Control method and control device of laser galvanometer system

Abstract

The embodiment of the invention provides a control method and a control device of a laser galvanometer system. The control method comprises the following steps: acquiring first sample data, second sample data and third sample data; performing parameter optimization through a genetic algorithm according to the first sample data, the second sample data and the third sample data, and determining an optimized target parameter combination; and the target parameter combination is imported into a control system so as to control a laser galvanometer system to conduct machining. According to the embodiment of the invention, global optimization is carried out through the genetic algorithm, the complex mutual influence between the galvanometer motion and the laser output can be fully considered, the optimal parameter balance point under the limited condition is automatically found, the processing track effect and the final product quality are improved, and the processing efficiency is improved through the data driving and model optimization mode. The integrated intelligent operation of galvanometer control and laser control is realized, the working efficiency and the product consistency are improved, and the adaptability to different application scenes is enhanced.

Description

Technical Field

[0001] This invention relates to the field of laser galvanometer system technology, and specifically to a control method and control device for a laser galvanometer system. Background Technology

[0002] Laser galvanometer systems have wide applications in industrial fields such as marking, welding, cleaning, and additive manufacturing. This system guides the laser beam scanning by controlling the deflection of the galvanometer and simultaneously adjusting the laser's output power and switching timing, thereby forming a predetermined processing trajectory on the workpiece surface to achieve the desired processing effect.

[0003] The key factors affecting laser processing quality are complex and diverse. For example, the power stability of the laser itself is affected by component aging, pump source attenuation, and changes in the temperature and humidity of the working environment, which may lead to output fluctuations. Changes in galvanometer motion parameters, such as velocity and acceleration, will alter the residence time of the laser beam on the material surface, thus affecting the energy deposition effect. At the same time, sudden acceleration changes may cause galvanometer instability, increasing the risk of position control deviation. In addition, the geometric characteristics of the processing trajectory have a significant impact on energy distribution, and uneven energy distribution or pulse overlap is prone to occur in regions of curvature variation.

[0004] In related technologies, laser control systems typically treat galvanometer motion control and laser output control as relatively independent components, lacking a multi-parameter collaborative optimization mechanism. This control approach is limited by technical conditions and equipment performance, making it difficult to achieve global optimization in complex application scenarios. Summary of the Invention

[0005] This invention provides a control method and device for a laser galvanometer system. Taking into account various influencing factors of the laser system and galvanometer system during processing, a genetic algorithm is used to jointly optimize multiple operating parameters in the laser galvanometer system, designing an optimized control method to find the best combination. This improves the working efficiency and product quality of the laser galvanometer system, integrating galvanometer motion control and laser control. Different control weights and thresholds are set according to specific applications, allowing one system to complete laser and galvanometer control for multiple applications, making operation more convenient.

[0006] The control method for a laser galvanometer system provided in this embodiment of the invention includes: acquiring first sample data, second sample data, and third sample data; the first sample data includes the galvanometer position feedback, control parameters, and corresponding first relational functions of the laser galvanometer system under various conditions; the second sample data includes the laser frequency, control parameters, and corresponding second relational functions of the laser galvanometer system under various conditions; and the third sample data includes the galvanometer motion parameters, laser power, and corresponding third relational functions of the laser galvanometer system under various conditions; optimizing parameters using a genetic algorithm based on the first sample data, the second sample data, and the third sample data to determine an optimized target parameter combination; and importing the target parameter combination into a control system to control the laser galvanometer system for processing.

[0007] In some implementations, the step of obtaining the first sample data includes: obtaining the position feedback data of the galvanometer; calculating the position error between the position feedback data and the specified position data; correcting the galvanometer control parameters in real time based on the position error to determine the control parameters corresponding to the galvanometer position feedback; and using a big data fitting algorithm to fit the galvanometer position feedback and control parameters of the laser galvanometer system under various conditions to determine the first relationship function.

[0008] In some embodiments, the step of obtaining the second sample data includes: obtaining the control parameters of the galvanometer processing and the geometric features of the processing trajectory; dynamically adjusting the laser frequency according to the geometric features of the processing trajectory to determine the laser frequency corresponding to the control parameters; and using a big data fitting algorithm to fit the laser frequency and control parameters of the laser galvanometer system under various conditions to determine the second relationship function.

[0009] In some embodiments, dynamically adjusting the laser frequency based on the geometric features of the processing trajectory to determine the laser frequency corresponding to the control parameters includes: adjusting the laser frequency according to the curvature of the arc when the geometric features of the processing trajectory are arc-shaped, wherein the laser frequency and the curvature have a non-linear proportional relationship; reducing the laser frequency when the geometric features of the processing trajectory are that the marking length is less than a predetermined length; and increasing the laser frequency when the geometric features of the processing trajectory are that the marking length is greater than a predetermined length.

[0010] In some embodiments, the step of obtaining the third sample data includes: obtaining velocity data and acceleration data of the galvanometer, wherein the velocity data and acceleration data of the galvanometer are recorded as galvanometer motion parameters; dynamically adjusting the laser power based on the velocity data and acceleration data of the galvanometer to determine the laser power corresponding to the galvanometer motion parameters; and using a big data fitting algorithm to fit the galvanometer motion parameters and laser power of the laser galvanometer system under various conditions to determine the third relationship function.

[0011] In some embodiments, the step of dynamically adjusting the laser power based on the velocity and acceleration data of the galvanometer to determine the laser power corresponding to the motion parameters of the galvanometer includes: dynamically adjusting the laser power based on the acceleration data of the galvanometer when the acceleration data of the galvanometer is higher than a predetermined acceleration; and dynamically adjusting the laser power based on the velocity data of the galvanometer when the acceleration data of the galvanometer is lower than a predetermined acceleration.

[0012] In some embodiments, the step of using a big data fitting algorithm to fit the galvanometer motion parameters and laser power of the laser galvanometer system under various conditions to determine the third relational function includes: constructing an experimental system comprising a laser system, a galvanometer system, and a processing platform; conducting processing experiments and collecting galvanometer position data and laser power data; converting the galvanometer position data into velocity and acceleration data within a unit period to form a galvanometer velocity sample library and an acceleration sample library; converting the physical quantity of laser power into a digital quantity to form a laser power sample library; performing preprocessing on the data in the galvanometer velocity sample library and the acceleration sample library, including filtering duplicate data, normalization, and anomaly identification, to obtain preprocessed calibration data; fitting the preprocessed calibration data using a big data fitting algorithm to obtain a fitting function; evaluating the fitting function, and determining the fitting function as the third relational function if the fitting degree of the fitting function is not lower than a predetermined fitting degree.

[0013] In some implementations, the step of optimizing parameters using a genetic algorithm based on the first sample data, the second sample data, and the third sample data to determine the optimized target parameter combination includes: encoding parameters such as galvanometer position, velocity, acceleration, laser power, and laser frequency; generating an initial population based on the galvanometer position, velocity, acceleration, laser power, and laser frequency data in the first sample data, the second sample data, and the third sample data; constructing a fitness function using multiple indicators such as position accuracy, spot quality, motion performance, laser stability, and processing efficiency; and optimizing the initial population using the number of iterations and the fitness threshold as termination conditions for the optimization process to determine the optimized target parameter combination.

[0014] In some embodiments, the step of importing the target parameter combination into the control system to control the laser galvanometer system to perform processing includes: evaluating whether the control of the laser galvanometer system has achieved the expected control based on the monitoring signal at the current moment; adjusting the control parameters of the laser galvanometer system if the control of the laser galvanometer system has not achieved the expected control; and providing an alarm and controlling the laser galvanometer system to stop processing if the number of times the control parameters of the laser galvanometer system are adjusted reaches a predetermined number and the control of the laser galvanometer system has not achieved the expected control.

[0015] The control device provided in this embodiment of the invention includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the above-described control method through the computer program.

[0016] The control method for a laser galvanometer system provided in this embodiment of the invention includes: acquiring first sample data, second sample data, and third sample data; the first sample data includes the galvanometer position feedback, control parameters, and corresponding first relational functions of the laser galvanometer system under various conditions; the second sample data includes the laser frequency, control parameters, and corresponding second relational functions of the laser galvanometer system under various conditions; and the third sample data includes the galvanometer motion parameters, laser power, and corresponding third relational functions of the laser galvanometer system under various conditions; optimizing parameters using a genetic algorithm based on the first sample data, the second sample data, and the third sample data to determine an optimized target parameter combination; and importing the target parameter combination into a control system to control the laser galvanometer system for processing.

[0017] This invention utilizes a genetic algorithm for global optimization, comprehensively considering the complex interplay between galvanometer motion and laser output. It automatically finds the optimal parameter balance point under constrained conditions, fundamentally solving the problems of traditional methods that struggle to balance multiple control objectives and rely on manual trial and error. This directly improves the processing trajectory effect and final product quality. Through data-driven and model-optimized approaches, it achieves integrated intelligent operation of galvanometer control and laser control, not only improving work efficiency and product consistency but also enhancing adaptability to different application scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1A schematic diagram illustrating the working steps of the control method for the laser galvanometer system provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the change in galvanometer processing speed provided in an embodiment of this application.

[0021] Figure 3 This is a schematic diagram of the process for obtaining sample data in an embodiment of this application.

[0022] Figure 4 This is a schematic diagram of the marking length of the galvanometer processing trajectory provided in an embodiment of this application.

[0023] Figure 5 This is a schematic diagram illustrating the construction of relational functions provided in an embodiment of this application.

[0024] Figure 6 This is a flowchart illustrating the parameter optimization process using a genetic algorithm.

[0025] Figure 7 This is a schematic diagram of the signal adjustment process based on the monitoring signal in an embodiment of this application.

[0026] Figure 8 for Figure 7 The process of signal adjustment and preprocessing.

[0027] Figure 9 A schematic diagram illustrating the verification of the control method for the laser galvanometer system provided in this application embodiment. Detailed Implementation

[0028] Laser galvanometer systems have wide applications in industrial fields such as marking, welding, cleaning, and additive manufacturing. This system guides the laser beam scanning by controlling the deflection of the galvanometer and simultaneously adjusting the laser's output power and switching timing, thereby forming a predetermined processing trajectory on the workpiece surface to achieve the desired processing effect.

[0029] The key factors affecting laser processing quality are complex and diverse. For example, the power stability of the laser itself is affected by component aging, pump source attenuation, and changes in the temperature and humidity of the working environment, which may lead to output fluctuations. Changes in galvanometer motion parameters, such as velocity and acceleration, will alter the residence time of the laser beam on the material surface, thus affecting the energy deposition effect. At the same time, sudden acceleration changes may cause galvanometer instability, increasing the risk of position control deviation. In addition, the geometric characteristics of the processing trajectory have a significant impact on energy distribution, and uneven energy distribution or pulse overlap is prone to occur in regions of curvature variation.

[0030] In related technologies, laser control systems typically treat galvanometer motion control and laser output control as relatively independent components, lacking a multi-parameter collaborative optimization mechanism. This control approach is limited by technical conditions and equipment performance, making it difficult to achieve global optimization in complex application scenarios.

[0031] To address the aforementioned technical problems, this invention provides a control method and device for a laser galvanometer system. Taking into account various influencing factors of the laser system and galvanometer system during processing, a genetic algorithm is used to jointly optimize multiple operating parameters in the laser galvanometer system, designing an optimized control method to find the best combination. This improves the working efficiency and product quality of the laser galvanometer system, integrating galvanometer motion control and laser control. Different control weights and thresholds are set according to specific applications, allowing one system to complete laser and galvanometer control for multiple applications, making operation more convenient.

[0032] The embodiments of the present invention can be applied to application scenarios of laser galvanometer systems, such as industrial fields such as marking, welding, cleaning and additive manufacturing.

[0033] The control method of the laser galvanometer system provided by the exemplary embodiment of the present invention will be described below with reference to the accompanying drawings and the application scenarios described above. It should be noted that the above application scenarios are only shown to facilitate understanding of the principles of the present invention, and the embodiments of the present invention are not limited in any way in this respect.

[0034] Figure 1 This is a schematic diagram illustrating the working steps of the control method for the laser galvanometer system provided in an embodiment of the present invention. Figure 1 As shown, the control method for the laser galvanometer system provided in this embodiment of the invention includes: Step 01: Obtain the first sample data, the second sample data, and the third sample data. The first sample data includes the galvanometer position feedback, control parameters, and corresponding first relational functions of the laser galvanometer system under various conditions. The second sample data includes the laser frequency, control parameters, and corresponding second relational functions of the laser galvanometer system under various conditions. The third sample data includes the galvanometer motion parameters, laser power, and corresponding third relational functions of the laser galvanometer system under various conditions. Step 02: Based on the first sample data, the second sample data, and the third sample data, optimize the parameters using a genetic algorithm to determine the optimized target parameter combination; Step 03: Import the target parameter combination into the control system to control the laser galvanometer system for processing.

[0035] Specifically, the first sample data involves synchronously recording the real-time position feedback values ​​and corresponding control command parameters of the laser galvanometer system under various operating conditions. Based on this data, a first relational function describing the intrinsic relationship between the two is derived using a big data fitting algorithm. The second sample data involves collecting the laser's pulse frequency settings and corresponding control parameters in different processing scenarios, and establishing a second relational function reflecting the matching law between "laser frequency and control parameters." The third sample data involves collecting motion parameters such as the galvanometer's velocity and acceleration under various motion states, along with the laser's output power at the same moment, and then fitting a third relational function revealing the dynamic coupling relationship between "galvanometer motion parameters and laser power." These three relational functions together constitute the mathematical model foundation describing the dynamic behavior of the entire processing system.

[0036] Based on a complete model composed of three relational functions, a genetic algorithm is used for multi-objective joint optimization. The combination of objective parameters obtained by the genetic algorithm is converted into specific control commands and imported into the real-time control software of the laser galvanometer system. The control system drives the galvanometer movement and synchronously regulates the laser output according to these optimized parameters, executing predetermined tasks during actual processing, thereby transforming the theoretical optimal solution obtained from offline optimization into actual processing actions.

[0037] In this way, by using genetic algorithms for global optimization, the complex interaction between galvanometer motion and laser output can be comprehensively considered, and the optimal parameter balance point under constrained conditions can be automatically found. This fundamentally solves the problem that traditional methods cannot take into account multiple control objectives and rely on manual trial and error, directly improving the processing trajectory effect and the final product quality. Through data-driven and model optimization, integrated intelligent operation of galvanometer control and laser control is realized, which not only improves work efficiency and product consistency, but also enhances the adaptability to different application scenarios.

[0038] Figure 2 This is a schematic diagram illustrating the variation of the galvanometer processing speed provided in an embodiment of this application. Figure 2 As shown, the galvanometer begins processing, and the speed gradually increases from zero to a stable working speed. Processing is carried out at a stable maximum working speed. After processing is completed, the speed gradually decreases to zero, and the processing stops.

[0039] Due to the limitations of laser processing area, in practical applications, processing does not always begin only after entering the stable region; it begins as soon as the effective speed region is reached, meaning processing starts during the acceleration and deceleration phases. Taking 3D printing applications as an example, the graphic position trajectory is marked during the acceleration and deceleration phases, forming a series of light spots along the trajectory.

[0040] In theory, the speed and acceleration (speed change) of a galvanometer are unlimited. However, in practical applications, the range of speed and acceleration changes is limited by the magnitude of the galvanometer's moment of inertia, driving capability, control system performance, and application requirements. The speed of the galvanometer, from its minimum to its maximum value and from its maximum value to its minimum value, is actually a non-linear change due to system disturbances, especially in applications requiring frequent speed changes.

[0041] In the control method of the laser galvanometer system provided in the embodiments of the present invention, multiple control objectives are taken into account during the processing. Simultaneously, the position / velocity / acceleration of the galvanometer and the power / frequency of the laser are controlled, and different weights are applied to different control items to achieve the best processing effect that meets the requirements.

[0042] In some implementations, the step of obtaining the first sample data in step 01 above includes: Step 0111: Obtain the position feedback data of the galvanometer; Step 0112: Calculate the position error between the position feedback data and the specified position data; Step 0113: Correct the galvanometer control parameters in real time based on the position error to determine the control parameters corresponding to the galvanometer position feedback; Step 0114: Using a big data fitting algorithm, fit the data of the galvanometer position feedback and control parameters of the laser galvanometer system under various conditions to determine the first relationship function.

[0043] Specifically, in step 0111, the actual position feedback data of the galvanometer during laser processing is acquired in real time by a position detection device installed on the galvanometer system.

[0044] In step 0112, the collected position feedback data is compared with the target position data specified by the system (i.e., the position setting value), and the deviation between the two is calculated, i.e., the position error. When the calculated average error exceeds the preset position accuracy threshold, the system enters the correction phase.

[0045] In step 0113, the control parameters for driving the galvanometer motion are dynamically and in real time adjusted and corrected based on the calculated position error, in order to enable the actual position of the galvanometer to quickly and accurately track the specified position, thereby determining the optimal control parameters for achieving precise positioning under this working condition.

[0046] In step 0114, massive amounts of "galvanometer position feedback-control parameter" data pairs, repeatedly collected and recorded under various processing speeds, loads, and trajectory conditions, are analyzed in depth using a big data fitting algorithm. This algorithm extracts patterns from these data and fits a function that can universally describe the intrinsic mathematical relationship between the position feedback value and the required control parameters—the first relationship function. This first relationship function encapsulates the control characteristics of the galvanometer system under complex dynamic responses.

[0047] Figure 3 This is a schematic diagram illustrating the process of obtaining the first sample data in an embodiment of this application. For example... Figure 3 As shown, a certain amount of position feedback data is collected in real time and compared with the position setting value to obtain the average error. If the average error exceeds the position accuracy threshold, the same number of position errors are corrected for the next set, and position feedback values ​​are collected again for error correction, repeating this process continuously. Each "position feedback - control parameter" data is recorded to obtain the first sample data.

[0048] In some implementations, the step of obtaining the second sample data in step 01 above includes: Step 0121: Obtain the control parameters for galvanometer machining and the geometric features of the machining trajectory; Step 0122: Based on the geometric characteristics of the machining trajectory, dynamically adjust the laser frequency to determine the laser frequency corresponding to the control parameters; Step 0123: Using a big data fitting algorithm, fit the laser frequency and control parameters of the laser galvanometer system under various conditions to determine the second relationship function.

[0049] Specifically, the system first acquires the control parameters and geometric features of the machining trajectory during the galvanometer processing, such as the curvature or straightness of the trajectory. These features determine the adjustment requirements of the laser frequency. Then, the laser frequency is dynamically adjusted based on the geometric features. For example, the pulse frequency is redefined for curvature trajectories to adapt to curvature changes, or the spot distance is corrected by fine-tuning the frequency when there is a deviation in the marking length, thereby determining the optimal laser frequency that matches the current control parameters. Finally, a big data fitting algorithm is used to comprehensively analyze the laser frequency and control parameter data collected under various working conditions, fitting a second relationship function describing the inherent mathematical relationship between the two, laying the model foundation for subsequent optimization.

[0050] In some embodiments, step 0122 above includes: When the geometric features of the processing trajectory are arc-shaped, the laser frequency is adjusted according to the arc curvature, and the laser frequency has a non-linear proportional relationship with the arc curvature. If the geometric characteristics of the processing trajectory indicate that the marking length is lower than the predetermined length, the laser frequency will be reduced. If the geometric characteristics of the processing trajectory indicate that the marking length is longer than the predetermined length, the laser frequency is increased.

[0051] Specifically, for the arc trajectory, the laser pulse frequency can be re-determined according to relevant formulas, as follows:

[0052]

[0053]

[0054] It is understandable that when the geometric characteristics of the processing trajectory are arc-shaped, the laser frequency is adjusted according to the arc curvature, and the laser frequency and arc curvature have a non-linear proportional relationship.

[0055] Figure 4 This is a schematic diagram of the marking length of the galvanometer processing trajectory provided in an embodiment of this application. (See attached diagram.) Figure 4 As shown, for situations where the spot distance needs to be corrected, if the marking length is insufficient, it indicates that the laser pulse frequency is too fast or the galvanometer speed is too slow. It is necessary to reduce the laser pulse frequency to give the galvanometer more time to move to the target position; or increase the galvanometer speed to compensate for the pulse interval.

[0056] If the marking length is too long, it indicates that the laser pulse frequency is too slow or the galvanometer speed is too fast. It is necessary to increase the laser pulse frequency and pulse density, or reduce the galvanometer speed to prevent the galvanometer from moving beyond the desired position.

[0057] The adjustable range of the galvanometer speed is affected by the galvanometer's design, drive, and control system, and its adjustable range is smaller than that of the laser pulse frequency. Therefore, adjusting the laser pulse frequency is chosen to correct this type of deviation. When there is a deviation in the marking length, especially when the deviation distance is not an integer multiple of the spot size, the laser pulse frequency is adjusted within a small range near the original laser pulse frequency point.

[0058]

[0059] Obtain the laser pulse frequency and marking length results, adjust the scale value and offset value, record the "laser frequency-control parameter" data, and obtain the sample database.

[0060] In some implementations, the step of obtaining the third sample data in step 01 above includes: Step 0131: Obtain the velocity and acceleration data of the galvanometer, and record the velocity and acceleration data of the galvanometer as the galvanometer motion parameters; Step 0132: Based on the velocity and acceleration data of the galvanometer, dynamically adjust the laser power to determine the laser power corresponding to the motion parameters of the galvanometer; Step 0133: Using a big data fitting algorithm, fit the mirror motion parameters and laser power of the laser galvanometer system under various conditions to determine the third relationship function.

[0061] Specifically, by collecting the position data of the galvanometer and converting it into velocity and acceleration per unit period, key parameters characterizing the motion state of the galvanometer are obtained. Next, based on the acquired real-time velocity and acceleration data, the output power of the laser is dynamically adjusted. The core objective is to ensure that the laser energy density acting on the material by the galvanometer remains stable under different motion states, thereby obtaining a uniform and consistent processing spot quality. Finally, using a big data fitting algorithm, the massive amount of "galvanometer motion parameter-laser power" data collected under various operating conditions is analyzed in depth. From this analysis, a mathematical model that can accurately describe the complex nonlinear relationship between the two—the third relationship function—is extracted and determined, providing a crucial basis for the intelligent control of the system.

[0062] In some embodiments, step 0132 above includes: dynamically adjusting the laser power based on the acceleration data of the galvanometer when the acceleration data of the galvanometer is higher than a predetermined acceleration; If the acceleration data of the galvanometer is lower than the predetermined acceleration, the laser power is dynamically adjusted according to the velocity data of the galvanometer.

[0063] Specifically, the ideal state of "galvanometer speed / acceleration - laser power" is to dynamically adjust the "galvanometer speed / acceleration - laser power" parameter pair to obtain the same quality of light spot effect.

[0064] When the galvanometer's acceleration data is higher than the predetermined acceleration, it is in the acceleration / deceleration phase, and acceleration takes precedence, adjusting the "galvanometer acceleration - laser power" relationship. When the galvanometer's acceleration data is lower than the predetermined acceleration, it is in the velocity stabilization phase, and velocity takes precedence, adjusting the "galvanometer velocity - laser power" relationship.

[0065] When the system detects that the acceleration is higher than the predetermined value (i.e., in the acceleration / deceleration phase), it uses acceleration as the primary adjustment parameter, increasing or decreasing the laser power to compensate for the drastic change in the laser beam's dwell time on the material surface caused by the acceleration change. This avoids shallow processing due to a sudden drop in energy density or overheating due to energy accumulation. When the acceleration is lower than the predetermined value (i.e., entering the speed stabilization phase), it switches to speed as the primary adjustment parameter, fine-tuning the laser power to balance the differences in energy accumulation caused by minor fluctuations in uniform motion, ensuring a uniform and consistent spot effect across the entire processing trajectory.

[0066] In some embodiments, step 0133 above includes: building an experimental system including a laser system, a galvanometer system and a processing platform, conducting processing experiments and collecting galvanometer position data and laser power data; The galvanometer position data is converted into velocity and acceleration data within a unit period to form a galvanometer velocity sample library and an acceleration sample library. Convert the physical quantity of laser power into a digital quantity to form a laser power sample library; Preprocessing is performed on the data in the galvanometer velocity sample library and acceleration sample library to filter duplicate data, normalize the data, and identify outliers, resulting in preprocessed calibration data. The preprocessed calibration data is fitted using a big data fitting algorithm to obtain a fitting function; Evaluate the fitting function, and if the goodness of fit of the fitting function is not lower than the predetermined goodness of fit, determine the fitting function as the third relation function.

[0067] Specifically, Figure 5 This is a schematic diagram illustrating the construction of a third relation function as provided in an embodiment of this application. For example... Figure 5 As shown, the process of mathematical modeling the third relation function may include: (1) Set up the experimental platform, which mainly includes the laser system (laser, laser controller, laser power detection device), galvanometer system (galvanometer controller, galvanometer, position detection device), and processing platform. Adjust the processing parameters and conduct various processing experiments.

[0068] (2) Under the same working environment, the galvanometer position data and laser power data are collected at regular intervals.

[0069] (3) Convert the galvanometer position data into velocity data within a unit period to form a basic sample library of galvanometer velocity; then convert it into acceleration data to form a basic sample library of galvanometer acceleration.

[0070] (4) Convert the physical quantity of laser power into an electrical signal, and then further convert it into a digital quantity to obtain laser power data and form a basic sample library of laser power.

[0071] (5) Preprocess the basic sample data, filter out duplicate data, normalize the data, and classify the data that exceeds the limit, is faulty, etc. as abnormal data, and classify them into the regular data sample library and the abnormal data sample library respectively.

[0072] (6) Fit the sample data using big data fitting algorithms.

[0073] (7) Evaluate the fitting results. Apply the galvanometer velocity / acceleration and laser power data samples to the fitting function and check the consistency between the fitting results and the actual data.

[0074] by The value is used as the basis for evaluating the fitting results. The numerator is the sum of squares of the regression of laser power, and the denominator is the total sum of squares of laser power. The value should be less than 1; the closer it is to 1, the better the fit is considered.

[0075] (8) Repeat steps (5) and (6) multiple times until the desired result is obtained. If the result is not ideal (underfitting, overfitting), adjust the data model based on the existing results.

[0076] (9) Following the steps (5)-(7), fit the abnormal data of “galvanometer speed-laser power” and “galvanometer acceleration-laser power”.

[0077] (10) After (3)-(7), the expected results could not be obtained. Data was collected again, the sample size was increased, and the data was re-screened.

[0078] Under different working environments (such as different temperatures and different humidity), repeat (2)-(10) to model the data of “galvanometer velocity-laser power” and “galvanometer acceleration-laser power”.

[0079] It should be noted that the modeling of the first and second relational functions can refer to the mathematical modeling method of the third relational function. The relational functions of "position feedback - control parameters" and "laser frequency - control parameters" can be obtained through big data fitting methods.

[0080] In some embodiments, step 02 above includes: Step 021: Encode the parameters of galvanometer position, velocity, acceleration, laser power, and laser frequency; Step 022: Based on the mirror position, velocity, acceleration, laser power, and laser frequency data in the first, second, and third sample data, generate an initial population; Step 023: Construct a fitness function using multiple indicators such as position accuracy, spot quality, motion performance, laser stability, and processing efficiency. Use the number of iterations and fitness threshold as termination conditions for the optimization process to optimize the initial population and determine the optimized target parameter combination.

[0081] Figure 6 This is a flowchart illustrating parameter optimization using a genetic algorithm. Figure 6As shown, the five key physical parameters in the laser processing process—galvanometer position, velocity, acceleration, laser power, and laser frequency—are first encoded. This involves converting these parameters, which have different physical meanings and dimensions, into a unified format that the genetic algorithm can directly process and calculate, laying the foundation for subsequent evolutionary operations. Through extensive experiments and data fitting, a database of first sample data (galvanometer position feedback - control parameters), second sample data (laser frequency - control parameters), and third sample data (galvanometer motion parameters - laser power) is established. High-quality historical parameter combinations are extracted from these databases to generate the initial population for the genetic algorithm. This allows the optimization process to start from a high level, significantly improving optimization efficiency and success rate.

[0082] A comprehensive fitness function is constructed, which simultaneously considers multiple target indicators such as position accuracy, spot quality, motion performance, laser stability, and processing efficiency. This function evaluates the merits of each parameter combination in the population and uses a preset number of iterations and fitness threshold as the termination conditions for the optimization process. The population is continuously evolved through operations such as selection, crossover, and mutation, and finally the target parameter combination with the best overall performance is selected.

[0083] The function of the weighted control objective is:

[0084] The values ​​of f1, f2, and f3 can represent the control objectives of the first, second, and third relational functions mentioned above, respectively. Specifically, f1 represents the control objective as a function of the mathematical relationship between the position feedback value and the required control parameters, f2 represents the control objective as a function of the mathematical relationship between the laser frequency and the control parameters, and f3 represents the control objective as a function of the mathematical relationship between the galvanometer motion parameters (including velocity and acceleration) and the laser power.

[0085] Choose appropriate weights based on the specific application field mirror and laser performance. For example: When laser performance degrades (due to component aging, changes in ambient temperature and humidity), the mathematical relationship between the galvanometer motion parameters (including velocity and acceleration) and laser power is significantly affected. Therefore, the weight w3 of the aforementioned third relationship function can be increased, providing a crucial basis for intelligent system control. Conversely, when laser performance is good, the weight w3 of the aforementioned third relationship function can be appropriately decreased.

[0086] In high-speed cutting applications, the mathematical relationship between laser frequency and control parameters is significantly affected. The weight w2 of the second relationship function can be increased to provide a key basis for intelligent control of the system, so as to achieve an equally spaced spot effect and improve the quality of the cutting edge.

[0087] In the process of using a genetic algorithm to find the optimal solution set, position (velocity, acceleration), laser power, and laser frequency are used as parameters for encoding. An initial solution set is generated based on the sample library data obtained during the experimental phase, serving as the initial population. An evaluation threshold is used as the criterion for fitness, including position / spot accuracy, velocity / acceleration performance, laser stability, and processing efficiency.

[0088]

[0089] The weights can be adjusted adaptively. The number of iterations and the fitness threshold are used as termination conditions. If the number of iterations is less than or equal to the expected threshold, the optimal parameter combination is obtained. If the number of iterations exceeds the expected threshold, the optimization stops, and the final parameter combination is used as the usable parameter combination.

[0090] In some embodiments, step 03 above includes: Step 031: Based on the monitoring signals at the current moment, evaluate whether the control of the laser galvanometer system has achieved the expected control. Step 032: If the control of the laser galvanometer system fails to achieve the expected control results, adjust the control parameters of the laser galvanometer system. Step 033: If the control parameters of the laser galvanometer system have been adjusted a predetermined number of times, and the control of the laser galvanometer system fails to meet the expected control requirements, an alarm is provided and the laser galvanometer system is stopped from processing.

[0091] Specifically, Figure 7 This is a schematic diagram of the signal adjustment process based on the monitoring signal in an embodiment of this application.

[0092] like Figure 7 As shown, by analyzing the monitoring signals at the current moment in real time and comparing them with the expected control targets set based on the relational function model, the overall control effect of the system can be quickly evaluated to determine whether it meets the standards. If the evaluation finds that the control effect does not meet expectations, the control parameters of the galvanometer or the output parameters of the laser will be dynamically corrected. Problems arising during signal adjustment will also be alerted through alarms, enabling timely correction of deviations in the processing, effectively preventing continuous production of defective products, thereby significantly reducing the defect rate and improving the reliability of the production process.

[0093] Figure 8 for Figure 7 The process of signal adjustment and preprocessing. For example... Figure 8 As shown, during the signal adjustment preprocessing, it is necessary to determine the expected number of times, the signal adjustability, and the alarm.

[0094] If the control of the laser galvanometer system fails to achieve the expected control results, and the number of adjustments has not reached the predetermined number, the control parameters can be adjusted. If some control parameters cannot be adjusted, other methods can be used to adjust the operating mode of the laser galvanometer system, triggering a low-level alarm.

[0095] When the system has dynamically adjusted the control parameters of the laser galvanometer system a maximum number of times, but real-time monitoring reveals that the actual control effect (such as processing accuracy or spot quality) still fails to meet the expected goals, the system will automatically trigger a final safeguard mechanism. This mechanism will immediately provide the operator with a clear alarm and simultaneously control the laser galvanometer system to stop the current processing. The core purpose of this measure is to promptly interrupt the processing flow that may continuously produce defective products, thereby effectively avoiding material waste and preventing the generation of batch defects, fundamentally ensuring production quality and reducing the risk of loss.

[0096] Figure 9 A schematic diagram illustrating the verification of the control method for the laser galvanometer system provided in this application embodiment. (See attached diagram.) Figure 9 As shown, dashed lines are marked from top to bottom, from left to right, and then from right to left. The control system controls the speed / acceleration of the galvanometer during the jump / marking action. The dashed line effect in the middle of the line segment is achieved by controlling the laser pulse power and laser frequency in units of pixels, while maintaining a constant speed. The speed at both ends of the line segment is maintained by adding a small section of laser-free processing path in units of position coordinates to keep the overall speed constant.

[0097] In summary, the control method for the laser galvanometer system provided in this application has at least the following technical effects: (1) Taking into account the various influencing factors of the laser system and the galvanometer system in the processing, an optimized control method is designed by using a genetic algorithm to optimize the "galvanometer position|velocity|acceleration-laser power|frequency", find the best combination, and improve work efficiency and product quality.

[0098] (2) Integrate galvanometer motion control and laser control, set different control weights and thresholds according to specific applications, and complete laser control and galvanometer control for multiple applications in one system, making operation more convenient and achieving twice the result with half the effort.

[0099] (3) Combine conventional laser power adjustment methods to better improve the stability and lifespan of the laser.

[0100] (4) If multiple adjustments fail to achieve the expected results, the system will issue an alarm and stop working, reducing the probability of producing defective products.

[0101] (5) The data sample library can be used for debugging and fault diagnosis, and can provide guidance on the selection of lasers and galvanometers.

[0102] The control device provided in this embodiment of the invention includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the control method of the above-described embodiments through the computer program. The embodiments of the control device include the control method of the laser galvanometer system described above, which will not be repeated here.

[0103] For example, the control method of the laser galvanometer system provided in this embodiment of the invention can be executed by a computer device, which can be a terminal or a server. The terminal can be a tablet computer, laptop computer, desktop computer, or personal computer (PC). The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, and big data and artificial intelligence platforms. However, it is not limited to these. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the invention.

[0104] The laser galvanometer system provided in this embodiment of the invention includes the control device provided in this embodiment of the invention. The implementation methods of the laser galvanometer system include the control methods and control devices of the laser galvanometer system described above, and will not be repeated here.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0107] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0108] If the functionality 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 this invention, or the part that contributes to the prior art, or a 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 or a server) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0109] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method of a laser galvanometer system, characterized by, The control method comprises: obtaining first sample data, second sample data and third sample data, the first sample data comprising mirror position feedback, control parameters and corresponding first relationship functions of the laser galvanometer system in various cases, the second sample data comprising laser frequency, control parameters and corresponding second relationship functions of the laser galvanometer system in various cases, and the third sample data comprising mirror motion parameters, laser power and corresponding third relationship functions of the laser galvanometer system in various cases; determining an optimized target parameter combination through genetic algorithm parameter optimization according to the first sample data, the second sample data and the third sample data; introducing the target parameter combination into a control system to control the laser galvanometer system to process.

2. The control method of a laser galvanometer system according to claim 1, characterized by, The step of obtaining the first sample data comprises: obtaining position feedback data of the mirror; calculating position error of the position feedback data and specified position data; real-time correcting mirror control parameters according to the position error to determine the control parameters corresponding to the mirror position feedback; determining the first relationship function through big data fitting algorithm data fitting of the mirror position feedback and the control parameters of the laser galvanometer system in various cases.

3. The control method of a laser galvanometer system according to claim 1, characterized by, The step of obtaining the second sample data comprises: obtaining control parameters of mirror processing and geometric characteristics of processing tracks; dynamically adjusting laser frequency according to the geometric characteristics of the processing tracks to determine the laser frequency corresponding to the control parameters; determining the second relationship function through big data fitting algorithm data fitting of the laser frequency and the control parameters of the laser galvanometer system in various cases.

4. The control method of a laser galvanometer system according to claim 2, characterized by, The step of dynamically adjusting laser frequency according to the geometric characteristics of the processing tracks to determine the laser frequency corresponding to the control parameters comprises: in the case that the geometric characteristics of the processing tracks are radian tracks, adjusting the laser frequency according to radian curvature, and the laser frequency is in a non-linear proportional relationship with the radian curvature; in the case that the geometric characteristics of the processing tracks are that the marking length is lower than a predetermined length, reducing the laser frequency; in the case that the geometric characteristics of the processing tracks are that the marking length is higher than a predetermined length, increasing the laser frequency.

5. The control method of a laser galvanometer system according to claim 1, wherein, The step of obtaining the third sample data comprises: obtaining speed data and acceleration data of the mirror, which are denoted as mirror motion parameters; dynamically adjusting laser power according to the speed data and the acceleration data of the mirror to determine the laser power corresponding to the mirror motion parameters; determining the third relationship function through big data fitting algorithm data fitting of the mirror motion parameters and the laser power of the laser galvanometer system in various cases.

6. The control method of a laser galvanometer system according to claim 5, wherein, The step of dynamically adjusting laser power according to the speed data and the acceleration data of the mirror to determine the laser power corresponding to the mirror motion parameters comprises: in the case that the acceleration data of the mirror is higher than a predetermined acceleration, dynamically adjusting the laser power according to the acceleration data of the mirror; in the case that the acceleration data of the mirror is lower than a predetermined acceleration, dynamically adjusting the laser power according to the speed data of the mirror.

7. The control method of a laser galvanometer system according to claim 5, wherein, The third relationship function is determined by fitting the galvanometer motion parameters and the laser power of the laser galvanometer system in various cases through a big data fitting algorithm, and the third relationship function includes: An experimental system including a laser system, a galvanometer system, and a machining platform is built, machining tests are performed, and galvanometer position data and laser power data are collected; The galvanometer position data is converted into speed data and acceleration data in a unit period to form a galvanometer speed sample library and an acceleration sample library; The laser power physical quantity is converted into a digital quantity to form a laser power sample library; The data in the galvanometer speed sample library and the acceleration sample library are preprocessed by filtering repeated data, normalization processing, and abnormal data identification to obtain preprocessed calibration data; The preprocessed calibration data is fitted through a big data fitting algorithm to obtain a fitting function; The fitting function is evaluated, and in the case that the fitting degree of the fitting function is not less than a predetermined fitting degree, the fitting function is determined as the third relationship function.

8. The control method of a laser galvanometer system according to claim 1, wherein, The target parameter combination is determined by optimizing parameters through a genetic algorithm based on the first sample data, the second sample data, and the third sample data, and the target parameter combination includes: The galvanometer position, speed, acceleration, laser power, and laser frequency are parameter-encoded; Based on the galvanometer position, speed, acceleration, laser power, and laser frequency data in the first sample data, the second sample data, and the third sample data, an initial population is generated; A fitness function is constructed with multiple indicators such as position accuracy, spot quality, motion performance, laser stability, and machining efficiency, and the number of iterations and the fitness threshold are used as termination conditions for the optimization process to optimize the initial population and determine the optimized target parameter combination.

9. The control method of a laser galvanometer system according to claim 1, wherein, The target parameter combination is imported into a control system to control the laser galvanometer system to perform machining, and the target parameter combination includes: Based on the monitoring signal at the current time, it is evaluated whether the control of the laser galvanometer system achieves the control expectation; In the case that the control of the laser galvanometer system does not achieve the control expectation, the control parameters of the laser galvanometer system are adjusted; In the case that the number of times of adjusting the control parameters of the laser galvanometer system reaches a predetermined number of times and the control of the laser galvanometer system does not achieve the control expectation, an alarm prompt is provided and the laser galvanometer system is controlled to stop machining.

10. A control device for a laser galvanometer system, characterized by The control device includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the control method in any one of claims 1 to 9 through the computer program.

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