Control optimization method and system of battery combination cover plate laser marking equipment

Abstract

The invention discloses a control optimization method and system for battery combination cover plate laser marking equipment, and relates to the technical field of intelligent control. The method comprises the steps that three-dimensional contour data and basic material indexes of the battery combination cover plate are uploaded, and the battery combination cover plate is placed on a workbench of laser marking equipment; the marking sequence and the laser parameter combination of all the cover plate components are determined; according to the marking sequence and the laser parameter combination, a laser beam focusing path matched with the three-dimensional contour data and the basic material indexes is drawn up; and a material heat affected zone is determined, a laser beam focusing path is compensated and corrected to obtain a thermal compensation path, the thermal compensation path, the marking sequence of the cover plate components and the laser parameter combination are loaded to laser marking equipment, and battery combination cover plate marking control is carried out. The technical problem that in the prior art, laser marking is conducted on different components of the battery combination cover plate, and consequently the marking quality is not stable due to contour differences and different material characteristics is solved, and the technical effect of improving the marking quality is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a control optimization method and system for battery assembly cover laser marking equipment. Background Art

[0002] As an important component of the battery packaging structure, the battery combination cover is usually made of components made of multiple materials. Its three-dimensional contour structure is complex, and the material properties of the components vary significantly, such as hardness, density, and melting point. In order to achieve product traceability, quality control, and brand identification, laser marking on the cover surface has become an important process link. However, existing laser marking equipment generally adopts unified marking parameters and path planning, and fails to fully consider the contour changes and material differences of the various components of the cover. As a result, in the actual marking process, problems such as uneven marking depth, edge melting or excessive ablation, and expansion of the heat-affected zone occur, seriously affecting the marking quality and the functional integrity of the cover. Summary of the Invention

[0003] The present application provides a control optimization method and system for a battery combination cover laser marking device, which solves the technical problem in the prior art of unstable marking quality caused by contour differences and material properties on different components of the battery combination cover.

[0004] In a first aspect of the present application, a control optimization method for a battery assembly cover laser marking device is provided, the method comprising:

[0005] Upload the three-dimensional contour data and basic material indicators of the battery combination cover, and place the battery combination cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point; according to the three-dimensional contour data of the battery combination cover, perform data segmentation in comparison with the various cover components of the battery combination cover, and perform depth consistency processing in combination with the basic material indicators to determine the marking sequence and laser parameter combination of each cover component; according to the marking sequence and laser parameter combination of each cover component, formulate a laser beam focusing path that matches the three-dimensional contour data and basic material indicators; determine the heat-affected zone of the material, compensate and correct the laser beam focusing path to obtain a thermal compensation path, load the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment to perform battery combination cover marking control.

[0006] A second aspect of the present application provides a control optimization system for a battery pack cover laser marking device, the system comprising:

[0007] Data uploading module: uploading the three-dimensional contour data and basic material indicators of the battery combination cover, and placing the battery combination cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point; data processing module: according to the three-dimensional contour data of the battery combination cover, data segmentation is performed in comparison with each cover component of the battery combination cover, and depth consistency processing is performed in combination with the basic material indicators to determine the marking sequence and laser parameter combination of each cover component; focusing path formulation module: according to the marking sequence and laser parameter combination of each cover component, a laser beam focusing path that matches the three-dimensional contour data and basic material indicators is formulated; control module: determining the heat-affected zone of the material, compensating and correcting the laser beam focusing path to obtain a thermal compensation path, and loading the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment to perform battery combination cover marking control.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the 3D contour data and basic material specifications of the battery assembly cover are uploaded, and the battery assembly cover is placed on the worktable of the laser marking equipment. Basic material specifications include material hardness, material density, and material melting point. Next, based on the 3D contour data of the battery assembly cover, data segmentation is performed on each cover component of the battery assembly cover. Deep consistency processing is performed in combination with the basic material specifications to determine the marking sequence and laser parameter combination for each cover component. Then, based on the marking sequence and laser parameter combination for each cover component, a laser beam focusing path is formulated to match the 3D contour data and basic material specifications. Finally, the material's heat-affected zone is determined, and the laser beam focusing path is compensated and corrected to obtain a thermal compensation path. The thermal compensation path, marking sequence for each cover component, and laser parameter combination are loaded into the laser marking equipment to control the battery assembly cover marking. This solves the technical problem of unstable marking quality caused by profile differences and different material properties on different components of the battery assembly cover in the existing laser marking technology, achieving the technical effect of improving marking quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flow chart of a control optimization method for a battery combination cover laser marking device according to an embodiment of the present application;

[0012] Figure 2 Schematic diagram of the control optimization system structure of the battery combination cover laser marking equipment provided in an embodiment of the present application.

[0013] Description of the accompanying drawings: data uploading module 11, data processing module 12, focusing path formulation module 13, control module 14. DETAILED DESCRIPTION

[0014] This application solves the technical problem in the prior art of unstable marking quality caused by contour differences and material properties on different components of a battery assembly cover by providing a control optimization method and system for a battery assembly cover laser marking device.

[0015] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Example 1, as Figure 1 As shown, the present application provides a control optimization method for a battery combination cover laser marking device, wherein the method includes:

[0018] Upload the three-dimensional contour data and basic material indicators of the battery combination cover, and place the battery combination cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point.

[0019] The 3D contour data of the battery combination cover to be processed is obtained through 3D scanning equipment, computer-aided design (CAD) files or other industrial detection systems. The 3D contour data includes the spatial geometric structure, surface characteristics, curvature information, etc. of the cover as a whole and its components. At the same time, the basic material indicators of each component in the battery combination cover are collected from the material data manual. The basic material indicators include but are not limited to material hardness (for example, measured in Rockwell hardness or Vickers hardness), material density (in g / cm 3 or kg / m 3 ) and the melting point of the material (in °C).

[0020] Upload the 3D contour data and basic material indicators to the control system of the laser marking equipment. During the upload process, the system will verify the format and pre-process the data, including coordinate system 1, defect point filtering and model standardization, to ensure data accuracy and compatibility.

[0021] After the data is successfully loaded, the battery assembly cover is positioned and fixed on the workbench of the laser marking equipment to ensure that its physical posture is consistent with the reference coordinate system set in the 3D data.

[0022] According to the three-dimensional contour data of the battery combination cover plate, data segmentation is performed in comparison with each cover plate component of the battery combination cover plate, and depth consistency processing is performed in combination with the basic material indicators to determine the marking sequence and laser parameter combination of each cover plate component.

[0023] After uploading the 3D contour data and basic material specifications of the battery assembly cover to the laser marking equipment's control system, the 3D model of the battery assembly cover is segmented at the component level. By identifying geometric feature changes (such as sudden edge changes, sudden curvature changes, and seam areas) on the cover's surface or volume model, the component boundaries are extracted and divided into several substructures. Specifically, the system uses boundary detection algorithms and 3D topological analysis methods, combined with point cloud density differences, surface normal angle changes, or assembly features defined in the CAD model, to identify and annotate different components.

[0024] After completing the component division, the system unifies the basic material indicators (material hardness, density, melting point) associated with each component and couples them with its three-dimensional surface information for depth consistency processing. That is, based on the marking depth requirements of each component, while ensuring consistent visual effects, the laser marking parameter combination (such as laser power, scanning speed, frequency, focus depth, etc.) is adjusted according to the material properties to achieve equivalent energy depth consistency. The system can use an empirical database or a material laser response model to predict the ablation depth of various materials under different laser parameter combinations, and then perform reverse regulation to achieve consistent apparent marking. Ultimately, the system comprehensively considers the spatial position, connection relationship, material properties and process adaptability of the components, and determines the marking order of each component based on an optimization algorithm (such as heuristic sorting, priority scheduling or path cost minimization strategy). At the same time, a corresponding laser parameter combination table is generated for each component and used as input data for marking control for subsequent path planning and energy control.

[0025] Furthermore, according to the three-dimensional contour data of the battery assembly cover, data segmentation is performed by comparing each cover member of the battery assembly cover, and the method includes:

[0026] The boundaries of each cover plate component are identified by the geometric feature differences of the battery combination cover plate; the sharp feature points in the three-dimensional contour data of the battery combination cover plate are extracted, and a cover plate component connection diagram is constructed based on the topological relationship of the battery combination cover plate; data segmentation is performed based on the cover plate component connection diagram and the boundaries of each cover plate component, and removable component identifiers and fixed component identifiers are added to the three-dimensional contour data.

[0027] First, the system analyzes the geometric features of the overall 3D contour data of the battery assembly cover to identify the boundaries of its individual cover components. Specifically, based on surface feature changes in 3D modeling data (such as point cloud models, mesh models, or voxel data), the system uses methods such as curvature mutation recognition, normal vector change rate analysis, and boundary surface sharp angle detection to determine the joint areas or assembly interfaces between different components. For example, if the normal angle between adjacent facets exceeds a set threshold (such as 45°), it can be considered a component boundary; alternatively, by detecting points where the surface curvature transitions from continuous and smooth to discontinuous, it can assist in determining the dividing line. Next, sharp feature points are extracted from the 3D contour data, including structurally significant geometric feature points such as hole edges, corners, steps, notches, and rib ends. The system combines the spatial distribution of these geometric feature points to further construct a topological relationship model for the battery assembly cover, forming a cover component connection diagram. This connection diagram is an undirected graph structure with components as nodes and the actual connections or assembly relationships between components as edges, thereby establishing the structural network relationship of the entire cover.

[0028] After constructing the cover component connection diagram, the system uses the diagram in combination with the aforementioned geometric boundary recognition results to perform data segmentation operations. Specifically, it includes: dividing the components according to the connection edges between the components and according to a certain segmentation priority (such as structural independence, geometric complexity, and material differences). After the division is completed, the system labels each component and distinguishes it as "detachable component" and "fixed component". The identification is determined based on the structural design rules of the cover product, the distribution of screw holes, the nested plug-in structure and other features. For example, components with reserved disassembly through-holes, mechanical fasteners or clamping slots can be marked as "detachable components", while the integrally formed or welded packaged parts are classified as "fixed components".

[0029] According to the marking sequence of each cover plate component and the combination of laser parameters, a laser beam focusing path matching the three-dimensional contour data and basic material indicators is formulated.

[0030] After determining the marking sequence for each cover component and the corresponding laser parameter combinations, the system formulates a matching laser beam focusing path based on the 3D contour data and basic material specifications. Specifically, the system first performs a spatial geometric analysis of the 3D contour data to extract parameters such as the cover surface curvature, local tilt angles, and surface undulations. This curvature analysis identifies areas of high curvature (such as corners, edges, and raised areas) and areas of low curvature (such as flat surfaces and gentle slopes). The laser head's focusing method is adjusted accordingly: a "point-by-point focusing" strategy is used in high-curvature areas, where the laser focus point is adjusted point by point and closely follows the curved surface trajectory, ensuring that the focus remains on the component surface. A "continuous scanning focusing" strategy is used in low-curvature areas to improve processing efficiency. Next, the system determines the required energy density distribution of the laser beam across the different components based on their basic material specifications, such as hardness, density, and melting point. Specifically, for materials with higher hardness or melting points, the laser power needs to be increased or the focus dwell time needs to be extended. For materials with higher density, the scanning speed needs to be reduced to achieve sufficient energy deposition. The system uses a built-in material response model or a database of laser marking experience to determine parameter matching relationships through table lookup or interpolation, and then maps these relationships to spatial path planning. The final output laser beam focusing path is a set of three-dimensional curves that describe the trajectory of the laser focus in three dimensions during the marking process.

[0031] Furthermore, the method for formulating a laser beam focusing path that matches the three-dimensional profile data and basic material indicators includes:

[0032] Perform a curvature analysis on the three-dimensional contour data and make a judgment based on the first curvature threshold. If the results are consistent, the data is defined as a high curvature area, and the operation is switched to point-by-point focusing in the high curvature area. Perform a curvature analysis on the three-dimensional contour data and make a judgment based on the second curvature threshold. If the results are consistent, the data is defined as a low curvature area, and the operation is switched to continuous scanning focusing in the low curvature area.

[0033] First, the system performs surface curvature analysis on the three-dimensional contour data of the battery combination cover. The curvature analysis can be performed based on a triangular mesh model (such as STL format) or point cloud data, using Gaussian curvature or average curvature as a curvature quantification indicator. By calculating the curvature value for each mesh face or point cloud node, the curvature distribution map of the cover surface in different areas is obtained. Then, the calculated curvature value is compared with the preset first curvature threshold. If the curvature value of a certain area is higher than the first curvature threshold (for example, set to 0.2mm), the curvature value is calculated. -1), then the area is judged to be a high curvature area. High curvature areas usually correspond to geometrically complex parts such as sharp corners, bends, tiny bumps and so on, and these areas are more sensitive to laser focus position errors. In high curvature areas, the system automatically switches the laser focus mode to point-by-point focusing operation, which means that the laser head adjusts the Z-axis height and focal length in real time during the marking process, so that the laser focus and the cover plate surface are always kept perpendicular and equidistant, thereby improving the marking consistency and accuracy. Further, the above curvature calculation is repeated for the remaining areas, and the curvature value is compared with the second curvature threshold. If the curvature value of an area is lower than the second curvature threshold (for example, set to 0.05mm -1 ), the area is determined to be low curvature. In such areas, the surface topography is relatively flat or gradually changing, and the focus tolerance requirement is relatively low. In low curvature areas, the system uses continuous scanning focusing, meaning the laser head moves continuously along the marking path while maintaining a constant focus or performing low-frequency adjustments, thereby improving marking efficiency. For areas of medium curvature between the first and second curvature thresholds, the system can configure intermediate states, such as semi-continuous scanning mode and low-speed focus compensation mode, to achieve smooth transitions and avoid control instability caused by frequent switching of focus modes.

[0034] Furthermore, in the high curvature region, switching to point-by-point focusing operation is performed, and the method includes:

[0035] The marking dot matrix density is determined by the first curvature threshold; in the high curvature area, surface roughness is introduced, and combined with the marking dot matrix density, it is determined whether to activate the marking dot matrix reorganization instruction.

[0036] First, the system determines the marking dot density of the area based on the difference between the curvature value in the high curvature area and the first curvature threshold. Dot density D = D0 + α (KK t ), where D0 is the basic lattice density, K is the average curvature value of the region, and K t is the first curvature threshold value, and α is the empirical adjustment coefficient. Secondly, the system performs surface roughness analysis on the high curvature area. The surface roughness can be obtained by a laser displacement sensor or a three-dimensional profile scanner to extract the Ra (arithmetic mean roughness) or Rq (root mean square roughness) index. High roughness indicates that there are microscopic convex and concave structures on the surface, which may cause the laser focus to deviate from the ideal focusing plane in a local area, thereby affecting the integrity and clarity of the marking pattern. Combining the marking dot density and surface roughness, the system evaluates whether the current dot arrangement meets the marking requirements. If any of the following conditions are met, the marking dot reorganization instruction is activated: the current dot density exceeds the density critical value, but the roughness is greater than the set threshold; the joint judgment result of the dot density and roughness does not meet the target focus energy coverage.

[0037] Once the marking dot matrix reorganization command is activated, the system will reconstruct the dot matrix arrangement of the area, including adjusting the dot matrix shape (from an equidistant matrix to an adaptive hexagon, grid or staggered arrangement), fine-tuning the local point coordinates, adjusting the focus dwell time, etc., to ensure that each focus point can still maintain effective energy deposition and pattern reconstruction capabilities in areas with high curvature and complex surface undulations.

[0038] Furthermore, in combination with the marking dot matrix density, determining whether to activate the marking dot matrix reorganization instruction includes:

[0039] The marking dot matrix density and focus dwell time are mapped to the multi-objective Pareto front to determine the initial population; using the laser head start and laser head stop as penalty factors, non-dominated sorting iterative optimization is performed in the initial population to screen out a non-dominated solution set; from the non-dominated solution set, the solution with the optimal fitness function value is selected as the target solution, and the target solution is used to execute the activation decision of the marking dot matrix reorganization instruction.

[0040] First, the system obtains the marking point density and the corresponding laser focus dwell time within the current high-curvature region. The point density measures the distribution density of marking points per unit area, and the focus dwell time evaluates the intensity of laser energy deposition at a single point. Next, the marking point density and focus dwell time are input into a multi-objective optimization model as two optimization objectives and mapped to the Pareto optimal frontier space to construct an initial solution set (i.e., initial population). The multi-objective optimization model can be initialized using NSGA-II or similar evolutionary computation methods.

[0041] The objective function is as follows: Objective 1: Minimize the marking time per unit area (= dot density × dwell time); Objective 2: Maximize the clarity of the marking pattern (positively correlated with energy coverage uniformity).

[0042] After forming the initial population, the system incorporates the laser head start and stop frequencies as penalty factors into the fitness function to control the device response burden during the dot matrix reorganization process. Frequent laser head starts and stops affect device stability. Therefore, a penalty weight is added for each start / stop; the total penalty term is included in the negative indicator portion of the fitness function. Then, based on the principles of non-dominated sorting and congestion comparison, multiple rounds of iterative optimization are performed on the initial population to screen for several non-dominated solution sets. Non-dominated solutions are defined as a set of solutions that are not completely surpassed by other solutions on multiple objectives and represent the frontier solution with the optimal performance balance. Finally, the solution with the optimal fitness function value from the non-dominated solution set is selected as the target solution for the current area. The fitness function comprehensively considers dot matrix coverage, marking efficiency, and device stability. Under the parameters corresponding to the target solution, if the results show that the original dot matrix density or focus duration combination cannot achieve uniform coverage and the device load is high, the system automatically activates the marking dot matrix reorganization command to perform operations such as point reconstruction and focus strategy adjustment.

[0043] Determine the heat-affected zone of the material, compensate and correct the laser beam focusing path to obtain a thermal compensation path, load the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment, and perform battery assembly cover marking control.

[0044] First, by combining three-dimensional contour data with basic material indicators (including material hardness, density, melting point) and thermal conductivity, the temperature field distribution inside the material after laser action is predicted, and the area where the material structure changes is identified, which is defined as the heat-affected zone of the material. Subsequently, based on the thermal response characteristics of each component, the degree of thermal deformation under unit energy input is calculated, and then a thermal response function is constructed to generate the corresponding energy density compensation coefficient. On this basis, the initial laser beam focusing path is vector-level power adjustment and trajectory fine-tuning are performed so that parameters such as laser power, dwell time, and scanning speed can dynamically change according to the distribution characteristics of the heat-affected zone, thereby forming a thermal compensation path that matches the thermal response characteristics of the material. The thermal compensation path, marking sequence, and laser parameter combination are uniformly loaded into the laser marking equipment. The control system performs the marking operation accordingly, while monitoring the marking status in real time and dynamically adjusting the laser output according to the thermal compensation path to ensure that the marking pattern of each component area meets the preset process standards in terms of geometric accuracy, line width, color depth, etc., effectively improving the overall marking quality and consistency.

[0045] Furthermore, the heat-affected zone of the material is determined, and the laser beam focusing path is compensated and corrected to obtain a thermal compensation path. The method includes:

[0046] In the heat-affected zone of the material, thermal deformation is predicted using thermal conductivity and the material melting point in the basic material indicators to determine an energy density compensation coefficient; based on the energy density compensation coefficient, the laser energy density distribution of the laser marking equipment is synchronously adjusted until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, thereby determining a thermal compensation path.

[0047] First, sensitive areas where laser exposure may induce structural changes, known as the heat-affected zone (HAZ), are identified within the three-dimensional contour data of the battery assembly cover. This identification process, based on the material's thermal conductivity and melting point parameters, models the transient temperature distribution and thermal diffusion behavior induced by laser irradiation through numerical simulation or analytical calculation. This predicts the temperature rise and deformation trends of the surface and interior of different components under heat input during laser scanning, thereby identifying areas of most significant local thermal deformation. Based on these predictions, an energy density compensation coefficient is generated for each component region. This coefficient represents the magnitude of the adjustment required for laser input parameters to ensure energy consistency across materials with different thermal responses. Subsequently, the laser marking equipment's power output curve is synchronously corrected based on the energy density compensation coefficient to align the laser energy density distribution spatially with the material's thermal response. The laser focus power, scanning speed, and spot overlap are dynamically adjusted to ensure that the error between the actual melting zone boundary and the predicted boundary of the theoretical HAZ remains within a preset process threshold. Ultimately, a laser scanning path that has been corrected by thermal compensation, namely the thermal compensation path, is formed, and this path is loaded into the equipment control module as the actual marking trajectory, effectively improving the thermal stability and pattern accuracy of the marking process.

[0048] Furthermore, the thermal compensation path, the marking sequence of each cover plate component, and the laser parameter combination are loaded into the laser marking device, and the method further includes:

[0049] Before the laser marking equipment is started, the temperature field distribution and stress field distribution during the laser marking process are simulated through virtual simulation; through the virtual simulation marking data, if the boundary error between the material melting zone and the material heat-affected zone does not meet the marking process threshold, the energy density compensation coefficient is automatically adjusted retrospectively.

[0050] Before the laser marking system is activated, a coupled thermal-mechanical simulation model is constructed based on the generated thermal compensation path, the spatial structure of the cover plate components, and their material parameters. Finite element analysis or numerical heat conduction simulation is used to simulate the temperature field distribution and the resulting stress field distribution in different regions of the cover plate caused by the laser energy input during the laser marking process. This virtual simulation is used to dynamically track the formation range of the material's melt zone and the diffusion boundary of the heat-affected zone. If the simulation results indicate a deviation between the actual melt zone boundary and the heat-affected zone boundary exceeding the process threshold, the system automatically activates a compensation feedback mechanism to retroactively adjust the previously set energy density compensation coefficient. This involves recalculating the local energy input distribution, adjusting the laser power-time curve, and adjusting the path center of gravity, thereby fine-tuning parameters in critical areas. After simulation verification confirms that the revised thermal compensation path and marking parameters meet marking consistency and thermal control requirements, they are loaded into the laser marking system for subsequent high-precision, low-heat-loss battery assembly cover marking control tasks.

[0051] Furthermore, the method for controlling the marking of the battery assembly cover plate further includes:

[0052] Collect the equipment operating parameters of the laser marking equipment, including laser power fluctuation value, galvanometer scanning speed deviation, and workbench vibration amplitude; configure health indicators, and associate the equipment operating parameters with the laser emission component stability assessment dimension, galvanometer scanning component accuracy assessment dimension, and workbench transmission component smoothness assessment dimension of the health indicators, and obtain the equipment health score through weighted calculation; when the equipment health score is lower than the basic health threshold, the marking speed is automatically reduced, and the equipment fault diagnosis program is started at the same time. The potential fault point is located through historical fault data matching and machine learning, and a fault reminder is issued.

[0053] Preferably, key parameters generated by the laser marking equipment during operation are collected in real time, including but not limited to laser power fluctuation, galvanometer scanning speed deviation, and worktable vibration amplitude, to form a comprehensive data set on the equipment's operating status. Subsequently, based on a pre-set health evaluation model, these operating parameters are mapped to multiple health indicator evaluation dimensions, including those for laser emission component stability, galvanometer scanning component accuracy, and worktable transmission component smoothness. After mapping, the results of each evaluation dimension are comprehensively calculated using pre-set weighting coefficients to determine the equipment's current comprehensive health score. When this health score falls below a pre-set baseline health threshold, the system automatically triggers an operational protection mechanism: First, it automatically reduces the laser marking speed to mitigate the risk of accuracy deviation caused by equipment instability; second, it immediately initiates a device fault diagnosis program, utilizing a built-in fault database and historical operation and maintenance records, combined with a machine learning-based pattern recognition algorithm, to match and analyze the current operating status with historical abnormal patterns to quickly locate potential fault points. Once a potential fault trend with significant similarity is detected, the system will proactively alert the user of the fault using a graphical interface or audio / visual signals, and output possible faulty components and recommended treatment measures, assisting maintenance personnel in conducting repairs or replacements in advance to ensure the continuity of the laser marking process and the life of the equipment.

[0054] Furthermore, it also includes:

[0055] After the laser marking is completed, the laser marking pattern is collected and the key features of the pattern are extracted; the key features of the pattern are compared with the preset marking pattern for similarity to determine the geometric size error, line clarity error, and color depth error; based on the geometric size error, line clarity error, and color depth error, the battery combination cover batch and the laser marking quality are statistically analyzed to determine strongly correlated defects, and the strongly correlated defects are combined with the corresponding battery combination cover batch to perform compensation and correction intervention on the laser beam focusing path.

[0056] First, the laser marking pattern image of the battery combination cover surface is collected, and the collected image is analyzed through the image processing algorithm to extract the key feature information of the pattern. The key features include but are not limited to geometric contours, edge lines, pattern filling forms and color level changes. Then, the key features of the pattern are compared with the preset standard marking pattern for similarity analysis, and the geometric dimension error (such as the length, width and height deviation of the characters / patterns), line clarity error (such as the degree of edge jaggedness and blurriness) and color depth error (such as the difference in light and dark caused by the change in ablation depth) are calculated and identified. Based on the error results of the above three dimensions, the system statistically models the laser marking quality of the current batch of battery combination covers, extracts the key defect types and their frequency and distribution trends, and associates them with the specific battery combination cover batch information to build a batch-defect strong correlation model. After determining the presence of significant, strongly correlated defects, the system will perform targeted compensation and correction interventions based on the identified error type and distribution location, combined with the laser beam focus path data used in the marking process. This includes focusing position adjustment, laser energy density fine-tuning, scanning speed optimization, and other operations to ensure that the probability of the same type of defects occurring in subsequent marking tasks for similar batches is significantly reduced, thereby achieving intelligent and adaptive laser marking path optimization and continuous improvement in marking quality.

[0057] In summary, the embodiments of the present application have at least the following technical effects:

[0058] First, the 3D contour data and basic material specifications of the battery assembly cover are uploaded, and the battery assembly cover is placed on the worktable of the laser marking equipment. Basic material specifications include material hardness, material density, and material melting point. Next, based on the 3D contour data of the battery assembly cover, data segmentation is performed on each cover component of the battery assembly cover. Deep consistency processing is performed in combination with the basic material specifications to determine the marking sequence and laser parameter combination for each cover component. Then, based on the marking sequence and laser parameter combination for each cover component, a laser beam focusing path is formulated to match the 3D contour data and basic material specifications. Finally, the material's heat-affected zone is determined, and the laser beam focusing path is compensated and corrected to obtain a thermal compensation path. The thermal compensation path, marking sequence for each cover component, and laser parameter combination are loaded into the laser marking equipment to control the battery assembly cover marking. This solves the technical problem of unstable marking quality caused by profile differences and different material properties on different components of the battery assembly cover in the existing laser marking technology, achieving the technical effect of improving marking quality.

[0059] Example 2, based on the same inventive concept as the control optimization method of the battery combination cover laser marking device in the above embodiment, Figure 2 As shown, the present application provides a control optimization system for a battery combination cover laser marking device, wherein the system includes:

[0060] Data uploading module 11: Upload the three-dimensional contour data and basic material indicators of the battery combination cover, and place the battery combination cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point; Data processing module 12: According to the three-dimensional contour data of the battery combination cover, data segmentation is performed against each cover component of the battery combination cover, and depth consistency processing is performed in combination with the basic material indicators to determine the marking sequence and laser parameter combination of each cover component; Focusing path formulation module 13: According to the marking sequence and laser parameter combination of each cover component, a laser beam focusing path that matches the three-dimensional contour data and basic material indicators is formulated; Control module 14: Determine the heat-affected zone of the material, compensate and correct the laser beam focusing path to obtain a thermal compensation path, load the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment to control the marking of the battery combination cover.

[0061] Furthermore, the control module 14 is configured to execute the following method:

[0062] In the heat-affected zone of the material, thermal deformation is predicted using thermal conductivity and the material melting point in the basic material indicators to determine an energy density compensation coefficient; based on the energy density compensation coefficient, the laser energy density distribution of the laser marking equipment is synchronously adjusted until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, thereby determining a thermal compensation path.

[0063] Furthermore, the data processing module 12 is configured to execute the following method:

[0064] The boundaries of each cover plate component are identified by the geometric feature differences of the battery combination cover plate; the sharp feature points in the three-dimensional contour data of the battery combination cover plate are extracted, and a cover plate component connection diagram is constructed based on the topological relationship of the battery combination cover plate; data segmentation is performed based on the cover plate component connection diagram and the boundaries of each cover plate component, and removable component identifiers and fixed component identifiers are added to the three-dimensional contour data.

[0065] Furthermore, the focus path planning module 13 is configured to execute the following method:

[0066] Perform a curvature analysis on the three-dimensional contour data and make a judgment based on the first curvature threshold. If the results are consistent, the data is defined as a high curvature area, and the operation is switched to point-by-point focusing in the high curvature area. Perform a curvature analysis on the three-dimensional contour data and make a judgment based on the second curvature threshold. If the results are consistent, the data is defined as a low curvature area, and the operation is switched to continuous scanning focusing in the low curvature area.

[0067] Furthermore, the focus path planning module 13 is configured to execute the following method:

[0068] The marking dot matrix density is determined by the first curvature threshold; in the high curvature area, surface roughness is introduced, and combined with the marking dot matrix density, it is determined whether to activate the marking dot matrix reorganization instruction.

[0069] Furthermore, the focus path planning module 13 is configured to execute the following method:

[0070] The marking dot matrix density and focus dwell time are mapped to the multi-objective Pareto front to determine the initial population; using the laser head start and laser head stop as penalty factors, non-dominated sorting iterative optimization is performed in the initial population to screen out a non-dominated solution set; from the non-dominated solution set, the solution with the optimal fitness function value is selected as the target solution, and the target solution is used to execute the activation decision of the marking dot matrix reorganization instruction.

[0071] Furthermore, the control module 14 is configured to execute the following method:

[0072] Before the laser marking equipment is started, the temperature field distribution and stress field distribution during the laser marking process are simulated through virtual simulation; through the virtual simulation marking data, if the boundary error between the material melting zone and the material heat-affected zone does not meet the marking process threshold, the energy density compensation coefficient is automatically adjusted retrospectively.

[0073] Furthermore, the control module 14 is configured to execute the following method:

[0074] Collect the equipment operating parameters of the laser marking equipment, including laser power fluctuation value, galvanometer scanning speed deviation, and workbench vibration amplitude; configure health indicators, and associate the equipment operating parameters with the laser emission component stability assessment dimension, galvanometer scanning component accuracy assessment dimension, and workbench transmission component smoothness assessment dimension of the health indicators, and obtain the equipment health score through weighted calculation; when the equipment health score is lower than the basic health threshold, the marking speed is automatically reduced, and the equipment fault diagnosis program is started at the same time. The potential fault point is located through historical fault data matching and machine learning, and a fault reminder is issued.

[0075] Furthermore, the control module 14 is configured to execute the following method:

[0076] After the laser marking is completed, the laser marking pattern is collected and the key features of the pattern are extracted; the key features of the pattern are compared with the preset marking pattern for similarity to determine the geometric size error, line clarity error, and color depth error; based on the geometric size error, line clarity error, and color depth error, the battery combination cover batch and the laser marking quality are statistically analyzed to determine strongly correlated defects, and the strongly correlated defects are combined with the corresponding battery combination cover batch to perform compensation and correction intervention on the laser beam focusing path.

[0077] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0079] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A control optimization method for a battery combination cover laser marking device, characterized in that: include: Upload the three-dimensional contour data and basic material indicators of the battery assembly cover, and place the battery assembly cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point; According to the three-dimensional contour data of the battery assembly cover plate, data segmentation is performed in comparison with each cover plate component of the battery assembly cover plate, and depth consistency processing is performed in combination with the basic material indicators to determine the marking order and laser parameter combination of each cover plate component; According to the marking sequence and laser parameter combination of each cover plate component, a laser beam focusing path matching the three-dimensional profile data and basic material indicators is formulated; Determine the heat-affected zone of the material, compensate and correct the laser beam focusing path to obtain a thermal compensation path, load the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment, and perform battery assembly cover marking control.

2. The control optimization method for the battery combination cover laser marking device according to claim 1, characterized in that: Determining the heat-affected zone of the material and compensating and correcting the laser beam focusing path to obtain a thermal compensation path, the method comprising: In the heat-affected zone of the material, thermal deformation prediction is performed using thermal conductivity and the melting point of the material in the basic material index to determine an energy density compensation coefficient; According to the energy density compensation coefficient, the laser energy density distribution of the laser marking device is synchronously adjusted until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, and the thermal compensation path is determined.

3. The control optimization method for the battery combination cover laser marking device according to claim 2, characterized in that: According to the three-dimensional contour data of the battery assembly cover plate, data segmentation is performed by comparing each cover plate component of the battery assembly cover plate, and the method includes: Identify the boundaries of each cover component based on the geometric feature differences of the battery assembly cover; Extracting sharp feature points from the three-dimensional contour data of the battery assembly cover plate, and constructing a cover plate component connection diagram based on the topological relationship of the battery assembly cover plate; Data segmentation is performed according to the cover plate component connection diagram and the boundaries of each cover plate component, and a detachable component identifier and a fixed component identifier are added to the three-dimensional contour data.

4. The control optimization method for the battery assembly cover laser marking device according to claim 1, characterized in that: Formulating a laser beam focusing path that matches the three-dimensional profile data and basic material indicators, the method includes: Performing curvature analysis on the three-dimensional contour data and comparing it with a first curvature threshold, defining it as a high curvature area if it meets the requirements, and switching to point-by-point focusing operation in the high curvature area; The three-dimensional contour data is subjected to curvature analysis and judged against a second curvature threshold. If the data meets the requirements, it is defined as a low curvature area, and the continuous scanning and focusing operation is switched in the low curvature area.

5. The control optimization method for the battery assembly cover laser marking device according to claim 4, characterized in that: Switching to a point-by-point focusing operation in the high curvature region, the method comprises: Determining the marking dot density by using the first curvature threshold; In the high curvature area, surface roughness is introduced, and combined with the marking dot matrix density, it is determined whether to activate the marking dot matrix reorganization instruction.

6. The control optimization method for the battery pack cover laser marking device according to claim 5, characterized in that: In combination with the marking dot matrix density, determining whether to activate the marking dot matrix reorganization instruction, the method includes: Mapping the marking dot density and focus dwell time to the multi-target Pareto frontier to determine the initial population; Using the laser head start and laser head stop as penalty factors, performing non-dominated sorting iterative optimization in the initial population to screen out a non-dominated solution set; From the non-dominated solution set, a solution with an optimal fitness function value is selected as a target solution, and the target solution is used to execute the activation decision of the marking dot matrix reorganization instruction.

7. The control optimization method for the battery pack cover laser marking device according to claim 2, characterized in that: The thermal compensation path, the marking sequence of each cover plate component, and the laser parameter combination are loaded into the laser marking device, and the method further includes: Before the laser marking device is started, the temperature field distribution and stress field distribution during the laser marking process are simulated by virtual simulation; Through virtual simulation marking data, if the boundary error between the material melting zone and the material heat-affected zone does not meet the marking process threshold, the energy density compensation coefficient is automatically adjusted retrospectively.

8. The control optimization method for the battery pack cover laser marking device according to claim 7, characterized in that: Performing battery assembly cover marking control, the method further includes: Collect equipment operating parameters of laser marking equipment, including laser power fluctuation value, galvanometer scanning speed deviation, and workbench vibration amplitude; Configure health indicators, associate and map the equipment operating parameters to the health indicators' laser emission component stability assessment dimension, galvanometer scanning component accuracy assessment dimension, and workbench transmission component smoothness assessment dimension, and obtain the equipment health score through weighted calculation; When the equipment health score is lower than the basic health threshold, the marking speed is automatically reduced, and the equipment fault diagnosis program is started at the same time. The potential fault point is located through historical fault data matching and machine learning, and a fault reminder is issued.

9. The control optimization method for the battery pack cover laser marking device according to claim 8, characterized in that: The method further comprises: After the laser marking is completed, the laser marking pattern is collected and the key features of the pattern are extracted; Comparing the key features of the pattern with the preset marking pattern for similarity to determine geometric size error, line clarity error, and color depth error; Based on the geometric dimension error, line clarity error, and color depth error, statistics are taken on the battery combination cover plate batches and the laser marking quality to determine strongly correlated defects. The strongly correlated defects are combined with the corresponding battery combination cover plate batches to perform compensation correction intervention on the laser beam focusing path.

10. The control optimization system of the battery combination cover laser marking equipment is characterized by: A control optimization method for implementing a battery combination cover laser marking device according to any one of claims 1 to 9, the system comprising: Data upload module: upload the three-dimensional contour data and basic material indicators of the battery combination cover, and place the battery combination cover on the workbench of the laser marking equipment, wherein the basic material indicators include material hardness, material density, and material melting point; Data processing module: based on the three-dimensional contour data of the battery assembly cover, data segmentation is performed in comparison with each cover plate component of the battery assembly cover, and depth consistency processing is performed in combination with the basic material indicators to determine the marking order and laser parameter combination of each cover plate component; Focusing path planning module: plans a laser beam focusing path that matches the three-dimensional profile data and basic material indicators according to the marking sequence of each cover component and the combination of laser parameters; Control module: Determine the heat-affected zone of the material, compensate and correct the laser beam focusing path to obtain a thermal compensation path, load the thermal compensation path, the marking sequence of each cover component and the laser parameter combination into the laser marking equipment, and perform battery assembly cover marking control.

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