Method and system for control optimization of battery assembly cover plate laser marking equipment
By segmenting and unifying the three-dimensional contour data and material properties of the battery pack cover, and optimizing the laser beam focusing path, the problem of unstable marking quality on the battery pack cover was solved, and a high-quality marking effect was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG ZHONGZE PRECISION TECHNOLOGY CO LTD
- Filing Date
- 2025-06-05
- Publication Date
- 2026-06-23
Smart Images
Figure CN120587682B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent control technology, specifically to a control optimization method and system for laser marking equipment for battery pack covers. Background Technology
[0002] As a crucial component of the battery packaging structure, the battery pack cover is typically assembled from multiple material components, resulting in a complex three-dimensional profile and significant differences in material properties between components, such as hardness, density, and melting point. To achieve product traceability, quality control, and brand identification, laser marking on the cover surface has become an important process. However, existing laser marking equipment generally employs uniform marking parameters and path planning, failing to fully consider the contour variations and material differences among the various cover components. This leads to problems such as inconsistent marking depth, excessive edge melting or ablation, and expansion of the heat-affected zone during actual marking, severely impacting marking quality and the functional integrity of the cover. Summary of the Invention
[0003] This application provides a control optimization method and system for laser marking equipment for battery pack covers, which solves the technical problem of unstable marking quality caused by different contours and material properties on different components of the battery pack cover in the prior art.
[0004] A first aspect of this application provides a control optimization method for a laser marking device for a battery pack cover, the method comprising:
[0005] Upload the 3D contour data and basic material specifications of the battery pack cover. Place the battery pack cover on the worktable of the laser marking equipment. The basic material specifications include material hardness, material density, and material melting point. Based on the 3D contour data of the battery pack cover, segment the data of each cover component and perform depth consistency processing in conjunction with the basic material specifications to determine the marking sequence and laser parameter combination for each cover component. Based on the marking sequence and laser parameter combination of each cover component, formulate a laser beam focusing path that matches the 3D contour data and basic material specifications. Determine the heat-affected zone of the material and compensate and correct the laser beam focusing path to obtain a heat compensation path. Load the heat 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 pack cover.
[0006] A second aspect of this application provides a control optimization system for a laser marking equipment for battery pack covers, the system comprising:
[0007] Data Upload Module: Uploads the 3D contour data and basic material specifications of the battery pack cover, and places the battery pack cover on the worktable of the laser marking equipment. The basic material specifications include material hardness, material density, and material melting point. Data Processing Module: Based on the 3D contour data of the battery pack cover, segments the data of each cover component and performs depth consistency processing based on the basic material specifications to determine the marking sequence and laser parameter combination for each cover component. Focusing Path Determination Module: Based on the marking sequence and laser parameter combination of each cover component, determines a laser beam focusing path that matches the 3D contour data and basic material specifications. Control Module: Determines the heat-affected zone of the material, compensates and corrects the laser beam focusing path to obtain a heat-compensated path, and loads the heat-compensated path, the marking sequence of each cover component, and the laser parameter combination into the laser marking equipment for battery pack 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 properties of the battery pack cover are uploaded. The battery pack cover is then placed on the worktable of the laser marking equipment. The basic material properties include material hardness, density, and melting point. Next, based on the 3D contour data of the battery pack cover, data segmentation is performed on each cover component, and depth consistency processing is performed using the basic material properties to determine the marking sequence and laser parameter combinations for each component. Then, based on the marking sequence and laser parameter combinations for each cover component, a laser beam focusing path matching the 3D contour data and basic material properties is determined. Finally, the heat-affected zone of the material is identified, and the laser beam focusing path is compensated and corrected to obtain a heat-compensated path. The heat-compensated path, the marking sequence for each cover component, and the laser parameter combinations are loaded into the laser marking equipment for battery pack cover marking control. This solves the technical problem of unstable marking quality on different components of the battery pack cover due to differences in contour and material properties, achieving a significant improvement in marking quality. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 This is a schematic diagram of the control optimization method for the laser marking equipment for battery pack cover provided in an embodiment of this application;
[0012] Figure 2 A schematic diagram of the control and optimization system structure of the laser marking equipment for battery pack cover provided in this application embodiment.
[0013] Figure labeling: Data upload module 11, data processing module 12, focus path determination module 13, control module 14. Detailed Implementation
[0014] This application provides a control optimization method and system for laser marking equipment on battery pack covers, which solves the technical problem of unstable marking quality caused by differences in contour and material properties on different components of battery pack covers in the prior art.
[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. 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 that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.
[0017] Example 1, as Figure 1 As shown, this application provides a control optimization method for a laser marking equipment for battery pack covers, wherein the method includes:
[0018] Upload the three-dimensional contour data and basic material parameters of the battery pack cover plate, and place the battery pack cover plate on the worktable of the laser marking equipment. The basic material parameters include material hardness, material density, and material melting point.
[0019] The 3D contour data of the battery pack cover to be processed is obtained through 3D scanning equipment, computer-aided design (CAD) files, or other industrial inspection systems. This 3D contour data includes the spatial geometry, surface features, and curvature information of the cover as a whole and its individual components. Simultaneously, basic material properties of each component in the battery pack cover are collected from material data sheets. These basic material properties include, but are not limited to, material hardness (e.g., measured by Rockwell or Vickers hardness) and material density (in g / cm³). 3 or kg / m 3 ) and the melting point of the material (in °C).
[0020] The three-dimensional contour data and basic material indicators are uploaded to the control system of the laser marking equipment. During the upload process, the system performs format verification and preprocessing on the data, including coordinate system 1, defect point filtering and model standardization, to ensure the accuracy and compatibility of the data.
[0021] After the data is successfully loaded, the battery pack cover is positioned and fixed on the worktable of the laser marking equipment to ensure that its physical orientation is consistent with the reference coordinate system set in the three-dimensional data.
[0022] Based on the three-dimensional contour data of the battery pack cover, the data is segmented according to each cover component of the battery pack 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.
[0023] After uploading and loading the 3D contour data and basic material specifications of the battery pack cover into the control system of the laser marking equipment, the 3D model of the battery pack cover is segmented at the component level. By identifying geometric feature changes (such as abrupt changes in edges, curvature, and seam areas) in the cover surface or volume model, the component boundaries are extracted and divided into several substructures. Specifically, the system uses boundary detection algorithms and 3D topology analysis methods, combined with point cloud density differences, surface normal angle changes, or assembly features defined in the CAD model, to identify and label different components.
[0024] After component segmentation, the system uniformly collects the basic material indicators (hardness, density, melting point) associated with each component and couples them with its three-dimensional surface information for depth consistency processing. This involves adjusting the laser marking parameter combinations (such as laser power, scanning speed, frequency, and focusing depth) based on material characteristics, while ensuring visual consistency, to achieve consistent equivalent energy depth. 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 control to achieve consistent surface marking. Finally, the system comprehensively considers the spatial location, connectivity, material properties, and process adaptability of the components, and determines the marking order of each component based on optimization algorithms (such as heuristic sorting, priority scheduling, or path cost minimization strategies). Simultaneously, a corresponding laser parameter combination table is generated for each component and used as input data for subsequent path planning and energy control.
[0025] Furthermore, based on the three-dimensional contour data of the battery pack cover, data segmentation is performed by comparing the various cover components of the battery pack cover. The method includes:
[0026] By identifying the geometric differences of the battery pack cover, the boundaries of each cover component are identified; sharp feature points are extracted from the three-dimensional contour data of the battery pack cover, and a connection diagram of the cover components is constructed by combining the topological relationship of the battery pack cover; data segmentation is performed based on the connection diagram of the cover components and the boundaries of each cover component, and detachable 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 pack cover to identify the boundaries of its individual 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 abrupt change recognition, normal vector change rate analysis, and boundary surface acute angle detection to determine the joint areas or assembly interfaces between different components. For example, if the normal angle between adjacent surfaces exceeds a set threshold (such as 45°), it can be considered a component boundary; or the system can detect points where the surface curvature changes from continuous and smooth to discontinuous to assist in determining the boundary line. Next, sharp feature points are further extracted from the 3D contour data, including structurally strong geometric feature points such as hole edges, corners, steps, grooves, and rib ends. Combining the spatial distribution relationship of these geometric feature points, the system further constructs a topological relationship model of the battery pack cover, forming a connection diagram of the cover components. 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 connection diagram of the cover plate components, the system combines this diagram with the aforementioned geometric boundary identification results to perform data segmentation. Specifically, this includes dividing the components according to the connection edges between them, based on a certain segmentation priority (such as structural independence, geometric complexity, and material differences). After segmentation, the system labels each component, distinguishing them as "detachable components" or "fixed components." This labeling is determined based on characteristics such as the cover plate product's structural design rules, bolt hole distribution, and nested insertion structure. For example, components with reserved through holes for disassembly, mechanical fasteners, or clamping slots can be marked as "detachable components," while integrally formed or welded encapsulated parts are classified as "fixed components."
[0029] Based on the marking sequence and laser parameter combination of each cover plate component, a laser beam focusing path matching the three-dimensional contour data and basic material properties is determined.
[0030] After determining the marking sequence and corresponding laser parameter combinations for each cover plate component, the system formulates a matching laser beam focusing path based on 3D contour data and basic material properties. Specifically, the system first performs spatial geometric analysis on the 3D contour data, extracting parameters such as curvature information, local tilt angles, and surface undulations of the cover plate surface. Through curvature analysis, high-curvature areas (such as corners, edges, and protrusions) and low-curvature areas (such as planes and gentle slopes) are identified, and the laser head focusing method is adjusted accordingly: a "point-by-point focusing" strategy is adopted in high-curvature areas, where the laser focus point is adjusted point by point and closely follows the curved surface trajectory to ensure that the focus is always on the component surface; while a "continuous scanning focusing" strategy is adopted in low-curvature areas to improve processing efficiency. Next, the system determines the energy density distribution requirements of the laser beam on different components based on the basic material properties of each component, such as material hardness, density, and melting point. Specifically, for materials with higher hardness or higher melting points, the laser power needs to be increased or the focusing dwell time extended; for materials with higher density, the scanning speed needs to be appropriately reduced to achieve sufficient energy deposition. The system uses a built-in material response model or laser marking experience database to determine parameter matching relationships through table lookup or interpolation, and maps these relationships to spatial path planning. The final output laser beam focusing path is a set of three-dimensional curves, describing the trajectory changes of the laser focus in three-dimensional space during the marking process.
[0031] Furthermore, the method for determining a laser beam focusing path that matches the three-dimensional contour data and basic material properties includes:
[0032] The curvature analysis of the three-dimensional contour data is performed and compared with the first curvature threshold. If the first threshold is met, the data is defined as a high curvature region, and the point-by-point focusing operation is switched in the high curvature region. The curvature analysis of the three-dimensional contour data is performed and compared with the second curvature threshold. If the second threshold is met, the data is defined as a low curvature region, and the continuous scanning focusing operation is switched in the low curvature region.
[0033] First, the system performs surface curvature analysis on the 3D contour data of the battery pack cover. This curvature analysis can be based on a triangular mesh model (such as STL format) or point cloud data, using Gaussian curvature or average curvature as the curvature quantification index. By calculating the curvature value for each mesh patch or point cloud node, a curvature distribution map of the cover surface in different regions is obtained. Next, the calculated curvature values are compared with a preset first curvature threshold. If the curvature value of a certain region exceeds the first curvature threshold (e.g., set to 0.2 mm), the system detects the curvature. -1If a region exhibits high curvature, it is identified as such. High curvature regions typically correspond to geometrically complex areas such as sharp corners, bends, and minor irregularities, making them more sensitive to laser focus position errors. Within high curvature regions, the system automatically switches the laser focusing mode to point-by-point focusing. This operation involves real-time adjustment of the laser head's Z-axis height and focal length during marking, ensuring the laser focus remains perpendicular and equidistant from the cover plate surface, thus improving marking consistency and accuracy. Further, the curvature calculation is repeated for the remaining regions, and the curvature values are compared to a second curvature threshold. If the curvature value of a region is lower than this second curvature threshold (e.g., set to 0.05mm), the region is considered high-curvature. -1 If the surface morphology is relatively flat or gently changing, the system is considered a low-curvature region. In such regions, the surface topography is relatively smooth or gently changing, and the focal tolerance requirement is relatively low. The system employs continuous scanning focusing in low-curvature regions, meaning the laser head moves continuously along the marking path while maintaining a constant focal length or adjusting it at a low frequency, thereby improving marking efficiency. For medium-curvature regions between the first and second curvature thresholds, the system can be configured with intermediate states, such as semi-continuous scanning mode or low-speed focusing compensation mode, to achieve smooth transitions and avoid control instability caused by frequent switching of focusing modes.
[0034] Furthermore, the method for switching to point-by-point focusing operation in the high curvature region includes:
[0035] The marking dot density is determined by the first curvature threshold; in the high curvature region, surface roughness is introduced, and in combination with the marking dot density, it is determined whether to activate the marking dot recombination command.
[0036] First, the system determines the marking lattice density of the high-curvature region based on the difference between the curvature value and the first curvature threshold. The lattice density is D = D0 + α(KK). t ), where D0 is the basic lattice density, K is the region average curvature value, and K t The first curvature threshold is set, and α is an empirical adjustment coefficient. Next, the system performs surface roughness analysis on this high-curvature region. Surface roughness can be obtained through a laser displacement sensor or a 3D contour scanner, extracting Ra (arithmetic mean roughness) or Rq (root mean square roughness) indices. High roughness indicates the presence of microscopic uneven structures on the surface, which may cause the laser focus to deviate from the ideal focusing plane in a localized area, thus 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 reassembly command is activated: the current dot density exceeds the density critical value, but the roughness is greater than the set threshold; the combined judgment result of dot density and roughness does not meet the target focus energy coverage.
[0037] Once the marking dot matrix reconstruction command is activated, the system will reconstruct the dot matrix arrangement in the area, including adjusting the dot matrix shape (from an equidistant matrix to an adaptive hexagon, grid, or staggered arrangement), fine-tuning the local dot coordinates, and adjusting the focus dwell time, to ensure that each focal point can still maintain effective energy deposition and pattern reconstruction capabilities in areas with high curvature and complex surface undulations.
[0038] Furthermore, in conjunction with the marked dot matrix density, the method for determining whether to activate the marked dot matrix recombination instruction includes:
[0039] The density of the marking lattice and the focusing dwell time are mapped to the multi-target Pareto front to determine the initial population. The laser head start and stop are used as penalty factors to perform non-dominated sorting iterative optimization in the initial population to select 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. The target solution is used to execute the activation decision of the marking lattice recombination command.
[0040] First, the system acquires the marking density and corresponding laser focusing dwell time within the current high-curvature region. The marking density measures the distribution density of marking points per unit area, while the focusing dwell time evaluates the laser energy deposition intensity at a single point. Next, the marking density and focusing dwell time are input into a multi-objective optimization model as two optimization objectives and mapped to the Pareto optimal front space to construct an initial solution set (i.e., an initial population). The multi-objective optimization model can be initialized using NSGA-II or similar evolutionary computation methods.
[0041] The objective functions are as follows: Objective 1: Minimize the marking time per unit area (= lattice density × dwell time); Objective 2: Maximize the clarity of the marking pattern (positively correlated with the uniformity of energy coverage).
[0042] After forming the initial population, the system incorporates the laser head start-up frequency and laser head stop-up frequency as penalty factors into the fitness function to control the equipment response burden during the dot matrix reassembly process. Frequent laser head starts or stops affect equipment stability; therefore, each start / stop increases the penalty weight, and the total penalty term is included in the negative index of the fitness function. Then, based on the principles of non-dominated sorting and crowding comparison, the initial population is iteratively optimized in multiple rounds to select several non-dominated solution sets. Non-dominated solutions refer to the set of solutions that are not completely surpassed by other solutions on multiple objectives, representing the frontier solutions with optimal performance balance. Finally, from the non-dominated solution set, the solution with the optimal fitness function value is selected as the target solution for the current region. The fitness function comprehensively considers dot matrix coverage, marking efficiency, and equipment stability. Under the parameter conditions corresponding to the target solution, if the results show that the original dot matrix density or focusing time combination cannot achieve uniform coverage and the equipment load is high, the system automatically activates the dot matrix reassembly command and performs operations such as dot reconstruction and focusing strategy adjustment.
[0043] The heat-affected zone of the material is determined, and the focusing path of the laser beam is compensated and corrected to obtain a heat compensation path. The heat compensation path, the marking sequence of each cover plate component, and the combination of laser parameters are loaded into the laser marking equipment to control the marking of the battery assembly cover plate.
[0044] First, by combining 3D contour data with basic material properties (including material hardness, density, and melting point) and thermal conductivity, the temperature field distribution inside the material after laser treatment is predicted, and the areas where the material structure changes are identified and defined as the heat-affected zone (HAZ). Then, based on the thermal response characteristics of each component, the degree of thermal deformation per unit energy input is calculated, and a thermal response function is constructed to generate the corresponding energy density compensation coefficient. On this basis, the initial laser beam focusing path is adjusted at the vector level for power and trajectory fine-tuning, allowing parameters such as laser power, dwell time, and scanning speed to dynamically change according to the distribution characteristics of the HAZ, thus forming a thermal compensation path that matches the material's thermal response characteristics. The thermal compensation path, marking sequence, and laser parameter combination are uniformly loaded into the laser marking equipment. The control system executes the marking operation accordingly, while simultaneously monitoring the marking status in real time and dynamically adjusting the laser output based on the thermal compensation path. This ensures that the marking patterns in each component area meet the preset process standards in terms of geometric accuracy, line width, and color depth, effectively improving the overall marking quality and consistency.
[0045] Furthermore, the method for determining the heat-affected zone of the material and compensating and correcting the focusing path of the laser beam to obtain a thermal compensation path 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, and an energy density compensation coefficient is determined. Based on the energy density compensation coefficient, the laser energy density distribution of the laser marking equipment is adjusted synchronously until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, and a heat compensation path is determined.
[0047] First, sensitive areas where laser irradiation might induce structural changes—the heat-affected zones (HAZs)—are identified in the 3D contour data of the battery pack cover. This identification process, based on the material's thermal conductivity and melting point parameters, models the transient temperature distribution and thermal diffusion behavior caused by laser irradiation through numerical simulation or analytical calculation. This predicts the temperature rise and deformation trends of different components' surfaces and interiors under heat input during laser scanning, thereby determining the areas with the most significant local thermal deformation. Based on these predictions, a corresponding energy density compensation coefficient is generated for each component region. This coefficient characterizes the extent to which laser input parameters need adjustment to ensure energy consistency among materials with different thermal responses. Subsequently, based on the energy density compensation coefficient, the power output curve of the laser marking equipment is simultaneously corrected to match the laser energy density distribution spatially with the material's thermal response behavior. The laser focusing power, scanning speed, and spot overlap rate are dynamically adjusted to ensure that the error between the actual melting zone boundary and the predicted theoretical HAZ boundary remains within a preset process threshold. Ultimately, a laser scanning path corrected by thermal compensation is formed, namely the thermal compensation path, 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 method further includes loading the thermal compensation path, the marking sequence of each cover plate component, and the laser parameter combination into the laser marking equipment:
[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. 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 back based on the virtual simulation marking data.
[0050] Before starting the laser marking equipment, a thermo-mechanical coupling simulation model is first constructed based on the generated thermal compensation path, the spatial structure of the cover plate components, and their material parameters. Through finite element analysis or numerical heat conduction simulation, the temperature field distribution and stress field distribution caused by the laser energy input on different areas of the cover plate during laser marking are simulated. This virtual simulation is used to dynamically track the formation range of the material's molten zone and the diffusion boundary of the heat-affected zone. When the simulation results show a deviation exceeding the process-set threshold between the actual molten zone boundary and the heat-affected zone boundary, the system automatically activates the compensation feedback mechanism to retrospectively adjust the previously set energy density compensation coefficient. This includes recalculating the local energy input distribution and adjusting the laser power-time curve and path centroid, thereby achieving fine-tuning of parameters for key areas. Only after the thermal compensation path and marking parameters, verified by simulation, are confirmed to meet marking consistency and thermal control indicators are loaded into the laser marking equipment for subsequent high-precision, low-heat-loss battery pack cover plate marking control tasks.
[0051] Furthermore, methods for controlling the marking of battery pack cover plates also include:
[0052] The system collects equipment operating parameters for laser marking equipment, including laser power fluctuation, galvanometer scanning speed deviation, and worktable vibration amplitude. It configures a health index, mapping these operating parameters to the laser emission component stability assessment dimension, galvanometer scanning component accuracy assessment dimension, and worktable transmission component stability assessment dimension of the health index. A weighted calculation yields the equipment health score. When the equipment health score falls below a basic health threshold, the marking speed is automatically reduced, and a fault diagnosis program is initiated. This program uses historical fault data matching and machine learning to locate potential fault points and provides fault alerts.
[0053] Preferably, key parameters generated during the operation of the laser marking equipment are collected in real time, including but not limited to laser power fluctuations, galvanometer scanning speed deviations, and worktable vibration amplitudes, forming a comprehensive equipment operating status dataset. Subsequently, based on a preset health evaluation model, these operating parameters are mapped to multiple evaluation dimensions of the health indicators, including the stability evaluation dimension of the laser emitting component, the accuracy evaluation dimension of the galvanometer scanning component, and the stability evaluation dimension of the worktable transmission component. After mapping, the results of each evaluation dimension are comprehensively calculated using set weighting coefficients to obtain the current comprehensive health score of the equipment. When this health score falls below a set basic health threshold, the system will automatically trigger an operational protection mechanism: on the one hand, it automatically reduces the laser marking speed to mitigate the risk of accuracy deviation caused by equipment instability; on the other hand, it immediately initiates the equipment fault diagnosis program, utilizing the built-in fault database and historical maintenance records, combined with a machine learning-based pattern recognition algorithm, to match and analyze the current operating status with historical abnormal patterns, quickly locating potential fault points. Once a potential fault trend with significant similarity is detected, the system will proactively alert users to the fault through a graphical interface or audio-visual signals, and output possible faulty components and suggested handling measures to assist maintenance personnel in carrying out inspections or replacements in advance, ensuring the continuity of the laser marking process and the lifespan of the equipment.
[0054] Furthermore, it also includes:
[0055] After laser marking is completed, the laser marking pattern is acquired and key features of the pattern are extracted. The key features of the pattern are compared with the preset marking pattern 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 batch of battery pack cover plates and the quality of laser marking are statistically analyzed to determine strongly correlated defects. The strongly correlated defects are then combined with the corresponding batch of battery pack cover plates to perform compensation and correction intervention on the laser beam focusing path.
[0056] First, images of the laser-marked pattern on the surface of the battery pack cover are acquired. Image processing algorithms are then used to analyze the acquired images and extract key feature information of the pattern. These key features include, but are not limited to, geometric contours, edge lines, pattern filling shapes, and color layer variations. Subsequently, the key features of the pattern are compared with preset standard marking patterns for similarity analysis. Geometric size errors (e.g., deviations in length, width, and height of characters / patterns), line clarity errors (e.g., edge jaggedness and blurriness), and color depth errors (e.g., differences in brightness caused by variations in ablation depth) are calculated and identified. Based on the error results in these three dimensions, the system statistically models the laser marking quality of the current batch of battery pack covers, extracting key defect types and their frequency and distribution trends. These are then correlated with specific batch information of the battery pack covers to construct a strong batch-defect correlation model. After identifying significant and 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 focusing path data used in the marking process. These interventions include focusing position adjustment, laser energy density fine-tuning, and scanning speed optimization. This ensures that the probability of the same type of defect occurring is significantly reduced in subsequent marking tasks of the same batch, thereby achieving intelligent and adaptive laser marking path optimization and continuous improvement in marking quality.
[0057] In summary, the embodiments of this application have at least the following technical effects:
[0058] First, the 3D contour data and basic material properties of the battery pack cover are uploaded. The battery pack cover is then placed on the worktable of the laser marking equipment. The basic material properties include material hardness, density, and melting point. Next, based on the 3D contour data of the battery pack cover, data segmentation is performed on each cover component, and depth consistency processing is performed using the basic material properties to determine the marking sequence and laser parameter combinations for each component. Then, based on the marking sequence and laser parameter combinations for each cover component, a laser beam focusing path matching the 3D contour data and basic material properties is determined. Finally, the heat-affected zone of the material is identified, and the laser beam focusing path is compensated and corrected to obtain a heat-compensated path. The heat-compensated path, the marking sequence for each cover component, and the laser parameter combinations are loaded into the laser marking equipment for battery pack cover marking control. This solves the technical problem of unstable marking quality on different components of the battery pack cover due to differences in contour and material properties, achieving a significant improvement in marking quality.
[0059] Example 2, based on the same inventive concept as the control optimization method for the laser marking equipment for the battery pack cover in the foregoing examples, such as... Figure 2 As shown, this application provides a control optimization system for a laser marking equipment for battery pack covers, wherein the system includes:
[0060] Data upload module 11: Uploads the three-dimensional contour data and basic material indicators of the battery pack cover plate, and places the battery pack cover plate on the worktable of the laser marking equipment. The basic material indicators include material hardness, material density, and material melting point. Data processing module 12: Based on the three-dimensional contour data of the battery pack cover plate, performs data segmentation on each cover plate component of the battery pack cover plate, and performs depth consistency processing in combination with the basic material indicators to determine the marking sequence and laser parameter combination of each cover plate component. Focusing path formulation module 13: Based on the marking sequence and laser parameter combination of each cover plate component, formulates a laser beam focusing path that matches the three-dimensional contour data and basic material indicators. Control module 14: Determines the heat-affected zone of the material, compensates and corrects the laser beam focusing path to obtain a heat compensation path, and loads the heat compensation path, the marking sequence of each cover plate component, and the laser parameter combination into the laser marking equipment to control the marking of the battery pack cover plate.
[0061] Furthermore, the control module 14 is used to perform the following methods:
[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, and an energy density compensation coefficient is determined. Based on the energy density compensation coefficient, the laser energy density distribution of the laser marking equipment is adjusted synchronously until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, and a heat compensation path is determined.
[0063] Furthermore, the data processing module 12 is used to perform the following methods:
[0064] By identifying the geometric differences of the battery pack cover, the boundaries of each cover component are identified; sharp feature points are extracted from the three-dimensional contour data of the battery pack cover, and a connection diagram of the cover components is constructed by combining the topological relationship of the battery pack cover; data segmentation is performed based on the connection diagram of the cover components and the boundaries of each cover component, and detachable component identifiers and fixed component identifiers are added to the three-dimensional contour data.
[0065] Furthermore, the focusing path formulation module 13 is used to perform the following method:
[0066] The curvature analysis of the three-dimensional contour data is performed and compared with the first curvature threshold. If the first threshold is met, the data is defined as a high curvature region, and the point-by-point focusing operation is switched in the high curvature region. The curvature analysis of the three-dimensional contour data is performed and compared with the second curvature threshold. If the second threshold is met, the data is defined as a low curvature region, and the continuous scanning focusing operation is switched in the low curvature region.
[0067] Furthermore, the focusing path formulation module 13 is used to perform the following method:
[0068] The marking dot density is determined by the first curvature threshold; in the high curvature region, surface roughness is introduced, and in combination with the marking dot density, it is determined whether to activate the marking dot recombination command.
[0069] Furthermore, the focusing path formulation module 13 is used to perform the following method:
[0070] The density of the marking lattice and the focusing dwell time are mapped to the multi-target Pareto front to determine the initial population. The laser head start and stop are used as penalty factors to perform non-dominated sorting iterative optimization in the initial population to select 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. The target solution is used to execute the activation decision of the marking lattice recombination command.
[0071] Furthermore, the control module 14 is used to perform the following methods:
[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. 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 back based on the virtual simulation marking data.
[0073] Furthermore, the control module 14 is used to perform the following methods:
[0074] The system collects equipment operating parameters for laser marking equipment, including laser power fluctuation, galvanometer scanning speed deviation, and worktable vibration amplitude. It configures a health index, mapping these operating parameters to the laser emission component stability assessment dimension, galvanometer scanning component accuracy assessment dimension, and worktable transmission component stability assessment dimension of the health index. A weighted calculation yields the equipment health score. When the equipment health score falls below a basic health threshold, the marking speed is automatically reduced, and a fault diagnosis program is initiated. This program uses historical fault data matching and machine learning to locate potential fault points and provides fault alerts.
[0075] Furthermore, the control module 14 is used to perform the following methods:
[0076] After laser marking is completed, the laser marking pattern is acquired and key features of the pattern are extracted. The key features of the pattern are compared with the preset marking pattern 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 batch of battery pack cover plates and the quality of laser marking are statistically analyzed to determine strongly correlated defects. The strongly correlated defects are then combined with the corresponding batch of battery pack cover plates to perform compensation and correction intervention on the laser beam focusing path.
[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0078] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0079] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A control optimization method for laser marking equipment for battery pack covers, characterized in that the method... include: Upload the three-dimensional contour data and basic material parameters of the battery pack cover plate, and place the battery pack cover plate on the worktable of the laser marking equipment. The basic material parameters include material hardness, material density, and material melting point. Based on the three-dimensional contour data of the battery pack cover, the data is segmented according to each cover component of the battery pack 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. Based on the marking sequence and laser parameter combination of each cover plate component, a laser beam focusing path matching the three-dimensional contour data and basic material properties is determined. The heat-affected zone of the material is determined, and the focusing path of the laser beam is compensated and corrected to obtain a thermal compensation path. This includes: predicting thermal deformation in the heat-affected zone of the material using thermal conductivity and the melting point of the material in the basic material index, and determining an energy density compensation coefficient; and simultaneously adjusting the laser energy density distribution of the laser marking equipment according to the energy density compensation coefficient until the boundary error between the material melting zone and the material heat-affected zone meets the marking process threshold, thereby determining the thermal compensation path. The process of loading the thermal compensation path, the marking sequence of each cover plate component, and the combination of laser parameters into the laser marking equipment also includes: before the laser marking equipment is started, simulating the temperature field distribution and stress field distribution during the laser marking process through virtual simulation; and automatically adjusting the energy density compensation coefficient if the boundary error between the material melting zone and the material heat-affected zone does not meet the marking process threshold based on the virtual simulation marking data. Control the marking of the battery pack cover.
2. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 1, characterized in that, Based on the three-dimensional contour data of the battery pack cover, data segmentation is performed by referring to each cover component of the battery pack cover, specifically including: Identify the boundaries of each cover plate component by the differences in geometric features of the battery pack cover plate; Sharp feature points are extracted from the three-dimensional contour data of the battery pack cover, and a connection diagram of the cover components is constructed by combining the topological relationship of the battery pack cover. Data segmentation is performed based on the connection diagram of the cover plate components and the boundaries of each cover plate component. Detachable component identifiers and fixed component identifiers are added to the three-dimensional contour data.
3. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 1, characterized in that, Determining a laser beam focusing path that matches the three-dimensional contour data and basic material properties specifically includes: The curvature analysis of the three-dimensional contour data is performed and compared with the first curvature threshold. If the threshold is met, it is defined as a high curvature region. In the high curvature region, the point-by-point focusing operation is switched. The curvature analysis of the three-dimensional contour data is performed and compared with the second curvature threshold. If the threshold is met, it is defined as a low curvature region, and the continuous scanning focusing operation is switched to the low curvature region.
4. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 3, characterized in that, Switching to point-by-point focusing operation in the high curvature region specifically includes: The density of the marking lattice is determined by the first curvature threshold; In the high curvature region, surface roughness is introduced, and combined with the marking dot density, it is determined whether to activate the marking dot recombination command.
5. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 4, characterized in that, Based on the marked dot matrix density, determine whether to activate the marked dot matrix recombination command, specifically including: The density of the marking lattice and the focusing dwell time are mapped to the multi-target Pareto front to determine the initial population; Using laser head start-up and laser head stop-up as penalty factors, non-dominated sorting iterative optimization is performed on the initial population to select the 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 tagged dot matrix recombination instruction.
6. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 1, characterized in that, The method for controlling the marking of the battery pack cover also includes: The equipment operating parameters collected for laser marking equipment include laser power fluctuation values, galvanometer scanning speed deviation, and worktable vibration amplitude. Configure health indicators, and map the equipment operating parameters to the stability evaluation dimension of the laser emitting component, the accuracy evaluation dimension of the galvanometer scanning component, and the stability evaluation dimension of the worktable transmission component of the health indicators. The equipment health score is obtained by weighted calculation. When the device health score is lower than the basic health threshold, the marking speed is automatically reduced, and the device fault diagnosis program is started. By matching historical fault data and machine learning, potential fault points are located and fault reminders are issued.
7. The control optimization method for the laser marking equipment for battery pack cover plates as described in claim 6, characterized in that, The method further includes: After laser marking is completed, the laser marking pattern is acquired and key features of the pattern are extracted. The key features of the pattern are compared with the preset marking pattern to determine the geometric size error, line clarity error, and color depth error. Based on the aforementioned geometric dimension error, line clarity error, and color depth error, the batches of battery pack cover plates and the quality of laser marking are statistically analyzed to identify strongly correlated defects. These strongly correlated defects are then combined with the corresponding batches of battery pack cover plates to perform compensation and correction intervention on the laser beam focusing path.
8. A control and optimization system for laser marking equipment for battery pack covers, characterized in that, A control optimization method for implementing the laser marking equipment for battery pack cover plates according to any one of claims 1-7, the system comprising: Data upload module: Uploads the three-dimensional contour data and basic material indicators of the battery pack cover plate, and places the battery pack cover plate on the worktable of the laser marking equipment. 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 pack cover, the module performs data segmentation by comparing the various cover components of the battery pack cover, and performs depth consistency processing in conjunction with the basic material indicators to determine the marking sequence and laser parameter combination of each cover component. Focusing path formulation module: Based on the marking sequence and laser parameter combination of each cover plate component, formulate a laser beam focusing path that matches the three-dimensional contour data and basic material indicators; Control module: Determines the heat-affected zone of the material, compensates and corrects the focusing path of the laser beam to obtain a heat compensation path, and loads the heat compensation path, the marking sequence of each cover plate component, and the combination of laser parameters into the laser marking equipment to control the marking of the battery assembly cover plate.