Laser spot energy distribution measurement methods, devices, equipment and storage media

Abstract

This invention relates to the field of laser measurement technology, and in particular to a method, apparatus, device, and storage medium for measuring the energy distribution of a laser spot. The method first acquires a laser beam to be simulated, calls a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, and obtains the simulated spot energy distribution parameters. Based on the simulated spot energy distribution parameters, an initial beam energy distribution model is constructed. Then, the model parameters of the initial beam energy distribution model are optimized using the beetle whisker algorithm to obtain the target beam energy distribution model. Next, the real-time laser spot energy parameters are acquired, and the real-time laser spot energy parameters are input into the target beam energy distribution model for fitting calculation to obtain the real-time spot energy distribution parameters. Finally, the simulated spot energy distribution parameters and the real-time spot energy distribution parameters are visualized to obtain visualized data, aiming to achieve high-precision, real-time reconstruction of the actual spot morphology of a micro-laser.

Description

Technical Field

[0001] This invention relates to the field of laser measurement technology, and in particular to a method, apparatus, equipment and storage medium for measuring the energy distribution of a laser spot. Background Technology

[0002] In the micro-laser-assisted ultra-precision machining of high-precision quartz glass components for inkjet printing OLED (Organic Light Emitting Diode) manufacturing, the precise control of laser spot energy distribution directly determines the thermal control accuracy and forming quality of the printhead micro-nozzle, optical alignment marks, and mask. Existing technologies generally build temperature field models to optimize process parameters based on the premise that the laser maintains an ideal Gaussian distribution after being transmitted through the tool. However, the geometric characteristics of diamond tools, such as the rake face inclination angle and cutting edge curvature, can cause significant refraction and total internal reflection effects, resulting in the actual emitted laser spot energy deviating significantly from the ideal distribution. This leads to a disconnect between theoretical models and machining practice, restricting the forming accuracy of core components.

[0003] The practical bottlenecks in microscale spot detection technology further exacerbate this problem. On the hardware level, mainstream beam quality analyzers generally have a spatial resolution greater than 5μm, which cannot meet the requirements for submicron-level energy distribution detection. Furthermore, offline measurement modes require interrupting the processing flow, making them unsuitable for continuous production scenarios. On the software level, optical path simulation, primarily based on the finite element method, is time-consuming for each calculation, making it difficult to support real-time monitoring and closed-loop control of the processing. Existing detection techniques such as the hole-punch method and the knife-edge scanning method are mostly applicable to regular, symmetrical Gaussian beams, with limited accuracy for measuring complex, asymmetrical spots. While CCD (charge-coupled device) imaging can observe irregular spots, its application is severely limited by pixel spacing and high-power laser saturation issues. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the present invention aims to provide a laser spot energy distribution measurement method, device, equipment and storage medium, which aims to achieve high-precision, real-time reconstruction of the actual spot morphology of micro-lasers.

[0005] The first aspect of this invention provides a method for measuring the energy distribution of a laser spot, comprising: acquiring a laser beam to be simulated; calling a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, thereby obtaining simulated spot energy distribution parameters; constructing an initial beam energy distribution model based on the simulated spot energy distribution parameters; optimizing the model parameters of the initial beam energy distribution model using a beetle whisker algorithm to obtain a target beam energy distribution model; acquiring real-time laser spot energy parameters; inputting the real-time laser spot energy parameters into the target beam energy distribution model for fitting calculation, thereby obtaining real-time spot energy distribution parameters; and visualizing the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the step of calling a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated and obtaining the simulated spot energy distribution parameters includes: acquiring preset tool parameters and laser incident configuration parameters; performing parameter standardization processing on the laser beam to be simulated based on the tool parameters and the laser incident configuration parameters to obtain a standardized laser beam; and calling the laser optical path transmission model to simulate the beam propagation behavior of the standardized laser beam to obtain the simulated spot energy distribution parameters.

[0007] Optionally, in a second implementation of the first aspect of the present invention, constructing an initial beam energy distribution model based on the simulated beam energy distribution parameters includes: obtaining the laser power density from the simulated beam energy distribution parameters; based on the distribution characteristics of the laser power density, introducing coordinate transformation coefficients into the ordinary Gaussian distribution form to obtain a modified Gaussian distribution form, and determining the modified Gaussian distribution form as the fitting form for the energy distribution model; and constructing the initial beam energy distribution model based on the fitting form for the energy distribution model and the simulated beam energy distribution parameters.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of using the beetle whisker algorithm to optimize the model parameters of the initial beam energy distribution model to obtain the target beam energy distribution model includes: performing data filtering and grouping processing on the simulated beam energy distribution parameters to obtain a training sample set; constructing a fitness function based on the simulated beam energy distribution parameters; and using the beetle whisker algorithm to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model.

[0009] Optionally, in the fourth implementation of the first aspect of the present invention, constructing a fitness function based on the simulated beam energy distribution parameters includes: obtaining coordinate values ​​of multiple sampling points and laser energy reference values ​​from the simulated beam energy distribution parameters; calling the initial beam energy distribution model and performing prediction calculations based on the coordinate values ​​of each sampling point to obtain a predicted laser energy value corresponding to each sampling point; performing an arithmetic mean operation based on the multiple laser energy reference values ​​to obtain an average laser energy value; performing error quantization calculations based on the multiple laser energy reference values ​​and the multiple laser energy predicted values ​​to obtain a root mean square percentage error; performing goodness-of-fit calculations based on the multiple laser energy reference values, the multiple laser energy predicted values, and the average laser energy value to obtain a coefficient of determination; and constructing the fitness function based on the root mean square percentage error and the coefficient of determination.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of employing the beetle whisker algorithm to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model includes: performing parameter initialization based on the simulated beam energy distribution parameters to obtain the beetle's initial position and initial step size; calculating the antenna positions based on the beetle's initial position to obtain the initial positions of the left and right antennas; calculating antenna comfort based on the fitness function, the initial positions of the left and right antennas to obtain the left and right antenna fitness; comparing the left and right antenna fitnesss to obtain a comparison result, and determining the parameter update direction based on the comparison result; obtaining a preset attenuation coefficient, and iteratively updating the beetle's initial position based on the attenuation coefficient, the initial step size, and the parameter update direction; and optimizing the initial beam energy distribution model based on the updated beetle position when a preset iteration stopping condition is met to obtain the target beam energy distribution model.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the step of visualizing the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data includes: performing coordinate alignment and dimension unification processing on the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain standardized simulated spot energy distribution parameters and standardized real-time spot energy distribution parameters; performing error calculation and feature comparison on the standardized simulated spot energy distribution parameters and the standardized real-time spot energy distribution parameters to obtain energy distribution error data and key feature difference results; and performing visualization processing on the energy distribution error data and key feature difference results to obtain the visualized data.

[0012] A second aspect of the present invention provides a laser spot energy distribution measurement device, comprising: a beam propagation simulation module for acquiring a laser beam to be simulated, calling a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, and obtaining simulated spot energy distribution parameters; a model construction module for constructing an initial beam energy distribution model based on the simulated spot energy distribution parameters; a model optimization module for optimizing the model parameters of the initial beam energy distribution model using a beetle whisker algorithm to obtain a target beam energy distribution model; a fitting calculation module for acquiring real-time laser spot energy parameters, inputting the real-time laser spot energy parameters into the target beam energy distribution model for fitting calculation, and obtaining real-time spot energy distribution parameters; and a visualization module for visualizing the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data.

[0013] A third aspect of the present invention provides a laser spot energy distribution measurement device, the laser spot energy distribution measurement device comprising: a memory and at least one processor, the memory storing instructions; at least one processor calling the instructions in the memory to cause the laser spot energy distribution measurement device to perform each step of the laser spot energy distribution measurement method described in any of the preceding claims.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of the laser spot energy distribution measurement method described in any of the preceding claims.

[0015] In the technical solution of this invention, the laser beam to be simulated is first obtained, and a pre-trained laser optical path transmission model is called to simulate the beam propagation behavior of the laser beam to be simulated, thereby obtaining the simulated spot energy distribution parameters. Based on the simulated spot energy distribution parameters, an initial beam energy distribution model is constructed. Then, the model parameters of the initial beam energy distribution model are optimized using the beetle whisker algorithm to obtain the target beam energy distribution model. Next, the real-time laser spot energy parameters are obtained and input into the target beam energy distribution model for fitting calculation to obtain the real-time spot energy distribution parameters. Finally, the simulated spot energy distribution parameters and the real-time spot energy distribution parameters are visualized to obtain visualized data. This effectively solves the problems of insufficient resolution and large fitting deviation of traditional detection methods in micron-level processing, significantly simplifies the operation process, and provides a real-time and reliable spot monitoring and quality control means for laser-assisted processing systems in inkjet printing manufacturing, effectively improving the processing consistency and production efficiency of microstructures such as high-density nozzle arrays. Attached Figure Description

[0016] Figure 1 A logic flowchart of a laser spot energy distribution measurement method provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the structure of the laser spot energy distribution measurement device provided in an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of the structure of a laser spot energy distribution measurement device provided in an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a method, apparatus, device, and storage medium for measuring the energy distribution of a laser spot. In this invention, the terms "first," "second," "third," "fourth," etc. (if present)," in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the laser spot energy distribution measurement method in this invention includes:

[0021] 101. Obtain the laser beam to be simulated, call the pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, and obtain the simulated spot energy distribution parameters.

[0022] In this embodiment, the pre-trained laser beam propagation model is based on the COMSOL (COMSOL Multiphysics) optical simulation module and constructed using a ray tracing algorithm. It is designed and developed for the beam propagation scenario of laser-diamond tool interaction. Other simplified simulation methods (such as simplified models based on the finite element method or ray tracing algorithm) can also be introduced to reduce computational resource consumption and improve real-time feedback capabilities. This model integrates the geometric features of the tool and the optical properties of its material into the simulation logic through optical physics modeling. It assumes that the focused laser beam to be simulated is incident in a parallel beam form. By configuring the grid type and the total number of rays, the spatial sampling density and computational reliability of the simulation are ensured. Simultaneously, a detector is placed in the critical action area of ​​the tool's cutting edge to capture the beam's morphological evolution and energy distribution information after exiting the beam, thereby obtaining the simulated beam energy distribution parameters. Compared to relying on resource-intensive detailed simulations or time-consuming and inconvenient experimental power adjustments, the basic data provided by this model can lay a crucial foundation for the construction of subsequent beam energy models, support the accurate derivation of the relationship between input and output laser power, thereby efficiently determining the input parameters corresponding to the target output power, avoiding fitting deviations caused by the disconnect between theory and reality, and avoiding the resource waste of detailed simulations and the inefficiency of experimental adjustments.

[0023] 102. Construct an initial beam energy distribution model based on the simulated beam energy distribution parameters;

[0024] In this embodiment, when constructing the initial beam energy distribution model based on the simulated beam energy distribution parameters, a formula fitting method is used to establish a mathematical framework that adapts to the laser energy distribution at the cutting edge of the diamond tool, addressing the resolution limitations of optical simulation. Considering that the beam energy distribution after passing through the diamond tool follows a Gaussian distribution trend, but exhibits compression in the vertical direction with a more significant compression in the upper half, a modified Gaussian distribution is adopted, and coordinate transformation coefficients are introduced to adapt to the asymmetric energy distribution characteristics of the actual beam. Specifically, the model integrates key information such as energy density, beam center coordinates, and energy attenuation trend from the simulated beam energy distribution parameters, transforming the power transfer laws involved in the simulation into parameter constraints for the model. This ensures that the initial model, in both structure and parameters, closely matches the actual interaction process between the laser and the tool. This model overcomes the limitations of the traditional ideal Gaussian distribution assumption, accurately adapting to the asymmetric energy distribution of the actual beam through coordinate transformation coefficients, solving the problem of microscopic energy distribution characterization caused by insufficient resolution in optical simulation. Furthermore, the model structure and initial parameters determined based on the simulated beam energy distribution parameters provide a scientific starting point for subsequent algorithm optimization, significantly reducing the computational complexity of the global optimization problem.

[0025] 103. The model parameters of the initial beam energy distribution model are optimized using the beetle whisker algorithm to obtain the target beam energy distribution model;

[0026] In this embodiment, the beetle whisker algorithm simulates the beetle's environmental perception mechanism to efficiently optimize unknown variables in the model globally. The algorithm simulates the detection behavior of the beetle's left and right whiskers within the parameter space, compares the error values ​​under different parameter combinations, dynamically adjusts the optimization direction and step size of the parameters, and iteratively improves the model's predicted laser power density, spot center coordinates, energy distribution trends, etc., to a high degree of consistency with the actual physical process, ultimately obtaining an accurate target beam energy distribution model. The beetle whisker algorithm possesses powerful global search and fast convergence capabilities, effectively avoiding the shortcomings of traditional optimization methods that get trapped in local optima, ensuring the accuracy and global optimality of model parameters. The optimized target model accurately adapts to the asymmetric energy distribution and power transfer law of the actual spot, significantly improving the prediction accuracy of laser energy distribution.

[0027] 104. Obtain real-time laser spot energy parameters, input the real-time laser spot energy parameters into the target beam energy distribution model for fitting calculation, and obtain real-time spot energy distribution parameters;

[0028] In this embodiment, real-time laser spot energy parameters are obtained using a professional laser power meter combined with a LabVIEW (Laboratory Virtual Instrument Engineering Workbench) program. The laser power meter establishes a direct communication link with the LabVIEW program via a high-speed communication interface (such as USB) to transmit the raw data of the collected spot energy distribution in real time. Furthermore, the fusion of multiple sensors can be considered, such as combining infrared imaging technology and pressure sensors, to achieve more comprehensive spot detection and compensation, improving the system's robustness. The LabVIEW program utilizes its built-in data acquisition and processing functions to perform preprocessing operations such as noise filtering and format normalization on the raw data, thereby obtaining real-time laser spot energy parameters containing core information such as the measured power density values ​​and spatial coordinates of each sampling point of the spot. After obtaining the real-time laser spot energy parameters, they are input into a target beam energy distribution model optimized by the beetle whisker algorithm to obtain the optimal parameter combination for fitting calculation. The key parameters of this model, such as the coordinate transformation coefficients and the laser equivalent radius, have been determined as optimal solutions through global optimization. At this point, the model uses a fixed optimal parameter framework as a basis for fitting calculation of the real-time laser spot energy parameters. Specifically, the model substitutes the measured power density values ​​and spatial coordinates of each sampling point of the light spot acquired in real time into the optimized mathematical expression. Through a built-in fitting mechanism, it quickly solves the problem, outputting real-time light spot energy distribution parameters that highly match the measured energy distribution gradient and morphological distortion characteristics of the light spot. These parameters include accurate power density fitting values, standardized spatial coordinates, light spot center coordinates, and energy peak values, among other core feature parameters. The target model's parameters, after global optimization using the beetle-whisker algorithm, possess optimal adaptability. After inputting real-time parameters, there is no need for dynamic adjustment of the model parameters; high-precision real-time light spot energy distribution results can be quickly output through fitting calculations. This ensures data real-time performance and avoids errors that may be introduced by secondary parameter adjustments. This collaborative mode of fixed optimal parameters and real-time data fitting overcomes the accuracy bottleneck caused by parameter uncertainty in traditional models, significantly improving the efficiency and accuracy of energy distribution monitoring and control in microstructure fabrication.

[0029] 105. Visualize the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data.

[0030] In this embodiment, abstract numerical parameters are transformed into intuitive and easy-to-understand graphical information. This not only fully preserves the spatial distribution characteristics and key quantitative indicators of both types of data, but also presents the evolution trend of the light spot shape, regional differences in energy distribution, and numerical deviations of core characteristic parameters through comparative logic. The resulting visualized data includes comprehensive charts that directly support analytical decisions, allowing technicians to quickly grasp the core patterns of light spot energy distribution in simulation predictions and actual processing without needing to deeply interpret complex numerical values. This significantly reduces the difficulty and time cost of data analysis. The intuitive comparison charts not only help technicians quickly determine the prediction accuracy of the optical path transmission model and the fitting effect of the target beam energy distribution model, but also accurately locate abnormalities such as light spot distortion and energy shift that may occur during processing. This provides clear directional guidance for subsequent process parameter adjustments, optical path optimization, or tool condition calibration.

[0031] This application discloses a method for measuring the energy distribution of a laser spot. First, a laser beam to be simulated is acquired. A pre-trained laser path transmission model is then used to simulate the beam propagation behavior of the simulated laser beam, obtaining the simulated spot energy distribution parameters. Based on these parameters, an initial beam energy distribution model is constructed. Then, the model parameters of the initial beam energy distribution model are optimized using a beetle whisker algorithm to obtain the target beam energy distribution model. Next, real-time laser spot energy parameters are acquired and input into the target beam energy distribution model for fitting calculation, yielding the real-time spot energy distribution parameters. Finally, the simulated and real-time spot energy distribution parameters are visualized to obtain visualized data. This method effectively solves the problems of insufficient resolution and large fitting deviation in micron-level processing using traditional detection methods, significantly simplifies the operation process, and provides a real-time and reliable spot monitoring and quality control method for laser-assisted processing systems in inkjet printing manufacturing. It effectively improves the processing consistency and production efficiency of microstructures such as high-density nozzle arrays.

[0032] In this embodiment of the invention, the step of calling a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated and obtaining the simulated spot energy distribution parameters includes: acquiring preset tool parameters and laser incident configuration parameters; performing parameter standardization processing on the laser beam to be simulated based on the tool parameters and the laser incident configuration parameters to obtain a standardized laser beam; and calling the laser optical path transmission model to simulate the beam propagation behavior of the standardized laser beam to obtain the simulated spot energy distribution parameters.

[0033] In this embodiment, preset tool parameters and laser incident configuration parameters are obtained. The tool parameters cover the structural features and optical properties of the diamond tool. For example, the rake angle is set to -45°, the incident surface is tilted at 5°, and the refractive index at a laser wavelength of 1064nm is 2.39 and the reflectivity is 0.168. The laser incident configuration parameters are set according to the processing requirements, tool and material characteristics, and the compatibility between simulation and actual measurement. For example, the wavelength of the incident laser is set to 1064nm, the initial spot diameter is 80μm, the standard Gaussian energy distribution characteristics are set, and the simulation sets the total number of hexagonal grid rays to 67591, the incident position to be the rear end of the tool, and the incident height to be 0.6mm. Based on the above parameters, parameter standardization is performed on the simulated laser beam. The core of this process is to standardize the format and dimensions of the dispersed tool geometry parameters, material optical parameters, laser incident parameters, and energy distribution parameters, eliminating input adaptation differences between different types of parameters. This results in a standardized dataset containing laser beam characteristics, tool operating conditions, and incident environment parameters—the complete input information corresponding to the standardized laser beam. This ensures that the parameters can be effectively recognized and invoked by the laser optical path transmission model. The invoked laser optical path transmission model is built based on the COMSOL optical simulation module, using a ray tracing algorithm as its core simulation logic. After receiving the standardized dataset, the model reproduces the complete propagation process of the laser beam from air into the diamond tool, including the optical path offset caused by changes in the refractive index of the medium, the refraction and reflection effects caused by the tool's rake angle and cutting edge structure, and the secondary reflection behavior of some beams. By using a detector placed at the tool's cutting edge position, the beam's spot morphology and energy distribution information after exiting the tool are captured, ultimately obtaining simulated beam energy distribution parameters including the spot's spatial coordinates, power density in each region, energy peak position, and asymmetric distortion characteristics. By integrating and standardizing the key parameters of the cutting tool and the laser, the integrity and consistency of the model input information are ensured, laying the foundation for the accuracy of the simulation results. Relying on the ray tracing algorithm and the COMSOL simulation platform, the model accurately reproduces the physical mechanism of the interaction between the laser and the cutting tool, breaking through the limitations of low efficiency in traditional pure mathematical calculations and efficiently outputting simulation parameters that have both physical realism and feature integrity.

[0034] In this embodiment of the invention, constructing an initial beam energy distribution model based on the simulated beam energy distribution parameters includes: obtaining the laser power density from the simulated beam energy distribution parameters; based on the distribution characteristics of the laser power density, introducing coordinate transformation coefficients into the ordinary Gaussian distribution form to obtain a modified Gaussian distribution form, and determining the modified Gaussian distribution form as the fitting form for the energy distribution model; and constructing the initial beam energy distribution model based on the fitting form for the energy distribution model and the simulated beam energy distribution parameters.

[0035] In this embodiment, the laser power density is obtained from the simulated beam energy distribution parameters. These parameters originate from the emitted beam energy data captured by the detector at the cutting edge of the cutting tool in the optical simulation. They accurately reflect the spatial distribution of laser energy after being processed by the diamond tool, including the power density values ​​and energy gradient characteristics at each sampling point. Based on these laser power density distribution characteristics, although the beam energy distribution after passing through the diamond tool follows a Gaussian distribution trend, there is compression in the vertical direction, with the upper half showing more significant compression. Therefore, a coordinate transformation coefficient is introduced into the ordinary Gaussian distribution form to construct a modified Gaussian distribution form, thereby adapting to the asymmetric energy distribution shape of the actual beam. This modified Gaussian distribution form is determined as the fitting form for the energy distribution model. The calculation expression for the ordinary Gaussian distribution is:

[0036] ,

[0037] Where Q represents laser power density; P represents laser power; represents the radius of the laser spot; r represents the distance between the sampling point and the center of the beam. The sampling point refers to a discrete point within the laser spot area used to measure or calculate the laser power density.

[0038] By introducing coordinate transformation coefficients into the ordinary Gaussian distribution, we obtain the modified Gaussian distribution. The calculation expression for the modified Gaussian distribution is as follows:

[0039] ,

[0040] in, This represents the predicted laser power density. Indicates the output laser power. This represents the coordinates of the laser spot calculation point along the horizontal axis. This represents the coordinates of the laser spot calculation point along the vertical axis. This represents the coordinates of the laser spot center along the horizontal axis. This represents the coordinates of the laser spot center along the vertical axis. and Represents the coordinate transformation coefficients. This represents the transformation factor in the horizontal axis direction. This represents the transformation factor in the vertical axis direction. Representing the equivalent radius of a laser, using parameters and The value is calculated by fitting the actual laser energy distribution to a Gaussian distribution and is used to represent the equivalent spot size.

[0041] In addition, input power and output power The relational calculation expression is:

[0042] ,

[0043] in, This represents the proportionality coefficient. This represents a constant term.

[0044] Subsequently, based on the fitted form of this energy distribution model and the simulated spot energy distribution parameters, an initial beam energy distribution model is constructed, that is, the mathematical structure of the modified Gaussian distribution is combined with the simulated laser power density, spot center coordinates, and laser equivalent radius. By combining parameters, the unknown variables in the model (such as coordinate transformation coefficients) can be identified. and proportionality coefficient constant term The initial constraints and value ranges of (etc.) are established to form a mathematical framework that can preliminarily describe the distribution law of laser energy. The problem of insufficient resolution in optical simulation is solved by formula fitting method, which can accurately capture the microscopic energy distribution at the cutting edge; the coordinate transformation coefficient is introduced by modifying the Gaussian distribution, which effectively adapts to the asymmetric distortion characteristics of the actual spot and breaks through the application limitations of ordinary Gaussian distribution.

[0045] In this embodiment of the invention, the step of optimizing the model parameters of the initial beam energy distribution model using the beetle whisker algorithm to obtain the target beam energy distribution model includes: performing data filtering and grouping processing on the simulated beam energy distribution parameters to obtain a training sample set; constructing a fitness function based on the simulated beam energy distribution parameters; and using the beetle whisker algorithm to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model.

[0046] In this embodiment, data filtering and grouping based on simulated light spot energy distribution parameters requires selecting samples that reflect key energy distribution characteristics from a large amount of spatial coordinate and power density data. These samples include high-energy central regions, edge attenuation regions, and areas with significant asymmetric distortion. The samples are then grouped according to dimensions such as power density level to form a training sample set covering all energy distribution characteristics, providing comprehensive input for subsequent algorithm training. When using the beetle whisker algorithm, based on the training sample set and fitness function, unknown model parameters (such as coordinate transformation coefficients) are used to further refine the data. and proportionality coefficient constant term In a parameter space composed of parameters such as the beetle's left and right whiskers, the sensing mechanism is simulated to detect the fitness values ​​of different parameter combinations. By comparing the errors, the parameter search direction and step size are dynamically adjusted, and continuous iterative optimization is performed until the fitness function value is minimized, thereby obtaining the optimal parameter combination and the target beam energy distribution model. The fitness function accurately quantifies the fitting accuracy, providing a clear guide for algorithm optimization. The global search and fast convergence characteristics of the beetle whisker algorithm avoid the risk of local optima, ensuring the global optimality and fitting accuracy of the target model parameters. The final model can accurately adapt to the asymmetric energy distribution of the actual laser spot, significantly improving the prediction accuracy of laser energy distribution, providing reliable mathematical support for real-time spot monitoring and processing technology optimization, effectively ensuring the accuracy and stability of energy control in microstructure processing, and significantly improving parameter optimization efficiency to meet the real-time requirements of actual processing.

[0047] In this embodiment of the invention, constructing a fitness function based on the simulated beam energy distribution parameters includes: obtaining coordinate values ​​of multiple sampling points and laser energy reference values ​​from the simulated beam energy distribution parameters; calling the initial beam energy distribution model and performing prediction calculations based on the coordinate values ​​of each sampling point to obtain a predicted laser energy value corresponding to each sampling point; performing an arithmetic mean operation on the multiple laser energy reference values ​​to obtain an average laser energy value; performing error quantization calculations on the multiple laser energy reference values ​​and the multiple laser energy predicted values ​​to obtain a root mean square percentage error; performing goodness-of-fit calculations on the multiple laser energy reference values, the multiple laser energy predicted values, and the average laser energy value to obtain a coefficient of determination; and constructing the fitness function based on the root mean square percentage error and the coefficient of determination.

[0048] In this embodiment, the coordinate values ​​of multiple sampling points and the laser energy reference value are obtained from the simulated spot energy distribution parameters. The coordinate values ​​correspond to the spatial location information of the spot calculation points, and the laser energy reference value of each sampling point is obtained through optical simulation. As the baseline data for model fitting, the initial beam energy distribution model, i.e., the mathematical framework of the corrected Gaussian distribution, is invoked. The coordinate values ​​of each sampling point are substituted into the model for prediction calculation, yielding the laser energy prediction value corresponding to each sampling point. The average laser energy is obtained by performing an arithmetic mean calculation based on multiple laser energy reference values. This value is a key intermediate parameter for measuring the goodness of fit of the model. Then, based on multiple laser energy benchmark values ​​and predicted laser energy values, error quantification is performed according to the formula for calculating the root mean square percentage error (RMSPE). This is achieved by averaging and taking the square root of the sum of squared relative errors between the actual and predicted values ​​at each sampling point, and converting this sum to a percentage. The specific calculation expression is as follows:

[0049] ,

[0050] Wherein, RMSPE represents the root mean square percentage error. Indicates the number of sampling points. Indicates the first Predicted laser energy value at each sampling point Indicates the first The laser energy reference value for each sampling point.

[0051] Then, based on multiple laser energy benchmark values, predicted laser energy values, and average laser energy values, the goodness of fit is calculated according to the formula for calculating the coefficient of determination. The index measuring the goodness of fit of the model is obtained through the relationship between the sum of squares of the actual values, predicted values, and average values. The specific calculation expression is as follows:

[0052] ,

[0053] in, The coefficient of determination is represented by the coefficient of determination. Indicates the number of sampling points. Indicates the first Predicted laser energy value at each sampling point Indicates the first The laser energy reference value at each sampling point This represents the average laser energy.

[0054] A fitness function is constructed based on the root mean square percentage error and the coefficient of determination. The ratio of the two is used as the quantitative objective for the iterative optimization of the beetle whisker algorithm. This function balances the dual requirements of error minimization and fitting accuracy. The specific calculation expression is as follows:

[0055] ,

[0056] in, This represents the fitness function, and RMSPE represents the root mean square percentage error. This represents the coefficient of determination.

[0057] The fitness function integrates two core dimensions: error and goodness of fit. This avoids the limitations of optimizing a single index and enables the beetle beard algorithm to simultaneously ensure the accuracy of model prediction and the ability to interpret data during the iteration process. The target beam energy distribution model optimized based on this function achieves a good balance between fitting accuracy and generalization performance, providing accurate and reliable mathematical support for subsequent real-time spot monitoring and optimization of processing parameters.

[0058] In this embodiment, the method employing the beetle whisker algorithm to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model includes: performing parameter initialization based on the simulated beam energy distribution parameters to obtain the beetle's initial position and initial step size; calculating the antenna positions based on the beetle's initial position to obtain the initial positions of the left and right antennas; calculating antenna comfort based on the fitness function, the initial positions of the left and right antennas to obtain the left and right antenna fitness; comparing the left and right antenna fitness to obtain a comparison result, and determining the parameter update direction based on the comparison result; obtaining a preset attenuation coefficient, and iteratively updating the beetle's initial position based on the attenuation coefficient, the initial step size, and the parameter update direction; and optimizing the initial beam energy distribution model based on the updated beetle position when a preset iteration stopping condition is met to obtain the target beam energy distribution model.

[0059] In this embodiment, parameter initialization is performed based on the simulated light spot energy distribution parameters. This requires setting the search space dimension (i.e., the number of unknown parameters in the model, such as coordinate transformation coefficients, laser equivalent radius, etc.), the maximum number of iterations, the initial step size, and the initial search distance. Simultaneously, the beetle's position is initialized. Then, based on the beetle's initial position, combined with the unit vector of the random direction and the search distance, the initial positions of the left and right antennae are calculated respectively, simulating the beetle's antennae's detection mechanism in the parameter space. The expressions for the initial positions of the right and left antennae are defined as follows:

[0060] ,

[0061] ,

[0062] in, express The initial position of the right tentacle at that moment. express The initial position of the left tentacle at that moment. Indicates the initial position of the longhorn beetle. express The beetle's search distance at all times. Let represent the unit vector in a random direction. The expression for calculating the unit vector in a random direction is:

[0063] ,

[0064] in, Indicates the interval [ Generating a uniformly distributed random number within [1, 1]. Indicates to Calculate its Euclidean norm.

[0065] Then, based on the constructed fitness function, the parameters of the initial positions of the left and right tentacles are substituted into the calculation to obtain the fitness of the left and right tentacles, quantifying the fitting accuracy and goodness of the model under different parameter combinations. By comparing the fitness values ​​of the left and right tentacles, the moving direction of the longhorn beetle is determined. If the fitness of the right tentacles is greater than that of the left tentacles, it means that the parameter position of the left tentacles is closer to the optimal solution, and the longhorn beetle will move towards the left tentacles. If the fitness of the right tentacles is less than or equal to that of the left tentacles, it will move towards the right tentacles. Next, the preset search distance and step size decay coefficient are obtained. Combined with the initial step size and the determined parameter update direction, the initial position of the longhorn beetle is iteratively updated. At the same time, the search distance and step size gradually decay with the number of iterations, taking into account both the global exploration in the early stage of the search and the local fine search in the later stage. The specific calculation expression is as follows:

[0066] ,

[0067] ,

[0068] in, express The beetle's search distance at all times. express The beetle's stride length at any given moment This represents the attenuation coefficient of the search distance. This represents the decay coefficient of the step size.

[0069] The initial position of the longhorn beetle is iteratively updated by combining the initial step size and the determined parameter update direction. Simultaneously, the search distance and step size gradually decrease with each iteration, balancing global exploration in the early stages of the search with fine-grained local searching in the later stages. When the number of iterations reaches a preset value or the fitness function value meets the accuracy requirements, the initial beam energy distribution model is optimized based on the updated longhorn beetle position to obtain the target beam energy distribution model. The specific calculation expression is as follows:

[0070] ,

[0071] in, Indicates the fitness of the right antenna. Indicates the fitness of the left antenna. express The beetle's stride length at any given moment Represents a unit vector in a random direction. Indicates the initial position of the longhorn beetle. This indicates the update position of the longhorn beetle.

[0072] To clearly demonstrate the application logic of the beetle whisker algorithm in beam energy model parameter optimization, the following examples illustrate this with specific parameter settings, training and verification strategies, and final model results: First, the parameter configuration of the beetle whisker algorithm is defined as follows: the search distance attenuation coefficient is 0.99, the step size attenuation coefficient is 0.95, the initial search distance and initial step size are both set to 1, and the maximum number of iterations is set to 100. The retrieval range of unknown parameters in the model needs to take into account both physical rationality and simulation data characteristics. For example, the equivalent radius of the laser is in the range of [32, 48], the coordinate transformation coefficients a and b are in the ranges of [0.6, 1.4] and [0.4, 1.6] respectively, the coordinate retrieval range of the laser spot center in the horizontal direction is in the range of [-1, 1], and the coordinate retrieval range of the laser spot center in the horizontal direction is in the range of [-1, 5]. To verify the effectiveness of the model, five sets of simulated energy distribution results at different laser powers (5W, 10W, 15W, 20W, and 25W) were selected as the training set to solve for the optimal parameters corresponding to each power. Simulation data at 18W laser power was used as the test set to evaluate the model's generalization and prediction capabilities. Each set of calculations was performed 30 times to enhance statistical reliability. The computing environment was a computer running Windows 10 (64-bit), and the programming environment was MATLAB 2020a.

[0073] The final optimization yields definite beam energy model parameters. The specific calculation expression is as follows:

[0074] ,

[0075] Where P represents laser power, This represents the coordinates of the laser spot calculation point along the horizontal axis. This represents the coordinates of the laser spot calculation point along the vertical axis.

[0076] Input power and output power The relational calculation expression is:

[0077] .

[0078] Based on the strategy of multi-group power training, independent testing and multiple iterations of verification, a balance between global exploration and local fine optimization is achieved through reasonable algorithm parameter settings, and the stability of the optimal parameters is ensured by repeated calculations.

[0079] In this embodiment of the invention, the visualization processing of the simulated light spot energy distribution parameters and the real-time light spot energy distribution parameters to obtain visualized data includes: performing coordinate alignment and dimension unification processing on the simulated light spot energy distribution parameters and the real-time light spot energy distribution parameters to obtain standardized simulated light spot energy distribution parameters and standardized real-time light spot energy distribution parameters; performing error calculation and feature comparison on the standardized simulated light spot energy distribution parameters and the standardized real-time light spot energy distribution parameters to obtain energy distribution error data and key feature difference results; and performing visualization processing on the energy distribution error data and key feature difference results to obtain the visualized data.

[0080] In this embodiment, to achieve effective comparison between simulated and real-time beam energy distribution, the simulated and real-time beam energy distribution parameters need to be aligned in coordinates and standardized in dimensions. The simulated beam energy distribution parameters originate from the beam model simulation results built using the COMSOL and MATLAB interface, while the real-time beam energy distribution parameters are acquired by the energy meter measurement system and transmitted via a LabVIEW program. Coordinate alignment ensures that both parameters maintain consistency in their spatial coordinate system references, while standardization of dimensions involves standardizing the units and numerical ranges of parameters such as power density and spatial coordinates, resulting in directly comparable standardized simulated and real-time beam energy distribution parameters. Subsequently, error calculation and feature comparison are performed on the two types of standardized parameters. Error calculation uses quantitative indicators such as root mean square percentage error to quantify the numerical deviation between simulation and measurement. Feature comparison focuses on key features such as beam center coordinates, peak energy, and asymmetric distortion coefficients, analyzing the differences in core features to ultimately obtain energy distribution error data and key feature difference results. Finally, these error data and key feature difference results are visualized. Leveraging LabVIEW's results plotting module, visual charts such as energy distribution heatmaps, error distribution cloud maps, and key feature comparison curves are generated, providing intuitive visual data. Coordinate alignment and dimensional consistency ensure the consistency of the comparison data, laying the foundation for the accuracy of error calculation and feature comparison. Error calculation and feature comparison comprehensively reveal the degree of fit between simulation and actual measurement from both numerical and feature dimensions, providing quantitative basis for evaluating model accuracy and identifying processing anomalies. Visualization transforms abstract data into intuitive charts, enabling technicians to quickly grasp the differences in light spot energy distribution and providing intuitive support for decisions such as process optimization and optical path adjustment.

[0081] The laser spot energy distribution measurement method in the embodiments of the present invention has been described above. The laser spot energy distribution measurement device in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 2 One embodiment of the laser spot energy distribution measurement device in this invention includes:

[0082] Beam propagation simulation module 201: used to acquire the laser beam to be simulated, call the pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, and obtain the simulated spot energy distribution parameters;

[0083] Model building module 202: used to build an initial beam energy distribution model based on the simulated beam energy distribution parameters;

[0084] Model optimization module 203: used to optimize the model parameters of the initial beam energy distribution model using the beetle whisker algorithm to obtain the target beam energy distribution model;

[0085] Fitting calculation module 204: used to obtain real-time laser spot energy parameters, input the real-time laser spot energy parameters into the target beam energy distribution model for fitting calculation, and obtain real-time spot energy distribution parameters;

[0086] Visualization module 205: used to perform visualization processing on the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualization data.

[0087] Based on the same ideas as the methods in the above embodiments, the apparatus provided in this application can implement the methods in the above embodiments.

[0088] above Figure 2 The laser spot energy distribution measuring device in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The laser spot energy distribution measuring device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0089] Figure 3 This is a schematic diagram of a laser spot energy distribution measurement device 300 provided in an embodiment of the present invention. The laser spot energy distribution measurement device 300 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the laser spot energy distribution measurement device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the laser spot energy distribution measurement device 300 to implement the steps of the laser spot energy distribution measurement method provided in the above-described method embodiments.

[0090] The laser spot energy distribution measurement device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The illustrated structure of the laser spot energy distribution measurement device does not constitute a limitation on the laser spot energy distribution measurement device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0091] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the laser spot energy distribution measurement method.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring the energy distribution of a laser spot, characterized in that, include: The laser beam to be simulated is obtained, and a pre-trained laser optical path transmission model is called to simulate the beam propagation behavior of the laser beam to be simulated, so as to obtain the energy distribution parameters of the simulated spot. The laser power density is obtained from the simulated spot energy distribution parameters; based on the distribution characteristics of the laser power density, coordinate transformation coefficients are introduced into the ordinary Gaussian distribution form to obtain a modified Gaussian distribution form, and the modified Gaussian distribution form is determined as the energy distribution model fitting form; an initial beam energy distribution model is constructed based on the energy distribution model fitting form and the simulated spot energy distribution parameters. The simulated light spot energy distribution parameters are filtered and grouped to obtain a training sample set; a fitness function is constructed based on the simulated light spot energy distribution parameters. The longhorn beetle whisker algorithm is used to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model. The real-time laser spot energy parameters are obtained, and the real-time laser spot energy parameters are input into the target beam energy distribution model for fitting calculation to obtain the real-time spot energy distribution parameters. The simulated spot energy distribution parameters and the real-time spot energy distribution parameters are visualized to obtain visualized data.

2. The laser spot energy distribution measurement method according to claim 1, characterized in that, The process involves calling a pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, obtaining simulated spot energy distribution parameters, including: Obtain the preset tool parameters and laser incident configuration parameters; Based on the tool parameters and the laser incident configuration parameters, the laser beam to be simulated is subjected to parameter standardization processing to obtain a standardized laser beam; The laser optical path transmission model is invoked to simulate the beam propagation behavior of the standardized laser beam, and the energy distribution parameters of the simulated spot are obtained.

3. The laser spot energy distribution measurement method according to claim 1, characterized in that, A fitness function is constructed based on the simulated light spot energy distribution parameters, including: The coordinate values ​​of multiple sampling points and the laser energy reference value are obtained from the simulated spot energy distribution parameters; The initial beam energy distribution model is invoked, and prediction calculations are performed based on the coordinate values ​​of each sampling point to obtain the predicted laser energy value corresponding to each sampling point. The average laser energy value is obtained by performing an arithmetic mean operation on the multiple laser energy reference values. Error quantification calculation is performed based on multiple laser energy reference values ​​and multiple laser energy prediction values ​​to obtain the root mean square percentage error. The goodness-of-fit calculation is performed based on multiple laser energy reference values, multiple laser energy predicted values, and the average laser energy value to obtain the coefficient of determination. The fitness function is constructed based on the root mean square percentage error and the coefficient of determination.

4. The laser spot energy distribution measurement method according to claim 1, characterized in that, The step of employing the beetle whisker algorithm to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model includes: Based on the simulated light spot energy distribution parameters, parameter initialization processing is performed to obtain the initial position and initial step size of the longhorn beetle; Based on the initial position of the longhorn beetle, the antenna positions are calculated to obtain the initial positions of the left and right antennae; Antennae comfort is calculated based on the fitness function, the initial position of the left antenna, and the initial position of the right antenna to obtain the left antennae fitness and the right antennae fitness; The fitness of the left antenna and the fitness of the right antenna are compared to obtain a comparison result, and the parameter update direction is determined based on the comparison result; Obtain a preset attenuation coefficient, and iteratively update the initial position of the longhorn beetle based on the attenuation coefficient, the initial step size, and the parameter update direction; When the preset iteration stopping condition is met, the initial beam energy distribution model is optimized based on the updated beetle position to obtain the target beam energy distribution model.

5. The laser spot energy distribution measurement method according to claim 1, characterized in that, The visualization processing of the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data includes: The simulated spot energy distribution parameters and the real-time spot energy distribution parameters are subjected to coordinate alignment and dimension unification processing to obtain standardized simulated spot energy distribution parameters and standardized real-time spot energy distribution parameters. The standardized simulated spot energy distribution parameters and the standardized real-time spot energy distribution parameters are used to calculate errors and compare features to obtain energy distribution error data and key feature differences. The energy distribution error data and key feature differences are visualized to obtain the visualized data.

6. A laser spot energy distribution measuring device, characterized in that, include: Beam propagation simulation module: used to acquire the laser beam to be simulated, call the pre-trained laser optical path transmission model to simulate the beam propagation behavior of the laser beam to be simulated, and obtain the simulated spot energy distribution parameters; Model building module: used to obtain laser power density from the simulated spot energy distribution parameters; based on the distribution characteristics of the laser power density, introduce coordinate transformation coefficients into the ordinary Gaussian distribution form to obtain a modified Gaussian distribution form, and determine the modified Gaussian distribution form as the energy distribution model fitting form; construct an initial beam energy distribution model based on the energy distribution model fitting form and the simulated spot energy distribution parameters; Model optimization module: used to perform data filtering and grouping processing on the simulated light spot energy distribution parameters to obtain a training sample set; and to construct a fitness function based on the simulated light spot energy distribution parameters; The longhorn beetle whisker algorithm is used to iteratively search the initial beam energy distribution model based on the training sample set and the fitness function to obtain the target beam energy distribution model. Fitting calculation module: used to obtain real-time laser spot energy parameters, input the real-time laser spot energy parameters into the target beam energy distribution model for fitting calculation, and obtain real-time spot energy distribution parameters; Visualization module: used to visualize the simulated spot energy distribution parameters and the real-time spot energy distribution parameters to obtain visualized data.

7. A laser spot energy distribution measurement device, characterized in that, The laser spot energy distribution measurement device includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the laser spot energy distribution measurement device to perform the steps of the laser spot energy distribution measurement method as described in any one of claims 1-5.

8. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the laser spot energy distribution measurement method as described in any one of claims 1-5.

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