A laser cutting machine rail path planning optimization method and system

Abstract

The application discloses a kind of laser cutting machine rail searching path planning optimization method and system, it is related to laser cutting machine path optimization technical field, specifically includes the following steps: when the prediction result is that there is heat distribution overlap, all path groups with this condition are screened out, and it is calibrated as to be adjusted cutting path group;Real-time acquisition of the thermal sensitive characteristic information of each group of to-be-adjusted cutting path group, and after acquisition, analysis is carried out, the heat interference intensity generated by each group of to-be-adjusted cutting path group under current order is evaluated, and each group of to-be-adjusted cutting path group is divided into low heat interference path group, medium heat interference path group and high heat interference path group according to the evaluation result;According to the division result, corresponding cutting path order optimization measures are respectively executed.The application solves the problem that path heat overlap cannot be dynamically avoided, realizes intelligent optimization of cutting order based on heat sensing and accurate control of heat interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laser cutting machine path optimization, and particularly relates to a laser cutting machine rail searching path planning optimization method and system. BACKGROUND

[0002] Laser cutting machine rail searching path planning refers to trajectory calculation and control required for ensuring that the cutting head moves according to an optimal path to accurately complete material cutting during the laser cutting process. Since the laser cutting machine mainly relies on high-energy laser beams 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 rail searching path planning is to calculate an optimal path so that the cutting head completes all cutting tasks in the shortest time, while avoiding unnecessary repeated movements, reducing path intersections and reducing cutting head empty running (i.e. invalid movement in a non-cutting state) to improve processing efficiency and reduce energy consumption. However, in actual applications, the existing rail searching path planning often has some problems, such as time waste caused by unreasonable cutting path, increased mechanical wear due to redundant trajectories, lack of dynamic adjustment capability in path planning during the cutting process, and inability to adapt to the processing needs of complex parts. Therefore, optimizing the laser cutting machine rail searching path planning is to redesign the path planning method based on mathematical optimization, artificial intelligence algorithms or sensor data analysis technology, so as to make it more intelligent and adaptive, thereby reducing cutting time, improving precision, reducing equipment wear and tear, and improving overall processing efficiency, ultimately improving the productivity and economic benefits of industrial manufacturing.

[0003] The existing laser cutting machine path planning optimization technology mainly improves the efficiency and accuracy of the cutting path through mathematical modeling, artificial intelligence algorithm, sensor data analysis and advanced control strategy. The specific optimization methods include several aspects. First, the path planning based on mathematical optimization usually uses graph theory algorithm (such as shortest path Dijkstra algorithm) or intelligent optimization algorithm (such as genetic algorithm, particle swarm optimization) to calculate the optimal cutting sequence, ensure the shortest path of the cutting head, reduce the empty running distance, and thus reduce the processing time and energy consumption. Second, the dynamic optimization method based on artificial intelligence can learn from a large amount of historical cutting data through deep learning or reinforcement learning, so that the system can predict the optimal trajectory and dynamically adjust during the cutting process, improving the adaptive ability of the path. Third, by using real-time sensor feedback and computer vision technology, the system can detect the irregularities of the material surface, thermal deformation and other influencing factors during the processing, and adjust the path in real time to ensure that the cutting accuracy is not affected by the change of the material. In addition, the combination of intelligent scheduling and path optimization can consider the sequence of multiple cutting tasks for complex parts, and use parallel path planning or partition optimization strategy to reduce path intersection and repeated cutting, improve processing efficiency. At the same time, combined with advanced CNC (CNC) system and high-speed motion control, the 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. In summary, the application of these optimization technologies makes the path planning of laser cutting machine more intelligent and adaptive, not only improves the processing accuracy and cutting efficiency, but also reduces the equipment wear and energy consumption, so as to meet the demand of modern intelligent manufacturing for efficient, accurate and low-cost processing.

[0004] The prior art has the following disadvantages:

[0005] In the process of structural part contour machining, when multiple closed paths are close to each other in space, especially in the case of collinear or semi-enclosed relationship, the cutting task often needs to be processed continuously in a short time. At this time, the phenomenon of local heat distribution overlap is easy to appear. Because a heat affected zone is formed on the material surface during laser cutting, continuous cutting of adjacent paths will cause heat to be unable to diffuse in time, resulting in heat accumulation and abnormal temperature rise in the local area, causing thermal expansion and internal stress changes of the material, and finally leading to material warping or path deviation. However, the existing laser cutting machine path planning optimization technology cannot dynamically optimize the path order according to the local heat distribution overlap trend in the path sorting link. It still relies on geometric distance or path level for static sorting, and cannot identify and avoid the risk of heat overlap. The local heat shock caused by this may cause cutting edge melting, subsequent path precision decline, or even cause the entire workpiece to be deformed beyond the tolerance requirement, affecting the quality of the finished product, while damaging the stability of path reuse and reducing batch production efficiency.

[0006] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present application is to provide a laser cutting machine path planning optimization method and system to solve the problems in the background.

[0008] In order to achieve the above purpose, the present application provides the following technical scheme: a laser cutting machine path planning optimization method, specifically comprising the following steps:

[0009] In the process of laser cutting machine contour machining of structural parts, spatial geometric analysis is performed on all preset cutting paths, the spatial relationship between each cutting path is identified, a cutting path group set with spatial proximity relationship is constructed, and whether there is heat distribution overlap in each path group in the set during cutting is predicted;

[0010] When the prediction result is that there is heat distribution overlap, all path groups with this condition are selected and marked as to-be-adjusted cutting path groups;

[0011] Real-time acquisition of the heat-sensitive feature information of each group of to-be-adjusted cutting path groups, and analysis after acquisition, evaluation of the heat interference intensity generated by each group of to-be-adjusted cutting path groups under the current order, and division of each group of to-be-adjusted cutting path groups into low heat interference path groups, medium heat interference path groups and high heat interference path groups according to the evaluation result;

[0012] According to the division result, the corresponding cutting path order optimization measures are respectively executed;

[0013] After the cutting path sequence is optimized, a thermal simulation is performed to verify 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.

[0014] Preferably, the thermal characteristic information of each group of cutting paths to be adjusted is acquired in real time, analyzed after acquisition, and the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting is evaluated. Based on the evaluation results, each group of cutting paths to be adjusted is divided into low thermal interference path group, medium thermal interference path group, and high thermal interference path group. Specifically, the following steps are included:

[0015] The thermal feature information of each group of cutting paths to be adjusted is acquired in real time and preprocessed after acquisition;

[0016] Extract path geometric aggregation feature information and path thermal energy distribution gradient information from the thermally sensitive feature information of each group of pre-processed cutting path groups to be adjusted, and analyze them after extraction to generate the path group thermal coupling aggregation coefficient and local laser thermal pressure index of each group of cutting path groups to be adjusted.

[0017] A sorting thermal interference evaluation model is constructed for the path group thermal coupling aggregation coefficient and local laser thermal pressure index of each group of cutting paths to be adjusted. The sorting thermal interference coefficient of each group of cutting paths to be adjusted is generated by weighted summation.

[0018] Determine the pre-set threshold range of the sorting thermal interference coefficient, and compare it with the sorting thermal interference coefficient of each group of cutting paths to be adjusted generated. Evaluate the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting based on the comparison results, and divide each group of cutting paths to be adjusted into low thermal interference path group, medium thermal interference path group and high thermal interference path group based on the evaluation results.

[0019] Preferably, the logic for obtaining the thermal coupling aggregation coefficient of each group of cutting paths to be adjusted is as follows:

[0020] The path geometric aggregation feature information is extracted from the thermal feature information of each group of cutting paths to be adjusted after preprocessing. Specifically, this includes 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 rate of curvature change of all cutting paths, and is labeled as follows: , and , Indicates the first The shortest edge distance between all cutting paths in the group of cutting paths to be adjusted. Indicates the first The sum of the path lengths of all cutting paths in the group to be adjusted. represents the first average curvature change rate of all cutting paths in the group of cutting paths to be adjusted, , is a positive integer;

[0021] The path group thermal coupling aggregation coefficient of each group of cutting paths to be adjusted is calculated, and the specific calculation formula is as follows:

[0022]

[0023] In the formula, is the path group thermal coupling aggregation coefficient of the first group of cutting paths to be adjusted.

[0024] Preferably, the acquisition logic of the local laser thermal pressure index of each group of cutting paths to be adjusted is as follows:

[0025] The path thermal energy distribution gradient information is extracted from the thermal sensitive feature information of each group of cutting paths to be adjusted after preprocessing, specifically including the average laser power used by the laser cutting machine in 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 is respectively marked as , and , represents the average laser power used by the laser cutting machine in all paths in the first group of cutting paths to be adjusted, represents the path group envelope area of the first group of cutting paths to be adjusted, represents the path energy density change rate of the first group of cutting paths to be adjusted, , is a positive integer;

[0026] The local laser thermal pressure index of each group of cutting paths to be adjusted is calculated, and the specific calculation formula is as follows:

[0027]

[0028] In the formula, is the local laser thermal pressure index of the first group of cutting paths to be adjusted.

[0029] Preferably, the path group thermal coupling aggregation coefficient and the local laser thermal pressure index The sorting thermal interference evaluation model is constructed, and the sorting thermal interference coefficients of each group of to-be-adjusted cutting path groups are generated by weighted summation. The specific calculation formula is as follows:

[0030]

[0031] In the formula, is the sorting thermal interference coefficient of the i-th group of to-be-adjusted cutting path groups, and are non-zero weight coefficients of the path group thermal coupling aggregation coefficient and the local laser thermal pressure index respectively, and .

[0032] Preferably, a pre-set sorting thermal interference coefficient threshold interval is determined, and the generated sorting thermal interference coefficients of each group of to-be-adjusted cutting path groups are compared. According to the comparison result, the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting is evaluated, and according to the evaluation result, each group of to-be-adjusted cutting path groups is divided into a low thermal interference path group, a medium thermal interference path group and a high thermal interference path group. The specific comparison analysis and division are as follows:

[0033] If , the thermal interference intensity generated by the group of to-be-adjusted cutting path groups under the current sorting is low thermal interference intensity, and the group of to-be-adjusted cutting path groups is divided into a low thermal interference path group;

[0034] If , the thermal interference intensity generated by the group of to-be-adjusted cutting path groups under the current sorting is medium thermal interference intensity, and the group of to-be-adjusted cutting path groups is divided into a medium thermal interference path group;

[0035] If , the thermal interference intensity generated by the group of to-be-adjusted cutting path groups under the current sorting is high thermal interference intensity, and the group of to-be-adjusted cutting path groups is divided into a high thermal interference path group.

[0036] Preferably, according to the division result, corresponding cutting path order optimization measures are respectively executed, specifically:

[0037] For all groups of to-be-adjusted cutting path groups divided into low thermal interference path groups, the cutting path order optimization measures executed are specifically: keeping the original cutting order of all cutting paths in the path group unchanged, and not performing sorting optimization operation;

[0038] ​For all the cutting path groups to be adjusted, which are classified as medium thermal interference path groups, the specific measures to optimize the cutting path order are as follows: adjust the order of all cutting paths in the path group and insert buffer path segments between the paths to reduce the local thermal interference effect.

[0039] For all the cutting path groups to be adjusted, which are classified as high thermal interference path groups, the specific measures to optimize the cutting path order are as follows: the path order of all cutting paths in the path group is broken up, and a thermal isolation control strategy is implemented to reduce the phenomenon of local thermal coupling superposition and control the thermal diffusion path.

[0040] Preferably, a laser cutting machine track-finding path planning and optimization system includes a path identification and thermal overlap prediction module, a thermal overlap screening and calibration module, a thermal feature evaluation module, a path hierarchical optimization module, and a thermal simulation and closed-loop correction module.

[0041] The path recognition and thermal overlap prediction module performs spatial geometric analysis on all preset cutting paths during the laser cutting machine's processing of the structural component contour, identifies the spatial relationships between each cutting path, constructs a set of cutting path groups with spatial proximity relationships, and predicts whether there is thermal distribution overlap between each path group in the set during the cutting process.

[0042] The thermal overlap screening and calibration module, when the prediction result indicates that there is thermal distribution overlap, filters out all path groups with this situation and calibrates them as path groups to be adjusted and cut.

[0043] The thermal feature evaluation module acquires the thermal feature information of each group of cutting paths to be adjusted in real time, and analyzes it after acquisition. It evaluates the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting, and divides each group of cutting paths to be adjusted into low thermal interference path group, medium thermal interference path group and high thermal interference path group according to the evaluation results.

[0044] The path hierarchical optimization module executes corresponding path cutting order optimization measures according to the division results.

[0045] The thermal simulation and closed-loop correction module performs thermal field simulation verification on the optimized path sequence after the cutting path sequence is optimized. If the verification result does not meet the thermal safety conditions, the path sequence is corrected based on the verification result.

[0046] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0047] 1. The application introduces an inter-path spatial geometry analysis and thermal distribution overlap trend prediction mechanism, enabling the laser cutting machine to identify potential heat concentration areas in advance before processing the structure contour, and establishing a path recognition framework based on thermal behavior prediction. On this basis, a double-parameter evaluation model (path group thermal coupling aggregation coefficient and local laser thermal pressure index) is constructed to quantitatively determine the thermal interference intensity of each path under the current ranking, and then through the linkage control of path level division and differentiated optimization strategy, the defects of the existing ranking technology that cannot dynamically respond to local heat accumulation are effectively avoided. In this way, the application realizes the technical leap of cutting path ranking from static geometry driving to dynamic thermal sensing driving, which can significantly reduce the quality defects caused by thermal interference such as thermal deformation, path deviation and edge melting.

[0048] 2. The ranking thermal interference evaluation model proposed by the application is based on multi-dimensional quantitative feature construction, which integrates the path geometry aggregation characteristics and thermal input density variation characteristics through weighted fusion, not only having physical meaning in index design, but also having good scalability and engineering adaptability. The ranking thermal interference coefficient output by the model is used to divide the path group into low, medium and high levels, and the system executes differentiated optimization strategies according to the thermal interference level, including maintaining the original order, adjusting the order, inserting a buffer segment, dispersing the path and controlling thermal isolation. Compared with the traditional method of static ranking based on path distance or topological relationship, this mechanism realizes the dynamic linkage of path behavior and thermal risk for precise control, 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.

[0049] 3. After completing the path optimization ranking, the application introduces a thermal field simulation verification mechanism to perform visual thermal analysis and safety check on the optimization results 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 a closed-loop correction logic to re-optimize the path order based on simulation feedback information until a path structure that meets the thermal safety boundary is formed. This closed-loop design realizes self-verification, self-correction and self-convergence of path ranking optimization, not only improving the intelligence level of the system, but also providing higher adaptability and process fault tolerance space for different workpiece layouts and complex process materials, greatly enhancing the quality consistency and process reusability in batch cutting tasks. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0051] Figure 1 A flowchart of a laser cutting machine rail path planning optimization method and system of the present application.

[0052] Figure 2 A module diagram of a laser cutting machine rail path planning optimization method and system of the present application. DETAILED DESCRIPTION

[0053] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0054] The present application provides a laser cutting machine rail path planning optimization method as shown in Figure 1 The laser cutting machine rail path planning optimization method specifically includes the following steps:

[0055] In the process of laser cutting machine processing the structure contour, the spatial geometry of all the preset cutting paths is analyzed, the spatial relationship between each cutting path is identified, the cutting path group set with spatial proximity relationship is constructed, and whether there is heat distribution overlap in each path group in the set during the cutting process is predicted;

[0056] In order to identify the spatial relationship between each preset cutting path in the laser cutting task, the software system needs to first analyze the structure of the imported geometric figure or CAD cutting file, and extract all closed or open path vector data (such as starting point, ending point, control point, path type, etc.). Subsequently, the system analyzes the geometric relationship between each path based on two-dimensional or three-dimensional coordinate system, which can include path boundary envelope calculation, path barycenter coordinate calculation, bounding box intersection detection, minimum distance calculation, direction vector angle analysis and other means, to determine whether there is spatial adjacency, edge, intersection, nesting, inclusion, overlap, angle close and other spatial geometric relationships between any two paths. By establishing the "spatial relationship matrix" or "path adjacency graph" between the above path pairs, the software can realize the systematic modeling of the structural position between all paths. The purpose of this step is to provide accurate structural support data for subsequent heat interference analysis, to ensure that the system can identify which paths will be close to each other in space when the execution order is close, and then form a potential risk area of heat superposition. This operation is completed through software logic judgment, spatial geometry modeling algorithm and data structure processing, without relying on any external hardware conditions, with high automation and scalability.

[0057] On the basis of the completion of the path space relationship identification, the software can further construct a "path group set with spatial proximity relationship". This process usually sets a certain path proximity threshold (such as minimum edge distance, less than a certain angle included angle, etc.), and automatically divides all paths that meet the adjacency conditions into one or more "adjacent path groups". The paths in each group have a close relative relationship in structure, and there may be potential needs for continuous processing. The system then predicts the modeling of the heat distribution superposition trend based on the path group set, combined with the cutting sequence, path length, historical cutting energy model and material thermal diffusion characteristics. Specifically, the software can call the heat conduction simulation module or build a heat affected zone expansion model to simulate the diffusion radius and duration of the heat affected zone in space during the cutting process of a path, and determine whether the heat zone covers other path locations in the path group in time and space. If it meets the condition, 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 the paths before the task is executed, to warn in advance of possible local heat accumulation problems, and to provide data basis for subsequent path ordering optimization. This mechanism not only improves the heat stability design capability of the path finding, but also enhances the intelligent decision level of the software, which is a key pre-step in this optimization method.

[0058] When the prediction result is that there is heat distribution overlap, all path groups that exist in this case are screened out and marked as "to be adjusted cutting path groups";

[0059] After the heat distribution overlap prediction is completed, the software system will structure the prediction results and screen out those path groups that have spatial heat affected area intersection, coverage or superposition phenomenon in the cutting process from all path groups through the internally preset thermal interference judgment logic. Specifically, the system will label the heat affected prediction results of each path group, such as labeling "whether there is heat overlap", "heat influence coverage", "estimated temperature rise level" and other key fields. Then, the system automatically screens based on these labels: all path groups that have heat influence overlap judgment as "yes" or heat interference parameters exceeding the set threshold will be included in the "to be adjusted cutting path group" set. In the software system, this set will be numbered, archived and attached with a set of attribute information for subsequent heat optimization operations, such as cutting sequence in the path group, estimated heat diffusion area, path spacing model, etc., to be called by the next stage of evaluation and ordering optimization logic. The whole process is automatically completed by the software without human intervention, realizing the automation, modularization and data structuring of path thermal coupling identification and management.

[0060] The purpose of the screening and calibration is to actively identify and extract the thermal safety risk path group as the subsequent optimization object by software means, so as to realize the targeted optimization and resource focusing of the path planning. Due to the complex structure of the part contour and the dense path in the cutting task, only part of the path group will appear local heat accumulation under certain sorting and heat conduction conditions. Therefore, if not screened, the system may blindly optimize all paths, causing heavy calculation burden, low efficiency, and even unnecessary path disturbance. Through accurate screening of the "to-be-adjusted cutting path group", the system can focus the optimization resources and scheduling algorithm on the local area where there is a real thermal overlap risk, thereby significantly improving the response efficiency, thermal control accuracy and cutting quality stability of the path planning. At the same time, the screening mechanism also provides a structural input basis for the subsequent hierarchical evaluation and classified optimization of the path, and is a key intermediate link in the entire heat-sensing driven path optimization logic.

[0061] The thermal sensitive characteristic information of each group of to-be-adjusted cutting path groups is acquired in real time, and after acquisition, analysis is performed, the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting is evaluated, and each group of to-be-adjusted cutting path groups is divided into low thermal interference path groups, medium thermal interference path groups and high thermal interference path groups according to the evaluation results;

[0062] In this embodiment, the thermal sensitive characteristic information of each group of to-be-adjusted cutting path groups is acquired in real time, and after acquisition, analysis is performed, the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting is evaluated, and each group of to-be-adjusted cutting path groups is divided into low thermal interference path groups, medium thermal interference path groups and high thermal interference path groups according to the evaluation results, and the specific steps include the following steps:

[0063] The thermal sensitive characteristic information of each group of to-be-adjusted cutting path groups is acquired in real time, and after acquisition, analysis is performed, the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting is evaluated, and each group of to-be-adjusted cutting path groups is divided into low thermal interference path groups, medium thermal interference path groups and high thermal interference path groups according to the evaluation results;

[0064] In the path planning optimization process, the system can extract the key geometric and energy data related to the thermal behavior from the cutting path definition data and the cutting parameter database in real time before the task scheduling is executed or in the path simulation stage through the data acquisition module built in the software. The "thermal sensitive characteristic information" includes the control point sequence of the path, the curvature change rate, the path bounding box, the path barycenter coordinates, the total length, the laser power it belongs to, the energy density, etc. After the system identifies the to-be-adjusted cutting path group, it maps its number to the path database, calls the path attribute field and process parameter field through association, and forms the "thermal behavior characteristic snapshot" of the path group under the current sorting. The extraction process is completely executed by the software scheduling system, based on the data interface composed of the CAD layer analysis module of the cutting task, the G code parameter parser, the device laser process library, etc., to ensure that the extraction process is real-time, automatic and continuous, and is updated synchronously with the path sorting state, so as to realize "real-time acquisition".

[0065] The purpose of preprocessing the thermal sensitive feature information is to standardize, structure and multi-dimensionally correlate the raw data, so as to facilitate the unified execution of feature extraction, parameter calculation and thermal interference evaluation operations in subsequent modeling analysis. Since the raw data may come from different structural dimensions (such as vector for geometric data and scalar for process data, with different unit scales), direct input into the analysis model may easily cause inconsistent dimensions, data distortion or skew influence. Therefore, after obtaining the thermal sensitive feature information, the system will perform a series of software preprocessing procedures, including but not limited to: (1) data unit uniform conversion, such as converting path length to mm and 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 structure of different paths in the same path group to a uniform vector dimension; (4) outlier detection and interpolation completion, for example, automatically completing the intermediate segment points when the path missing control point data; (5) path group feature aggregation processing, merging the path-level data into a group-level thermal sensitive information matrix according to the path group unit. These preprocessing operations are automatically completed by the software through the embedded geometric calculation module, data standardization function library and abnormality recognition logic, ensuring that the data input quality meets the operation requirements of complex mathematical models, and improving the stability of subsequent parameter calculation and the reliability of evaluation results.

[0066] From the thermal sensitive feature information of each group of to-be-adjusted cutting path groups after preprocessing, path geometry aggregation feature information and path thermal energy distribution gradient information are extracted, and after extraction, analysis is performed to generate path group thermal coupling aggregation coefficients and local laser thermal pressure indexes of each group of to-be-adjusted cutting path groups, respectively.

[0067] After obtaining and completing the thermal sensitive feature information of each group of to-be-adjusted cutting path groups after preprocessing, the system automatically extracts and constructs two types of high-level information dimensions, "path geometry aggregation feature information" and "path thermal energy distribution gradient information", according to the preset parameter correlation rules and structured field mapping logic, through the built-in feature deconstruction and label mapping module of the software. Specifically, the system first retrieves the preprocessing fields related to the path spatial 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 labeled as path geometry aggregation feature information. Then, the system extracts fields related to thermal input, including laser power, path energy density, and path power difference of each path in 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 analysis, vectorization reorganization and feature label recognition, ensuring that each group of paths can be mapped to two feature dimension information sets as direct input for subsequent parameter modeling, while improving the explainability and responsiveness of the system to path thermal behavior.

[0068] A sorting thermal interference evaluation model is constructed for the path group thermal coupling aggregation coefficient and local laser thermal pressure index of each group of cutting paths to be adjusted. The sorting thermal interference coefficient of each group of cutting paths to be adjusted is generated by weighted summation.

[0069] Determine the pre-set threshold range of the sorting thermal interference coefficient, and compare it with the sorting thermal interference coefficient of each group of cutting paths to be adjusted generated. Evaluate the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting based on the comparison results, and divide each group of cutting paths to be adjusted into low thermal interference path group, medium thermal interference path group and high thermal interference path group based on the evaluation results.

[0070] The threshold range for the thermal interference coefficient in the ranking process can be determined through the software system's built-in parameter learning module and empirical model construction mechanism. Specifically, the system automatically extracts representative thermal interference evaluation coefficient values ​​of typical path groups as a sample dataset, combining historical cutting task data, thermal interference level labels, and path execution effect feedback. 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 ranking thermal interference coefficient and the actual thermal impact results. Then, it uses clustering algorithms (such as K-means or hierarchical clustering) to perform density clustering on the evaluation coefficients of all known path groups, identifying natural boundary points or inflection points in the coefficient distribution as preliminary threshold division criteria. Statistical methods (such as quantile analysis and box analysis) are then used to determine the critical intervals, forming reference intervals for thermal interference level stratification. Finally, the system sets the two identified boundary coefficient values ​​as the first and second thresholds, dividing the system into three intervals: low thermal interference, medium thermal interference, and high thermal interference. The entire process is completed automatically through the software's data modeling and adaptive update mechanism, and supports differentiated adjustments based on material type, plate thickness specifications, or cutting process parameters to achieve dynamic adaptation and continuous optimization of parameter thresholds.

[0071] In this embodiment, the logic for obtaining the thermal coupling aggregation coefficient of each group of cutting paths to be adjusted is as follows:

[0072] The path geometric aggregation feature information is extracted from the thermal feature information of each group of cutting paths to be adjusted after preprocessing. Specifically, this includes 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 rate of curvature change of all cutting paths, and is labeled as follows: , and , Indicates the first The shortest edge distance between all cutting paths in the group of cutting paths to be adjusted. Indicates the first The sum of the path lengths of all cutting paths in the group to be adjusted. Indicates the first The average rate of change of curvature of all cutting paths in the group of cutting paths to be adjusted. , It is a positive integer;

[0073] During the path planning system's operation, the software can perform vector analysis and topological modeling on imported cutting graphic data (such as DXF, G-code, or internal CAD models) to obtain the path geometry information in each group of cutting paths to be adjusted in real time, and then automatically calculate several key data points required. First, the "shortest edge distance between all cutting paths" refers to the minimum Euclidean distance between the bounding boxes or contour lines of any two paths in the path group. The software constructs the minimum bounding box for each path and performs pairwise nearest edge calculations within the path group to obtain the path pair corresponding to the minimum distance in the current path group in real time, serving as... The input reflects the spatial compactness of the path. Secondly, the "sum of 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 calculate the actual length of each path using integration or curve parameterization algorithms, then sum them up to obtain the final length. This is used to evaluate the total heat input scale of the path group. Finally, the "average rate of curvature change of all cutting paths" represents the trend and complexity of path morphology changes. The system first discretizes the geometric representation of each path (such as Bezier or NURBS) to obtain multiple consecutive tangent points and their tangent directions, then calculates the local curvature change through the difference in tangent angles, and finally averages the rate of curvature change of all paths in the group to obtain the mean curvature change. This data reflects the frequency of geometric perturbations within the path group's internal structure. All three types of data originate from the coordinates and topological attributes of the path graph itself. They can be automatically extracted in real-time through the software's geometric modeling module, curve analysis engine, and numerical analysis components, requiring no external input or manual intervention. This allows for synchronous updates 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.

[0074] Calculate the thermal coupling aggregation coefficient of each group of cutting paths to be adjusted. The specific calculation formula is as follows:

[0075]

[0076] In the formula, For the first The thermal coupling aggregation coefficient of the path group to be adjusted.

[0077] path group thermal coupling aggregation coefficient The formula is used to comprehensively express the trend of the path group in space due to the geometric aggregation, path density concentration, and path complexity enhancement, which leads to the enhancement of thermal coupling effect. The numerator term in the formula represents the total path thermal accumulation intensity of the path group, wherein reflects the total scale of the regional heat input, and the square operation enhances the sensitivity to the sharp change in curvature (i.e., the complex structure of the path), which means that the more paths and the more turns, the stronger the thermal coupling generated by the path in the local area. The denominator represents the shortest boundary distance between the paths plus 1, which is an attenuation factor to avoid mathematical anomalies of division by 0 and also plays a role in spatial buffering of thermal diffusion. The closer the paths, the easier the thermal influence area is to overlap, and the stronger the coupling effect. Adding 1 also plays a role in mathematical balancing of the converging logarithmic input. The external natural logarithm function is used to introduce a nonlinear growth model, so that the coefficient value rises more rapidly after the thermal coupling trend reaches a critical state, improving the model's ability to identify sensitive areas of thermal overlap. Squaring the logarithmic term further amplifies the weight of high-coupling areas in the model, so that they are prioritized for optimization in subsequent thermal interference assessment. The second part of the formula extracts the nonlinear coupling relationship between curvature and path proximity, and strengthens its modeling ability for thermal behavior complexity in the "path tight + frequent bending" scenario through 0.75 power. Overall, the formula enhances growth sensitivity through logarithmic operation, constructs nonlinear characteristic response through power operation, and constructs an antagonistic relationship between thermal accumulation and thermal diffusion through the numerator and denominator. It is a highly abstract and reasonable mapping of the thermal coupling aggregation phenomenon in the geometric space dimension, has clear physical meaning and engineering guidance value, and can be automatically calculated by a software system through structured data.

[0078] The first path group thermal coupling aggregation coefficient of the path group to be adjusted The numerical size of the path group is positively correlated with the thermal interference intensity that the path group may cause under the current sorting, that is: The larger the value, the more the path group tends to be tightly aggregated in the spatial structure, the longer the total path length, the more complex the geometric structure, the smaller the minimum boundary distance between the paths, the more bending and dense the path form, the more concentrated the heat input in the unit space, and the shorter the heat diffusion path, so that the thermal accumulation effect in the local area during continuous laser cutting is more significant, the thermal superposition risk is higher, and the resulting thermal interference intensity is stronger. Therefore, the A higher value indicates that the path group is more prone to high-intensity thermal coupling and cross-influence of heat conduction under the current sorting arrangement. This directly leads to problems such as thermal deformation in local areas of the material, fluctuations in cutting accuracy, or path execution deviation, making it an important metric for the risk of thermal interference during sorting. This parameter quantifies the heat transfer trend through geometric aggregation characteristics, providing a strong mathematical basis for the identification and priority optimization of thermally sensitive path groups.

[0079] In this embodiment, the logic for obtaining the local laser thermal pressure index of each group of cutting paths to be adjusted is as follows:

[0080] The thermal energy distribution gradient information of each group of preprocessed cutting paths to be adjusted is extracted from the thermal feature information. Specifically, this includes the average laser power used by the laser cutting machine in all paths of each group, the path envelope area of ​​each group, and the path energy density change rate of each group. These are then calibrated as follows: , and , Indicates the laser cutting machine in the first The average laser power used by all paths in the group of cutting paths to be adjusted. Indicates the first The envelope area of ​​the path group to be adjusted for the cutting path group. Indicates the first The rate of change of path energy density of the group of cutting paths to be adjusted , It is a positive integer;

[0081] During path planning and optimization, the software system can extract physical data related to laser thermal input from the cutting task data in real time through the integrated cutting parameter analysis module and geometric modeling subsystem, thereby obtaining the required path thermal energy distribution gradient information for each group of cutting paths to be adjusted. Among these, "the average laser power used by each path" is... "This 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-codes, processing configuration files, or reading actual power commands from the equipment control logic, and then calculate the average value to reflect the overall heat input level of the path group; "Path group envelope area" "Path energy density change rate" refers to the total area covered by the smallest bounding polygon or envelope rectangle formed by all paths within the group on the plane. It is used to measure the spatial distribution area of ​​thermal energy. The software can quickly generate the contour envelope of the path group and calculate the area by calling the boundary detection and convex hull calculation algorithms in the geometry engine; "Power density" refers to the ratio of the maximum difference to the average value of the laser power per unit length carried by each path in the path group. It reflects the degree of non-uniformity of heat energy distribution within the path group. The system calculates the power density by dividing the power value of each path by its length, and then statistically analyzes the maximum, minimum, and average values ​​of the density to finally calculate the rate of change. All three types of data can be automatically acquired in the early stage of path sorting optimization through the software's real-time task parsing and geometric analysis process. They correspond one-to-one with the path group number, geometric model, and process parameters, requiring no manual intervention. This provides real-time, accurate, and structured data support for subsequent calculations of the local laser thermal pressure index.

[0082] Calculate the local laser thermal stress index for each group of cutting paths to be adjusted. The specific calculation formula is as follows:

[0083]

[0084] In the formula, For the first The local laser thermal pressure index of the group to be adjusted for the cutting path.

[0085] Local laser thermo-pressing index of each group of cutting paths to be adjusted The reason for using this calculation formula is to comprehensively characterize the first... The group of cutting paths to be adjusted faces the risk of thermal shock due to transient thermal pressure intensity and uneven energy distribution caused by laser energy input in local spatial regions. Specifically, the left side of the formula... This represents the laser power density per unit area. This is the square of the average laser power, reflecting the heat input intensity of the laser power. This represents the envelope area of ​​the path group plus 1, which prevents the division-by-zero problem caused by an area of ​​zero, and also reflects the tendency for higher thermal pressure caused by smaller areas and more concentrated heat energy. Amplifying this ratio as a power of 1.2 introduces a nonlinear response mechanism, giving high-power-density regions greater identification weight in thermal pressure risk assessment. (Right-hand side of the formula) This reflects the gradient of laser energy density variation among the paths within the path group. A larger value indicates significant non-uniformity of heat input in a local area, which easily induces internal temperature gradients and stress disturbances in the material. By taking the logarithm of this value and amplifying it exponentially, the thermal shock sensitivity of local high-gradient regions can be enhanced. The entire exponential model comprehensively considers key factors such as the intensity, distribution density, and non-uniformity of laser heat input, making it... It can effectively characterize the ability of path groups to cause transient thermal stress concentration in materials under the current order, providing strong thermophysical support for the accurate assessment of the order thermal interference level.

[0086] No. Local laser thermo-pressing index of the group to be adjusted for the cutting path The magnitude of this value is positively correlated with the intensity of thermal interference that the path group may generate under the current sorting. Specifically, The larger the value, the higher the laser energy density the path group experiences per unit space, and the more uneven the laser power distribution between paths within the group. This indicates that the heat input is both strong and concentrated, while also exhibiting significant local gradient changes. This combination of high thermal pressure and high inhomogeneity easily leads to rapid temperature differences in the material within a short period, resulting in inconsistent thermal expansion and abrupt changes in internal stress. Ultimately, this manifests as thermal deformation during path execution, erosion of cutting edges, path deviation, and other thermal interference phenomena. Therefore, A higher value indicates a higher risk of local thermal shock and stronger transient thermal disturbance for the path group under the current cutting sequence. It is a key indicator used in thermal interference intensity assessment to measure the "concentration of thermal input and complexity of distribution gradient." It complements the thermal coupling trend caused by the path structure, together forming a two-factor judgment basis for thermal interference intensity assessment.

[0087] In this embodiment, the thermal coupling aggregation coefficient of each group of cutting paths to be adjusted is... and local laser thermo-pressure index A thermal interference assessment model for sorting is constructed. The thermal interference coefficients for each group of cutting paths to be adjusted are generated through weighted summation. The specific calculation formula is as follows:

[0088]

[0089] In the formula, For the first The thermal interference coefficient of the sorting of the group of cutting paths to be adjusted. and The thermal coupling aggregation coefficients of the path groups to be adjusted are respectively the path group thermal coupling aggregation coefficients of each group of cutting paths to be adjusted. and local laser thermo-pressure index The non-zero weight coefficients, and .

[0090] 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, incorporating the previously calculated thermal coupling aggregation coefficient of the path groups. With local laser thermo-pressure index As input variables, the sorting thermal interference coefficient of this path group is generated by weighted summation. The core logic of the model is that the thermal interference performance of the path group on the geometric aggregation trend and the heat input distribution is comprehensively reflected through the fusion of two-dimensional parameters. In the specific implementation, the software automatically generates two non-zero weight coefficients through the built-in evaluation module according to the influence weight of the thermal coupling type parameter and the thermal power type parameter in the historical cutting sample, the sensitivity of the objective function, and the influence correlation on the finished product quality and . Among them, represents the contribution degree of path geometric aggregation to thermal interference, which usually dominates the weight when the path structure is complex and the spatial layout is compact; and reflects the dominant degree of local heat density and change gradient in thermal shock, which 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 historical process sample training to ensure that the final generated sorting thermal interference coefficient can truly reflect the potential thermal interference intensity of the path group under the current sorting structure. The fusion evaluation method is automatically completed by the software end without human intervention, and has good real-time performance and scalability.

[0091] In this embodiment, the pre-set sorting thermal interference coefficient threshold interval is determined, and the generated sorting thermal interference coefficient of each group of to-be-adjusted cutting path groups is compared. According to the comparison result, the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sorting is evaluated, and according to the evaluation result, each group of to-be-adjusted cutting path groups is divided into low thermal interference path group, medium thermal interference path group and high thermal interference path group. The specific comparison and analysis and division are as follows:

[0092] If , the thermal interference intensity generated by the group of to-be-adjusted cutting path groups under the current sorting is low thermal interference intensity, and the group of to-be-adjusted cutting path groups is divided into low thermal interference path group;

[0093] This case shows that the spatial layout of the path group under the current cutting sorting structure is relatively dispersed, the geometric structure is simple, the thermal coupling relationship between the paths is weak, the laser energy input is relatively uniform and the density is not high, and the overall heat distribution is stable, which will not form obvious heat accumulation in the local area. In the processing process of this kind of path group, the risk of material thermal deformation is extremely low, the cutting precision is less affected by heat, and phenomena such as path deviation, edge melting, geometric error caused by thermal stress basically do not occur, and usually the cutting order or path strategy does not need to be adjusted. The system can directly retain the original sorting in the optimization stage, thereby improving the optimization efficiency and avoiding excessive intervention.

[0094] If The heat interference intensity generated by the group of to-be-adjusted cutting path groups under the current order is medium heat interference intensity, and the group of to-be-adjusted cutting path groups is divided into a medium heat interference path group;

[0095] The case indicates that the heat interference intensity of the path group under the current order is in a critical state, although there is no obvious heat runaway risk, but it has shown a certain degree of heat accumulation trend, for example, the path spacing is slightly small, the curvature is relatively complex, the power distribution has certain fluctuation and the like. Such path group has certain material thermal expansion sensitivity, if continues to cut according to the current order, the cutting deviation, edge micro deformation, local error amplification and the like may be caused due to temperature rise superposition in the subsequent stage. Therefore, the system should take the order fine tuning, insert buffer path, adjust path execution sequence and the like heat optimization strategies to realize the active control of heat risk on the basis of guaranteeing the production efficiency.

[0096] If The heat interference intensity generated by the group of to-be-adjusted cutting path groups under the current order is high heat interference intensity, and the group of to-be-adjusted cutting path groups is divided into a high heat interference path group.

[0097] The case indicates that the heat interference intensity of the path group under the current order is in a critical state, although there is no obvious heat runaway risk, but it has shown a certain degree of heat accumulation trend, for example, the path spacing is slightly small, the curvature is relatively complex, the power distribution has certain fluctuation and the like. Such path group has certain material thermal expansion sensitivity, if continues to cut according to the current order, the cutting deviation, edge micro deformation, local error amplification and the like may be caused due to temperature rise superposition in the subsequent stage. Therefore, the system should take the order fine tuning, insert buffer path, adjust path execution sequence and the like heat optimization strategies to realize the active control of heat risk on the basis of guaranteeing the production efficiency.

[0098] According to the division result, corresponding cutting path order optimization measures are respectively executed, wherein: the original order is maintained for the low heat interference path group, the order is adjusted and the buffer path segment is inserted for the medium heat interference path group, and the order is scattered and the heat isolation control is implemented for the high heat interference path group;

[0099] In this embodiment, according to the division result, corresponding cutting path order optimization measures are respectively executed, specifically:

[0100] For all the groups of to-be-adjusted cutting path groups divided into the low heat interference path group, the cutting path order optimization measures executed are specifically: the original cutting order of all the cutting paths in the path group is maintained unchanged, and no order optimization operation is executed;

[0101] For the path set divided into the low thermal interference path group, the software system detects that the sorting thermal interference coefficient is lower than the threshold lower limit through the preset thermal interference judgment logic in the path sorting optimization stage, and is automatically marked as a "thermal safe path group". The system takes the mark as a skip condition of the path sorting module, avoids any reordering, inserting paths or switching order operations on the path group, and retains the path definition order in the CAD original layer or performs the processing task scheduling based on the initial process parameter specified execution order. The reason for doing so is that the path group is relatively scattered in spatial structure, the thermal input intensity is low, and the thermal influence area of the paths almost does not overlap, which will not cause significant thermal interference to the cutting process. Unnecessary sorting processing may introduce additional path offset or processing time redundancy, so the "identification and exclusion optimization" lightweight scheme is adopted in the software control strategy, which effectively saves the computing resources and maintains the real-time response of the system.

[0102] For all the cutting path groups to be adjusted divided into the medium thermal interference path group, the specific cutting path order optimization measures are: adjusting the order of all cutting paths in the path group and inserting buffer path segments between the paths to reduce the local thermal interference effect.

[0103] For the path set divided into the medium thermal interference path group, the system identifies it and includes it in the "optimizable path group" list after identification, and adjusts the order through the built-in path local rearrangement algorithm. The optimization goal is to maximize the thermal diffusion interval between the paths. First, the system analyzes the "thermal sensitive impact index" (calculated from power density, path area, curvature, etc.) of each path in the path group, and then rearranges the execution order in a relatively balanced thermal load manner, and preferentially schedules the paths with weak thermal input to disperse the execution order of continuous high-heat segment paths. Secondly, the system inserts "buffer path segments" between the rearranged paths. These buffer segments can be pseudo paths, non-cutting transition paths, or other low-heat path segments inserted for execution, which aims to artificially lengthen the time interval between high-heat paths and increase the material cooling time. This strategy can effectively reduce the risk of thermal influence area overlap through dual adjustment of time logic and path spatial order, and is particularly suitable for path areas with cumulative thermal influence characteristics but not yet out of control, which can improve cutting stability without significantly affecting the process cycle.

[0104] For all the cutting path groups to be adjusted divided into the high thermal interference path group, the specific cutting path order optimization measures are: performing path order dispersion processing on all cutting paths in the path group, implementing thermal isolation control strategy to reduce local thermal coupling superposition and control thermal diffusion path.

[0105] For the path set divided into high thermal interference path groups, the system regards it as a high-risk thermal shock unit and needs to take a strong intervention path optimization strategy. First, the software identifies the coupling strength threshold relationship between high thermal coupling path segments through the thermal interference evaluation module, splits the continuous path with high thermal aggregation into multiple sub-path segments, and rearranges their original order through the "path scattering algorithm" to make them inserted between the path groups with low thermal load in the global path sorting, so as to pull apart the processing time sequence and spatial thermal zone distribution between paths. Second, the system calls the "thermal isolation control strategy" according to the material thickness and thermal diffusion characteristics, automatically inserts "thermal barrier segments" or "pre-cooling segments" between these high-thermal paths, such as cutting non-critical edge paths, performing device idle running, temporarily diverting to auxiliary path areas, etc., so that the local material obtains a thermal release time window. This optimization scheme effectively prevents local material warping, size precision out-of-tolerance, or processing area carbonization caused by continuous heat superposition by combining path splitting + priority adjustment + cooling control, and is suitable for processing tasks of complex profile or structure aggregated workpieces with high thermal sensitivity requirements.

[0106] After the optimization of the cutting path sequence is completed, the optimized path sequence is verified by thermal field simulation. If the verification result does not meet the thermal safety condition, the path sequence is modified based on the verification result to realize thermal control closed-loop optimization.

[0107] After the optimization of the cutting path sequence is completed, the system verifies the current path execution sequence through the integrated thermal field simulation module. Based on numerical analysis methods such as finite difference method (FDM) or finite element method (FEM), the module simulates the heat conduction, heat diffusion and material thermal response characteristics during the movement of the laser heat source along the path, generates the thermal distribution map of the entire workpiece during the cutting process, and outputs the temperature field distribution matrix and thermal gradient atlas at each time node. 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, combines the spatial geometric model and grid division algorithm to simulate dynamic heat energy injection and diffusion for each cutting area, and finally outputs the temperature field distribution matrix and thermal gradient atlas at each time node. The system then compares these simulation outputs with the preset "thermal safety condition indicators", including local area maximum temperature rise threshold, thermal gradient mutation rate, and thermal influence zone overlap degree. If any of these exceeds the thermal safety threshold, it is automatically determined that the current path sorting still has potential thermal interference risks and is not executable.

[0108] The reason for adding a thermal field simulation verification step after path sequencing optimization is that, in actual cutting, thermal interference between paths is not solely determined by geometric order and power values, but is a dynamic, superimposed, and nonlinear thermal diffusion process. Even if the optimization strategy has rearranged or isolated high-thermal-interference path groups, transient heat accumulation may still occur in small areas in some workpieces with particularly complex structures or materials with slow thermal diffusion (such as stainless steel and aluminum alloys), leading to problems such as processing deformation, cracks, and erosion. Therefore, preliminary optimization based solely on the thermal interference coefficient model is insufficient to guarantee process safety. Thermal field simulation must be used to predict the entire heat propagation process to ensure that the optimization results do not trigger uncontrollable thermal response behavior during actual execution. Introducing simulation verification not only improves the accuracy of path sequencing optimization but also implements a self-feedback closed-loop logic: if the simulation results do not meet thermal safety indicators, the system can automatically correct the path sequence, triggering a new round of optimization until the simulation passes, thereby constructing a complete thermal sensing-optimization-verification-correction-convergence path control closed loop, improving the system's intelligence level and cutting quality stability.

[0109] like Figure 2 The system shown is a laser cutting machine track-finding path planning and optimization system, including a path identification and thermal overlap prediction module, a thermal overlap screening and calibration module, a thermal feature evaluation module, a path hierarchical optimization module, and a thermal simulation and closed-loop correction module.

[0110] The path recognition and thermal overlap prediction module performs spatial geometric analysis on all preset cutting paths during the laser cutting machine's processing of the structural component contour, identifies the spatial relationships between each cutting path, constructs a set of cutting path groups with spatial proximity relationships, and predicts whether there is thermal distribution overlap between each path group in the set during the cutting process.

[0111] The thermal overlap screening and calibration module, when the prediction result indicates that there is thermal distribution overlap, filters out all path groups with this situation and calibrates them as path groups to be adjusted and cut.

[0112] The thermal feature evaluation module acquires the thermal feature information of each group of cutting paths to be adjusted in real time, and analyzes it after acquisition. It evaluates the thermal interference intensity generated by each group of cutting paths to be adjusted under the current sorting, and divides each group of cutting paths to be adjusted into low thermal interference path group, medium thermal interference path group and high thermal interference path group according to the evaluation results.

[0113] The path hierarchical optimization module executes corresponding path cutting order optimization measures according to the division results.

[0114] 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, and if the verification result does not meet the thermal safety condition, the path sequence is corrected based on the verification result.

[0115] The above formulas are dimensionless values calculated, and the formulas are obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0116] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially 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 wholly or partially generated. 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 transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. 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, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0117] It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0118] 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 realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0119] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other manners. For example, the embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0121] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0122] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing a rail-seeking path planning of a laser cutting machine, characterized in that, Specifically comprising the following steps: In the process of machining the structural part profile on the laser cutting machine, the spatial geometry of all the preset cutting paths is analyzed, the spatial relationship between the cutting paths is identified, a cutting path group set with spatial proximity relationship is constructed, and whether there is heat distribution overlap in the cutting process of each path group in the set is predicted; When the prediction result is that there is heat distribution overlap, all path groups with this condition are screened out and marked as to-be-adjusted cutting path groups; Real-time acquisition of the heat-sensitive characteristic information of each group of to-be-adjusted cutting path groups, and analysis after acquisition, evaluation of the heat interference intensity generated by each group of to-be-adjusted cutting path groups under the current order, and division of each group of to-be-adjusted cutting path groups into low heat interference path groups, medium heat interference path groups and high heat interference path groups according to the evaluation result; According to the division result, corresponding cutting path order optimization measures are respectively executed; After the cutting path order optimization is completed, the optimized path order is verified by thermal field simulation, and if the verification result does not meet the thermal safety condition, the path order is modified based on the verification result.

2. The method of claim 1, wherein, Real-time acquisition of the heat-sensitive characteristic information of each group of to-be-adjusted cutting path groups, and analysis after acquisition, evaluation of the heat interference intensity generated by each group of to-be-adjusted cutting path groups under the current order, and division of each group of to-be-adjusted cutting path groups into low heat interference path groups, medium heat interference path groups and high heat interference path groups according to the evaluation result, specifically comprising the following steps: Real-time acquisition of the heat-sensitive characteristic information of each group of to-be-adjusted cutting path groups, and analysis after acquisition, evaluation of the heat interference intensity generated by each group of to-be-adjusted cutting path groups under the current order, and division of each group of to-be-adjusted cutting path groups into low heat interference path groups, medium heat interference path groups and high heat interference path groups according to the evaluation result, specifically comprising the following steps: Real-time acquisition of the heat-sensitive characteristic information of each group of to-be-adjusted cutting path groups, and analysis after acquisition, evaluation of the heat interference intensity generated by each group of to-be-adjusted cutting path groups under the current order, and division of each group of to-be-adjusted cutting path groups into low heat interference path groups, medium heat interference path groups and high heat interference path groups according to the evaluation result, specifically comprising the following steps: The acquisition logic of the path group thermal coupling aggregation coefficient of each group of to-be-adjusted cutting path groups is as follows: The path group thermal coupling aggregation coefficient of each group of to-be-adjusted cutting path groups is calculated, and the specific calculation formula is as follows:

3. The method of claim 2, wherein, The acquisition logic of the local laser thermal pressure index of each group of to-be-adjusted cutting path groups is as follows: extracting path geometry aggregation feature information from the preprocessed thermal sensitive feature information of each group of to-be-adjusted cutting path groups, specifically including the shortest edge distance between all cutting paths in each group of to-be-adjusted cutting path groups, the sum of path lengths of all cutting paths, and the average curvature change rate of all cutting paths, and respectively marking as , and , represents the shortest edge distance between all cutting paths in the first group of to-be-adjusted cutting path groups, represents the sum of path lengths of all cutting paths in the first group of to-be-adjusted cutting path groups, represents the average curvature change rate of all cutting paths in the first group of to-be-adjusted cutting path groups, , is a positive integer; The local laser thermal pressure index of each group of to-be-adjusted cutting path groups is calculated, and the specific calculation formula is as follows: In the formula, is the first path group thermal coupling aggregation coefficient of the path group of the cutting path group to be adjusted.

4. The method of claim 3, wherein, According to the division result, corresponding cutting path order optimization measures are respectively executed, specifically: The path thermal energy distribution gradient information is extracted from the thermal sensitive feature information of each group of the preprocessed to-be-adjusted cutting path groups, specifically including the average laser power used by the laser cutting machine in all paths of each group of the to-be-adjusted cutting path groups, the path group envelope area of each group of the to-be-adjusted cutting path groups, and the path energy density change rate of each group of the to-be-adjusted cutting path groups, and are respectively marked as , and , represents the average laser power used by the laser cutting machine in all paths of the first group of the to-be-adjusted cutting path groups, represents the path group envelope area of the first group of the to-be-adjusted cutting path groups, represents the path energy density change rate of the first group of the to-be-adjusted cutting path groups, , is a positive integer; ​ In the formula, is the first local laser heat pressing index of the group of cutting paths to be adjusted.

5. The method of claim 4, wherein, path group thermal coupling aggregation coefficient of each group of generated to-be-adjusted cutting path groups and local laser hot pressing index The sorting thermal interference evaluation model is constructed, and the sorting thermal interference coefficient of each group of to-be-adjusted cutting path groups is generated by weighted summation. The specific calculation formula is as follows: wherein is the first is the sorting thermal interference coefficient of the group of cutting path groups to be adjusted, and are the path group thermal coupling aggregation coefficients of the respective group of cutting path groups to be adjusted and the non-zero weight coefficient of the local laser thermal pressure index , and .

6. The method of claim 5, wherein, Determine a pre-set ranking thermal interference coefficient threshold interval And after determining, compare with the generated ranking thermal interference coefficient of each group of to-be-adjusted cutting path groups According to the comparison result, evaluate the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current ranking, and according to the evaluation result, divide each group of to-be-adjusted cutting path groups into low thermal interference path group, medium thermal interference path group and high thermal interference path group. The specific comparison analysis and division are as follows: If If the heat interference intensity generated by the set of to-be-adjusted cutting path groups under the current order is low heat interference intensity, the set of to-be-adjusted cutting path groups is divided into low heat interference path groups. If If the heat interference intensity generated by the set of to-be-adjusted cutting path groups under the current order is a medium heat interference intensity, the set of to-be-adjusted cutting path groups is divided into a medium heat interference path group. If If the heat interference intensity generated by the set of to-be-adjusted cutting path groups under the current ranking is high heat interference intensity, the set of to-be-adjusted cutting path groups is divided into a high heat interference path group.

7. The method of claim 6, wherein, ​ For all the to-be-adjusted cutting path groups divided into the low thermal interference path group, the specific cutting path sequence optimization measures executed are: keeping the original cutting sequence of all the cutting paths in the path group unchanged, and not performing the sequence optimization operation; For all the to-be-adjusted cutting path groups divided into the medium thermal interference path group, the specific cutting path sequence optimization measures executed are: adjusting the sequence of all the cutting paths in the path group and inserting a buffer path segment between the paths to reduce the local thermal interference effect; For all the to-be-adjusted cutting path groups divided into the high thermal interference path group, the specific cutting path sequence optimization measures executed are: performing path sequence scattering processing on all the cutting paths in the path group, implementing thermal isolation control strategy, to reduce the local thermal coupling superposition phenomenon and control the thermal diffusion path.

8. A laser cutting machine rail-seeking path planning optimization system for implementing a laser cutting machine rail-seeking path planning optimization method according to any one of claims 1-7, characterized in that, The path recognition and thermal overlap prediction module, the thermal overlap screening and marking module, the thermal feature evaluation module, the path hierarchical optimization module, and the thermal simulation and closed-loop correction module are included. The path recognition and thermal overlap prediction module performs spatial geometric analysis on all the preset cutting paths during the laser cutting machine processing the structure contour, recognizes the spatial relationship between the cutting paths, constructs a cutting path group set with spatial proximity relationship, and predicts whether there is thermal distribution overlap in each path group in the set during the cutting process. The thermal overlap screening and marking module screens all the path groups with the thermal distribution overlap when the prediction result is that there is thermal distribution overlap, and marks them as to-be-adjusted cutting path groups. The thermal feature evaluation module obtains the thermal sensitivity feature information of each group of to-be-adjusted cutting path groups in real time, analyzes after obtaining, evaluates the thermal interference intensity generated by each group of to-be-adjusted cutting path groups under the current sequence, and divides 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. The path hierarchical optimization module executes corresponding cutting path sequence optimization measures according to the division result. 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, and if the verification result does not meet the thermal safety condition, the path sequence is corrected based on the verification result.

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