Processing control method and control system for laser engraving machine

Abstract

The invention discloses a machining control method and system for a laser engraving machine, and belongs to the technical field of laser engraving machines. The machining control method comprises the steps that a closed contour, a gradually-changed filling area and a high-precision edge in a pattern are automatically recognized; a cutting path is optimized in combination with an improved ant colony algorithm and a hierarchical genetic algorithm, and meanwhile, a real-time obstacle avoidance subsystem is included to avoid collision; an S-shaped acceleration and deceleration curve planner and a laser power dynamic compensation model are used for optimizing the motion process and laser output. According to the machining control method for the laser engraving machine, automatic and intelligent control over the whole process from design file import to machining completion is achieved, the machining efficiency and accuracy are improved, and the machining cost and risk are remarkably reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of laser engraving machines, and in particular relates to a processing control method and a control system for laser engraving machines. Background Art

[0002] In the related technology, in the field of laser processing technology, especially in the field of laser engraving, with the advancement of Industry 4.0 and smart manufacturing, the requirements for the intelligence, automation and efficiency of laser engraving machines are increasing. In the existing technology, the control system of the laser engraving machine adopts fixed control logic and algorithm, resulting in low processing efficiency. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one object of the present invention is to provide a processing control method for a laser engraving machine.

[0004] The present invention also provides a control system having the above processing control method for a laser engraving machine.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a processing control method for a laser engraving machine, comprising: automatically identifying closed contours, gradient fill areas and high-precision edges in graphics; optimizing cutting paths by combining an improved ant colony algorithm and a hierarchical genetic algorithm, and including a real-time obstacle avoidance subsystem to avoid collisions; and using an S-shaped acceleration and deceleration curve planner and a laser power dynamic compensation model to optimize motion processes and laser output.

[0007] According to the processing control method for a laser engraving machine of the present invention,.

[0008] Furthermore, the improved ant colony algorithm introduces a dynamic pheromone volatility coefficient and a directional heuristic function to improve the efficiency of path planning.

[0009] Furthermore, the hierarchical genetic algorithm adopts a double-layer encoding structure of region level and path level, and integrates a probability control strategy of simulated annealing as a part of the mutation operation.

[0010] Furthermore, the real-time obstacle avoidance subsystem is capable of dynamically calculating a safe distance based on an octree-based spatial segmentation collision detection model.

[0011] Furthermore, it also includes: an empty trip optimization strategy, wherein the empty trip optimization strategy realizes the regional jump priority principle by establishing a motion energy consumption model.

[0012] The control system according to the present invention is briefly described below.

[0013] According to the control system of the present invention, the control system of the present invention is suitable for executing the processing control method of the laser engraving machine described in any one of the above embodiments, and the control system includes a multimodal file parsing layer, the multimodal file parsing layer is used to process multiple design file formats and extract vector features, that is, the multimodal file parsing layer is suitable for automatically identifying closed contours, gradient fill areas and high-precision edges in graphics; a topology optimization path planning module, the topology optimization path planning module is used to generate an optimal cutting path; a motion control optimization engine, the motion control optimization engine is used to adjust the movement speed and laser power of the laser head; a real-time replanning module, the real-time replanning module is used to respond to hardware-level microsecond path correction requests.

[0014] Furthermore, the topology optimization path planning module includes an adaptive region segmentation algorithm based on the Voronoi diagram, so as to be suitable for dynamically adjusting segmentation parameters according to material properties and pattern complexity.

[0015] Furthermore, the real-time re-planning module is used to implement a fast response mechanism at the FPGA level and is equipped with an exception handling function. At the same time, the real-time re-planning module is used to perform material displacement compensation and thermal deformation prediction.

[0016] Furthermore, it also includes: a quality backtracking module, which is used to record data during the processing for subsequent analysis and improvement.

[0017] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0019] Figure 1 is a schematic diagram of a control system of the present invention;

[0020] Figure 2 It is a schematic diagram of the laser engraving machine of the present invention.

[0021] The following are marked in the accompanying drawings:

[0022] 1. Laser engraving machine; 10. Laser head. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0024] In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is apparent to one of ordinary skill in the art that these specific details are not necessarily employed to practice the present invention. In other instances, well-known structures, circuits, materials, or methods are not specifically described in order to avoid obscuring the present invention.

[0025] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment," "an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. In addition, it will be appreciated by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] In the description of the present invention, it should be understood that the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the scope of protection of the present invention.

[0027] Embodiment 1:

[0028] like Figure 1-Figure 2 As shown, the present invention provides a processing control method for a laser engraving machine 1, including: automatically identifying closed contours, gradient fill areas and high-precision edges in graphics; optimizing the cutting path by combining an improved ant colony algorithm and a hierarchical genetic algorithm, and including a real-time obstacle avoidance subsystem to avoid collisions; using an S-type acceleration and deceleration curve planner and a laser power dynamic compensation model to optimize the motion process and laser output.

[0029] In some embodiments, the multimodal file parsing layer uses an adaptive image processor, which is highly flexible and intelligent and can automatically identify and parse design files in multiple formats (such as SVG, DXF, etc.). During the processing, the adaptive image processor can pay special attention to elements such as closed contours, gradient fill areas, and high-precision edges in the graphics. Through the multimodal file parsing layer, users can easily import and process various complex design files without manual adjustment or preprocessing, thereby greatly improving work efficiency and accuracy.

[0030] The topology optimization path planning module of this application combines an improved ant colony algorithm and a hierarchical genetic algorithm. The improved ant colony algorithm simulates the foraging behavior of ants to find the optimal path in the search space; while the hierarchical genetic algorithm iteratively optimizes the path by simulating the biological evolution process. In addition, the topology optimization path planning module also includes a real-time obstacle avoidance subsystem to utilize advanced sensor technology and collision detection algorithms to monitor and avoid potential collision risks in real time. The topology optimization path planning module can generate efficient, collision-free cutting paths, significantly reducing processing time and material waste. At the same time, the real-time obstacle avoidance subsystem ensures the safety of the processing process and avoids equipment damage or processing errors caused by collisions.

[0031] The motion control optimization engine uses an S-shaped acceleration and deceleration curve planner to optimize the motion process of the laser head 10, ensuring that the laser head 10 can maintain a smooth and impact-free motion state during the acceleration, uniform speed and deceleration stages. In addition, the laser power dynamic compensation model adjusts the laser output power in real time according to factors such as cutting speed and material properties to ensure cutting quality and efficiency. The motion control optimization engine can significantly improve the processing accuracy and stability of the laser engraving machine 1, and reduce processing defects caused by unstable motion or unstable laser power. At the same time, by dynamically adjusting the laser power, the motion control optimization engine can also maintain the best cutting effect under different materials and cutting conditions.

[0032] It is worth mentioning that the laser power dynamic compensation model is designed to ensure that the laser output power can be automatically adjusted to maintain consistent engraving results at different engraving speeds. This is because when the engraving speed changes, if the laser power remains unchanged, it may cause changes in engraving depth or width, affecting the final product quality.

[0033] The program is (Python):

[0034] def power_compensation(speed,material):

[0035] base_power=material_lookup[material]

[0036] Reternbase_power*(1+0.0 / XMLSchema=0.05*(speed / MAX_SPEED)^2)

[0037] in:

[0038] base_power: The base power value obtained from the preset material_lookup dictionary based on the material type.

[0039] speed: current engraving speed.

[0040] MAX_SPEED: The maximum engraving speed allowed by the device.

[0041] (speed / MAX_SPEED)^2: The square of the speed ratio, which is used to measure the ratio of the current speed to the maximum speed, and to enhance the effect of this proportional relationship on power through the square.

[0042] 0.05: Adjustment coefficient, used to control the influence of speed change on power compensation.

[0043] The core idea of ​​the laser power dynamic compensation model is to appropriately increase the laser power as the engraving speed increases to compensate for the decrease in energy density caused by the increase in speed (because the energy density is inversely proportional to the speed), thereby ensuring the consistency of engraving.

[0044] According to the processing control method for the laser engraving machine 1 of the present invention, by integrating a multimodal file parsing layer, a topology optimization path planning module and a motion control optimization engine, the whole process from design file import to processing completion is realized with automation and intelligent control, which not only improves processing efficiency and accuracy, but also significantly reduces processing costs and risks, and provides strong support for the application and development of laser engraving technology.

[0045] Embodiment 2:

[0046] Based on the first embodiment, the improved ant colony algorithm in this embodiment introduces a dynamic pheromone volatility coefficient and a directional heuristic function to improve the efficiency of path planning.

[0047] In the prior art, in the traditional ant colony algorithm, the pheromone volatility coefficient is usually a fixed value, which is used to simulate the process of pheromone gradually dissipating over time. However, in a complex processing environment, the fixed pheromone volatility coefficient cannot adapt to the changing path search requirements.

[0048] It is understandable that the improved ant colony algorithm of the present application introduces a dynamic pheromone volatility coefficient. The dynamic pheromone volatility coefficient is adjusted in real time according to the current search progress or environmental status. For example, when the algorithm is close to finding the optimal path, the pheromone volatility coefficient can be appropriately reduced to retain more path information and help the algorithm converge to the optimal solution faster. On the contrary, in the early stage of the search or when encountering a complex environment, the pheromone volatility coefficient can be increased to promote the exploration ability of the algorithm.

[0049] Therefore, the introduction of the dynamic pheromone volatility coefficient enables the algorithm to adapt to different search environments more flexibly and improves the efficiency of path planning. By adjusting the pheromone volatility coefficient in real time, the algorithm can find a better balance between exploration and utilization, thereby finding the optimal path faster.

[0050] At the same time, in other existing technologies, directional heuristic function is another mechanism used to improve the efficiency of path planning. In the traditional ant colony algorithm, ants mainly rely on the pheromone concentration at the current location when choosing the next moving direction. However, this method often ignores the directional characteristics of the path, causing the algorithm to fall into a local optimal solution during the search process.

[0051] Therefore, the improved ant colony algorithm of the present application introduces a directional heuristic function. The directional heuristic function provides ants with additional selection basis according to the directional characteristics of the path. For example, in the path planning of the laser engraving machine 1, those path segments that are consistent or nearly consistent with the current cutting direction can be given priority to reduce the number of turns and cutting errors. That is, the introduction of the directional heuristic function makes the algorithm more intelligent and efficient in selecting a path. By considering the directional characteristics of the path, the algorithm can avoid unnecessary turns and repeated cutting, thereby significantly improving the quality and efficiency of path planning.

[0052] Here, the dynamic pheromone volatilization coefficient ρ is used in the improved ant colony algorithm, and its value is adjusted according to the progress of task completion. The formula is expressed as ρ = 0.1 + 0.4 * (completion progress). That is, as the task is close to completion, the speed of pheromone volatilization will gradually slow down. A higher volatilization rate in the early stage helps to explore more possible paths and avoid falling into the local optimal solution too early; while reducing the volatilization rate in the later stage is conducive to increasing the selection probability of the good path found and promoting global convergence.

[0053] The directional heuristic function is one of the strategies used when selecting the next node. It aims to guide the search process in the direction that is more likely to contain the optimal solution. Here, the neighboring nodes with θ < 45° are preferred. Specifically, in the path planning of the laser engraving machine 1, when moving from the current point to the next point, the neighboring nodes whose angle with the previous path is less than 45 degrees are given priority.

[0054] This can reduce the steering angle, and a smaller angle change can make the movement of the laser head 10 smoother, reducing the speed loss caused by frequent steering. Moreover, by keeping the motion trajectory as straight or close to a straight line as possible, the target position can be reached faster, thereby improving the overall processing efficiency. Since continuous large-angle steering may cause heat concentration and affect the surface quality of the material, the present application uses a directional heuristic function to help disperse the heat and obtain a better engraving effect.

[0055] According to some embodiments of the present invention, the hierarchical genetic algorithm adopts a double-layer encoding structure of region level and path level, and integrates a probability control strategy of simulated annealing as a part of the mutation operation.

[0056] In some embodiments, at the regional level, the algorithm divides the entire processing area into several sub-areas, each of which contains a set of possible path points. The coding at this level focuses on selecting which sub-areas (or path point sets) should be included in the final path; at the path level, the algorithm further refines the path points in each selected sub-area, determines the connection order and specific path between them, and the coding at this level focuses on optimizing the specific shape and length of the path. Therefore, through the double-layer coding structure, the algorithm can simultaneously consider the global and local characteristics of the path at different levels, thereby improving the global optimization capability of path planning.

[0057] That is, the introduction of the double-layer coding structure enables the algorithm to explore the solution space more effectively and avoid falling into the local optimal solution. At the same time, the double-layer coding structure can also improve the computational efficiency of the algorithm because the double-layer coding structure allows the algorithm to search at a coarser granularity, thereby reducing unnecessary computational overhead.

[0058] It is worth mentioning that the simulated annealing algorithm simulates the annealing process in physics. During the annealing process, the system gradually cools from a high temperature state to a low temperature state, and the energy of the system gradually decreases to reach a stable state. The simulated annealing algorithm uses this principle to adjust the probability of accepting a new solution according to the current temperature (or number of iterations) during the search process, thereby balancing the breadth and depth of the search.

[0059] In the hierarchical genetic algorithm, the simulated annealing probability control strategy is integrated into the mutation operation. When the algorithm performs a mutation operation, it adjusts the probability of accepting a new solution (i.e., the mutated solution) according to the current temperature (or number of iterations). If the new solution is better than the current solution, it is accepted unconditionally; if the new solution is slightly worse than the current solution, it is accepted with a certain probability, which gradually decreases as the temperature decreases (or the number of iterations increases).

[0060] Therefore, the introduction of simulated annealing probability control strategy enables the algorithm to better balance the breadth and depth of search during the mutation process. By adjusting the probability of accepting new solutions, the algorithm can maintain a high exploration ability in the early stage of the search to discover more potential solutions; and gradually converge to the optimal solution in the later stage of the search to improve the search efficiency, which helps to improve the global optimization ability and search efficiency of path planning.

[0061] Embodiment three:

[0062] In this embodiment, based on the second embodiment, the real-time obstacle avoidance subsystem is based on the spatial segmentation collision detection model of the octree and can dynamically calculate the safety distance.

[0063] In some embodiments, an octree is a data structure for space segmentation, which recursively divides a three-dimensional space into eight equal-sized subspaces (i.e., octaves) until a certain stopping condition is met (such as no obstacles in the subspace or a predetermined segmentation depth is reached). In collision detection, the octree model can effectively organize and manage obstacle information in the space, thereby accelerating the collision detection process.

[0064] When the laser engraving machine 1 is processing, the real-time obstacle avoidance subsystem will use the octree model to spatially segment the processing area and update the obstacle information in real time. By traversing the octree structure, the system can quickly determine whether the laser head 10 collides with an obstacle or is about to enter a dangerous area.

[0065] Therefore, the octree-based spatial segmentation collision detection model can significantly improve the efficiency of collision detection and reduce computational overhead. At the same time, the model can also effectively process obstacle information in complex scenes and improve the robustness and accuracy of the obstacle avoidance system.

[0066] It is worth mentioning that during the processing of the laser engraving machine 1, the safety distance refers to the minimum distance between the laser head 10 and the obstacle to ensure the safety of the processing process. The real-time obstacle avoidance subsystem needs to be able to dynamically calculate and adjust this safety distance to adapt to different processing environments and conditions.

[0067] In order to dynamically calculate the safety distance, the real-time obstacle avoidance subsystem will calculate a suitable safety distance value in real time based on the movement speed, acceleration, position and shape of the obstacle, etc. of the laser head 10. This value will be dynamically adjusted as the processing process changes to ensure that the laser head 10 always stays in the safe area.

[0068] The introduction of dynamically calculated safety distance enables the real-time obstacle avoidance subsystem to more flexibly respond to different processing environments and conditions, improving the safety and stability of the processing process. By adjusting the safety distance value in real time, the system can effectively avoid the risk of collision between the laser head 10 and obstacles, ensuring the smooth progress of the processing process.

[0069] It is worth mentioning that the dynamic safety distance calculation formula is: D_safe = v 2 / (2μ)+50mm.

[0070] in:

[0071] v represents the current speed of the laser head.

[0072] μ is the friction coefficient, which reflects the friction characteristics between surfaces of different materials.

[0073] This formula calculates the safety distance that must be maintained in order to prevent the vehicle from being stopped too quickly. (The relationship between the square of the speed and the friction is taken into account to ensure that there is enough braking distance to avoid a collision even at high speeds. An additional 50 mm is added as a fixed safety margin to ensure that there is enough buffer even in the most unfavorable conditions).

[0074] According to some embodiments of the present invention, the processing control method further includes: an idle stroke optimization strategy, wherein the idle stroke optimization strategy implements a regional jump priority principle by establishing a motion energy consumption model.

[0075] In some embodiments, the idle stroke optimization strategy reduces unnecessary idle strokes by analyzing and optimizing the moving path of the laser head 10. Specifically, the idle stroke optimization strategy is suitable for considering the overall layout of the processing tasks and the relationship between the various cutting areas, and reducing the idle strokes by adjusting the cutting order or merging adjacent cutting tasks.

[0076] Therefore, the introduction of the idle stroke optimization strategy enables the laser engraving machine 1 to use time more efficiently and reduce unnecessary movements, thereby improving processing efficiency. At the same time, the idle stroke optimization strategy also helps to reduce energy consumption and extend the service life of the laser engraving machine 1.

[0077] The motion energy consumption model is a mathematical model used to evaluate the energy consumption of the laser engraving machine 1 under different motion states. The motion energy consumption model takes into account factors such as the mass, acceleration, speed and motion distance of the laser head 10, and can accurately calculate the energy consumption of the laser head 10 under different paths.

[0078] Based on the motion energy consumption model, the regional jump priority principle is an optimization strategy. The motion energy consumption model determines the cutting order according to the energy consumption of each cutting area. Specifically, the regional jump priority principle will give priority to processing areas with lower energy consumption so as to complete as many cutting tasks as possible under limited energy. After processing the area with lower energy consumption, the system will turn to the area with higher energy consumption for processing.

[0079] Therefore, by establishing a motion energy consumption model and implementing the regional jump priority principle, the motion control optimization engine can more intelligently manage the energy consumption of the laser engraving machine 1. This can not only improve processing efficiency, but also extend the service life of the laser engraving machine 1 and reduce operating costs. At the same time, this strategy also helps to reduce carbon emissions and is in line with environmental protection concepts.

[0080] Of course, the motion energy consumption model is used to evaluate and optimize the motion energy consumption of the laser head 10 in a non-cutting state, aiming to reduce the total processing time and energy consumption. It satisfies: E = Σ(k1*Δx+k2*Δy).

[0081] in:

[0082] E represents the total energy consumption.

[0083] Δx and Δy represent the displacement increments in the X-axis and Y-axis directions, respectively.

[0084] k1 and k2 are constants associated with the system and represent the energy consumption per unit distance moved in the corresponding direction.

[0085] The motion energy consumption model is mainly used to implement the regional jump priority principle (Jump-first Policy). When planning the path, priority is given to those routes that can minimize the empty stroke (i.e. non-cutting movement). By calculating the total energy consumption under different paths, the path with the lowest energy consumption is selected as the optimal path. This can effectively reduce the unnecessary movement of the laser head 10 on the work surface, thereby improving work efficiency and reducing energy consumption.

[0086] Embodiment 4:

[0087] The present invention provides a control system, which is suitable for executing the processing control method for a laser engraving machine 1 described in any one of the above embodiments. The control system includes: a multimodal file parsing layer, a topology optimization path planning module, a motion control optimization engine and a real-time replanning module. The multimodal file parsing layer is used to process various design file formats and extract vector features, that is, the multimodal file parsing layer is suitable for automatically identifying closed contours, gradient fill areas and high-precision edges in graphics, the topology optimization path planning module is used to generate an optimal cutting path, the motion control optimization engine is used to adjust the moving speed and laser power of the laser head 10, and the real-time replanning module is used to respond to hardware-level microsecond path correction requests.

[0088] In some embodiments, the multimodal file parsing layer is responsible for processing multiple design file formats, including but not limited to AI, CDR, DWG, DXF, etc. The multimodal file parsing layer can accurately identify and extract vector features in the file, providing basic data for subsequent path planning. The multimodal file parsing layer enhances the compatibility of the control system, allowing users to use it directly without converting the file format, greatly improving work efficiency.

[0089] The topology optimization path planning module generates the optimal cutting path based on the extracted vector features. The topology optimization path planning module takes into account multiple factors such as material properties, cutting efficiency, energy consumption, etc. to ensure that the cutting path is both efficient and economical. The topology optimization path planning module can significantly reduce cutting time and energy consumption while ensuring cutting quality.

[0090] The motion control optimization engine is responsible for adjusting the moving speed and laser power of the laser head 10 to adapt to different cutting requirements and material characteristics. The motion control optimization engine dynamically adjusts the control parameters based on the path planning results and real-time feedback data to ensure the stability and accuracy of the cutting process. The motion control optimization engine achieves the best balance between cutting speed, quality and energy consumption through fine parameter adjustment, thereby improving the overall processing efficiency.

[0091] The real-time replanning module can respond to path correction requests within microseconds at the hardware level. When an abnormal situation (such as material deformation, equipment failure, etc.) is detected during the cutting process, the real-time replanning module can immediately adjust the cutting path to ensure the smooth completion of the cutting task. The real-time replanning module greatly enhances the flexibility and robustness of the control system, allowing the laser engraving machine 1 to maintain efficient and stable operation in a complex and changeable processing environment.

[0092] Therefore, the control system of the laser engraving machine 1 of the present application realizes intelligent control of the entire chain from design to processing by integrating multiple key components such as multimodal file analysis, topology optimization path planning, motion control optimization and real-time replanning. It not only improves the accuracy and efficiency of laser engraving, but also reduces energy consumption and operating costs, bringing significant economic benefits to users. At the same time, the flexibility and robustness of the control system also enable the laser engraving machine 1 to adapt to more diverse processing requirements and environmental challenges.

[0093] According to some embodiments of the present invention, the topology optimization path planning module includes an adaptive region segmentation algorithm based on the Voronoi diagram, so as to be suitable for dynamically adjusting the segmentation parameters according to the material properties and pattern complexity.

[0094] In some embodiments, the adaptive region segmentation algorithm based on the Voronoi diagram utilizes the characteristics of the Voronoi diagram to divide the area to be cut into multiple sub-regions, and the shapes and sizes of the multiple sub-regions are dynamically adjusted according to the complexity of the pattern and the characteristics of the material. The adaptive region segmentation algorithm based on the Voronoi diagram determines the optimal segmentation parameters (such as the number, size, shape, etc. of sub-regions) by analyzing the geometric features of the pattern and the physical properties of the material (such as hardness, thermal conductivity, etc.). These parameters change dynamically during the cutting process to ensure the continuity and efficiency of the cutting path.

[0095] The adaptive region segmentation algorithm based on the Voronoi diagram can significantly improve the flexibility and adaptability of the cutting path. It can not only process complex patterns, but also optimize according to the characteristics of different materials, thereby reducing energy consumption and improving efficiency while ensuring cutting quality.

[0096] In the intelligent control system of the laser engraving machine 1, the adaptive region segmentation algorithm based on the Voronoi diagram is integrated into the topology optimization path planning module. After receiving the design file, the topology optimization path planning module first uses the multimodal file parsing layer to extract vector features, and then applies the adaptive region segmentation algorithm to divide the cutting area. Next, the optimal cutting path is generated according to the division results and the characteristics of the material.

[0097] It is worth mentioning that, first, a Voronoi diagram is generated according to the geometric shape or specified point set in the design file (these points can be user-defined key positions or automatically identified important feature points). The Voronoi diagram is a space division method. The Voronoi diagram divides the plane into several regions so that the distance from all points in each region to the nearest predetermined point is shorter than the distance to any other predetermined point.

[0098] The maximum segmentation size is set according to the laser spot size and processing accuracy requirements. The formula is d_max = 2*spot_size + 0.1mm, where spot_size is the laser spot diameter. This parameter ensures that the processing accuracy will not be affected due to the partition being too large.

[0099] Set a threshold (for example 0.25 mm 2 ) to prevent too small an area from being processed separately, which helps to avoid wasting time caused by frequently adjusting the position of the laser head 10.

[0100] In practical applications, the above parameters are dynamically adjusted according to the material properties and pattern complexity. For example, when processing a part with rich details, it may be necessary to reduce d_max to obtain a higher resolution; while in a large area of ​​uniform filling, d_max can be appropriately increased to improve efficiency.

[0101] For each sub-region divided by the Voronoi diagram, the optimal cutting path is calculated respectively, and the most efficient transition from one sub-region to the next is considered to minimize the non-cutting moving distance.

[0102] Therefore, the present application can significantly reduce the non-cutting time by intelligently dividing the processing areas and optimizing the movement paths within and between each area, thereby speeding up the overall processing speed. Moreover, the reasonable setting of parameters such as d_max and minimum effective area can not only ensure the quality of detail processing, but also avoid the efficiency loss caused by unnecessary precision. At the same time, the adaptive region segmentation algorithm based on the Voronoi diagram can flexibly adjust parameters according to different material properties and design requirements to adapt to a variety of application scenarios.

[0103] According to some embodiments of the present invention, the real-time replanning module is used to implement a fast response mechanism at the FPGA level and is equipped with an exception handling function. The real-time replanning module is used to perform material displacement compensation and thermal deformation prediction.

[0104] In some embodiments, the FPGA-level fast response mechanism in the real-time replanning module means that the real-time replanning module can use the hardware acceleration capability of the Field-Programmable Gate Array (FPGA) to achieve microsecond-level path correction request response. As a high-performance hardware platform, FPGA can significantly improve data processing speed and real-time replanning module response capability through parallel processing and hardware acceleration.

[0105] The fast response mechanism at the FPGA level has brought significant performance improvements to the laser engraving machine 1. In complex cutting tasks, the real-time replanning module detects and responds to abnormal conditions in the cutting process, such as material displacement and equipment vibration, in real time, so as to adjust the cutting path in time to ensure cutting accuracy and efficiency. In addition, the hardware acceleration capability of the FPGA also enables the real-time replanning module to handle more complex and finer cutting patterns, improving the overall processing quality.

[0106] Of course, during the laser engraving process, the material may move due to factors such as thermal expansion and mechanical vibration. The material displacement compensation function in the real-time replanning module can monitor the displacement of the material in real time and automatically adjust the cutting path according to the displacement to ensure that the cutting accuracy is not affected. This function is realized through high-precision sensors and advanced algorithms, which can significantly improve the stability and reliability of cutting tasks.

[0107] Moreover, during the laser engraving process, the material may be thermally deformed after being heated, thus affecting the cutting accuracy. The thermal deformation prediction function in the real-time replanning module can predict the thermal deformation of the material during the cutting process based on the physical properties of the material and the cutting parameters, and adjust the cutting path accordingly. This function combines advanced technologies such as material science, thermodynamics and machine learning to achieve accurate prediction and effective compensation of thermal deformation, further improving cutting accuracy and efficiency.

[0108] According to some embodiments of the present invention, the control system further comprises: a quality backtracking module, which is used to record data during the processing for subsequent analysis and improvement.

[0109] In some embodiments, the quality traceability module can record a variety of key data, including but not limited to the following aspects:

[0110] 1. Processing parameters: such as laser power, cutting speed, focal position, etc.;

[0111] 2. Material information: including material type, thickness, hardness and other physical properties, as well as material pretreatment status (such as whether it has been preheated, coated, etc.);

[0112] 3. Equipment status: record various performance indicators of the laser engraving machine 1 during the processing, such as equipment temperature, vibration, laser head 10 position accuracy, etc.;

[0113] 4. Environmental parameters: such as workshop temperature, humidity, air cleanliness, etc.;

[0114] 5. Processing results: including quality indicators such as the roughness of the cutting surface, the width of the heat-affected zone, and the verticality of the cutting edge.

[0115] The collected key data will be stored in a centralized database for subsequent analysis and query. Enterprises can use this data to analyze and improve the following aspects:

[0116] 1. Quality trend analysis: By comparing data from different time periods or batches, analyze the changing trends of product quality and discover potential quality problems in a timely manner.

[0117] 2. Process parameter optimization: Based on the data analysis results, adjust the processing parameters, such as laser power, cutting speed, etc., to optimize the cutting quality and efficiency.

[0118] 3. Equipment maintenance and upgrade: By analyzing equipment status data, timely discover equipment wear or failure trends, formulate maintenance plans, and upgrade equipment when necessary.

[0119] 4. Material selection and application: Based on material information and quality results, optimize material selection and application strategies to improve the overall performance of the product.

[0120] Therefore, the introduction of the quality traceability module makes the processing of the laser engraving machine 1 more transparent and controllable. By recording and analyzing key data, enterprises can gain an in-depth understanding of how various factors in the processing process affect product quality, and thus take corresponding measures to optimize it.

[0121] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A processing control method for a laser engraving machine, characterized in that: include: Automatically identify closed contours, gradient fill areas and high-precision edges in graphics; The cutting path is optimized by combining the improved ant colony algorithm and the hierarchical genetic algorithm, and a real-time obstacle avoidance subsystem is included to avoid collisions; Use S-shaped acceleration and deceleration curve planner and laser power dynamic compensation model to optimize the motion process and laser output.

2. The processing control method for a laser engraving machine according to claim 1, characterized in that: The improved ant colony algorithm introduces a dynamic pheromone volatility coefficient and a directional heuristic function to improve the efficiency of path planning.

3. The processing control method for a laser engraving machine according to claim 2, characterized in that: The hierarchical genetic algorithm adopts a double-layer encoding structure of region level and path level, and integrates a probability control strategy of simulated annealing as a part of the mutation operation.

4. The processing control method for a laser engraving machine according to claim 3, characterized in that: The real-time obstacle avoidance subsystem is based on an octree-based spatial segmentation collision detection model and can dynamically calculate a safe distance.

5. The processing control method for a laser engraving machine according to claim 3, characterized in that: Also includes: The idle trip optimization strategy realizes the regional jump priority principle by establishing a motion energy consumption model.

6. A control system, characterized in that: The control system is suitable for executing the processing control method for a laser engraving machine according to any one of claims 1 to 5, and the control system includes: A multimodal file parsing layer, which is used to process various design file formats and extract vector features, that is, the multimodal file parsing layer is suitable for automatically identifying closed contours, gradient fill areas and high-precision edges in graphics; A topology optimization path planning module, wherein the topology optimization path planning module is used to generate an optimal cutting path; A motion control optimization engine, wherein the motion control optimization engine is used to adjust the moving speed and laser power of the laser head; A real-time re-planning module is used to respond to hardware-level microsecond-level path correction requests.

7. The control system according to claim 6, characterized in that: The topology optimization path planning module includes an adaptive region segmentation algorithm based on the Voronoi diagram, which is suitable for dynamically adjusting segmentation parameters according to material properties and pattern complexity.

8. The control system according to claim 7, characterized in that: The real-time re-planning module is used to implement a fast response mechanism at the FPGA level and is equipped with an exception handling function. At the same time, the real-time re-planning module is used to perform material displacement compensation and thermal deformation prediction.

9. The control system according to claim 6, characterized in that: Also includes: The quality backtracking module is used to record the data in the processing process for subsequent analysis and improvement.

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