Laser cutting line positioning method and system

Abstract

The invention discloses a laser cutting line positioning method and system, and relates to the technical field of laser processing, and the method comprises the steps: collecting historical cutting data, constructing a cutting path prediction model according to the historical cutting data, and predicting the change trend of a cutting path; according to the cutting path prediction model, current cutting material characteristics and laser parameters are input in the cutting process, and real-time path adjustment data are obtained; an edge detection algorithm is used for monitoring the position of an actual cutting line in real time in the laser cutting process, and deviation data of an actual cutting path and a predicted path are obtained; and according to the deviation data, a cutting parameter set is adjusted through dynamic feedback, and positioning of the cutting line is completed. The cutting effect is controllable, and potential deviation is corrected in time, so that the positioning precision and the working efficiency of the laser cutting line are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of laser processing, and particularly to a method and system for positioning a laser cutting line. Background Art

[0002] In the field of laser cutting technology, with the increasing demand for precision machining in the manufacturing industry, laser cutting technology has witnessed remarkable development. From early simple laser cutting equipment to today's widely used high-power laser systems, laser cutting technology has gradually become an indispensable process in industrial production. Currently, laser cutting technology is not only widely applied in industries such as metal processing, aerospace, and automotive manufacturing, but also gradually extended to the processing of various materials such as wood, plastic, and ceramics. The progress of technology has promoted the intelligent and automated development of laser cutting machines, including automated cutting path planning, real-time monitoring, and feedback adjustment, aiming to improve cutting accuracy and efficiency. However, existing cutting path positioning methods still have some deficiencies. Especially when the material properties and cutting conditions change, how to dynamically adapt and adjust the cutting path has become an urgent technical problem to be solved.

[0003] Although the prior art realizes path control by setting fixed cutting parameters during the laser cutting process, this method often faces the problem that the actual cutting effect does not match the preset path. Due to the changes in the physical properties of materials (such as thickness, hardness, reflectivity, etc.) and laser parameters (such as power, focus position, cutting speed, etc.), it often leads to deviations in the cutting line, which not only affects the cutting quality but also may cause waste of resources. In addition, the application effect of traditional edge detection algorithms is also limited in a high-dynamic environment, and it is unable to effectively monitor the actual position of the cutting line in real time, thus unable to timely adjust the cutting path to adapt to the changes. Therefore, existing cutting path positioning methods lack real-time performance and self-adaptability, and it is difficult to ensure high-precision cutting effects. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, but such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] The present invention aims to provide a method for positioning a laser cutting line to achieve prediction and real-time correction of the cutting path, which can dynamically adapt to different materials and cutting patterns, and improve the accuracy and efficiency of laser cutting.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method and system for positioning a laser cutting line, which can solve the problems mentioned in the background art.

[0008] To solve the above technical problems, the present invention provides the following technical solutions:

[0009] In the first aspect, the present invention provides a method for positioning a laser cutting line, which includes collecting historical cutting data, constructing a cutting path prediction model according to the historical cutting data, and predicting the change trend of the cutting path;

[0010] According to the cutting path prediction model, input the current cutting material characteristics and laser parameters during the cutting process to obtain real-time path adjustment data;

[0011] Use an edge detection algorithm to monitor the position of the actual cutting line in real time during the laser cutting process, and obtain the deviation data between the actual cutting path and the predicted path;

[0012] According to the deviation data, complete the positioning of the cutting line by dynamically feedback-adjusting the cutting parameter set.

[0013] As a preferred scheme of the laser cutting line positioning method of the present invention, wherein: the historical cutting data includes the type, thickness, hardness, and reflectivity of the cutting material each time, as well as the path accuracy, speed, laser power, and focus position of the cutting line.

[0014] As a preferred scheme of the laser cutting line positioning method of the present invention, wherein: the construction of the cutting path prediction model includes,

[0015] Input the collected historical cutting data as the feature vector x into the model, and the calculation formula of the model is as follows:

[0016]

[0017] Wherein, is the predicted cutting path value of the i-th sample, f k is the k-th decision tree, x i is the feature vector of the i-th sample, and K is the number of trees, that is, the number of weak learners of the model;

[0018] Add a regularization term to the objective function, and the calculation formula is as follows:

[0019]

[0020] Wherein, is the loss function between the predicted value and the true value, n is the total number of samples, and Ω(f k ) is the regularization term of the tree, which includes the number of leaf nodes T of the tree and the sum of the squares of the weights w j of each leaf node;

[0021] Combined with specific parameters of the cutting path, a penalty term for path deviation is introduced into the loss, and the calculation formula is as follows:

[0022]

[0023] Among them, α is the weight coefficient for controlling the prediction error, β is the weight coefficient for controlling the path deviation penalty, and Δ i is the deviation between the actual path and the predicted path of the i-th sample.

[0024] As a preferred solution of the laser cutting line positioning method described in the present invention, wherein: constructing the cutting path prediction model further includes,

[0025] Performing correlation analysis on the material properties in the cutting dataset and the actual deviation of the cutting path, and extracting key parameters during the cutting process based on feature engineering;

[0026] Training based on multiple cutting results to predict the change trend of the path under different materials and specific cutting conditions.

[0027] As a preferred solution of the laser cutting line positioning method described in the present invention, wherein: obtaining the real-time path adjustment data includes,

[0028] During the real-time cutting process, input the characteristics of the current cutting material and the laser parameters as input data into the trained cutting path prediction model to generate real-time path adjustment prediction values, and optimize using incremental learning. The calculation formula is as follows:

[0029]

[0030] Among them, L i is the target loss function of incremental learning, m is the number of newly input samples, y new,i is the actual cutting path value of the i-th new sample, is the predicted cutting path value of the i-th new sample.

[0031] As a preferred solution of the laser cutting line positioning method described in the present invention, wherein: monitoring the position of the actual cutting line includes,

[0032] Using an edge detection algorithm to extract the cutting line edge in the image. The calculation formula is as follows:

[0033]

[0034] Among them, E(x, y) is the edge intensity at the coordinate (x, y), is the gradient of the image in the x direction, is the gradient of the image in the y direction;

[0035] By analyzing the edge detection results, the position of the actual cutting line is extracted, and contour detection technology is used to obtain the coordinate data of the cutting path. The calculation formula is as follows:

[0036] P actual = FindContours(E)

[0037] Where P actual is the coordinate of the actual cutting path extracted;

[0038] Compare the extracted actual cutting path with the predicted cutting path, and calculate the deviation data between the two. The calculation formula is as follows:

[0039]

[0040] Where Di is the deviation at the i-th point, x pred,i is the coordinate of the predicted path point, and x actual,i is the coordinate of the actual path point;

[0041] Store the calculated deviation data in the system database.

[0042] As a preferred solution of the laser cutting line positioning method described in the present invention, wherein: the adjusted cutting parameter set includes,

[0043] Input the calculated deviation data into the dynamic feedback adjustment system;

[0044] Use gradient descent to adjust the parameter set, and set the objective function to minimize the deviation of the cutting path. The calculation formula is as follows:

[0045]

[0046] Where J(θ) is the objective function, representing the deviation of the cutting path, N is the total number of cutting path points, and θ is the cutting parameter set to be optimized;

[0047] According to the feedback of the deviation data and the objective function, use the dynamic adjustment algorithm to update the cutting parameter set in real time. The update rule calculation formula is as follows:

[0048] θ new = θ old - η▽J(θ old )

[0049] Where θ new is the updated cutting parameter set, θ old is the current cutting parameter set, η is the learning rate, used to control the update step size, is the gradient of the objective function;

[0050] Apply the adjusted cutting parameters to the laser cutting machine in real time;

[0051] After implementing the parameter adjustment, continue to monitor the actual cutting path, compare the deviation data between the new actual path and the predicted path, and verify the effectiveness of the adjustment;

[0052] Record the parameters, deviation data, and cutting effect after each adjustment into the system database to complete the positioning of the laser cutting line.

[0053] In a second aspect, the present invention provides a laser cutting line positioning system, which includes: a historical data acquisition module, a real-time adjustment data generation module, a real-time monitoring and deviation calculation module, and a dynamic feedback adjustment module;

[0054] The historical data acquisition module is used to collect historical cutting data, construct a cutting path prediction model based on the historical cutting data, and predict the change trend of the cutting path;

[0055] The real-time adjustment data generation module is used to input the current cutting material characteristics and laser parameters during the cutting process according to the cutting path prediction model to obtain real-time path adjustment data;

[0056] The real-time monitoring and deviation calculation module is used to use an edge detection algorithm to monitor the position of the actual cutting line in real time during the laser cutting process and obtain the deviation data between the actual cutting path and the predicted path;

[0057] The dynamic feedback adjustment module is used to complete the positioning of the cutting line by dynamically feedback-adjusting the cutting parameter set according to the deviation data.

[0058] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, the steps of the laser cutting line positioning method are implemented.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by a processor, the steps of the laser cutting line positioning method are implemented.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting historical cutting data and constructing a cutting path prediction model, the prediction of the changing trend of the cutting path is realized. This step makes full use of past cutting experience and improves the degree of intelligence in the cutting process. At the same time, real-time path adjustment data is obtained based on the real-time cutting material characteristics and laser parameters to ensure that the cutting machine can respond in a timely manner to changes in material characteristics, thereby improving the cutting accuracy and response speed. Finally, an edge detection algorithm is used to monitor the position of the actual cutting line in real time, and the cutting parameters are dynamically adjusted according to the deviation data, making the cutting effect controllable and correcting potential deviations in a timely manner, thereby significantly improving the positioning accuracy and working efficiency of the laser cutting line. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0062] Figure 1 It is a method flowchart of a laser cutting line positioning method and system provided by an embodiment of the present invention;

[0063] Figure 2 It is an internal structure diagram of a computer device of a laser cutting line positioning method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To make the above objects, features, and advantages of the present invention more comprehensible, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0066] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0067] Example 1

[0068] Reference Figure 1 - Figure 2 , which is the first embodiment of the present invention. This embodiment provides a method for positioning a laser cutting line, including:

[0069] Before introducing the embodiments of the present application in detail, for the sake of clarity, some related concepts are first explained.

[0070] XGBoost (Extreme Gradient Boosting): XGBoost is an efficient and flexible boosting algorithm widely used in regression and classification problems. It is based on the gradient boosting framework and continuously optimizes the loss function in the way of an additive model. XGBoost has excellent performance and scalability, can handle missing values and categorical features, and is suitable for the construction of large-scale datasets and complex models;

[0071] Edge Detection: Edge detection is an image processing technique aimed at identifying regions with significant brightness changes in an image to extract important features in the image. Common edge detection algorithms include the Canny, Sobel, and Prewitt algorithms. Edge detection plays an important role in image analysis, object recognition, and tracking, and can improve the accuracy of subsequent processing;

[0072] Incremental Learning: Incremental learning is a machine learning strategy that allows the model to be gradually updated when new data is received without retraining the entire model. This method is suitable for dynamically changing environments, enabling the model to continuously adapt to new information and improve the accuracy and effectiveness of predictions.

[0073] The present application can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement the method for positioning a laser cutting line;

[0074] Figure 1 The method flow chart of a method for positioning a laser cutting line and system is shown, including:

[0075] S1: Collect historical cutting data, construct a cutting path prediction model according to the historical cutting data, and predict the change trend of the cutting path;

[0076] Further, the historical cutting data includes the type, thickness, hardness, and reflectivity of the material cut each time, as well as the path accuracy, speed, laser power, and focus position of the cutting line;

[0077] Further, constructing the cutting path prediction model includes,

[0078] The XGBoost regression model is used as the basic model, and the collected historical cutting data is input into the model as the feature vector x. The calculation formula of the model is as follows:

[0079]

[0080] Among them, is the predicted cutting path value of the i-th sample, and f k is the k-th decision tree in XGBoost, and x i is the feature vector of the i-th sample, including information such as material properties and laser parameters. K is the number of trees, that is, the number of weak learners of the model;

[0081] To improve the prediction accuracy, a regularization term is added to the objective function of XGBoost. The calculation formula is as follows:

[0082]

[0083] Among them, is the loss function between the predicted value and the true value, n is the total number of samples, and Ω(f k ) is the regularization term of the tree, which is used to control the model complexity and includes the sum of the squares of the number of leaf nodes T of the tree and the weight w j of each leaf node;

[0084] Combined with the specific parameters of the cutting path, the present invention further optimizes the loss function by introducing a penalty term for path deviation in the loss. The calculation formula is as follows:

[0085]

[0086] Among them, α is the weight coefficient for controlling the prediction error, β is the weight coefficient for controlling the path deviation penalty, and Δ i is the deviation between the actual path and the predicted path of the i-th sample;

[0087] Furthermore, constructing the cutting path prediction model also includes,

[0088] Performing correlation analysis on the material properties in the cutting dataset and the actual deviation of the cutting path, and extracting key parameters during the cutting process based on feature engineering;

[0089] Training based on multiple cutting results to predict the change trend of the path under different materials and specific cutting conditions, so as to provide precise path optimization suggestions for subsequent cutting;

[0090] By continuously iterating and updating the algorithm, improving its prediction ability for different materials and complex paths, and realizing more efficient path change prediction.

[0091] S2: According to the cutting path prediction model, input the current cutting material characteristics and laser parameters during the cutting process to obtain real-time path adjustment data;

[0092] Further, obtaining real-time path adjustment data includes,

[0093] During the real-time cutting process, input the characteristics of the current cutting material and laser parameters as input data into the trained XGBoost regression model to generate real-time path adjustment prediction values, and optimize them using incremental learning. The calculation formula is as follows:

[0094]

[0095] Among them, L i is the target loss function of incremental learning, m is the number of newly input samples, y new,i is the actual cutting path value of the i-th new sample, is the predicted cutting path value of the i-th new sample.

[0096] S3: Use an edge detection algorithm to monitor the position of the actual cutting line in real time during laser cutting, and obtain the deviation data between the actual cutting path and the predicted path;

[0097] Further, during laser cutting, use a high-resolution camera or a laser sensor to perform real-time acquisition of the cutting area, and the acquired images will be used for subsequent edge detection and position calibration;

[0098] Preprocess the acquired images to improve the accuracy of edge detection. The preprocessing steps include grayscale conversion and noise removal.

[0099] Further, the monitoring of the position of the actual cutting line includes,

[0100] Use an edge detection algorithm to extract the edge of the cutting line in the image. The present invention adopts the Canny edge detection algorithm, which can effectively identify the edges in the image and provide the precise position of the cutting line. The calculation formula is as follows:

[0101]

[0102] Among them, E(x, y) is the edge intensity at the coordinate (x, y), is the gradient of the image in the x direction, is the gradient of the image in the y direction;

[0103] By analyzing the edge detection results, extract the position of the actual cutting line, and adopt contour detection technology to obtain the coordinate data of the cutting path. The calculation formula is as follows:

[0104] P actual= FindContours(E)

[0105] where P actual are the coordinates of the actual cutting path extracted;

[0106] Compare the extracted actual cutting path with the predicted cutting path, and calculate the deviation data between the two. The calculation formula is as follows:

[0107]

[0108] where Di is the deviation at the i-th point, x pred,i are the coordinates of the predicted path point, and x actual,i are the coordinates of the actual path point;

[0109] Store the calculated deviation data in the system database to provide a basis for subsequent dynamic feedback adjustment. The real-time recording of the deviation data will help to further optimize the cutting parameters and path adjustment.

[0110] S4: According to the deviation data, adjust the cutting parameter set through the dynamic feedback adjustment system to complete the positioning of the cutting line.

[0111] Furthermore, the cutting parameter set includes laser power, cutting speed, and focus position;

[0112] Furthermore, the adjustment of the cutting parameter set by the adjustment system includes

[0113] Input the calculated deviation data into the dynamic feedback adjustment system as the basis for adjusting the cutting parameter set;

[0114] Use gradient descent to adjust the parameter set, and set the objective function to minimize the deviation of the cutting path. The calculation formula is as follows:

[0115]

[0116] where J(θ) is the objective function, representing the deviation of the cutting path, N is the total number of cutting path points, and θ is the cutting parameter set to be optimized;

[0117] According to the feedback of the deviation data and the objective function, use the dynamic adjustment algorithm to update the cutting parameter set in real time. The update rule calculation formula is as follows:

[0118] θ new = θ old - η▽J(θ old )

[0119] where θ new is the updated cutting parameter set, θ old is the current cutting parameter set, and η is the learning rate, used to control the update step size, is the gradient of the objective function;

[0120] Apply the adjusted cutting parameters to the laser cutting machine in real time to ensure that the cutting equipment can perform cutting according to the new parameters, so as to achieve path calibration and optimization of cutting effect;

[0121] After implementing the parameter adjustment, continue to monitor the actual cutting path, compare the deviation data between the new actual path and the predicted path, and verify the effectiveness of the adjustment;

[0122] Record the parameters, deviation data and cutting effect after each adjustment into the system database to complete the positioning of the laser cutting line.

[0123] Furthermore, this embodiment also provides a laser cutting line positioning system, including: a historical data acquisition module, a real-time adjustment data generation module, a real-time monitoring and deviation calculation module, and a dynamic feedback adjustment module;

[0124] The historical data acquisition module is used to collect historical cutting data, construct a cutting path prediction model according to the historical cutting data, and predict the change trend of the cutting path;

[0125] The real-time adjustment data generation module is used to input the current cutting material characteristics and laser parameters during the cutting process according to the cutting path prediction model to obtain real-time path adjustment data;

[0126] The real-time monitoring and deviation calculation module is used to use an edge detection algorithm to monitor the position of the actual cutting line in real time during the laser cutting process and obtain the deviation data between the actual cutting path and the predicted path;

[0127] The dynamic feedback adjustment module is used to complete the positioning of the cutting line by dynamically feedback adjusting the cutting parameter set according to the deviation data.

[0128] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 2As shown in the figure. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device 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 and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a comprehensive evaluation method suitable for the orderly access of large-scale distributed power sources. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0129] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: collecting historical cutting data, constructing a cutting path prediction model according to the historical cutting data, and predicting the change trend of the cutting path;

[0130] According to the cutting path prediction model, input the current cutting material characteristics and laser parameters during the cutting process to obtain real-time path adjustment data;

[0131] Use an edge detection algorithm to monitor the position of the actual cutting line in real time during laser cutting, and obtain the deviation data between the actual cutting path and the predicted path;

[0132] According to the deviation data, complete the positioning of the cutting line by dynamically feedback-adjusting the cutting parameter set.

[0133] Embodiment 2

[0134] Refer to Figure 1 - Figure 2 , which is the second embodiment of the present invention. This embodiment provides a laser cutting line positioning method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0135] In this embodiment, first, the effectiveness of the laser cutting line positioning method is experimentally verified. Six materials with different characteristics are selected, including stainless steel (SS304), aluminum alloy (6061-T6), polycarbonate (PC), acrylic (PMMA), carbon steel (Q235), and titanium alloy (Ti6Al4V). These materials have different thicknesses, hardnesses, and reflectivities, and can comprehensively test the path accuracy and cutting time comparison during the cutting process. The test preparation stage includes the following steps:

[0136] Select materials with different thicknesses and hardnesses to verify the path accuracy and cutting time during the cutting process. The selected material thicknesses are stainless steel (5mm), aluminum alloy (8mm), polycarbonate (6mm), acrylic (10mm), carbon steel (15mm), and titanium alloy (12mm), and the physical properties of each material, including the reflectivity, are recorded in detail.

[0137] Use a laser cutting machine with different laser power and cutting speed settings. The laser power ranges from 100W to 250W, and the cutting speed is set at 1.8 to 3.5 meters per minute to simulate various cutting scenarios.

[0138] During the cutting process, parameters such as the cutting time, actual path deviation, and predicted path accuracy of each material are recorded. The actual cutting line position is monitored through an edge detection algorithm to obtain the deviation data between the real-time path and the predicted path.

[0139] Based on the collected historical cutting data, a cutting path prediction model is constructed. This model predicts the change of the cutting path according to the feature vector of the historical cutting data and adjusts the path in real time during the cutting process.

[0140] After the experiment, all the data is collected and statistically analyzed. Focus is placed on the cutting accuracy, cutting time, and actual path deviation of each material to verify the superiority of the method of the present invention.

[0141] Through the implementation of the above steps, the experimental data is recorded as shown in the following table:

[0142] Table 1 Results of Laser Cutting Experiment Parameters

[0143]

[0144] Table 2: Comparison of Laser Cutting Performance

[0145] Parameter Name Prior Art The Present Invention Cutting Precision (mm) 0.7 0.4 Cutting Time (s) 180 120 Material Utilization Rate (%) 90 95 Energy Consumption (kWh) 5.5 4.0 Scrap Generation Quantity (kg) 2.0 1.0

[0146] Through the comparison of the above experimental data, it can be clearly seen that the laser cutting line positioning method of the present invention has significant innovation and advantages compared with the prior art. In terms of cutting accuracy, the cutting accuracy of the prior art is 0.7 mm, while the present invention achieves a higher cutting accuracy of only 0.4 mm through real-time monitoring and dynamic feedback mechanisms, which indicates that the present invention can more effectively control the laser cutting path during the actual cutting process and reduce errors; at the same time, in terms of cutting time, the cutting time of the prior art is 180 s, while the optimized cutting time of the present invention is only 120 s, which shows that the present invention performs excellently in improving cutting efficiency through real-time path adjustment.

[0147] In terms of material utilization rate, the material utilization rate of the prior art is 90%, while the material utilization rate of the present invention is increased to 95%. This increase not only reduces material waste but also lowers production costs, making it more economically competitive. At the same time, in terms of energy consumption, the energy consumption of the present invention is 4.0 kWh, significantly lower than 5.5 kWh of the prior art, which indicates that the present invention is more efficient in resource utilization and meets the requirements of current sustainable development. Finally, in terms of the amount of waste generated, the amount of waste generated by the present invention is 1.0 kg, while the amount of waste generated by the prior art is 2.0 kg, further proving the optimization effect of the present invention during the cutting process and reducing the generation of waste.

[0148] In summary, by implementing the method of the present invention, not only the improvement of cutting accuracy and efficiency is achieved, but also its creativity and novelty are demonstrated in resource utilization and environmental protection, fully proving the practical application value of this method in the field of laser cutting technology.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0150] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0151] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one or more of the flows Figure One one or more flows and / or blocks Figure One or a means for implementing the functions specified in one or more of the blocks.

[0152] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one or more of the flows Figure One one or more flows and / or blocks Figure One or a means for implementing the functions specified in one or more of the blocks.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure One one or more flows and / or blocks Figure One or a means for implementing the functions specified in one or more of the blocks.

[0154] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0155] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for positioning a laser cutting line, characterized in that: Including, Collecting historical cutting data, constructing a cutting path prediction model based on the historical cutting data, and predicting the change trend of the cutting path; According to the cutting path prediction model, inputting the current cutting material characteristics and laser parameters during the cutting process to obtain real-time path adjustment data; Using an edge detection algorithm to monitor the position of the actual cutting line in real time during laser cutting, and obtaining the deviation data between the actual cutting path and the predicted path; According to the deviation data, adjusting the cutting parameter set through dynamic feedback to complete the positioning of the cutting line.

2. The laser cutting line positioning method according to claim 1, wherein: The historical cutting data includes the type, thickness, hardness, and reflectivity of the material cut each time, as well as the path accuracy, speed, laser power, and focus position of the cutting line.

3. The laser cutting line positioning method according to claim 2, characterized in that: The constructing of the cutting path prediction model includes, Taking the collected historical cutting data as the feature vector x and inputting it into the model. The calculation formula of the model is as follows: Among them, is the predicted cutting path value of the i-th sample, f k is the k-th decision tree, x i is the feature vector of the i-th sample, and K is the number of trees, that is, the number of weak learners of the model; Adding a regularization term to the objective function. The calculation formula is as follows: Among them, is the loss function between the predicted value and the true value, n is the total number of samples, and Ω(f k ) is the regularization term of the tree, which includes the number of leaf nodes T of the tree and the sum of squares of the weights w j of each leaf node; Combining specific parameters of the cutting path, introducing a penalty term for path deviation in the loss. The calculation formula is as follows: Among them, α is the weight coefficient for controlling the prediction error, β is the weight coefficient for controlling the path deviation penalty, and Δ i is the deviation between the actual path and the predicted path of the i-th sample.

4. The laser cutting line positioning method according to claim 3, characterized in that: The constructing of the cutting path prediction model also includes, Performing correlation analysis on the material characteristics in the cutting dataset and the actual deviation of the cutting path, and extracting key parameters during the cutting process based on feature engineering; Training according to multiple cutting results to predict the change trend of the path under different materials and specific cutting conditions.

5. The laser cutting line positioning method according to claim 4, wherein: The obtaining of the real-time path adjustment data includes, During the real-time cutting process, taking the characteristics of the current cutting material and the laser parameters as input data and inputting them into the trained cutting path prediction model to generate a real-time path adjustment prediction value, and optimizing it using incremental learning. The calculation formula is as follows: Among them, L i is the objective loss function of incremental learning, m is the number of newly input samples, and y new,i is the actual cutting path value of the i-th new sample, and is the predicted cutting path value of the i-th new sample.

6. The laser cutting line positioning method according to claim 5, characterized in that: The monitoring of the position of the actual cutting line includes, Using an edge detection algorithm to extract the cutting line edge in the image. The calculation formula is as follows: where E(x, y) is the edge intensity at coordinates (x, y), is the gradient of the image in the x direction, and is the gradient of the image in the y direction; By analyzing the edge detection results, extracting the position of the actual cutting line, and using contour detection technology to obtain the coordinate data of the cutting path. The calculation formula is as follows: P actual = FindContours(E) Among them, P actual is the actual cutting path coordinates extracted; Comparing the extracted actual cutting path with the predicted cutting path, and calculating the deviation data between the two. The calculation formula is as follows: where Di is the deviation of the i-th point, x pred,i is the predicted path point coordinate, and x actual,i is the actual path point coordinate; Storing the calculated deviation data in the system database.

7. The laser cutting line positioning method according to claim 6, wherein: The adjusting of the cutting parameter set includes, Inputting the calculated deviation data into the dynamic feedback adjustment system; Using gradient descent to adjust the parameter set, and setting the objective function to minimize the deviation of the cutting path. The calculation formula is as follows: Among them, J(θ) is the objective function, representing the deviation of the cutting path, N is the total number of cutting path points, and θ is the cutting parameter set to be optimized; According to the feedback of the deviation data and the objective function, using a dynamic adjustment algorithm to update the cutting parameter set in real time. The update rule calculation formula is as follows: where θ new is the updated set of cutting parameters, θ old is the current set of cutting parameters, η is the learning rate, which is used to control the update step size, is the gradient of the objective function; Applying the adjusted cutting parameters to the laser cutting machine in real time; After implementing the parameter adjustment, continue to monitor the actual cutting path, compare the deviation data between the new actual path and the predicted path, and verify the effectiveness of the adjustment; Recording the parameters, deviation data, and cutting effect after each adjustment into the system database to complete the positioning of the laser cutting line.

8. A laser cutting line positioning system, based on the laser cutting line positioning method according to any one of claims 1 to 7, characterized in that: Including a historical data collection module, a real-time adjustment data generation module, a real-time monitoring and deviation calculation module, and a dynamic feedback adjustment module; The historical data acquisition module is used to collect historical cutting data, construct a cutting path prediction model according to the historical cutting data, and predict the change trend of the cutting path; The real-time adjustment data generation module is used to input the current cutting material characteristics and laser parameters during the cutting process according to the cutting path prediction model to obtain real-time path adjustment data; The real-time monitoring and deviation calculation module is used to use an edge detection algorithm to monitor the position of the actual cutting line in real time during the laser cutting process and obtain the deviation data between the actual cutting path and the predicted path; The dynamic feedback adjustment module is used to complete the positioning of the cutting line by dynamically feedback-adjusting the cutting parameter set according to the deviation data.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the laser cutting line positioning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the laser cutting line positioning method according to any one of claims 1 to 7 are implemented.

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