constraints on the candidate cutting models to generate material constraint data;

[0105] Step S242: Perform constraint integration processing on the boundary condition constraint data and the material constraint data based on the preset constraint weight data to obtain constraint cutting conditions;

[0106] Step S243: Perform optimal cutting target matching on the candidate cutting models based on the constraint cutting conditions to obtain an optimal cutting sequence; calculate the adjacent distances for the optimal cutting sequence to generate a target spacing matrix;

[0107] Step S244: Perform thermal influence analysis according to the optimal cutting sequence to obtain thermal accumulation distribution data; identify conflict regions for the thermal accumulation distribution data to generate a conflict time sequence point set; perform time reallocation on the optimal cutting sequence based on the conflict time sequence point set to generate time sequence optimization data;

[0108] Step S245: Calculate the safety distance for the target spacing matrix to obtain gap safety distance data; perform spacing adjustment processing on the target spacing matrix according to the gap safety distance to generate spacing optimization data;

[0109] Step S246: Perform integration mapping on the time sequence optimization data and the spacing optimization data to generate an optimized cutting blueprint.

[0110] In this embodiment, when imposing boundary condition constraints on the candidate cutting model, first, the geometric boundaries of the candidate cutting target model are precisely analyzed. The shape of the cutting model is extracted using a 3D model processing tool, its edge contour is analyzed and docked with the working area of the cutting device to ensure that the layout of the cutting path is within the working range of the device. The specific boundary constraint data includes the size and shape of the cutting target and its distance from the device boundary. A geometric transformation algorithm is used for position adjustment to meet the motion range requirements of the cutting device. Then, material utilization rate constraints are imposed on the candidate cutting model. By analyzing the ratio between the material thickness and the cutting area of the lens assembly, a linear programming algorithm is used to calculate the maximum material utilization rate. By optimizing the distribution of cutting targets, the overlap degree between each cutting area is minimized to reduce material waste, generating material constraint data. The weight values of the preset boundary constraints and material constraints are obtained, and the weight values are adjusted according to the priority of the cutting task. The weight of the boundary condition constraint mainly considers the device motion range and the safety of the cutting path, while the weight of the material constraint is more inclined towards the optimization of the cutting area and the maximization of the material utilization rate. The two constraint data are integrated through a weighted summation method to obtain comprehensive constraint data. During the integration process, a multi-objective optimization algorithm (e.g., Particle Swarm Optimization, PSO) is used to handle the conflicts between the boundary constraint data and the material constraint data. The optimized result ensures that the arrangement of each cutting target not only meets the device boundary requirements but also maximally improves the material utilization rate. Finally, the constrained cutting conditions are obtained. The constrained cutting conditions are analyzed to ensure that the geometric features of each cutting target match the cutting ability of the device. A Dynamic Programming (DP) algorithm is used to compare each candidate cutting target with the defined cutting conditions one by one, calculating the matching degree of each cutting target. Finally, the target with the highest matching degree is selected as the final cutting target, generating an optimized cutting sequence. Then, the adjacent distance of the optimized cutting sequence is calculated, and the Euclidean Distance is used to calculate the distance between cutting targets, obtaining a target distance matrix. The target distance matrix contains the shortest distance between every two cutting targets. Considering the thermophysical properties of the lens assembly, the heat distribution during the laser cutting process is simulated through a heat conduction model, simulating the heat accumulation during the cutting process and its impact on the material. The finite element analysis method (FEA) is used to calculate the heat accumulation amount in each cutting area, obtaining heat accumulation distribution data. Then, conflict regions are identified from the heat accumulation distribution data. First, a threshold for the heat-affected region is defined, and a thermal analysis software is used to identify the regions where the temperature exceeds the threshold. These regions will cause material deformation or damage, generating a set of conflict time sequence points and recording the timestamps of these conflict regions.Time reallocation is performed on the preferred cutting sequence based on the conflict time point set. By adjusting the cutting order and cutting time, material damage caused by excessive heat accumulation is avoided, and timing optimization data is generated. The safety distance standard is defined to ensure that there is enough space between cutting targets to avoid mutual interference. The minimum distance between targets is calculated using a geometric algorithm and compared with the preset safety distance to obtain the clearance safety distance data. Then, the target spacing matrix is adjusted according to the clearance safety distance. The spatial distribution of cutting targets is adjusted using an optimization algorithm (e.g., Genetic Algorithm) to ensure that the spacing of each target meets the safety standard and to avoid cross - over or interference of the cutting paths, generating spacing optimization data. The timing optimization data and the spacing optimization data are fused, and the two are integrated by the method of weighted average to obtain the final preferred cutting blueprint. The data in the blueprint includes the timing arrangement of the cutting path and the spatial layout of the target spacing to ensure the smooth progress of the cutting process. The optimized cutting blueprint is visualized using a Computer - Aided Design (CAD) system, and finally a complete laser cutting control chart is generated. This control chart is transmitted to the laser cutting equipment to ensure that the equipment can perform cutting operations according to the optimized cutting path and order, achieving an efficient and precise cutting effect.

[0111] Preferably, step S245 includes the following steps:

[0112] Calculate the space requirement of the target spacing matrix to obtain the space requirement matrix; perform effective utilization analysis based on the space requirement matrix to obtain the effectively utilized space data;

[0113] Extract the cutting clearance based on the effectively utilized space data and the target spacing matrix to generate the cutting clearance data;

[0114] Calculate the safety distance for the cutting clearance data to obtain the clearance safety distance data;

[0115] Map the safety - required reserved space to the target spacing matrix according to the clearance safety distance data to generate the safety - required reserved space data;

[0116] Perform interval adjustment on the target spacing matrix based on the safety - required reserved space data to generate the spacing optimization data.

[0117] In this embodiment, the geometric shapes and dimensional data of each cutting target are obtained. By precisely modeling the boundaries of the cutting targets, the target spacing matrix is transformed into grid data using a spatial meshing method. The positions and dimensions of each cutting target are mapped into a three-dimensional spatial grid. A spatial analysis algorithm is used to calculate the space required for each cutting target, obtaining the free space between this target and other targets, and calculating the spatial range occupied by each target. Further, a space requirement matrix is generated. The space requirement matrix reflects the space requirement situation of each cutting target within the entire cutting area. The value of each element in the matrix represents the degree of space requirement at that position. The data is organized as a multi-dimensional matrix, showing the space requirements of each cutting target and its surrounding area. The data in the space requirement matrix is screened to identify the free parts in the cutting area and calculate their utilization efficiency. The region growing algorithm (Region Growing Algorithm) is used to divide the free space, and the usability of each region is evaluated. By calculating the space utilization rate within each region, the effective space and the ineffective space are further distinguished. The space regions that can be effectively utilized are screened out, and the maximum available space of these regions is calculated through an optimization algorithm (for example, the maximum minimum area method). Finally, effective utilization space data is generated, indicating which parts can be efficiently utilized and which parts have potential space waste within the given cutting area. The effective space is further divided. A contour detection algorithm (for example, Canny edge detection) is used to identify the gap regions between the cutting targets. The dimensions, shapes, and positions of the gap regions are confirmed through a step-by-step scanning method to ensure that the extracted gaps meet the actual working requirements of the cutting equipment. During this process, the minimum size of each gap is calculated, and according to the target spacing matrix, the gap size is ensured to meet the requirements of the laser cutting path. Finally, the identified effective gap regions are saved as cutting gap data, which contains information such as the gap positions, sizes, and shapes between the cutting targets. A safety distance threshold is defined. According to the physical characteristics and safety requirements of the cutting equipment, a geometric distance algorithm (for example, Manhattan distance method) is used to calculate the cutting gaps, checking whether the actual distances between each cutting target meet the safety preset standards, calculating the safety distances of the gaps between each cutting target, determining which gaps meet the predetermined safety requirements and which need to be adjusted, and generating gap safety distance data, which contains the minimum safety distances between each pair of cutting targets and shows which gaps do not meet the safety requirements and need further adjustment. The gap safety distances of each cutting target are compared with its surrounding area. The gap safety distance data is combined with the target spacing matrix through a spatial mapping algorithm, and the minimum constraint priority algorithm (Minimum Constraint Priority Algorithm) is used to calculate the amount of space to be reserved around each target. According to the preset safety standards, it is ensured that the gaps between the targets meet the safety requirements.Generate the data of the reserved space required for safety through mapping. The data includes the minimum safety space to be reserved around each target, ensuring that the space around each target is large enough to avoid interference during the cutting process. According to the data of the reserved space required for safety, adjust the distribution of the cutting targets in the space. Use the Greedy Algorithm to dynamically adjust the spacing between the cutting targets. By optimizing the distance between each pair of targets, maximize the space utilization efficiency while ensuring that the safety distance between each target meets the requirements. After adjustment, generate the data of optimized spacing, which reflects the spacing between the targets after the calculation of the safety reserved space, making the arrangement of the targets meet the safety requirements and be able to efficiently utilize the space. The finally generated data of optimized spacing provides a safe and efficient space layout for the cutting path planning.

[0118] Preferably, step S3 includes the following steps:

[0119] Step S31: Perform path constraint mapping on the lens assembly model based on the preferred cutting blueprint to obtain path constraint data;

[0120] Step S32: Plan the cutting path for the lens assembly model according to the path constraint data to obtain candidate cutting paths;

[0121] Step S33: Perform cutting simulation based on the candidate cutting paths to obtain the cutting data of the simulated assembly; predict the deformation of the cutting data of the simulated assembly to generate cutting deformation prediction data;

[0122] Step S34: Calculate the stress distribution based on the cutting deformation prediction data to generate stress distribution data; perform stress path compensation according to the stress distribution data to obtain the compensated cutting path.

[0123] In this embodiment, the lens component model is scanned using the optimized cutting blueprint obtained through calculation. The cutting paths in the blueprint are mapped into the three-dimensional space of the lens component model. The space mapping algorithm (such as the Bézier curve mapping method) is used to calibrate the cutting paths within the cutting area, determining the spatial position, shape, and variation trend of each cutting path. During the mapping process, precise spatial registration is performed for each path node according to the path requirements of the cutting blueprint and the geometric features of the lens component model, obtaining the constraint conditions for each cutting path to ensure that the path perfectly matches the geometric shape of the lens component. The path constraint data includes the geometric information of each cutting path, its movement trajectory during the cutting process, and the minimum safety distance between paths. The lens component model is divided into multiple cutting areas, and independent path planning is performed for each cutting area according to the path constraint data. The shortest path algorithm based on graph theory (such as the Dijkstra algorithm) is used to determine the cutting paths for each cutting area. During the planning process, the movement trajectory and working range of the cutting device are considered, and the paths between all cutting areas are globally optimized to ensure the continuity of the paths and the cutting efficiency. During planning, the curvature and intersection of the paths are strictly controlled to generate a set of candidate cutting paths. These paths reflect the best path scheme from the cutting start point to the end point, and each candidate cutting path conforms to the requirements in the path constraint data. The candidate cutting paths are simulated, and the finite element analysis (FEA) technique is used to simulate the cutting process of the lens component. Through refined simulation of the candidate cutting paths, simulated component cutting data is generated. The simulation data includes parameters such as the cutting trajectory, cutting depth, and cutting speed of each path. Combining with the mechanical properties of the material, the forces and heat during the cutting process are analyzed during the simulation, calculating the stress distribution on each cutting path, and further predicting the deformation that will occur. Based on the cutting simulation data, using the stress-strain relationship of the material, further cutting deformation prediction is performed. The lens deformation occurring during the cutting process is predicted through the thermal expansion and stress concentration models, generating cutting deformation prediction data. The data includes the deformation amplitude, deformation direction of each cutting path, and the stress concentration areas that will appear. According to the cutting deformation prediction data, the finite element method is used to model the stress field of the lens component, simulating the stress distribution on the surface and inside of the material during the cutting process, calculating the stress distribution data on each cutting path during the cutting process, combining the simulation data of the cutting path with the physical properties of the material to obtain the stress intensity, stress direction on each path, and its influence on the lens deformation. Based on the stress distribution data, the cutting paths are adjusted using the reverse compensation method. For the stress concentration area of each path, stress path compensation is performed. During the compensation process, adjustments are made according to the springback characteristics of the material and the geometric shape of the cutting path to ensure that the cutting path can effectively offset the stress changes generated during the cutting process, generating compensated cutting paths.The compensated path can more precisely reduce the deformation during the cutting process, ensuring the final cutting accuracy and the quality of the lens assembly.

[0124] Preferably, step S33 includes the following steps:

[0125] Obtain laser cutting heat source data; perform cutting temperature field simulation on the laser cutting heat source data to obtain the simulated temperature field distribution;

[0126] Perform cutting cooling field simulation on the laser cutting heat source data to generate the simulated cooling field distribution; perform stress calculation on the candidate cutting path to obtain stress field data;

[0127] Based on the simulated temperature field distribution, the simulated cooling field distribution, and the stress field data, perform cutting simulation on the candidate cutting path to obtain the simulated component cutting data;

[0128] Perform local strain calculation on the simulated component cutting data to obtain the local strain field; perform thermal deformation evaluation based on the local strain field to generate local thermal deformation data;

[0129] According to the local thermal deformation data, perform global deformation cumulative calculation on the simulated component cutting data to obtain the cumulative deformation field;

[0130] Perform deformation prediction based on the cumulative deformation field to generate cutting deformation prediction data.

[0131] In this embodiment, sensors in the laser cutting system record parameters such as the power output of the laser, the focusing mode of the laser beam, the laser pulse frequency, the cutting speed, and the beam irradiation area. These parameters are combined with the temperature data monitored in real time, and the intensity and distribution of the laser heat source during the laser cutting process are calculated using a heat source model (such as the Gauss distribution model) to obtain laser heat source data. These data describe the heat input and distribution when the laser beam contacts the lens assembly. Subsequently, the laser heat source data is used to simulate the cutting temperature field using the heat conduction equation and the Finite Element Method (FEM). The temperature field distribution on and inside the lens surface is obtained through numerical solution methods. During the simulation, a high-precision mesh generation technique is adopted to conduct a detailed spatial distribution analysis of the temperature field changes, and the temperature data on the lens surface and at different depths are output to obtain the simulated temperature field distribution data. Based on the laser cutting heat source data and considering the cooling methods during the cutting process, such as spray cooling, gas cooling, or contact cooling, a cooling field model is established. The heat exchange efficiency and temperature changes when the cooling fluid contacts the lens surface are calculated through numerical simulation methods. Computational Fluid Dynamics (CFD) is used to analyze the flow state of the cooling medium to obtain the velocity field, pressure field, and temperature field data of the cooling medium. The calculation of the cooling field considers parameters such as the temperature, flow rate, and cooling efficiency of the cooling medium. Based on these parameters, the influence of cooling on the temperature distribution during cutting is calculated to generate the simulated cooling field distribution data. The cooling field data includes the effects of the cooling medium contacting the lens at different time points. The simulated cooling field can truly reflect the distribution of the cooling medium during the cutting process. According to the simulated temperature field distribution data and the simulated cooling field distribution data, combined with mechanical parameters such as the thermal expansion coefficient and elastic modulus of the lens material, the Finite Element Analysis (FEA) method is used for stress calculation to establish a thermo-mechanical coupling model to calculate the stress distribution caused by thermal expansion during the laser cutting process. Considering the sharp changes in the temperature field during the laser cutting process, the spatio-temporal distribution of the thermal stress is calculated to generate the stress field data. The stress field data includes the stress intensity, direction, and distribution of each point on the cutting path. By analyzing the stress of different cutting paths, the stress concentration areas that occur during the cutting process are obtained. For the simulated temperature field distribution, the simulated cooling field distribution, and the stress field data, a multi-physics coupled finite element analysis method is used to simulate the cutting of the lens assembly. By coupling models such as heat conduction, fluid mechanics, and mechanical stress, the material response on the cutting path is simulated. During the simulation, the temperature changes, cooling effects, and stress changes of the material are considered to generate the simulated component cutting data, which includes the temperature changes, stress distribution, and plastic deformation of the material during the cutting process. Through different simulations of the cutting path,The cutting quality, deformation conditions, and potential defect areas on each path can be obtained. Based on the temperature and stress data obtained from the simulated component cutting data, the strain in the cutting area is calculated using the strain-stress relationship (such as Hooke's law). Special attention is paid to the strain distribution in the area near the cutting path. The finite element method is used to accurately calculate the strain in different areas, and local strain field data is obtained. The local strain field data reflects the small deformation of the material near the cutting path during the cutting process, and can accurately describe the material strain in the cutting area, including elastic strain and plastic strain. Based on the simulation results, the areas with larger local strain are identified, and these areas are potential risk points for cutting deformation. Based on the local strain field data, combined with the thermal expansion characteristics and deformation behavior of the material, the thermal deformation is evaluated. A thermo-mechanical coupling analysis model is adopted to combine the temperature field and the strain field to calculate the deformation caused by temperature changes in the local area. Special attention is paid to the areas with larger thermal expansion. Based on the thermal expansion coefficient and stress-strain model of the material, the thermal deformation behavior of different areas during the cutting process is simulated, and local thermal deformation data is generated. The data includes the thermal deformation amount, deformation direction, and timeliness of the deformation in the cutting area. In the global deformation cumulative calculation, based on the local thermal deformation data, the thermal deformation amounts of each local area are accumulated through numerical integration to obtain the global deformation field. Considering the influence of the deformation of each area on the overall structure, combined with the temperature field, cooling field, and stress field on the cutting path, the total deformation amount of the entire lens component during the cutting process is calculated, and cumulative deformation field data is obtained. The cumulative deformation field reflects the deformation of the entire lens component during the cutting process and can accurately show the deformation trend of each area. Based on the cumulative deformation field data, a thermo-mechanical coupling model is used to predict the deformation of the cutting path. Special attention is paid to the overall or local deformation that occurs during the cutting process. Prediction algorithms are used to analyze the deformation trends of different cutting paths, and cutting deformation prediction data is generated. The prediction data includes the deformation amount, deformation direction that will occur during the cutting process, and its impact on the final product quality. The prediction results will provide guidance for optimizing the cutting path to ensure that the cutting accuracy and quality meet the requirements.

[0132] Preferably, step S34 includes the following steps:

[0133] Perform stress tensor analysis on the cutting deformation prediction data to obtain stress component data;

[0134] Perform distribution calculation on the stress component data to generate stress distribution data;

[0135] Perform compensation amount calculation on the stress distribution data to obtain path compensation data;

[0136] Perform compensation superposition processing on the simulated component cutting data according to the path compensation data to obtain a compensated cutting path.

[0137] In this embodiment, based on the temperature field, cooling field, and stress field information in the cutting deformation prediction data, the finite element analysis method (FEA) is used to analyze the stress of each cutting path in detail, calculate the stress distribution of the lens during laser cutting due to temperature changes, material expansion, and laser beam interaction, and use the mathematical model of stress tensor (e.g., Cauchy stress tensor) to decompose the stress in each cutting area to obtain stress components in different directions, including normal stress and shear stress. By solving the principal stress values and principal directions of the tensor, the stress component data of each point during cutting is obtained. According to the stress component data, the finite element solution method is used to calculate the spatial distribution of the stress in each cutting area. The stress data of different areas is spatially weighted averaged by the numerical integration method, and combined with factors such as the geometric shape of the cutting path, material properties, and cooling effect, to generate global stress distribution data. The stress distribution data includes the stress values and the gradient changes of the stress in each cutting path and its surrounding areas. The calculated stress field reflects the stress state of the lens assembly during laser cutting. Based on the stress distribution data, considering the stress distribution and deformation trend in different areas, a thermal-mechanical coupling model is used to calculate the compensation amount. First, the high-stress areas in the stress field are analyzed. These areas are often the key points of cutting deformation. By introducing a path correction factor, according to the stress magnitude, deformation trend, and specific requirements of the cutting path, the stress field data is adjusted to calculate the compensation amount for each cutting path. The calculation of the compensation amount involves the relationship between the adjusted value of the stress and the geometric position of the cutting path. The path is optimized by the integration method to generate path compensation data. The path compensation data includes the compensation amount for each cutting path. These compensation amounts will be applied to the simulated component cutting data in the subsequent steps to make necessary corrections to the cutting path. The path compensation data is combined with the simulated component cutting data, and the compensation amount is applied to the coordinate points of the simulated cutting path by the numerical calculation method. For each point of each cutting path, the coordinates of the cutting path are adjusted according to the calculated compensation amount, and the compensation is superimposed point by point. The adjusted path takes into account the actual stress and deformation conditions to ensure that the influence of deformation and stress is effectively controlled during actual cutting. After this process, the generated compensated cutting path is the corrected path and is suitable for actual laser cutting operations. These compensated cutting paths will be used for subsequent control signal generation to ensure that the lens assembly reaches the expected shape and size during laser cutting.

[0138] Preferably, step S4 includes the following steps:

[0139] Step S41: Analyze the control characteristics of the compensated cutting path to obtain compensated control characteristics; extract the curvature of the compensated cutting path to obtain path curvature data;

[0140] Step S42: Calculate the rate of change of speed for the path curvature data to obtain the curvature speed change rate data; map the control parameters of the compensation control feature according to the curvature speed change rate data to obtain the variable control parameters;

[0141] Step S43: Based on the variable control parameters, perform multi-objective optimization modeling to generate a multi-objective optimization model; perform parameter substitution on the variable control parameters according to the multi-objective optimization model to obtain the optimized control instruction;

[0142] Step S44: Perform laser cutting control according to the optimized control instruction.

[0143] In this embodiment, the compensated cutting path data is obtained, the path is represented by a set of coordinate points, and the path is segmented using numerical methods. The second-order difference method is used for control feature analysis to calculate feature data such as the curvature and smoothness of each path segment. A suitable algorithm is selected to model the shape of the path, and the shape changes of each segment in the path are analyzed, including bending, broken lines, etc. Control feature data is further extracted based on the geometric features of the path segments. The obtained control features include the curvature, tangent direction, angle, etc. of the path. Then, the curvature of the path is extracted. Based on the discrete point set of the path, the continuous difference algorithm is used to calculate the curvature of the path at each point. The calculation results of the curvature are the change rate, curvature extreme value, etc. of each path segment. By processing these data, the path curvature data is obtained, where the curvature data reflects the degree of bending of the path. Through the first-order numerical differential processing of the path curvature data, the curvature change rate at each point of the path is calculated. The curvature data is differentiated using the time step or the path length unit to obtain the speed change rate of each path point. These change rates reflect the change of the path curvature over time during the cutting process. Next, according to the calculated curvature speed change rate data, an adaptive control model is used to associate the curvature change rate with control parameters. The control parameters include laser power, cutting speed, etc. The curvature speed change rate is mapped to the corresponding control parameters through the control model to obtain the variable control parameters. These parameters are used to adjust the power and cutting speed of the laser during the laser cutting process to ensure the accuracy and stability during the cutting process. The interpolation method is used during the mapping process to ensure the smooth transition of the control parameters for each path segment and avoid cutting errors caused by mutations. Multiple objective functions are considered, including cutting quality, cutting time, energy consumption, etc. The genetic algorithm is used to perform multi-objective optimization on the objective functions. The effects of different control parameter combinations are evaluated through the optimization algorithm. The initial population is generated according to the variable control parameters, and each population is evaluated to calculate the fitness value. The fitness value reflects the comprehensive effect of each set of parameters. After multiple optimization iteration processes, the parameter combination with a higher fitness is selected for crossover and mutation operations to update the parameter set, and finally, an optimized control model is generated. Then, according to the optimized model, the variable control parameters are replaced, and the optimization result is replaced with the new control parameters to obtain the final optimized control instructions. These instructions are used in the subsequent laser cutting process. The optimization process ensures the balance between various objectives during the cutting process, making the cutting efficiency and quality reach the best. The optimized control instructions are transmitted to the laser cutting control system, and the control system adjusts parameters such as the power, frequency, and cutting speed of the laser according to the instructions to ensure that the cutting conditions for each path segment meet the preset requirements. The temperature, pressure, etc. parameters during the cutting process are monitored in real time, and the data is fed back through sensors to dynamically adjust the laser output power to maintain the stability and accuracy of the cutting. During the cutting process, the control system also makes adaptive adjustments according to environmental changes. Finally, through the closed-loop control method, the efficient and precise cutting of the cutting path is ensured.And avoid any overheating or error generation to complete the laser cutting task of the entire lens assembly.

[0144] The present invention also provides a laser cutting control system for a lens assembly, which is used to execute the laser cutting control method for a lens assembly as described above. The laser cutting control system for a lens assembly includes:

[0145] A lens reconstruction module, which is used to scan the lens assembly azimuthally to obtain the point cloud data of the lens assembly; remove abnormal point positions from the point cloud data of the lens assembly to obtain the filtered point cloud data of the lens assembly; perform three-dimensional reconstruction on the filtered point cloud data of the lens assembly to generate a lens assembly model;

[0146] A space-time arrangement module, which is used to obtain cutting task data; identify cutting targets from the cutting task data to obtain cutting target data; perform optimal cutting space-time arrangement on the lens assembly model based on the cutting target data to generate an optimal cutting blueprint;

[0147] A dynamic compensation module, which is used to plan a cutting path for the lens assembly model based on the optimal cutting blueprint to obtain a candidate cutting path; perform dynamic compensation on the candidate cutting path to obtain a compensated cutting path;

[0148] A parameter replacement module, which is used to perform control parameter mapping according to the compensated cutting path to obtain variable control parameters; perform parameter replacement based on the variable control parameters to execute the intelligent laser cutting control method.

[0149] Through the implementation of the lens reconstruction module of the present invention, a comprehensive scan of the lens assembly can be realized, detailed point cloud data can be obtained, the implementation of abnormal point position removal can improve the accuracy of the data, the generated filtered point cloud data of the lens assembly provides a reliable basis for subsequent three-dimensional reconstruction, the implementation of three-dimensional reconstruction can generate an accurate lens assembly model, providing a clear reference for the cutting process. The implementation of the space-time arrangement module can effectively obtain cutting task data, accurately identify cutting targets, and the implementation of optimal cutting space-time arrangement on the lens assembly model based on the cutting target data can optimize the cutting plan. The generated optimal cutting blueprint provides clear guidance for cutting path planning. The implementation of the dynamic compensation module can accurately plan the cutting path, and the generation of the candidate cutting path provides a basis for subsequent compensation. The implementation of dynamic compensation can ensure the accuracy and effectiveness of the cutting path. The implementation of the parameter replacement module can convert the compensated cutting path into operable control parameters, the generation of variable control parameters lays a foundation for the precise control of laser cutting, and the implementation of parameter replacement based on the variable control parameters can execute efficient intelligent laser cutting control, improving the accuracy and efficiency of lens assembly processing.

[0150] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0151] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A laser cutting control method for a lens assembly, characterized in that: The following steps are involved: Step S1: scanning the lens assembly in all directions to obtain lens assembly point cloud data; removing abnormal points from the lens assembly point cloud data to obtain filtered lens assembly point cloud data; performing three-dimensional reconstruction on the filtered lens assembly point cloud data to generate a lens assembly model; Step S2: Obtain cutting task data; Perform cutting target recognition on the cutting task data to obtain cutting target data; Based on the cutting target data, the lens component model is optimally arranged in time and space to generate an optimal cutting blueprint; Step S3: planning a cutting path for the lens component model based on the preferred cutting blueprint to obtain a candidate cutting path; dynamically compensating the candidate cutting path to obtain a compensated cutting path, wherein step S3 includes the following steps: Step S31: performing path constraint mapping on the lens component model based on the preferred cutting blueprint to obtain path constraint data; Step S32: planning a cutting path for the lens component model according to the path constraint data to obtain a candidate cutting path; Step S33: performing cutting simulation based on the candidate cutting path to obtain simulated component cutting data; performing deformation prediction on the simulated component cutting data to generate cutting deformation prediction data, wherein step S33 includes the following steps: Acquire laser cutting heat source data; simulate the cutting temperature field of the laser cutting heat source data to obtain simulated temperature field distribution; Perform cutting cooling field simulation on laser cutting heat source data to generate simulated cooling field distribution; perform stress calculation on candidate cutting paths to obtain stress field data; Perform cutting simulation on the candidate cutting path based on the simulated temperature field distribution, simulated cooling field distribution and stress field data to obtain simulated component cutting data; Perform local strain calculation on the simulated component cutting data to obtain the local strain field; perform thermal deformation evaluation based on the local strain field to generate local thermal deformation data; Perform global deformation accumulation calculation on the simulated component cutting data according to the local thermal deformation data to obtain the accumulated deformation field; Deformation prediction is performed based on the accumulated deformation field to generate cutting deformation prediction data; Step S34: performing stress distribution calculation based on the cutting deformation prediction data to generate stress distribution data; performing stress path compensation according to the stress distribution data to obtain a compensated cutting path; Step S4: mapping control parameters according to the compensation cutting path to obtain changed control parameters; performing parameter replacement based on the changed control parameters to execute the intelligent laser cutting control method.

2. The laser cutting control method for lens components according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: performing omnidirectional laser scanning on the lens assembly to obtain original point cloud data; performing spatial registration on the original point cloud data to obtain lens assembly point cloud data; Step S12: performing local density analysis on the lens component point cloud data to obtain point cloud density distribution data; performing outlier detection based on the point cloud density distribution data to generate an abnormal point set; Step S13: performing an abnormal elimination process on the abnormal point set to generate point cloud data of the filter lens component; performing a normal vector estimation process on the point cloud data of the filter lens component to obtain a normal vector field; Step S14: Perform three-dimensional reconstruction on the filtered lens component point cloud data based on the normal vector field to generate a lens component model.

3. The laser cutting control method for lens components according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining cutting task data; classifying cutting targets on the cutting task data to obtain target classification results; Step S22: performing cutting target recognition on the cutting task data based on the target classification result to generate cutting target data; Step S23: performing feature matching on the lens component model according to the cutting target data to obtain a candidate cutting target model; Step S24: performing arrangement constraint processing on the candidate cutting models to obtain the constrained cutting conditions; performing optimal spatiotemporal arrangement processing on the candidate cutting models based on the constrained cutting conditions to generate an optimal cutting blueprint.

4. The laser cutting control method for lens components according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing boundary condition constraints on the candidate cutting model to obtain boundary condition constraint data; performing material utilization rate constraints on the candidate cutting model to generate material constraint data; Step S242: performing constraint integration processing on the boundary condition constraint data and the material constraint data based on the preset constraint weight data to obtain the constraint cutting conditions; Step S243: performing optimal cutting target matching on the candidate cutting models based on the constraint cutting conditions to obtain the optimal cutting sequence; performing adjacent distance calculation on the optimal cutting sequence to generate a target spacing matrix; Step S244: performing a heat impact analysis according to the preferred cutting sequence to obtain heat accumulation distribution data; performing conflict area identification on the heat accumulation distribution data to generate a conflict timing point set; performing time reallocation on the preferred cutting sequence based on the conflict timing point set to generate timing optimization data; Step S245: Calculate the safety distance of the target spacing matrix to obtain gap safety distance data; perform spacing adjustment processing on the target spacing matrix according to the gap safety distance to generate spacing optimization data; Step S246: Integrate and map the timing optimization data and the spacing optimization data to generate a preferred cutting blueprint.

5. The laser cutting control method for lens components according to claim 4, characterized in that: Step S245 includes the following steps: Calculate the space requirement of the target spacing matrix to obtain the space requirement matrix; perform effective utilization analysis based on the space requirement matrix to obtain effective utilization space data; Extract cutting gaps based on effective use of spatial data and target spacing matrix to generate cutting gap data; Calculate the safety distance of the cutting gap data to obtain the gap safety distance data; According to the gap safety distance data, the target spacing matrix is ​​mapped to the safety reserved space to generate the reserved space data required for safety; The target spacing matrix is ​​adjusted based on the reserved space data required for safety to generate spacing optimization data.

6. The laser cutting control method for lens components according to claim 1, characterized in that: Step S34 includes the following steps: Perform stress tensor analysis on the cutting deformation prediction data to obtain stress component data; Perform distribution calculation on stress component data to generate stress distribution data; Calculate the compensation amount for the stress distribution data to obtain the path compensation data; The simulated component cutting data is compensated and superimposed according to the path compensation data to obtain a compensated cutting path.

7. The laser cutting control method for lens components according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing control feature analysis on the compensation cutting path to obtain compensation control features; performing curvature extraction on the compensation cutting path to obtain path curvature data; Step S42: calculating the speed change rate of the path curvature data to obtain curvature speed change rate data; mapping the control parameters of the compensation control characteristics according to the curvature speed change rate data to obtain the change control parameters; Step S43: Perform multi-objective optimization modeling based on the change control parameters to generate a multi-objective optimization model; perform parameter replacement on the change control parameters according to the multi-objective optimization model to obtain an optimization control instruction; Step S44: Perform laser cutting control according to the optimized control instruction.

8. A laser cutting control system for lens components, characterized in that: For executing the laser cutting control method for a lens assembly as claimed in claim 1, the laser cutting control system for the lens assembly comprises: The lens reconstruction module is used to scan the lens assembly in azimuth to obtain the lens assembly point cloud data; remove abnormal points from the lens assembly point cloud data to obtain filtered lens assembly point cloud data; perform three-dimensional reconstruction on the filtered lens assembly point cloud data to generate a lens assembly model; The spatiotemporal arrangement module is used to obtain cutting task data; identify cutting targets on the cutting task data to obtain cutting target data; and perform optimal spatiotemporal arrangement of cutting on the lens component model based on the cutting target data to generate an optimal cutting blueprint; A dynamic compensation module is used to plan a cutting path for the lens component model based on the preferred cutting blueprint to obtain a candidate cutting path; and dynamically compensate the candidate cutting path to obtain a compensated cutting path; The parameter replacement module is used to map the control parameters according to the compensation cutting path to obtain the changed control parameters; and perform parameter replacement based on the changed control parameters to execute the intelligent laser cutting control method.

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