Ultra-precision laser cutting path optimization method and system

Abstract

The invention relates to the technical field of laser cutting, in particular to an ultra-precision laser cutting path optimization method and system.The method comprises the steps that a CAD model of a mechanical part is obtained, contour information of the mechanical part is extracted, the contour is discretized into a series of nodes, and technological parameters of laser cutting and motion constraint conditions of cutting equipment are determined; a laser cutting path is obtained according to the extracted contour information, nodes in the cutting path are coded into chromosomes in sequence, and each chromosome represents a cutting path scheme; designing a fitness function to evaluate each chromosome to obtain a fitness value of each chromosome; and performing genetic manipulation and iterative optimization on each chromosome according to the fitness value until a termination condition is met, obtaining the chromosome with the highest fitness, and decoding the chromosome into an optimized ultra-precision laser cutting path. According to the method, the genetic algorithm and the fitness function are planned and designed for the ultra-precision laser cutting path, global optimization of the cutting path is achieved, and the cutting efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of laser cutting technology, and in particular to an ultra-precision laser cutting path optimization method and system. Background Art

[0002] In the field of mechanical parts processing, ultra-precision laser cutting has become an important processing method. However, as the structure of mechanical parts becomes increasingly complex, the requirements for cutting accuracy and efficiency continue to increase, and traditional cutting path planning methods are gradually unable to meet the needs. When processing parts with complex shapes, current cutting path planning methods often have problems such as long cutting paths, long idle travel time, and low cutting efficiency. At the same time, it is also difficult to effectively control thermal effects, resulting in reduced cutting quality and affecting the performance and service life of mechanical parts. Although there are some optimization methods available, they are still insufficient in global optimization capabilities and adaptability to complex situations, and it is impossible to achieve optimal planning of cutting paths. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the present invention provides an ultra-precision laser cutting path optimization method and system to achieve global optimization of the ultra-precision laser cutting path and improve laser cutting efficiency and quality.

[0004] The present invention is achieved through the following technical solutions:

[0005] A method for optimizing an ultra-precision laser cutting path is provided, the method comprising the following steps:

[0006] Step S10: Obtain a CAD model of a mechanical component, extract its contour information, generate a series of discrete nodes based on the extracted contour information, and simultaneously determine the laser cutting process parameters, such as laser power, cutting speed, gas pressure, etc., and the motion constraints of the cutting equipment, such as maximum acceleration, maximum speed, etc.;

[0007] Step S20: obtaining a laser cutting path according to the extracted contour information, and encoding the node sequence in the laser cutting path into chromosomes, where each chromosome represents a cutting path scheme;

[0008] Step S30: Design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome;

[0009] Step S40: Genetically operate each chromosome and iteratively optimize it according to the fitness value until the termination condition is met, such as reaching the set maximum number of iterations or the fitness value converges to a certain degree, and obtain the chromosome with the highest fitness and decode it into a cutting path as the optimized ultra-precision laser cutting path.

[0010] Preferably, the step of generating a series of discrete nodes according to the extracted contour information in step S10 includes:

[0011] Determine the discretization method: According to the complexity of the mechanical part contour and the subsequent processing accuracy requirements, select the appropriate discretization method. Equal-interval discretization or adaptive discretization can be used. Equal-interval discretization is to evenly select points on the mechanical part contour curve according to a fixed distance. It is suitable for the case where the contour shape is relatively simple and the curvature changes little. Adaptive discretization is to dynamically adjust the spacing of the points according to the curvature change of the mechanical part contour curve. The points are denser in places with larger curvature and sparser in places with smaller curvature. It reduces the number of nodes while ensuring the discretization accuracy, improves the calculation efficiency, and is suitable for contours with complex shapes.

[0012] Set discrete precision parameters: Determine discrete precision parameters based on the accuracy requirements of laser cutting and the size specifications of mechanical parts. For ultra-precision laser cutting, the spacing between discrete points is set to tens of microns or less. The setting of discrete precision parameters is achieved by comprehensively considering processing efficiency and processing quality to achieve the best balance;

[0013] Generate discrete nodes: According to the determined discrete method and the set discrete accuracy parameters, the extracted contour information is discretized to generate a series of nodes. Each node represents a coordinate position. This series of nodes can be connected in sequence to approximately represent the contour curve of the mechanical component CAD model. In the discretization process, the order and connection relationship between the nodes also need to be recorded for subsequent path planning and processing. At the same time, special nodes, such as the starting point, end point, corner point, etc. of the contour, need to be specially marked so that they can be specially processed in path optimization and processing control.

[0014] Preferably, the step of sequentially encoding the nodes in the laser cutting path into chromosomes in step S20 includes:

[0015] Establishing a node index table: Numbering the discrete nodes in step S10 in sequence, such as from 1 to N, where N represents the total number of nodes, and establishing a mapping relationship between the numbers and the physical coordinates of the nodes. Prioritize numbering or marking key feature points in the contour, such as corner points, hole starting points, and contour connection points, to ensure that these points are correctly processed in path planning;

[0016] Chromosome structure design: Integer permutation encoding is used. Each chromosome is a one-dimensional array containing N nodes. The value of each node is the node number (1 to N), and each number appears only once to avoid path duplication. The chromosome length is equal to the total number of nodes N to ensure that the entire contour path is covered.

[0017] Path constraint processing: When the process requires starting or ending cutting at a specific node, such as a positioning hole on a part, the starting and ending nodes are fixed at the beginning and end of the chromosome; when the starting and ending points can be freely selected, any position on the chromosome can be used as the starting point, but the impact of the starting and ending points on the empty stroke must be considered in the fitness function. For parts containing more than one independent contour, such as inner and outer nested contours, ensure that the node sequence within each subpath is continuous to avoid cross-contour jump cutting;

[0018] Chromosome legitimacy check: Verify the uniqueness of the node number in each chromosome to avoid self-intersection of the path or repeated visits to the same node. Verify whether the nodes corresponding to each chromosome form a continuous, breakpoint-free cutting path to ensure that adjacent nodes can be directly connected physically to avoid crossing the internal area of the part.

[0019] Preferably, in step S30, a fitness function is designed to evaluate each chromosome. The fitness function comprehensively considers factors such as cutting time T, cutting quality Q, and idle stroke length L. The calculation formula of the fitness value F is shown in formula (1):

[0020]

[0021] Among them, α, β, and γ are weight coefficients, which are adjusted according to actual processing requirements and satisfy α+β+γ=1.

[0022] Preferably, the step of performing genetic operations and iterative optimization on each chromosome according to the fitness value in step S40 includes:

[0023] Confirm the parent generation: Use the roulette wheel selection method to select a certain number of chromosomes from the generated chromosomes as parent chromosomes. The probability of each chromosome being selected is proportional to its fitness value. The higher the fitness value, the greater the probability of being selected. In this way, chromosomes with high fitness have more chances to be inherited to the next generation;

[0024] Crossover operation: Perform a crossover operation on the selected parent chromosome to generate a new daughter chromosome. Using the partial matching crossover method, two crossover points are randomly selected, and the segments between the two parent chromosomes are exchanged. Conflicts are handled to ensure the legitimacy of the daughter chromosome.

[0025] Mutation operation: Mutate some offspring chromosomes with a certain mutation probability to introduce new combinations, increase diversity, and avoid the algorithm falling into local optimality. The random exchange mutation method is used to randomly select two fragments in the chromosome to exchange positions to generate the mutated chromosome;

[0026] Iterative optimization: Repeat the parent confirmation, crossover operation and mutation operation to generate new chromosomes, set the maximum number of iterations, calculate the fitness value of each newly generated chromosome, and iterate continuously until the number of iterations meets the maximum number of iterations.

[0027] In addition, to achieve the above objectives, the present invention also proposes an ultra-precision laser cutting path optimization system, which includes:

[0028] Mechanical parts information acquisition module: used to obtain the CAD model of mechanical parts, extract their contour information, generate a series of discrete nodes based on the extracted contour information, and simultaneously determine the laser cutting process parameters, such as laser power, cutting speed, gas pressure, etc., and the motion constraints of the cutting equipment, such as maximum acceleration and maximum speed;

[0029] Laser cutting path encoding module: used to obtain the laser cutting path according to the extracted contour information, and encode the node sequence in the laser cutting path into chromosomes, each chromosome representing a cutting path scheme;

[0030] Fitness evaluation module: used to design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome;

[0031] Laser cutting path optimization module: It is used to perform genetic operations on each chromosome according to the fitness value and iteratively optimize until the termination conditions are met, such as reaching the set maximum number of iterations or the fitness value converges to a certain level. The chromosome with the highest fitness is obtained and decoded into a cutting path as the optimized ultra-precision laser cutting path.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also proposes an ultra-precision laser cutting path optimization device, which includes: a memory, a processor, and programs such as an ultra-precision laser cutting path optimization algorithm stored in the memory and capable of running on the processor. The ultra-precision laser cutting path optimization algorithm and other programs are steps for implementing an ultra-precision laser cutting path optimization method as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, which includes programs such as an ultra-precision laser cutting path optimization algorithm. When the ultra-precision laser cutting path optimization algorithm and other programs are executed by the processor, an ultra-precision laser cutting path optimization method as described above is implemented.

[0034] The advantages and effects of the present invention are:

[0035] The present invention proposes an ultra-precision laser cutting path optimization method and system, which optimizes the ultra-precision laser cutting path of mechanical parts through a genetic algorithm. By utilizing the global search capability of the genetic algorithm, it is possible to find the optimal or approximately optimal cutting path in a complex path planning space, thereby improving the laser cutting efficiency and reducing the cutting time and idle stroke length compared with traditional methods. In addition, by designing a fitness function that comprehensively considers the cutting time, cutting quality and idle stroke length, the optimized cutting path not only improves the processing efficiency, but also ensures the cutting quality, reduces the thermal impact, improves the processing accuracy and surface quality of mechanical parts, and enhances the performance and service life of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a flow chart of an ultra-precision laser cutting path optimization method of the present invention.

[0038] Figure 2 This is a structural schematic diagram of an ultra-precision laser cutting path optimization system of the present invention.

[0039] Figure 3 This is a schematic block diagram of the structure of an ultra-precision laser cutting path optimization electronic device of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, in one embodiment of the present invention, a method for optimizing an ultra-precision laser cutting path includes the following steps:

[0042] Step S10: Obtain the CAD model of the mechanical part, extract its contour information, generate a series of discrete nodes based on the extracted contour information, and determine the laser cutting process parameters, such as laser power, cutting speed, gas pressure, etc., and the motion constraints of the cutting equipment, such as maximum acceleration, maximum speed, etc. For example, obtain the CAD model of the mechanical part, use professional CAD / CAM software to extract its contour information, discretize the contour into 100 nodes, determine the laser cutting process parameters, the laser power is 00W, the cutting speed is 50mm / s, the gas pressure is 0.5MPa, and the motion constraints of the cutting equipment are the maximum acceleration of 10m / s 2 , maximum speed 80mm / s.

[0043] Specifically, the step of generating a series of discrete nodes according to the extracted contour information in step S10 includes:

[0044] Determine the discretization method: According to the complexity of the mechanical part contour and the subsequent processing accuracy requirements, select the appropriate discretization method. Equal-interval discretization or adaptive discretization can be used. Equal-interval discretization is to evenly select points on the mechanical part contour curve according to a fixed distance. It is suitable for the case where the contour shape is relatively simple and the curvature changes little. Adaptive discretization is to dynamically adjust the spacing of the points according to the curvature change of the mechanical part contour curve. The points are denser in places with larger curvature and sparser in places with smaller curvature. It reduces the number of nodes while ensuring the discretization accuracy, improves the calculation efficiency, and is suitable for contours with complex shapes.

[0045] Set discrete precision parameters: Determine discrete precision parameters based on the accuracy requirements of laser cutting and the size specifications of mechanical parts. For ultra-precision laser cutting, the spacing between discrete points is set to tens of microns or less. The setting of discrete precision parameters is achieved by comprehensively considering processing efficiency and processing quality to achieve the best balance;

[0046] Generate discrete nodes: According to the determined discrete method and the set discrete accuracy parameters, the extracted contour information is discretized to generate a series of nodes. Each node represents a coordinate position. This series of nodes can be connected in sequence to approximately represent the contour curve of the mechanical component CAD model. In the discretization process, the order and connection relationship between the nodes also need to be recorded for subsequent path planning and processing. At the same time, special nodes, such as the starting point, end point, corner point, etc. of the contour, need to be specially marked so that they can be specially processed in path optimization and processing control.

[0047] Step S20: Obtain a laser cutting path according to the extracted contour information, and encode the nodes in the laser cutting path into chromosomes, where each chromosome represents a cutting path scheme.

[0048] Specifically, the step of sequentially encoding the nodes in the laser cutting path into chromosomes in step S20 includes:

[0049] Establish a node index table: Number the series of discrete nodes in step S10 in sequence, such as from 1 to N, where N represents the total number of nodes, and establish a mapping relationship between the numbers and the physical coordinates of the nodes. For example, node 1 corresponds to the coordinates (x1, y1), node 2 corresponds to (x2, y2), and so on. Prioritize numbering or marking key feature points in the contour, such as corner points, hole starting points, and contour connection points, to ensure that these points are correctly processed in path planning;

[0050] Chromosome structure design: Integer permutation encoding is used. Each chromosome is a one-dimensional array containing N nodes. The value of each node is the node number (1 to N), and each number appears only once to avoid path duplication. The chromosome length is equal to the total number of nodes N to ensure that the entire contour path is covered.

[0051] Path constraint processing: When the process requires starting or ending cutting at a specific node, such as a positioning hole on a part, the starting and ending nodes are fixed at the beginning and end of the chromosome; when the starting and ending points can be freely selected, any position on the chromosome can be used as the starting point, but the impact of the starting and ending points on the empty stroke must be considered in the fitness function. For parts containing more than one independent contour, such as inner and outer nested contours, ensure that the node sequence within each subpath is continuous to avoid cross-contour jump cutting;

[0052] Chromosome legitimacy check: Check the uniqueness of the node number in each chromosome to avoid self-intersection of the path or repeated visits to the same node. Check whether the nodes corresponding to each chromosome form a continuous, breakpoint-free cutting path to ensure that adjacent nodes can be directly connected physically and avoid crossing the internal area of the part. Suppose 5 nodes are generated, numbered 1 to 5, where node 1 is the starting point and node 5 is the ending point. The legal chromosome is [1,3,2,4,5], indicating that the path is 1-3-2-4-5. The illegal chromosome is [1,2,3,5,4], node 4 is not the ending node, the illegal chromosome is [1,3,2,2,5], and node 2 is repeated.

[0053] Step S30: Design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome.

[0054] Specifically, in step S30, a fitness function is designed to evaluate each chromosome. The fitness function comprehensively considers factors such as cutting time T, cutting quality Q, and empty run length L. The calculation formula of the fitness value F is shown in formula (1):

[0055]

[0056] Among them, α, β and γ are weight coefficients, which are adjusted according to actual processing requirements and satisfy α+β+γ=1. For example, the weight coefficients are set to α=0.4, β=0.4, and γ=0.2.

[0057] Step S40: Genetically operate each chromosome and iteratively optimize it according to the fitness value until the termination condition is met, such as reaching the set maximum number of iterations or the fitness value converges to a certain degree, and obtain the chromosome with the highest fitness and decode it into a cutting path as the optimized ultra-precision laser cutting path.

[0058] Specifically, the steps of performing genetic operations and iterative optimization on each chromosome according to the fitness value in step S40 include:

[0059] Confirm the parent generation: Use the roulette wheel selection method to select a certain number of chromosomes from the generated chromosomes as parent chromosomes. The probability of each chromosome being selected is proportional to its fitness value. The higher the fitness value, the greater the probability of being selected. In this way, chromosomes with high fitness have more opportunities to be inherited to the next generation. For example, there are 50 chromosomes generated, and the roulette wheel selection method is used to select 25 chromosomes as parents.

[0060] Crossover operation: A crossover operation is performed on the selected parent chromosomes to generate new daughter chromosomes. The partial matching crossover method is used to randomly select two crossover points and exchange the segments between the two parent chromosomes at the crossover points. For example, a partial matching crossover (PMX) operation is performed on the selected 25 parent chromosomes with a crossover probability of 0.8 to generate 25 daughter chromosomes. Conflicts are also handled to ensure the legitimacy of the daughter chromosomes. For example, for two parent chromosomes [1, 2, 3, 4, 5] and [5, 4, 3, 2, 1], the two middle segments are selected for crossover. The resulting daughter chromosomes may be [1, 4, 3, 2, 5] and [5, 2, 3, 4, 1].

[0061] Mutation operation: Mutate some offspring chromosomes with a certain mutation probability. For example, set the mutation probability to 0.5 to introduce new combinations, increase diversity, and prevent the algorithm from falling into local optimality. Use the random exchange mutation method to randomly select two segments in the chromosome and exchange their positions to generate the mutated chromosome. For example, for chromosome [1,2,3,4,5], randomly exchange the positions of node 2 and node 4 to obtain the mutated chromosome [1,4,3,2,5];

[0062] Iterative optimization: Repeat the parent confirmation, crossover and mutation operations to generate new chromosomes, set the maximum number of iterations, for example, set the maximum number of iterations to 100 times, calculate the fitness value of each newly generated chromosome, and continue to iterate until the number of iterations meets the maximum number of iterations. When the number of iterations reaches 100 times, select the chromosome with the highest fitness value from the last generation of chromosomes, decode it into a cutting path, and output it to the control system of the laser cutting equipment, which controls the laser cutting equipment to perform ultra-precision laser cutting processing on mechanical parts according to the path.

[0063] In addition, if Figure 2 As shown, in one embodiment of the present invention, an ultra-precision laser cutting path optimization system is proposed, and the ultra-precision laser cutting path optimization system includes:

[0064] Mechanical parts information acquisition module: used to obtain the CAD model of mechanical parts, extract their contour information, generate a series of discrete nodes based on the extracted contour information, and simultaneously determine the laser cutting process parameters, such as laser power, cutting speed, gas pressure, etc., and the motion constraints of the cutting equipment, such as maximum acceleration and maximum speed;

[0065] Laser cutting path encoding module: used to obtain the laser cutting path according to the extracted contour information, and encode the node sequence in the laser cutting path into chromosomes, each chromosome representing a cutting path scheme;

[0066] Fitness evaluation module: used to design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome;

[0067] Laser cutting path optimization module: It is used to perform genetic operations on each chromosome according to the fitness value and iteratively optimize until the termination conditions are met, such as reaching the set maximum number of iterations or the fitness value converges to a certain level. The chromosome with the highest fitness is obtained and decoded into a cutting path as the optimized ultra-precision laser cutting path.

[0068] The present application provides an ultra-precision laser cutting path optimization system that utilizes an ultra-precision laser cutting path optimization method described in the aforementioned embodiment, capable of resolving the technical issue of low efficiency in conventional ultra-precision laser cutting. Compared to the prior art, the present application provides the same beneficial effects as the ultra-precision laser cutting path optimization method described in the aforementioned embodiment, and the other technical features of the ultra-precision laser cutting path optimization system are the same as those disclosed in the aforementioned embodiment, which are not further elaborated here.

[0069] The present application provides an ultra-precision laser cutting path optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an ultra-precision laser cutting path optimization method in the above-mentioned embodiment one.

[0070] like Figure 3 As shown, in one embodiment of the present invention, a schematic diagram of the structure of an ultra-precision laser cutting path optimization device suitable for implementing the embodiment of the present application is shown. The ultra-precision laser cutting path optimization device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The ultra-precision laser cutting path optimization device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0071] Figure 3The ultra-precision laser cutting path optimization device shown may include a processing system 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage system 1003 into a random access memory (RAM) 1004. Various programs and data required for the operation of the ultra-precision laser cutting path optimization device are also stored in RAM 1004. The processing system 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: an input system 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output system 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage system 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication system 1009. The communication system 1009 can allow an ultra-precision laser cutting path optimization device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an ultra-precision laser cutting path optimization device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0072] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication system, or installed from a storage system 1003, or installed from a ROM 1002. When the computer program is executed by the processing system 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0073] The ultra-precision laser cutting path optimization device provided in this application utilizes an ultra-precision laser cutting path optimization method in the aforementioned embodiment to address the technical issue of low efficiency in conventional ultra-precision laser cutting. Compared to the prior art, the beneficial effects of the ultra-precision laser cutting path optimization device provided in this application are the same as those of the ultra-precision laser cutting path optimization method provided in the aforementioned embodiment. The other technical features of this ultra-precision laser cutting path optimization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0074] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0075] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned ultra-precision laser cutting path optimization method when executed by a processor.

[0076] The computer program product provided in this application can solve the technical problem of low efficiency in traditional ultra-precision laser cutting. Compared with the existing technology, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the ultra-precision laser cutting path optimization method provided in the above embodiment, and will not be repeated here.

[0077] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for optimizing ultra-precision laser cutting paths, characterized in that: The method comprises the following steps: Step S10: Obtaining a CAD model of a mechanical component, extracting its contour information, generating a series of discrete nodes based on the extracted contour information, and determining laser cutting process parameters and motion constraints of the cutting equipment; Step S20: obtaining a laser cutting path according to the extracted contour information, and encoding the node sequence in the laser cutting path into chromosomes, where each chromosome represents a cutting path scheme; Step S30: Design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome; Step S40: performing genetic operations on each chromosome according to the fitness value and iteratively optimizing until the termination condition is met, obtaining the chromosome with the highest fitness and decoding it into a cutting path as the optimized ultra-precision laser cutting path.

2. The method for optimizing an ultra-precision laser cutting path according to claim 1, wherein: The step of generating a series of discrete nodes according to the extracted contour information in step S10 includes: Determine the discretization method: Select the appropriate discretization method based on the complexity of the mechanical component contour and the subsequent processing accuracy requirements; Set discrete precision parameters: Determine discrete precision parameters based on the precision requirements of laser cutting and the size specifications of mechanical parts; Generate discrete nodes: According to the determined discretization method and the set discrete accuracy parameters, the extracted contour information is discretized to generate a series of nodes. Each node represents a coordinate position. This series of nodes are connected in sequence to represent the contour curve of the mechanical component CAD model. During the discretization process, the order and connection relationship between the nodes are recorded, and special nodes are specially marked.

3. The method for optimizing an ultra-precision laser cutting path according to claim 1, wherein: The step of sequentially encoding the nodes in the laser cutting path into chromosomes in step S20 includes: Establishing a node index table: sequentially numbering a series of discrete nodes in step S10, establishing a mapping relationship between the numbers and the physical coordinates of the nodes, and prioritizing numbering or marking key feature points in the contour; Chromosome structure design: Integer permutation encoding is used. Each chromosome is a one-dimensional array containing N nodes. The value of each node is the node number, and each node number only appears once. The chromosome length is equal to the total number of nodes N. Path constraint processing: When the process requires starting or ending cutting at a specific node, the starting and ending nodes are fixed at the beginning and end of the chromosome; when the starting and ending points are freely selected, any position of the chromosome is used as the starting point. For independent contours containing more than one, the node sequence within each subpath is ensured to be continuous. Chromosome legitimacy check: Check the uniqueness of the node number in each chromosome and check whether the nodes corresponding to each chromosome form a continuous, breakpoint-free cutting path.

4. The method for optimizing an ultra-precision laser cutting path according to claim 1, wherein: In step S30, a fitness function is designed to evaluate each chromosome. The fitness function comprehensively considers the cutting time T, cutting quality Q and empty run length L. The calculation formula of the fitness value F is shown in formula (1): Among them, α, β, and γ are weight coefficients, which are adjusted according to actual processing requirements and satisfy α+β+γ=1.

5. The method for optimizing an ultra-precision laser cutting path according to claim 1, wherein: The steps of performing genetic operations and iterative optimization on each chromosome according to the fitness value in step S40 include: Confirm the parent generation: Use the roulette wheel selection method to select a certain number of chromosomes from the generated chromosomes as parent chromosomes. The probability of each chromosome being selected is proportional to its fitness value. The higher the fitness value, the greater the probability of being selected. Crossover operation: Perform a crossover operation on the selected parent chromosome to generate a new daughter chromosome. Using the partial matching crossover method, two crossover points are randomly selected, and the segments between the two parent chromosomes are exchanged. Conflicts are handled to ensure the legitimacy of the daughter chromosome. Mutation operation: Mutate some offspring chromosomes with a certain mutation probability, introduce new combinations, and use the random exchange mutation method to randomly select two fragments in the chromosome to exchange positions to produce mutated chromosomes; Iterative optimization: Repeat the parent confirmation, crossover operation and mutation operation to generate new chromosomes, set the maximum number of iterations, calculate the fitness value of each newly generated chromosome, and continue to iterate until the number of iterations meets the maximum number of iterations.

6. An ultra-precision laser cutting path optimization system, characterized in that: The ultra-precision laser cutting path optimization system comprises: Mechanical parts information acquisition module: used to obtain the CAD model of mechanical parts, extract their contour information, generate a series of discrete nodes based on the extracted contour information, and determine the laser cutting process parameters and the motion constraints of the cutting equipment; Laser cutting path encoding module: used to obtain the laser cutting path according to the extracted contour information, and encode the node sequence in the laser cutting path into chromosomes, each chromosome representing a cutting path scheme; Fitness evaluation module: used to design a fitness function to evaluate each chromosome and calculate the fitness value of each chromosome; Laser cutting path optimization module: used to perform genetic operations on each chromosome according to the fitness value and iteratively optimize until the termination condition is met, obtain the chromosome with the highest fitness and decode it into a cutting path as the optimized ultra-precision laser cutting path.

7. An ultra-precision laser cutting path optimization device, characterized in that: The ultra-precision laser cutting path optimization device comprises: A memory, a processor, and an ultra-precision laser cutting path optimization program stored in the memory and executable on the processor, wherein the ultra-precision laser cutting path optimization program, when executed by the processor, implements an ultra-precision laser cutting path optimization method as described in any one of claims 1 to 5.

8. A computer program product, characterized in that The computer program product includes an ultra-precision laser cutting path optimization program, and when the ultra-precision laser cutting path optimization program is executed by a processor, an ultra-precision laser cutting path optimization method according to any one of claims 1 to 5 is implemented.

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