Intelligent laser cutting control method and system

Abstract

The present invention discloses an intelligent laser cutting control method and system, which relates to the field of laser cutting control technology, and specifically discloses performing structural analysis on a workpiece to be cut, constructing a three-dimensional model thereof, and determining changes in cutting thickness based on changes in the posture of a laser cutting line relative to the three-dimensional structure; conducting an in-depth analysis of the changes in cutting thickness, determining a number of key cutting focus nodes, and marking the positions of these nodes on the cutting surface; selecting a suitable initial strategy for cutting testing based on the material properties of the workpiece to be cut, and performing quality inspection on the cutting surface to determine the defect type and extract the quality characteristics of the cutting focus nodes; correcting the initial cutting control strategy to obtain a more optimized reference strategy, and applying it to the laser cutting process of other workpieces to be cut. The above technical scheme disclosed by the present invention achieves high precision and high quality of laser cutting through precise structural analysis, thickness change analysis, material property considerations and dynamic strategy adjustment.

Description

Technical Field

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

[0002] In modern manufacturing, laser cutting is widely used in cutting various materials as an efficient and precise processing method. However, there are a series of technical challenges in the laser cutting process, which affect the cutting accuracy and quality.

[0003] First, the structural complexity of the workpiece often leads to uneven thickness changes during the cutting process, which can have a significant impact on the effect of laser cutting. Traditional laser cutting methods are often unable to accurately sense and adapt to such thickness changes, resulting in defects on the cutting surface, such as cracks, incomplete cutting or over-cutting.

[0004] Secondly, different materials have different laser cutting characteristics, and targeted cutting strategies are needed to ensure cutting quality. However, most existing laser cutting control methods are based on experience or simple experimental adjustments, lacking in-depth understanding and precise control of the characteristics of different materials, and are difficult to meet the needs of high-precision cutting. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent laser cutting control method and system that can perform intelligent control based on the structural characteristics and material characteristics of the workpiece to be cut.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An intelligent laser cutting control method, comprising:

[0008] Perform structural analysis on the workpiece to be cut, construct a three-dimensional structure of the workpiece to be cut, and determine the cutting thickness change of the laser cutting line based on the cutting posture change of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut;

[0009] Analyze the cutting thickness change, determine several cutting focus nodes, and mark the corresponding positions of the cutting focus nodes on the cutting surface;

[0010] According to the material difference of the workpiece to be cut, several material-initial cutting control strategy relationships are established, and based on the material of the current workpiece to be cut, the initial cutting control strategy to be applied is selected, and the initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and the cutting quality is detected on the cutting surface after the cutting test. Based on the detection results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node;

[0011] Based on the determined defect type and the cutting quality characteristics of each cutting focus node, the initial cutting control strategy is corrected to obtain a reference cutting control strategy, and based on the reference cutting control strategy, the laser cutting component is driven to perform laser cutting on other parts to be cut.

[0012] In an embodiment disclosed in the present invention, a method for determining a change in cutting thickness of a laser cutting line includes:

[0013] Obtain the cutting trajectory of the laser cutting line and construct a vertical thickness analysis plane for the laser cutting line;

[0014] Construct a number of thickness detection points for the cutting trajectory of the laser cutting line. When the laser cutting line reaches each thickness detection point, the thickness analysis plane is moved, the critical overlap between the thickness analysis plane and the three-dimensional structure of the workpiece to be cut is marked, and the relative distance between the thickness analysis plane and the two critical overlaps is calculated. The relative distance is identified as the cutting thickness, and the cutting thickness is associated with the corresponding thickness detection point and recorded;

[0015] The overall performance of the cutting thickness corresponding to each thickness detection point on the cutting track is identified as the cutting thickness variation of the laser cutting line.

[0016] In an embodiment disclosed in the present invention, a method for analyzing cutting thickness changes and determining a number of cutting focus nodes includes:

[0017] Calculate the thickness difference of the cutting thickness corresponding to adjacent thickness detection points, where the thickness difference is an absolute value. If the thickness difference is greater than or equal to a preset value, the corresponding thickness detection point is identified as a thickness detection point that needs attention.

[0018] Analyze the continuity characteristics of the thickness detection points that need attention, associate the thickness detection points that need attention whose adjacent intervals are less than or equal to the preset number of intervals, and form a cluster of thickness detection points that need attention;

[0019] The number of intervals in the cluster of thickness detection points requiring attention is determined, and the sum of the thickness difference amounts of the thickness difference amounts is determined. Based on the number of intervals and the sum of the thickness difference amounts, the cutting attention node density influencing parameters of the cluster of thickness detection points requiring attention are determined;

[0020] Based on the position interval of the cluster of thickness detection points that need to be concerned, mapped on the three-dimensional structure of the workpiece to be cut, determine the position interval of the cutting surface that needs to be marked;

[0021] Based on the cutting attention node density influencing parameter, the number of attention nodes of the cutting attention node in the position interval marked on the cutting surface is determined, and several cutting attention nodes in the corresponding position interval are recorded as a cutting attention node cluster.

[0022] In some embodiments disclosed in the present invention, the expression for determining the influencing parameter of the cutting focus node density is:

[0023] ;

[0024] in, To cut the node density affecting parameters, is the density influencing parameter conversion coefficient, is the number of intervals in the cluster of thickness detection points that need attention, The weight adjustment coefficient is affected by the number of intervals. is the sum of the preset maximum thickness differences, is the total thickness difference, is the thickness difference influence weight adjustment coefficient, Adjustment constant for density influencing parameter.

[0025] In some embodiments disclosed in the present invention, the method for performing cutting quality inspection on the cutting surface after the cutting test includes:

[0026] Divide the defective blocks on the cutting surface after the cutting test, and determine the defect type of the defective blocks based on the position and shape of the defective blocks;

[0027] The cutting surface is photographed to obtain a cutting surface image, and the cutting surface image is gray-scaled to obtain a cutting surface grayscale image;

[0028] The grayscale features of the defect-free blocks in the grayscale image of the cutting surface are extracted and recorded as normal grayscale features. The cutting focus nodes are mapped on the grayscale image of the cutting surface, and the grayscale features of the blocks near the cutting focus nodes are extracted and recorded as cutting grayscale features.

[0029] Compare the cutting grayscale features of each cutting attention node with the normal grayscale features, and based on the comparison results, determine the cutting attention nodes with abnormal grayscale features, and mark the cutting attention nodes with abnormal grayscale features as defects;

[0030] The node ratio of the number of cutting attention nodes marked as defects to the number of all cutting attention nodes in the nearby cutting attention node cluster is calculated, and the initial cutting control strategy is modified based on the defect type and the node ratio.

[0031] In some embodiments disclosed in the present invention, a method for determining a defect type of a defective block based on the position and shape of the defective block includes:

[0032] Using manual or visual analysis technology, the defective blocks on the cutting surface are divided, and based on the location and shape of the defective blocks, the defect type of the defective blocks is determined;

[0033] Among them, the method of dividing the defective blocks on the cutting surface by using visual analysis technology includes:

[0034] The cutting surface is photographed to obtain a cutting surface image, and the cutting surface image is grayed to obtain a cutting surface grayscale map, and the defect edges in the cutting surface grayscale map are demarcated and marked using edge detection technology to obtain a number of defect blocks;

[0035] The relative position of each defective block relative to the edge of the cutting surface is determined, and combined with the shape characteristics of the defect edge of the defective block, a preset defect recognition model is used for analysis to output the defect type of each defective block.

[0036] In some embodiments disclosed in the present invention, a method for determining a relative position of each defective block relative to an edge of a cutting surface includes:

[0037] Divide the block between the edges of the cutting surface into a number of position sub-blocks, where the edges of the sub-blocks are parallel to the edges of the cutting surface;

[0038] A number of position analysis probes are applied to each defective block, and the ratio of the number of position analysis probes in different position sub-blocks is analyzed. The position sub-blocks containing position analysis probes and whose ratio of the number of probes is greater than or equal to a preset value are marked as main position sub-blocks. Based on the size relationship of the ratio of the number of probes, the main position sub-blocks are sorted to obtain a main position sub-block sequence.

[0039] In some embodiments disclosed in the present invention, the method for modifying the initial cutting control strategy based on the defect type and the node ratio includes:

[0040] Analyze the initial cutting control strategy to determine the laser cutting power and cutting rate of different trajectory nodes on the cutting trajectory of the laser cutting component;

[0041] Match the trajectory nodes with the cutting focus nodes, and determine the node ratio and defect type corresponding to the trajectory nodes;

[0042] Based on the defect type corresponding to the trajectory node, the correction direction of the laser cutting power and cutting rate is determined, and the node ratio is substituted into the preset node ratio range~correction coefficient correspondence table to determine the correction coefficient for correcting the laser cutting power or cutting rate.

[0043] In some embodiments disclosed in the present invention, there is also disclosed an intelligent laser cutting control system, including:

[0044] The first module is used to perform structural analysis on the workpiece to be cut, construct a three-dimensional structure of the workpiece to be cut, and determine the cutting thickness change of the laser cutting line based on the cutting posture change of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut;

[0045] The second module is used to analyze the cutting thickness change, determine a number of cutting focus nodes, and mark the corresponding positions of the cutting focus nodes on the cutting surface;

[0046] The third module is used to establish several material-initial cutting control strategy relationships according to the material difference of the workpiece to be cut, and select the initial cutting control strategy to be applied based on the material of the current workpiece to be cut. The initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and the cutting quality detection is performed on the cutting surface after the cutting test. Based on the detection results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node;

[0047] The fourth module is used to modify the initial cutting control strategy based on the determined defect type and the cutting quality characteristics of each cutting focus node to obtain a reference cutting control strategy, and based on the reference cutting control strategy, drive the laser cutting component to perform laser cutting on other parts to be cut.

[0048] The present invention discloses an intelligent laser cutting control method and system, which relates to the field of laser cutting control technology, and specifically discloses performing structural analysis on a workpiece to be cut, constructing a three-dimensional model thereof, and determining changes in cutting thickness based on changes in the posture of a laser cutting line relative to the three-dimensional structure; conducting an in-depth analysis of the changes in cutting thickness, determining a number of key cutting focus nodes, and marking the positions of these nodes on the cutting surface; selecting a suitable initial strategy for cutting testing based on the material properties of the workpiece to be cut, and performing quality inspection on the cutting surface to determine the defect type and extract the quality characteristics of the cutting focus nodes; correcting the initial cutting control strategy to obtain a more optimized reference strategy, and applying it to the laser cutting process of other workpieces to be cut. The above technical scheme disclosed by the present invention achieves high precision and high quality of laser cutting through precise structural analysis, thickness change analysis, material property considerations and dynamic strategy adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A method step diagram of an intelligent laser cutting control method disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0051] The purpose of the present invention is to provide an intelligent laser cutting control method and system that can perform intelligent control based on the structural characteristics and material characteristics of the workpiece to be cut.

[0052] In order to achieve the above object, the present invention adopts the following technical solutions:

[0053] See also Figure 1 , an intelligent laser cutting control method, comprising:

[0054] Step S100 , performing structural analysis on the workpiece to be cut, constructing a three-dimensional structure of the workpiece to be cut, and determining a change in cutting thickness of the laser cutting line based on a change in cutting posture of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut.

[0055] In the laser cutting process, accurate structural analysis of the part to be cut is a crucial first step. This step involves using advanced measurement technology and 3D modeling software, such as CAD (computer-aided design) tools, to build a 3D structural model of the part to be cut. This model not only accurately reflects the geometry and size of the part to be cut, but also takes into account the change in the attitude of the laser cutting line relative to the 3D structure during the cutting process. Since the thickness of laser cutting is affected by many factors such as cutting angle, speed, power and material properties, the change in cutting thickness can be accurately determined by simulating the cutting effect of the laser cutting line in different attitudes. The purpose of this step is to provide accurate data support for the subsequent cutting strategy formulation to ensure the accuracy and consistency of cutting.

[0056] For example, when cutting parts with complex surfaces, 3D modeling can accurately simulate the path of the laser cutting line on the surface, thereby calculating the changes in cutting thickness at different positions, providing a basis for subsequent cutting strategy adjustments.

[0057] In an embodiment disclosed in the present invention, a method for determining a change in cutting thickness of a laser cutting line includes:

[0058] Step S101, obtaining a cutting trajectory of a laser cutting line, and constructing a vertical thickness analysis plane for the laser cutting line.

[0059] In the preparation stage of laser cutting, the cutting trajectory of the laser cutting line needs to be obtained first to ensure that the laser cutting head can move along the predetermined path. Next, in order to deeply analyze the thickness change during the cutting process, a vertical thickness analysis plane is constructed for the laser cutting line. This plane is perpendicular to the cutting line and is used for subsequent measurement and calculation of cutting thickness.

[0060] Step S102, construct a number of thickness detection points for the cutting trajectory of the laser cutting line. When the laser cutting line reaches each thickness detection point, the thickness analysis plane is moved, the critical overlap between the thickness analysis plane and the three-dimensional structure of the workpiece to be cut is marked, and the relative distance between the two critical overlaps of the thickness analysis plane is calculated, and the relative distance is identified as the cutting thickness, and this cutting thickness is associated with the corresponding thickness detection point and recorded.

[0061] After the cutting trajectory is determined, several thickness detection points are constructed along this trajectory. These detection points are key positions for monitoring changes in cutting thickness. When the laser cutting line moves to each thickness detection point, the thickness analysis plane is moved to make it contact with the three-dimensional structure of the part to be cut. The critical overlaps between the thickness analysis plane and the structure of the part to be cut are marked. These two critical overlaps actually represent the upper and lower boundaries of the cutting line at the current detection point. By calculating the relative distance between these two critical overlaps on the thickness analysis plane, the cutting thickness of the current detection point can be obtained. This cutting thickness value is associated with the corresponding thickness detection point and recorded for subsequent analysis.

[0062] Step S103, identifying the overall performance of the cutting thickness corresponding to each thickness detection point on the cutting trajectory as the cutting thickness variation of the laser cutting line.

[0063] After completing the cutting thickness measurement of all thickness detection points, the overall performance of the cutting thickness corresponding to each thickness detection point on the cutting track is identified as the cutting thickness change of the laser cutting line. This means that by connecting these discrete cutting thickness values, a curve reflecting the change of cutting thickness along the cutting track can be obtained. This curve is of great significance for understanding the thickness change law during the cutting process, optimizing cutting parameters, and improving cutting quality.

[0064] Step S200, analyzing the cutting thickness variation, determining a number of cutting focus nodes, and marking the corresponding positions of the cutting focus nodes on the cutting surface.

[0065] After determining the changes in cutting thickness, the next step is to conduct an in-depth analysis of these changes to determine the key areas that have the greatest impact on cutting quality, namely the cutting focus nodes. These nodes may be the locations where the cutting thickness changes the most, or the areas where defects are most likely to occur in the cutting path. Through professional analysis software, the cutting simulation of the 3D model can be performed to find these key nodes and mark the corresponding positions on the cutting surface. The purpose of this step is to focus on monitoring and regulating these key areas in the subsequent cutting process to ensure the stability of the cutting quality.

[0066] In an embodiment disclosed in the present invention, a method for analyzing cutting thickness changes and determining a number of cutting focus nodes includes:

[0067] Step S201, calculating the thickness difference of the cutting thickness corresponding to adjacent thickness detection points, wherein the thickness difference is an absolute value, and if the thickness difference is greater than or equal to a preset value, the corresponding thickness detection point is identified as a thickness detection point requiring attention.

[0068] Step S202 , analyzing the continuity characteristics of the thickness detection points requiring attention, associating the thickness detection points requiring attention whose adjacent intervals are less than or equal to a preset number of intervals, and forming a cluster of thickness detection points requiring attention.

[0069] Step S203, determining the number of intervals in the thickness detection point cluster requiring attention, and determining the total thickness difference of the thickness difference, and determining the cutting attention node density influencing parameter of the thickness detection point cluster requiring attention based on the number of intervals and the total thickness difference.

[0070] Step S204: determining the position interval of the cutting surface that needs to be marked based on the position interval of the cluster of thickness detection points that need to be concerned mapped on the three-dimensional structure of the workpiece to be cut.

[0071] Step S205, based on the cutting attention node density influencing parameter, determine the number of cutting attention nodes in the position interval marked on the cutting surface, and record several cutting attention nodes in the corresponding position interval as a cutting attention node cluster.

[0072] In some embodiments disclosed in the present invention, the expression for determining the influencing parameter of the cutting focus node density is:

[0073] .

[0074] in, To cut the node density affecting parameters, is the density influencing parameter conversion coefficient, is the number of intervals in the cluster of thickness detection points that need attention, The weight adjustment coefficient affected by the number of intervals, is the sum of the preset maximum thickness differences, is the total thickness difference, is the thickness difference influence weight adjustment coefficient, Adjustment constant for density influencing parameter.

[0075] Step S300, according to the material difference of the workpiece to be cut, a number of material-initial cutting control strategy relationships are established, and based on the material of the current workpiece to be cut, the initial cutting control strategy to be applied is selected, and the initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and the cutting surface after the cutting test is tested for cutting quality. Based on the test results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node. For example, when cutting metal sheets, due to changes in material thickness and hardness, problems such as expansion of the heat-affected zone and uneven cutting surface may occur during the cutting process. By marking the cutting focus nodes, these areas can be specially processed during the cutting process, such as adjusting parameters such as cutting speed and power to reduce the occurrence of defects.

[0076] Different materials have different absorption, reflection and penetration capabilities for lasers. Therefore, different cutting strategies need to be adopted when cutting different materials. This step involves establishing a database of the relationship between materials and initial cutting control strategies, which contains the cutting parameter settings of various materials, such as cutting speed, power, frequency, etc. When a new piece to be cut needs to be cut, the corresponding initial cutting control strategy can be selected in the database according to its material type. Then, these strategies are used to drive the laser cutting parts to perform cutting tests and perform quality inspection on the cut surface after cutting. Through the test results, the defect types of the cut surface, such as cracks, incomplete cutting, overcutting, etc., can be determined, and the cutting quality features of each marked cutting focus node, such as the roughness and inclination of the cut surface, can be extracted. The purpose of this step is to evaluate the applicability of the initial cutting control strategy and provide a basis for subsequent strategy corrections. For example, when cutting stainless steel materials, due to the low thermal conductivity of stainless steel, heat accumulation is easy to occur, resulting in cracks on the cut surface. By selecting a suitable initial cutting control strategy, and conducting cutting tests and quality inspections, these problems can be discovered in a timely manner, and guidance can be provided for subsequent strategy adjustments.

[0077] In some embodiments disclosed in the present invention, the method for performing cutting quality inspection on the cutting surface after the cutting test includes:

[0078] Step 301 , dividing the defective blocks on the cutting surface after the cutting test, and determining the defect type of the defective blocks based on the positions and shapes of the defective blocks.

[0079] After the cutting test is completed, the defective blocks on the cut surface need to be divided first. This is usually done by visual inspection or using automated inspection equipment. Based on the location and shape of the defective blocks, the type of defect can be determined, such as cracks, incomplete cutting, over-cutting, etc. This step is to enable targeted defect analysis and strategy adjustment in the future.

[0080] In some embodiments disclosed in the present invention, a method for determining a defect type of a defective block based on the position and shape of the defective block includes:

[0081] Step S3011, using manual or visual analysis technology to divide the defective blocks on the cutting surface, and based on the position and shape of the defective blocks, determine the defect type of the defective blocks.

[0082] Among them, the method of dividing the defective blocks on the cutting surface by using visual analysis technology includes:

[0083] Step S30111, photograph the cutting surface to obtain a cutting surface image, and grayscale the cutting surface image to obtain a cutting surface grayscale map, and use edge detection technology to delineate and mark the defect edges in the cutting surface grayscale map to obtain a number of defect blocks.

[0084] Specifically include:

[0085] Shooting the cutting surface image: First, use a high-resolution camera to shoot the cutting surface to obtain a clear cutting surface image. This is the basis for subsequent image processing and analysis. Grayscale processing: In order to simplify the image information and highlight the defect features, the cutting surface image is grayscaled. Grayscale is the process of converting a color image into a grayscale image with only brightness information, which can reduce the amount of data and increase the processing speed. Edge detection technology: In the grayscale image, edge detection technology is used to identify the edges of the defect. Edge detection algorithms (such as the Canny edge detector) can detect areas with significant brightness changes in the image, which usually correspond to the edges of the defect. By setting appropriate thresholds and parameters, the edges of the marked defects can be accurately delineated to obtain several defect blocks. Defect block delineation: According to the results of edge detection, adjacent edge points are connected to form closed defect blocks. These blocks represent different defect areas on the cutting surface, and their location, shape, size and other information can be further analyzed. Determination of defect type: Based on information such as the position and shape of the defective block, combined with knowledge of cutting process and material properties, the defect type of the defective block (such as cracks, incomplete cutting, overcutting, etc.) can be determined. This is crucial for subsequent strategy adjustment and quality control. Step S30112, determine the relative position of each defective block relative to the edge of the cutting surface, and combine the shape characteristics of the defective edge of the defective block, use the preset defect recognition model to analyze, and output the defect type of each defective block.

[0086] In some embodiments disclosed in the present invention, a method for determining a relative position of each defective block relative to an edge of a cutting surface includes:

[0087] Step S301121, dividing the block between the cutting surface edges into a plurality of position sub-blocks, wherein the edges of the sub-blocks are parallel to the cutting surface edges.

[0088] Step S301122, applying a number of position analysis probes to each defective block, and analyzing the ratio of the number of position analysis probes in different position sub-blocks, and marking the position sub-blocks containing position analysis probes and having a ratio of the number of probes greater than or equal to a preset value as main position sub-blocks, and sorting the main position sub-blocks based on the size relationship of the ratio of the number of probes to obtain a main position sub-block sequence.

[0089] Step S302, photographing the cutting surface to obtain a cutting surface image, and gray-converting the cutting surface image to obtain a cutting surface grayscale map.

[0090] Next, the cutting surface is photographed to obtain a cutting surface image. In order to facilitate subsequent processing, the cutting surface image needs to be grayscaled to obtain a cutting surface grayscale image. Grayscale conversion is the process of converting a color image into a grayscale image, which can simplify the image information and highlight the brightness characteristics of the image.

[0091] Step S303, extract the grayscale features of the defect-free blocks in the cutting surface grayscale image, record them as normal grayscale features, map the cutting focus node on the cutting surface grayscale image, and extract the grayscale features of the blocks near the cutting focus node, record them as cutting grayscale features.

[0092] In the grayscale image of the cutting surface, the grayscale features of the defect-free blocks need to be extracted and recorded as normal grayscale features. At the same time, the previously determined cutting focus nodes are mapped to the cutting surface grayscale image, and the grayscale features of the blocks near these nodes are extracted and recorded as cutting grayscale features. This step is to compare the difference between the grayscale features near the cutting focus nodes and the normal grayscale features.

[0093] Step S304, comparing the cutting grayscale features of each cutting attention node with the normal grayscale features, and based on the comparison results, determining the cutting attention nodes with abnormal grayscale features, and marking defects on the cutting attention nodes with abnormal grayscale features.

[0094] Compare the cutting grayscale features of each cutting focus node with the normal grayscale features. If there is a significant difference between the cutting grayscale features and the normal grayscale features, the grayscale features of the cutting focus node are considered abnormal and marked as defective. This step is to identify possible problem areas during the cutting process.

[0095] Step S305, calculating the node ratio of the number of cutting attention nodes marked as defects to the number of all cutting attention nodes in the nearby cutting attention node cluster, and modifying the initial cutting control strategy based on the defect type and the node ratio.

[0096] Finally, the node ratio of the number of cutting attention nodes marked as defects to the number of all cutting attention nodes in the nearby cutting attention node cluster is calculated. This ratio reflects the frequency and severity of defects during the cutting process. Based on the defect type and node ratio, the initial cutting control strategy can be modified. For example, if the defect type is not cut through and the node ratio is high, it may be necessary to increase the cutting power or slow down the cutting speed to improve the cutting effect.

[0097] In some embodiments disclosed in the present invention, the method for modifying the initial cutting control strategy based on the defect type and the node ratio includes:

[0098] Step S3051, analyzing the initial cutting control strategy to determine the laser cutting power and cutting rate of different trajectory nodes of the laser cutting component on the cutting trajectory.

[0099] Step S3052, matching the trajectory nodes with the cutting focus nodes, and determining the node ratio and defect type corresponding to the trajectory nodes.

[0100] Step S3053, based on the defect type corresponding to the trajectory node, determine the correction direction of the laser cutting power and cutting rate, and substitute the node ratio into the preset node ratio interval~correction coefficient correspondence table to determine the correction coefficient for correcting the laser cutting power or cutting rate.

[0101] Step S400, based on the determined defect type and the cutting quality characteristics of each cutting focus node, the initial cutting control strategy is modified to obtain a reference cutting control strategy, and based on the reference cutting control strategy, the laser cutting component is driven to perform laser cutting on other parts to be cut.

[0102] After determining the defect type of the cutting surface and the cutting quality characteristics of each cutting focus node, the last step is to modify the initial cutting control strategy. This step involves adjusting and optimizing the parameters in the initial cutting control strategy based on the quality inspection results and the information extracted from the cutting quality characteristics to obtain a reference cutting control strategy that is more suitable for the current piece to be cut. Then, this optimized strategy is used to drive the laser cutting component to laser cut other pieces to be cut. Through continuous iteration and optimization, the cutting quality and efficiency can be gradually improved to meet different cutting requirements. For example, when cutting thick metal sheets, the initial cutting strategy may cause defects such as incomplete cutting on the cutting surface due to excessive cutting speed or insufficient power. By correcting the cutting speed and power parameters in the strategy and re-performing cutting tests and quality inspections, a more optimized cutting strategy can be obtained to improve cutting quality and efficiency.

[0103] In some embodiments disclosed in the present invention, there is also disclosed an intelligent laser cutting control system, including:

[0104] The first module is used to perform structural analysis on the workpiece to be cut, construct a three-dimensional structure of the workpiece to be cut, and determine the change in cutting thickness of the laser cutting line based on the change in cutting posture of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut.

[0105] The second module is used to analyze the cutting thickness change, determine a number of cutting focus nodes, and mark the corresponding positions of the cutting focus nodes on the cutting surface.

[0106] The third module is used to establish several material-initial cutting control strategy relationships according to the material differences of the workpiece to be cut, and select the initial cutting control strategy to be applied based on the material of the current workpiece to be cut. The initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and to perform a cutting quality test on the cutting surface after the cutting test. Based on the test results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node.

[0107] The fourth module is used to modify the initial cutting control strategy based on the determined defect type and the cutting quality characteristics of each cutting focus node to obtain a reference cutting control strategy, and based on the reference cutting control strategy, drive the laser cutting component to perform laser cutting on other parts to be cut.

[0108] The present invention discloses an intelligent laser cutting control method and system, which relates to the field of laser cutting control technology, and specifically discloses performing structural analysis on a workpiece to be cut, constructing a three-dimensional model thereof, and determining changes in cutting thickness based on changes in the posture of a laser cutting line relative to the three-dimensional structure; conducting an in-depth analysis of the changes in cutting thickness, determining a number of key cutting focus nodes, and marking the positions of these nodes on the cutting surface; selecting a suitable initial strategy for cutting testing based on the material properties of the workpiece to be cut, and performing quality inspection on the cutting surface to determine the defect type and extract the quality characteristics of the cutting focus nodes; correcting the initial cutting control strategy to obtain a more optimized reference strategy, and applying it to the laser cutting process of other workpieces to be cut. The above technical scheme disclosed by the present invention achieves high precision and high quality of laser cutting through precise structural analysis, thickness change analysis, material property considerations and dynamic strategy adjustment.

[0109] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent laser cutting control method, It is characterized in that include: Perform structural analysis on the workpiece to be cut, construct a three-dimensional structure of the workpiece to be cut, and determine the cutting thickness change of the laser cutting line based on the cutting posture change of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut; Analyze the cutting thickness change, determine several cutting focus nodes, and mark the corresponding positions of the cutting focus nodes on the cutting surface; According to the material difference of the workpiece to be cut, several material-initial cutting control strategy relationships are established, and based on the material of the current workpiece to be cut, the initial cutting control strategy to be applied is selected, and the initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and the cutting quality is detected on the cutting surface after the cutting test. Based on the detection results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node; Based on the determined defect type and the cutting quality characteristics of each cutting focus node, the initial cutting control strategy is modified to obtain a reference cutting control strategy, and based on the reference cutting control strategy, the laser cutting component is driven to perform laser cutting on other parts to be cut; Methods for determining the variation in cutting thickness of laser cutting wire include: Obtain the cutting trajectory of the laser cutting line and construct a vertical thickness analysis plane for the laser cutting line; Construct a number of thickness detection points for the cutting trajectory of the laser cutting line. When the laser cutting line reaches each thickness detection point, the thickness analysis plane is moved, the critical overlap between the thickness analysis plane and the three-dimensional structure of the workpiece to be cut is marked, and the relative distance between the thickness analysis plane and the two critical overlaps is calculated. The relative distance is identified as the cutting thickness, and the cutting thickness is associated with the corresponding thickness detection point and recorded; The overall performance of the cutting thickness corresponding to each thickness detection point on the cutting track is identified as the cutting thickness change of the laser cutting line; Methods for analyzing the cutting thickness variation and determining several cutting focus nodes include: Calculate the thickness difference of the cutting thickness corresponding to adjacent thickness detection points, where the thickness difference is an absolute value. If the thickness difference is greater than or equal to a preset value, the corresponding thickness detection point is identified as a thickness detection point that needs attention. Analyze the continuity characteristics of the thickness detection points that need attention, associate the thickness detection points that need attention whose adjacent intervals are less than or equal to the preset number of intervals, and form a cluster of thickness detection points that need attention; The number of intervals in the cluster of thickness detection points requiring attention is determined, and the sum of the thickness difference amounts of the thickness difference amounts is determined. Based on the number of intervals and the sum of the thickness difference amounts, the cutting attention node density influencing parameters of the cluster of thickness detection points requiring attention are determined; Based on the position interval of the cluster of thickness detection points that need to be concerned, mapped on the three-dimensional structure of the workpiece to be cut, determine the position interval of the cutting surface that needs to be marked; Based on the cutting attention node density influencing parameter, the number of attention nodes of the cutting attention node in the position interval marked on the cutting surface is determined, and several cutting attention nodes in the corresponding position interval are recorded as a cutting attention node cluster.

2. According to claim 1, an intelligent laser cutting control method, It is characterized in that The expression for determining the influencing parameter of the cutting focus node density is: ； in, To cut the node density affecting parameters, is the density influencing parameter conversion coefficient, is the number of intervals in the cluster of thickness detection points that need attention, The weight adjustment coefficient affected by the number of intervals, is the sum of the preset maximum thickness differences, is the total thickness difference, is the thickness difference influence weight adjustment coefficient, Adjustment constant for density influencing parameter.

3. According to the intelligent laser cutting control method of claim 1, It is characterized in that The method for testing the cutting quality of the cutting surface after the cutting test includes: Divide the defective blocks on the cutting surface after the cutting test, and determine the defect type of the defective blocks based on the position and shape of the defective blocks; The cutting surface is photographed to obtain a cutting surface image, and the cutting surface image is gray-scaled to obtain a cutting surface grayscale image; The grayscale features of the defect-free blocks in the grayscale image of the cutting surface are extracted and recorded as normal grayscale features. The cutting focus nodes are mapped on the grayscale image of the cutting surface, and the grayscale features of the blocks near the cutting focus nodes are extracted and recorded as cutting grayscale features. Compare the cutting grayscale features of each cutting attention node with the normal grayscale features, and based on the comparison results, determine the cutting attention nodes with abnormal grayscale features, and mark the cutting attention nodes with abnormal grayscale features as defects; The node ratio of the number of cutting attention nodes marked as defects to the number of all cutting attention nodes in the nearby cutting attention node cluster is calculated, and the initial cutting control strategy is modified based on the defect type and the node ratio.

4. According to claim 3, an intelligent laser cutting control method, It is characterized in that Based on the position and shape of the defective block, the method for determining the defect type of the defective block includes: Using manual or visual analysis technology, the defective blocks on the cutting surface are divided, and based on the location and shape of the defective blocks, the defect type of the defective blocks is determined; Among them, the method of dividing the defective blocks on the cutting surface by using visual analysis technology includes: The cutting surface is photographed to obtain a cutting surface image, and the cutting surface image is grayed to obtain a cutting surface grayscale map, and the defect edges in the cutting surface grayscale map are demarcated and marked using edge detection technology to obtain a number of defect blocks; The relative position of each defective block relative to the edge of the cutting surface is determined, and combined with the shape characteristics of the defect edge of the defective block, a preset defect recognition model is used for analysis to output the defect type of each defective block.

5. According to claim 4, an intelligent laser cutting control method, It is characterized in that The method of determining the relative position of each defective block relative to the edge of the cutting surface includes: Divide the block between the edges of the cutting surface into a number of position sub-blocks, where the edges of the sub-blocks are parallel to the edges of the cutting surface; A number of position analysis probes are applied to each defective block, and the ratio of the number of position analysis probes in different position sub-blocks is analyzed. The position sub-blocks containing position analysis probes and whose ratio of the number of probes is greater than or equal to a preset value are marked as main position sub-blocks. Based on the size relationship of the ratio of the number of probes, the main position sub-blocks are sorted to obtain a main position sub-block sequence.

6. According to claim 3, an intelligent laser cutting control method, It is characterized in that Methods for modifying the initial cutting control strategy based on defect type and node ratio include: Analyze the initial cutting control strategy to determine the laser cutting power and cutting rate of different trajectory nodes on the cutting trajectory of the laser cutting component; Match the trajectory nodes with the cutting focus nodes, and determine the node ratio and defect type corresponding to the trajectory nodes; Based on the defect type corresponding to the trajectory node, the correction direction of the laser cutting power and cutting rate is determined, and the node ratio is substituted into the preset node ratio range~correction coefficient correspondence table to determine the correction coefficient for correcting the laser cutting power or cutting rate.

7. An intelligent laser cutting control system, It is characterized in that The intelligent laser cutting control method for executing any one of claims 1 to 4 comprises: The first module is used to perform structural analysis on the workpiece to be cut, construct a three-dimensional structure of the workpiece to be cut, and determine the cutting thickness change of the laser cutting line based on the cutting posture change of the laser cutting line relative to the three-dimensional structure of the workpiece to be cut; The second module is used to analyze the cutting thickness change, determine a number of cutting focus nodes, and mark the corresponding positions of the cutting focus nodes on the cutting surface; The third module is used to establish several material-initial cutting control strategy relationships according to the material difference of the workpiece to be cut, and select the initial cutting control strategy to be applied based on the material of the current workpiece to be cut. The initial cutting control strategy is used to drive the laser cutting component to perform a cutting test on the workpiece to be cut, and the cutting quality detection is performed on the cutting surface after the cutting test. Based on the detection results, the defect type of the cutting surface is determined, and the cutting quality feature is extracted for each marked cutting focus node; The fourth module is used to modify the initial cutting control strategy based on the determined defect type and the cutting quality characteristics of each cutting focus node to obtain a reference cutting control strategy, and based on the reference cutting control strategy, drive the laser cutting component to perform laser cutting on other parts to be cut.

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