Processing system and method for real-time adaptive adjustment of galvanometer jitter based on motion speed

Abstract

The present invention discloses a processing system and method for real-time adaptive adjustment of the vibration mirror jitter based on the motion speed. In the foil cutting process, the instantaneous speed of the vibration mirror relative to the foil is obtained in real time, and the relationship model between the instantaneous speed of the vibration mirror and the jitter parameter is automatically established according to the machine learning method adopted in advance, and the jitter parameter of the vibration mirror is dynamically adjusted using the trained neural network model; according to the jitter parameter of the vibration mirror adjusted in real time, a new cutting path is generated, and the scanning speed of the vibration mirror is adjusted according to the adjusted jitter parameter to match the new cutting path requirement; the vibration mirror is adjusted with the new cutting path and scanning speed, and the output of the laser is controlled to continue to run to complete the foil cutting operation. It adaptively adjusts the jitter parameter of the vibration mirror in real time based on the instantaneous motion speed of the vibration mirror, corrects the flying cutting trajectory, so as to ensure the stability and consistency of the cutting shape, thereby improving the overall quality of the final product.

Description

Technical Field

[0001] The present invention relates to the technical field of laser precision cutting processing, and in particular to a processing system and method for real-time adaptive adjustment of galvanometer jitter based on movement speed. Background Art

[0002] The foil cutting process includes cutting pole pieces and tabs of different types of batteries. Tabs are key components that connect the positive and negative poles of the battery. Their shape and size directly affect the performance and service life of the battery. With the improvement of production line automation and efficiency, the foil cutting system is in high-speed motion. Therefore, the foil cutting system needs to perform flying cutting and ensure that the cutting shape is consistent with the preset, and accurate cutting on the high-speed moving material strip has become a technical difficulty.

[0003] During the flying cutting process, the material strip moves continuously at a certain speed under the foil cutting system. In the current advanced production line, the minimum movement speed of the material strip can reach 80m / min. The galvanometer system is a scanning system based on high-speed rotating lenses. By driving the motor to control the rotation angle and speed of the lens, the laser beam can be accurately positioned and quickly moved. During the flying cutting process, the galvanometer system needs to be precisely controlled synchronously with the high-speed material strip movement. This high-speed movement makes the precise feedback and control of the galvanometer system a top priority.

[0004] In order to achieve this precise flying cutting, the system needs to adopt a real-time motion compensation method, which can correct the galvanometer jitter parameters according to the speed of the material strip, the path of the laser cutting and the shape requirements of the cutting, so as to adjust the motion trajectory of the laser beam in real time. In this case, the cutting path can maintain extremely high accuracy during high-speed processing. This method requires the combination of material strip speed feedback and galvanometer position control to ensure that the motion trajectory of the galvanometer is consistent with the actual motion of the material strip. Even in the case of high-speed motion, it can avoid the accumulation of cutting errors and ensure that the shape and size of each cutting part can meet the unified standard.

[0005] At present, the motion correction methods of the galvanometer system include trajectory offset compensation through the difference between the initial processing pattern and the actual processing curve, designing an adjustable anti-shake structure for the laser galvanometer, adding rotary encoder feedback coupled with the production line motion to obtain motion displacement compensation, etc.

[0006] However, the prior art has the following defects:

[0007] (1) The existing technology requires initial processing to obtain the initial processing graphics and the trajectory of the actual processing curve, and calculate the trajectory offset compensation parameters, which lacks real-time feedback;

[0008] (2) The galvanometer anti-shake capability is limited;

[0009] (3) The lack of vibration compensation capability of the galvanometer under non-uniform motion makes it difficult to meet the requirements of high-speed and high-precision cutting. Summary of the invention

[0010] In order to solve the problem that in the existing foil cutting system, due to the fast processing speed and non-uniform motion, the galvanometer system is prone to jitter when performing precision cutting according to a preset flying cutting algorithm, resulting in changes in the cutting shape, thereby affecting the cutting quality, the present invention provides a processing system and method for real-time adaptive adjustment of the galvanometer jitter based on the motion speed, which introduces real-time adaptive adjustment, enhances the galvanometer jitter compensation capability of non-uniform motion, improves the galvanometer anti-shake design, and adaptively adjusts the jitter parameters of the galvanometer in real time based on the instantaneous motion speed of the galvanometer, corrects the flying cutting trajectory, so as to ensure the stability and consistency of the cutting shape, thereby improving the overall quality of the final product.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] A first aspect of the present invention provides a processing method for real-time adaptive adjustment of galvanometer jitter based on motion speed, which is used in a foil cutting system. The processing method comprises:

[0013] Based on the initial constant motion speed of the foil, and according to the galvanometer oscillation frequency, oscillation amplitude, scanning speed and output power of the laser, the corresponding initial galvanometer oscillation parameters are set;

[0014] During the foil cutting process, the instantaneous speed of the galvanometer relative to the foil is obtained in real time, and a relationship model between the speed and the jitter parameters is automatically established based on the machine learning method used in advance, so as to dynamically adjust the jitter parameters of the galvanometer;

[0015] Generate a new cutting path according to the jitter parameters of the galvanometer adjusted in real time, and adjust the scanning speed of the galvanometer according to the adjusted jitter parameters to match the new cutting path requirements;

[0016] The galvanometer adjusts to the new cutting path and scanning speed, controls the output of the laser, and continues to run to complete the foil cutting operation.

[0017] The laser in the present invention can be a solution of multiple lasers working together, and lasers with different powers, wavelengths or pulse widths are used for cutting according to different track sections during the cutting process. For example, high-power laser cutting is used in thicker areas, while low-power laser cutting is used in thinner areas or positioning hole areas. This solution can improve cutting accuracy and efficiency.

[0018] In addition, the laser power can also be adjusted based on beam shaping technology, by changing the intensity distribution of the laser beam to adapt to different cutting requirements, without frequently adjusting the laser power. This method can be achieved by dynamically adjusting the spatial distribution of laser energy through optical elements.

[0019] Furthermore, a multi-layer perceptron is used to automatically establish a relationship model between speed and jitter parameters.

[0020] In the present invention, a relationship model between the instantaneous speed and the jitter parameter of the galvanometer is automatically established by a machine learning method, thereby replacing the manually set speed-jitter parameter comparison table, which can continuously update the model and optimize the adjustment strategy of the galvanometer jitter parameter according to different batches of foil cutting data (data sets of instantaneous speed V and jitter parameter d of different galvanometers relative to the foil). The relationship model between the speed and the jitter parameter is automatically established using a multilayer perceptron, which solves the problem of the nonlinear mapping relationship between the galvanometer motion speed and the jitter parameter.

[0021] Multilayer perceptron is a deep learning model that consists of multiple layers, including an input layer, one or more hidden layers, and an output layer. The input layer inputs the instantaneous velocity V of the galvanometer relative to the foil. The input layer contains one node and normalizes the input data. The hidden layer is used to learn the nonlinear relationship between speed and jitter parameter. There are three hidden layers in total. The number of neurons in the first layer is 64 (the specific number can be adjusted according to the complexity of the data), and the ReLU activation function is used; the number of neurons in the second layer is 64, and the ReLU activation function is also used; the number of neurons in the third layer is 32, and the ReLU activation function is also used. The number of these hidden layers and the number of neurons can be adjusted according to specific data and performance requirements. The output layer outputs the jitter parameter d, and the output node is 1 node, corresponding to the jitter parameter d generated by the instantaneous velocity V of each input, and the corresponding activation function is a linear activation function. The loss function uses the mean square error (MSE), which is defined as:

[0022] ;

[0023] in, is the jitter parameter predicted by the model, is the actual jitter parameter. The neural network is optimized using the Adam optimizer.

[0024] Furthermore, the method of automatically establishing a relationship model between speed and jitter parameters using a multilayer perceptron specifically includes the following steps:

[0025] Firstly, a batch of data sets of different instantaneous speeds V and jitter parameters d of the galvanometer relative to the foil during the foil cutting process are obtained, the data sets are divided into a training set and a validation set, and the input parameters are normalized;

[0026] Secondly, construct a multi-layer perceptron network structure and randomly initialize the weights;

[0027] Then, the instantaneous speed V in the training set is input through each layer of the neural network to obtain the predicted jitter parameters , calculate the loss function, and use the Adam optimizer to update the model weights so that the loss function gradually decreases;

[0028] Finally, after multiple rounds of training iterations, the model training is completed, the prediction ability of the model is evaluated by the validation set, and the trained model is saved for subsequent operations of real-time adaptive extraction of galvanometer jitter parameters based on motion speed for regulation.

[0029] In practical applications, when the cutting process involves multiple variables, the multilayer perceptron can be expanded to a multi-input network structure instead of relying solely on a single speed variable, which allows the model to more comprehensively reflect changes in working conditions.

[0030] In actual operation, the neural network structure can be more complex to optimize model performance, and machine learning methods such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning algorithms can be applied to the present invention.

[0031] Furthermore, an encoder or a displacement sensor is used to obtain the instantaneous speed of the galvanometer relative to the foil.

[0032] Specifically, a laser ranging sensor or image recognition technology can also be used to identify the movement state of the foil by scanning, thereby dynamically adjusting the jitter parameters of the galvanometer. Laser ranging and image recognition (CCD image sensor) provide more accurate feedback, which is particularly suitable for high-precision foil cutting.

[0033] In specific applications, an acceleration sensor can also be used to monitor the acceleration changes of the galvanometer in real time. The motion trajectory and jitter parameters of the galvanometer can be adjusted through real-time feedback of the acceleration. This solution is particularly suitable for situations where there are large acceleration changes during the foil cutting process.

[0034] A second aspect of the present invention provides a processing system for real-time adaptive adjustment of galvanometer jitter based on motion speed, wherein the processing system is provided with a control device, and the control device comprises:

[0035] The parameter setting module is used to set the galvanometer jitter parameters. It uses machine learning methods to automatically establish a relationship model between the instantaneous speed of the galvanometer and the jitter parameters, replacing the manually set speed-jitter parameter comparison table;

[0036] A signal acquisition module is used to obtain the current speed signal or position signal of the galvanometer relative to the foil in real time during the foil cutting process;

[0037] The signal processing module is used to process the instantaneous speed of the galvanometer relative to the foil according to the speed signal or position signal of the galvanometer relative to the foil acquired in real time, and adjust the jitter parameter of the galvanometer in real time according to the instantaneous speed;

[0038] The signal control module is used to synchronously generate a new foil cutting path according to the jitter parameters of the galvanometer adjusted in real time during the foil cutting process, and adjust the scanning speed of the galvanometer according to the adjusted jitter parameters. The galvanometer is adjusted with the new cutting path and scanning speed, controls the output of the laser, and continues to run to complete the foil cutting operation.

[0039] Further, the jitter parameter of the galvanometer is based on the initial constant motion speed of the foil, and is obtained according to the jitter frequency, jitter amplitude, scanning speed and output power of the laser.

[0040] Furthermore, a speed signal or a position signal of the galvanometer relative to the foil is fed back through an encoder, a displacement sensor, a laser distance sensor or an image recognition technology.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The present invention is suitable for high-speed and complex trajectory laser cutting: the existing jitter welding method mainly solves the welding quality problem caused by the uneven speed of the motion mechanism, while the present invention not only copes with the change of instantaneous speed by real-time adaptive adjustment of the jitter parameters, but is also suitable for high-speed motion conditions in foil cutting. Especially when the material strip runs at a high speed of more than 80m / min, the present invention can dynamically compensate for the relative speed change between the laser and the foil to ensure the cutting accuracy and consistency, which greatly improves the application capability in efficient and high-speed production.

[0043] (2) The present invention has the function of adjusting the jitter parameters in segments to adapt to the cutting of foils of different shapes: the present invention adaptively adjusts the jitter parameters of the galvanometer in real time according to the different cutting characteristics of each segment for different trajectory segments, so that each segment can obtain the optimal laser cutting effect. However, the prior art fails to solve the problem of motion speed changes caused by the complexity of different shapes, which limits its application in nonlinear trajectories.

[0044] (3) The present invention automatically establishes a relationship model between the instantaneous speed of the galvanometer and the jitter parameters through a machine learning method. The neural network does not need to mechanically search for the relationship between the instantaneous speed and the jitter parameters. The neural network will adaptively learn the best jitter adjustment strategy through training, thereby optimizing the jitter parameters. It also has a dynamic adjustment function. Under different foil cutting conditions, the neural network can dynamically adjust the jitter parameters according to historical data and real-time input, so that the system has stronger adaptability to different working conditions.

[0045] (4) Flexible adaptation to various cutting requirements: The present invention is not only suitable for the cutting of single-pole ears, but can also be extended to the laser cutting of double-pole ears, multi-pole ears and ears with unconventional shapes. It also supports the cutting requirements of ears with positioning holes. By setting the jitter parameters and adaptive control based on real-time feedback, the system can adapt to different workpiece shapes and trajectory requirements and has a wide range of application scenarios. The existing technology is limited to the single shape and trajectory of the welding workpiece and has poor adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Figure 1 It is a flow chart of a processing method for real-time adaptive adjustment of the vibration mirror jitter based on the motion speed in the present invention;

[0048] Figure 2 Schematic diagram of laser energy segmentation for conventional tabs of lithium batteries during foil cutting;

[0049] Figure 3 The basic structure of the neural network model between speed and jitter parameters is automatically established by using a machine learning method in the present invention;

[0050] Figure 4 A conventional lithium battery tab flying cutting trajectory in foil cutting generated without real-time adaptive adjustment of the jitter parameters of the motion speed in the prior art;

[0051] Figure 5 A conventional tab flight cutting trajectory of a lithium battery in foil cutting generated by real-time adaptive adjustment of the jitter parameter of the motion speed in the present invention;

[0052] Figure 6 It is a schematic diagram of a processing system for real-time adaptive adjustment of the vibration of a galvanometer based on the motion speed in the present invention;

[0053] Figure 7 It is a schematic diagram of setting the initial galvanometer jitter parameters in the present invention.

[0054] Among them, the specific drawings are marked as:

[0055] Control device 1, parameter setting module 2, signal acquisition module 3, signal processing module 4, signal control module 5. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The embodiment of the present invention provides a processing method for real-time adaptive adjustment of the vibration of the galvanometer based on the motion speed, which is used in a foil cutting system, such as Figure 1 As shown, the specific steps include:

[0058] S1. First, based on the initial constant motion speed of the foil, and according to the galvanometer jitter frequency, jitter amplitude, scanning speed and output power of the laser, the corresponding initial galvanometer jitter parameters are set; the above parameters assume that the foil is in a uniform motion state by default, and the galvanometer moves to obtain the cutting trajectory. During the foil cutting, as the cutting path changes between straight lines and curves, the scanning speed of the galvanometer will change accordingly. During the foil cutting process, the galvanometer is in a variable speed motion state.

[0059] The initial galvanometer jitter parameter setting process is as follows: Figure 7 As shown, the specific operation steps are as follows: First, set the initial constant movement speed of the foil , the initial scanning speed of the galvanometer , the average scanning speed of the galvanometer in a very short time t during the foil cutting process was measured , the ideal instantaneous velocity of the galvanometer relative to the foil is obtained and the actual instantaneous speed , relative moving speed of the galvanometer , the vibration frequency of the galvanometer , defined as:

[0060]

[0061]

[0062] in θ is the angle between the two velocities, and are the ideal and actual cutting track widths, respectively;

[0063] According to the ideal cutting width and actual cutting width Determining the jitter amplitude :

[0064] ;

[0065] Under a certain fixed laser output power, the corresponding initial galvanometer jitter parameters are obtained:

[0066] .

[0067] S2. During the foil cutting process, the instantaneous speed of the galvanometer relative to the foil is obtained in real time, and a relationship model between the speed and the jitter parameters is automatically established based on a machine learning method used in advance, so as to dynamically adjust the jitter parameters of the galvanometer.

[0068] The instantaneous speed of the galvanometer relative to the foil is obtained by accurately feeding back its speed signal or position signal through an encoder or a displacement sensor, and then calculated. In other specific embodiments, a laser distance sensor or image recognition technology can also be used to scan and identify the motion state of the foil, thereby dynamically adjusting the jitter parameters of the galvanometer. Laser distance measurement and image recognition provide more accurate feedback, which is particularly suitable for high-precision foil cutting.

[0069] There are two main methods for setting the galvanometer jitter parameters in the prior art:

[0070] The first method: You need to pre-set a specific reference instantaneous speed of the galvanometer relative to the foil V n , and set the corresponding reference jitter parameters at the reference instantaneous speed d n This reference instantaneous speed V n and reference jitter parameters d n The results are obtained by trial cutting with the same laser cutting machine. V n and reference jitter parameters d n , and then get the galvanometer jitter parameters at the current speed. The specific calculation formula is as follows:

[0071] .

[0072] The second method: the jitter parameters need to be set in advance, the jitter parameters at different speeds are measured in advance, and a speed-jitter parameter comparison table is generated, as shown in Table 1:

[0073] Table 1

[0074]

[0075] in, V 1. V 2... V i-1 , V iare the speeds of the galvanometer relative to the foil, and the speeds are uniformly distributed. d 1. d 2... d i-1 , d i In order V 1. V 2... V i-1 , V i The corresponding jitter parameters. The calculation formula of the galvanometer jitter parameters in this case is as follows:

[0076] ;

[0077] in, d is the jitter parameter of the galvanometer after adjustment, V is the instantaneous velocity of the galvanometer relative to the foil, V n-1 It is the speed range of the instantaneous speed of the galvanometer relative to the foil in the speed-jitter parameter comparison table. d n-1 is the jitter parameter corresponding to the speed range of the galvanometer's current instantaneous speed relative to the foil, d n It is the jitter parameter corresponding to the previous speed interval of the speed interval in which the instantaneous speed of the galvanometer relative to the foil is currently located.

[0078] The above speed-jitter parameter comparison table is obtained by using the same galvanometer for foil cutting at a fixed laser output power. In actual situations, different galvanometers and cutting shape trajectories have different galvanometer initial jitter frequencies and amplitudes and laser output powers. Depending on the material partition, the output energy is also different. Figure 2 As shown in the figure, this is a segmented laser energy design for conventional tabs of lithium batteries in foil cutting. In the four tab trajectories of A, B, C, and D, it is necessary to establish respective speed-jitter parameter comparison tables. In addition, when using different galvanometers, the original speed-jitter parameter comparison table is no longer applicable, and a new speed-jitter parameter comparison table needs to be created.

[0079] In the present invention, a relationship model between the instantaneous speed and the jitter parameter of the galvanometer is automatically established by a machine learning method, thereby replacing the manually set speed-jitter parameter comparison table, which can continuously update the model and optimize the adjustment strategy of the galvanometer jitter parameter according to different batches of foil cutting data (data sets of instantaneous speed V and jitter parameter d of different galvanometers relative to the foil). The relationship model between the speed and the jitter parameter is automatically established using a multilayer perceptron, which solves the problem of the nonlinear mapping relationship between the galvanometer motion speed and the jitter parameter.

[0080] A multilayer perceptron is a deep learning model that consists of multiple layers, including an input layer, one or more hidden layers, and an output layer. Figure 3 As shown, the input layer inputs the instantaneous velocity V of the galvanometer relative to the foil. The input layer contains one node and normalizes the input data. The hidden layer is used to learn the nonlinear relationship between the speed and the jitter parameter. There are three hidden layers in total. The number of neurons in the first layer is 64 (the specific number can be adjusted according to the complexity of the data), and the ReLU activation function is used; the number of neurons in the second layer is 64, and the ReLU activation function is also used; the number of neurons in the third layer is 32, and the ReLU activation function is also used. The number of these hidden layers and the number of neurons can be adjusted according to specific data and performance requirements. The output layer outputs the jitter parameter d, and the output node is 1 node, corresponding to the jitter parameter d generated by the instantaneous velocity V of each input, and the corresponding activation function is a linear activation function. The loss function uses the mean square error (MSE), which is defined as:

[0081] ;

[0082] in, is the jitter parameter predicted by the model, is the actual jitter parameter. The neural network is optimized using the Adam optimizer.

[0083] In a specific embodiment, a multi-layer perceptron is used to automatically establish a relationship model between speed and jitter parameters, which specifically includes the following steps:

[0084] Firstly, a batch of data sets of different instantaneous speeds V and jitter parameters d of the galvanometer relative to the foil during the foil cutting process are obtained, the data sets are divided into a training set and a validation set, and the input parameters are normalized;

[0085] Secondly, construct a multi-layer perceptron network structure and randomly initialize the weights;

[0086] Then, the instantaneous speed V in the training set is input through each layer of the neural network to obtain the predicted jitter parameters , calculate the loss function, use the Adam optimizer to update the model weights, and gradually reduce the loss function. When the loss function value is less than the preset threshold ε, if the loss function is less than this threshold in the next few rounds and the change of the loss function is very small, it can be considered that the loss function has converged. Further training of the model will lead to overfitting, so the training needs to be terminated;

[0087] Finally, after multiple rounds of training iterations, the training is terminated when the preset upper limit of training rounds is reached or the loss function converges, completing the model training. The prediction ability of the model is evaluated by the validation set, and the trained model is saved for subsequent operations based on real-time adaptive extraction of the galvanometer jitter parameters for regulation based on motion speed.

[0088] If the training process reaches the preset upper limit of the number of training rounds, the loss function has not converged and the model's generalization ability is insufficient, consider adjusting the model structure, optimizing hyperparameters, or adding training data for retraining to improve model performance.

[0089] Use the validation set to predict the current model and calculate the loss function to help monitor overfitting and underfitting during training. During training, use the validation set to predict the model regularly, and the validation set loss function should continue to decrease until convergence. In order to prevent overfitting, an early stopping mechanism is introduced, which means that training can be stopped early when the loss function on the validation set no longer decreases. When the model accuracy on the validation set is high, the loss function is small, and the performance of the validation set is close to the performance of the model on the training set, it can be considered that the model has good generalization ability and the trained model can be saved.

[0090] In practical applications, when the cutting process involves multiple variables, the multilayer perceptron can be expanded to a multi-input network structure instead of relying solely on a single speed variable, which allows the model to more comprehensively reflect changes in working conditions.

[0091] In actual operation, the neural network structure can be more complex to optimize model performance, and machine learning methods such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning algorithms can be applied to the present invention.

[0092] S3. Generate a new flying cutting path according to the jitter parameters of the galvanometer adjusted in real time. Figure 4 Shown in the figure is the conventional lithium battery tab flight cutting trajectory generated in the prior art without real-time adaptive adjustment of the jitter parameters of the movement speed. Since the material strip moves rapidly in the lateral direction, when the tab and the galvanometer move relative to each other, the jitter of the galvanometer in the lateral direction is much greater than the jitter of the galvanometer in the longitudinal direction. The shape of the tab formed on the tab based on the flight cutting trajectory will change, thereby affecting the battery quality. Figure 5What is shown in the figure is the flight cutting trajectory of a conventional pole ear of a lithium battery in foil cutting generated by real-time adaptive adjustment of the jitter parameters of the motion speed using the method in the present invention, and the generated trajectory is close to the theoretical simulation. It should be noted that the present invention only takes the trajectory within the cutting cycle of a single pole ear as an example for illustration, but is not limited to this, and can also be a bipolar ear, a multi-pole ear, an unconventional-shaped pole ear, a pole ear with a positioning hole, or the cutting of different materials such as circuit boards, flexible materials, film materials, and plastics. The scanning speed of the galvanometer is the speed at which the galvanometer guides the laser beam to scan the path. Due to changes in the jitter parameters of the galvanometer, the scanning speed of the galvanometer will also change accordingly. The scanning speed of the galvanometer is adjusted according to the jitter parameters of the adjusted galvanometer so that the new flight cutting trajectory is compensated for the preset trajectory.

[0093] S4, the galvanometer is adjusted with a new cutting path and scanning speed, the output of the laser is controlled, and the operation continues to complete the foil cutting operation.

[0094] In specific applications, an acceleration sensor can also be used to monitor the acceleration changes of the galvanometer in real time. The motion trajectory and jitter parameters of the galvanometer can be adjusted through real-time feedback of the acceleration. This solution is particularly suitable for situations where there are large acceleration changes during the foil cutting process.

[0095] The laser in the present invention can be a solution of multiple lasers working together, and lasers with different powers, wavelengths or pulse widths are used for cutting according to different track sections during the cutting process. For example, high-power laser cutting is used in thicker areas, while low-power laser cutting is used in thinner areas or positioning hole areas. This solution can improve cutting accuracy and efficiency.

[0096] In addition, the laser power can also be adjusted based on beam shaping technology, by changing the intensity distribution of the laser beam to adapt to different cutting requirements, without frequently adjusting the laser power. This method can be achieved by dynamically adjusting the spatial distribution of laser energy through optical elements.

[0097] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0098] Based on the same inventive concept, an embodiment of the present invention also provides a control system for implementing the above-mentioned processing method for adaptively adjusting the vibration mirror jitter based on the motion speed in real time. The implementation scheme for solving the problem provided by the control system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiment of the processing system for adaptively adjusting the vibration mirror jitter based on the motion speed provided below can be referred to the limitations of the processing method for adaptively adjusting the vibration mirror jitter based on the motion speed in real time above, and will not be repeated here.

[0099] like Figure 6 As shown, in an exemplary embodiment, the embodiment of the present application provides a processing system for real-time adaptive adjustment of galvanometer jitter based on motion speed. The processing system is provided with a control device 1, which includes a parameter setting module 2, a signal acquisition module 3, a signal processing module 4 and a signal control module 5.

[0100] Parameter setting module 2 is used to set the galvanometer jitter parameters. It uses machine learning methods to automatically establish a relationship model between the instantaneous speed of the galvanometer and the jitter parameters, replacing the manually set speed-jitter parameter comparison table; the galvanometer jitter parameters are based on the initial constant motion speed of the foil, and are obtained according to the galvanometer jitter frequency, jitter amplitude, scanning speed and output power of the laser.

[0101] The signal acquisition module 3 is used to obtain the current speed signal or position signal of the galvanometer relative to the foil in real time during the foil cutting process; specifically, the speed signal or position signal of the galvanometer relative to the foil is fed back through an encoder, a displacement sensor, a laser ranging sensor or an image recognition technology, and the signal is transmitted to the signal processing module 4.

[0102] The signal processing module 4 is used to process the instantaneous speed of the galvanometer relative to the foil according to the speed signal or position signal of the galvanometer relative to the foil obtained in real time during the foil cutting process, and adjust the jitter parameter of the galvanometer in real time according to the instantaneous speed.

[0103] The signal control module 5 is used to synchronously generate a new foil cutting path according to the jitter parameters of the galvanometer adjusted in real time during the foil cutting process, and adjust the scanning speed of the galvanometer according to the adjusted jitter parameters. The galvanometer is adjusted with the new cutting path and scanning speed, controls the output of the laser, and continues to run to complete the foil cutting operation.

[0104] Each module in the control device 1 is responsible for the setting of jitter parameters, the acquisition of instantaneous speed, the calculation and adjustment of parameters, and the synchronous control of trajectory generation and laser cutting operation. This modular design enhances the real-time response capability and control accuracy of the system.

[0105] The processing method and control system of the present invention for real-time adaptive adjustment of the vibration mirror vibration based on the motion speed can be applied to the cutting of different types of battery tabs:

[0106] It is used for cutting tabs of different materials and shapes such as lithium batteries, aluminum-air batteries, sodium batteries, solid-state batteries, etc. Depending on the material and shape, the vibration parameters, scanning speed and laser power of the galvanometer can be customized and optimized respectively.

[0107] It can also be used for other high-speed cutting processes:

[0108] In addition to foil cutting, the method of the present invention is also applicable to other precision laser cutting processes under high-speed motion conditions, such as circuit board cutting, flexible material cutting, thin film material, plastic and other thin material cutting. In these applications, similar speed feedback and real-time adjustment technology can be used to improve cutting accuracy and efficiency.

[0109] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A processing method for real-time adaptive adjustment of galvanometer jitter based on motion speed, characterized in that: Used in a foil cutting system, in which the moving speed of the material strip in the foil cutting system is above 80 m / min, the processing method comprises: Based on the initial constant motion speed of the foil, and according to the galvanometer oscillation frequency, oscillation amplitude, scanning speed and output power of the laser, the corresponding initial galvanometer oscillation parameters are set; During the foil cutting process, the instantaneous speed of the galvanometer relative to the foil is obtained in real time, and a relationship model between the instantaneous speed of the galvanometer and the jitter parameters is automatically established based on a machine learning method used in advance, so as to dynamically adjust the jitter parameters of the galvanometer; Generate a new cutting path according to the jitter parameters of the galvanometer adjusted in real time, and adjust the scanning speed of the galvanometer according to the adjusted jitter parameters to match the new cutting path requirements; The galvanometer adjusts to the new cutting path and scanning speed, controls the output of the laser, and continues to run to complete the foil cutting operation; The process of setting the initial galvanometer jitter parameters is as follows: First, set the initial constant motion speed V of the foil x , the initial scanning speed V of the galvanometer t , the average scanning speed V′ of the galvanometer in a very short time t during the foil cutting process was measured t , we can obtain the ideal instantaneous velocity V1 and actual instantaneous velocity V′1 of the galvanometer relative to the foil, the relative moving velocity V of the galvanometer, and the galvanometer's oscillation frequency f1, which are defined as: where θ is V x With V t The angle between the two speeds, θ′ is V x and V′ t The angle between the two speeds, W1 and W′1 are the ideal and actual cutting track widths, respectively; Determine the jitter amplitude A1 based on the ideal cutting width W1 and the actual cutting width W′1: Under a certain fixed laser output power, the corresponding initial galvanometer jitter parameters are obtained: d1=f1·A1.

2. The processing method for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 1, characterized in that: A multi-layer perceptron is used to automatically build a relationship model between speed and jitter parameters.

3. The processing method for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 2, characterized in that: The method of automatically establishing a relationship model between speed and jitter parameters using a multi-layer perceptron includes: Obtain a data set of different instantaneous speeds V and jitter parameters d of the galvanometer relative to the foil during the foil cutting process, and divide the data set into a training set and a validation set; Construct a multi-layer perceptron network structure and randomly initialize weights; The instantaneous velocity V of the galvanometer relative to the foil in the training set is input through each layer of the neural network to obtain the predicted jitter parameter d pred , calculate the loss function, and use the Adam optimizer to update the model weights so that the loss function gradually decreases; After multiple rounds of training iterations, the training is terminated when the preset upper limit of the number of training rounds is reached or the loss function converges, and the model training is completed; The prediction ability of the model is evaluated on the validation set and the trained model is saved.

4. The processing method for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 3 is characterized in that: The loss function uses mean square error.

5. The processing method for real-time adaptive adjustment of galvanometer vibration based on motion speed according to any one of claims 2 to 4, characterized in that: The multilayer perceptron has three hidden layers, the number of neurons in the first and second layers is 64, the number of neurons in the third layer is 32, and the hidden layers all use the ReLU activation function.

6. The processing method for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 1, characterized in that: An encoder, a displacement sensor, a laser distance sensor or an image recognition technology is used to obtain the instantaneous motion state of the galvanometer relative to the foil.

7. A processing system for real-time adaptive adjustment of galvanometer vibration based on motion speed, characterized in that: Based on the processing method according to claim 1, a control device is provided in the processing system, and the control device includes: A parameter setting module is used to set the vibration parameters of the galvanometer, and automatically establish a relationship model between the instantaneous speed of the galvanometer and the vibration parameters through machine learning methods; A signal acquisition module is used to obtain the current speed signal or position signal of the galvanometer relative to the foil in real time during the foil cutting process; The signal processing module is used to process the instantaneous speed of the galvanometer relative to the foil according to the speed signal or position signal of the galvanometer relative to the foil obtained in real time, and dynamically adjust the jitter parameter of the galvanometer in real time according to the instantaneous speed; The signal control module is used to synchronously generate a new foil cutting path according to the jitter parameters of the galvanometer adjusted in real time during the foil cutting process, and adjust the scanning speed of the galvanometer according to the adjusted jitter parameters. The galvanometer is adjusted with the new cutting path and scanning speed, controls the output of the laser, and continues to run to complete the foil cutting operation.

8. The processing system for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 7, characterized in that: The jitter parameters of the galvanometer are based on the initial constant motion speed of the foil and are obtained according to the jitter frequency, jitter amplitude, scanning speed and output power of the laser.

9. The processing system for real-time adaptive adjustment of galvanometer vibration based on motion speed according to claim 7, characterized in that: The speed signal or position signal of the galvanometer relative to the foil is fed back through an encoder, a displacement sensor, a laser distance sensor or an image recognition technology.

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