Laser cutting control method and apparatus for improving cutting quality

Abstract

The application discloses a laser cutting control method and device for improving cutting quality, relates to the related technical field of laser cutting, and comprises the following steps: reading first preset cutting characteristics of a first cutting point in a predetermined cutting track; inputting the characteristics into a heat integration prediction model to obtain first predicted heat; collecting first material characteristics of the first cutting point material; fitting a first thermal expansion curve to obtain a first predicted thermal expansion index under the first predicted heat; forming a first focal point optimization constraint; performing matching iteration in a laser cutting database to obtain a first optimal focal point and form a target optimal focal point sequence; and performing laser cutting control on a target material. The application solves the technical problem that existing laser cutting control cannot automatically and accurately regulate and control laser cutting parameters, which leads to cutting quality fluctuation and further affects the overall performance of products, and achieves the technical effects of accurately controlling cutting parameters and improving cutting quality.

Description

Technical Field

[0001] The present application relates to the field related to laser cutting technology, and specifically to a laser cutting control method and device for improving cutting quality. Background Art

[0002] With the continuous development of science and technology, laser cutting technology has become an indispensable and important process in the field of modern industrial manufacturing. Laser cutting is widely used in many fields such as automobiles, aerospace, electronics, metal processing, etc. with its advantages of high precision, high efficiency and high flexibility. With the intensification of market competition, companies have increasingly stringent requirements on cutting quality. Traditional cutting methods are difficult to meet the needs of higher precision and higher efficiency cutting. Traditional laser cutting control is difficult to effectively reduce the heat-affected zone, improve the flatness of the cutting surface, and reduce the generation of burrs and slag by optimizing the cutting path, adjusting laser power and cutting speed and other parameters, and cannot guarantee the cutting quality and the overall performance of the product.

[0003] Therefore, in the current laser cutting control related technologies, there is a technical problem that it is difficult to automatically and accurately regulate the laser cutting parameters, resulting in fluctuations in cutting quality and thus affecting the overall performance of the product. Summary of the Invention

[0004] This application provides a laser cutting control method and device for improving cutting quality. It adopts cutting feature extraction, thermal expansion curve fitting and other technical means to solve the technical problem of existing laser cutting control that it is difficult to automatically and accurately regulate laser cutting parameters, resulting in fluctuations in cutting quality and thus affecting the overall performance of the product. The application achieves the technical effect of accurately controlling cutting parameters and improving cutting quality.

[0005] The present application provides a laser cutting control method for improving cutting quality, the method comprising: reading a first preset cutting feature of a first cutting point in a predetermined cutting trajectory, the first preset cutting feature comprising a first preset cutting laser feature and a first preset cutting control feature; inputting the first preset cutting laser feature and the first preset cutting control feature into a heat integration prediction model to obtain a first predicted heat of the first cutting point; collecting and obtaining a first material feature of the first cutting point material in a target material corresponding to the first cutting point, the first material feature comprising first material thermal expansion information, first material thickness and first material surface state index; fitting a first thermal expansion curve obtained based on the first material thermal expansion information to obtain a first thermal expansion polynomial, and combining the first thermal expansion polynomial to obtain a first predicted thermal expansion index under the first predicted heat; forming a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness and the first material surface state index; traversing and matching the first focus optimization constraint in a laser cutting database to obtain a first optimal focus, and forming a target optimal focus sequence based on the first optimal focus; and laser cutting control of the target material according to the target optimal focus sequence.

[0006] In a possible implementation, the first preset cutting laser feature and the first preset cutting control feature are input into a heat integration prediction model to obtain a first predicted heat of the first cutting point, and the following processing is also performed: extracting a first historical record from a historical laser cutting control record, the historical laser cutting control record refers to a record of laser cutting of similar materials of the target material in history, the first historical record including a first historical cutting feature record and a first historical material heat; extracting the first historical cutting laser feature and the first historical cutting control feature from the first historical cutting feature record in sequence; performing supervised learning and fusion on a data group based on the first historical cutting laser feature, the first historical cutting control feature and the first historical material heat to obtain the heat integration prediction model.

[0007] In a possible implementation, the first historical cutting laser feature and the first historical cutting control feature in the first historical cutting feature record are sequentially extracted, and the following processing is performed: the first historical cutting feature record is used as an independent variable; the first historical material heat is used as a dependent variable;

[0008] Performing maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, the correlation analysis result includes multiple factor features; clustering the multiple factor features to obtain a clustering result, the clustering result includes a cutting laser feature cluster cluster and a cutting control feature cluster cluster; combining the cutting laser feature cluster cluster to extract the first historical cutting laser feature in the first historical cutting feature record, and combining the cutting control feature cluster cluster to extract the first historical cutting control feature in the first historical cutting feature record.

[0009] In a possible implementation, the multiple factor features are clustered to obtain clustering results, which include cutting laser feature clustering clusters and cutting control feature clustering clusters, and the following processing is also performed: the cutting laser feature clustering clusters include at least laser power and beam quality, and the cutting control feature clustering clusters include at least cutting speed, pulse frequency and auxiliary gas.

[0010] In a possible implementation, the first focus optimization constraint is traversed and matched in the laser cutting database to obtain the first optimal focus, and the following processing is also performed: the first laser cutting mapping relationship in the laser cutting database is randomly extracted, and the first laser cutting mapping relationship refers to the mapping relationship between the first focus constraint, the first focus position, the first incision feature and the first cutting time; when the first constraint consistency of the first focus optimization constraint and the first focus constraint reaches a predetermined consistency threshold, a fitness evaluation instruction is generated; based on the fitness evaluation instruction, the first position fitness of the first focus position is obtained in combination with the first cutting time and the first incision quality obtained by analyzing the first incision feature; when the first position fitness reaches a predetermined fitness threshold, the first focus position is used as the first optimal focus.

[0011] In a possible implementation, based on the fitness evaluation instruction, a first position fitness of the first focus position is obtained in combination with the first cutting time and the first incision quality obtained by analyzing the first incision feature, and the following processing is further performed: a predetermined fitness evaluation function is called based on the fitness evaluation instruction;

[0012] The first position fitness is obtained by analyzing the first incision feature based on the predetermined fitness evaluation function, wherein the expression of the predetermined fitness evaluation function is as follows:

[0013] ;

[0014] characterizing the first position fitness of the first focus position, The first position fitness, Characterization is based on the first of the predetermined incision quality indicators The first incision feature extracted by the incision quality index Characteristic parameters, wherein the predetermined incision quality index includes incision quality indicators, and , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0015] In a possible implementation, the first incision feature is analyzed based on the predetermined fitness evaluation function to obtain the first position fitness, and the following processing is also performed: the predetermined incision quality indicators include at least incision width, incision edge smoothness, incision straightness, incision verticality, incision taper and material deflection.

[0016] In a possible implementation, after obtaining the first optimal focus, the following processing is further performed: obtaining a first laser beam diameter at the first optimal focus through dynamic monitoring by a laser beam analyzer; and verifying the first optimal focus according to the first laser beam diameter.

[0017] The present application also provides a laser cutting control device for improving cutting quality, comprising:

[0018] A first preset cutting feature reading module, the first preset cutting feature reading module is used to read a first preset cutting feature of a first cutting point in a predetermined cutting trajectory, the first preset cutting feature including a first preset cutting laser feature and a first preset cutting control feature;

[0019] a first predicted heat acquisition module, the first predicted heat acquisition module being configured to input the first preset cutting laser feature and the first preset cutting control feature into a heat integration prediction model to obtain a first predicted heat of the first cutting point;

[0020] A first material feature acquisition module, the first material feature acquisition module is used to acquire first material features of the first cutting point material in the target material corresponding to the first cutting point, the first material features including first material thermal expansion information, first material thickness, and first material surface state index;

[0021] a first predicted thermal expansion index determination module, configured to fit a first thermal expansion curve obtained based on the thermal expansion information of the first material to obtain a first thermal expansion polynomial, and to obtain a first predicted thermal expansion index at the first predicted heat level in combination with the first thermal expansion polynomial;

[0022] a first focus optimization constraint forming module, configured to form a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness, and the first material surface condition index;

[0023] A target optimal focus sequence composition module is used to traverse and match the first focus optimization constraint in the laser cutting database to obtain a first optimal focus, and to compose a target optimal focus sequence based on the first optimal focus, and to perform laser cutting control on the target material according to the target optimal focus sequence.

[0024] The laser cutting control method and device proposed in this application for improving cutting quality are intended to read the first preset cutting feature of the first cutting point in the predetermined cutting trajectory; input the feature into the heat integration prediction model to obtain the first predicted heat; collect the first material feature of the material at the first cutting point; fit the first thermal expansion curve and obtain the first predicted thermal expansion index under the first predicted heat; form a first focus optimization constraint; traverse and match in the laser cutting database to obtain the first optimal focus, and form a target optimal focus sequence; and control the laser cutting of the target material. This solves the technical problem of the existing laser cutting control that it is difficult to automatically and accurately adjust the laser cutting parameters, resulting in fluctuations in cutting quality and thus affecting the overall performance of the product, and achieves the technical effect of accurately controlling cutting parameters and improving cutting quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0026] Figure 1 A schematic flow chart of a laser cutting control method for improving cutting quality provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of a laser cutting control device for improving cutting quality provided in an embodiment of the present application.

[0028] Explanation of the accompanying drawings: first preset cutting feature reading module 10, first predicted heat acquisition module 20, first material feature acquisition module 30, first predicted thermal expansion index determination module 40, first focus optimization constraint formation module 50, target optimal focus sequence composition module 60. DETAILED DESCRIPTION

[0029] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0030] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0031] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0032] The present application provides a laser cutting control method for improving cutting quality, such as Figure 1 As shown, the method includes:

[0033] Step S100, read the first preset cutting feature of the first cutting point in the predetermined cutting trajectory, wherein the first preset cutting feature includes a first preset cutting laser feature and a first preset cutting control feature. Specifically, the first preset cutting feature is a preset cutting feature of a random cutting point in the predetermined cutting trajectory, which mainly refers to the parameter setting related to the laser beam itself, including the power, wavelength, beam shape and other aspects of the laser beam, which directly determines the energy distribution and cutting ability of the laser beam during the cutting process. For example, the size of the laser power directly affects the cutting speed and cutting depth, while the beam shape determines the flatness and accuracy of the cutting surface; the first preset cutting control feature refers to the parameter setting related to the cutting process control, including control parameters such as cutting speed, cutting depth, and cutting path, which determine the movement trajectory and cutting method of the laser beam during the cutting process. For example, the cutting speed will affect the roughness of the cutting surface and the size of the heat-affected zone, while the cutting depth determines the degree of material cutting.

[0034] Step S200, input the first preset cutting laser feature and the first preset cutting control feature into the heat integration prediction model to obtain the first predicted heat of the first cutting point. The heat integration prediction model is a trained and optimized mathematical model that can comprehensively consider various factors in the laser cutting process, including laser features such as laser power, wavelength, and beam shape, as well as control features such as cutting speed, depth, and path, to predict the heat distribution and changes at the cutting point. Specifically, the first preset cutting laser feature and the first preset cutting control feature are extracted and sorted to form the input data required by the model, which is input into the heat integration prediction model. The heat integration prediction model processes and analyzes the cutting control features according to the internal algorithm and parameter settings, obtains the relationship between the control features, and the impact on the heat distribution during the cutting process, to predict the heat situation at the first cutting point. Finally, the heat integration prediction model will output the first predicted heat of the first cutting point, which represents the amount of heat that may be generated during the cutting process at this cutting point based on the preset laser features and control features.

[0035] In one possible implementation, step S200 further includes step S210, extracting a first historical record from a historical laser cutting control record, wherein the historical laser cutting control record refers to a record of laser cutting of a material similar to the target material in the past, and the first historical record includes a first historical cutting feature record and a first historical material temperature. The historical laser cutting control record refers to all operation data and result data recorded by the control system during past laser cutting of materials similar to the target material, and may include cutting parameter settings, laser beam motion trajectory, real-time temperature monitoring during the cutting process, and final cutting effect evaluation. The first historical record is the earliest or most representative record selected from these historical records. The first historical cutting feature record refers to the laser cutting feature parameters used in the cutting operation at that time, such as laser power, scanning speed, cutting depth, etc. The first historical material temperature refers to the actual temperature reached by the material during that historical cutting process, which can be monitored in real time by a temperature sensor and recorded in the control system. The process further includes step S220, sequentially extracting the first historical cutting laser feature and the first historical cutting control feature from the first historical cutting feature record. Historical data related to laser beam characteristics and cutting control are extracted from the first historical records. Specifically, the first historical cutting laser characteristics mainly refer to the specific parameters and performance of the laser beam in the historical cutting operation, which may include the laser power, wavelength, beam mode (such as continuous wave or pulse wave), beam diameter, etc.; the first historical cutting control characteristics refer to the relevant parameters for controlling the movement and operation of the laser cutting equipment, including cutting speed, scanning path, cutting depth, focal length adjustment, etc. The control characteristics determine the movement trajectory and cutting method of the laser beam on the material. The process also includes step S230, in which supervised learning and fusion are performed on the data group based on the first historical cutting laser characteristics, the first historical cutting control characteristics and the first historical material heat to obtain the heat integration prediction model. The first historical cutting laser characteristics and the first historical cutting control characteristics are used as input, and the first historical material heat is used as output to train a prediction model. Specifically, through supervised learning, the model will learn the correlation and regularity between the laser characteristics and control characteristics and the material heat, so that the model can more accurately predict the heat of the material under specific laser characteristics and control conditions. Multiple prediction results are integrated to obtain the final heat integrated prediction model, which can reduce deviations and errors and predict the heat of the material more accurately and reliably.

[0036] In one possible implementation, step S220 further includes step S221, using the first historical cutting feature record as an independent variable. It also includes step S222, using the first historical material popularity as a dependent variable. It also includes step S223, performing a maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, wherein the correlation analysis result includes multiple factor features. The first historical cutting feature record is used as the independent variable and the first historical material heat is used as the dependent variable. A maximum information coefficient analysis is performed to obtain a correlation analysis result, which is used to explore the degree of association between the independent variable (i.e., the first historical cutting feature record) and the dependent variable (i.e., the first historical material heat). Specifically, the maximum information coefficient (MIC) is a method for measuring the association between two variables. It can capture various types of relationships between variables, including linear, nonlinear, monotonic, and non-monotonic relationships. By calculating the MIC value, the strength of the correlation between the independent variable and the dependent variable can be obtained. By performing the MIC analysis on the independent variable and the dependent variable, a correlation analysis result can be obtained, including multiple factor features, namely, those cutting features that are significantly correlated with material heat. This not only shows the cutting parameters that affect material heat, but also reveals the mode and degree of influence between them. The method also includes step S224, clustering the multiple factor features to obtain a clustering result, wherein the clustering result includes a cutting laser feature cluster and a cutting control feature cluster. A clustering algorithm is used to group multiple cutting features (i.e., factor features) that are significantly correlated with material heat, such that features within the same group have a high degree of similarity under a certain metric, while features between different groups have a low degree of similarity. Specifically, the clustering results include cutting laser feature clusters and cutting control feature clusters. The cutting laser feature clusters are clusters of factor features related to laser beam characteristics, while the cutting control feature clusters are clusters of factor features related to cutting equipment control. The method further includes step S225 of extracting the first historical cutting laser features from the first historical cutting feature record in combination with the cutting laser feature clusters, and extracting the first historical cutting control features from the first historical cutting feature record in combination with the cutting control feature clusters. Historical data falling within the laser feature category, i.e., the first historical cutting laser features, are screened from the first historical cutting feature record; historical data falling within the control feature category, i.e., the first historical cutting control features, are screened from the first historical cutting feature record.

[0037] In one possible implementation, step S224 further includes that the cutting laser feature cluster includes at least laser power and beam quality, and the cutting control feature cluster includes at least cutting speed, pulse frequency and auxiliary gas. Laser power is used to describe the strength of the laser beam energy output, which directly affects the energy input and cutting ability of laser cutting. The size of the laser power determines the speed of the cutting speed and the depth of the cutting depth; the beam quality refers to the focusing performance of the laser beam and the uniformity of the beam distribution. The quality of the beam directly affects the roughness of the cutting edge and the quality of the cutting surface; the cutting speed refers to the speed at which the laser beam moves on the surface of the material, which determines the speed of the cutting process; the pulse frequency refers to the rate at which the laser beam emits pulses, which affects the frequency and mode of interaction between the laser and the material. By adjusting the pulse frequency, the heat-affected zone and cutting accuracy during the cutting process can be controlled; the auxiliary gas refers to the auxiliary medium used to blow away the molten material and cool the cutting area during the cutting process. Different types of auxiliary gases have different effects on the cutting effect.

[0038] Step S300 , collecting and obtaining first material characteristics of the first cutting point material in the target material corresponding to the first cutting point, wherein the first material characteristics include first material thermal expansion information, first material thickness, and first material surface state index. Before the actual cutting operation, the specific properties and state of the target material at the first cutting point are measured and analyzed in detail to obtain relevant material characteristic parameters, namely the first material characteristics, including the first material thermal expansion information, the first material thickness, and the first material surface state index. Specifically, the first material thermal expansion information refers to the possible expansion or contraction of the target material at the first cutting point under different temperature conditions. Thermal expansion is a physical phenomenon in which a material changes volume after being heated. For laser cutting, understanding the thermal expansion characteristics of the material helps predict deformation and dimensional changes during the cutting process, thereby adjusting cutting parameters and reducing cutting errors caused by thermal expansion. The first material thickness refers to the specific thickness of the target material at the first cutting point. The material thickness directly affects the penetration ability of the laser beam and the cutting effect. By measuring the material thickness, parameters such as laser power and cutting speed can be more accurately set. The first material surface state index is a quantitative description of the surface state of the target material at the first cutting point. The surface state includes factors such as surface roughness, oil contamination, and oxide layer, all of which may affect the interaction between the laser beam and the material, thereby affecting the cutting quality. In summary, the first material characteristics, including the first material thermal expansion information, the first material thickness, and the first material surface state index, collectively reflect the physical and chemical properties of the target material at the first cutting point.

[0039] Step S400 is to fit a first thermal expansion curve obtained based on the thermal expansion information of the first material to obtain a first thermal expansion polynomial, and then combine the first thermal expansion polynomial to obtain a first predicted thermal expansion index under the first predicted heat. The thermal expansion information of the first material is usually obtained through experimental measurement or a material database, and describes the thermal expansion behavior of the material at different temperatures. Specifically, based on the thermal expansion information of the first material, a first thermal expansion curve can be drawn, which intuitively shows the relationship between the thermal expansion of the material and the temperature. The thermal expansion curve is then fitted to obtain a first thermal expansion polynomial, wherein polynomial fitting is a mathematical method that finds a set of coefficients so that the polynomial function can be as close to or match the original data points as possible. After obtaining the first thermal expansion polynomial, it can be combined with the first predicted heat obtained previously through the heat integration prediction model for calculation, that is, the first predicted heat is substituted into the first thermal expansion polynomial to solve the first predicted thermal expansion index of the material under the predicted heat. The first predicted thermal expansion index reflects the degree of thermal expansion that may occur in the material due to the action of heat during the predicted cutting process. Through this process, we can combine the thermal expansion characteristics of the material with the heat prediction during the cutting process, so as to more comprehensively evaluate the impact of the cutting process on the material, which will help to subsequently optimize the cutting parameters, reduce the heat-affected zone, and thus improve the cutting quality.

[0040] Step S500: forming a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness, and the first material surface state index. During the laser cutting process, the focus position of the laser beam is set and adjusted according to the first predicted thermal expansion index, the first material thickness, and the first material surface state index to achieve the optimal cutting effect. Specifically, since thermal expansion can cause changes in material size and materials of different thicknesses have different absorption and reflection characteristics of the laser beam, it is necessary to adjust the focus depth and focus size of the laser beam according to the thickness of the material and adjust the focus position of the laser beam according to the thermal expansion index to ensure that the laser beam can accurately act on the predetermined cutting path, to ensure that the laser energy can fully act on the material and achieve efficient cutting. At the same time, the roughness and oiliness of the material surface affect the interaction between the laser beam and the material surface. When forming the focus constraint, the surface state index also needs to be considered to avoid a decrease in cutting quality due to poor surface condition.

[0041] Step S600, traverse and match the first focus optimization constraint in the laser cutting database to obtain the first optimal focus, and form a target optimal focus sequence based on the first optimal focus. Traverse and match the first focus optimization constraint in the laser cutting database to obtain the first optimal focus, and form a target optimal focus sequence based on the first optimal focus, which is used to determine the optimal laser beam focus point position for each cutting point in the laser cutting process. Specifically, the first focus optimization constraint is a condition set according to the characteristics of the target material (such as the first predicted thermal expansion index, the first material thickness and the first material surface state index) and the expected cutting effect, reflecting the requirements that the laser beam focus point should meet at different cutting points in order to obtain the best cutting effect; the laser cutting database is a data set that stores a large number of laser cutting parameters and results. The laser cutting data comes from previous experiments, simulations or actual cutting operations, and contains focus positions under different materials and different cutting conditions. , laser power, cutting speed and other parameters and their corresponding cutting effects; compare and screen the constraints with the data in the database to find the data records that best match the constraints, that is, find the laser beam focal point position that has achieved good cutting effects under similar material properties and cutting conditions. The focus position that is finally determined to best match the first focus optimization constraint is the first optimal focus. The first optimal focus is the laser beam focal point that is expected to achieve the best cutting effect under the current constraints. Finally, based on the first optimal focus, a target optimal focus sequence can be formed, which includes the optimal focus position of each cutting point on the entire cutting path, which is used to guide the laser cutting equipment to adjust the focal point of the laser beam during the actual cutting process to achieve the globally optimal cutting effect.

[0042] In one possible implementation, step S600 further includes step S610, randomly extracting a first laser cutting mapping relationship from the laser cutting database, wherein the first laser cutting mapping relationship refers to a mapping relationship between a first focus constraint, a first focus position, a first incision feature, and a first cutting duration. A cutting record is randomly selected from the laser cutting database, comprising a first laser mapping relationship between a plurality of key parameters and features, wherein the first laser cutting mapping relationship refers to a mapping relationship between a first focus constraint, a first focus position, a first incision feature, and a first cutting duration. Specifically, the first focus constraint refers to a focusing constraint condition of the laser beam during the cutting process, which determines the focusing method and the size of the focus point of the laser beam; the first focus position refers to the specific location of the laser beam focus point, i.e., the location on the material where the laser energy is most concentrated; the first incision feature describes the morphology and properties of the material incision after cutting, such as the width, roughness, and size of the heat-affected zone of the incision; and the first cutting duration refers to the time required to complete the entire cutting process. The step further includes step S620, generating a fitness evaluation instruction when the consistency of the first focus optimization constraint and the first constraint of the first focus constraint reaches a predetermined consistency threshold. The first constraint consistency refers to the degree of match or similarity between the first focus optimization constraint and the first focus constraint. The predetermined consistency threshold is a preset similarity or matching threshold used to determine whether the constraint consistency reaches a satisfactory level. When the first constraint consistency between the first focus optimization constraint and the first focus constraint reaches the predetermined consistency threshold, it means that the focus configuration found through the optimization process matches the constraint conditions in the actual operation, and the degree of match reaches the preset satisfactory level, and a fitness evaluation instruction is generated. It also includes step S630, based on the fitness evaluation instruction, combined with the first cutting time and the first incision quality obtained by analyzing the first incision feature, to obtain the first position fitness of the first focus position. By analyzing the first incision feature, a quantitative evaluation result of the incision quality can be obtained. The first cutting time is used as part of the efficiency evaluation and combined with the incision quality. Based on the incision quality and the cutting time, the first position fitness of the first focus position is calculated, reflecting the overall performance of the focus position in the cutting process. It also includes step S640, when the first position fitness reaches the predetermined fitness threshold, the first focus position is used as the first optimal focus. The predetermined fitness threshold is a preset fitness level used to determine whether the focus position meets the optimization requirements. When the first position fitness reaches the predetermined fitness threshold, it means that the focus position is appropriate, exhibits good performance during the cutting process, and can meet or exceed the expected cutting quality and efficiency requirements. The focus position is determined as the first optimal focus.

[0043] In one possible implementation, step S630 further includes step S631, calling a predetermined fitness evaluation function based on the fitness evaluation instruction; and further includes step S632, analyzing the first incision feature based on the predetermined fitness evaluation function to obtain the first position fitness, wherein the expression of the predetermined fitness evaluation function is as follows:

[0044] ;

[0045] characterizing the first position fitness of the first focus position, The first position fitness, Characterization is based on the first of the predetermined incision quality indicators The first incision feature extracted by the incision quality index Characteristic parameters, wherein the predetermined incision quality index includes incision quality indicators, and , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0046] In one possible implementation, step S630 further includes S634, wherein the predetermined incision quality index includes at least incision width, incision edge smoothness, incision straightness, incision verticality, incision taper, and material deflection. Incision width refers to the actual width of the incision formed by the laser beam on the material, which depends on the size and power of the laser spot and the properties of the material; incision edge smoothness refers to the smoothness and flatness of the incision edge after the cutting is completed; incision straightness is to evaluate the degree of deviation between the incision and the theoretical straight line during the cutting process, reflecting the ability and accuracy of the laser cutting system in maintaining straight cutting; incision verticality is used to measure the degree of deviation of the incision perpendicular to the cutting direction; incision taper describes the difference in width between the top and bottom of the incision, and the size of the taper reflects the uniformity of energy distribution and cutting speed during the cutting process; material deflection reflects the influence of the cutting force on the material, as well as the deformation of the material after cutting.

[0047] In a possible implementation, step S600 further includes step S660, in which the first laser beam diameter at the first optimal focus is obtained by dynamic monitoring using a laser beam analyzer. The diameter of the laser beam at a specific focal position (i.e., the first optimal focus) is measured and monitored in real time and dynamically using a laser beam analyzer. Specifically, the laser beam diameter is used to evaluate the laser beam quality and focusing performance, reflecting the energy distribution and focusing effect of the laser beam at a specific position. Step S670 is also included, in which the first optimal focus is verified based on the first laser beam diameter. Verify whether the first optimal focus meets the expected laser beam diameter requirements to ensure cutting quality and accuracy. Specifically, when actually performing laser cutting control, it will be affected by external factors such as the environment and machine tool vibration. Therefore, the laser beam diameter is dynamically monitored by a laser beam analyzer. The smaller the diameter, the better the corresponding focal position. Once the diameter exceeds the predetermined diameter threshold, an early warning should be issued and the focal position should be adjusted in time to ensure cutting accuracy.

[0048] Step S700: Controlling the laser cutting of the target material based on the target optimal focus sequence. During the laser cutting process, the laser cutting equipment is precisely controlled based on the predetermined target optimal focus sequence to ensure that the laser beam cuts the target material at the optimal focus point. Specifically, the control system adjusts the focus point position of the laser beam in real time based on the information in the target optimal focus sequence. By precisely controlling the focus depth and position of the laser beam, it ensures that the laser energy accurately acts on the predetermined cutting path of the target material, thereby achieving high-quality cutting.

[0049] In the above, refer to Figure 1 The laser cutting control method for improving cutting quality according to an embodiment of the present invention is described in detail. Figure 2 A laser cutting control device for improving cutting quality according to an embodiment of the present invention is described.

[0050] The laser cutting control device for improving cutting quality according to an embodiment of the present invention is designed to address the technical problem of existing laser cutting control systems, which suffers from the difficulty in automatically and accurately regulating laser cutting parameters, leading to fluctuations in cutting quality and thus affecting the overall performance of the product. The device achieves the technical effect of precisely controlling cutting parameters and improving cutting quality. The laser cutting control device for improving cutting quality includes: a first preset cutting feature reading module 10, a first predicted heat acquisition module 20, a first material feature acquisition module 30, a first predicted thermal expansion index determination module 40, a first focus optimization constraint formation module 50, and a target optimal focus sequence composition module 60.

[0051] A first preset cutting feature reading module 10, the first preset cutting feature reading module 10 is used to read a first preset cutting feature of a first cutting point in a predetermined cutting trajectory, the first preset cutting feature including a first preset cutting laser feature and a first preset cutting control feature;

[0052] a first predicted heat acquisition module 20, configured to input the first preset cutting laser feature and the first preset cutting control feature into a heat integration prediction model to obtain a first predicted heat of the first cutting point;

[0053] A first material characteristic acquisition module 30 is configured to acquire first material characteristics of the first cutting point material in the target material corresponding to the first cutting point, wherein the first material characteristics include first material thermal expansion information, first material thickness, and first material surface condition index;

[0054] a first predicted thermal expansion index determination module 40, configured to fit a first thermal expansion curve obtained based on the thermal expansion information of the first material to obtain a first thermal expansion polynomial, and to obtain a first predicted thermal expansion index at the first predicted heat level in combination with the first thermal expansion polynomial;

[0055] A first focus optimization constraint forming module 50, the first focus optimization constraint forming module 50 is used to form a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness and the first material surface state index;

[0056] The target optimal focus sequence composition module 60 is used to traverse and match the first focus optimization constraint in the laser cutting database to obtain the first optimal focus, and compose a target optimal focus sequence based on the first optimal focus, and perform laser cutting control on the target material according to the target optimal focus sequence.

[0057] The specific configuration of the first predicted heat acquisition module 20 will be described in detail below. The first predicted heat acquisition module 20 may further include: extracting a first historical record from a historical laser cutting control record, wherein the historical laser cutting control record refers to a record of laser cutting of a material similar to the target material in history, and the first historical record includes a first historical cutting feature record and a first historical material heat; sequentially extracting the first historical cutting laser feature and the first historical cutting control feature from the first historical cutting feature record; and performing supervised learning and fusion on a data set composed of the first historical cutting laser feature, the first historical cutting control feature, and the first historical material heat to obtain the heat integrated prediction model.

[0058] The specific configuration of the first predicted heat acquisition module 20 will be described in detail below. The first predicted heat acquisition module 20 further includes: using the first historical cutting feature record as an independent variable; using the first historical material heat as a dependent variable; performing a maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, wherein the correlation analysis result includes multiple factor features; clustering the multiple factor features to obtain a clustering result, wherein the clustering result includes a cutting laser feature cluster cluster and a cutting control feature cluster cluster; extracting the first historical cutting laser feature from the first historical cutting feature record in combination with the cutting laser feature cluster cluster, and extracting the first historical cutting control feature from the first historical cutting feature record in combination with the cutting control feature cluster cluster.

[0059] The specific configuration of the first predicted heat acquisition module 20 will be described in detail below. The first predicted heat acquisition module 20 may further include: the cutting laser feature cluster includes at least laser power and beam quality, and the cutting control feature cluster includes at least cutting speed, pulse frequency and auxiliary gas.

[0060] The specific configuration of the target optimal focus sequence composition module 60 will be described in detail below. The target optimal focus sequence composition module 60 may further include: randomly extracting a first laser cutting mapping relationship from the laser cutting database, wherein the first laser cutting mapping relationship refers to a mapping relationship between a first focus constraint, a first focus position, a first incision feature, and a first cutting time; when the first constraint consistency of the first focus optimization constraint and the first focus constraint reaches a predetermined consistency threshold, generating a fitness evaluation instruction; based on the fitness evaluation instruction, combining the first cutting time and the first incision quality obtained by analyzing the first incision feature, obtaining a first position fitness of the first focus position; when the first position fitness reaches a predetermined fitness threshold, using the first focus position as the first optimal focus.

[0061] The specific configuration of the target optimal focus sequence composition module 60 will be described in detail below. The target optimal focus sequence composition module 60 may further include: calling a predetermined fitness evaluation function based on the fitness evaluation instruction; analyzing the first incision feature based on the predetermined fitness evaluation function to obtain the first position fitness, wherein the expression of the predetermined fitness evaluation function is as follows:

[0062] ;

[0063] characterizing the first position fitness of the first focus position, The first position fitness, Characterization is based on the first of the predetermined incision quality indicators The first incision feature extracted by the incision quality index Characteristic parameters, wherein the predetermined incision quality index includes incision quality indicators, and , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0064] The following will further describe the specific configuration of the target optimal focus sequence component module 60. The target optimal focus sequence component module 60 further includes: the predetermined incision quality indicators include at least incision width, incision edge smoothness, incision straightness, incision verticality, incision taper and material deflection.

[0065] The following will further describe the specific configuration of the target optimal focus sequence composition module 60. The target optimal focus sequence composition module 60 further includes: dynamically monitoring the first laser beam diameter at the first optimal focus through a laser beam analyzer; and verifying the first optimal focus based on the first laser beam diameter.

[0066] The laser cutting control device for improving cutting quality provided by an embodiment of the present invention can execute the laser cutting control method for improving cutting quality provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0068] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A laser cutting control method for improving cutting quality, characterized in that: include: Reading a first predetermined cutting feature of a first cutting point in a predetermined cutting trajectory, wherein the first predetermined cutting feature includes a first predetermined cutting laser feature and a first predetermined cutting control feature, wherein the first predetermined cutting laser feature refers to a parameter setting related to the laser beam itself, and the first predetermined cutting control feature refers to a parameter setting related to cutting process control; Inputting the first preset cutting laser feature and the first preset cutting control feature into a heat integration prediction model to obtain a first predicted heat of the first cutting point includes: Extracting a first historical record from a historical laser cutting control record, wherein the historical laser cutting control record refers to a record of laser cutting of a material similar to the target material in history, wherein the first historical record includes a first historical cutting feature record and a first historical material heat; Sequentially extracting the first historical cutting laser feature and the first historical cutting control feature from the first historical cutting feature record includes: Using the first historical cutting feature record as an independent variable; The first historical material heat is used as the dependent variable; Performing a maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, wherein the correlation analysis result includes multiple factor characteristics, namely, those cutting characteristics that are significantly correlated with the heat of the material; Clustering the multiple factor features to obtain clustering results, wherein the clustering results include cutting laser feature clusters and cutting control feature clusters; extracting the first historical cutting laser feature in the first historical cutting feature record in combination with the cutting laser feature cluster, and extracting the first historical cutting control feature in the first historical cutting feature record in combination with the cutting control feature cluster; Performing supervised learning and fusing a data set based on the first historical cutting laser feature, the first historical cutting control feature, and the first historical material heat to obtain the heat integrated prediction model; Acquiring a first material characteristic of the first cutting point material in the target material corresponding to the first cutting point, the first material characteristic including first material thermal expansion information, first material thickness, and first material surface condition index, wherein the first material surface condition index is a quantitative description of the surface condition of the target material at the first cutting point, the surface condition including surface roughness, oil contamination level, and oxide layer; Fitting a first thermal expansion curve obtained based on the thermal expansion information of the first material to obtain a first thermal expansion polynomial, and combining the first thermal expansion polynomial to obtain a first predicted thermal expansion index at the first predicted heat, the first predicted thermal expansion index being a prediction of the degree of thermal expansion of the material under the action of heat during the cutting process; forming a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness, and the first material surface condition index; Traversing and matching the first focus optimization constraint in the laser cutting database to obtain a first optimal focus, and forming a target optimal focus sequence based on the first optimal focus; Laser cutting of the target material is controlled according to the target optimal focus sequence.

2. The laser cutting control method for improving cutting quality according to claim 1, wherein: The cutting laser feature clusters include at least laser power and beam quality, and the cutting control feature clusters include at least cutting speed, pulse frequency and auxiliary gas.

3. The laser cutting control method for improving cutting quality according to claim 1, wherein: Traversing and matching the first focus optimization constraint in the laser cutting database to obtain a first optimal focus includes: Randomly extracting a first laser cutting mapping relationship from the laser cutting database, where the first laser cutting mapping relationship refers to a mapping relationship among a first focus constraint, a first focus position, a first incision feature, and a first cutting duration; When the first focus optimization constraint and the first constraint consistency of the first focus constraint reach a predetermined consistency threshold, generating a fitness evaluation instruction; Obtaining a first position fitness of the first focus position based on the fitness evaluation instruction and in combination with the first cutting time and a first incision quality obtained by analyzing the first incision feature; When the first position fitness reaches a predetermined fitness threshold, the first focus position is used as the first optimal focus.

4. The laser cutting control method for improving cutting quality according to claim 3, wherein: Obtaining a first position fitness of the first focus position based on the fitness evaluation instruction and in combination with the first cutting time and a first incision quality obtained by analyzing the first incision feature includes: Calling a predetermined fitness evaluation function based on the fitness evaluation instruction; The first position fitness is obtained by analyzing the first incision feature based on the predetermined fitness evaluation function, wherein the expression of the predetermined fitness evaluation function is as follows: ； Characterizing the first focal position The first position fitness, Characterization is based on the first of the predetermined incision quality indicators The first incision feature extracted by the incision quality index Characteristic parameters, wherein the predetermined incision quality index includes incision quality indicators, and , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

5. The laser cutting control method for improving cutting quality according to claim 4, wherein: The predetermined incision quality indexes include at least incision width, incision edge smoothness, incision straightness, incision verticality, incision taper and material deflection.

6. The laser cutting control method for improving cutting quality according to claim 1, wherein: The method also includes: Dynamically monitoring the first laser beam diameter at the first optimal focus using a laser beam analyzer; The first optimal focus is verified according to the first laser beam diameter.

7. A laser cutting control device for improving cutting quality, characterized in that: The device is used to implement the laser cutting control method for improving cutting quality according to any one of claims 1 to 6, and the device includes: A first preset cutting feature reading module, the first preset cutting feature reading module is used to read a first preset cutting feature of a first cutting point in a predetermined cutting trajectory, the first preset cutting feature including a first preset cutting laser feature and a first preset cutting control feature; a first predicted heat acquisition module, the first predicted heat acquisition module being configured to input the first preset cutting laser feature and the first preset cutting control feature into a heat integration prediction model to obtain a first predicted heat of the first cutting point; A first material feature acquisition module, the first material feature acquisition module is used to acquire first material features of the first cutting point material in the target material corresponding to the first cutting point, the first material features including first material thermal expansion information, first material thickness, and first material surface state index; a first predicted thermal expansion index determination module, configured to fit a first thermal expansion curve obtained based on the thermal expansion information of the first material to obtain a first thermal expansion polynomial, and to obtain a first predicted thermal expansion index at the first predicted heat level in combination with the first thermal expansion polynomial; a first focus optimization constraint forming module, configured to form a first focus optimization constraint based on the first predicted thermal expansion index, the first material thickness, and the first material surface condition index; A target optimal focus sequence composition module is used to traverse and match the first focus optimization constraint in the laser cutting database to obtain a first optimal focus, and to compose a target optimal focus sequence based on the first optimal focus, and to perform laser cutting control on the target material according to the target optimal focus sequence.

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