Automatic control method and control system for laser cutting

Abstract

The invention relates to the technical field of laser cutting, in particular to an automatic control method and system for laser cutting, and the method and system achieve the automatic control of laser cutting through the key technologies of multi-source data fusion, intelligent path planning, vibration compensation, parameter optimization and the like. Full-process automatic control from data collection to final cutting effect evaluation is achieved, and the precision and efficiency of laser cutting are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of laser cutting, and in particular to an automatic control method and control system for laser cutting. Background Art

[0002] As one of the core processes in modern precision machining, metal laser cutting technology plays a key role in high-end manufacturing industries such as aerospace, automotive, and electronics, thanks to its non-contact processing, high energy density, and excellent machining accuracy. However, as industrial scenarios continue to increase their requirements for cutting quality and efficiency, the limitations of existing technologies in practical applications are gradually becoming apparent. Current laser cutting control has the following deficiencies:

[0003] 1) Insufficient data collection accuracy and synchronization:

[0004] In traditional laser cutting systems, there may be problems with inaccurate data collection due to the lack of an effective synchronization mechanism between a single sensor or sensors.

[0005] 2) Lack of efficient multi-source data preprocessing and fusion technology:

[0006] Existing systems may not be able to effectively filter and denoise data from multiple sensors, remove outliers, or align the data in time and space. This can result in inaccurate 3D models that fail to accurately reflect the geometry, temperature distribution, and changes in cutting quality during the cutting process.

[0007] 3) The level of intelligent path planning is not high:

[0008] Path planning in traditional laser cutting systems often relies on simple algorithms or manual settings. It is difficult to dynamically adjust the cutting path based on real-time geometric information, temperature field data, and cutting quality parameters to avoid surface defects and high-temperature areas, thereby limiting cutting efficiency and quality.

[0009] 4) Imperfect vibration compensation mechanism:

[0010] Laser cutting heads are subject to mechanical vibrations during operation. Failure to effectively and timely compensate for the path deviations caused by these vibrations can severely impact cutting accuracy. Existing compensation solutions may not be sufficient to handle complex vibration patterns, especially in high-speed cutting scenarios.

[0011] 5) Parameter optimization and control are not flexible enough:

[0012] Key parameters such as cutting speed and laser power are typically set based on experience or fixed rules, lacking a dynamic adjustment mechanism based on real-time feedback. Furthermore, methods for establishing a regression relationship between cutting speed and slit width through experimental data analysis and using optimization algorithms to find the optimal parameter combination are still underdeveloped. Summary of the Invention

[0013] The present invention provides a laser cutting automatic control method and control system. By integrating high-precision sensors, advanced data processing algorithms, intelligent path planning, vibration compensation mechanisms and parameter optimization strategies, it provides a more efficient, accurate and adaptable laser cutting automatic control solution. This method not only improves the cutting quality and efficiency, but also enhances the degree of automation and flexibility of the entire processing process.

[0014] In order to achieve the purpose of the present invention, the technical solution adopted is: a laser cutting automatic control method, comprising:

[0015] S1. 3D laser scanner generates three-dimensional point cloud data of the metal surface, infrared thermal imager monitors the temperature field to obtain temperature field data, visual camera captures the cutting picture to obtain cutting quality visual image data, encoder and inertial measurement unit monitor the position and vibration frequency of the cutting head to obtain multi-source data;

[0016] S2. Preprocess the multi-source data, map the preprocessed data into three-dimensional space, and generate a dynamic three-dimensional model containing geometric information, temperature distribution, and cutting quality;

[0017] S3, cutting path planning and obstacle avoidance based on dynamic 3D models; combined with dynamic 3D models, the optimal cutting path is generated in real time, avoiding surface defects and high-temperature areas;

[0018] S4. Establish the regression relationship between cutting speed and seam width, fit the coefficients through experimental data, define the objective function to evaluate the cutting effect, and use the optimization algorithm to find the optimal cutting speed and laser power.

[0019] As an optimization solution of the present invention, in step S1, a 3D laser scanner is used to obtain three-dimensional point cloud data of the metal surface to be cut in real time, and provide metal surface features, including undulations, holes and edge features, to help identify and locate key features on the cutting path; the 3D laser scanner is linked with the encoder to ensure that the scanning data is strictly synchronized with the cutting head position, and a high-speed processor module is added to achieve hardware-level trigger synchronization, and the clock signals of the 3D laser scanner, encoder and inertial measurement unit are unified to μs-level accuracy; the visual camera is used to capture the real-time cutting picture and analyze the cutting seam quality, which includes cutting width and slag residue, so as to evaluate the cutting effect; an encoder and an inertial measurement unit are also provided to track the three-dimensional motion trajectory of the laser cutting head, thereby compensating for mechanical vibration errors.

[0020] As an optimization solution of the present invention, in step S2, preprocessing includes filtering and denoising the three-dimensional point cloud data, using Kalman filtering to eliminate the measurement noise of the 3D laser scanner; mapping the preprocessed data into three-dimensional space to generate a dynamic three-dimensional model containing geometric information, temperature distribution, and cutting quality, specifically including:

[0021] 1) Spatiotemporal alignment of multi-source data: unify the coordinate system and time base of 3D point cloud data, temperature field data, and cutting quality visual image data, and align multi-source data by calibrating parameters to ensure spatial consistency;

[0022] 2) 3D geometric model reconstruction: To generate a continuous surface model from the filtered point cloud data, a Poisson surface reconstruction method combined with a dynamic update strategy is used. In the real-time point cloud data streaming scenario, the surface model is dynamically updated to reflect the latest geometric changes. Incremental Delaunay triangulation is used to gradually update the mesh vertices V(t) and facets F(t).

[0023] 3) Temperature distribution mapping: associate temperature field data with vertices / faces of the 3D geometric model, and use inverse distance weighted interpolation for areas not directly measured.

[0024] 4) Cutting quality parameter integration: quantify cutting quality visual image data and map it to a 3D geometric model;

[0025] 5) Dynamic 3D model update, to achieve the dynamic effect of the model evolving over time, and to update the point cloud position x t and temperature T t To make a status update:

[0026] The state prediction equation is:

[0027] The observation equation is:

[0028] in: is the estimated value of the state at the current time t, A is the state transfer matrix, which describes how the system state is transferred from the previous time t-1 to the current time t, and B is the control input matrix, which describes the control input u t Impact on system status; w t is process noise, which indicates the random error in the system state transition process; v t is the observation noise, which represents the random error in the sensor measurement process; H is the observation matrix, which maps the state variables to the observation space; z t is the observation value at the current time t.

[0029] As an optimization solution of the present invention, the cutting quality parameters are integrated; the cutting quality visual image data is quantified and mapped to a three-dimensional geometric model, specifically including:

[0030] (1) Calculate the surface roughness C(p) using the point cloud curvature formula and measure the surface smoothness by the normal vector difference;

[0031] Where: n(p) is the normal vector of point p, p i is its neighboring point;

[0032] (2) Establishing the regression relationship between cutting speed v and slit width W to predict and optimize cutting process parameters;

[0033] (3) Display cutting quality parameters on the geometric model by color coding or label overlay.

[0034] As an optimization solution of the present invention, in step S3, it specifically includes:

[0035] S31, use A* algorithm to generate the optimal cutting path, the formula is:

[0036]

[0037] Where: c(si,si+1): from node s i to s i+1 Cost function, Path is the optimal path;

[0038] S32, comprehensively consider the performance indicators of cutting efficiency and cutting quality, and the cutting efficiency is to minimize the total length of the path L path ; Cutting quality is to avoid high temperature and high roughness areas; the specific implementation is:

[0039] 31) Initialization; Define the starting point and end point: Determine the starting point and end point according to the cutting task. Set the initial path: Use a straight line to connect the starting point and end point as the initial path;

[0040] 32) Path evaluation; cost function calculation: based on geometric distance, temperature constraints, surface characteristics factors,

[0041] Calculate the total cost of the current path;

[0042] J=w1·L path +w2·J T +w3·J C

[0043] Where: w1, w2, w3: weight coefficients, representing the importance attached to path length, temperature constraint and surface characteristics respectively;

[0044] 33) Use the A* algorithm to gradually optimize the path; each iteration selects the path expansion direction with the minimum cost until the termination condition is met;

[0045] S33. Using encoder and inertial measurement unit measurement data to correct path deviation caused by mechanical vibration in real time: Vibration compensation model:

[0046]

[0047] a i : vibration amplitude; ω i : vibration angular frequency; φ i : Phase angle; ΔP(t) represents the vibration offset of the laser cutting head in three-dimensional space.

[0048] As an optimization solution of the present invention, in step S4, it specifically includes:

[0049] S41, establishing a regression relationship between cutting speed v and seam width W: W = k1×v+k2×P+b;

[0050] Where: W is the cutting slit width; v is the cutting speed, P is the laser power; k1, k2, b are fitting coefficients;

[0051] S42. Define an objective function to evaluate the cutting effect; J(v,P) = λ1·W + λ2·C(p) + λ3·T; where: J(v,P) is the objective function value, W is the cutting slit width, C(p) is the surface roughness, and T is the temperature distribution. λ1, λ2, λ3 are weight coefficients;

[0052] S43, using a particle swarm optimization algorithm to find the optimal cutting speed v and laser power P during the cutting process, continuously monitoring the changes in geometric information, temperature distribution, and cutting quality parameters, and timely adjusting the cutting speed v and laser power P;

[0053] [v * ,P * ]=arg min J(v,P); used to find the optimal cutting speed v and laser power P.

[0054] Finally, a PID controller is used to adjust the laser power and cutting speed in real time.

[0055] As an optimization solution of the present invention, in step S43, a particle swarm optimization algorithm is used to simulate the foraging behavior of a flock of birds and find the global optimal solution through group collaboration. The steps are as follows:

[0056] 1) Initialize a group of particles, each particle represents a candidate solution (v, P);

[0057] 2) Calculate the objective function value J(v,P) of each particle;

[0058] 3) Update the velocity and position of each particle to move it towards a better solution;

[0059] 4) Repeat steps 2 and 3 until the termination condition is met, which includes reaching the maximum number of iterations or the objective function value no longer changes significantly.

[0060] In order to achieve the purpose of the present invention, the technical solution adopted is: a control system for a laser cutting automatic control method, characterized in that it includes a data acquisition module, a data preprocessing and modeling module, a path planning and obstacle avoidance module, a vibration compensation module and a parameter optimization and control module, the data acquisition module generates three-dimensional point cloud data of the metal surface through a 3D laser scanner, the infrared thermal imager monitors the temperature field to obtain temperature field data, the visual camera captures the cutting picture to obtain cutting quality visual image data, the encoder and inertial measurement unit monitor the position and vibration frequency of the cutting head to obtain multi-source data; the data preprocessing and modeling module processes the multi-source data Preprocessing is performed, and the preprocessed data is mapped into three-dimensional space to generate a dynamic three-dimensional model containing geometric information, temperature distribution and cutting quality; the path planning and obstacle avoidance module is based on the cutting path planning and obstacle avoidance of the dynamic three-dimensional model; combined with the dynamic three-dimensional model, the optimal cutting path is generated in real time, and surface defects and high-temperature areas are avoided; the vibration compensation module uses encoder and IMU measurement data to correct the path deviation caused by mechanical vibration in real time; the parameter optimization and control module establishes a regression relationship between cutting speed and seam width, defines the objective function to evaluate the cutting effect through the experimental data fitting coefficient, and uses the optimization algorithm to find the optimal cutting speed and laser power.

[0061] As an optimization solution of the present invention, a 3D laser scanner acquires high-precision point cloud data of the metal surface to be cut in real time; provides metal surface features to help identify and locate key features on the cutting path, thereby improving the accuracy of cutting path planning; an infrared thermal imager monitors the temperature distribution in the cutting area, maps the temperature field data to the geometric model, and provides temperature constraint information for path planning to avoid high-temperature areas; a visual camera captures the real-time cutting image and analyzes the quality of the cutting seam; an encoder and an inertial measurement unit track the three-dimensional motion trajectory of the laser cutting head, compensates for mechanical vibration errors, and reduces dynamic position errors caused by mechanical vibrations.

[0062] The present invention has positive effects: 1) High-precision data acquisition: comprehensive monitoring of the cutting process is achieved through multiple sensors, providing high-quality original data.

[0063] 2) Accurate data preprocessing and modeling: Through preprocessing steps such as filtering, denoising, and outlier removal, as well as dynamic 3D model generation technology, the quality of the data and the accuracy of the model are ensured.

[0064] 3) Intelligent path planning and obstacle avoidance: Combining the A* algorithm and multi-source data, intelligent path planning is achieved, effectively avoiding high-temperature areas and surface defects, and improving cutting efficiency and quality.

[0065] 4) Effective vibration compensation: Through the vibration compensation model, the path deviation caused by mechanical vibration is corrected in real time, thereby improving cutting accuracy.

[0066] 5) Optimized parameter control: By establishing regression relationships, defining objective functions, and applying optimization algorithms, the optimal configuration of cutting speed and laser power is achieved, further improving cutting quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0069] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0070] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of a laser cutting automatic control method according to an embodiment of the present invention includes:

[0071] S1. 3D laser scanner generates three-dimensional point cloud data of the metal surface, infrared thermal imager monitors the temperature field to obtain temperature field data, visual camera captures the cutting picture to obtain cutting quality visual image data, encoder and inertial measurement unit monitor the position and vibration frequency of the cutting head to obtain multi-source data;

[0072] Specifically, the 3D laser scanner is used to acquire high-precision point cloud data of the metal surface to be cut in real time, providing surface features such as undulations, holes, and edges, helping to identify and locate key features along the cutting path. The 3D laser scanner uses triangulation to obtain a three-dimensional point cloud of the metal surface, with coverage customized to the cutting format. The 3D laser scanner is linked to an encoder to ensure strict synchronization of the scan data with the cutting head position. A high-speed processor module is added to achieve hardware-level trigger synchronization, unifying the clock signals of the 3D laser scanner, encoder, and inertial measurement unit to μs-level accuracy. The infrared thermal imager uses a short-wave infrared (SWIR, 1-3μm) thermal imager to monitor the temperature distribution in the cutting area to prevent local overheating and material deformation. A visual camera captures real-time cutting images and analyzes kerf quality, including cut width and slag residue, to assess cutting performance. An encoder and inertial measurement unit are also provided to track the three-dimensional motion trajectory (X / Y / Z axes) of the laser cutting head, compensating for mechanical vibration errors and improving cutting accuracy.

[0073] S2. Preprocess the multi-source data, map the preprocessed data into three-dimensional space, and generate a dynamic three-dimensional model containing geometric information, temperature distribution, and cutting quality;

[0074] Preprocessing of multi-source data includes filtering and denoising of 3D point cloud data, using Kalman filtering to eliminate measurement noise of 3D laser scanners, and improving point cloud stability and accuracy.

[0075] An improved threshold segmentation algorithm is used to remove abnormal points (such as reflection interference from the oxide layer) and extract effective metal surface information. Specifically:

[0076] Dynamic threshold calculation: m=u1×(L max -L min )+u2×σ;

[0077] Among them: m is the dynamic threshold, which is used to judge which points of the 3D point cloud data belong to the normal area and which points belong to the abnormal area; u1 is used to balance the impact of local range on the dynamic threshold, u2 is used to balance the impact of standard deviation on the dynamic threshold, L max is the maximum value of the local point cloud height, L max is the minimum height of the local point cloud. σ is the standard deviation of the local point cloud height. Points with heights exceeding [μ-m,μ+m] are marked as noise (μ is the local mean), and a RANSAC algorithm is used to fit a plane and remove outliers. A local point cloud is a set of points within a limited range in the point cloud data.

[0078] The pre-processed data is mapped into 3D space to generate a dynamic 3D model containing geometric information, temperature distribution and cutting quality, including:

[0079] 1) Spatiotemporal alignment of multi-source data;

[0080] Unify the coordinate systems and time bases of 3D point cloud data, temperature field data, and cut quality visual image data. Align multi-source data using calibration parameters (rotation matrix R and translation vector t) to ensure spatial consistency. Align data streams based on timestamp interpolation, and design interpolation strategies for different sampling rates to ensure that geometric, temperature, and cut quality data at the same moment correspond.

[0081] 2) 3D geometric model reconstruction;

[0082] To generate a continuous surface model from filtered point cloud data, a Poisson surface reconstruction method combined with a dynamic update strategy is employed. Poisson surface reconstruction is an implicit function-based method that fits the point cloud surface using the implicit function χ and solves the Poisson equation to generate a continuous surface model from point cloud data. This method can handle noisy point clouds and generate a smooth, continuous surface. It is also robust to sparse and irregularly distributed point clouds.

[0083] Dynamically update the surface model to reflect the latest geometric changes in real-time point cloud data streams. Using incremental Delaunay triangulation, mesh vertices V(t) and facets F(t) are progressively updated. Supporting real-time updates, this approach is suitable for dynamic point cloud data streams. Incremental updates reduce the overhead of recalculating the entire mesh, improving efficiency. It quickly adapts to changes in the point cloud and generates high-quality, continuous surface models.

[0084] 3) Temperature distribution mapping;

[0085] The temperature field data is associated with the vertices / faces of the 3D geometric model, and inverse distance weighted (IDW) interpolation is used for areas not directly measured.

[0086]

[0087] Where: T(x,y,z) is the estimated temperature value of the interpolation point (x,y,z), T i is the temperature value of the i-th known temperature point. i The distance from the interpolation point (x, y, z) to the i-th known temperature point (x i ,y i ,z i ), where n is the number of known temperature points. During texture mapping, the temperature field is converted into a UV texture map, which is rendered through vertex color or heat map shader to intuitively display the temperature distribution. Through attribute interpolation and texture mapping methods, the temperature data can be effectively associated with the vertices or patches of the geometric model, and the temperature distribution can be intuitively displayed on the three-dimensional model. It is suitable for monitoring the surface temperature of metal during laser cutting. UV coordinates are used to map the geometric model to the texture image, and the shader dynamically adjusts the color according to the temperature value to realize the visualization of the temperature distribution.

[0088] 4) Cutting quality parameter integration: quantify cutting quality visual image data and map it to a 3D geometric model.

[0089] (1) The surface roughness C(p) is calculated using the point cloud curvature formula, and the surface smoothness is measured by the normal vector difference.

[0090]

[0091] Where: n(p) is the normal vector of point p, p i Its neighboring points.

[0092] (2) A regression relationship between cutting speed v and slit width W is established to predict and optimize cutting process parameters. W = k1·v + k2·P + b; where: W: cutting slit width; v: cutting speed; P: laser power. k1, k2, b: fitting coefficients in the regression model, obtained by fitting experimental data, are used to describe the influence of cutting speed and laser power on slit width.

[0093] (3) Display cutting quality parameters (such as roughness, seam width, temperature, etc.) on the geometric model through color coding or label superposition. Use different colors to represent different attribute values. The color gradient can be dynamically adjusted according to the actual attribute value to form an intuitive visualization effect. Add numerical labels at key positions of the geometric model to display specific attribute values (such as roughness value, seam width value, etc.). Numerical label: Directly display specific attribute values (such as C(p), W, etc.) to facilitate users to quickly understand the quality information of key areas. Through color coding and label superposition, the spatial distribution of cutting quality parameters can be intuitively displayed, helping to quickly identify problem areas (such as overheating, excessive roughness, etc.) and adjust process parameters in a timely manner.

[0094] 5) Dynamic 3D model update, to achieve the dynamic effect of the model evolving over time, and to update the point cloud position x t and temperature T t To make a status update:

[0095] The state prediction equation is:

[0096] The observation equation is:

[0097] in: is the estimated value of the state at the current time t, A is the state transfer matrix, which describes how the system state is transferred from the previous time t-1 to the current time t, and B is the control input matrix, which describes the control input u t Impact on system status; w t is the process noise, which represents the random error in the system state transition process. t is the observation noise, which represents the random error in the sensor measurement process. H is the measurement matrix, which maps the state variables to the observation space. t is the observation value at the current time t.

[0098] S3. Cutting path planning and obstacle avoidance based on dynamic 3D models; combined with dynamic 3D models, the optimal cutting path is generated in real time, avoiding surface defects (holes) and high-temperature areas.

[0099] The dynamic 3D model generated in step S2 includes the following information:

[0100] Geometric information: continuous surface model reconstructed from point cloud data.

[0101] Temperature Distribution: The temperature field is mapped onto the geometry model through interpolation.

[0102] Cutting quality parameters: surface roughness, cutting slit width, etc.

[0103] 1. Use heuristic algorithm (A* algorithm) to generate the optimal cutting path. The formula is:

[0104]

[0105] Where: c(si,si+1): from node s i to s i+1 Path is the optimal path. The cost function c(si,si+1) is usually composed of multiple sub-items, each of which reflects a different optimization goal or constraint. The following are the components and their meanings:

[0106] 1) Geometric distance: L path =||s i+1 -s i ||, where L path : Total length of the cutting path. ||s i+1 -s i || is the Euclidean distance between nodes.

[0107] 2) Temperature constraint: J T =T i ·Δt i , where T i is the temperature Δt of the area where the i-th path is located i is the stay time on the i-th path. T is the temperature constraint. It needs to satisfy T i ≤T max (Local Temperature Threshold).

[0108] 3) Surface features: Smooth areas are preferred to reduce cutting errors. Where: JC is the cumulative cost of surface roughness. C(pi): surface roughness at the i-th node.

[0109] 2. Comprehensively consider multiple performance indicators:

[0110] Cutting efficiency: Minimize the total path length L path .

[0111] Cutting quality: Avoid high temperature and high roughness areas.

[0112] 31) The steps to achieve the optimal cutting path include:

[0113] 31) Initialization; Define the starting and ending points: Determine the starting and ending points based on the cutting task. Set the initial path: Use a straight line to connect the starting and ending points as the initial path.

[0114] 32) Path evaluation; cost function calculation: Calculate the total cost of the current path based on factors such as geometric distance, temperature constraints, surface characteristics, etc.

[0115] J=w1·L path +w2·J T +w3·J C

[0116] Where: w1, w2, w3: weight coefficients, representing the importance of path length, temperature constraint and surface characteristics respectively.

[0117] 33) Use the A* algorithm to gradually optimize the path. Each iteration selects the path expansion direction with the minimum cost until the termination condition is met. The termination condition is to reach the predetermined path length or optimization times or to meet all constraints (such as temperature threshold T i ≤T max ). Finally, the final path is generated, and the optimal cutting path is generated based on the optimization results. The path is superimposed on the dynamic 3D model, and key path information (such as path length, temperature distribution, surface roughness, etc.) is displayed through color coding or labels. By combining the geometric information, temperature distribution, and cutting quality parameters provided by the dynamic 3D model, a heuristic algorithm (A* algorithm) is used for path planning, which can effectively balance processing efficiency, cutting quality, and safety. This not only improves the intelligence level of path planning, but also significantly improves the overall efficiency and accuracy of laser cutting.

[0118] 3. Use encoder and inertial measurement unit measurement data to correct path deviation caused by mechanical vibration in real time: Vibration compensation model:

[0119]

[0120] a i : Vibration amplitude (X / Y / Z axis components). i : Vibration angular frequency (obtained through FFT analysis). i : Phase angle. ΔP(t) represents the vibration offset of the laser cutting head in three-dimensional space (X / Y / Z axes), which is the dynamic position error caused by mechanical vibration.

[0121] S4. Establish a regression relationship between cutting speed and slit width, and use the fitting coefficients of experimental data. Define the objective function to evaluate the cutting effect, and use the optimization algorithm to find the optimal cutting speed and laser power. Specifically include:

[0122] S41. Establish a regression relationship between the cutting speed v and the slit width W: W = k1×v+k2×P+b.

[0123] Where: W is the slit width. v is the cutting speed. P is the laser power. k1, k2, and b are fitting coefficients obtained by fitting experimental data. Slit width W data at different cutting speeds v and laser powers P were collected, and the coefficients k1, k2, and b were fitted using the least squares method.

[0124] S42. Define an objective function to evaluate cutting performance: J(v,P) = λ1·W + λ2·C(p) + λ3·T, where: J(v,P) is the objective function value. W is the cutting kerf width. C(p) is the surface roughness. T is the temperature distribution. λ1, λ2, and λ3 are weight coefficients, which are set according to actual needs.

[0125] S43. Use an optimization algorithm (particle swarm optimization algorithm) to find the optimal cutting speed v and laser power P. During the cutting process, continuously monitor the changes in geometric information, temperature distribution and cutting quality parameters, and adjust the cutting speed v and laser power P in a timely manner.

[0126] [v * ,P * ]=arg min J(v,P) is used to find the optimal cutting speed v and laser power P. * ,P * ] is the optimal solution vector, which represents the cutting speed v and laser power P that minimize the objective function J(v,P).

[0127] Particle Swarm Optimization (PSO) simulates the foraging behavior of bird flocks and finds the global optimal solution through group collaboration.

[0128] The steps are:

[0129] 1) Initialize a group of particles, each particle represents a candidate solution (v, P);

[0130] 2) Calculate the objective function value J(v,P) of each particle;

[0131] 3) Update the velocity and position of each particle to move it towards a better solution;

[0132] 4) Repeat steps 2 and 3 until the termination condition is met (such as reaching the maximum number of iterations or the objective function value no longer changes significantly).

[0133] Finally, a PID controller is used to adjust the laser power and cutting speed in real time.

[0134] The above describes the laser cutting automatic control method in an embodiment of the present invention. The following describes the laser cutting automatic control system in an embodiment of the present invention. An embodiment of the laser cutting automatic control system in an embodiment of the present invention includes: a data acquisition module, a data preprocessing and modeling module, a path planning and obstacle avoidance module, a vibration compensation module and a parameter optimization and control module.

[0135] 1. Data acquisition module;

[0136] 3D laser scanner: acquires high-precision point cloud data of the metal surface to be cut in real time.

[0137] Provides metal surface features (such as undulations, holes, and edges) to help identify and locate key features on the cutting path, improving the accuracy of cutting path planning.

[0138] Infrared thermal imager: monitors the temperature distribution in the cutting area to prevent local overheating and material deformation.

[0139] By mapping the temperature field data onto the geometric model, temperature constraint information is provided for path planning to avoid high temperature areas.

[0140] Visual camera: Captures real-time cutting images and analyzes kerf quality (such as cutting width and slag residue). Evaluates cutting results and provides cutting quality feedback for subsequent parameter optimization.

[0141] Encoder and Inertial Measurement Unit (IMU): Track the three-dimensional motion trajectory of the laser cutting head and compensate for mechanical vibration errors. This improves cutting accuracy and reduces dynamic position errors caused by mechanical vibration.

[0142] 2. Data preprocessing and modeling module;

[0143] Data preprocessing: Perform preprocessing operations such as filtering, denoising, and outlier removal on multi-source data.

[0144] Improve point cloud stability and accuracy, ensure data quality, and provide a reliable foundation for subsequent modeling.

[0145] Dynamic 3D model generation: Map preprocessed data into 3D space to generate a dynamic 3D model that includes geometric information, temperature distribution, and cut quality. This allows for spatiotemporal alignment of multi-source data, generating a continuous surface model and integrating temperature fields and cut quality parameters into the model, supporting real-time updates and visualization.

[0146] 3. Path Planning and Obstacle Avoidance Module: Based on a dynamic 3D model, it generates the optimal cutting path in real time, avoiding surface defects (such as holes) and high-temperature areas. By comprehensively considering geometric distances, temperature constraints, and surface characteristics, it balances processing efficiency, cutting quality, and safety, significantly improving the overall efficiency and accuracy of laser cutting.

[0147] 4. Vibration compensation module;

[0148] Vibration compensation model: Utilizes encoder and IMU measurement data to correct path deviation caused by mechanical vibration in real time. This reduces dynamic position errors caused by mechanical vibration and improves cutting accuracy.

[0149] 5. Parameter optimization and control module;

[0150] Regression relationship establishment: Establish a regression relationship between cutting speed v and slit width W, and use experimental data to fit the coefficients. Provide a prediction model for cutting process parameters and provide a basis for parameter optimization.

[0151] Objective function definition and optimization: Define the objective function to evaluate the cutting effect, and use optimization algorithms (such as particle swarm optimization) to find the optimal cutting speed v and laser power P. The optimization algorithm finds the best combination of process parameters to improve cutting quality and efficiency.

[0152] PID controller: adjusts laser power and cutting speed in real time, ensuring that parameters remain optimal during the cutting process and adapt to dynamically changing environmental conditions.

[0153] The effects of the present invention are summarized as follows:

[0154] 1) High-precision data acquisition: Through a variety of sensors such as 3D laser scanners, infrared thermal imagers, visual cameras and IMU, comprehensive monitoring of the cutting process is achieved, providing high-quality raw data.

[0155] 2) Accurate data preprocessing and modeling: Through preprocessing steps such as filtering, denoising, and outlier removal, as well as dynamic 3D model generation technology, the quality of the data and the accuracy of the model are ensured.

[0156] 3) Intelligent path planning and obstacle avoidance: Combining the A* algorithm and multi-source data, intelligent path planning is achieved, effectively avoiding high-temperature areas and surface defects, and improving cutting efficiency and quality.

[0157] 4) Effective vibration compensation: Through the vibration compensation model, the path deviation caused by mechanical vibration is corrected in real time, thereby improving cutting accuracy.

[0158] 5) Optimized parameter control: By establishing regression relationships, defining objective functions, and applying optimization algorithms, the optimal configuration of cutting speed and laser power is achieved, further improving cutting quality and efficiency.

[0159] In summary, the laser cutting automatic control method and system realizes full-process automated control from data acquisition to final cutting effect evaluation through key technologies such as multi-source data fusion, intelligent path planning, vibration compensation and parameter optimization, significantly improving the accuracy and efficiency of laser cutting.

[0160] An embodiment of the present invention further provides a computer device, which may be a server. The computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor of the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above-mentioned method is implemented.

[0161] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0162] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program code.

[0163] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A laser cutting automatic control method, characterized in that: include: S1. 3D laser scanner generates three-dimensional point cloud data of the metal surface, infrared thermal imager monitors the temperature field to obtain temperature field data, visual camera captures the cutting picture to obtain cutting quality visual image data, encoder and inertial measurement unit monitor the position and vibration frequency of the cutting head to obtain multi-source data; S2. Preprocess the multi-source data, map the preprocessed data into three-dimensional space, and generate a dynamic three-dimensional model containing geometric information, temperature distribution, and cutting quality; S3, cutting path planning and obstacle avoidance based on dynamic 3D model; Combined with dynamic 3D models, the optimal cutting path is generated in real time, avoiding surface defects and high-temperature areas; S4. Establish the regression relationship between cutting speed and seam width, fit the coefficients through experimental data, define the objective function to evaluate the cutting effect, and use the optimization algorithm to find the optimal cutting speed and laser power.

2. The laser cutting automatic control method according to claim 1, characterized in that: In step S1, a 3D laser scanner is used to acquire three-dimensional point cloud data of the metal surface to be cut in real time, providing metal surface features, including undulations, holes, and edge features, to help identify and locate key features on the cutting path; The 3D laser scanner is linked with the encoder to ensure strict synchronization of the scanning data and the cutting head position. A high-speed processor module is added to achieve hardware-level trigger synchronization, and the clock signals of the 3D laser scanner, encoder and inertial measurement unit are unified to μs-level accuracy. The visual camera is used to capture the real-time cutting image and analyze the cutting seam quality, which includes the cutting width and slag residue, to evaluate the cutting effect. An encoder and inertial measurement unit are also set up to track the three-dimensional motion trajectory of the laser cutting head to compensate for mechanical vibration errors.

3. The automatic control method for laser cutting according to claim 2, characterized in that: In step S2, preprocessing includes filtering and denoising of the 3D point cloud data, using Kalman filtering to eliminate measurement noise of the 3D laser scanner; The pre-processed data is mapped into 3D space to generate a dynamic 3D model containing geometric information, temperature distribution and cutting quality, including: 1) Spatiotemporal alignment of multi-source data: unify the coordinate system and time base of 3D point cloud data, temperature field data, and cutting quality visual image data, and align multi-source data by calibrating parameters to ensure spatial consistency; 2) 3D geometric model reconstruction: To generate a continuous surface model from the filtered point cloud data, a Poisson surface reconstruction method combined with a dynamic update strategy is used. In the real-time point cloud data streaming scenario, the surface model is dynamically updated to reflect the latest geometric changes. Incremental Delaunay triangulation is used to gradually update the mesh vertices V(t) and facets F(t). 3) Temperature distribution mapping: associate temperature field data with vertices / faces of the 3D geometric model, and use inverse distance weighted interpolation for areas not directly measured. 4) Cutting quality parameter integration: quantify cutting quality visual image data and map it to a 3D geometric model; 5) Dynamic 3D model update, to achieve the dynamic effect of the model evolving over time, and to update the point cloud position x t and temperature T t To make a status update: The state prediction equation is: The observation equation is: in: is the estimated value of the state at the current time t, A is the state transfer matrix, which describes how the system state is transferred from the previous time t-1 to the current time t, and B is the control input matrix, which describes the control input u t Impact on system status; w t is process noise, which indicates the random error in the system state transition process; v t is the observation noise, which represents the random error in the sensor measurement process; H is the observation matrix, which maps the state variables to the observation space; z t is the observation value at the current time t.

4. The laser cutting automatic control method according to claim 3, characterized in that: Integration of cutting quality parameters; quantification of cutting quality visual image data and mapping to 3D geometric models. Specifically including: (1) Calculate the surface roughness C(p) using the point cloud curvature formula and measure the surface smoothness by the normal vector difference; Where: n(p) is the normal vector of point p, p i is its neighboring point; (2) Establishing the regression relationship between cutting speed v and slit width W to predict and optimize cutting process parameters; (3) Display cutting quality parameters on the geometric model by color coding or label overlay.

5. The laser cutting automatic control method according to claim 4, characterized in that: In step S3, it specifically includes: S31, use A* algorithm to generate the optimal cutting path, the formula is: Where: c(si,si+1): from node s i to s i+1 Cost function, Path is the optimal path; S32, comprehensively consider the performance indicators of cutting efficiency and cutting quality, and the cutting efficiency is to minimize the total length of the path L path ; Cutting quality is to avoid high temperature and high roughness areas; the specific implementation is: 31) Initialization; Define the starting point and end point: Determine the starting point and end point according to the cutting task. Set the initial path: Use a straight line to connect the starting point and end point as the initial path; 32) Path evaluation; cost function calculation: Calculate the total cost of the current path based on geometric distance, temperature constraints, and surface characteristics; J=w1·L path +w2·J T +w3·J C ; Where: w1, w2, w3: weight coefficients, representing the importance attached to path length, temperature constraint and surface characteristics respectively; 33) Use the A* algorithm to gradually optimize the path; each iteration selects the path expansion direction with the minimum cost until the termination condition is met; S33. Using encoder and inertial measurement unit measurement data to correct path deviation caused by mechanical vibration in real time: Vibration compensation model: a i : vibration amplitude; ω i : vibration angular frequency; φ i : Phase angle; ΔP(t) represents the vibration offset of the laser cutting head in three-dimensional space.

6. The laser cutting automatic control method according to claim 5, characterized in that: In step S4, it specifically includes: S41, establishing a regression relationship between cutting speed v and seam width W: W = k1×v+k2×P+b; Where: W is the cutting slit width; v is the cutting speed, P is the laser power; k1, k2, b are fitting coefficients; S42. Define an objective function to evaluate the cutting effect; J(v,P) = λ1·W+λ2·C(p)+λ3·T; Where: J(v,P) is the objective function value, W is the cutting slit width, C(p) is the surface roughness, T is the temperature distribution, λ1, λ2, λ3 are weight coefficients; S43, using a particle swarm optimization algorithm to find the optimal cutting speed v and laser power P during the cutting process, continuously monitoring the changes in geometric information, temperature distribution, and cutting quality parameters, and timely adjusting the cutting speed v and laser power P; [v * ,P * ]=arg min J(v,P); used to find the optimal cutting speed v and laser power P; finally, a PID controller is used to adjust the laser power and cutting speed in real time.

7. The automatic control method for laser cutting according to claim 6, characterized in that: In step S43, the particle swarm optimization algorithm is used to simulate the foraging behavior of the bird flock and find the global optimal solution through group collaboration. The steps are as follows: 1) Initialize a group of particles, each particle represents a candidate solution (v, P); 2) Calculate the objective function value J(v,P) of each particle; 3) Update the velocity and position of each particle to move it towards a better solution; 4) Repeat steps 2 and 3 until the termination condition is met, which includes reaching the maximum number of iterations or the objective function value no longer changes significantly.

8. A control system using the laser cutting automatic control method according to claim 7, characterized in that: It includes a data acquisition module, a data preprocessing and modeling module, a path planning and obstacle avoidance module, a vibration compensation module and a parameter optimization and control module. The data acquisition module generates three-dimensional point cloud data of the metal surface through a 3D laser scanner, the infrared thermal imager monitors the temperature field to obtain temperature field data, the visual camera captures the cutting picture to obtain cutting quality visual image data, and the encoder and inertial measurement unit monitor the position and vibration frequency of the cutting head to obtain multi-source data; the data preprocessing and modeling module preprocesses the multi-source data, maps the preprocessed data into three-dimensional space, and generates a dynamic three-dimensional model containing geometric information, temperature distribution and cutting quality; the path planning and obstacle avoidance module performs cutting path planning and obstacle avoidance based on the dynamic three-dimensional model; Combined with the dynamic three-dimensional model, the optimal cutting path is generated in real time, avoiding surface defects and high-temperature areas; the vibration compensation module uses the encoder and IMU measurement data to correct the path deviation caused by mechanical vibration in real time; the parameter optimization and control module establishes the regression relationship between cutting speed and seam width, defines the objective function to evaluate the cutting effect through the experimental data fitting coefficient, and uses the optimization algorithm to find the optimal cutting speed and laser power.

9. The control system of the automatic laser cutting control method according to claim 8, characterized in that: The 3D laser scanner acquires high-precision point cloud data of the metal surface to be cut in real time; provides metal surface features to help identify and locate key features on the cutting path, thereby improving the accuracy of cutting path planning; the infrared thermal imager monitors the temperature distribution in the cutting area, maps the temperature field data to the geometric model, and provides temperature constraint information for path planning to avoid high-temperature areas; The visual camera captures the real-time cutting image and analyzes the cutting quality; the encoder and inertial measurement unit track the three-dimensional motion trajectory of the laser cutting head, compensate for mechanical vibration errors, and reduce the dynamic position error caused by mechanical vibration.

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