Intelligent short pulse laser processing cutting path optimization method and system

Abstract

The invention discloses an intelligent short-pulse laser processing cutting path optimization method and system. The intelligent short-pulse laser processing cutting path optimization method comprises the steps that S1, coordinate data of all cutting points in a laser processing area are obtained and processed; s2, simulating the heat distribution condition in the laser processing process according to the coordinate data, and outputting a heat distribution diagram; s3, based on the heat distribution diagram, calculating a moving path of the laser head through a path planning algorithm, and optimizing the path to minimize the total moving distance and reduce the influence of a heat accumulation area; s4, the optimized path is adjusted according to specific machining requirements, and parameters are dynamically adjusted through a real-time feedback mechanism; and S5, outputting the optimal cutting path, and applying the optimal cutting path to actual machining equipment to improve the machining efficiency and quality. According to the method, the cutting point coordinate data of the laser processing area are obtained and processed, the heat distribution condition is simulated, path planning is conducted by adopting the ant colony algorithm in combination with the heat distribution diagram, the idle stroke time is minimized, and the influence of a heat gathering area is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to an intelligent short-pulse laser processing cutting path optimization method and system. Background Art

[0002] Short pulse laser technology, especially ultrashort pulse lasers in the picosecond and femtosecond range, can produce extremely high peak power density on the surface of materials due to its extremely short pulse width. This laser technology can achieve nonlinear absorption processes, reduce heat-affected zones, and achieve fine processing effects. Short pulse laser processing has the advantages of high processing accuracy, small heat-affected zones, and wide material adaptability. By precisely controlling the pulse width and energy, precise cutting, drilling, engraving and other processing operations can be achieved on materials.

[0003] Laser processing and cutting technology uses the energy of laser beam to cut materials. Due to its high precision, high efficiency, non-contact and other characteristics, it is widely used in the processing of various materials. The development of laser cutting technology, especially ultrashort pulse laser cutting technology, has moved from mode-locked sub-picosecond pulses to femtosecond pulses, and has been widely used and popularized in recent years. In laser micromachining technology, the peak power is related to the duration of the laser pulse, so the pulsed laser can obtain a much higher peak than the continuous wave. Pulsed lasers with different pulse widths have different performances in material removal rate, processing accuracy and quality. Nanosecond lasers have a higher material removal rate at the same average power, so they have a higher throughput than picosecond and femtosecond lasers.

[0004] Laser cutting path planning is a key step in ensuring processing efficiency and quality. Determining the best laser moving path based on the shape and requirements of the workpiece can reduce non-working distances and reduce the temperature rise of the plate caused by laser processing, thereby improving cutting efficiency and quality.

[0005] Therefore, it is necessary to improve the short pulse laser processing cutting path method and system in the prior art to solve the above problems. Summary of the invention

[0006] The present invention overcomes the shortcomings of the prior art and provides an intelligent short pulse laser processing cutting path optimization method and system, aiming to solve the problems of long idle travel time and uneven heat distribution caused by unreasonable path planning in short pulse laser processing cutting in the prior art.

[0007] To achieve the above object, the technical solution adopted by the present invention is: an intelligent short pulse laser processing cutting path optimization method, comprising:

[0008] S1, obtaining and processing the coordinate data of all cutting points in the laser processing area;

[0009] S2. Simulate the heat distribution during laser processing according to the coordinate data and output a heat distribution map;

[0010] S3. Based on the heat distribution map, the moving path of the laser head is calculated through the path planning algorithm, and the path is optimized to minimize the total moving distance and reduce the impact of the heat accumulation area;

[0011] S4, adjust the optimized path according to specific processing requirements, and dynamically adjust parameters through real-time feedback mechanism;

[0012] S5. Output the optimal cutting path and apply it to the actual processing equipment to improve processing efficiency and quality.

[0013] In a preferred embodiment of the present invention, in step S1, the step of obtaining coordinate data includes:

[0014] S11, determine the target area for laser processing, including all points that need to be cut;

[0015] S12, obtaining the coordinate data of the cutting point by manual input, database import, GPS positioning or laser scanning;

[0016] S13, performing feature extraction and noise reduction processing on the acquired data.

[0017] In a preferred embodiment of the present invention, in step S11, the target area is the portion to be cut, punched or engraved; the coordinate data of the cutting point is the coordinates in a two-dimensional plane (X, Y) or a three-dimensional space (X, Y, Z).

[0018] In a preferred embodiment of the present invention, in step S13, key cutting points are identified, including: turning points, intersections or special processing points of the workpiece contour; for each pair of adjacent points, the vector between the adjacent points is calculated, and the angle change of the vector is further calculated; an angle change threshold is set, and when the angle change of the adjacent points exceeds this threshold, the point is marked as a key point; the threshold range is 5°-15°.

[0019] In a preferred embodiment of the present invention, in step S2, the heat distribution simulation step is:

[0020] S21. Based on the physical principles of laser processing, establish a model of heat propagation in materials;

[0021] S22, dividing the laser processing area into a plurality of grid units;

[0022] S23, calculating the heat absorbed by the grid cells during laser processing according to the thermal model and the IDW interpolation method;

[0023] S24 visualizes the heat values ​​calculated by all grid cells and generates a heat distribution map.

[0024] In a preferred embodiment of the present invention, a three-dimensional heat conduction model is established based on the physical principle of the interaction between laser and material. The energy density distribution of the laser beam can be expressed as:

[0025] Where I(x,y,z) is the energy density at position (x,y,z), I0 is the energy density at the center of the laser beam, and σ xy and σ z are the standard deviations of the Gaussian distribution in the xy plane and z direction, respectively;

[0026] For a grid cell, the absorbed energy Q = ∫ V I(x,y,z)·Vvα·dV, where V is the volume of the grid cell and α is the absorptivity of the material to the laser.

[0027] The three-dimensional heat conduction equation can be expressed as Where T is the temperature, t is the pulse duration, a is the thermal diffusivity, ρ is the material density, and c is the specific heat capacity;

[0028] The distance between the unknown grid cell and the known grid cell is then calculated by the IDW interpolation method, and a weight is assigned to each sampled grid cell to predict the temperature and heat of the grid cell.

[0029] The weight is calculated using the inverse distance weight formula Among them, mi is the power, d i From unknown point P′ to known point P i distance;

[0030] The interpolated heat value calculation formula is: Among them, Q i is the calorific value of the grid cell with known calorific value, Q′ is the calorific value of the grid cell to be interpolated, and n is the number of grid cells with known calorific value.

[0031] In a preferred embodiment of the present invention, in step S3, the path planning step includes:

[0032] S31, initializing path information;

[0033] Set the number of ant colonies m, and each ant represents a laser cutting path solution; initialize the pheromone concentration on the connection paths between all cutting points to the same value, indicating that the probability of all paths being selected is the same at the beginning;

[0034] Set the pheromone volatilization rate p, pheromone enhancement coefficient G, and number of iterations N;

[0035] S32, each ant builds a path until all points are visited to form a complete path;

[0036] The starting point randomly selects an unvisited cut point as the starting point;

[0037] Based on the current position, calculate the transition probability to all unvisited points Among them, P ij represents the probability of an ant moving from node i to node j, τ ij is the pheromone concentration, η ij is the heuristic information, which is the inverse of the distance between two points or the negative effect of heat accumulation; β and γ are parameters that control the importance of pheromone and heuristic information, and k is the unvisited cutting point;

[0038] The ant moves to the selected cutting point and updates its current position;

[0039] When all ants have completed the path construction, i.e. visited all the cutting points and returned to the starting point, the path of each ant and its total movement distance are recorded;

[0040] S33, update path pheromone;

[0041] Simulate the pheromone volatilization over time, and the pheromone on all paths decreases by the volatilization rate τ ij =(1-p)τ ij ;

[0042] According to the length of the path constructed by each ant, pheromones are deposited on its path. The shorter the path, the more pheromones are deposited. Among them, L k is the length of the path constructed by the kth ant;

[0043] S34, dynamically adjusting the heuristic information according to the heat distribution map so that the path planning is more inclined to the low heat area;

[0044] In the calculation of transfer probability, the influence factor of heat distribution is introduced Among them, b is a regulating factor that determines the influence of heat value on the transfer probability. For the area where heat is concentrated, its attractiveness is reduced, that is, the corresponding heuristic information η is reduced. ij , to avoid the laser head staying in these areas for a long time, the new heuristic information

[0045] S35, repeating steps S32 and S33 until the set number of iterations is reached, and outputting the recorded optimal path as the moving path of the laser head.

[0046] In a preferred embodiment of the present invention, a sensor is used to monitor the quality of laser cutting, including: the smoothness of the cutting edge and the cutting depth; when it is found that the cutting quality does not meet expectations, the speed, power or other parameters of the laser head are adjusted to correct it: adj =P g +K e (Q t -Q a ), where P adj is the adjusted power, K e is the error gain factor, Q t is the target cutting quality, Q a is the actual measured cut quality.

[0047] In a preferred embodiment of the present invention, the cutting path is dynamically adjusted based on real-time data during processing, including temperature and cutting speed, to cope with problems caused by changes in material properties or environmental factors:

[0048] D new =D old +K d (ΔT), where D new is the new cutting depth, D old Original cutting depth, K d is the temperature sensitivity coefficient and ΔT is the temperature change.

[0049] The present invention provides an intelligent short pulse laser processing cutting path optimization system, comprising:

[0050] Data acquisition and processing module, used to acquire and process the coordinate data of all cutting points in the laser processing area;

[0051] The heat distribution simulation module is used to simulate the heat distribution during laser processing according to the coordinate data and output the heat distribution map;

[0052] The path planning module uses path planning algorithms such as ant colony algorithm to calculate the moving path of the laser head.

[0053] Path optimization module, used to optimize the path to minimize the total moving distance and reduce the impact of heat accumulation areas;

[0054] Real-time feedback module, used for real-time feedback mechanism to monitor the processing process;

[0055] Adjustment module, used to dynamically adjust relevant parameters and optimize the path according to real-time data such as cutting quality and temperature changes;

[0056] The path execution module is used to output the optimal cutting path and apply it to the actual processing equipment.

[0057] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0058] (1) The present invention provides an intelligent short-pulse laser processing cutting path optimization method, which obtains and processes the cutting point coordinate data of the laser processing area, simulates the heat distribution, and uses the ant colony algorithm combined with the heat distribution map to perform path planning to minimize the idle travel time and reduce the impact of the heat accumulation area. At the same time, the path and parameters are adjusted in real time according to the specific processing requirements, and the optimal cutting path is finally output and applied to the actual processing equipment, which effectively improves the processing efficiency and quality, reduces the idle travel time and path, avoids problems such as material deformation or burning, and achieves fine processing effects.

[0059] (2) The present invention combines the heat distribution model with the IDW interpolation method to more accurately simulate the heat distribution inside the material during laser processing. This method uses the heat conduction model and the inverse distance weighted interpolation method to make the heat distribution map closer to the actual situation, providing a reliable basis for path planning. Compared with the traditional method that only relies on experience or simple mathematical models, this method greatly improves the accuracy of heat distribution prediction and helps to avoid material damage caused by overheating during the processing process.

[0060] (3) The present invention uses an ant colony algorithm to optimize the laser cutting path. By simulating the behavior pattern of ants in nature looking for food, the optimal path can be found in multiple iterations. In step S34, the heuristic information is dynamically adjusted so that the path planning is more inclined to the low-heat area, thereby effectively avoiding the processing quality problems caused by excessive local heat. Compared with the existing fixed path planning, this method not only reduces the idle travel time, but also improves the cutting quality and efficiency.

[0061] (4) The present invention combines the real-time feedback mechanism with the specific processing requirements, allowing the speed, power and other parameters of the laser head to be dynamically adjusted according to the actual monitored cutting quality during the processing, so as to correct the processing deviation in time and ensure the consistency of processing quality. Compared with the traditional static parameter setting, it has stronger adaptability and flexibility, and can better cope with the changes of different material properties and environmental conditions.

[0062] (5) The present invention combines the heat distribution simulation in step S2 with the path planning algorithm in step S3, and fully considers the influence of heat distribution in the path planning process. Specifically, by simulating the heat distribution during laser processing, the path planning algorithm can avoid areas with excessive heat concentration and optimize the movement path of the laser head, thereby reducing the problem of material deformation or burning caused by heat accumulation, and achieving a significant improvement in processing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 is a flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0067] Application Overview:

[0068] Short pulse laser technology, especially ultrashort pulse lasers in the picosecond and femtosecond range, can produce extremely high peak power density on the material surface due to its extremely short pulse width, realize nonlinear absorption process, reduce heat-affected zone, and achieve fine processing effect; by precisely controlling pulse width and energy, precise cutting, punching and engraving of materials can be achieved; laser processing and cutting technology uses the energy of laser beam to cut materials, and is widely used in the processing of various materials due to its high precision, high efficiency and non-contact characteristics.

[0069] However, there are some shortcomings in the existing short-pulse laser processing cutting path methods and systems. For example, the idle stroke generated when the laser cutting head moves between contours seriously affects the cutting efficiency and increases unnecessary processing time and cost. In addition, the path planning in the prior art often fails to fully consider the problem of heat distribution, resulting in excessive heat concentration in certain areas, which may cause material deformation or burns, thereby affecting the cutting quality and accuracy. Therefore, how to effectively reduce the idle stroke time and optimize the heat distribution has become a key issue that needs to be solved urgently.

[0070] In order to solve the above problems, the present application proposes an intelligent short-pulse laser processing cutting path optimization method and system to calculate the optimal cutting path that can both reduce the idle stroke and effectively control the heat distribution.

[0071] Exemplary Methods

[0072] An intelligent short pulse laser processing cutting path optimization method comprises the steps of:

[0073] S1, obtaining and processing the coordinate data of all cutting points in the laser processing area;

[0074] S2. Simulate the heat distribution during laser processing according to the coordinate data and output a heat distribution map;

[0075] S3. Based on the heat distribution map, the moving path of the laser head is calculated through the path planning algorithm, and the path is optimized to minimize the total moving distance and reduce the impact of the heat accumulation area;

[0076] S4, adjust the optimized path according to specific processing requirements, and dynamically adjust parameters through real-time feedback mechanism;

[0077] S5. Output the optimal cutting path and apply it to the actual processing equipment to improve processing efficiency and quality.

[0078] In step S1, the step of obtaining coordinate data includes:

[0079] S11, determine the target area for laser processing, including all points that need to be cut;

[0080] S12, obtaining the coordinate data of the cutting point by manual input, database import, GPS positioning or laser scanning;

[0081] S13, extracting features and performing noise reduction processing on the acquired data to ensure the accuracy and reliability of the data;

[0082] Specifically, in step S11, the target area is the part that needs to be cut, punched or engraved; the coordinate data of the cutting point is the coordinates in a two-dimensional plane (X, Y) or a three-dimensional space (X, Y, Z);

[0083] Specifically, in step S12, manual input is suitable for simple or a small number of cutting points, and the operator enters the position information of each cutting point in the control system; when the cutting points have been digitally recorded in a database, the coordinate data is directly imported from the database; for large or outdoor applications, the position information of the cutting points is obtained through GPS technology; for workpieces with complex shapes, the workpiece is fully scanned by a laser scanning device to automatically detect and record the position information of all points that need to be processed;

[0084] Specifically, in step S13, a filtering algorithm is used to remove noise in the data, and the filtering method includes mean filtering, median filtering or Gaussian filtering;

[0085] Taking mean filtering as an example, in three-dimensional coordinates, the filtering algorithm is applied to the values ​​of the coordinates (X, Y, Z) respectively;

[0086] Suppose there are n three-dimensional coordinate points (x1, y1, z1), (x2, y2, z2), ..., (x n ,y n ,z n ), the window size is w;

[0087] For the ith x,y,z coordinate:

[0088] Among them, j is the point of the window;

[0089] The values ​​on each axis are smoothed, and the result (x′ i ,y′ i ,z′ i ) is used for key point identification in the next step;

[0090] Identify key cutting points, including turning points, intersections or special processing points of the workpiece contour;

[0091] For each pair of adjacent points, the vector between the adjacent points is calculated, and the angle change of the vector is further calculated;

[0092] Set an angle change threshold. When the angle change of adjacent points exceeds this threshold, the point is marked as a key point. Specifically, the range of this threshold is 5°-15°.

[0093] For the marked key points, further check whether the points in its neighborhood need to be merged or deleted to simplify the path without affecting the processing accuracy;

[0094] Taking inflection point detection as an example, suppose there is a sequence of n points (P0, P1, ..., P n );Calculate P between the i-th adjacent points i-1 ,P i ,P i+1 Direction vector

[0095] Calculate the angle between two vectors

[0096] During the laser processing, the distribution of heat is crucial to the optimization of the cutting path. In order to accurately simulate the heat distribution during the laser processing, the inverse distance weighted interpolation method is used to achieve it.

[0097] In step S2, the steps of heat distribution simulation are:

[0098] S21. Based on the physical principles of laser processing, establish a model of heat propagation in materials;

[0099] S22, dividing the laser processing area into a plurality of grid units;

[0100] S23, calculating the heat absorbed by the grid cells during laser processing according to the thermal model and the IDW interpolation method;

[0101] S24 visualizes the heat values ​​calculated by all grid cells and generates a heat distribution map;

[0102] Taking the three-dimensional case as an example,

[0103] Based on the physical principle of the interaction between laser and material, a three-dimensional heat conduction model is established, and the energy density distribution of the laser beam can be expressed as: Where I(x,y,z) is the energy density at position (x,y,z), I0 is the energy density at the center of the laser beam, and σ xy and σ z are the standard deviations of the Gaussian distribution in the xy plane and z direction, respectively;

[0104] For a grid cell, the absorbed energy Q = ∫ V I(x,y,z)·V·α·dV, where V is the volume of the grid cell and α is the absorptivity of the material to the laser.

[0105] The three-dimensional heat conduction equation can be expressed as Where T is the temperature, t is the pulse duration, a is the thermal diffusivity, ρ is the material density, and c is the specific heat capacity;

[0106] The distance between the unknown grid cell and the known grid cell is then calculated by the IDW interpolation method, and a weight is assigned to each sampled grid cell to predict the temperature and heat of the grid cell.

[0107] The weight is calculated using the inverse distance weight formula Among them, mi is the power, d i From unknown point P′ to known point P i distance;

[0108] The interpolated heat value calculation formula is: Among them, Q i is the calorific value of the grid cell with known calorific value, Q′ is the calorific value of the grid cell to be interpolated, and n is the number of grid cells with known calorific value.

[0109] In the machining process, the process of repositioning the laser in each laser cycle is called an idle stroke. The idle stroke caused by the laser cutting head moving between contours seriously affects the cutting efficiency.

[0110] In this method, the ant colony algorithm is used to optimize the path, shorten the idle stroke and save the processing time;

[0111] In step S3, the path planning step includes:

[0112] S31, initializing path information;

[0113] Set the number of ant colonies m, and each ant represents a laser cutting path solution; initialize the pheromone concentration on the connection paths between all cutting points to the same value, indicating that the probability of all paths being selected is the same at the beginning;

[0114] Set the pheromone volatilization rate p, pheromone enhancement coefficient G, and number of iterations N;

[0115] S32, each ant builds a path until all points are visited to form a complete path;

[0116] The starting point randomly selects an unvisited cut point as the starting point;

[0117] Based on the current position, calculate the transition probability to all unvisited points Among them, P ij represents the probability of an ant moving from node i to node j, τ ij is the pheromone concentration, η ij is the heuristic information, which is the inverse of the distance between two points or the negative effect of heat accumulation; β and γ are parameters that control the importance of pheromone and heuristic information, and k is the unvisited cutting point;

[0118] The ant moves to the selected cutting point and updates its current position;

[0119] When all ants have completed the path construction, i.e. visited all the cutting points and returned to the starting point, the path of each ant and its total movement distance are recorded;

[0120] S33, update path pheromone;

[0121] Simulate the pheromone volatilization over time, and the pheromone on all paths decreases by the volatilization rate τ ij =(1-p)τ ij ;

[0122] According to the length of the path constructed by each ant, pheromones are deposited on its path. The shorter the path, the more pheromones are deposited. Among them, L k is the length of the path constructed by the kth ant;

[0123] S34, dynamically adjusting the heuristic information according to the heat distribution map so that the path planning is more inclined to the low heat area;

[0124] In the calculation of transfer probability, the influence factor of heat distribution is introduced Among them, b is a regulating factor that determines the influence of heat value on the transfer probability. For the area where heat is concentrated, its attractiveness is reduced, that is, the corresponding heuristic information η is reduced. ij , to avoid the laser head staying in these areas for a long time, the new heuristic information

[0125] S35, repeating steps S32 and S33 until the set number of iterations is reached, and outputting the recorded optimal path as the moving path of the laser head.

[0126] In step S4, the path is fine-tuned and optimized according to the specific requirements of laser processing, including cutting speed, power and material properties, and the processing process is monitored through a real-time feedback mechanism to dynamically adjust relevant parameters to ensure processing quality and efficiency; Among them, v cut is the cutting speed, P g is the laser power, D W is the material thickness;

[0127] In the actual processing process, sensors are used to monitor the quality of laser cutting, including the smoothness of the cutting edge and the cutting depth. When the cutting quality is found to be not as expected, it is corrected by adjusting the speed, power or other parameters of the laser head: adj =P g +K e (Q t -Q a ), where P adj is the adjusted power, K e is the error gain factor, Q t is the target cutting quality, Q a is the actual measured cut quality;

[0128] Based on real-time data during processing, including temperature and cutting speed, the cutting path is dynamically adjusted to deal with problems caused by changes in material properties or environmental factors: D new =D old +K d (ΔT), where D new is the new cutting depth, D old Original cutting depth, K d is the temperature sensitivity coefficient, ΔT is the temperature change;

[0129] For complex three-dimensional path optimization problems, GPU can be used for parallel computing to speed up path planning.

[0130] In step S5, after the calculation and optimization of the previous steps S1-S4, the system obtains one or more optimal cutting paths, which are the best solutions obtained after comprehensively considering factors such as the shortest idle travel time and the best heat distribution;

[0131] The calculated optimal path is uploaded to the control system of the laser cutting machine. The user selects the corresponding path, and the control system will drive the laser cutting head to perform actual cutting operations according to the path instructions.

[0132] Exemplary Systems

[0133] An intelligent short pulse laser processing cutting path optimization system, comprising:

[0134] Data acquisition and processing module, used to acquire and process the coordinate data of all cutting points in the laser processing area;

[0135] The heat distribution simulation module is used to simulate the heat distribution during laser processing according to the coordinate data and output the heat distribution map;

[0136] The path planning module uses path planning algorithms such as ant colony algorithm to calculate the moving path of the laser head.

[0137] Path optimization module, used to optimize the path to minimize the total moving distance and reduce the impact of heat accumulation areas;

[0138] Real-time feedback module, used for real-time feedback mechanism to monitor the processing process;

[0139] Adjustment module, used to dynamically adjust relevant parameters and optimize the path according to real-time data such as cutting quality and temperature changes;

[0140] The path execution module is used to output the optimal cutting path and apply it to the actual processing equipment.

[0141] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. An intelligent short pulse laser processing cutting path optimization method, characterized in that: Includes steps: S1, obtaining and processing the coordinate data of all cutting points in the laser processing area; S2. Simulate the heat distribution during laser processing according to the coordinate data and output a heat distribution map; S3. Based on the heat distribution map, the moving path of the laser head is calculated through the path planning algorithm, and the path is optimized to minimize the total moving distance and reduce the impact of the heat accumulation area; S4, adjust the optimized path according to specific processing requirements, and dynamically adjust parameters through real-time feedback mechanism; S5. Output the optimal cutting path and apply it to the actual processing equipment to improve processing efficiency and quality.

2. The intelligent short pulse laser processing cutting path optimization method according to claim 1, characterized in that: In step S1, the step of obtaining coordinate data includes: S11, determine the target area for laser processing, including all points that need to be cut; S12, obtaining the coordinate data of the cutting point by manual input, database import, GPS positioning or laser scanning; S13, performing feature extraction and noise reduction processing on the acquired data.

3. The intelligent short pulse laser processing cutting path optimization method according to claim 2 is characterized in that: In step S11, the target area is the part that needs to be cut, punched or engraved; the coordinate data of the cutting point is the coordinates in a two-dimensional plane (X, Y) or a three-dimensional space (X, Y, Z).

4. The intelligent short pulse laser processing cutting path optimization method according to claim 2, characterized in that: In step S13, key cutting points are identified, including turning points, intersections or special processing points of the workpiece contour; for each pair of adjacent points, the vector between the adjacent points is calculated, and the angle change of the vector is further calculated; an angle change threshold is set, and when the angle change of the adjacent points exceeds this threshold, it is marked as a key point; the threshold range is 5°-15°.

5. The intelligent short pulse laser processing cutting path optimization method according to claim 1, characterized in that: In step S2, the steps of heat distribution simulation are: S21. Based on the physical principles of laser processing, establish a model of heat propagation in materials; S22, dividing the laser processing area into a plurality of grid units; S23, calculating the heat absorbed by the grid cells during laser processing according to the thermal model and the IDW interpolation method; S24 visualizes the heat values ​​calculated by all grid cells and generates a heat distribution map.

6. The intelligent short pulse laser processing cutting path optimization method according to claim 5, characterized in that: Based on the physical principle of the interaction between laser and material, a three-dimensional heat conduction model is established, and the energy density distribution of the laser beam can be expressed as: Where I(x,y,z) is the energy density at position (x,y,z), I0 is the energy density at the center of the laser beam, and σ xy and σ z are the standard deviations of the Gaussian distribution in the xy plane and z direction, respectively; For a grid cell, the absorbed energy Q = ∫ V I(x,y,z)·V·α·dV, where V is the volume of the grid cell and α is the absorptivity of the material to the laser. The three-dimensional heat conduction equation can be expressed as Where T is the temperature, t is the pulse duration, a is the thermal diffusivity, ρ is the material density, and c is the specific heat capacity; The distance between the unknown grid cell and the known grid cell is then calculated by the IDW interpolation method, and a weight is assigned to each sampled grid cell to predict the temperature and heat of the grid cell. The weight is calculated using the inverse distance weight formula Among them, mi is the power, d i From unknown point P′ to known point P i distance; The interpolated heat value calculation formula is: Among them, Q i is the calorific value of the grid cell with known calorific value, Q′ is the calorific value of the grid cell to be interpolated, and n is the number of grid cells with known calorific value.

7. The intelligent short pulse laser processing cutting path optimization method according to claim 1, characterized in that: In step S3, the path planning step includes: S31, initializing path information; Set the number of ant colonies m, and each ant represents a laser cutting path solution; initialize the pheromone concentration on the connection paths between all cutting points to the same value, indicating that the probability of all paths being selected is the same at the beginning; Set the pheromone volatilization rate p, pheromone enhancement coefficient G, and number of iterations N; S32, each ant builds a path until all points are visited to form a complete path; The starting point randomly selects an unvisited cut point as the starting point; Based on the current location, calculate the transition probability to all unvisited points Among them, P ij represents the probability of an ant moving from node i to node j, τ ij is the pheromone concentration, η ij is the heuristic information, which is the inverse of the distance between two points or the negative effect of heat accumulation; β and γ are parameters that control the importance of pheromone and heuristic information, and k is the unvisited cutting point; The ant moves to the selected cutting point and updates its current position; When all ants have completed the path construction, i.e. visited all the cutting points and returned to the starting point, the path of each ant and its total movement distance are recorded; S33, update path pheromone; Simulate the pheromone volatilization over time, and the pheromone on all paths decreases by the volatilization rate τ ij =(1-p)τ ij ; According to the length of the path constructed by each ant, pheromones are deposited on its path. The shorter the path, the more pheromones are deposited. Among them, L k is the length of the path constructed by the kth ant; S34, dynamically adjusting the heuristic information according to the heat distribution map so that the path planning is more inclined to the low heat area; In the calculation of transfer probability, the influence factor of heat distribution is introduced Among them, b is a regulating factor that determines the influence of heat value on the transfer probability. For the area where heat is concentrated, its attractiveness is reduced, that is, the corresponding heuristic information η is reduced. ij , to avoid the laser head staying in these areas for a long time, the new heuristic information S35, repeating steps S32 and S33 until the set number of iterations is reached, and outputting the recorded optimal path as the moving path of the laser head.

8. The intelligent short pulse laser processing cutting path optimization method according to claim 1, characterized in that: Use sensors to monitor the quality of laser cutting, including: the smoothness of the cutting edge and the cutting depth; when it is found that the cutting quality does not meet expectations, it can be corrected by adjusting the speed, power or other parameters of the laser head: adj =P g +K e (Q t -Q a ), where P adj is the adjusted power, K e is the error gain factor, Q t is the target cutting quality, Q a is the actual measured cut quality.

9. The intelligent short pulse laser processing cutting path optimization method according to claim 1, characterized in that: Based on real-time data during processing, including temperature and cutting speed, the cutting path is dynamically adjusted to deal with problems caused by changes in material properties or environmental factors: D new =D old +K d (ΔT), where D new is the new cutting depth, D old Original cutting depth, K d is the temperature sensitivity coefficient and ΔT is the temperature change.

10. An intelligent short pulse laser processing cutting path optimization system, based on an intelligent short pulse laser processing cutting path optimization method and system according to any one of claims 1 to 9, characterized in that: include: Data acquisition and processing module, used to acquire and process the coordinate data of all cutting points in the laser processing area; The heat distribution simulation module is used to simulate the heat distribution during laser processing according to the coordinate data and output the heat distribution map; The path planning module uses path planning algorithms such as ant colony algorithm to calculate the moving path of the laser head; Path optimization module, used to optimize the path to minimize the total moving distance and reduce the impact of heat accumulation areas; Real-time feedback module, used for real-time feedback mechanism to monitor the processing process; Adjustment module, used to dynamically adjust relevant parameters and optimize the path according to real-time data such as cutting quality and temperature changes; The path execution module is used to output the optimal cutting path and apply it to the actual processing equipment.

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