Laser cutting machine track searching path planning optimization method and system

Abstract

The invention discloses a laser cutting machine track searching path planning optimization method and system, and relates to the technical field of laser cutting machine path optimization, and the method specifically comprises the following steps: when a prediction result is that heat distribution overlap exists, screening out all path groups with the condition, and calibrating the path groups as to-be-adjusted cutting path groups; thermosensitive feature information of each to-be-adjusted cutting path group is obtained in real time, analysis is carried out after obtaining, and the thermal interference intensity generated by each to-be-adjusted cutting path group under the current sequence is evaluated; dividing the to-be-adjusted cutting path groups into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group according to an evaluation result; and respectively executing corresponding cutting path sequence optimization measures according to a division result. According to the method, the problem that path thermal overlapping cannot be dynamically avoided is solved, and intelligent optimization of the cutting sequence and accurate control of thermal interference based on thermal sensing are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of laser cutting machine path optimization, and particularly relates to a method and system for optimizing the path planning of a laser cutting machine to find the track. Background Art

[0002] The path planning of a laser cutting machine to find the track refers to the trajectory calculation and regulation required to ensure that the cutting head can move along the optimal path and accurately complete the material cutting during the laser cutting process. Since a laser cutting machine mainly relies on a high-energy laser beam to perform high-precision cutting on materials, the movement path of the cutting head directly affects the cutting quality, efficiency, and production cost. The core of the path planning to find the track is to calculate an optimal path so that the cutting head can complete all cutting tasks in the shortest time, while avoiding unnecessary repeated movements, reducing path intersections, and reducing the idle running of the cutting head (i.e., ineffective movements in the non-cutting state), in order to improve the processing efficiency and reduce energy consumption. However, in practical applications, the existing path planning to find the track often has some problems, such as time waste caused by unreasonable cutting paths, increased mechanical wear due to redundant trajectories, and lack of dynamic adjustment ability in path planning during the cutting process, which cannot meet the processing requirements of complex parts. Therefore, optimizing the path planning of a laser cutting machine to find the track is to redesign the path planning method based on technologies such as mathematical optimization, artificial intelligence algorithms, or sensor data analysis, making it more intelligent and adaptable, thereby reducing the cutting time, improving the accuracy, reducing equipment wear, and enhancing the overall processing efficiency, ultimately improving the productivity and economic benefits of industrial manufacturing.

[0003] The existing laser cutting machine tracking path planning optimization technology mainly improves the efficiency and accuracy of the cutting path through mathematical modeling, artificial intelligence algorithms, sensor data analysis and advanced control strategies. The specific optimization methods include multiple aspects. First, the path planning based on mathematical optimization usually uses graph theory algorithms (such as the shortest path Dijkstra algorithm) or intelligent optimization algorithms (such as genetic algorithms, particle swarm optimization) to calculate the optimal cutting sequence to ensure that the path of the cutting head is the shortest and reduce the empty running distance, thereby reducing processing time and energy consumption. Secondly, the dynamic optimization method based on artificial intelligence can be learned through deep learning or reinforcement learning, combined with a large amount of historical cutting data, so that the system can predict the optimal trajectory and make dynamic adjustments during the cutting process to improve the path's adaptability. Furthermore, using real-time sensor feedback and computer vision technology, the system can detect factors such as irregularities on the surface of the material, thermal deformation during processing, and adjust the path in real time to ensure that the cutting accuracy is not affected by material changes. In addition, the combination of intelligent scheduling and path optimization can comprehensively consider the order of multiple cutting tasks for complex parts, adopt parallel path planning or partition optimization strategies, reduce path intersections and repeated cutting, and improve processing efficiency. At the same time, by combining advanced CNC systems with high-speed motion control, motion control parameters such as acceleration, deceleration and corner speed are optimized, so that the laser cutting machine can maintain high precision while ensuring high-speed cutting, thereby further improving production efficiency. Overall, the application of these optimization technologies makes the path planning of the laser cutting machine more intelligent and adaptive, which not only improves processing accuracy and cutting efficiency, but also reduces equipment wear and energy consumption, thereby meeting the needs of modern intelligent manufacturing for efficient, precise and low-cost processing.

[0004] The prior art has the following deficiencies: During the contour processing of structural parts, when multiple closed paths are close to each other in space, especially when there is a collinear or semi-enclosed relationship, the cutting task often requires continuous processing of these adjacent areas in a short period of time, and local heat distribution overlap is prone to occur at this time. Since a heat-affected zone will be formed on the surface of the material during the laser cutting process, continuous cutting of adjacent paths will cause the heat to fail to diffuse in time, resulting in heat accumulation and abnormal temperature rise in local areas, causing thermal expansion and internal stress changes of the material, and ultimately causing material warping or path deviation. However, the existing laser cutting machine track-seeking path planning optimization technology cannot dynamically optimize the path sequence according to the overlapping trend of local heat distribution between paths in the path sorting link, and still performs static sorting based on geometric distance or path hierarchy, and cannot identify and avoid the risk of thermal overlap. The resulting local thermal shock may cause cutting edge melting, a decrease in subsequent path accuracy, and even cause the thermal deformation of the entire workpiece to exceed the tolerance requirements, affecting the quality of the finished product, while destroying the stability of path reuse and reducing mass production efficiency.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for optimizing the path planning of a laser cutting machine's track seeking to solve the problems in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for optimizing the path planning of a laser cutting machine's track seeking, specifically including the following steps: During the process of the laser cutting machine machining the contour of the structural part, perform a spatial geometric analysis on all preset cutting paths, identify the spatial relationship between each cutting path, construct a set of cutting path groups with spatial proximity relationships, and predict whether there is thermal distribution overlap during the cutting process for each path group in the set; When the prediction result is that there is thermal distribution overlap, screen out all path groups with this situation and label them as the cutting path groups to be adjusted; Obtain the thermal sensitivity characteristic information of each group of cutting path groups to be adjusted in real time, and perform analysis after obtaining it, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation result; According to the division result, execute the corresponding cutting path sequence optimization measures respectively; After the cutting path sequence optimization is completed, perform a thermal field simulation verification on the optimized path sequence. If the verification result does not meet the thermal safety condition, correct the path sequence based on the verification result.

[0008] Preferably, obtaining the thermal sensitivity characteristic information of each group of cutting path groups to be adjusted in real time, and performing analysis after obtaining it, evaluating the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and dividing each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group specifically includes the following steps: Obtain the thermal sensitivity characteristic information of each group of cutting path groups to be adjusted in real time, and perform preprocessing after obtaining it; Extract the path geometric aggregation characteristic information and the path thermal energy distribution gradient information from the thermal sensitivity characteristic information of each group of cutting path groups to be adjusted after preprocessing, and perform analysis after extraction to generate the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of cutting path groups to be adjusted respectively; Construct a sorting thermal interference evaluation model for the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of to-be-adjusted cutting path groups to be generated, and generate the sorting thermal interference coefficient of each group of to-be-adjusted cutting path groups through weighted summation; Determine the preset sorting thermal interference coefficient threshold interval, and after determination, compare it with the sorting thermal interference coefficients of each group of to-be-adjusted cutting path groups generated, evaluate the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting according to the comparison result, and divide each group of to-be-adjusted cutting path groups into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation result.

[0009] Preferably, the acquisition logic of the path group thermal coupling aggregation coefficient of each group of to-be-adjusted cutting path groups is as follows: Extract the path geometric aggregation feature information from the thermosensitive feature information of each group of to-be-adjusted cutting path groups after preprocessing, specifically including the shortest edge distance between all cutting paths in each group of to-be-adjusted cutting path groups, the sum of the path lengths of all cutting paths, and the average curvature change rate of all cutting paths, and calibrate them respectively as 、 and , represents the shortest edge distance between all cutting paths in the th group of to-be-adjusted cutting path groups, represents the sum of the path lengths of all cutting paths in the th group of to-be-adjusted cutting path groups, represents the average curvature change rate of all cutting paths in the th group of to-be-adjusted cutting path groups, , where is a positive integer; Calculate the path group thermal coupling aggregation coefficient of each group of to-be-adjusted cutting path groups. The specific calculation formula is as follows: In the formula, is the path group thermal coupling aggregation coefficient of the th group of to-be-adjusted cutting path groups.

[0010] Preferably, the acquisition logic of the local laser thermal pressure index of each group of to-be-adjusted cutting path groups is as follows: Extract the path thermal energy distribution gradient information from the thermosensitive feature information of each group of to-be-adjusted cutting path groups after preprocessing, specifically including the average laser power used by the laser cutting machine for all paths in each group of to-be-adjusted cutting path groups, the path group envelope area of each group of to-be-adjusted cutting path groups, and the path energy density change rate of each group of to-be-adjusted cutting path groups, and calibrate them respectively as 、 and , represents the average laser power used for all paths in the th group of cutting path groups to be adjusted, represents the path group envelope area of the th group of cutting path groups to be adjusted, represents the rate of change of path energy density of the th group of cutting path groups to be adjusted, , where \(n\) is a positive integer; Calculate the local laser thermal pressure index for each group of cutting path groups to be adjusted. The specific calculation formula is as follows: In the formula, is the local laser thermal pressure index of the th group of cutting path groups to be adjusted.

[0011] Preferably, for the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each generated group of cutting path groups to be adjusted, construct a sorting thermal interference evaluation model, and generate the sorting thermal interference coefficient of each group of cutting path groups to be adjusted through weighted summation. The specific calculation formula is as follows: In the formula, is the sorting thermal interference coefficient of the th group of cutting path groups to be adjusted, and are the non-zero weight coefficients of the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of cutting path groups to be adjusted, respectively, and .

[0012] Preferably, determine a preset sorting thermal interference coefficient threshold interval , and after determination, compare it with the sorting thermal interference coefficient of each generated group of cutting path groups to be adjusted. According to the comparison result, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation result. The specific comparison analysis and division are as follows: If , the thermal interference intensity generated by this group of cutting path groups to be adjusted under the current sorting is low thermal interference intensity, and this group of cutting path groups to be adjusted is divided into a low thermal interference path group; If , the thermal interference intensity generated by this group of cutting paths to be adjusted under the current sorting is medium thermal interference intensity, and this group of cutting paths to be adjusted is classified as the medium thermal interference path group; If , the thermal interference intensity generated by this group of cutting paths to be adjusted under the current sorting is high thermal interference intensity, and this group of cutting paths to be adjusted is classified as the high thermal interference path group.

[0013] Preferably, according to the classification results, corresponding cutting path sequence optimization measures are respectively implemented, specifically: For all groups of cutting paths to be adjusted classified as the low thermal interference path group, the cutting path sequence optimization measure implemented is: keep the original cutting sequence of all cutting paths in this path group unchanged, and no sorting optimization operation is performed; For all groups of cutting paths to be adjusted classified as the medium thermal interference path group, the cutting path sequence optimization measure implemented is: adjust the sequence of all cutting paths in this path group and insert buffer path segments between the paths to reduce the local thermal interference effect; For all groups of cutting paths to be adjusted classified as the high thermal interference path group, the cutting path sequence optimization measure implemented is: perform path sequence scrambling on all cutting paths in this path group, implement a thermal isolation control strategy to reduce the local thermal coupling superposition phenomenon and control the thermal diffusion path.

[0014] Preferably, a path planning optimization system for a laser cutting machine includes a path recognition and thermal overlap prediction module, a thermal overlap screening and calibration module, a thermal feature evaluation module, a path grading optimization module, and a thermal simulation and closed-loop correction module; The path recognition and thermal overlap prediction module, during the process of the laser cutting machine machining the contour of the structural part, performs spatial geometric analysis on all preset cutting paths, identifies the spatial relationship between each cutting path, constructs a set of cutting path groups with spatial proximity relationships, and predicts whether there is thermal distribution overlap during the cutting process for each path group in this set; The thermal overlap screening and calibration module, when the prediction result is that there is thermal distribution overlap, screens out all path groups with this situation and calibrates them as the cutting paths to be adjusted; The thermal feature evaluation module, in real time obtains the thermal sensitive feature information of each group of cutting paths to be adjusted, and analyzes it after obtaining, evaluates the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting, and classifies each group of cutting paths to be adjusted into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation results; The path grading optimization module, according to the classification results, respectively implements corresponding cutting path sequence optimization measures; The thermal simulation and closed-loop correction module, after the optimization of the cutting path sequence is completed, conducts thermal field simulation verification on the optimized path sequence. If the verification result does not meet the thermal safety conditions, the path sequence is corrected based on the verification result.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: 1. By introducing the mechanism of spatial geometric analysis between paths and prediction of the overlapping trend of thermal distribution, the present invention enables the laser cutting machine to identify potential thermal concentration areas in advance before processing the contour of the structural part, and establish a tracking path recognition framework based on the prediction of thermal behavior. On this basis, a dual-parameter evaluation model (thermal coupling aggregation coefficient of the path group and local laser thermal pressure index) is constructed to quantitatively determine the thermal interference intensity of each path group under the current sorting, and then through the linkage control of path level division and differential optimization strategy, effectively avoiding the defect that the existing sorting technology cannot dynamically respond to local thermal accumulation. In this way, the present invention realizes the technological leap of cutting path sorting from static geometric drive to dynamic thermal perception drive, and can significantly reduce quality defects caused by thermal interference such as thermal deformation, path deviation, and edge erosion.

[0016] 2. The sorting thermal interference evaluation model proposed by the present invention is constructed based on multi-dimensional quantitative features. By weighted fusion of the path geometric aggregation characteristics and the change characteristics of thermal input density, it not only has physical significance in index design, but also has good scalability and engineering adaptability. The sorting thermal interference coefficient output by the model is used to divide the path group into three levels: low, medium, and high. The system executes differential optimization strategies according to the thermal interference level, including maintaining the original order, fine-tuning the order and inserting a buffer section, path scattering, and thermal isolation control, etc. Compared with the traditional method of static sorting based on path distance or topological relationship, this mechanism realizes the precise control of the dynamic linkage between path behavior and thermal risk, and the optimization measures are highly matched with the thermal level, ensuring that the cutting quality and process stability are significantly improved without sacrificing processing efficiency.

[0017] 3. After completing the path optimization sorting, the present invention introduces a thermal field simulation verification mechanism to conduct visual thermal analysis and safety verification on the optimization result before execution, ensuring that the path adjustment effect meets the thermal safety requirements at the physical level. If the simulation verification result does not meet the preset conditions, the system can automatically trigger the closed-loop correction logic to re-optimize the path sequence based on the simulation feedback information until a path structure that meets the thermal safety boundary is formed. This closed-loop design realizes the self-verification, self-correction, and self-convergence of path sorting optimization, not only improving the intelligent level of the system, but also providing higher adaptability and process fault tolerance space for dealing with different workpiece layouts and complex process materials, greatly enhancing the quality consistency and process reusability in batch cutting tasks. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of a method and system for optimizing the path planning of a laser cutting machine for tracking the rail in the present invention.

[0020] Figure 2 It is a schematic block diagram of a method and system for optimizing the path planning of a laser cutting machine for tracking the rail in the present invention. Detailed implementation manners

[0021] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] The present invention provides a method for optimizing the path planning of a laser cutting machine for tracking the rail as shown in Figure 1 and specifically includes the following steps: During the process of the laser cutting machine machining the contour of the structural member, perform a spatial geometric analysis on all preset cutting paths, identify the spatial relationships between the cutting paths, construct a set of cutting path groups with spatial proximity relationships, and predict whether there is thermal distribution overlap among the path groups in the set during the cutting process; To identify the spatial relationships between preset cutting paths in a laser cutting task, the software system first needs to perform a structural analysis on the imported geometric graphics or CAD cutting files, extracting all closed or open path vector data (such as start points, end points, control points, path types, etc.). Subsequently, based on a two-dimensional or three-dimensional coordinate system, the system analyzes the geometric relationships between the paths. The methods that can be used include path boundary envelope calculation, path centroid coordinate calculation, bounding box intersection detection, minimum distance calculation, direction vector angle analysis, etc., to determine whether there are spatial geometric relationships such as spatial adjacency, common edge, intersection, nesting, inclusion, overlap, and close angle between any two paths. By establishing a "spatial relationship matrix" or "path adjacency graph" between the above path pairs, the software can achieve a systematic modeling of the structural positions of all paths. The purpose of this step is to provide accurate structural support data for subsequent thermal interference analysis, ensuring that the system can identify which paths will be close to each other in space when the execution order is similar, thereby forming potential risk areas of thermal superposition. This operation is all completed through software logic judgment, spatial geometric modeling algorithms, and data structure processing, without relying on any external hardware conditions, and has high automation and scalability.

[0023] Based on the completion of path spatial relationship identification, the software can further construct a "set of path groups with spatial proximity relationships". This process usually divides all paths that meet the adjacency conditions into one or more "adjacent path groups" by setting a certain path proximity threshold (such as minimum edge distance, angle less than a certain angle, etc.). The paths in each group have a close relative relationship in structure and may have potential requirements for continuous processing. Subsequently, based on this set of path groups, combined with the cutting order, path length, historical cutting energy model, and material heat diffusion characteristics, the software predicts and models the superposition trend of thermal distribution. Specifically, the software can call the heat conduction simulation module or construct a heat affected zone expansion model to simulate the diffusion radius and duration of the heat affected area in space during the cutting process of a certain path by the laser, and determine whether this heat zone covers other path positions in the path group in terms of time and space. If so, it is predicted that there is a heat overlap trend. The purpose of this step is to actively identify whether there is a "thermal interference coupling" relationship between paths by the software before the task execution, and give an early warning of possible local heat accumulation problems, so as to provide data basis for subsequent path sorting optimization. This mechanism not only improves the thermal stability design ability of the tracking path, but also enhances the intelligent decision-making level of the software, and is a key pre-step in this optimization method.

[0024] When the prediction result is that there is a thermal distribution overlap, screen out all path groups with this situation and label them as the cutting path groups to be adjusted; After the prediction of thermal distribution overlap is completed, the software system will conduct a structured analysis of the prediction results and, through the internally preset thermal interference determination logic, screen out those path groups from all path groups that have spatial thermal influence area intersections, coverage, or superposition phenomena during the cutting process. Specifically, the system will label the thermal influence prediction results of each path group, such as marking keyword fields like "whether there is thermal overlap", "thermal influence coverage rate", "predicted temperature rise level", etc. Then, the system will automatically screen based on these labels: Any path group with a thermal influence overlap determined as "yes" or a thermal interference parameter exceeding the set threshold will be included in the "path group to be adjusted for cutting" set. In the software system, this set will be individually numbered, archived, and attached with a set of attribute information for subsequent thermal optimization operations, such as the cutting order within the path group, estimated thermal diffusion area, path spacing model, etc., for the evaluation and sorting optimization logic in the next stage to call. The entire process is automatically completed by the software without manual intervention, achieving the automation, modularization, and data structuring of path thermal coupling identification and management.

[0025] The purpose of this screening and calibration is to actively identify and extract the path groups with thermal safety risks as the subsequent optimization objects through software means, so as to achieve targeted optimization and resource focus of the path planning for tracking. Since in the cutting tasks with complex structural part contours and dense paths, only some path groups will have local heat accumulation phenomena under specific sorting and heat conduction conditions, if not screened, the system may blindly optimize all paths, resulting in an overly heavy computational burden, low efficiency, and even introducing unnecessary path disturbances. By accurately screening the "path group to be adjusted for cutting", the system can concentrate the optimization resources and scheduling algorithms on the local areas where there are truly thermal overlap risks, thereby significantly improving the response efficiency, thermal control accuracy, and cutting quality stability of the path planning for tracking. At the same time, this screening mechanism also provides a structural input basis for the subsequent hierarchical evaluation and classification optimization of paths, and is a key intermediate link in the entire heat perception-driven path optimization logic.

[0026] Obtain the thermal sensitivity feature information of each group of path groups to be adjusted for cutting in real time, and conduct analysis after obtaining it, evaluate the thermal interference intensity generated by each group of path groups to be adjusted for cutting under the current sorting, and divide each group of path groups to be adjusted for cutting into low thermal interference path groups, medium thermal interference path groups, and high thermal interference path groups according to the evaluation results; In this embodiment, obtaining the thermal sensitivity feature information of each group of path groups to be adjusted for cutting in real time, and conducting analysis after obtaining it, evaluating the thermal interference intensity generated by each group of path groups to be adjusted for cutting under the current sorting, and dividing each group of path groups to be adjusted for cutting into low thermal interference path groups, medium thermal interference path groups, and high thermal interference path groups specifically include the following steps: Obtain the thermal characteristic information of each group of cutting path groups to be adjusted in real time, and perform preprocessing after obtaining it; During the path planning optimization process, the system can extract key geometric and energy data related to thermal behavior from the cutting path definition data and cutting parameter database in real time through the built-in data acquisition module in the software before the task scheduling execution or during the path simulation phase. This "thermal characteristic information" includes the control point sequence of the path, the curvature change rate, the path bounding box, the path centroid coordinates, the total length, the laser power to which it belongs, the energy density, etc. After the system identifies the cutting path groups to be adjusted, it maps their numbers to the path database, and forms a "thermal behavior characteristic snapshot" of this path group under the current sorting by associatively calling the path attribute field and the process parameter field. This extraction process is completely executed by the software scheduling system, and based on the data interface composed of the CAD layer parsing module, the G-code parameter parser, the device laser process library, etc. of the cutting task, it ensures that the extraction process is real-time, automatic, continuous, and synchronized with the path sorting state update, so as to achieve "real-time acquisition".

[0027] The purpose of preprocessing the thermal characteristic information is to standardize, structure, and multi-dimensionally associate the original data, so as to facilitate the unified execution of feature extraction, parameter calculation, and thermal interference evaluation operations in subsequent modeling analysis. Since the original data may come from different structural dimensions (such as geometric data being vectors, process data being scalars, and the unit scales being different), directly inputting into the analysis model is likely to cause dimensional inconsistency, data distortion, or skewness effects. Therefore, after the system obtains the thermal characteristic information, it will execute a series of software preprocessing processes, including but not limited to: (1) unified conversion of data units, such as unifying the path length to mm and the energy density to W / mm²; (2) normalization processing, such as normalizing the path curvature change rate to the [0,1] interval; (3) feature alignment processing, such as converting the feature value structures of different paths within the same path group into a unified vector dimension; (4) outlier detection and interpolation completion, for example, automatically completing the intermediate point positions when the control point data of the path is missing; (5) path group feature aggregation processing, aggregating the path-level data by path group as a unit into a group-level thermal information matrix. These preprocessing operations are all automatically completed by the software through the embedded geometric calculation module, data standardization function library, and anomaly recognition logic, ensuring that the data input quality meets the operation requirements of complex mathematical models, while improving the stability of subsequent parameter calculation and the reliability of evaluation results.

[0028] Extract the path geometric aggregation feature information and the path thermal energy distribution gradient information from the preprocessed thermal characteristic information of each group of cutting path groups to be adjusted, and perform analysis after extraction to generate the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of cutting path groups to be adjusted respectively; After obtaining and completing the preprocessing of the thermal characteristic information of each group of cutting path groups to be adjusted, the system, through the feature deconstruction and label mapping module built into the software, automatically extracts and constructs two types of high-level information dimensions, namely "path geometric aggregation feature information" and "path thermal energy distribution gradient information", according to the preset parameter association rules and structured field mapping logic. Specifically, the system first retrieves the preprocessing fields related to the path space form in each group of path data, including the total path length, the minimum boundary distance between paths, and the average curvature change rate, and constructs a feature vector set through a geometric processor, which is marked as path geometric aggregation feature information; subsequently, the system extracts the fields related to the heat input, including the laser power, path energy density, path power difference, etc. of each path within the path group, and aggregates these fields into path thermal energy distribution gradient information through a laser energy analysis module. This extraction process is automatically completed by the software through field parsing, vectorized recombination, and feature label recognition, ensuring that each group of paths can be mapped to two sets of feature dimension information, serving as the direct input for subsequent parameter modeling, and at the same time enhancing the system's interpretability and responsiveness to the path thermal behavior.

[0029] Construct a sorting thermal interference evaluation model for the path group thermal coupling aggregation coefficient and local laser thermal pressure index of each group of cutting path groups to be adjusted, and generate the sorting thermal interference coefficient of each group of cutting path groups to be adjusted through weighted summation; Determine the preset sorting thermal interference coefficient threshold interval, and after determination, compare it with the sorting thermal interference coefficient of each group of cutting path groups to be adjusted, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting according to the comparison result, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation result.

[0030] The threshold interval of the sorting thermal interference coefficient can be determined through the parameter learning module and the empirical model construction mechanism built into the software system. Specifically, the system combines historical cutting task data, thermal interference level labels, and path execution effect feedback to automatically extract representative typical path group thermal interference evaluation coefficient values as the sample data set. The software first performs data mapping analysis on the evaluation coefficients and actual cutting results in existing cutting cases to construct a correspondence model between the sorting thermal interference coefficient and the actual thermal influence result. Subsequently, density clustering processing is performed on all known path group evaluation coefficients through clustering algorithms (such as K-means or hierarchical clustering) to identify natural boundary points or inflection points in the coefficient distribution as the preliminary basis for threshold division. Then, statistical methods (such as quantile analysis, box analysis, etc.) are used to determine the critical interval, forming a reference interval for thermal interference level stratification. The system finally sets the two identified boundary coefficient values as the first threshold and the second threshold, dividing into three interval segments of low thermal interference, medium thermal interference, and high thermal interference. The entire process is automatically completed through the data modeling and adaptive update mechanism of the software, and supports differential adjustment based on material type, plate thickness specification, or cutting process parameters to achieve dynamic adaptation and continuous optimization of the parameter threshold.

[0031] In this embodiment, the acquisition logic of the path group thermal coupling aggregation coefficient of each group of cutting paths to be adjusted is as follows: Extract the path geometric aggregation feature information from the thermal-sensitive feature information of each group of cutting paths to be adjusted after preprocessing, specifically including the shortest edge distance between all cutting paths in each group of cutting paths to be adjusted, the sum of the path lengths of all cutting paths, and the average curvature change rate of all cutting paths, and calibrate them respectively as 、 and , represents the shortest edge distance between all cutting paths in the th group of cutting paths to be adjusted, represents the sum of the path lengths of all cutting paths in the th group of cutting paths to be adjusted, represents the average curvature change rate of all cutting paths in the th group of cutting paths to be adjusted, , is a positive integer; During the operation of the path planning system, the software can perform vector analysis and topological modeling on the imported cutting graphic data (such as DXF, G code or internal CAD model), obtain the path geometry information in each group of cutting paths to be adjusted in real time, and then automatically calculate the required key data. First of all, the "shortest edge distance between all cutting paths" refers to the minimum Euclidean distance between the bounding boxes or contours of any two paths in the path group. The software constructs the minimum bounding box of each path and performs the nearest edge calculation of each pair in the path group. It can obtain the path pair corresponding to the minimum distance in the current path group in real time as a result. The input reflects the tightness of the path in space. Secondly, the "sum of the path lengths of all cutting paths" refers to the sum of the actual cutting lengths of each path in the path group. The software can split the path segments based on the curve type (straight line, arc, spline), and obtain the true length of each path through the integral method or curve parameterization algorithm, and accumulate it to get , which is used to evaluate the total heat input scale of the path group. Finally, the "average curvature change rate of all cutting paths" represents the change trend and complexity of the path shape. The system first discretely samples the geometric expression of each path (such as Bezier or NURBS), obtains multiple consecutive tangent points and their tangent directions, and then calculates the local curvature change through the difference in tangent angles. Finally, the curvature change rate of all paths in the group is averaged to obtain , which is used to reflect the frequency of geometric disturbances in the internal structure of the path group. The above three types of data are all derived from the coordinates and topological properties of the path graphics themselves, and can be automatically extracted in real time through the software's geometric modeling module, curve parsing engine and numerical analysis components. No external input or manual intervention is required, and it can be updated synchronously at any stage of path identification, sorting or optimization, providing an efficient and stable data foundation for thermal behavior modeling and path group feature analysis.

[0032] Calculate the path group thermal coupling aggregation coefficient of each group of cutting path groups to be adjusted. The specific calculation formula is as follows: In the formula, For the The path group thermal coupling aggregation coefficient of the cutting path group to be adjusted.

[0033] Path group thermal coupling aggregation coefficient The calculation formula is used to comprehensively express the trend of thermal coupling effect enhancement caused by geometric aggregation, path density concentration and path complexity improvement of a path group in space. represents the total path heat accumulation intensity of the path group, where reflects the total scale of heat input to the region. The sensitivity to drastic changes in curvature (i.e., complex path structures) is enhanced through a squaring operation, which means that the more paths and the more turns there are, the stronger the thermal coupling generated in the local area. The denominator represents the shortest boundary distance between paths plus 1. To avoid mathematical anomalies of dividing by zero and also serve as an attenuation factor for the thermal diffusion space buffer, the closer the paths are, the easier it is for the heat affected zones to overlap, and the stronger the coupling effect; adding 1 also serves a mathematical balancing role of "converging logarithmic inputs". Applying the natural logarithm function outside the entire expression is to introduce a non-linear growth model, so that after the thermal coupling trend reaches the critical state, the coefficient value rises more rapidly, enhancing the model's ability to identify heat overlap sensitive areas; then squaring this logarithmic term is to further amplify the weight of the high-coupling area in the model, making it be preferentially optimized in subsequent thermal interference evaluations. The second part of the formula separately extracts the non-linear coupling relationship between curvature and path proximity, and strengthens its modeling ability for the complexity of thermal behavior in the scenario of "tight paths + frequent bends" through the 0.75th power. Overall, this formula enhances the growth sensitivity through logarithmic operations, constructs non-linear characteristic responses through power operations, and constructs an adversarial relationship between heat accumulation and heat diffusion in the numerator and denominator. It is a highly abstract and reasonable mapping of the thermal coupling aggregation phenomenon in the geometric space dimension, with clear physical meanings and engineering guiding values, and can be completely automatically calculated by a software system through structured data.

[0034] The path group thermal coupling aggregation coefficient of the group of cutting paths to be adjusted has a positive correlation with the intensity of thermal interference that the path group may cause under the current sorting, that is: The larger it is, the more closely aggregated the path group tends to be in the spatial structure, the longer the total path length, the more complex the geometric structure, the smaller the minimum boundary distance between paths, the more densely bent the path shape, the more concentrated the heat input in the unit space, and the shorter the heat diffusion path. As a result, the thermal accumulation effect in the local area during the continuous laser cutting process is more significant, the risk of heat superposition is higher, and the resulting thermal interference intensity is stronger. Therefore, the value of the path group is higher, indicating that the path group is more likely to generate high-intensity thermal coupling phenomena and cross-effects of heat conduction under the current sorting arrangement, directly leading to problems such as local thermal deformation of the material, cutting accuracy fluctuations, or path execution offsets. It is an important metric for sorting thermal interference risks. This parameter quantifies the heat transfer trend through geometric aggregation characteristics, providing a strong mathematical basis for the identification and priority optimization of heat-sensitive path groups.

[0035] In this embodiment, the acquisition logic of the local laser thermal pressure index of each group of cutting paths to be adjusted is as follows: Extract the path thermal energy distribution gradient information from the thermal characteristics information of each group of cutting paths to be adjusted after preprocessing, specifically including the average laser power used by all paths in each group of cutting paths to be adjusted, the path group envelope area of each group of cutting paths to be adjusted, and the path energy density change rate of each group of cutting paths to be adjusted, and calibrate them respectively as 、 and , represents the average laser power used by all paths in the th group of cutting paths to be adjusted, represents the path group envelope area of the th group of cutting paths to be adjusted, represents the path energy density change rate of the th group of cutting paths to be adjusted, , is a positive integer; During the path planning optimization process, the software system can extract the physical data related to laser heat input from the cutting task data in real time through the integrated cutting parameter analysis module and geometric modeling subsystem, and then obtain the path thermal energy distribution gradient information of each group of cutting paths to be adjusted as required. Among them, "the average laser power used by each path" refers to the arithmetic mean of the laser power values configured or executed by all paths within the path group. The system can collect the power parameters associated with each path by parsing G-code, machining process configuration files, or reading actual power instructions from the device control logic, and then calculate the average value uniformly to reflect the overall heat input level of the path group; "the path group envelope area " refers to the total area covered by the smallest circumscribed polygon or envelope rectangle formed by all paths within the group on the plane, which is used to measure the spatial distribution area of thermal energy. The software can quickly generate the path group contour envelope and calculate the area by calling the boundary detection and convex hull calculation algorithms in the geometric engine; "the path energy density change rate " refers to the ratio of the maximum difference between the laser powers (i.e., power densities) carried by each unit length of the paths in the path group to the average value, which reflects the unevenness of the thermal energy distribution within the path group. The system calculates the power density by dividing the power value of each path by its length, then statistically calculates the maximum, minimum, and average values of the density, and finally calculates the change rate. The above three types of data can be automatically obtained through the real-time task parsing and geometric analysis process of the software in the early stage of path sorting optimization, and are in one-to-one correspondence with the path group number, geometric model, and process parameters, without manual intervention, and can provide real-time, accurate, and structured data support for the subsequent calculation of the local laser heat pressing index.

[0036] Calculate the local laser thermocompression index for each group of cutting path groups to be adjusted. The specific calculation formula is as follows: In the formula, is the local laser thermocompression index of the th group of cutting path groups to be adjusted.

[0037] The local laser thermocompression index of each group of cutting path groups to be adjusted The reason for adopting this calculation formula is to comprehensively characterize the transient thermocompression intensity generated by laser energy input and the thermal shock risk brought by uneven energy distribution in the local area of space of the th group of cutting path groups to be adjusted. Among them, the left part of the formula represents the laser power density per unit area, is the square of the average laser power, reflecting the thermal input intensity of the laser power. And represents the envelope area of the path group plus 1, which can not only prevent the division-by-zero problem caused by zero area, but also reflect the higher thermocompression trend caused by the smaller area and more concentrated heat energy; magnifying this ratio in the form of the 1.2th power as a whole is to introduce a non-linear response mechanism, so that the high-power dense area has a greater identification weight in the thermocompression risk assessment. The right part of the formula reflects the change gradient of the laser energy density between the paths in the path group. The larger the value, the more significant the uneven thermal input in the local area, which is likely to cause temperature difference gradient and stress disturbance inside the material; by taking the logarithm of this value and exponential amplification, the thermal shock sensitivity of the local high-gradient area can be enhanced for identification. The construction of the entire index model comprehensively considers key factors such as the intensity, distribution density, and unevenness of laser thermal input, so that can effectively characterize the ability of the path group to cause transient thermal stress concentration to the material under the current sorting, providing strong support at the thermophysical level for the accurate assessment of the sorting thermal interference level.

[0038] The local laser thermocompression index of the th group of cutting path groups to be adjusted is positively correlated with the thermal interference intensity that the path group may generate under the current sorting. Specifically, The larger it is, the higher the laser energy density borne by the path group within the unit space range, and the more uneven the laser power distribution between the paths in the group, indicating that the thermal input is both strong and concentrated, and there are obvious local gradient changes at the same time. This combination of high thermocompression + high unevenness is extremely likely to cause a sharp temperature rise difference in the material in a short time, resulting in inconsistent thermal expansion and sudden internal stress changes, and ultimately manifested as thermal interference phenomena such as thermal deformation, cutting edge melting, and path deviation during the path execution process. Therefore, The higher the value, the higher the local thermal shock risk and the stronger the transient thermal disturbance of the path group under the current cutting sequence. It is a key indicator for measuring the "concentration of heat input and complexity of distribution gradient" in the evaluation of thermal interference intensity. It forms a complement with the thermal coupling trend caused by the path structure and together constitutes the dual-factor judgment basis for the evaluation of thermal interference intensity.

[0039] In this embodiment, for the path group thermal coupling aggregation coefficient of each generated group of cutting paths to be adjusted and the local laser thermal pressure index a sorting thermal interference evaluation model is constructed, and the sorting thermal interference coefficient of each group of cutting paths to be adjusted is generated by weighted summation. The specific calculation formula is as follows: In the formula, is the sorting thermal interference coefficient of the th group of cutting paths to be adjusted, and are the non-zero weight coefficients of the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of cutting paths to be adjusted respectively, and .

[0040] In order to comprehensively evaluate the thermal interference intensity of each group of cutting paths to be adjusted under the current sorting, the software system constructs a sorting thermal interference evaluation model, and takes the path group thermal coupling aggregation coefficient calculated previously and the local laser thermal pressure index as input variables, and generates the sorting thermal interference coefficient of this path group in the way of weighted summation. The core logic of this model is that through the parameter fusion of two dimensions, it comprehensively reflects the thermal interference performance of the path group in terms of geometric aggregation trend and thermal energy input distribution. In specific implementation, the software automatically generates two non-zero weight coefficients and according to the influence weights of thermal coupling type parameters and thermal power type parameters in historical cutting samples, the sensitivity of the objective function, and the influence correlation on the finished product quality. Among them, represents the contribution degree of path geometry aggregation to thermal interference, and usually accounts for the dominant weight when the path structure is complex and the spatial layout is compact; while reflects the dominant degree of local thermal energy density and change gradient in thermal shock, and has higher sensitivity in high-power density cutting scenarios. The system supports adaptive adjustment according to material type, cutting process or equipment model, and can form an optimal weight matching strategy through training with historical process samples to ensure that the finally generated sorting thermal interference coefficient It can truly reflect the potential thermal interference intensity of the path group under the current sorting structure. This fusion evaluation method is fully automated on the software side without manual intervention, and has good real-time performance and scalability.

[0041] In this embodiment, a preset sorting thermal interference coefficient threshold interval is determined , and after determination, it is compared with the sorting thermal interference coefficients of each group of cutting path groups to be adjusted generated . According to the comparison results, the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting is evaluated, and each group of cutting path groups to be adjusted is divided into a low thermal interference path group, a medium thermal interference path group, and a high thermal interference path group according to the evaluation results. The specific comparison analysis and division are as follows: If , the thermal interference intensity generated by this group of cutting path groups to be adjusted under the current sorting is a low thermal interference intensity, and this group of cutting path groups to be adjusted is divided into a low thermal interference path group; This situation indicates that under the current cutting sorting structure of this path group, its spatial layout is relatively scattered, the geometric structure is simple, the thermal coupling relationship between paths is weak, and at the same time, the laser energy input is relatively uniform and the density is not high, and the overall heat distribution is relatively stable, and obvious heat accumulation will not form in local areas. During the processing of this type of path group, the risk of material thermal deformation is extremely low, the cutting accuracy is less affected by heat, and phenomena such as path deviation, edge melting, and geometric errors caused by thermal stress basically do not occur. Usually, there is no need to adjust the cutting order or path strategy. The system can directly retain its original sorting during the optimization stage, thereby improving the optimization efficiency and avoiding excessive intervention.

[0042] If , the thermal interference intensity generated by this group of cutting path groups to be adjusted under the current sorting is a medium thermal interference intensity, and this group of cutting path groups to be adjusted is divided into a medium thermal interference path group; This situation indicates that the thermal interference intensity of this path group under the current sorting is in a critical state. Although there is no obvious risk of thermal runaway, a certain degree of heat accumulation trend has been shown, such as slightly smaller path spacing, relatively complex curvature, and certain fluctuations in power distribution. This type of path group has a certain sensitivity to material thermal expansion. If the cutting continues in the current order, phenomena such as cutting deviation, edge micro-deformation, and local error amplification may occur in the subsequent stage due to the superposition of temperature rise. Therefore, the system should adopt mild optimization strategies for the medium thermal interference path group, such as sequential fine-tuning, inserting buffer paths, and adjusting the execution order of paths, to actively control the thermal risk while ensuring production efficiency.

[0043] If , the thermal interference intensity generated by this group of cutting path groups to be adjusted under the current sorting is a high thermal interference intensity, and this group of cutting path groups to be adjusted is divided into a high thermal interference path group.

[0044] This situation indicates that the thermal interference intensity of this path group is significantly too high under the current sorting. It is manifested as dense space between paths, obvious edge overlap, sharp curvature change, and concentrated laser power, or large power difference between paths leading to serious local hot spots. This state is extremely likely to cause transient thermal shock of the material during the cutting process, resulting in thermal deformation exceeding the tolerance range, obvious path execution error, serious melting and erosion of the cutting edge, and even problems such as increased rework rate and uncontrollable process. For such path groups, the system should take strong intervention optimization measures, such as scrambling the path order, setting cooling buffer segments, introducing thermal isolation paths, or forced reordering, to effectively reduce its thermal interference level and ensure the finished product quality and process stability.

[0045] According to the division results, corresponding cutting path order optimization measures are executed respectively, where: the original sorting is maintained for the low thermal interference path group, the order is adjusted and buffer path segments are inserted for the medium thermal interference path group, and the order is scrambled and thermal isolation control is implemented for the high thermal interference path group; In this embodiment, according to the division results, corresponding cutting path order optimization measures are executed respectively, specifically: For all the cutting path groups to be adjusted that are divided into the low thermal interference path group, the specific cutting path order optimization measure executed is: keeping the original cutting order of all the cutting paths in this path group unchanged and not performing sorting optimization operations; For the path set divided into the low thermal interference path group, the software system will detect that its sorting thermal interference coefficient is lower than the lower threshold through the preset thermal interference judgment logic during the path sorting optimization stage and automatically mark it as a "thermally safe path group". The system uses this mark as a skip condition for the path sorting module to avoid any operations of reordering, inserting paths, or switching the order of precedence for this path group, but retains the path definition order in the CAD original layer or schedules the processing tasks based on the execution order specified by the initial process parameters. The reason for this is that this type of path group is relatively dispersed in the spatial structure, has a low thermal input intensity, and the thermal influence areas between paths hardly overlap, and will not cause significant thermal interference to the cutting process. Unnecessary sorting processing for it may instead introduce additional path offsets or processing time redundancy. Therefore, in the software control strategy, a lightweight solution of "identifying and excluding optimization" is adopted to effectively save computing resources and maintain the real-time response of the system.

[0046] For all the cutting path groups to be adjusted that are divided into the medium thermal interference path group, the specific cutting path order optimization measure executed is: adjusting the order of all the cutting paths in this path group and inserting buffer path segments between the paths to reduce the local thermal interference effect; For the set of paths classified into the medium heat interference path group, after identification, the system incorporates them into the "optimizable path group" list and performs sequential fine-tuning through the built-in path local rearrangement algorithm in the software. The goal of optimization is to maximize the thermal diffusion interval between paths. First, the system analyzes the "thermal shock index" (calculated from power density, path area, curvature, etc.) of each path within this path group, and then rearranges the execution order in a relatively balanced thermal load manner, preferentially scheduling paths with weaker heat input to disrupt the execution order of continuously high-heat segments. Second, the system inserts "buffer path segments" between the rearranged paths. These buffer segments can be dummy paths, non-cutting transition paths, or schedule the interspersed execution of other low-heat path segments. The purpose is to artificially extend the time interval between high-heat paths and increase the material cooling time. Such strategies can effectively reduce the risk of overlapping heat-affected zones through the dual adjustment of time logic and path space order, and are particularly suitable for path regions where heat effects have cumulative characteristics but have not yet gotten out of control, and can improve cutting stability without significantly affecting the process cycle time.

[0047] For all the cutting path groups to be adjusted classified into the high heat interference path group, the specific cutting path order optimization measures are as follows: perform path order disruption processing on all cutting paths in this path group and implement a thermal isolation control strategy to reduce local thermal coupling superposition phenomena and control the thermal diffusion path.

[0048] For the set of paths classified into the high heat interference path group, the system regards it as a high-risk thermal shock unit and needs to adopt a path optimization strategy with strong intervention. First, the software identifies the coupling strength threshold relationship between high-heat coupling path segments through the thermal interference evaluation module, splits the continuous paths with highly concentrated heat into multiple sub-path segments, and disrupts their original sequence through the "path disruption algorithm" so that they are interspersed between path groups with lower thermal loads in the global path sorting to widen the processing time sequence and spatial heat zone distribution between paths. Second, the system calls the "thermal isolation control strategy" according to the material thickness and thermal diffusion characteristics, and automatically inserts "thermal barrier segments" or "pre-cooling segments" between these high-heat paths, such as: cutting non-critical edge paths, performing equipment no-load operation, temporarily turning to the auxiliary path area, etc., to enable the local material to obtain a heat release time window. This optimization scheme combines path splitting + priority adjustment + cooling control to effectively prevent problems such as local material warping, dimensional accuracy out-of-tolerance, or carbonization in the processing area caused by continuous heat superposition, and is applicable to the processing tasks of complex contour or structural aggregation workpieces with high requirements for thermal sensitivity.

[0049] After the cutting path order optimization is completed, perform thermal field simulation verification on the optimized path order. If the verification result does not meet the thermal safety conditions, correct the path order based on the verification result to achieve closed-loop optimization of thermal control.

[0050] After the optimization of the cutting path sequence is completed, the system quickly verifies the current path execution sequence through an integrated thermal field simulation module. This module is based on numerical analysis methods such as the finite difference method (FDM) or the finite element method (FEM). By simulating the heat conduction, heat diffusion, and material heat response characteristics during the process of the laser heat source moving segment by segment along the path, it generates a thermal distribution map that evolves over time during the cutting process of the entire workpiece. In specific implementation, the system inputs the optimized path sequence, laser power parameters, path execution time window, and material thermal property parameters (such as thermal conductivity, thermal diffusivity, specific heat capacity, etc.) into the simulation engine. Combining the spatial geometric model and the mesh generation algorithm, it conducts dynamic thermal energy injection and diffusion simulation for each cutting area, and finally outputs the temperature field distribution matrix and thermal gradient map at each time node. Subsequently, the system compares and analyzes these simulation outputs with the preset "thermal safety condition indicators", including indicators such as the maximum temperature rise threshold in the local area, the thermal gradient mutation rate, and the overlap degree of the heat affected zone. If any one exceeds the thermal safety threshold, it is automatically determined that there is still a potential thermal interference risk in the current path sorting and it is not executable.

[0051] The reason why it is necessary to add a thermal field simulation verification link after the path sorting optimization is completed is that during the actual cutting process, the thermal interference between paths is not only determined by the geometric sequence and power values, but is a dynamic, superimposed, and non-linear heat diffusion process. Even if the optimization strategy has rearranged or isolated the high-thermal-interference path groups, in some materials with particularly complex workpiece structures or slow heat diffusion (such as stainless steel and aluminum alloy), transient heat accumulation may still occur in small areas, resulting in problems such as processing deformation, cracks, and melting corrosion. Therefore, the preliminary optimization based only on the thermal interference coefficient model is not sufficient to ensure process safety. It is necessary to predict the entire process of heat propagation through thermal field simulation to ensure that the optimization results will not trigger uncontrollable heat response behaviors during actual execution. Introducing simulation verification not only improves the accuracy of path sorting optimization, but also realizes a self-feedback closed-loop logic: if the simulation results do not meet the thermal safety indicators, the system can automatically correct the path sequence based on this, trigger a new round of optimization until the simulation passes, thus constructing a complete closed-loop of thermal perception - optimization - verification - correction - convergence for path control and improving the system's intelligent level and cutting quality stability.

[0052] As Figure 2 shown, a laser cutting machine rail-seeking path planning optimization system includes a path recognition and thermal overlap prediction module, a thermal overlap screening and calibration module, a thermal feature evaluation module, a path grading optimization module, and a thermal simulation and closed-loop correction module; Path recognition and thermal overlap prediction module: During the process of a laser cutting machine machining the contour of a structural part, perform spatial geometric analysis on all preset cutting paths, identify the spatial relationships between the cutting paths, construct a set of cutting path groups with spatial proximity relationships, and predict whether there is thermal distribution overlap among the path groups in the set during the cutting process; Thermal overlap screening and calibration module: When the prediction result is that there is thermal distribution overlap, screen out all path groups with this situation and calibrate them as cutting path groups to be adjusted; Thermal feature evaluation module: Real-time obtain the thermal sensitive feature information of each group of cutting path groups to be adjusted, and analyze it after obtaining, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divide each group of cutting path groups to be adjusted into low thermal interference path groups, medium thermal interference path groups, and high thermal interference path groups according to the evaluation results; Path grading and optimization module: According to the division results, execute corresponding cutting path sequence optimization measures respectively; Thermal simulation and closed-loop correction module: After the cutting path sequence optimization is completed, perform thermal field simulation verification on the optimized path sequence. If the verification result does not meet the thermal safety conditions, correct the path sequence based on the verification result.

[0053] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0054] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0055] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not indicate the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0056] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0057] In several embodiments provided by the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices or units, and can be in an electrical, mechanical or other form.

[0058] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0059] In addition, each functional unit in various embodiments of the present application may be integrated into a processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit.

[0060] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A laser cutting machine track-seeking path planning optimization method, characterized in that: The specific steps include: When the laser cutting machine processes the contour of the structural part, the spatial geometric analysis of all preset cutting paths is performed to identify the spatial relationship between the cutting paths, to construct a set of cutting path groups with spatial proximity relationships, and to predict whether there is thermal distribution overlap between the path groups in the set during the cutting process; When the prediction result shows that there is overlap in heat distribution, all path groups with this situation are screened out and marked as cutting path groups to be adjusted; Acquire the thermal characteristic information of each group of cutting path groups to be adjusted in real time, analyze it after acquisition, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group according to the evaluation result; According to the division results, corresponding cutting path sequence optimization measures are executed respectively; After the cutting path sequence optimization is completed, the optimized path sequence is verified by thermal field simulation. If the verification result does not meet the thermal safety conditions, the path sequence is corrected based on the verification result.

2. A laser cutting machine track-seeking path planning optimization method according to claim 1, characterized in that: Acquire the thermal characteristic information of each group of cutting path groups to be adjusted in real time, analyze it after acquisition, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group according to the evaluation result, specifically including the following steps: Acquire the thermal characteristic information of each group of cutting paths to be adjusted in real time, and perform preprocessing after acquisition; Extracting path geometry aggregation feature information and path thermal energy distribution gradient information from the preprocessed thermosensitive feature information of each group of cutting path groups to be adjusted, and analyzing them after extraction to generate path group thermal coupling aggregation coefficient and local laser thermal pressure index of each group of cutting path groups to be adjusted respectively; A ranking thermal interference evaluation model is constructed for the path group thermal coupling aggregation coefficient and the local laser thermal pressure index of each group of cutting path groups to be adjusted, and a ranking thermal interference coefficient of each group of cutting path groups to be adjusted is generated by weighted summation; Determine a pre-set sorting thermal interference coefficient threshold interval, and after determination, compare it with the generated sorting thermal interference coefficients of each group of cutting path groups to be adjusted, evaluate the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting based on the comparison results, and divide each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group based on the evaluation results.

3. A laser cutting machine track-seeking path planning optimization method according to claim 2, characterized in that: The logic for obtaining the path group thermal coupling aggregation coefficient of each group of cutting path groups to be adjusted is as follows: The path geometry aggregation feature information is extracted from the preprocessed thermal feature information of each group of cutting paths to be adjusted, including the shortest edge distance between all cutting paths in each group of cutting paths to be adjusted, the sum of the path lengths of all cutting paths, and the average curvature change rate of all cutting paths, and is calibrated as , and , Indicates The shortest edge distance between all cutting paths in the group of cutting paths to be adjusted. Indicates The sum of the path lengths of all cutting paths in the cutting path group to be adjusted. Indicates The average curvature change rate of all cutting paths in the group of cutting paths to be adjusted. , is a positive integer; Calculate the path group thermal coupling aggregation coefficient of each group of cutting path groups to be adjusted. The specific calculation formula is as follows: In the formula, For the The path group thermal coupling aggregation coefficient of the cutting path group to be adjusted.

4. A laser cutting machine track-seeking path planning optimization method according to claim 3, characterized in that: The logic for obtaining the local laser thermal pressure index of each group of cutting path groups to be adjusted is as follows: The path thermal energy distribution gradient information is extracted from the preprocessed thermal characteristic information of each group of cutting path groups to be adjusted, specifically including the average laser power used by the laser cutting machine for all paths in each group of cutting path groups to be adjusted, the path group envelope area of ​​each group of cutting path groups to be adjusted, and the path energy density change rate of each group of cutting path groups to be adjusted, and calibrated as , and , Indicates that the laser cutting machine is The average laser power used by all paths in the cutting path group to be adjusted. Indicates The path group envelope area of ​​the cutting path group to be adjusted. Indicates The path energy density change rate of the cutting path group to be adjusted, , is a positive integer; Calculate the local laser thermal pressure index of each group of cutting paths to be adjusted. The specific calculation formula is as follows: In the formula, For the The local laser heat pressure index of the cutting path group to be adjusted.

5. A laser cutting machine track-seeking path planning optimization method according to claim 4, characterized in that: The path group thermal coupling aggregation coefficient of each group of cutting path groups to be adjusted is and local laser thermal pressure index A ranking thermal interference evaluation model is constructed, and the ranking thermal interference coefficient of each group of cutting paths to be adjusted is generated by weighted summation. The specific calculation formula is as follows: In the formula, For the The thermal interference coefficient of the group of cutting paths to be adjusted. and are the path group thermal coupling aggregation coefficients of each group of cutting path groups to be adjusted and local laser thermal pressure index The non-zero weight coefficient of .

6. A laser cutting machine track-seeking path planning optimization method according to claim 5, characterized in that: Determine the preset sorting thermal interference coefficient threshold interval , and after determination, the thermal interference coefficients of the sorting of the generated groups of cutting paths to be adjusted are A comparison is performed, and the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting is evaluated according to the comparison results, and each group of cutting path groups to be adjusted is divided into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group according to the evaluation results. The specific comparison analysis and division are as follows: like , the thermal interference intensity generated by the group of cutting paths to be adjusted under the current sorting is low thermal interference intensity, and the group of cutting paths to be adjusted is divided into a low thermal interference path group; like , the thermal interference intensity generated by the group of cutting paths to be adjusted under the current sorting is medium thermal interference intensity, and the group of cutting paths to be adjusted is divided into a medium thermal interference path group; like The thermal interference intensity generated by the group of cutting paths to be adjusted under the current sorting is high thermal interference intensity, and the group of cutting paths to be adjusted is divided into a high thermal interference path group.

7. A laser cutting machine track-seeking path planning optimization method according to claim 6, characterized in that: According to the division results, the corresponding cutting path sequence optimization measures are executed respectively, specifically: For all the cutting path groups to be adjusted that are divided into the low thermal interference path group, the cutting path sequence optimization measures performed are specifically: keeping the original cutting sequence of all the cutting paths in the path group unchanged, and not performing the sorting optimization operation; For all the cutting path groups to be adjusted that are classified as medium thermal interference path groups, the cutting path sequence optimization measures performed are specifically: sequentially adjusting all cutting paths in the path group and inserting buffer path segments between the paths to reduce the local thermal interference effect; For all cutting path groups to be adjusted that are divided into high thermal interference path groups, the cutting path sequence optimization measures performed are specifically: path sequence scattering processing is performed on all cutting paths in the path group, and a thermal isolation control strategy is implemented to reduce local thermal coupling superposition and control the heat diffusion path.

8. A laser cutting machine track-seeking path planning optimization system, used to implement a laser cutting machine track-seeking path planning optimization method as described in any one of claims 1 to 7, characterized in that: It includes a path identification and thermal overlap prediction module, a thermal overlap screening and calibration module, a thermal feature evaluation module, a path classification optimization module, and a thermal simulation and closed-loop correction module; The path identification and thermal overlap prediction module performs spatial geometric analysis on all preset cutting paths during the process of the laser cutting machine processing the contour of the structural part, identifies the spatial relationship between the cutting paths, constructs a set of cutting path groups with spatial proximity relationships, and predicts whether there is thermal distribution overlap between the path groups in the set during the cutting process; The thermal overlap screening and calibration module, when the prediction result shows that there is thermal distribution overlap, screens out all path groups with this situation and calibrates them as cutting path groups to be adjusted; The thermal characteristic evaluation module acquires the thermal characteristic information of each group of cutting path groups to be adjusted in real time, analyzes it after acquisition, evaluates the thermal interference intensity generated by each group of cutting path groups to be adjusted under the current sorting, and divides each group of cutting path groups to be adjusted into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group according to the evaluation results; The path classification optimization module executes the corresponding cutting path sequence optimization measures according to the division results; The thermal simulation and closed-loop correction module performs thermal field simulation verification on the optimized path sequence after the cutting path sequence optimization is completed. If the verification result does not meet the thermal safety conditions, the path sequence is corrected based on the verification result.

Images