Laser cutting systems for complex shape cutting

Abstract

The present invention discloses a laser cutting system for cutting complex shapes, which relates to the field of laser cutting technology, including: obtaining shape segmentation information; configuring laser cutting parameters of each segmented area according to the shape segmentation information, and obtaining cutting control parameters of each segmented area; clustering the cutting control parameters of each segmented area, and obtaining the clustering relationship of the cutting control parameters; identifying the segmentation position relationship according to the shape segmentation information, and determining the segmentation position relationship; planning the cutting path according to the clustering relationship of the cutting control parameters and the segmentation position relationship, and obtaining the cutting control information, the cutting control information including the cutting path and the corresponding cutting control parameters, and controlling the cutting of the cutting position area according to the cutting control parameters based on the cutting order. The present invention solves the technical problems of insufficient control accuracy, low cutting efficiency, and poor stability in the prior art when processing complex shape cutting, and achieves the technical effect of improving cutting efficiency and quality.

Description

Technical Field

[0001] The invention relates to the technical field of laser cutting, and in particular to a laser cutting system for cutting complex shapes. Background Art

[0002] With the continuous advancement of modern industrial technology, laser cutting technology has been widely used in the field of complex shape cutting due to its high precision and high efficiency. Especially in high-precision manufacturing industries such as automobile manufacturing, aerospace, and electronic devices, laser cutting technology has become an indispensable processing method. However, with the increasing complexity of product structures and the continuous improvement of cutting requirements, traditional laser cutting systems face many challenges.

[0003] Traditional laser cutting systems often have problems such as insufficient control accuracy, low cutting efficiency, and poor stability when dealing with complex shape cutting. Especially when facing complex shapes with multiple segmented areas and fine structural features, traditional cutting methods are difficult to guarantee cutting quality and efficiency. In addition, due to the large differences in materials, thicknesses and other characteristics of different segmented areas, traditional laser cutting systems often find it difficult to achieve precise control of different areas, resulting in deviations or damage during the cutting process. Summary of the invention

[0004] The present application provides a laser cutting system for cutting complex shapes, which is used to solve the technical problems of insufficient control accuracy, low cutting efficiency and poor stability in the prior art when dealing with cutting complex shapes.

[0005] In view of the above problems, the present application provides a laser cutting system for cutting complex shapes.

[0006] The present application provides a laser cutting system for cutting complex shapes, the system comprising:

[0007] A shape segmentation information acquisition module, wherein the shape segmentation information acquisition module acquires shape segmentation information, wherein the shape segmentation information is the result of clustering segmentation according to the structural features of the complex shape; a cutting control parameter acquisition module, wherein the cutting control parameter acquisition module configures the laser cutting parameters of each segmented area according to the shape segmentation information, and acquires the cutting control parameters of each segmented area, wherein the cutting control parameters of each segmented area include constraint parameter identifiers; a cutting control parameter clustering relationship acquisition module, wherein the cutting control parameter clustering relationship acquisition module clusters the cutting control parameters of each segmented area based on the constraint parameter identifiers, and acquires the cutting control parameter clustering relationship; a segmentation position relationship determination module, wherein the segmentation position relationship determination module identifies the segmentation position relationship according to the shape segmentation information, and determines the segmentation position relationship; a cutting control module, wherein the cutting control module plans the cutting path according to the cutting control parameter clustering relationship and the segmentation position relationship, and acquires the cutting control information, wherein the cutting control information includes the cutting path and the corresponding cutting control parameters, which are used to determine the cutting position area and the cutting sequence according to the cutting path, and to control the cutting of the cutting position area according to the cutting sequence according to the cutting control parameters.

[0008] The technical solution provided in this application has at least the following technical effects or advantages:

[0009] The present application obtains shape segmentation information, which is the result of clustering segmentation according to the structural features of complex shapes; configures laser cutting parameters of each segmented area according to the shape segmentation information, and obtains cutting control parameters of each segmented area, wherein the cutting control parameters of each segmented area include constraint parameter identification; clusters the cutting control parameters of each segmented area based on the constraint parameter identification, and obtains the clustering relationship of the cutting control parameters; identifies the segmentation position relationship according to the shape segmentation information, and determines the segmentation position relationship; plans the cutting path according to the clustering relationship and the segmentation position relationship of the cutting control parameters, and obtains the cutting control information, which includes the cutting path and the corresponding cutting control parameters, and is used to determine the cutting position area and the cutting order according to the cutting path, and control the cutting of the cutting position area according to the cutting control parameters based on the cutting order. The present invention solves the technical problems of insufficient control accuracy, low cutting efficiency, and poor stability in the prior art when processing complex shape cutting, and achieves the technical effect of improving cutting efficiency and quality by intelligently configuring laser cutting parameters and planning paths. 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 schematic diagram of the structure of a laser cutting system for cutting complex shapes provided in an embodiment of the present application;

[0012] Figure 2 A schematic flow chart of a laser cutting system for cutting complex shapes provided in an embodiment of the present application.

[0013] Explanation of reference numerals: shape segmentation information acquisition module 11 , cutting control parameter acquisition module 12 , cutting control parameter clustering relationship acquisition module 13 , segmentation position relationship determination module 14 , cutting control module 15 . DETAILED DESCRIPTION

[0014] The present application provides a laser cutting system for cutting complex shapes, aiming to solve the technical problems of insufficient control accuracy, low cutting efficiency and poor stability in the prior art when dealing with complex shape cutting. By intelligently configuring laser cutting parameters and planning paths, the technical effect of improving cutting efficiency and quality is achieved.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof 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 explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the present application provides a laser cutting system for cutting complex shapes, which is used to perform the following steps: Figure 2 The laser cutting method for complex shape cutting shown in the figure comprises:

[0019] A shape segmentation information acquisition module 11, wherein the shape segmentation information acquisition module 11 obtains shape segmentation information, wherein the shape segmentation information is a result of clustering segmentation according to structural features of a complex shape;

[0020] In the embodiment of the present application, the shape segmentation information acquisition module first receives the complex shape information to be cut. The complex shape information is provided in the form of a CAD file, an image or other format, and contains detailed geometric information and possible topological structures of the complex shape.

[0021] Next, the shape segmentation information acquisition module performs feature analysis on the input complex shape to identify geometric features such as the shape's boundaries, corners, curves, symmetry, and possible repeated patterns or specific structures.

[0022] According to the characteristics of the shape, the shape segmentation information acquisition module selects a suitable clustering algorithm, such as K-means, hierarchical clustering or DBSCAN, to perform cluster segmentation on the shape. This process divides the shape into several regions, and the shape features in each region are similar or related.

[0023] After the segmentation is completed, the shape segmentation information acquisition module verifies the results to check whether the segmented regions meet the expected geometric and topological conditions, and whether there is over-segmentation or under-segmentation.

[0024] If the segmentation result is found to be unsatisfactory during the verification process, adjustments and optimizations can be made by adjusting the parameters of the clustering algorithm, re-executing the clustering process, etc.

[0025] Once the segmentation result is verified and reaches a satisfactory effect, the shape segmentation information acquisition module outputs shape segmentation information. The shape segmentation information includes the boundary coordinates of each segmented area, area identification, and other related data for subsequent cutting control.

[0026] A cutting control parameter acquisition module 12, wherein the cutting control parameter acquisition module 12 configures laser cutting parameters of each segmented area according to the shape segmentation information to obtain cutting control parameters of each segmented area, wherein the cutting control parameters of each segmented area include constraint parameter identifiers;

[0027] In the embodiment of the present application, the cutting control parameter acquisition module uses image processing technology and CA to identify the material properties, thickness and geometric shapes of different regions. Based on these characteristics, each region is classified and evaluated to determine the appropriate cutting strategy.

[0028] Then refer to the pre-set parameter library or expert system, which contains the optimal cutting parameters corresponding to different materials and thicknesses based on historical data and experimental results.

[0029] Apply algorithms such as fuzzy logic and neural networks to automatically select and adjust parameters such as laser power and cutting speed according to regional characteristics.

[0030] At the same time, a set of standardized constraint parameter identification systems are defined, each of which corresponds to a specific cutting requirement or restriction. According to the characteristics and cutting requirements of each segmented area, a corresponding constraint parameter identification is assigned to it.

[0031] Conduct simulated cutting and evaluate the effectiveness of parameters by observing and measuring indicators such as cutting quality, speed, and heat-affected zone. Then, based on the evaluation results, use optimization algorithms such as genetic algorithms and particle swarm optimization to fine-tune the parameters to achieve better cutting results.

[0032] Finally, the verified and adjusted cutting control parameters and constraint parameter identifiers are organized into cutting control parameters for each segmentation area.

[0033] A cutting control parameter clustering relationship acquisition module 13, wherein the cutting control parameter clustering relationship acquisition module 13 clusters the cutting control parameters of each segmented area based on the constraint parameter identifier to obtain a cutting control parameter clustering relationship;

[0034] In an embodiment of the present application, the cutting control parameter clustering relationship acquisition module pre-processes the cutting control parameters, such as standardization and denoising, to ensure the consistency and reliability of the data, and at the same time extracts features closely related to the cutting effect, such as laser power, cutting speed, gas pressure, etc.

[0035] According to the characteristics of the data, such as data distribution, size, etc. and actual needs, select a suitable clustering algorithm such as K-means to cluster the cutting control parameters of each segmentation area. When clustering, first initialize the cluster center and set the number of clusters, that is, the K value. The K value is determined according to actual needs or through some heuristic methods, such as the elbow rule. Then execute the clustering algorithm to divide the data into different clusters, each cluster represents a group of segmentation areas with similar cutting control parameters.

[0036] Verify the clustering results and check whether the data points in the same cluster have similar cutting control parameters and whether the parameters between different clusters are significantly different. If the clustering effect is not good, adjust the parameters of the clustering algorithm, such as K value, number of iterations, etc., or try other clustering algorithms for optimization.

[0037] Through the above steps, the clustering relationship of cutting control parameters is obtained.

[0038] a segmentation position relationship determination module 14, wherein the segmentation position relationship determination module 14 performs segmentation position relationship recognition based on the shape segmentation information to determine the segmentation position relationship;

[0039] In an embodiment of the present application, the segmentation position relationship determination module extracts the boundaries, shapes and other features of the region through image processing technology for the received shape segmentation information, and determines the boundaries of each region through feature recognition algorithms such as edge detection and contour extraction.

[0040] By comparing the features of different segmented regions, such as boundary coordinates, shapes, etc., the relative positional relationships between them are determined. These relationships may include adjacency, overlap, inclusion, etc.

[0041] When making comparisons, spatial relationship analysis techniques are used to calculate the spatial distance, overlapping area and other parameters between different regions to determine their relative positional relationships. Geometric algorithms are used to calculate the spatial relationship between regions to determine whether two regions overlap or are adjacent.

[0042] Based on the identified positional relationships, a reasonable segmentation order is determined. When determining the segmentation order, a topological sorting algorithm is used to ensure the stability of each region during the cutting process and avoid damaging adjacent or overlapping regions when cutting a region.

[0043] Finally, the segmentation position relationship is determined through the above steps.

[0044] The cutting control module 15 performs cutting path planning according to the cutting control parameter clustering relationship and the segmentation position relationship to obtain cutting control information. The cutting control information includes a cutting path and corresponding cutting control parameters, and is used to determine a cutting position area and a cutting sequence according to the cutting path, and perform cutting control on the cutting position area according to the cutting control parameters based on the cutting sequence.

[0045] In the embodiment of the present application, the cutting control module receives the clustering relationship and segmentation position relationship of the cutting control parameters, cleans, converts the format and standardizes the received data to ensure the accuracy and compatibility of the data.

[0046] Then, advanced path planning algorithms, such as the Dijkstra algorithm, are used to calculate the optimal cutting path based on the specific shape of the workpiece and the split position relationship. This path is designed to minimize the moving distance and idle travel time of the laser head. In the path planning process, multiple factors are considered, such as the complexity of the shape of the workpiece, the thickness and type of the material, and the performance of the laser cutting machine.

[0047] To reduce the impact of heat-affected zones on material properties during cutting, priority is given to cutting those areas that are less affected by heat. A specific sorting algorithm, such as rule-based sorting or heuristic sorting, is used to determine the final cutting order.

[0048] Match the optimized cutting path with the corresponding optimal cutting control parameters to ensure that the most appropriate parameters are used when cutting each area. Integrate the cutting path, cutting sequence and corresponding cutting control parameters into cutting control information.

[0049] Furthermore, the cutting control parameter acquisition module 12 in the system provided in the embodiment of the application further includes:

[0050] Obtaining the cutting shape, material information, and thickness information of the shape segmentation information;

[0051] According to the cutting shape, material information, and thickness information, simulation optimization is performed through a simulation optimization space to obtain optimized cutting parameters;

[0052] Acquire a record data set, and perform fitting of a relationship between a cutting parameter and a cutting effect according to the record data set to obtain an influence coefficient of the cutting parameter on the cutting effect;

[0053] The cutting parameters whose influence coefficients meet preset requirements are selected for constraint parameter identification, the optimized cutting parameters are used as cutting control parameters, and a mapping relationship between the cutting control parameters and each segmented area is established.

[0054] In the embodiment of the present application, the cutting shape, material information, and thickness information are extracted from the shape segmentation information, and these information are predetermined.

[0055] Based on the obtained cutting shape, material information and thickness information, a virtual laser cutting environment is constructed through ANSYS, Abaqus or other professional laser cutting simulation software.

[0056] Then, based on the numerical simulation method of the physical model, such as finite element analysis, adjust the laser power, cutting speed, focus position, auxiliary gas and other parameters, perform multiple simulated cutting in the simulation environment, and record the cutting quality and efficiency of each simulation. Compare the simulation results under different parameter combinations, and select the best cutting parameter combination as the optimized cutting parameters.

[0057] A large amount of laser cutting experimental data was collected from historical records, including various parameter combinations and their corresponding cutting effects. Using regression analysis technology, the relationship between cutting parameters and cutting effects was fitted in the data analysis software, and the influence coefficient of each parameter on the cutting effect was calculated.

[0058] Based on the influence coefficient, the cutting parameters that meet or exceed the preset requirements, such as the significance threshold, are selected, and these parameters are considered to be the key parameters that have a significant impact on the cutting effect.

[0059] The selected key parameters are then marked by setting specific labels or attributes for these parameters in the database to clarify their importance and role in the cutting process.

[0060] The optimized and screened cutting parameters are determined as cutting control parameters. These parameters will be directly used in subsequent cutting operations to control the behavior of the laser cutting machine.

[0061] According to the characteristics of the workpiece, such as shape, material and thickness, each segmented area is associated with the optimized cutting control parameters by creating a mapping table, wherein the characteristics of each segmented area correspond to a specific set of cutting control parameters.

[0062] Furthermore, the cutting control parameter acquisition module 12 also includes:

[0063] Based on the twin digital technology, a simulation space is constructed according to the laser cutting monitoring data, and the cutting shape, material information, and thickness information are fitted into the simulation space as simulation experimental data;

[0064] Establishing an optimization model, and fitting the optimization model to the simulation space for optimizing and adjusting simulation experiment data;

[0065] Acquire simulation experiment results, determine whether the simulation experiment results meet preset requirements, and if not, input the simulation experiment data into the optimization model to optimize cutting parameters, wherein the cutting parameters include power, speed, focus, gas and pressure;

[0066] The simulation experiment data is adjusted according to the cutting parameter optimization results, and the simulation experiment is repeated, and so on, until the simulation experiment results meet the preset requirements, and the current cutting parameters are used as the optimized cutting parameters.

[0067] In the embodiment of the present application, a virtual laser cutting environment is constructed using twin digital technology. The laser cutting environment can simulate the real laser cutting process, including the emission of the laser beam, the absorption and reflection of the laser by the material, the heat-affected zone during the cutting process, etc. Then, the collected cutting shape, material information, thickness, etc. are fitted into the simulation space.

[0068] According to the characteristics of laser cutting, select appropriate optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. to search for the best parameter combination in a given parameter space.

[0069] Based on the selected optimization algorithm, a mathematical model is constructed to optimize the cutting parameters in the simulation space. This model needs to consider multiple factors, such as cutting speed, laser power, focus position, auxiliary gas and pressure, etc.

[0070] Combining the optimization model with the simulation space allows the cutting parameters to be adjusted and optimized in real time in the simulation environment. By continuously adjusting the parameters of the model, the cutting effect in the simulation environment is optimized.

[0071] Conduct laser cutting experiments in the simulation space fitted with the optimization model. Record various data during the experiment, such as cutting speed, cutting quality, etc. Compare the results of the simulation experiment with the preset requirements. The preset requirements include the flatness of the cutting surface, cutting speed, size of the heat-affected zone, etc. Determine whether the experimental results have achieved the expected effect through comparison.

[0072] If the simulation experiment results do not meet the preset requirements, the cutting parameters are adjusted through the optimization model, including increasing the laser power, adjusting the cutting speed, changing the focus position, or adjusting the pressure of the auxiliary gas.

[0073] The simulation experiment is repeated according to the adjusted parameters. This cycle is repeated until a set of parameter combinations that can achieve the best cutting effect is found.

[0074] After multiple iterations and optimizations, if the experimental results meet or exceed the preset requirements, the currently used cutting parameters are determined as the optimized cutting parameters.

[0075] Furthermore, the step of inputting the simulation experiment data into the optimization model to optimize the cutting parameters includes:

[0076] Compare the simulation experiment results with the preset requirements to determine the deviation results;

[0077] Based on the deviation results, cutting parameters are traced to determine the optimization target parameters and optimization direction;

[0078] According to the optimization target parameters and optimization direction, the optimization step length is set to perform parameter optimization;

[0079] Monitoring the optimization approach efficiency, and when the optimization approach efficiency does not reach the target value, adjusting the optimization step length according to the optimization approach efficiency;

[0080] When the optimization iteration number or optimization target is reached, the cutting parameter optimization result is obtained.

[0081] In the embodiment of the present application, the cutting effect obtained by the simulation experiment, such as cutting depth, cutting surface quality, etc., is quantitatively compared with the preset cutting requirements. Through the comparison, the specific deviation between the experimental result and the preset requirements is calculated, and this deviation is the insufficient cutting depth, the roughness of the cutting surface, etc.

[0082] Then, based on the deviation results, the cutting parameters are traced back to analyze the possible causes of the deviation and traced back to specific cutting parameters, such as insufficient laser power, too fast cutting speed, etc. According to the deviation results, determine which parameters are the key parameters that need to be adjusted, that is, the target parameters for optimization. For each target parameter, determine the direction of adjustment, such as increasing laser power or reducing cutting speed.

[0083] When performing parameter optimization, first set the optimization step length. The optimization step length determines the amount of parameter adjustment each time. When setting the optimization step length, set it according to the required optimization accuracy.

[0084] Adjust the target parameters according to the set step size and conduct new simulation experiments to observe whether the cutting effect is improved.

[0085] When monitoring the optimization approach efficiency, define an indicator to measure the speed or degree of the cutting effect approaching the preset requirements after each parameter adjustment. After each parameter optimization, calculate the optimization approach efficiency to determine whether the optimization process is effective and whether the optimization strategy needs to be adjusted.

[0086] If the optimization approach efficiency does not reach the expected target value, it means that the current optimization step size may be too large or too small and needs to be adjusted according to the actual situation. By adjusting the step size, the optimization process is made more efficient and the number of invalid simulation experiments is reduced.

[0087] Set a maximum number of iterations to prevent infinite optimization loops. When the cutting effect reaches or approaches the preset requirements, the optimization goal is considered to have been achieved. When the maximum number of iterations is reached or the optimization goal is met, the current cutting parameter combination is recorded as the cutting parameter optimization result.

[0088] Further, based on the shape segmentation information, segmentation position relationship recognition is performed to determine the segmentation position relationship, including:

[0089] According to the drawing structure of the complex shape target, establish the position correspondence of each shape segmentation;

[0090] According to the drawing result, the positions of the shape segments are identified and the orientation relationship is determined, including the adjacent continuous relationship and the hierarchical wrapping relationship;

[0091] According to the adjacent continuous relationship and the hierarchical wrapping relationship, the constraint impact analysis of the cutting process is performed to obtain the segmentation position constraint relationship, and the segmentation position constraint relationship is mapped and integrated with the orientation relationship to determine the segmentation position relationship.

[0092] In the embodiment of the present application, the drawing file of the complex shape target object, such as DWG, DXF, etc., is automatically imported through CAD file format parsing technology. The key information in the drawing, such as size, annotation and shape data, is automatically recognized and extracted using optical character recognition technology.

[0093] Next, the drawings are cleaned and enhanced using image preprocessing techniques through steps such as noise reduction, binarization, and edge detection to obtain clear shape outlines.

[0094] After preprocessing, computational geometry techniques, such as polygon segmentation algorithms, are used to automatically segment complex shapes into a number of independent or associated simple shapes, and each shape is assigned a unique identifier. Subsequently, the spatial relationships between the shapes are calculated to determine their orientation. This includes using spatial database technology to store shape data and determining the adjacent, continuous, and hierarchical wrapping relationships between shapes through topological relationship analysis.

[0095] The adjacent continuous relationship refers to the shape segments being directly connected without gaps. In the cutting process, this relationship means that adjacent shape segments need to be cut in a certain order to avoid damaging other parts.

[0096] Order constraint: Determine the order of cutting to ensure that the first cut does not affect the cutting of subsequent parts.

[0097] Boundary protection: When cutting, pay special attention to the boundaries of adjacent parts to avoid destroying continuity.

[0098] Hierarchical wrapping relationship means that one shape segment is completely or partially wrapped by another shape segment. This relationship brings more constraints in the cutting process. For the wrapped part, it is necessary to cut it first to ensure the integrity of the outer part. When cutting the wrapped part, control the cutting depth to avoid damaging the outer wrapped part.

[0099] Through the above analysis, the constraints of each shape segmentation in the cutting process are determined, including cutting order, boundary protection, cutting depth, etc. These constraints together constitute the constraint relationship of the segmentation position.

[0100] The segmentation position constraint relationship is mapped and integrated with the determined orientation relationship, and the position, direction, hierarchical relationship and cutting constraint conditions of each shape segmentation are integrated into a unified model. Through mapping and integration, a complete segmentation position model that takes into account all constraints can be obtained. This model not only contains the geometric information of the shape segmentation, but also the constraint information during the cutting process. Based on this model, the cutting position and order of each shape segmentation are determined.

[0101] Furthermore, according to the adjacent continuous relationship and the hierarchical wrapping relationship, a constraint impact analysis of the cutting process is performed to obtain a segmentation position constraint relationship, including:

[0102] Based on the two shape segmentations of adjacent continuous relationship and hierarchical wrapping relationship, bidirectional process fitting is performed to determine the cutting evaluation result;

[0103] When the bidirectional cutting evaluation results are the same, the segmentation position constraint relationship is empty;

[0104] When the bidirectional cutting evaluation results are different, the flow direction with a higher cutting evaluation result is selected as the segmentation position constraint relationship, and the segmentation position constraint relationship includes a continuous direction constraint and a hierarchical flow constraint.

[0105] In an embodiment of the present application, a bidirectional process fitting is first performed on two shape segmentation regions having an adjacent continuous relationship or a hierarchical wrapping relationship to simulate the process of cutting from one region to another, and vice versa, to evaluate the efficiency and feasibility of two different cutting directions.

[0106] The cutting evaluation is based on a series of indicators, including cutting time, material waste, cutting quality, moving distance of the laser head, etc., and a comprehensive evaluation result is calculated for each process direction.

[0107] If the cutting evaluation results in both directions are the same, it means that no matter which direction the cutting is performed, its efficiency and effect are equivalent. In this case, the split position constraint relationship is considered to be empty, that is, there is no specific cutting direction constraint.

[0108] If the cutting evaluation results of the two directions are different, the process direction with a higher evaluation result is selected as the preferred cutting direction.

[0109] Based on the above analysis, the specific segmentation position constraint relationship is determined. These constraint relationships include continuous direction constraints and hierarchical process constraints. Continuous direction constraints refer to the specific direction that cutting should follow between adjacent continuous regions to ensure the continuity and quality of cutting. Hierarchical process constraints mean that between regions with hierarchical wrapping relationships, the outer region should be cut first and then the inner region, or vice versa, depending on which direction has a higher cutting evaluation result.

[0110] Through the above steps, determine the best cutting order and direction.

[0111] Furthermore, cutting path planning is performed according to the cutting control parameter clustering relationship and the segmentation position relationship to obtain cutting control information, including:

[0112] According to the clustering relationship of the cutting control parameters, the overlapping amount and the deviation amount of the cutting control parameters are analyzed to obtain overlapping control parameters and deviation control parameters;

[0113] Establishing a path continuity relationship according to the overlap control parameter;

[0114] Determining a deviation parameter adjustment mode according to the deviation control parameter, and establishing a path connection relationship when the deviation parameter adjustment mode is independent adjustment;

[0115] Performing path planning on the path continuity relationship according to the orientation relationship to determine a continuous planning path, and performing constraint adjustment on the continuous planning path using the segmentation position constraint relationship to obtain a control parameter integrated path;

[0116] Performing position insertion connection on the control parameter integrated path according to the orientation relationship and the path connection relationship, and performing constraints using the segmentation position constraint relationship to obtain a first path;

[0117] For the remaining area of ​​the segmented area, path planning is performed according to the consistency of the cutting control parameters, and constraint adjustment is performed using the segmentation position constraint relationship to obtain a second path;

[0118] The first path and the second path are connected to obtain a cutting path of the segmented area, the cutting paths of all the segmented areas are connected to obtain a cutting path, a mapping relationship between a cutting control parameter and a cutting path is established, and the cutting control information is generated.

[0119] In the embodiment of the present application, when performing the overlap analysis of the cutting control parameters, the overlap control parameters are obtained by calculating the distance between cluster centers or the overlap area of ​​cluster boundaries by Euclidean distance. When performing the deviation analysis, the deviation control parameters are obtained by calculating the standard deviation, variance, etc. of the parameters within each cluster.

[0120] When establishing the path continuity relationship, the coordinates of the continuous points are calculated according to the overlapping control parameters, and the continuous cutting path is drawn using the geometric modeling software to ensure a smooth transition during the cutting process.

[0121] Next, based on the analysis results of the deviation control parameters, the deviation parameter adjustment mode is determined. If the adjustment mode is independent adjustment, it means that the deviation of each cutting path will be adjusted individually, rather than based on the global adjustment of the entire cutting task. In the independent adjustment mode, the path connection relationship is established. The establishment of the path connection relationship is to ensure smooth transition between different cutting paths and avoid problems such as breakage, dislocation or repeated cutting at the connection. The connection points or transition sections between the paths are calculated by the algorithm to ensure that the paths can be naturally connected at the connection.

[0122] After that, the path continuity relationship is preliminarily planned according to the orientation relationship. By analyzing the orientation relationship, the relative positions and connection points between the paths are determined to ensure the rationality and feasibility of the paths in the continuity relationship. On the basis of the preliminary planning, the continuous planning path is further determined. The continuous planning path refers to a series of interconnected cutting paths that form a continuous, breakpoint-free path network in space. According to the preliminary planned path continuity relationship, the starting point, end point and trajectory of the path are calculated and adjusted to ensure the smooth transition and continuity between the paths. Next, the continuous planning path is constrained and adjusted using the segmentation position constraint relationship. The continuous planning path is further optimized and adjusted according to the segmentation position constraint relationship to ensure that the path meets specific requirements and conditions at the segmentation position. In the constraint adjustment process, the adjustment of the path length, the change of direction, the matching of speed, etc. are involved. Finally, the continuous planning path after constraint adjustment is used as the control parameter integrated path.

[0123] The starting point and end point of each cutting segment and their relative orientation are determined based on the orientation relationship. Based on the orientation relationship, determine which path segments are adjacent and which path segments have corner or curve connections. According to the path connection relationship, insert necessary connection actions such as deceleration, steering, acceleration, etc. at appropriate positions of the control parameter integrated path to ensure the continuity and quality of the cutting process. According to the orientation relationship and path connection relationship, insert connection points at appropriate positions of the control parameter integrated path. These connection points ensure a smooth transition of the path and take into account the dynamic performance of the equipment and the heat affected area of ​​the material. The process of inserting connection points includes calculating the corner radius, optimizing the acceleration and deceleration curves, and adjusting parameters such as laser power and cutting gas flow. The segmentation position constraint relationship involves the spatial relationship and accuracy requirements between the cutting pieces. The segmentation position constraint relationship is used to further optimize and adjust the path after the insertion connection to ensure the accuracy of the cutting and meet the design requirements. After the above steps are processed, an optimized cutting path, namely the first path, is generated.

[0124] After completing the first path planning, the parts that have not been planned, that is, the remaining areas of the segmented area, are automatically identified. These remaining areas are parts that are ignored in the preliminary path planning due to complex shapes, small sizes or other reasons. For the remaining areas, first analyze the required cutting control parameters, such as cutting speed, power, beam diameter, etc., to find out which of these areas have similar cutting conditions. Based on the consistency of the parameters, the path planning is performed for the remaining areas. When planning, priority is given to connecting areas with the same or similar cutting parameters in series to form a continuous cutting path. During the path planning process, adjustments are made using the previously determined segmentation position constraint relationships. These constraint relationships include adjacent relationships, hierarchical relationships, and the connection relationship between the end point of the previous cutting path and the starting point of the new path. By applying these constraint relationships, the system can ensure the continuity and optimization of the cutting path while avoiding possible collisions or repeated cutting. After the above steps, the second path for the remaining area is finally generated.

[0125] When connecting the first path and the second path, the endpoints of the first path and the second path are identified. These endpoints are the connection points of the two paths, which can be the starting point, the end point or a point in the middle of the path. To ensure the smoothness of the path connection, a smooth transition process is performed at the connection point, including adjusting the path curvature at the connection point, adding a transition section or using a specific connection algorithm to ensure that the connected path has no breakpoints and no mutations. Through the above steps, the first path and the second path are connected into a complete segmented area cutting path. This cutting path covers the entire segmented area and meets the requirements of the cutting control parameters. After that, all adjacent segmented areas are identified, and path connection is required between these areas. For adjacent segmented areas, a similar smooth transition process is used to connect their cutting paths. After the cutting paths of all segmented areas are connected, the system globally optimizes the entire cutting path to eliminate potential conflicts and redundancies and improve overall cutting efficiency.

[0126] According to the requirements of the cutting control parameters, each parameter is matched with the corresponding cutting path segment. This ensures that each path segment is cut according to the specified parameters. Through the matching process, a mapping relationship between the cutting control parameters and the cutting path is established. This mapping relationship clarifies the specific application location and method of each parameter in the cutting path.

[0127] Based on the established mapping relationship, cutting control information is generated, which includes specific parameters such as cutting speed, depth, direction, etc. of each path segment, as well as the starting and ending positions of the path segment.

[0128] Furthermore, the system also includes:

[0129] Obtain cutting energy monitoring data;

[0130] Based on the cutting energy monitoring data, construct an energy trend chart;

[0131] Establishing a corresponding relationship between the energy trend graph and the cutting control information, performing energy deviation calculation on the same cutting control parameters, and obtaining a deviation amount;

[0132] According to the time series fitting relationship of the deviation amount, an energy compensation parameter is set to compensate the cutting control information.

[0133] In the embodiment of the present application, sensors are used to monitor various parameters in the laser cutting process in real time, such as laser power, cutting speed, gas flow, etc. These data are collected at a high frequency to ensure that every detail change in the cutting process can be captured.

[0134] The collected raw data is cleaned, denoised and standardized to improve data quality. Using the processed data, a trend chart is constructed to reflect the trend of laser energy changes during the cutting process. This chart shows energy fluctuations, peaks and stable areas.

[0135] Match cutting control information, such as laser power settings, cutting speed, etc., with real-time monitored energy data. Through this matching, the actual performance of laser energy under specific control parameters is found, thus establishing an accurate corresponding relationship model.

[0136] By comparing the actual monitored energy data with the theoretical or preset energy value, the deviation is calculated. This deviation reflects the energy loss or instability in the cutting process.

[0137] Perform time series fitting on the calculated deviation, analyze its changing trend, and find out the potential cause of energy fluctuation.

[0138] According to the time series fitting results of the deviation, the corresponding energy compensation parameters are set. These parameters are used to correct the energy deviation during the cutting process and ensure the stability and accuracy of the laser output.

[0139] The set energy compensation parameters are used to compensate and adjust the original cutting control information, including adjusting key parameters such as laser power and cutting speed.

[0140] The adjusted control information is sent to the laser cutting machine, enabling it to more accurately control the energy output of the laser when performing the cutting task, thereby achieving the purpose of improving the cutting quality.

[0141] Furthermore, setting energy compensation parameters according to the time series fitting relationship of the deviation amount also includes:

[0142] Use exponential smoothing to fit the relationship between the deviation and time: ,in is the smoothing coefficient, is the deviation at time t;

[0143] Calculate the energy compensation parameters based on the deviation change trend obtained by fitting ,in, To improve the feedback coefficient of the current deviation, is the adjustment coefficient according to the deviation change trend, , Response time to eliminate deviations in the system, is the proportional factor, set according to experimental data .

[0144] In the embodiment of the present application, in order to achieve accurate energy compensation, the energy parameters in the laser cutting process are dynamically adjusted according to the time series change trend of the energy deviation. Specifically, the energy deviation is first smoothed by an exponential smoothing method to obtain the smoothed deviation , whose formula is ,in is the smoothing coefficient, set by technical experts, is the deviation at time t.

[0145] Next, the energy compensation parameters are calculated based on the trend of the smoothed deviation. ,in, Better feedback factor of current deviation, is the adjustment coefficient according to the deviation change trend, , Response time to eliminate deviations in the system, is the proportional factor, set according to experimental data .

[0146] Through the above process, the energy compensation parameters are calculated. , That is the energy compensation amount. Energy compensation is performed according to the energy compensation amount, thereby completing the setting of energy compensation parameters.

[0147] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0148] The present application obtains shape segmentation information, which is the result of clustering segmentation according to the structural features of complex shapes; configures laser cutting parameters of each segmented area according to the shape segmentation information, and obtains cutting control parameters of each segmented area, wherein the cutting control parameters of each segmented area include constraint parameter identification; clusters the cutting control parameters of each segmented area based on the constraint parameter identification, and obtains the clustering relationship of the cutting control parameters; identifies the segmentation position relationship according to the shape segmentation information, and determines the segmentation position relationship; plans the cutting path according to the clustering relationship and the segmentation position relationship of the cutting control parameters, and obtains the cutting control information, which includes the cutting path and the corresponding cutting control parameters, and is used to determine the cutting position area and the cutting order according to the cutting path, and control the cutting of the cutting position area according to the cutting control parameters based on the cutting order. The present invention solves the technical problems of insufficient control accuracy, low cutting efficiency, and poor stability in the prior art when processing complex shape cutting, and achieves the technical effect of improving cutting efficiency and quality by intelligently configuring laser cutting parameters and planning paths.

[0149] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0151] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. Laser cutting system for complex shape cutting, characterized in that, The system comprises: A shape segmentation information acquisition module, wherein the shape segmentation information acquisition module obtains shape segmentation information, wherein the shape segmentation information is a result of clustering segmentation according to structural features of complex shapes; A cutting control parameter acquisition module, which configures laser cutting parameters of each segmented area according to the shape segmentation information to obtain cutting control parameters of each segmented area, wherein the cutting control parameters of each segmented area include constraint parameter identifiers; A cutting control parameter clustering relationship acquisition module, wherein the cutting control parameter clustering relationship acquisition module clusters the cutting control parameters of each segmented area based on the constraint parameter identifier to obtain a cutting control parameter clustering relationship; a segmentation position relationship determination module, wherein the segmentation position relationship determination module performs segmentation position relationship recognition according to the shape segmentation information and determines the segmentation position relationship; A cutting control module, wherein the cutting control module performs cutting path planning according to the cutting control parameter clustering relationship and the segmentation position relationship to obtain cutting control information, wherein the cutting control information includes a cutting path and corresponding cutting control parameters, and is used to determine a cutting position area and a cutting sequence according to the cutting path, and perform cutting control on the cutting position area according to the cutting control parameters based on the cutting sequence; According to the shape segmentation information, segmentation position relationship recognition is performed to determine the segmentation position relationship, including: According to the drawing structure of the complex shape target, establish the position correspondence of each shape segmentation; According to the drawing structure, the positions of the shape segments are identified and the orientation relationships are determined, including adjacent continuous relationships and hierarchical wrapping relationships; According to the adjacent continuous relationship and the hierarchical wrapping relationship, the constraint impact analysis of the cutting process is performed to obtain the segmentation position constraint relationship, and the segmentation position constraint relationship is mapped and integrated with the orientation relationship to determine the segmentation position relationship.

2. The system according to claim 1, characterized in that, According to the shape segmentation information, laser cutting parameters of each segmented area are configured to obtain cutting control parameters of each segmented area, including: Obtaining the cutting shape, material information, and thickness information of the shape segmentation information; According to the cutting shape, material information, and thickness information, simulation optimization is performed through a simulation optimization space to obtain optimized cutting parameters; Acquire a record data set, and perform fitting of a relationship between a cutting parameter and a cutting effect according to the record data set to obtain an influence coefficient of the cutting parameter on the cutting effect; The cutting parameters whose influence coefficients meet preset requirements are selected for constraint parameter identification, the optimized cutting parameters are used as cutting control parameters, and a mapping relationship between the cutting control parameters and each segmented area is established.

3. The system according to claim 2, characterized in that According to the cutting shape, material information, and thickness information, simulation optimization is performed through the simulation optimization space to obtain optimized cutting parameters, including: Based on the twin digital technology, a simulation space is constructed according to the laser cutting monitoring data, and the cutting shape, material information, and thickness information are fitted into the simulation space as simulation experimental data; Establishing an optimization model, and fitting the optimization model to the simulation space for optimizing and adjusting simulation experiment data; Acquire simulation experiment results, determine whether the simulation experiment results meet preset requirements, and if not, input the simulation experiment data into the optimization model to optimize cutting parameters, wherein the cutting parameters include power, speed, focus, gas and pressure; The simulation experiment data is adjusted according to the cutting parameter optimization results, and the simulation experiment is repeated, and so on, until the simulation experiment results meet the preset requirements, and the current cutting parameters are used as the optimized cutting parameters.

4. The system according to claim 3, characterized in that The step of inputting the simulation experiment data into the optimization model to optimize the cutting parameters includes: Compare the simulation experiment results with the preset requirements to determine the deviation results; Based on the deviation results, cutting parameters are traced to determine the optimization target parameters and optimization direction; According to the optimization target parameters and optimization direction, the optimization step length is set to perform parameter optimization; Monitoring the optimization approach efficiency, and when the optimization approach efficiency does not reach the target value, adjusting the optimization step length according to the optimization approach efficiency; When the optimization iteration number or optimization target is reached, the cutting parameter optimization result is obtained.

5. The system according to claim 1, wherein: According to the adjacent continuous relationship and the hierarchical wrapping relationship, the constraint impact analysis of the cutting process is performed to obtain the segmentation position constraint relationship, including: Based on the two shape segmentations of adjacent continuous relationship and hierarchical wrapping relationship, bidirectional process fitting is performed to determine the cutting evaluation result; When the bidirectional cutting evaluation results are the same, the segmentation position constraint relationship is empty; When the bidirectional cutting evaluation results are different, the flow direction with a higher cutting evaluation result is selected as the segmentation position constraint relationship, and the segmentation position constraint relationship includes a continuous direction constraint and a hierarchical flow constraint.

6. The system according to claim 1, wherein: The cutting path is planned according to the cutting control parameter clustering relationship and the segmentation position relationship to obtain cutting control information, including: According to the clustering relationship of the cutting control parameters, the overlapping amount and the deviation amount of the cutting control parameters are analyzed to obtain overlapping control parameters and deviation control parameters; Establishing a path continuity relationship according to the overlap control parameter; Determining a deviation parameter adjustment mode according to the deviation control parameter, and establishing a path connection relationship when the deviation parameter adjustment mode is independent adjustment; Performing path planning on the path continuity relationship according to the orientation relationship to determine a continuous planning path, and performing constraint adjustment on the continuous planning path using the segmentation position constraint relationship to obtain a control parameter integrated path; Performing position insertion connection on the control parameter integrated path according to the orientation relationship and the path connection relationship, and performing constraints using the segmentation position constraint relationship to obtain a first path; For the remaining area of ​​the segmented area, path planning is performed according to the consistency of the cutting control parameters, and constraint adjustment is performed using the segmentation position constraint relationship to obtain a second path; The first path and the second path are connected to obtain a cutting path of the segmented area, the cutting paths of all the segmented areas are connected to obtain a cutting path, a mapping relationship between a cutting control parameter and a cutting path is established, and the cutting control information is generated.

7. The system according to claim 1, characterized in that The system further comprises: Obtain cutting energy monitoring data; Based on the cutting energy monitoring data, construct an energy trend chart; Establishing a corresponding relationship between the energy trend graph and the cutting control information, performing energy deviation calculation on the same cutting control parameters, and obtaining a deviation amount; According to the time series fitting relationship of the deviation amount, an energy compensation parameter is set to compensate the cutting control information.

8. The system according to claim 7, characterized in that According to the time series fitting relationship of the deviation amount, setting the energy compensation parameters includes: Use exponential smoothing to fit the relationship between the deviation and time: ,in is the smoothing coefficient, is the deviation at time t; Calculate the energy compensation parameters based on the deviation change trend obtained by fitting ,in, To improve the feedback coefficient of the current deviation, is the adjustment coefficient according to the deviation change trend, , , Response time to eliminate deviations in the system, is the proportional factor, set according to experimental data .

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