Laser cutting control method and device for improving cutting quality

Abstract

The invention discloses a laser cutting control method and device for improving cutting quality, and relates to the technical field related to laser cutting, and the method comprises the steps that a first preset cutting feature of a first cutting point in a preset cutting track is read; inputting the features into a popularity integrated prediction model to obtain a first predicted popularity; acquiring a first material feature of the first cutting point material; fitting the first thermal expansion curve, and obtaining a first predicted thermal expansion index under the first predicted heat; forming a first focus optimization constraint; traversing and matching in the laser cutting database to obtain a first optimal focus, and forming a target optimal focus sequence; and performing laser cutting control on the target material. The technical problem that in existing laser cutting control, laser cutting parameters are difficult to automatically and accurately regulate and control, cutting quality fluctuates, and then the overall performance of products is affected is solved, and the technical effects of accurately controlling the cutting parameters and improving the cutting quality are achieved.

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, which leads to fluctuations in cutting quality and thus affects the overall performance of the product. Summary of the invention

[0004] The present application provides a laser cutting control method and device for improving cutting quality, and adopts technical means such as cutting feature extraction and fitting of thermal expansion curves to solve the technical problem that it is difficult to automatically and accurately adjust the laser cutting parameters in the existing laser cutting control, resulting in fluctuations in cutting quality and thus affecting the overall performance of the product, thereby achieving 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 is performed 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 in a historical laser cutting control record, wherein the historical laser cutting control record refers to a record of laser cutting of a similar material 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 in the first historical cutting feature record; 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; 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 clustering cluster and a cutting control feature clustering cluster; extracting the first historical cutting laser feature in the first historical cutting feature record in combination with the cutting laser feature clustering cluster, and extracting the first historical cutting control feature in the first historical cutting feature record in combination with the cutting control feature clustering cluster.

[0008] In a possible implementation, the multiple factor features are clustered to obtain clustering results, and the clustering results include cutting laser feature clustering clusters and cutting control feature clustering clusters, and the following processing is also performed: the cutting laser feature clustering cluster includes at least laser power and beam quality, and the cutting control feature clustering cluster includes at least cutting speed, pulse frequency and auxiliary gas.

[0009] In a possible implementation, the first focus optimization constraint is traversed and matched in the laser cutting database to obtain a 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.

[0010] 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: based on the fitness evaluation instruction, a predetermined fitness evaluation function is called; 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: ; the first position fitness characterizing 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 The incision quality index is , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0011] 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.

[0012] 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.

[0013] The present application also provides a laser cutting control device for improving cutting quality, comprising: 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 comprising 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 used to input the first preset cutting laser feature and the first preset cutting control feature into a heat integrated 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 a first material feature of a first cutting point material in a target material corresponding to the first cutting point, the first material feature including first material thermal expansion information, first material thickness and first material surface state index; a first predicted thermal expansion index determination module, the first predicted thermal expansion index determination module being used 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 under the first predicted heat in combination with the first thermal expansion polynomial; A first focus optimization constraint forming module, the first focus optimization constraint forming module 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; 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.

[0014] 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 integrated 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 the first focus optimization constraint; traverse the matching in the laser cutting database to obtain the first optimal focus and form a target optimal focus sequence; and perform laser cutting control on the target material. This solves the technical problem that it is difficult to automatically and accurately adjust the laser cutting parameters in the existing laser cutting control, 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

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic flow chart of a laser cutting control method for improving cutting quality provided in an embodiment of the present application; 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.

[0017] Explanation of the accompanying drawings: first preset cutting feature reading module 10, first predicted heat obtaining module 20, first material feature acquisition module 30, first predicted thermal expansion index determination module 40, first focus optimization constraint forming module 50, target optimal focus sequence composition module 60. DETAILED DESCRIPTION

[0018] 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.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is 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 to distinguish similar objects and do not represent a specific ordering of 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 that are 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a laser cutting control method for improving cutting quality, such as Figure 1 As shown, the method includes: 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, and 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 the cutting speed, cutting depth, cutting path and other control parameters, which determine the movement trajectory and cutting method of the laser beam during the cutting process. For example, the speed of 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.

[0022] Step S200, input the first preset cutting laser feature and the first preset cutting control feature into the heat integrated prediction model to obtain the first predicted heat of the first cutting point. The heat integrated 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 are input into the heat integrated prediction model. The heat integrated 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 influence on the heat distribution during the cutting process, to predict the heat situation at the first cutting point. Finally, the heat integrated 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 the cutting point according to the preset laser features and control features.

[0023] In a possible implementation, step S200 further includes step S210, extracting the first historical record in the historical laser cutting control record, the historical laser cutting control record refers to the record of laser cutting of the same material of the target material in history, and the first historical record includes the first historical cutting feature record and the first historical material heat. The historical laser cutting control record refers to all the operation data and result data recorded by the control system when laser cutting the same material of the target material in the past, which may include the setting of cutting parameters, the movement trajectory of the laser beam, the real-time temperature monitoring during the cutting process, and the final cutting effect evaluation, etc.; 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 heat refers to the actual heat reached by the material during that historical cutting process, which can be monitored in real time by the temperature sensor and recorded in the control system. It also includes step S220, extracting the first historical cutting laser feature and the first historical cutting control feature in the first historical cutting feature record in sequence. Historical data related to laser beam characteristics and cutting control are extracted from the first historical records. Specifically, the first historical cutting laser feature mainly refers 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 feature refers 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 feature determines the movement trajectory and cutting method of the laser beam on the material. It also includes step S230, which performs supervised learning and fusion on the 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. 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 rules 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, and fuse multiple prediction results 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.

[0024] In a possible implementation, step S220 further includes step S221, taking the first historical cutting feature record as an independent variable. It also includes step S222, taking the first historical material heat as a dependent variable. It also includes step S223, performing maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, and the correlation analysis result includes multiple factor features. The first historical cutting feature record is used as an independent variable, and the first historical material heat is used as a dependent variable. The 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, which can capture various types of relationships between variables, including linear, nonlinear, monotonic and non-monotonic relationships. By calculating the MIC value, the correlation strength between the independent variable and the dependent variable can be obtained; by performing MIC analysis on the independent variable and the dependent variable, a correlation analysis result can be obtained, including multiple factor features, that is, those cutting features that are significantly associated with the material heat, which not only show the cutting parameters that affect the material heat, but also reveal the way and degree of influence between them. It also includes step S224, clustering the multiple factor features to obtain a clustering result, and the clustering result includes a cutting laser feature clustering cluster and a cutting control feature clustering cluster. A clustering algorithm is used to group multiple cutting features (i.e., factor features) that are significantly correlated with material heat, so that the features in the same group have a higher similarity under a certain metric, while the similarity between different groups is lower. Specifically, the clustering results include cutting laser feature clusters and cutting control feature clusters. The cutting laser feature clusters refer to clusters formed by clustering factor features related to laser beam characteristics, and the cutting control feature clusters refer to clusters formed by clustering factor features related to cutting equipment control. It also includes step S225, extracting the first historical cutting laser feature in the first historical cutting feature record in combination with the cutting laser feature clusters, and extracting the first historical cutting control feature in the first historical cutting feature record in combination with the cutting control feature clusters. Filter out the historical data belonging to the laser feature category from the first historical cutting feature record, i.e., the first historical cutting laser feature; filter out the historical data belonging to the control feature category from the first historical cutting feature record, i.e., the first historical cutting control feature.

[0025] In a possible implementation, step S224 also includes that the cutting laser feature clustering cluster includes at least laser power and beam quality, and the cutting control feature clustering cluster includes at least cutting speed, pulse frequency and auxiliary gas. Laser power is used to describe the strength of laser beam energy output, which directly affects the energy input and cutting ability of laser cutting. The size of laser power determines the speed of cutting and the depth of cutting depth; beam quality refers to the focusing performance of the laser beam and the uniformity of beam distribution. The quality of the beam directly affects the roughness of the cutting edge and the quality of the cutting surface; 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; 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 in the cutting process can be controlled; 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.

[0026] 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 states 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 expansion or contraction of the target material at the first cutting point under different temperature conditions. Thermal expansion is a physical phenomenon in which the volume of a material changes after being heated. For laser cutting, understanding the thermal expansion characteristics of the material helps to predict the deformation and dimensional changes during the cutting process, thereby adjusting the cutting parameters and reducing the cutting error 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 and cutting effect of the laser beam. By measuring the thickness of the material, parameters such as the laser power and cutting speed can be set more accurately. 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 pollution, and oxide layer, which may affect the interaction between the laser beam and the material, and thus affect the cutting quality. In summary, the first material characteristics include the first material thermal expansion information, the first material thickness and the first material surface state index, which together reflect the physical and chemical properties of the target material at the first cutting point.

[0027] Step S400, fitting the 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 under the first predicted heat. The thermal expansion information of the first material is usually obtained through experimental measurement or 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. Then, the thermal expansion curve is fitted to obtain a first thermal expansion polynomial, wherein polynomial fitting is a mathematical method, which 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 by the thermal integrated prediction model to perform calculations, that is, the first predicted heat is substituted into the first thermal expansion polynomial, and the first predicted thermal expansion index of the material under the predicted heat can be solved. 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.

[0028] 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 point 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 best cutting effect. Specifically, since thermal expansion will 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 point 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 state.

[0029] 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 focusing 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 focusing 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 focal point position of the laser beam that has achieved good cutting effects under similar material properties and cutting conditions. The focal position that best matches the first focus optimization constraint is finally determined to be 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 focal 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 global optimal cutting effect.

[0030] In a possible implementation, step S600 further includes step S610, randomly extracting the first laser cutting mapping relationship in the laser cutting database, 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. A cutting record is randomly selected from the laser cutting database, which contains the first laser mapping relationship between multiple key parameters and features, 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. Specifically, the first focus constraint refers to the focusing constraint condition of the laser beam during the cutting process, which determines the focusing mode and the size of the focus point of the laser beam; the first focus position refers to the specific position of the laser beam focus point, that is, the position of the point where the laser energy is most concentrated on the material; the first incision feature describes the morphology and properties of the material incision after the cutting is completed, such as the width, roughness, and heat-affected zone size of the incision; the first cutting time refers to the time required to complete the entire cutting process. It also includes step S620, when the first focus optimization constraint and the first constraint consistency of the first focus constraint reach a predetermined consistency threshold, a fitness evaluation instruction is generated. The first constraint consistency refers to the matching degree or similarity between the first focus optimization constraint and the first focus constraint. The predetermined consistency threshold is a preset similarity or matching threshold, which is used to determine whether the constraint consistency reaches a satisfactory level. When the first focus optimization constraint and the first constraint consistency of the first focus constraint reach the predetermined consistency threshold, it means that the focus configuration found through the optimization process matches the constraint condition in the actual operation, and the matching degree 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, the first position fitness of the first focus position is obtained. By analyzing the first incision feature, a quantitative evaluation result of the incision quality can be obtained, and the first cutting time is used as part of the evaluation efficiency, combined with the incision quality, and the first position fitness of the first focus position is calculated based on the incision quality and the cutting time, 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 focal position meets the optimization requirements. When the fitness of the first position reaches the predetermined fitness threshold, it means that the focal position is suitable, exhibits good performance during the cutting process, and can meet or exceed the expected cutting quality and efficiency requirements. The focal position is determined as the first optimal focus.

[0031] In a 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: ; the first position fitness characterizing 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 The incision quality index is , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0032] In a 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. The 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; the incision edge smoothness refers to the smoothness and flatness of the incision edge after the cutting is completed; the 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; the incision verticality is used to measure the degree of deviation of the incision perpendicular to the cutting direction; the incision taper describes the difference in the width of the top and bottom of the incision, and the size of the taper reflects the uniformity of the energy distribution and cutting speed during the cutting process; the material deflection reflects the influence of the cutting force on the material, as well as the deformation of the material after cutting.

[0033] 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 with 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, and reflects 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 according to 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.

[0034] Step S700, laser cutting control of the target material is performed according to the target optimal focus sequence. During the laser cutting process, the laser cutting equipment is precisely controlled according to the predetermined target optimal focus sequence to ensure that the laser beam can cut the target material according to the optimal focus point position. Specifically, the control system will adjust the focus point position of the laser beam in real time according to the information in the target optimal focus sequence. By precisely controlling the focus depth and position of the laser beam, it can be ensured that the laser energy can accurately act on the predetermined cutting path of the target material, thereby achieving high-quality cutting.

[0035] 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.

[0036] The laser cutting control device for improving cutting quality according to the embodiment of the present invention is used to solve the technical problem that it is difficult to automatically and accurately adjust the laser cutting parameters in the existing laser cutting control, 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. 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.

[0037] 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 comprising a first preset cutting laser feature and a first preset cutting control feature; A first predicted heat acquisition module 20, the first predicted heat acquisition module 20 is used to input the first preset cutting laser feature and the first preset cutting control feature into a heat integrated prediction model to obtain a first predicted heat of the first cutting point; A first material feature acquisition module 30, the first material feature acquisition module 30 is used to acquire a first material feature of the first cutting point material in the target material corresponding to the first cutting point, the first material feature including first material thermal expansion information, first material thickness and first material surface state index; A first predicted thermal expansion index determination module 40, the first predicted thermal expansion index determination module 40 is used 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 under the first predicted heat in combination with the first thermal expansion polynomial; 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; 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 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.

[0038] 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 in a historical laser cutting control record, wherein the historical laser cutting control record refers to a record of laser cutting of the same material as 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 in 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.

[0039] 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: taking the first historical cutting feature record as an independent variable; taking the first historical material heat as a dependent variable; performing maximum information coefficient analysis on the independent variable and the dependent variable to obtain a correlation analysis result, and the correlation analysis result includes multiple factor features; clustering the multiple factor features to obtain a clustering result, and the clustering result includes a cutting laser feature clustering cluster and a cutting control feature clustering cluster; extracting the first historical cutting laser feature in the first historical cutting feature record in combination with the cutting laser feature clustering cluster, and extracting the first historical cutting control feature in the first historical cutting feature record in combination with the cutting control feature clustering cluster.

[0040] 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.

[0041] 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 the first laser cutting mapping relationship in the laser cutting database, 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 focus optimization constraint and the first constraint consistency of the first focus constraint reach 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.

[0042] 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: ; the first position fitness characterizing 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 The incision quality index is , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

[0043] The specific configuration of the target optimal focus sequence component module 60 will be described in detail below. The target optimal focus sequence component module 60 further includes: the predetermined incision quality index at least includes incision width, incision edge smoothness, incision straightness, incision verticality, incision taper and material deflection.

[0044] The following will further describe in detail the specific configuration of the target optimal focus sequence component module 60. The target optimal focus sequence component 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 according to the first laser beam diameter.

[0045] The laser cutting control device for improving cutting quality provided in the embodiment of the present invention can execute the laser cutting control method for improving cutting quality provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0046] Although the present application makes various references to certain modules in the device 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.

[0047] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A laser cutting control method for improving cutting quality, characterized in that: include: Reading a first preset cutting feature of a first cutting point in a predetermined cutting trajectory, wherein the first preset cutting feature includes 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 integrated prediction model to obtain a first predicted heat of the first cutting point; Acquire a first material feature of a material at a first cutting point in a target material corresponding to the first cutting point, wherein the first material feature includes thermal expansion information of the first material, thickness of the first material, and surface state index of the first material; 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 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 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, characterized in that: Inputting the first preset cutting laser feature and the first preset cutting control feature into a heat integrated 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; The heat integrated prediction model is obtained by 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.

3. The laser cutting control method for improving cutting quality according to claim 2, characterized in that: 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 heat of the first historical material is used as the dependent variable; Performing 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; Clustering the multiple factor features to obtain clustering results, wherein the clustering results include cutting laser feature clustering clusters and cutting control feature clustering clusters; The first historical cutting laser feature in the first historical cutting feature record is extracted in combination with the cutting laser feature cluster, and the first historical cutting control feature in the first historical cutting feature record is extracted in combination with the cutting control feature cluster.

4. The laser cutting control method for improving cutting quality according to claim 3, characterized in that: The cutting laser feature clusters at least include laser power and beam quality, and the cutting control feature clusters at least include cutting speed, pulse frequency and auxiliary gas.

5. The laser cutting control method for improving cutting quality according to claim 1, characterized in that: The first focus optimization constraint is traversed and matched in the laser cutting database to obtain the first optimal focus, including: Randomly extracting a first laser cutting mapping relationship in 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; Based on the fitness evaluation instruction, a first position fitness of the first focus position is obtained by combining 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.

6. The laser cutting control method for improving cutting quality according to claim 5, characterized in that: Based on the fitness evaluation instruction, in combination with the first cutting time and the first incision quality obtained by analyzing the first incision feature, a first position fitness of the first focus position is obtained, including: 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: ； the first position fitness characterizing 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 The incision quality index is , Characterizing the first cutting time, and are the first coefficient and the second coefficient respectively, and .

7. The laser cutting control method for improving cutting quality according to claim 6, characterized in that: The predetermined incision quality indexes include at least incision width, incision edge smoothness, incision straightness, incision verticality, incision taper and material deflection.

8. The laser cutting control method for improving cutting quality according to claim 1, characterized in that: The method also includes: Dynamically monitoring and obtaining a first laser beam diameter at the first optimal focus by a laser beam analyzer; The first optimal focus is verified according to the first laser beam diameter.

9. 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 8, and the device comprises: 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 comprising 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 used to input the first preset cutting laser feature and the first preset cutting control feature into a heat integrated 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 a first material feature of a first cutting point material in a target material corresponding to the first cutting point, the first material feature including first material thermal expansion information, first material thickness and first material surface state index; a first predicted thermal expansion index determination module, the first predicted thermal expansion index determination module being used 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 under the first predicted heat in combination with the first thermal expansion polynomial; A first focus optimization constraint forming module, the first focus optimization constraint forming module 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; 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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