Method and System for Hybrid Common Edge Analysis of Nesting Layout Based on Laser Cutting of Lenses

Abstract

The present invention relates to the technical field of laser cutting, and particularly to a nesting layout hybrid common-edge analysis method and system based on lens laser cutting. The method includes the following steps: obtaining a laser cutting preparation data set; performing data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessing data set; performing geometric feature-material property correlation analysis on the laser cutting preprocessing data set to generate laser cutting geometry-material correlation data; constructing an intelligent common-edge analysis model based on the laser cutting geometry-material correlation data to generate a preliminary laser cutting common-edge analysis model; Therefore, by introducing intelligent common-edge analysis and optimization strategies, the present invention solves the problems of repeated cutting paths, material waste, and low cutting efficiency in the traditional laser cutting process, and improves the efficiency and accuracy of laser cutting operations.

Description

Technical Field

[0001] The present invention relates to the technical field of laser cutting, and particularly to a nesting layout hybrid common-edge analysis method and system based on lens laser cutting. Background Art

[0002] In current lens laser cutting technology, the planning of cutting paths is usually carried out based on simple geometric analysis and preset parameters. However, due to the complexity of lens materials, these traditional methods are difficult to effectively avoid the thermal expansion effect and stress concentration of materials, resulting in difficult control of the cutting accuracy during the cutting process. Moreover, for lenses of different materials (such as optical glass, polymer lenses, etc.) and different thicknesses, traditional methods often lack sufficient adaptability. In addition, traditional cutting algorithms lack a detailed analysis of the material characteristics of lenses and fail to comprehensively consider factors such as beam penetration, thermal distribution, and thickness uniformity during cutting, thus affecting the cutting efficiency and the quality of the final product. The special geometric shape of lenses also poses difficulties for traditional nesting layout and common-edge analysis. Lenses often require complex edge cutting and precise angle adjustment, while traditional laser cutting path planning techniques often cannot effectively adapt to the shape changes of lenses, easily resulting in problems such as excessive edge melting or uneven cutting. Especially during the production of multiple lenses, traditional nesting layout methods do not consider the common-edge effect between lenses and the optimization of cutting paths, often leading to unnecessary material waste and an extension of the cutting time. In addition, since lens cutting operations usually require high-precision optical testing and real-time feedback to ensure that the cutting quality meets the requirements, existing technologies also have defects in real-time evaluation and automatic adjustment. Most traditional laser cutting evaluation methods rely on manual monitoring and post-processing of data, lacking real-time monitoring and feedback adjustment during the cutting process, resulting in the inability to perform immediate optimization during the cutting process and making it difficult to cope with quality fluctuations and external environmental changes that may occur during lens cutting. Summary of the Invention

[0003] Based on this, it is necessary to provide a nesting layout hybrid common-edge analysis method and system based on lens laser cutting to solve at least one of the above technical problems.

[0004] To achieve the above object, a nesting layout hybrid common-edge analysis method based on lens laser cutting, the method includes the following steps:

[0005] Step S1: Obtain a laser cutting preparatory data set; perform data preprocessing on the laser cutting preparatory data set to generate a laser cutting preprocessed data set; perform geometric feature - material property correlation analysis on the laser cutting preprocessed data set to generate laser cutting geometry - material correlation data;

[0006] Step S2: Based on the laser cutting geometry-material correlation data, construct an intelligent common-edge analysis model to generate a preliminary laser cutting common-edge analysis model;

[0007] Step S3: Based on the preliminary laser cutting common-edge analysis model, perform global optimization of nesting layout to generate a global laser cutting optimization model; conduct local common-edge analysis on the global laser cutting optimization model to generate an intelligent laser cutting common-edge analysis model;

[0008] Step S4: Evaluate based on the intelligent laser cutting common-edge analysis model to generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting layout hybrid common-edge analysis operation for lens laser cutting.

[0009] The beneficial effects of the present invention are as follows. By performing intelligent common-edge analysis, global optimization of nesting layout, and local analysis on the laser cutting process, the efficiency and precision of the laser cutting process are significantly improved. First, by obtaining the laser cutting preparatory data set and performing preprocessing, the quality and consistency of the data can be ensured, which lays a foundation for the subsequent correlation analysis of geometric features and material properties. When conducting in-depth correlation analysis between geometric features and material properties, by analyzing the reaction characteristics of different materials during laser cutting (such as thermal expansion coefficient, hardness, thickness uniformity, etc.), a more accurate cutting parameter prediction model can be generated, thereby achieving better control and optimization during the cutting process. Then, based on this correlation data, an intelligent common-edge analysis model is constructed, enabling multiple parts to share the cutting path, reducing the movement time and unnecessary pauses of the cutting head, and thus improving the overall efficiency and precision of the cutting process. Subsequently, the global optimization of nesting layout based on the preliminary common-edge analysis model uses optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to ensure the maximum utilization rate of materials, and optimizes the cutting path through local analysis, minimizing the resource consumption during the cutting process and further improving the cutting quality. Finally, evaluating the cutting effect based on the intelligent common-edge analysis model and generating a detailed evaluation data report can provide reliable feedback information for the subsequent production process. Overall, the present invention greatly improves the efficiency, material utilization rate, and quality of the final product during the cutting process by precisely controlling and optimizing the laser cutting process at the data level. Therefore, the present invention solves the problems of repeated cutting paths, material waste, and low cutting efficiency in the traditional laser cutting process by introducing intelligent common-edge analysis and optimization strategies, and improves the efficiency and precision of laser cutting operations.

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

[0011] Step S11: Obtain the laser cutting preparatory data set;

[0012] Step S12: performing data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessing data set;

[0013] Step S13: extracting data from the laser cutting preprocessing data set to generate laser cutting extraction data; performing geometric feature-material property correlation analysis on the laser cutting extraction data to generate laser cutting geometry-material correlation data.

[0014] The present invention obtains the laser cutting preparatory data set, ensuring that various basic data required for the cutting process (such as the geometric dimensions of the lens, material properties and cutting parameters, etc.) can fully and accurately reflect the actual cutting environment. Data preprocessing is performed through the data set, eliminating noise and inconsistency in the data, and solving the inaccurate cutting results caused by data quality problems. In the data preprocessing process, through standardization, normalization and other technical means, data from different sources and formats can be unified, which is convenient for subsequent analysis and processing. Then, by extracting the preprocessed data, laser cutting extraction data is generated, providing a targeted key data set for further analysis. In this process, data extraction not only converts complex input data into effective and operational information, but also filters out irrelevant variables, thereby improving the efficiency and accuracy of data analysis. Most importantly, by performing correlation analysis between geometric features and material properties on the extracted data, the intrinsic relationship between the geometric properties of the material and its laser cutting behavior can be effectively revealed, and laser cutting geometry-material correlation data can be generated. These correlation data are crucial for optimizing cutting parameters, improving cutting quality and efficiency, and can provide strong support for subsequent cutting path planning and intelligent control. Through this series of data processing steps, the present invention improves the intelligence of decision-making in the laser cutting process on the basis of ensuring the high quality and availability of data, and realizes the precise matching and optimization between material properties and geometric features, thereby effectively reducing errors in the cutting process and improving cutting efficiency and finished product quality.

[0015] Preferably, step S12 comprises the following steps:

[0016] Step S121: normalizing the laser cutting preparation data set to generate laser cutting normalized data;

[0017] Step S122: performing data cleaning according to the laser cutting normalized data, and removing abnormal data to generate laser cutting cleaning data;

[0018] Step S123: performing data standardization processing on the laser cutting cleaning data to generate a laser cutting preprocessing data set, wherein the laser cutting preprocessing data set includes a laser cutting geometry preprocessing data set and a laser cutting material property preprocessing data set.

[0019] The present invention ensures that data of different scales and units can be compared and analyzed under the same standard through dataset normalization operations. Various input variables in the laser cutting preparation dataset, such as geometric dimensions, material properties, and laser cutting parameters, often have different dimensions or ranges. Through normalization, these differences can be eliminated, avoiding excessive influence of certain variables with too large or too small dimensions on subsequent analysis. Thus, all data can be processed on the same scale, ensuring the fairness and consistency of data analysis. Subsequently, in the data cleaning process, abnormal data (such as measurement errors, invalid values, or input errors) in the laser cutting preparation dataset are removed to ensure that the analysis process is not interfered by irrelevant data. By removing these abnormal data, the impact of data deviation on subsequent analysis results is avoided, improving the quality and accuracy of the dataset. After data cleaning, the dataset is further normalized through standardization processing. The generated preprocessed laser cutting dataset can be processed and analyzed under a unified scale and standard. The standardized dataset includes a geometric preprocessing dataset and a material property preprocessing dataset, which respectively normalize the geometric features and material properties of the lens, providing reliable basic data for subsequent geometric feature - material property correlation analysis. This series of data processing steps greatly improves the quality and consistency of the data, making subsequent analysis and modeling more accurate and efficient. Through precise data cleaning, standardization, and normalization processing, the dataset can provide more reliable support for the optimization of the laser cutting process and high-quality input data for model construction.

[0020] Preferably, step S13 includes the following steps:

[0021] Step S131: Extract size features from the laser cutting geometric preprocessing dataset to generate laser cutting size feature data; perform geometric shape analysis on the laser cutting size feature data to generate laser cutting geometric shape data; establish a three-dimensional image based on the laser cutting geometric shape data to generate a laser cutting three-dimensional image;

[0022] Step S132: Analyze the lens shape features of the laser cutting three-dimensional image to generate laser cutting lens shape data; detect the tilt angle of the lens edge of the laser cutting lens shape data to generate laser cutting lens edge tilt angle data; detect the cutting angle of the laser cutting lens shape data to generate laser cutting angle data; calculate the plane included angle between the laser cutting lens edge tilt angle data and the laser cutting angle data to generate laser cutting geometric feature extraction data;

[0023] Step S133: Perform a thermal expansion coefficient impact detection on the laser cutting material property preprocessing dataset to generate laser cutting thermal expansion coefficient data; perform a beam penetration analysis on the laser cutting thermal expansion coefficient data to generate laser cutting beam penetration data; perform a thickness uniformity measurement on the laser cutting beam penetration data to generate laser cutting material feature extraction data;

[0024] Step S134: Perform L1 regularization on the laser cutting geometric feature extraction data and the laser cutting material feature extraction data to generate laser cutting feature regularization data; create a geometric feature - material property feature matrix for the laser cutting feature regularization data to generate a laser cutting geometric feature - material property feature matrix; perform a geometric feature - material property correlation analysis on the laser cutting geometric feature - material property feature matrix to generate laser cutting geometric - material correlation data.

[0025] The present invention generates laser cutting dimension feature data through dimension feature extraction, providing basic information for subsequent geometric shape analysis. Through further geometric shape analysis, laser cutting geometric shape data is generated, which provides accurate shape information for the establishment of subsequent three - dimensional images, thereby enabling the visualization of the geometric model of the lens and facilitating subsequent processing and optimization. Next, based on the generated three - dimensional image, lens shape feature analysis is carried out to further refine the shape features, especially the detection of the tilt angle of the edge and the cutting angle. These data help to evaluate the geometric errors occurring during the cutting process and provide necessary information for subsequent cutting path optimization. Through plane angle calculation, the data of the tilt angle of the lens edge and the cutting angle are integrated to generate laser cutting geometric feature extraction data, which provides essential support for the precise control of laser cutting and ensures the accuracy of the geometric form during the cutting process. On the other hand, the detection of laser cutting material properties, including thermal expansion coefficient and beam penetration analysis, is crucial for the thermal impact and cutting quality during the cutting process. Through thickness uniformity measurement, laser cutting material feature data is further extracted, providing a quantitative basis for evaluating the thermal deformation of the material during the cutting process. Finally, the L1 regularization technique is used to normalize the geometric feature and material feature data of laser cutting, reducing the interference of redundant features and ensuring the simplicity and effectiveness of the data. Subsequently, a geometric feature - material property feature matrix is created, and the internal relationship between geometric features and material properties is revealed through correlation analysis, thus providing strong support for precise parameter setting and path optimization during the laser cutting process. Through this series of data extraction, standardization, and correlation analysis, it is ensured that all links of the cutting process can be coordinated with each other, laying a solid foundation for improving the accuracy and efficiency of laser cutting.

[0026] Preferably, step S2 includes the following steps:

[0027] Step S21: Obtain laser cutting parameter data;

[0028] Step S22: Use laser cutting geometry-material correlation data to construct a 3D model and generate a 3D solid model of laser cutting;

[0029] Step S23: Use the laser cutting parameter data to construct an algorithm model for the 3D solid model of laser cutting and generate a preliminary co-edge analysis model of laser cutting.

[0030] The present invention provides a key control basis for the entire cutting process through the obtained laser cutting parameter data, including parameters such as laser power, cutting speed, beam diameter, and focus position. These parameters have a direct impact on cutting quality, efficiency, and stability. By using laser cutting geometry-material correlation data to construct a 3D model, the geometric shape of the cutting object and its interaction relationship with material properties can be intuitively displayed. The generation of the 3D model not only helps to simulate and predict thermal deformation, stress distribution, and cutting effects during the cutting process but also provides a morphological basis for subsequent path planning and optimization, making the cutting operation more accurate and efficient. Further, by combining the laser cutting parameter data with the 3D model, an algorithm model is constructed, enabling multiple key factors in the laser cutting process (such as cutting angle, material thermal response, edge quality, etc.) to be uniformly described and optimized through a mathematical model. In the generated preliminary co-edge analysis model, through a comprehensive analysis of the cutting path, material reaction, and cutting process, decision-making support can be provided for subsequent path optimization and cutting parameter adjustment, thereby effectively reducing errors and waste in cutting, improving cutting efficiency, and ensuring the consistency and high precision of the finished product quality. Generally speaking, this series of data processing and modeling steps achieve precise control and intelligent optimization of the cutting process through a comprehensive consideration from geometric features, material properties to cutting parameters, ensuring the efficiency, accuracy, and stability of laser cutting operations.

[0031] Preferably, step S23 includes the following steps:

[0032] Step S231: Conduct a geometric overlap analysis on the 3D solid model of laser cutting to generate laser cutting shared boundary data;

[0033] Step S232: Plan the cutting path for the laser cutting shared boundary data to generate laser cutting path data;

[0034] Step S233: Specify the focus-speed cutting parameters for the laser cutting path data to generate cutting parameter data; Based on the cutting parameter data and the laser cutting path data, construct an algorithm model to generate a preliminary co-edge analysis model of laser cutting.

[0035] Through geometric overlap analysis of the laser-cut three-dimensional solid model, the present invention can accurately identify the shared boundaries between different cutting regions. This process not only effectively integrates the data of each cutting region but also provides a key data basis for subsequent cutting path planning. The shared boundary data reflects the mutual relationship between different regions and the geometric shape at the boundary junction during the laser cutting process, which is of great significance for optimizing cutting efficiency and improving material utilization rate. By performing cutting path planning on the shared boundary data, laser cutting path data is generated, ensuring the efficiency of the cutting process. Through path optimization, the laser cutting equipment can more reasonably select the cutting route, thereby reducing unnecessary movements and cutting times, improving the overall operation efficiency, and effectively reducing material waste. In addition, the accuracy of path planning also plays a crucial role in the final cutting quality, avoiding cutting errors caused by improper path selection. By specifying the focus-speed cutting parameters for the cutting path data, cutting parameter data is generated. These parameters not only affect the cutting quality but also relate to the thermal response and mechanical stress of the material during the cutting process. By accurately specifying the focus position and cutting speed, fine control of the cutting process can be achieved, avoiding problems such as uneven cutting and thermal deformation caused by improper parameters. Finally, through the algorithm model constructed based on the cutting parameter data and path data, a preliminary laser cutting common-edge analysis model is generated. This model integrates all key cutting factors, including path planning, parameter setting, and geometric features, etc., thus providing comprehensive optimization and decision support for the subsequent cutting process. Through this series of optimization steps, each link in the laser cutting process is precisely controlled, ensuring the efficiency, stability, and high quality of the cutting operation.

[0036] Preferably, step S3 includes the following steps:

[0037] Step S31: Perform global optimization of nesting layout based on the preliminary laser cutting common-edge analysis model to generate laser cutting global optimization data;

[0038] Step S32: Perform integer linear programming of model nesting on the laser cutting global optimization data to generate a laser cutting global optimization model;

[0039] Step S33: Perform local common-edge analysis on the laser cutting global optimization model to generate an intelligent laser cutting common-edge analysis model.

[0040] Through the global optimization of nesting layout for the preliminary analysis model of laser cutting with common edges, the global optimization data for laser cutting is generated. This step utilizes the data of geometric features, material properties, and path planning during the laser cutting process to achieve global optimization, ensuring that each cutting area and material plate can maximize the utilization of existing materials, reducing blank areas and cutting waste. This optimization not only improves the material utilization rate but also reduces the ineffective movement during the cutting process, enhancing the efficiency of the entire operation. Secondly, through the integer linear programming of the nesting layout for the global optimization data of laser cutting, the global optimization model for laser cutting is generated. Through the integer linear programming algorithm, the cutting path and material distribution are optimized, enabling the optimal coordination among various parameters such as position, angle, and path during the cutting process, thereby further improving the cutting efficiency and reducing energy consumption. The core of this step lies in accurately describing the relationship between material layout and cutting path through a mathematical model, enabling the finding of the optimal solution under multiple constraints and significantly enhancing the intelligent level of the cutting system. Finally, through the local analysis of common edges for the global optimization model of laser cutting, the intelligent common-edge analysis model for laser cutting is generated. This model further optimizes the local details during the cutting process, especially for the cutting paths in areas close to the edge and with complex shapes, ensuring the consistency and high precision of cutting quality. Local analysis helps reduce cutting defects caused by path errors and improves the accuracy and consistency of cutting. Through this series of optimization steps, comprehensive improvements are achieved from the global to the local level, ensuring the efficiency, precision, and stability of the laser cutting process.

[0041] Preferably, step S33 includes the following steps:

[0042] Step S331: Conduct path planning simulation on the global optimization model of laser cutting to generate laser cutting path planning simulation data; optimize the shortest path spanning tree for the laser cutting path planning simulation data to generate the optimized tree path for laser cutting;

[0043] Step S332: Conduct local material scanning on the global optimization model of laser cutting to generate laser cutting local material data; re-optimize the material utilization rate for the laser cutting local material data to generate the re-optimized data for laser cutting materials;

[0044] Step S333: Plot the two-dimensional optimization graph of cutting speed - power for the global optimization model of laser cutting to generate the two-dimensional optimization graph of local cutting speed - power for laser cutting; create a speed - power matrix based on the two-dimensional optimization graph of local cutting speed - power for laser cutting and determine the optimal speed - power combination to generate the data of the optimal speed - power combination for laser cutting;

[0045] Step S334: Conduct a common-edge local analysis on the global laser cutting optimization model based on the laser cutting optimized tree path, laser cutting material re-optimization data, and laser cutting speed-power optimal combination data to generate an intelligent common-edge analysis model for laser cutting.

[0046] In the present invention, by simulating the path planning of the global laser cutting optimization model, laser cutting path planning simulation data is generated. The path planning simulation data provides an important basis for further optimizing the cutting path and can accurately predict and evaluate the advantages and disadvantages of different path schemes. Immediately afterwards, the shortest path generation tree optimization process further optimizes the path selection through the shortest path algorithm to generate the laser cutting optimized tree path. This optimized tree path ensures the optimality of the path during the laser cutting process, reduces unnecessary path redundancy, thereby improving the cutting efficiency and reducing energy consumption. Subsequently, by locally scanning the materials of the global laser cutting optimization model, laser cutting local material data is generated, and then the material utilization rate of these data is re-optimized to generate laser cutting material re-optimization data. This step ensures the best utilization of materials through detailed local scanning and optimization adjustments, effectively reduces waste, and improves the economy and resource utilization efficiency in the production process. The material re-optimization data further optimizes the material configuration in the entire cutting process, enabling the cutting operation to maximize benefits. Next, by plotting the cutting speed-power two-dimensional optimization graph of the global laser cutting optimization model, a local cutting speed-power two-dimensional optimization graph for laser cutting is generated, and based on this, a speed-power matrix is created, and finally the optimal speed-power combination is determined. This optimization graph and matrix provide a precise adjustment basis for the speed and power in the cutting process, enabling each parameter in the cutting process to operate in the best state and avoiding problems such as uneven cutting or excessive loss caused by unreasonable parameter settings. Finally, by integrating the laser cutting optimized tree path, material re-optimization data, and speed-power optimal combination data, a common-edge local analysis is conducted on the global optimization model to generate an intelligent common-edge analysis model for laser cutting. This model integrates data from multiple aspects such as path optimization, material utilization, and speed-power adjustment, further improving the accuracy and intelligence level of the laser cutting process and ensuring the consistency and high precision of the cutting quality.

[0047] Preferably, step S4 includes the following steps:

[0048] Step S41: Obtain real-time laser cutting data;

[0049] Step S42: Perform laser cutting operations based on the intelligent common-edge analysis model for laser cutting to generate laser cutting model data; evaluate the real-time laser cutting data and the laser cutting model data to generate laser cutting evaluation data;

[0050] Step S43: Generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting layout hybrid common edge analysis operation based on lens laser cutting.

[0051] The present invention obtains real-time laser cutting data, which includes key parameters such as laser power, speed, focus position, and cutting path during the cutting process. By collecting these data in real time, it is possible to accurately monitor each link during the laser cutting process, providing a solid foundation for subsequent model optimization. Next, based on the intelligent common edge analysis model for laser cutting, operations are carried out to generate laser cutting model data. Through the application of the model, the system can simulate and optimize the cutting process, and combine real-time data to adjust the cutting path and parameters in real time to ensure that each cutting operation is performed in an optimal state. During this process, the generated laser cutting model data not only reflects the dynamic changes of the cutting process but also provides detailed data support for subsequent evaluations. The real-time laser cutting data is also evaluated with the laser cutting model data to generate laser cutting evaluation data. By comparing and analyzing the real-time data with the simulation data, the system can identify potential cutting errors, path deviations, and fluctuations in cutting quality, providing accurate feedback for subsequent optimization. This process ensures that the cutting operation is always in a feedback-regulated closed loop, reducing the need for human intervention and further improving the automation level of cutting. Finally, an evaluation report is generated based on the laser cutting evaluation data, ensuring the transparency and traceability of the entire laser cutting operation process. The evaluation report not only summarizes the key parameters and performance data during the cutting process but also analyzes the potential optimization space, providing data support for the optimization and improvement of subsequent operations.

[0052] In this specification, a nesting layout hybrid common edge analysis system based on lens laser cutting is provided for performing the above-mentioned nesting layout hybrid common edge analysis method based on lens laser cutting. The nesting layout hybrid common edge analysis system based on lens laser cutting includes:

[0053] A data processing module, configured to obtain a laser cutting preliminary data set; perform data preprocessing on the laser cutting preliminary data set to generate a laser cutting preprocessed data set; perform geometric feature-material property correlation analysis on the laser cutting preprocessed data set to generate laser cutting geometry-material correlation data;

[0054] A common edge analysis model construction module, configured to construct an intelligent common edge analysis model based on the laser cutting geometry-material correlation data to generate a preliminary laser cutting common edge analysis model;

[0055] An optimization and local analysis module is used to perform global optimization of nesting layout based on the preliminary co-edge analysis model of laser cutting, generating a global optimization model of laser cutting; performing co-edge local analysis on the global optimization model of laser cutting to generate an intelligent co-edge analysis model of laser cutting;

[0056] An evaluation and report generation module is used to evaluate based on the intelligent co-edge analysis model of laser cutting, generating laser cutting evaluation data; generating an evaluation report for the laser cutting evaluation data, thereby completing the mixed co-edge analysis operation of nesting layout based on lens laser cutting.

[0057] The beneficial effects of the present invention are as follows: By obtaining the preliminary data set of laser cutting and performing data preprocessing, the standardization degree of input data is improved, data noise and redundant information are reduced, the stability and accuracy of data processing are improved, the geometric feature - material property correlation analysis is performed on the preprocessed data set of laser cutting, the precise adaptability of laser cutting parameters is enhanced, the cutting quality and material utilization rate are improved, an intelligent co-edge analysis model is constructed based on the geometric - material correlation data of laser cutting, the co-edge adaptability between different lens geometries is enhanced, the layout compactness of materials and resource utilization rate are improved, material waste is reduced, global optimization of nesting layout is performed based on the preliminary co-edge analysis model of laser cutting, the rationality of the overall cutting plan is improved, redundant cutting paths are reduced, the laser cutting efficiency is improved, co-edge local analysis is performed on the global optimization model of laser cutting, the local co-edge path is optimized, the cumulative effect of the heat affected zone is reduced, the cutting edge quality is improved, and the loss of materials caused by thermal deformation is reduced. Evaluation is performed based on the intelligent co-edge analysis model of laser cutting, the stability and applicability of the nesting layout plan are improved, the reliability of the cutting plan under different production conditions is ensured, an evaluation report is generated for the laser cutting evaluation data, precise co-edge layout optimization suggestions are provided for the production process, the visualization degree and controllability of production management are improved, and the production efficiency and quality control level are improved. Description of the Drawings

[0058] Figure 1 It is a schematic diagram of the step flow of a mixed co-edge analysis method for nesting layout based on lens laser cutting;

[0059] Figure 2 is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in

[0060] Figure 3 is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in

[0061] Figure 4 is Figure 1 a schematic diagram of the detailed implementation step flow of step S4 in

[0062] The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0063] The technical method of the present invention for the patent 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 efforts belong to the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the 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.

[0065] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0066] To achieve the above object, please refer to Figures 1 to 4 , a nesting layout hybrid common-edge analysis method based on lens laser cutting, the method includes the following steps:

[0067] Step S1: Obtain a laser cutting preparation data set; perform data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessing data set; perform geometric feature-material property correlation analysis on the laser cutting preprocessing data set to generate laser cutting geometry-material correlation data;

[0068] Step S2: Build an intelligent common-edge analysis model based on the laser cutting geometry-material correlation data to generate a preliminary laser cutting common-edge analysis model;

[0069] Step S3: Perform global optimization of nesting layout based on the preliminary laser cutting common-edge analysis model to generate a global laser cutting optimization model; perform local common-edge analysis on the global laser cutting optimization model to generate an intelligent laser cutting common-edge analysis model;

[0070] Step S4: Evaluate based on the intelligent common-edge analysis model for laser cutting to generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thus completing the nesting and layout hybrid common-edge analysis operation for lens laser cutting.

[0071] The beneficial effects of the present invention are as follows. By performing intelligent common-edge analysis, global optimization of nesting and layout, and local analysis on the laser cutting process, the efficiency and precision of the laser cutting process are significantly improved. First, by obtaining the laser cutting preliminary data set and performing preprocessing, the quality and consistency of the data can be ensured, which lays a foundation for the subsequent correlation analysis of geometric features and material properties. When performing in-depth correlation analysis between geometric features and material properties, by analyzing the reaction characteristics of different materials during laser cutting (such as thermal expansion coefficient, hardness, thickness uniformity, etc.), a more accurate cutting parameter prediction model can be generated, thereby achieving better control and optimization during the cutting process. Then, based on this correlated data, an intelligent common-edge analysis model is constructed, enabling multiple parts to share the cutting path, reducing the movement time and unnecessary pauses of the cutting head, and thus improving the overall efficiency and precision of the cutting process. Subsequently, the global optimization of nesting and layout based on the preliminary common-edge analysis model uses optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to ensure the maximum utilization rate of materials, and optimizes the cutting path through local analysis, minimizing the resource consumption during the cutting process and further improving the cutting quality. Finally, evaluating the cutting effect based on the intelligent common-edge analysis model and generating a detailed evaluation data report can provide reliable feedback information for the subsequent production process. Overall, the present invention greatly improves the efficiency, material utilization rate, and quality of the final product during the cutting process by precisely controlling and optimizing the laser cutting process at the data level. Therefore, the present invention solves the problems of repeated cutting paths, material waste, and low cutting efficiency in the traditional laser cutting process by introducing intelligent common-edge analysis and optimization strategies, and improves the efficiency and precision of laser cutting operations.

[0072] In an embodiment of the present invention, refer to Figure 1 As shown, it is a schematic diagram of the step flow of a nesting and layout hybrid common-edge analysis method for lens laser cutting according to the present invention. In this example, the nesting and layout hybrid common-edge analysis method for lens laser cutting includes the following steps:

[0073] Step S1: Obtain the laser cutting preliminary data set; perform data preprocessing on the laser cutting preliminary data set to generate a laser cutting preprocessed data set; perform correlation analysis of geometric features - material properties on the laser cutting preprocessed data set to generate laser cutting geometric - material correlation data;

[0074] In the embodiments of the present invention, obtaining the laser cutting preparation data set is the starting point of the entire processing flow. First, various data types related to the laser cutting process need to be collected, including geometric data (such as dimensions, shapes, cutting paths, etc.), material property data (such as thermal expansion coefficients, hardness, thickness uniformity, etc.), and operation parameter data during the laser cutting process (such as laser power, cutting speed, focus position, etc.). These data are usually obtained through high-precision sensors, the control system of the laser cutting machine, and external measurement devices. The obtained data set needs to go through a series of preprocessing steps to ensure data quality. Specifically, the data preprocessing process includes conventional data cleaning operations such as data normalization, outlier removal, and missing value filling, to ensure that all data are processed at the same scale in subsequent processing, and to reduce the impact of noise and inconsistency on the model accuracy. In addition, the data from different data sources also need to be standardized to ensure the consistency of all input data, thereby improving the reliability and comparability of subsequent data analysis. Through this process, the generated laser cutting preprocessing data set contains various information obtained by cleaning, normalizing, and standardizing the original data. These data sets then provide a basis for performing the correlation analysis between geometric features and material properties. In the geometric feature - material property correlation analysis, data mining techniques such as statistical analysis and regression analysis are used to explore the mutual influence relationship between different geometric features and material properties, and these correlation relationships are quantified through mathematical models. In particular, through technical means such as feature selection and dimensionality reduction, features that have a significant impact on the cutting performance can be extracted from complex multi-dimensional data, generating geometric feature - material property correlation data.

[0075] Step S2: Based on the laser cutting geometry - material correlation data, construct an intelligent common-edge analysis model to generate a preliminary laser cutting common-edge analysis model;

[0076] In the embodiments of the present invention, in the process of constructing an intelligent common-edge analysis model based on laser cutting geometry-material correlation data, the core technical means lies in using advanced data analysis and machine learning methods to predict and optimize the cutting path and cutting effect by modeling the complex correlation relationship between geometric features and material properties. First, the correlation data between geometric features and material properties obtained in step S1 provides the key data input for subsequent model construction. These correlation data reveal the mutual relationship between different geometric features and material properties through statistical methods including regression analysis, clustering analysis, principal component analysis (PCA), etc., such as the influence between cutting angle and material thickness, and the interaction between cutting paths of different shapes and the coefficient of thermal expansion. Based on these data, an intelligent analysis method is used to construct a common-edge analysis model. The core objective of common-edge analysis is to optimize the cutting path so that the cutting paths between adjacent regions can share the cutting edge, thereby reducing energy consumption during cutting, improving material utilization rate, and reducing cutting time. In this process, first, support vector machine (SVM), neural network (NN), or deep learning models in machine learning are used to establish a prediction model between the cutting path and material properties by training historical data. Specifically, geometric features and material properties are regarded as input features, and the model learns the influence of various geometric shapes and different material properties on cutting effects (such as cutting accuracy, cutting time, etc.), and then derives the optimal strategy for common-edge cutting. In addition, the generation of the common-edge analysis model can be further refined through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). These optimization algorithms continuously adjust cutting parameters such as cutting path, laser power, and speed through iterative calculations, and finally generate a cutting path configuration and common-edge strategy with optimal performance.

[0077] Step S3: Based on the preliminary common-edge analysis model of laser cutting, perform global optimization of nesting layout to generate a global optimization model of laser cutting; perform local common-edge analysis on the global optimization model of laser cutting to generate an intelligent common-edge analysis model of laser cutting;

[0078] In the embodiments of the present invention, the core technical means for global optimization of nesting layout based on the preliminary analysis model of laser cutting with common edges is a multi-stage optimization process that combines data-driven global optimization and local analysis. First, the construction of the global optimization model depends on the common edge data and related features extracted from the preliminary analysis model of laser cutting with common edges. By deeply mining this data and using advanced optimization algorithms such as integer linear programming (ILP), mixed integer linear programming (MILP), or genetic algorithm (GA), the global optimization goal of nesting layout is achieved. Specifically, the data includes the geometric features of each cutting part, material properties, and the correlation relationship of the cutting paths. These data are used to construct the optimal matching between the cutting paths and material configurations. The optimization algorithm formulates a globally optimal nesting layout plan by minimizing the total length of the cutting paths, maximizing the material utilization rate, and considering multiple objectives such as cutting time and energy consumption. During the global optimization process, first, a general plan for all cutting tasks is made, resources are reasonably allocated, and parts with different materials and geometric shapes are optimally arranged. This stage focuses on large-scale optimization of common edge sharing between different parts, reducing unnecessary repetition of cutting paths, thereby improving cutting efficiency and reducing cutting energy consumption. By establishing a comprehensive evaluation model, different nesting plans can be simulated, their actual effects can be evaluated, and the optimization direction can be continuously adjusted according to the evaluation results, finally generating a global optimization model for laser cutting. After completing the global optimization, further local analysis of the common edges is carried out. The purpose is to finely adjust the cutting paths through further optimization of the local area to ensure the best effect during the local cutting process. Local analysis usually uses methods such as dynamic programming algorithm (DP) and particle swarm optimization (PSO). By refining and adjusting the cutting paths in a specific area, the cutting accuracy, material utilization rate, and production efficiency are further improved. Local analysis not only focuses on minimizing the cutting paths but also considers the influence of the material properties and geometric shapes of a specific area on the cutting effect. Finally, through the combination of global optimization and local optimization, the generated intelligent common edge analysis model for laser cutting can adaptively make the best decisions in complex cutting tasks, optimize the cutting paths, reduce material waste, and improve cutting efficiency.

[0079] Step S4: Evaluate based on the intelligent common edge analysis model for laser cutting to generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting layout hybrid common edge analysis operation based on lens laser cutting.

[0080] In the embodiments of the present invention, the evaluation process relies on various types of data generated in the intelligent common-edge analysis model for laser cutting, including information such as cutting paths, material utilization rates, cutting speed and power combinations, and common-edge sharing degrees. These data can comprehensively reflect the efficiency and effectiveness of laser cutting operations. Therefore, the first step in the evaluation is to input this data into a multi-dimensional evaluation framework, which typically adopts an evaluation model based on multi-objective optimization, such as the Weighted Scoring Method, the Analytic Hierarchy Process (AHP), or the Data Envelopment Analysis (DEA). This evaluation framework comprehensively considers multiple key indicators such as cutting accuracy, material utilization rate, production efficiency, and energy consumption, and quantitatively evaluates the overall performance of the cutting process through preset evaluation criteria. During the evaluation process, statistical analysis is first performed on the data in each dimension of the intelligent common-edge analysis model for laser cutting, including the optimization degree of the cutting path, the reduction of material waste, and the improvement of cutting quality, which are all key indicators for evaluation. For each cutting task, the evaluation system generates real-time evaluation data by comparing with standard values or historical best values. These evaluation data include, but are not limited to, cutting efficiency, energy efficiency, error range, material loss, etc. The real-time tracking and comparison of these indicators provide data support for subsequent optimization. Next, through further analysis of the evaluation data, an evaluation report is generated. The generation of the report depends on the in-depth interpretation of the evaluation results and data visualization techniques, such as pivot tables, heat maps, line charts, etc., to help users clearly understand the actual performance of the cutting operation. The evaluation report not only includes the numerical analysis of various optimization indicators but also includes improvement suggestions for specific problems. For example, if there are long invalid gaps in some cutting paths, the report will prompt this problem and recommend an optimization plan. At the same time, the report can also provide a comparison of multiple optimization plans according to different operating conditions to help operators make more reasonable decisions. Finally, this process of evaluation and report generation completes the nesting and layout hybrid common-edge analysis operation for lens laser cutting.

[0081] Preferably, step S1 includes the following steps:

[0082] Step S11: Obtain the laser cutting preliminary data set;

[0083] Step S12: Perform data preprocessing on the laser cutting preliminary data set to generate a laser cutting preprocessed data set;

[0084] Step S13: Extract data from the laser cutting preprocessed data set to generate laser cutting extracted data; perform geometric feature - material property correlation analysis on the laser cutting extracted data to generate laser cutting geometric - material correlation data.

[0085] In an embodiment of the present invention, the process of obtaining a laser cutting preparation data set requires collecting basic data related to the cutting task from multiple sources, including but not limited to the geometric size, shape, material properties (such as thermal expansion coefficient, hardness, etc.) of the lens and parameters of the laser cutting equipment (such as laser power, cutting speed, etc.). The acquired data often has multidimensional characteristics and includes raw data and potential noise or missing values, so it needs to go through a rigorous data preprocessing process. The laser cutting preparation data set is preprocessed mainly by technical means such as standardization, denoising and filling missing values ​​to ensure that the data quality meets the requirements of subsequent processing. Data preprocessing generally includes operations such as cleaning and normalization of the raw data (for example, linear transformation to a uniform scale), outlier detection and correction. These steps help to eliminate noise caused by measurement errors or external interference, and make various data features consistent in numerical range, reducing deviations in subsequent modeling processes. In the data processing process, cluster analysis and data interpolation methods (such as KNN interpolation, Lagrange interpolation, etc.) are used to fill missing data or reduce dimensions through principal component analysis (PCA) to retain important information in the data and reduce unnecessary complexity. The data extraction stage extracts key features related to cutting quality, efficiency, etc. from the processed preliminary data set. Common geometric features include cutting path, lens shape, edge quality, etc., while material properties include physical properties of materials (such as hardness, thermal expansion coefficient) and thickness uniformity of materials. By extracting these features, valuable information can be provided for subsequent analysis models. Geometric feature-material property association analysis mainly analyzes the influence of different geometric forms and material properties on laser cutting performance by establishing a mathematical model. This step usually uses regression analysis, statistical analysis, machine learning algorithms and other methods to explore the potential relationship between geometric features and material properties. For example, algorithms such as support vector machines (SVM), decision trees or random forests are used to model the data and extract the key factors affecting the quality and efficiency of laser cutting. Through this process, the generated "geometric feature-material property association data" can provide important decision-making basis for the subsequent optimization of the laser cutting process. The results of data association analysis are usually presented in the form of a feature matrix, in which each column represents a variable related to the cutting process and each row represents the characteristic value of a sample. These data provide a basis for further optimization of laser cutting parameters and path planning.

[0086] Preferably, step S12 comprises the following steps:

[0087] Step S121: normalizing the laser cutting preparation data set to generate laser cutting normalized data;

[0088] Step S122: performing data cleaning according to the laser cutting normalized data, and removing abnormal data to generate laser cutting cleaning data;

[0089] Step S123: Perform data standardization processing on the laser cutting cleaning data to generate a laser cutting preprocessing data set, where the laser cutting preprocessing data set includes a laser cutting geometric preprocessing data set and a laser cutting material property preprocessing data set.

[0090] In the embodiments of the present invention, the technical means of the laser cutting data preprocessing method mainly focus on the processing of the laser cutting preparation data set, including multiple links such as data normalization, data cleaning, and standardization processing, aiming to improve data quality, remove noise, and make the data applicable to subsequent analysis and modeling. From the data level, the implementation steps of these technical means first perform unified scale conversion through the normalization of the laser cutting preparation data set to ensure that different feature data have the same numerical range and standard. This process usually adopts linear normalization methods, such as min-max normalization, to compress the numerical values of each feature into the interval [0,1] or [-1,1], avoiding the improper influence of some features with large numerical values on the subsequent analysis process. In addition, if the preparation data set contains non-numerical data or composite data with high dimensions, encoding conversion or dimensionality reduction processing is also required to ensure the consistency of the data during the processing and simplify the subsequent analysis difficulty. The main task of data cleaning is to remove abnormal data and handle missing values. There are outliers in the laser cutting preparation data set caused by sensor errors, external interferences, or data transmission problems. If these abnormal data are not processed, they will have a negative impact on the training results of the model. Therefore, the data cleaning process usually combines statistical methods or machine learning algorithms, such as using the Z-score-based method for outlier detection, using the box plot method to identify and remove outliers, or identifying noise points through clustering algorithms. In addition, if there are missing values in the data set, common imputation methods (such as mean imputation, KNN imputation, or regression imputation, etc.) are used to fill in the missing data to ensure the integrity of the data so as not to affect the subsequent standardization and analysis processes. The main purpose of data standardization processing is to perform further unified scale processing on the cleaned data to eliminate the dimensionality differences between data features, so that different features participate in modeling and analysis under the same standard. Standardization methods include Z-score standardization, usually by subtracting the mean of the data and dividing by the standard deviation, so that the data distribution of each feature has a zero mean and a unit variance, which can eliminate the influence of different feature scale differences. The standardized data is particularly important for subsequent modeling work, especially when using machine learning or optimization algorithms, which can ensure that each feature has relatively fair weights during the training process. After the standardization processing is completed, the data will be divided into two major categories: geometric property data and material property data, which are respectively used for subsequent geometric feature analysis and material property analysis.

[0091] Preferably, step S13 includes the following steps:

[0092] Step S131: Extract size features from the laser cutting geometric preprocessing dataset to generate laser cutting size feature data; perform geometric shape analysis on the laser cutting size feature data to generate laser cutting geometric shape data; establish a 3D image based on the laser cutting geometric shape data to generate a laser cutting 3D image;

[0093] Step S132: Analyze the lens shape features of the laser cutting 3D image to generate laser cutting lens shape data; detect the tilt angle of the lens edge of the laser cutting lens shape data to generate laser cutting lens edge tilt angle data; detect the cutting angle of the laser cutting lens shape data to generate laser cutting angle data; calculate the plane angle between the laser cutting lens edge tilt angle data and the laser cutting angle data to generate laser cutting geometric feature extraction data;

[0094] Step S133: Detect the influence of the coefficient of thermal expansion on the laser cutting material property preprocessing dataset to generate laser cutting coefficient of thermal expansion data; perform beam penetration analysis on the laser cutting coefficient of thermal expansion data to generate laser cutting beam penetration data; measure the thickness uniformity of the laser cutting beam penetration data to generate laser cutting material feature extraction data;

[0095] Step S134: Perform L1 regularization on the laser cutting geometric feature extraction data and the laser cutting material feature extraction data to generate laser cutting feature regularization data; create a geometric feature - material property feature matrix for the laser cutting feature regularization data to generate a laser cutting geometric feature - material property feature matrix; perform geometric feature - material property correlation analysis on the laser cutting geometric feature - material property feature matrix to generate laser cutting geometric - material correlation data.

[0096] In the embodiment of the present invention, the laser cutting dimension feature data is generated by extracting the dimension feature of the laser cutting geometric preprocessing data set. This process relies on morphological analysis and geometric operations. By calculating the dimensions of various objects in the data set (such as length, width, thickness, etc.) and converting them into numerical features, it is intended to provide a basis for subsequent geometric shape analysis. When performing geometric shape analysis on the dimension feature data, the shape features of the laser cutting object are extracted based on edge detection, contour extraction and other technologies, thereby generating geometric shape data, which is then used for subsequent three-dimensional image establishment. The generation of three-dimensional images usually uses spatial interpolation and grid reconstruction technology in computer graphics to construct an accurate three-dimensional model using the spatial relationship in the geometric shape data. The three-dimensional image is further analyzed for the shape feature of the lens, and the geometric characteristics of the lens are identified by algorithms such as feature point extraction and shape matching. Then, the tilt angle detection algorithm is used to accurately measure the edge of the lens, and the plane angle is calculated by the plane geometry method, thereby generating laser cutting geometric feature extraction data. The laser cutting material property preprocessing data set processes multiple important steps. First, the thermal expansion coefficient influence detection technology is used to evaluate the thermal deformation of the material under different temperature conditions to generate thermal expansion coefficient data. Next, beam penetration analysis is performed. This process generates beam penetration data by calculating the penetration depth and absorption rate of the laser beam in the material, which is crucial for subsequent cutting paths and power adjustments. In addition, the thickness of the material is detected through thickness uniformity measurement to ensure consistency and accuracy during the cutting process, and finally generate material feature data. The geometric feature data and material property data are processed by L1 regularization. The L1 regularization method is usually used for feature selection and sparse processing. By constraining the data, some redundant or unimportant features are compressed to zero during the optimization process, retaining the most representative features. This process effectively removes noise from the data and improves the effectiveness and interpretability of the features. Next, the regularized data is used to create a geometric feature-material property feature matrix. This matrix not only summarizes the geometric and material property data, but also provides a mathematical basis for subsequent correlation analysis. In the last step, the geometric feature-material property correlation analysis reveals the relationship between geometric shape and material properties, further optimizes the parameter allocation of the cutting process, and generates laser cutting geometry-material correlation data, providing strong data support for accurate laser cutting path planning and optimization.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0098] Step S21: obtaining laser cutting parameter data;

[0099] Step S22: Use the laser cutting geometry-material correlation data to construct a 3D model and generate a 3D solid model for laser cutting;

[0100] Step S23: Use the laser cutting parameter data to construct an algorithm model for the 3D solid model of laser cutting and generate a preliminary analysis model for common-edge laser cutting.

[0101] In the embodiment of the present invention, the process of obtaining the laser cutting parameter data generally includes collecting various key parameters during the cutting process, including laser power, cutting speed, focal length, spot size, laser wavelength, etc. These parameters are often monitored in real time by sensors and stored through a data acquisition system, providing the necessary data support for subsequent model construction. The technical means of this step include real-time data acquisition and timing analysis, which can collect accurate cutting parameter data according to different cutting conditions, ensuring that the key physical characteristics in the real cutting environment can be accurately reflected during the 3D model construction process. The process of using the laser cutting geometry-material correlation data to construct a 3D model adopts modeling techniques based on computer-aided design (CAD) and computer graphics. By combining and analyzing the data of geometric features (such as object size, shape, contour, etc.) and material properties (such as material density, hardness, thermal conductivity, etc.), a 3D solid model integrating geometric information and material properties can be generated. This process usually uses methods such as spatial transformation and geometric interpolation to map the 2D features in the dataset to 3D space and generates a fine 3D model through 3D modeling techniques (such as voxel modeling, mesh modeling, etc.). The correlation data between geometric features and material properties plays a key role here, ensuring that the generated 3D model accurately reflects the physical and material properties of the cutting object, especially the material reaction and deformation under different cutting parameters. Based on the laser cutting parameter data, an algorithm model is constructed for the 3D solid model to generate a preliminary analysis model for common-edge laser cutting. This process relies on a variety of algorithms and optimization techniques, mainly including path planning based on geometric shape and cutting parameters, common-edge optimization, and the optimal connection algorithm between adjacent cutting surfaces. During this process, the cutting parameters (such as laser power, speed, etc.) directly affect the generation and optimization of the cutting path. Therefore, the algorithm model not only needs to consider the matching of geometric shapes but also comprehensively consider factors such as thermal effects and mechanical actions during the cutting process, and then generate an optimized cutting path plan.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Conduct a geometric overlap analysis on the 3D solid model of laser cutting to generate laser cutting shared boundary data;

[0104] Step S232: Plan the cutting path for the laser cutting shared boundary data to generate laser cutting path data;

[0105] Step S233: Specify the focus - speed cutting parameters for the laser cutting path data to generate cutting parameter data; construct an algorithm model based on the cutting parameter data and the laser cutting path data to generate a preliminary laser cutting common - edge analysis model.

[0106] In the embodiments of the present invention, the core technical means for geometric overlap analysis of the laser - cut three - dimensional model is the geometric matching and spatial overlap analysis algorithm. This process first performs precise geometric overlap analysis on the three - dimensional model of the laser - cut object to identify the shared boundaries of multiple cutting regions. This operation usually relies on efficient spatial data structures (such as octrees, BVH trees, etc.) to quickly search for and determine the boundary overlap parts between models. The shared boundary data generated after geometric overlap analysis further provides key reference information for cutting path planning, ensuring seamless connection of the cutting path and maximizing material utilization. When performing cutting path planning based on the laser - cut shared boundary data, path optimization algorithms and the shortest - path algorithm in graph theory are used. These algorithms plan an efficient cutting path by considering the geometric characteristics of the cutting object, the cutting sequence, and the characteristics of the material (such as thickness, density, etc.). The path planning algorithm also needs to consider physical factors such as thermal effects and stress changes during the cutting process, as well as minimizing the crossing of the path to reduce equipment pauses and reverse cutting. Heuristic algorithms (such as the A* algorithm, Dijkstra algorithm, etc.) are usually used to optimize the cutting path to minimize energy consumption and improve cutting efficiency. The technical means of specifying focus - speed cutting parameters based on the laser cutting path data enables two - way optimization of the laser cutting parameters and the path. The effect of laser cutting highly depends on the settings of parameters such as the position of the laser beam focus, cutting speed, and power. During this process, the selection of focus - speed cutting parameters depends on the thermal conduction characteristics of the actual material and the complexity of the cutting path, ensuring that each cutting segment can be cut with the optimal laser power and speed. These cutting parameters are determined through multi - dimensional data analysis and optimization algorithms and are dynamically adjusted based on the data model of the laser cutting path to ensure the balance between cutting quality and efficiency. Finally, through the operation of constructing an algorithm model based on the cutting parameter data and the path data, a preliminary laser cutting common - edge analysis model is further generated. This technical means combines the aforementioned geometric overlap analysis and path planning data. By establishing a mathematical model and a simulation system, it further verifies and optimizes the cutting path and parameter settings to ensure that the generated preliminary analysis model meets the actual cutting requirements. The main technologies used in this process include numerical optimization algorithms, simulated annealing algorithms, and genetic algorithms, etc. Through the integration of these technologies, the optimal solution can be found in complex cutting scenarios.

[0107] As an example of the present invention, referring to Figure 3 as shown, in this example, step S3 includes:

[0108] Step S31: Perform global optimization of nesting layout based on the preliminary co-edge analysis model for laser cutting to generate global optimization data for laser cutting;

[0109] Step S32: Conduct integer linear programming for model nesting on the global optimization data for laser cutting to generate a global optimization model for laser cutting;

[0110] Step S33: Conduct local co-edge analysis on the global optimization model for laser cutting to generate an intelligent co-edge analysis model for laser cutting.

[0111] In the embodiment of the present invention, the core technical means of "performing global optimization of nesting layout based on the preliminary co-edge analysis model for laser cutting" is to perform overall layout optimization on the cutting area through a data-driven optimization algorithm. This process usually uses heuristic optimization algorithms (such as genetic algorithms, simulated annealing algorithms, or particle swarm optimization, etc.) to efficiently solve the problem of maximizing material utilization. The goal of global optimization is to ensure that each component within the cutting area can be arranged with minimal material waste, optimize the overall cutting process, reduce waste, and improve cutting efficiency. This optimization process adjusts the cutting path and relative position of each component based on the boundary data in the preliminary co-edge analysis model for laser cutting, thereby achieving global optimization. The technical means of "conducting integer linear programming for model nesting on the global optimization data for laser cutting" is to use the integer linear programming (ILP) method in mathematical optimization to further refine the global optimization results. This method constructs a mathematical model to transform the cutting layout problem into a mathematical problem with linear constraints, ensuring the optimization of nesting layout on the premise of meeting certain physical constraints (such as cutting path, machine performance, material thickness, etc.). Specifically, the integer linear programming method can minimize material waste by setting an appropriate objective function, and the constraint conditions include material size, shape limitations, and the processing capabilities of the machine. By solving this linear programming problem, a set of optimal cutting plans can be generated to provide data support for subsequent cutting operations. "Conducting local co-edge analysis on the global optimization model for laser cutting to generate an intelligent co-edge analysis model for laser cutting" further refines and locally optimizes the cutting path after global optimization. This process mainly uses data analysis and intelligent algorithms, relying on machine learning or deep learning models, to conduct local intelligent analysis on the co-edge areas within the cutting area. Local co-edge analysis usually uses the previously generated global optimization data to optimize and adjust the intersection points, shared boundaries, and cutting sequences of each cutting path, aiming to further reduce the reverse paths during the cutting process, improve the material utilization rate, and at the same time reduce the thermal effect and stress change during cutting.

[0112] Preferably, step S33 includes the following steps:

[0113] Step S331: Conduct path planning simulation on the global optimization model for laser cutting to generate laser cutting path planning simulation data; perform shortest path spanning tree optimization on the laser cutting path planning simulation data to generate the optimized tree path for laser cutting;

[0114] Step S332: Conduct local material scanning on the global optimization model for laser cutting to generate laser cutting local material data; perform re-optimization of material utilization rate on the laser cutting local material data to generate re-optimized data for laser cutting materials;

[0115] Step S333: Draw a two-dimensional optimization graph of cutting speed - power on the global optimization model for laser cutting to generate a two-dimensional optimization graph of local cutting speed - power for laser cutting; create a speed - power matrix based on the two-dimensional optimization graph of local cutting speed - power for laser cutting, and determine the optimal speed - power combination to generate data on the optimal speed - power combination for laser cutting;

[0116] Step S334: Conduct common-edge local analysis on the global optimization model for laser cutting based on the optimized tree path for laser cutting, the re-optimized data for laser cutting materials, and the data on the optimal speed - power combination for laser cutting to generate an intelligent common-edge analysis model for laser cutting.

[0117] In the embodiments of the present invention, the technical means of "performing path planning simulation on the global optimization model of laser cutting to generate laser cutting path planning simulation data" mainly uses path planning algorithms to simulate the cutting path. At this time, the algorithm generates a preliminary cutting path planning data set through comprehensive analysis of data such as the geometric shape and material properties of the cutting area. On this basis, through the optimization of the shortest path generation tree (such as Dijkstra algorithm or A* algorithm), the path is further optimized to ensure that the cutting path reduces the reverse path and idle path of the machine movement on the premise of minimizing time cost and energy consumption, thereby generating the laser cutting optimized tree path. This process uses the shortest path algorithm in graph theory to solve the optimal problem of path selection in the cutting process. The technical means of "performing local material scanning on the global optimization model of laser cutting to generate laser cutting local material data" is to perform detailed scanning of the materials in the local area based on scanning technology and sensor data to obtain the actual characteristic data of the materials. These data include the thickness, density, hardness, etc. of the materials. By comparing with the global optimization model, local material data is generated. Next, through the re-optimization of material utilization rate (such as algorithms like linear programming or simulated annealing), according to the scanning data and the optimization model, the cutting path and material layout are further adjusted and optimized to generate laser cutting material re-optimization data. This optimization process can further improve the material utilization rate and reduce waste on the basis of ensuring cutting accuracy. The technical means of "drawing a two-dimensional optimization graph of cutting speed-power for the global optimization model of laser cutting to generate a two-dimensional optimization graph of local cutting speed-power for laser cutting" is based on the balance between energy consumption and cutting quality in the cutting process. Through experimental data and theoretical models, a two-dimensional optimization graph of cutting speed and power is drawn. This optimization graph shows the best combination between speed and power under different cutting conditions. Based on this graph, a speed-power matrix is created, and through optimization algorithms (such as gradient descent method or genetic algorithm), the best speed-power combination is determined, thereby generating laser cutting speed-power best combination data. This process helps to avoid problems such as material ablation or incomplete cutting caused by too high or too low power settings while improving cutting efficiency. The technical means of "performing common-edge local analysis on the global optimization model of laser cutting based on the laser cutting optimized tree path, laser cutting material re-optimization data, and laser cutting speed-power best combination data" is to integrate the optimized path, material data, and speed-power data, and perform local analysis on the common edges in the cutting process through intelligent algorithms (such as deep learning or fuzzy logic algorithms). This analysis not only considers the efficiency of path optimization but also combines the comprehensive effects of material properties and energy consumption to generate an intelligent common-edge analysis model for laser cutting. This model can adaptively adjust the cutting strategy to maximize material utilization rate, increase cutting speed, and ensure the stability of cutting quality.

[0118] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0119] Step S41: Obtain real-time laser cutting data;

[0120] Step S42: Perform laser cutting operations based on the intelligent common-edge analysis model for laser cutting to generate laser cutting model data; evaluate the real-time laser cutting data and the laser cutting model data to generate laser cutting evaluation data;

[0121] Step S43: Generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting layout hybrid common-edge analysis operation based on lens laser cutting.

[0122] In the embodiment of the present invention, "obtaining real-time laser cutting data" relies on sensors and monitoring devices to collect multi-dimensional data during the cutting process in real time, including laser power, cutting speed, temperature, material changes, etc. These data are transmitted to the computing platform through the data acquisition system to form a real-time laser cutting data set. The accurate acquisition of these data provides a basis for subsequent analysis, ensuring the timeliness and accuracy of the data. Then, in step S42, laser cutting model data is generated through operations based on the "intelligent common-edge analysis model for laser cutting". In this process, the intelligent common-edge analysis model uses a data model that combines the geometric characteristics and material properties of laser cutting. Through comprehensive evaluation of the cutting path, common-edge analysis, and material properties, a set of theoretically optimal cutting paths and process parameters are automatically generated. Using the output of this model, laser cutting model data that interacts with real-time data can be generated. These data include the specific values of various parameters during the cutting process, such as speed, power, focus position, etc. Then, the real-time laser cutting data and the laser cutting model data are evaluated, and laser cutting evaluation data is generated. This process is through comparative analysis between real-time data and the theoretical model to evaluate the deviation between the actual cutting effect and the expected effect. Specifically, the evaluation data includes the differences between the actual performance indicators during the cutting process, such as cutting quality, material utilization rate, machining accuracy, etc., and the model prediction values, and error correction is performed through intelligent algorithms to further optimize the operation process. Generate a report for the laser cutting evaluation data. Based on these evaluation data, the system generates a detailed evaluation report through certain algorithms (such as data mining, machine learning, etc.).

[0123] In this specification, a nesting layout hybrid common-edge analysis system based on lens laser cutting is provided for performing the above-mentioned nesting layout hybrid common-edge analysis method based on lens laser cutting. The nesting layout hybrid common-edge analysis system based on lens laser cutting:

[0124] A data processing module, configured to obtain a laser cutting preparation data set; perform data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessed data set; perform geometric feature - material property correlation analysis on the laser cutting preprocessed data set to generate laser cutting geometric - material correlation data;

[0125] A common - edge analysis model construction module, configured to construct an intelligent common - edge analysis model based on the laser cutting geometric - material correlation data to generate a preliminary laser cutting common - edge analysis model;

[0126] An optimization and local analysis module, configured to perform global nesting layout optimization based on the preliminary laser cutting common - edge analysis model to generate a global laser cutting optimization model; perform common - edge local analysis on the global laser cutting optimization model to generate an intelligent laser cutting common - edge analysis model;

[0127] An evaluation and report generation module, configured to perform evaluation based on the intelligent laser cutting common - edge analysis model to generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thereby completing the mixed common - edge analysis operation of nesting layout based on lens laser cutting.

[0128] The beneficial effects of the present invention are as follows: By obtaining the laser cutting preparation data set and performing data preprocessing, the standardization degree of the input data is improved, data noise and redundant information are reduced, the stability and accuracy of data processing are enhanced. By performing geometric feature - material property correlation analysis on the laser cutting preprocessed data set, the precise adaptability of laser cutting parameters is improved, the cutting quality and material utilization rate are increased. By constructing an intelligent common - edge analysis model based on the laser cutting geometric - material correlation data, the common - edge adaptability between different lens geometries is enhanced, the layout compactness of materials and resource utilization rate are improved, and material waste is reduced. By performing global nesting layout optimization based on the preliminary laser cutting common - edge analysis model, the rationality of the overall cutting scheme is improved, the redundancy of cutting paths is reduced, and the laser cutting efficiency is increased. By performing common - edge local analysis on the global laser cutting optimization model, the local common - edge path is optimized, the cumulative effect of the heat - affected zone is reduced, the cutting edge quality is improved, and the loss of materials caused by thermal deformation is reduced. By performing evaluation based on the intelligent laser cutting common - edge analysis model, the stability and applicability of the nesting layout scheme are improved, the reliability of the cutting scheme under different production conditions is ensured. By generating an evaluation report for the laser cutting evaluation data, precise common - edge layout optimization suggestions are provided for the production process, the visualization degree and controllability of production management are improved, and the production efficiency and quality control level are enhanced.

[0129] Therefore, in any aspect, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed by the present invention.

[0130] 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, and 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 nesting layout mixed common edge analysis method based on lens laser cutting, characterized in that: The following steps are involved: Step S1: Acquire laser cutting preparation data set; Performing data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessing data set; performing geometric feature-material property correlation analysis on the laser cutting preprocessing data set to generate laser cutting geometry-material correlation data; Step S2: Acquire laser cutting parameter data; construct a three-dimensional model using laser cutting geometry-material association data to generate a laser cutting three-dimensional solid model; construct a model of the laser cutting three-dimensional solid model using the laser cutting parameter data to generate a laser cutting common edge preliminary analysis model; Step S3: performing global optimization of nesting layout based on the preliminary analysis model of laser cutting common edge, and generating a global optimization model for laser cutting; Perform local co-edge analysis on the global optimization model of laser cutting to generate an intelligent co-edge analysis model for laser cutting; wherein step S3 includes: Step S31: performing global optimization of nesting layout based on the preliminary analysis model of laser cutting common edge, and generating global optimization data for laser cutting; Step S32: performing nesting integer linear programming on the laser cutting global optimization data to generate a laser cutting global optimization model; Step S33: perform local co-edge analysis on the global optimization model of laser cutting to generate an intelligent co-edge analysis model for laser cutting; wherein step S33 includes: step S331: perform path planning simulation on the global optimization model of laser cutting to generate laser cutting path planning simulation data; perform shortest path generation tree optimization on the laser cutting path planning simulation data to generate a laser cutting optimization tree path; Step S332: performing local material scanning on the global optimization model for laser cutting to generate local material data for laser cutting; re-optimizing the material utilization rate of the local material data for laser cutting to generate re-optimized material data for laser cutting; Step S333: Draw a two-dimensional optimization graph of cutting speed and power for the global optimization model of laser cutting to generate a two-dimensional optimization graph of local speed and power for laser cutting; create a speed-power matrix based on the two-dimensional optimization graph of local speed and power for laser cutting, determine the best speed-power combination, and generate data of the best combination of laser cutting speed and power; Step S334: performing local co-edge analysis on the laser cutting global optimization model based on the laser cutting optimization tree path, the laser cutting material re-optimization data and the laser cutting speed-power optimal combination data to generate an intelligent co-edge analysis model for laser cutting; Step S4: Evaluate based on the intelligent common edge analysis model of laser cutting to generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting and typesetting mixed common edge analysis operation based on lens laser cutting.

2. The nesting layout mixed common edge analysis method based on lens laser cutting according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire laser cutting preparation data set; Step S12: performing data preprocessing on the laser cutting preparation data set to generate a laser cutting preprocessing data set; Step S13: extracting data from the laser cutting preprocessing data set to generate laser cutting extraction data; performing geometric feature-material property correlation analysis on the laser cutting extraction data to generate laser cutting geometry-material correlation data.

3. The nesting layout mixed common edge analysis method based on lens laser cutting according to claim 2 is characterized in that: Step S12 includes the following steps: Step S121: normalizing the laser cutting preparation data set to generate laser cutting normalized data; Step S122: performing data cleaning according to the laser cutting normalized data, and removing abnormal data to generate laser cutting cleaning data; Step S123: performing data standardization processing on the laser cutting cleaning data to generate a laser cutting preprocessing data set, wherein the laser cutting preprocessing data set includes a laser cutting geometry preprocessing data set and a laser cutting material property preprocessing data set.

4. The nesting layout mixed common edge analysis method based on lens laser cutting according to claim 3 is characterized in that: Step S13 includes the following steps: Step S131: extracting the size features of the laser cutting geometric preprocessing data set to generate laser cutting size feature data; performing geometric shape analysis on the laser cutting size feature data to generate laser cutting geometric shape data; establishing a three-dimensional image based on the laser cutting geometric shape data to generate a laser cutting three-dimensional image; Step S132: performing lens shape feature analysis on the laser-cut three-dimensional image to generate laser-cut lens shape data; performing lens edge tilt angle detection on the laser-cut lens shape data to generate laser-cut lens edge tilt angle data; performing cutting angle detection on the laser-cut lens shape data to generate laser cutting angle data; performing plane angle calculation on the laser-cut lens edge tilt angle data and the laser cutting angle data to generate laser cutting geometric feature extraction data; Step S133: performing thermal expansion coefficient influence detection on the laser cutting material property preprocessing data set to generate laser cutting thermal expansion coefficient data; performing beam penetration analysis on the laser cutting thermal expansion coefficient data to generate laser cutting beam penetration data; performing thickness uniformity measurement on the laser cutting beam penetration data to generate laser cutting material property extraction data; Step S134: perform L1 regularization on the laser cutting geometric feature extraction data and the laser cutting material property extraction data to generate laser cutting feature regularization data; create a geometric feature-material property feature matrix for the laser cutting feature regularization data to generate a laser cutting geometric feature-material property feature matrix; perform geometric feature-material property correlation analysis on the laser cutting geometric feature-material property feature matrix to generate laser cutting geometry-material correlation data.

5. The nesting layout mixed common edge analysis method based on lens laser cutting according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: acquiring real-time laser cutting data; Step S42: performing laser cutting operation based on the laser cutting intelligent common edge analysis model to generate laser cutting model data; evaluating the real-time laser cutting data and the laser cutting model data to generate laser cutting evaluation data; Step S43: generating an evaluation report for the laser cutting evaluation data, thereby completing the nesting, layout and mixed common edge analysis operation based on lens laser cutting.

6. A nesting layout mixed common edge analysis system based on lens laser cutting, characterized in that: Used to execute the nesting layout mixed common edge analysis method based on lens laser cutting as claimed in claim 1, the nesting layout mixed common edge analysis system based on lens laser cutting comprises: The data processing module is used to obtain a laser cutting preparatory data set; perform data preprocessing on the laser cutting preparatory data set to generate a laser cutting preprocessing data set; perform geometric feature-material property correlation analysis on the laser cutting preprocessing data set to generate laser cutting geometry-material correlation data; The common edge analysis model building module is used to obtain laser cutting parameter data; use the laser cutting geometry-material association data to build a three-dimensional model to generate a laser cutting three-dimensional solid model; use the laser cutting parameter data to build a model of the laser cutting three-dimensional solid model to generate a laser cutting common edge preliminary analysis model; The optimization and local analysis module is used to perform global optimization of nesting layout based on the preliminary analysis model of laser cutting co-edge, and generate a global optimization model for laser cutting; perform local co-edge analysis on the global optimization model for laser cutting, and generate an intelligent co-edge analysis model for laser cutting; The evaluation and report generation module is used to evaluate based on the intelligent common edge analysis model of laser cutting and generate laser cutting evaluation data; generate an evaluation report for the laser cutting evaluation data, thereby completing the nesting and typesetting mixed common edge analysis operation based on lens laser cutting.

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