Invar laser-tig welding process, system, apparatus, medium and program

Abstract

The application discloses an invar laser-TIG welding process method, system, equipment, medium and program, relates to the technical field of metal material welding, and the method comprises the following steps: adjusting the process parameters of laser power, welding speed and TIG current, acquiring the images of the arc and the molten pool area in the welding process by using a high-speed photography system, and obtaining high-speed images; the high-speed images are subjected to feature extraction of the arc deflection angle, the arc width-length ratio and the keyhole stability, and corresponding feature parameters are obtained; the feature parameters are screened according to the change law of the feature parameters with the process parameters and the welding quality standard, and the optimized process parameter window of the feature parameters is obtained; a numerical model of the laser-TIG composite welding process is established according to the optimized process parameter window; and the final optimized process parameter combination is obtained according to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model. The application has the effect of improving the welding joint quality and the process stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal material welding, in particular to an invar laser-TIG welding process method, system, device, medium and program. BACKGROUND

[0002] Invar steel is widely used in precision instruments, aerospace and liquefied natural gas transport ships due to its extremely low thermal expansion coefficient. However, defects such as hot cracks, pores and undercutting are prone to occur during welding of invar steel, so laser-TIG hybrid welding technology is usually used to weld invar steel. Laser-TIG hybrid welding technology combines the advantages of strong deep penetration of laser welding and good stability of TIG welding process, but the interaction of the two heat sources is extremely complex, especially the unstable behavior of the arc and the difficulty in controlling the dynamic change of the molten pool.

[0003] In actual welding process, the following phenomena can be observed:

[0004] (1) When the laser power is too large, the TIG arc will be strongly attracted to the laser action point, resulting in arc contraction and drift;

[0005] (2) When the welding speed is too fast, the arc floats above the molten pool, and the arc shape is distorted;

[0006] (3) When the TIG current is too large, the arc stability is improved, but the heat input increases, resulting in the expansion of the heat affected zone.

[0007] The above phenomena directly affect the welding quality, therefore, it is necessary to systematically study the influence of process parameters on the behavior of the arc and the dynamic change of the molten pool. SUMMARY

[0008] The main purpose of the present application is to solve the technical problems of unstable arc behavior and difficult control of molten pool dynamic change during invar steel welding in the prior art, so as to realize the parameter optimization of invar steel laser-TIG welding process.

[0009] In order to achieve the above purpose, in the first aspect, the present application provides an invar laser-TIG welding process method, comprising:

[0010] By adjusting the process parameters of laser power, welding speed and TIG current, the images of the arc and the molten pool area during welding are obtained by using a high-speed photography system, and high-speed images are obtained;

[0011] The high-speed images are subjected to feature extraction of arc deflection angle, arc width-length ratio and keyhole stability, respectively, to obtain feature parameters corresponding to the arc deflection angle, arc width-length ratio and keyhole stability;

[0012] According to the variation law of the characteristic parameters with the process parameters and the welding quality standard, the characteristic parameters are screened to obtain an optimal process parameter window of the characteristic parameters;

[0013] According to the optimal process parameter window, a numerical model of the laser-TIG hybrid welding process is established, wherein the numerical model is used to simulate and calculate the molten pool flow field, temperature field and dynamic behavior of the keyhole, and analyze the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism;

[0014] According to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, difference analysis is performed to obtain an ultimately optimized process parameter combination.

[0015] Preferably, the step of obtaining the high-speed images by adjusting the process parameters of the laser power, welding speed and TIG current and using a high-speed photography system to acquire images of the arc and molten pool area in the welding process comprises:

[0016] The laser-TIG hybrid welding experiment is performed by the single factor variable method, and the process parameters of the laser power, welding speed and TIG current are changed respectively.

[0017] Preferably, the step of extracting the characteristic parameters corresponding to the arc deflection angle, arc width-length ratio and keyhole stability from the high-speed images comprises:

[0018] The center line of the arc bright area in the high-speed image is fitted, and then the tungsten electrode center axis is drawn, and the characteristic parameter of the arc deflection angle is obtained by using the included angle between the center line of the arc bright area and the tungsten electrode center axis;

[0019] The arc boundary in the high-speed image is identified to form an arc area, and the maximum width of the arc parallel to the welding direction and the maximum length of the arc along the direction of the tungsten electrode center axis are measured to obtain the characteristic parameter of the arc width-length ratio;

[0020] The characteristic parameters of the keyhole opening area in multiple frames of the high-speed images are extracted, and the average value and the standard deviation are calculated in sequence according to the characteristics, and the characteristic parameter of the keyhole stability is obtained by using the standard deviation.

[0021] Preferably, the step of screening the characteristic parameters according to the variation law of the characteristic parameters with the process parameters and the welding quality standard to obtain the optimal process parameter window of the characteristic parameters comprises:

[0022] The corresponding relationship between the characteristic parameters and the process parameters is constructed, and a relationship curve of the characteristic parameters and the process parameters is formed;

[0023] According to the relationship curve and each corresponding process parameter combination, a welding quality comprehensive score calculation is performed to obtain a comprehensive score calculation result;

[0024] The comprehensive score calculation result is compared with a welding quality standard, and parameters meeting the welding quality standard are used to form an optimal process parameter window of the characteristic parameters.

[0025] Preferably, the step of establishing a numerical model of the laser-TIG hybrid welding process according to the optimal process parameter window comprises:

[0026] According to the optimal process parameter window, a grid model of the laser-TIG hybrid welding process is established, wherein the grid model locally encrypts the grid of the molten pool and the crater dynamic region;

[0027] According to the physical parameters of the Invar steel and the boundary conditions, the grid model is configured to obtain a numerical model integrated with the physical parameters of the Invar steel;

[0028] The numerical model of the laser-TIG hybrid welding process is obtained by combining a composite heat source model with the numerical model integrated with the physical parameters of the Invar steel, wherein the composite heat source model comprises a composite model of double-ellipsoid heat sources and rotating Gaussian heat sources.

[0029] Preferably, the step of performing difference analysis according to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model to obtain the final optimized process parameter combination comprises:

[0030] According to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, difference comparison is performed to obtain a list of key differences between the experiment and the simulation;

[0031] According to the list of key differences between the experiment and the simulation, difference analysis at the mechanism level is performed to obtain an analysis report of the defect formation mechanism;

[0032] According to the analysis report of the defect formation mechanism, the optimal process parameter window is finally optimized to obtain the final optimized process parameter combination.

[0033] In a second aspect, the present application provides an Invar steel laser-TIG welding process system, which is applied to the Invar steel laser-TIG welding process method described above and comprises:

[0034] An acquisition unit is configured to acquire images of the arc and the molten pool region in the welding process by adjusting the process parameters of the laser power, the welding speed and the TIG current, and using a high-speed photography system to obtain high-speed images.

[0035] An extraction unit is configured to extract the arc deflection angle, the arc width-length ratio and the keyhole stability from the high-speed images, and obtain feature parameters corresponding to the arc deflection angle, the arc width-length ratio and the keyhole stability.

[0036] A screening unit is configured to screen the feature parameters according to the variation of the feature parameters with the process parameters and the welding quality standard, and obtain an optimal process parameter window of the feature parameters.

[0037] A modeling unit is configured to establish a numerical model of the laser-TIG hybrid welding process according to the optimal process parameter window, wherein the numerical model is used to simulate the flow field, the temperature field and the dynamic behavior of the keyhole, and analyze the flow characteristics of the molten pool surface and the keyhole stability from the mechanism.

[0038] A result unit is configured to perform difference analysis according to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, and obtain a final optimized process parameter combination.

[0039] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the invar laser-TIG welding process method when executing the computer program.

[0040] In a fourth aspect, the present application provides a computer storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the invar laser-TIG welding process method.

[0041] In a fifth aspect, the present application provides a computer program, which is executed by a processor to implement the steps of the invar laser-TIG welding process method.

[0042] In summary, the present application at least has the following beneficial effects:

[0043] In the present application, by adjusting the process parameters of laser power, welding speed and TIG current, the images of the arc and the molten pool area in the welding process are obtained by using a high-speed photography system to obtain high-speed images; the characteristic extraction of the arc deflection angle, the arc width-length ratio and the keyhole stability is carried out on the high-speed images to obtain the characteristic parameters corresponding to the arc deflection angle, the arc width-length ratio and the keyhole stability; according to the variation law of the characteristic parameters with the process parameters and the welding quality standard, the characteristic parameters are screened to obtain the optimized process parameter window of the characteristic parameters; according to the optimized process parameter window, a numerical model of the laser-TIG hybrid welding process is established, wherein the numerical model is used to simulate the calculation of the molten pool flow field, the temperature field and the dynamic behavior of the keyhole, and to analyze the flow characteristics of the molten pool surface and the keyhole stability from the mechanism; according to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, the difference analysis is carried out to obtain the final optimized process parameter combination. Through the quantitative experiment observation and the deep fusion of simulation calculation, the precise control of the invar steel laser-TIG hybrid welding process is realized, which can effectively inhibit the defects such as porosity and undercut, and significantly improve the welding joint quality and the process stability. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which form a part of this application, are used to provide further understanding of the application, and make the other features, purposes and advantages of the application more apparent. The illustrative embodiments of the drawings of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 A flow chart of an invar steel laser-TIG welding process in the embodiments of the present application;

[0046] Figure 2 A schematic diagram of the measurement of the characteristic parameters in step S200 of the present application;

[0047] Figure 3 A numerical simulation result cloud chart under the optimized parameters in step S500 of the present application;

[0048] Figure 4 A schematic diagram of the arc deflection angle at different welding speeds in the present application;

[0049] Figure 5 A schematic diagram of the arc width-height length at different welding speeds in the present application;

[0050] Figure 6 A schematic diagram of the keyhole opening area at different welding speeds in the present application;

[0051] Figure 7 A schematic diagram of the temperature field and the flow field at different welding speeds in the present application;

[0052] Figure 8 Schematic diagram of the groove depth fluctuation for different welding speeds in the present application;

[0053] Figure 9 Schematic diagram of the weld appearance in the present application. DETAILED DESCRIPTION

[0054] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0055] It should be noted that the terms "first", "second", and the like in the specification of the present application, the claims, and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0056] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not intended to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0057] The terms involved in the present application are explained below in order to understand the technical solutions:

[0058] Single-factor variable method, also commonly known as control variable method, is a scientific experimental method, the core idea of which is: when studying a problem with multiple factors (multiple variables), only one factor (independent variable) is changed each time, while the remaining factors are kept constant, so as to study the influence of the changed factor on the experimental result (dependent variable).

[0059] Image J is an open source, Java-based image processing and analysis software, the core feature is designed for scientific image processing, especially good at quantitative measurement analysis of physical space size known image (such as microscope photos, medical images, etc.).

[0060] Fluent is a computational fluid dynamics (CFD) software.

[0061] CFD, full name Computational Fluid Dynamics, Chinese translation for computational fluid dynamics, is a branch of fluid mechanics, through computer numerical calculation and image display method, the system containing fluid flow and heat transfer and other related physical phenomena are analyzed and simulated. On the computer, a "virtual fluid laboratory" is established, researchers can use this laboratory to simulate the behavior of fluid under various conditions, so as to replace or assist the costly and long physical experiment.

[0062] Marangoni effect refers to the physical phenomenon that liquid flows from the area with low surface tension to the area with high surface tension due to the difference in surface tension of the liquid surface.

[0063] VOF method, full name for fluid volume method, is a numerical technique used to track the position and shape of free interface (i.e. interface) between two or more mutually insoluble fluids in computational fluid dynamics (CFD). The core of VOF method is to determine the proportion of each fluid in the calculation grid indirectly by solving an additional scalar field (i.e. VOF function), so as to reconstruct the clear fluid interface.

[0064] As shown in Figure 1 The application provides an invar laser-TIG welding process method, the method comprising:

[0065] S100, by adjusting the process parameters of laser power, welding speed and TIG current, using high-speed photography system to obtain the image of arc and molten pool area in the welding process, and obtaining high-speed image.

[0066] Specifically, obtaining high-speed image can include the following steps:

[0067] The laser-TIG hybrid welding experiment is carried out by single factor variable method, and the process parameters of laser power, welding speed and TIG current are changed respectively.

[0068] Specifically, single factor variable method is used to design experiment: fixing part of the parameters, changing one parameter of laser power, welding speed or TIG current in turn, keeping other parameters unchanged. For example:

[0069] Change laser power (P): Fix welding speed v = 0.5 m / min, TIG current I = 150 A, gradually increase P from 3 kW to 7 kW.

[0070] Change welding speed (v): Fix P = 5 kW, I = 150 A, gradually increase v from 0.2 m / min to 1 m / min.

[0071] Change TIG current (I): Fix P = 5 kW, v = 0.5 m / min, gradually increase I from 100 A to 200 A.

[0072] After each change, conduct welding experiments and record the parameter combinations. The single-factor variable method can clearly observe the influence of a single parameter on the arc and molten pool by isolating the variable, avoiding multi-factor interaction interference.

[0073] During each welding experiment, trigger the high-speed photography system to capture images simultaneously. The camera needs to be aligned with the arc and molten pool area to ensure coverage of key dynamic processes such as arc ignition, molten pool formation, and spoon hole opening and closing. When shooting, set appropriate exposure time (such as microseconds) and frame rate (such as 2000 fps) to capture rapid changes. For example, a sequence of 21 frames corresponding to a 10 ms time length was shot to analyze spoon hole stability. After obtaining the images, store them in high-resolution formats (such as RAW or TIFF) for subsequent processing. High-speed photography can quantitatively record phenomena such as arc deflection and molten pool flow through time resolution enhancement, making up for the shortcomings of human eye observation.

[0074] S200, the high-speed image is subjected to feature extraction of arc deflection angle, arc width-length ratio and spoon hole stability, respectively, to obtain feature parameters corresponding to the arc deflection angle, arc width-length ratio and spoon hole stability.

[0075] Specifically, referring to the arc deflection angle, the arc width-length ratio and the spoon hole stability corresponding to the feature parameters can include the following steps: Figure 2

[0076] S201, the center line of the arc bright area in the high-speed image is fitted, and then the tungsten electrode center axis is drawn, and the included angle between the center line of the arc bright area and the tungsten electrode center axis is used to obtain the feature parameter of the arc deflection angle.

[0077] ​Specifically, first, a single frame of high-speed image can be imported into image processing software (e.g. Image J); then, the center line of the arc bright area is fitted, for example, by using the threshold segmentation tool in the image processing software, the brightest area of the arc is identified (based on the brightness difference of pixels), and the geometric center line of the arc bright area is drawn by using the line fitting function (e.g. "line segment selection" or "curve fitting"), which should extend from the arc root (near the tungsten electrode end) to the arc end; then, based on the image calibration tungsten electrode position, the tungsten electrode center axis (usually the vertical axis of the tungsten electrode) is drawn; finally, the included angle θ between the two lines is measured. The larger the θ value, the stronger the attraction of the laser to the arc, and the worse the arc stability; in addition, the output θ of this step will be one of the bases for the evaluation of the welding quality.

[0078] It should be noted that the principle of extracting the arc deflection angle is based on geometric optics and image analysis. The center line of the arc bright area represents the geometric center of the arc, and the included angle with the tungsten electrode center axis directly reflects the degree of arc deflection. The laser plasma will attract the arc, causing the center line to deviate, and the θ value thus becomes a stability indicator. The quantification of θ can avoid subjective judgment and improve evaluation accuracy.

[0079] S202, the arc boundary in the high-speed image is identified, the arc region is formed, the maximum width of the arc parallel to the welding direction is measured, and the maximum length of the arc along the tungsten electrode center axis direction is measured, to obtain the characteristic parameter of the arc width-length ratio.

[0080] Specifically, in the same frame of image, the arc boundary is first identified, for example, by using the region selection tool (e.g. "polygon selection" or "threshold segmentation") of Image J, the arc region is highlighted based on brightness contrast, and a clear contour is formed; then, the maximum width (W) of the arc parallel to the welding direction is measured, which is usually the horizontal distance at the widest part of the arc, and the maximum length (L) of the arc along the extension line of the tungsten electrode center axis is measured, which is the vertical distance from the tip of the tungsten electrode to the distal end of the arc; finally, the ratio of the arc width-length ratio is calculated. The smaller the R value, the more serious the compression and stretching of the arc, i.e. the arc is compressed along the welding direction (width W) and stretched along the tungsten direction (length L) under the action of the laser.

[0081] It should be noted that the principle of extracting the arc width-length ratio is based on morphological analysis. The R value reflects the deformation of the arc under the action of the laser: the laser heat source will compress the arc width (W) and stretch its length (L), resulting in a change in R. By using the R value, qualitative morphological observation is converted into a quantitative indicator, enhancing objectivity.

[0082] S203, extracting features of the keyhole opening area in the plurality of frames of the high-speed images, and sequentially calculating an average value and a standard deviation according to the features, and obtaining a characteristic parameter of the stability of the keyhole by using the standard deviation.

[0083] Specifically, first, batch processing is performed on the image sequence, for example, importing multiple frames of images (such as 21 consecutive frames) in Image J, and extracting the keyhole opening area frame by frame. In each frame, the keyhole opening contour is outlined by a region selection tool (such as "ellipse" or "free hand drawing"), and the area (converted to the actual area based on the number of pixels) is calculated; then, based on N frames of data (such as N = 21), the average value and the standard deviation of the keyhole area are calculated.

[0084] Further, the average value of N data is calculated first, and the average value represents the opening degree of the keyhole. The average value is calculated as follows: wherein A i represents the keyhole opening area of the i-th frame of image (unit: mm 2 ), wherein A represents the average value of the area of N frames of data (unit: mm 2 ), and n represents the total number of frames; then, the standard deviation S is calculated, and the standard deviation represents the average deviation of each instantaneous area value from the average value, wherein the larger the S value is, the more unstable the keyhole is, and the more likely the keyhole is to cause porosity defects; for example, when S > 0.05, the keyhole is unqualified, and S comprehensively reflects the dynamic behavior of the keyhole.

[0085] It should be noted that the principle of extracting the stability of the keyhole is based on statistics and dynamic analysis. The fluctuation of the keyhole opening area directly reflects the stability of the molten pool: the standard deviation S quantifies the dispersion degree of the area, and a small S value indicates that the keyhole is uniform in opening and closing and is not easy to collapse. By analyzing multiple frames to capture the transient behavior, the limitations of single-frame images are overcome.

[0086] S300, screening the characteristic parameter according to the variation law of the characteristic parameter with the process parameter and the welding quality standard, to obtain an optimal process parameter window of the characteristic parameter.

[0087] The step S300 aims to screen an optimal process parameter window according to the variation law of the characteristic parameter (arc deflection angle θ, arc width-length ratio R, keyhole opening area standard deviation S) with the process parameter (laser power P, welding speed v, TIG current I), and in combination with the welding quality standard.

[0088] Specifically, obtaining the optimal process parameter window of the characteristic parameter can include the following steps:

[0089] S301, constructing a corresponding relationship between the characteristic parameter and the process parameter, and forming a relationship curve of the characteristic parameter and the process parameter.

[0090] ​Specifically, the characteristic parameters (θ, R, S) and corresponding process parameter data (P, v, I) obtained in the foregoing steps are from single-factor experiments.

[0091] For example, based on the single-factor variable method, the list of characteristic parameter values (θ, R, S) corresponding to each process parameter change (such as the welding speed v from 0.2 m / min to 1 m / min) is formed, and then a relationship curve is drawn according to the list.

[0092] For each process parameter (such as v), a relationship curve of the characteristic parameters (θ, R, S) and the process parameter is drawn, respectively. For example, a θ-v curve is drawn with v as the horizontal coordinate and θ as the vertical coordinate; similarly, an R-v curve and an S-v curve are drawn.

[0093] The curve type can be a scatter plot or a line chart to highlight the trend changes.

[0094] When v increases, θ increases, R decreases, and the fluctuation of S intensifies (for example, when v = 0.2 m / min, θ = 5.814°, and when v = 1 m / min, θ = 14.459°).

[0095] After drawing, the curve trend is analyzed: for example, the θ-v curve shows an upward trend, indicating that the arc deflection intensifies with the increase of the speed; the R-v curve shows a downward trend, indicating that the arc shape distortion is aggravated. These curves provide intuitive basis for subsequent scoring.

[0096] The principle of constructing the relationship curve is based on data visualization, which converts experimental data into graphical trends to identify the quantitative influence of process parameters on characteristic parameters. The single-factor variable method ensures the reliability of the curve and avoids multi-factor interference.

[0097] S302, according to the relationship curve and the corresponding each process parameter combination, welding quality comprehensive score calculation is carried out, and comprehensive score calculation result is obtained.

[0098] Specifically, according to the relationship curve and the corresponding process parameter combination (such as θ, R, S data under different v values) output by S301, the WQS (WQS-Welding Quality Score) formula can be applied for calculation,

[0099] The WQS formula is:

[0100] WQS = 40% × (arc deflection angle score) + 30% × (arc shape score) + 30% × (keyhole stability score).

[0101] Specifically, the WQS calculation formula is:

[0102] WQS = 0.4 x (100 - 5θ) + 0.3 x min(100, 50R) + 0.3 x max(0, 100 - 2000S);

[0103] where θ represents the arc deflection angle (unit: degree), the coefficient 5 means that θ decreases by 5 points for every 1° increase, and the score is 0 when θ = 20°; R represents the arc width-length ratio (dimensionless), the coefficient 50 means that R = 2.0 gets full score 100 points; S represents the standard deviation of the spoon hole opening area (unit: mm²), the coefficient 2000 amplifies the value of S (0-0.05 range) to the degree of influence on the score (0-100 points).

[0104] Calculation process:

[0105] For each process parameter combination (such as v = 0.5 m / min, θ = 10.407°, R = 1.063, S = 0.011), first calculate the scores of the three items:

[0106] Arc deflection angle score = 100 - 5θ = 100 - 5 x 10.407 ≈ 47.97;

[0107] Arc shape score = min(100, 50R) = min(100, 50 x 1.063) = 53.15;

[0108] Spoon hole stability score = max(0, 100 - 2000S) = max(0, 100 - 2000 x 0.011) = 78.00;

[0109] Then weighted sum: WQS = 0.4 x 47.97 + 0.3 x 53.15 + 0.3 x 78.00 = 58.54.

[0110] Repeat this process until all parameter combinations, get the WQS score list. The principle of WQS scoring is based on weighted comprehensive evaluation, integrating multiple characteristic parameters (θ, R, S) into one score to balance arc stability, shape and spoon hole dynamics. The weight setting (0.4, 0.3, 0.3) reflects the degree of influence of each parameter on quality, such as the high weight of θ because it is directly related to arc drift. The weight setting (0.4, 0.3, 0.3) can be adjusted as needed.

[0111] It should be noted that in the calculation formula of WQS, each weight is a fixed value, which can be dynamically adjusted based on this, which can include the following steps:

[0112] According to the process parameter data, and historical experimental data or expert knowledge base, the influence law of the process parameters on the characteristic parameters is analyzed, and a dynamic weight adjustment rule set is obtained, the dynamic weight adjustment rule set includes a mapping relationship of the weight and the process parameters or a decision condition;

[0113] According to the current process parameter combination and the dynamic weight adjustment rule set, a dynamic weight value is obtained.

[0114] Using the dynamic weight value, the characteristic parameter value is calculated to obtain a dynamic WQS score.

[0115] The dynamic WQS score can be applied in the welding quality standard for comparison and screening.

[0116] Exemplarily, first, the influence law of the process parameters on the characteristic parameters (θ, R, S) is analyzed, for example, when the high-speed welding (v>0.8m / min), θ increases significantly, indicating that the arc stability decreases; when the high laser power (P>6kW), the S value fluctuates intensively, indicating that the keyhole is prone to instability.

[0117] Secondly, the conditional rules are established, and the process parameters are mapped to the weight adjustment direction. Rule examples:

[0118] If the laser power P>6kW, the weight of the keyhole opening area standard deviation S is increased (such as from 0.3 to 0.5), because the risk of keyhole collapse is high under high power.

[0119] If the welding speed v>0.8m / min, the weight of the arc deflection angle θ is increased (such as from 0.4 to 0.5), because the arc drift dominates the quality degradation at high speed.

[0120] If the welding stage is “arc striking”, the sensitivity of all weights is temporarily increased to capture transient changes.

[0121] The dynamic weight adjustment rule set can be a series of conditional judgment rules based on the process parameter threshold, which is constructed based on historical data analysis and expert knowledge, and aims to adjust the relative importance of each characteristic parameter under different welding conditions. The rule set is presented in the form of clear IF-THEN logic, for example: IF laser power P>6000W, THEN set the weight as (the weight of θ is 0.3, the weight of R is 0.2, and the weight of S is 0.5). The system automatically matches and applies the corresponding rules according to the real-time reading of the process parameters, and calculates the dynamic weight value.

[0122] Rule formalization, encode rules as executable logic, such as using threshold conditions. The adjustment of dynamic weights is based on the "heterogeneity of the importance of feature parameters under different welding conditions". Through rule mapping, the weight gives priority to the current most sensitive quality indicators, avoiding the evaluation deviation of fixed weights under extreme parameters.

[0123] Calculate dynamic weight values, obtain real-time parameters, such as reading the current P, v, and I values from the welding equipment or experimental setup.

[0124] Apply weight rules: Calculate weight values according to the rule set. For example:

[0125] If P = 5kW (≤ 6kW) and v = 0.5m / min (≤ 0.8m / min), use the default weights (θ weight is 0.4, R weight is 0.3, S weight is 0.3).

[0126] If P = 7kW (> 6kW), increase the weight of S according to the rules: 0.3 + 0.2 = 0.5, and adjust the weights of θ and R accordingly (such as θ weight is 0.3, R weight is 0.2), ensuring the sum is 1.

[0127] Finally, when the weights are determined, they are substituted into the WQS and calculated with the feature parameter values.

[0128] S303, compare the comprehensive score calculation results with the welding quality standard, and use the parameters that meet the welding quality standard to form the preferred process parameter window of the feature parameters.

[0129] Specifically, according to the WQS score calculation results output by S302 and the pre-defined welding quality standard (such as WQS ≥ 80 points ideal, 60 ≤ WQS ≤ 80 points qualified, WQS < 60 points unstable), comparison and screening are performed.

[0130] Exemplarily, first, the WQS score is screened according to the score standard:

[0131] Ideal parameters: WQS ≥ 80 points, directly included in the preferred window, such as v = 0.2m / min, WQS = 69.17 (qualified), but if a parameter combination WQS ≥ 80, it is preferred.

[0132] Qualified parameters: 60 points ≤ WQS ≤ 80 points, can be used as candidates, need to be combined with simulation verification, further optimization.

[0133] Unstable parameters: WQS < 60 points, excluded, such as v = 1m / min, WQS = 24.13.

[0134] Then, based on the screening results, the parameter window is determined: for example, by scoring the preliminary screening v in the range of 0.2-0.8 m / min, and then combined with numerical simulation to finally optimize v = 0.3-0.5 m / min.

[0135] Exemplarily, the score grade standard can further include:

[0136] The arc deflection angle score = 100-5θ, θ is the arc deflection angle, and the coefficient 5 is to achieve the design goal that the score is 0 when the deflection angle reaches 20°, which is a scaling factor to map the angle value (0-20°) to the score value (100-0 points). The arc deflection angle is 1°, and 5 points are deducted. The smaller the deflection, the less the score, and the higher the score.

[0137] When θ<10°, the arc deflection angle score is >50 points (excellent); when θ=10°-15°, the arc deflection angle score is 25-50 points (qualified); and when the arc deflection angle θ>15°, the score is <25 points (unqualified).

[0138] The arc shape score = min(100, 50R), R is the arc aspect ratio, and the coefficient 50 is to achieve the goal of obtaining a full score of 100 points when the aspect ratio reaches 2.0. The value of the aspect ratio R is directly multiplied by 50 to get the score. The larger the R, the higher the score.

[0139] When R>2.0, 50R>100 points, the arc shape score is taken as 100 points; when R=1.5-2.0, the arc shape score is 75-100 points (excellent); when R=1.0-1.5, the arc shape score is 50-75 points (qualified); and when R<1.0, the arc shape score is <50 points (unqualified).

[0140] The spoon hole stability score = max(0, 100-2000S), S is the standard deviation of the spoon hole opening area, and the coefficient 2000 is set because the value of S is very small (0.01 order of magnitude), and a large coefficient (2000) is needed to amplify its fluctuation range (0-0.05) to a sufficient extent to affect the total score (0-100 points). The fluctuation of the spoon hole S increases by 0.01, and the score is deducted by 20 points. The smaller the fluctuation, the higher the score.

[0141] When S<0.02, the spoon hole stability score is >60 points (excellent); when S=0.02-0.05, the spoon hole stability score is 0-60 points (qualified); and when S>0.05, 100-2000S<0, the spoon hole stability score is taken as 0 (unqualified).

[0142] S400, according to the preferred process parameter window, a numerical model of the laser-TIG hybrid welding process is established.

[0143] The numerical model is used to simulate the flow field and temperature field of the molten pool and the dynamic behavior of the keyhole, and analyze the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism.

[0144] The step S400 is aimed at establishing a numerical model of the laser-TIG hybrid welding process, simulating the flow field and temperature field of the molten pool and the dynamic behavior of the keyhole, and analyzing the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism level to support the defect cause analysis.

[0145] Specifically, the establishment of the numerical model of the laser-TIG hybrid welding process can include the following steps:

[0146] S401, according to the preferred process parameter window, a grid model of laser-TIG hybrid welding process is established.

[0147] The grid model is locally grid-encrypted in the dynamic area of the molten pool and the keyhole.

[0148] Specifically, according to the preferred process parameter window (including the specific range of laser power, welding speed, and TIG current, such as P=5kW, I=150A, v=0.3~0.5m / min) and the geometry size of the invar steel plate (such as 10mm thick, 200mm×100mm), the grid model is constructed.

[0149] Illustratively, according to the preferred parameter window and the workpiece size, a three-dimensional geometric model is created using numerical simulation software (such as Fluent software). The grid model covers the welding area, including the invar steel plate and the welding path. Then, the calculation grid is divided, and structured or unstructured grid can be used, and local grid encryption is performed in the key area (such as the molten pool formation area and the keyhole dynamic area). The encryption method can be realized by the grid refinement tool in the Fluent software, for example, smaller grid size (such as 0.1mm grid) is set in the expected area of the molten pool, and coarser grid (such as 1mm grid) is used in the non-key area. The grid model can be a "three-dimensional transient model", and the grid division is adapted to the dynamic calculation requirement to ensure the coordination of time step and spatial grid.

[0150] It should be noted that the principle of local grid encryption is based on the accuracy requirement of computational fluid dynamics (CFD). The molten pool and the keyhole area involve rapid change of temperature gradient and fluid motion, and fine grid can more accurately capture these transient behaviors, while coarse grid saves computing resources in non-key areas. By encryption, the authenticity of the simulation results (such as temperature field) is ensured, and numerical errors caused by too coarse grid are avoided.

[0151] S402, according to the physical parameters of invar steel and the boundary conditions, the numerical model integrated with the physical parameters of invar steel is obtained on the grid model.

[0152] Specifically, on the mesh model, first set the physical parameters of Invar steel, including thermal conductivity, specific heat capacity, density, viscosity, etc. These parameters can be imported from the material library or experimental data to ensure consistency with the real Invar steel. Then, configure the boundary conditions:

[0153] Physical force boundary: consider surface tension, gravity, buoyancy, vapor recoil pressure, arc pressure, and electromagnetic force. These forces are added to the mesh model in the form of source terms, such as surface tension simulating molten pool flow through the Marangoni effect;

[0154] Environmental boundary: set the initial temperature of the Invar steel plate (such as room temperature 20°C), and the environmental convective heat transfer coefficient to simulate the real welding environment.

[0155] At the same time, enable the VOF (Volume of Fluid) method to track the dynamic free surface of the molten pool. This method captures the liquid-gas interface through the fluid volume fraction, ensuring the accuracy of the physical process. The output of this step is a partially integrated numerical model, ready for heat source addition.

[0156] It should be noted that the principle of configuring physical parameters and boundary conditions is to simulate physical reality and conservation laws. Physical parameters determine how materials respond to heat and force, while boundary conditions simulate the influence of the external environment. The VOF method is based on mass conservation and can dynamically track the changes in the molten pool surface, which is crucial for analyzing keyhole stability. By integrating these elements, a reliable foundation is provided for mechanism analysis.

[0157] S403, combine the composite heat source model with the numerical model of the integrated Invar steel physical parameters to obtain a numerical model of the laser-TIG hybrid welding process.

[0158] The composite heat source model includes a composite model composed of double-ellipsoid heat sources and rotating Gaussian heat sources.

[0159] Combine the composite heat source model with the physical numerical model. The composite heat source includes two parts:

[0160] Double-ellipsoid heat source: simulating the heat input of TIG arc, the shape is double-ellipsoid, the heat flux is unevenly distributed in space, and the heat is concentrated in the focal region. The parameters are based on the preferred window settings, such as TIG current I = 150 A corresponding to the heat source power. Further, the double-ellipsoid heat source model imagines the heat-acting range of TIG arc as a shape spliced by two half-ellipsoids (one in front and one behind). The ellipsoid in front is used to simulate the "preheating" effect of the arc on the workpiece, and the ellipsoid behind simulates the "slow cooling" effect of the arc. The heat distribution of TIG arc is relatively dispersed, the acting area is larger but the energy density is lower. The double-ellipsoid model can well describe this asymmetric heat flow distribution in the welding direction, which is more accurate than the simple point heat source or columnar heat source.

[0161] Rotating Gaussian heat source: simulating the deep penetration effect of laser, adopting rotating Gaussian distribution, the heat flow attenuates along the beam direction, the parameters are as follows: laser power P = 5 kW. The rotating Gaussian heat source model describes the laser heat source as a three-dimensional, rotationally symmetric "bell-shaped" distribution body, with the highest energy density in the center and gradually attenuating to the edge, like a rotating Gaussian surface. The core feature of laser welding is extremely high energy density, which can instantly vaporize metal and form a deep penetration "keyhole", and the rotating Gaussian model can depict this highly concentrated and energy-attenuating deep penetration effect along the depth direction, which is the key to simulating the formation and stability of the keyhole.

[0162] The double-ellipsoid heat source model and the rotating Gaussian model are existing mechanical models, which are not adjusted by the present application.

[0163] When the heat sources are integrated, the two heat sources will interact with each other, for example, the TIG arc preheats the workpiece, increasing the metal's absorption rate of laser; at the same time, the plasma generated by the laser affects the stability of the TIG arc, the numerical model simulates this complex coupling effect by simultaneously calculating the energy fields of the two heat sources and considering the fluid flow, phase change, etc. caused by them; the heat source position (such as the distance between the laser action point and the TIG arc 3 mm), direction (along the welding path), and time-related parameters (such as moving heat source under welding speed v = 0.5 m / min) can be defined in the Fluent software. Then, configure the solver settings: select the transient solution mode, set the time step (such as 0.001 s), convergence criteria and output frequency to simulate the molten pool flow field, temperature field, etc.

[0164] It should be noted that the principle of heat source combination is based on energy superposition effect. Laser and TIG heat source interact with each other, and the composite model can more truly reflect the heat distribution in actual welding. The double-ellipsoid heat source simulates the wide-area heating of the arc, and the rotating Gaussian heat source simulates the local deep penetration of the laser. The combination of the two covers the heat input characteristics of composite welding, and through this composite model, in-depth mechanism analysis from experiment to simulation is realized.

[0165] S500, performing difference analysis according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, to obtain a final optimized process parameter combination.

[0166] The step S500 aims to perform systematic difference analysis through the high-speed photography experimental observation result and the numerical simulation analysis result, and finally determine the optimal process parameter combination. The numerical simulation result cloud diagram under the specific optimized parameter can refer to the following figure. Figure 3

[0167] Specifically, obtaining the final optimized process parameter combination can include the following steps:

[0168] S501, performing difference comparison according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, to obtain a key difference point list of the experiment and the simulation.

[0169] Specifically, first, compare the quantitative characteristic parameters extracted from the experiment with the physical field data output by the numerical simulation item by item:

[0170] Arc behavior comparison: compare the measured θ value and R value with the shape and temperature distribution of the arc heat source action area in the simulation. For example, the experiment measures θ = 10.407° (v = 0.5 m / min), and checks whether the arc in the simulation shows the corresponding deflection trend.

[0171] Melt pool dynamics comparison: compare the experimental observation of the spoon hole area fluctuation (S value) with the simulation calculation of the spoon hole depth fluctuation graph. For example, the experiment shows S = 0.011 (stable), and verifies whether the spoon hole remains in a stable open state in the simulation.

[0172] Temperature field verification: compare the melt pool morphology observed by high-speed photography with the isotherm distribution of the simulation temperature field, and confirm whether the high-temperature region is consistent.

[0173] Through comparison, a difference point list is generated, for example: "the experiment shows that the arc deflection angle is small (θ = 5.814°), and the arc heat source distribution in the simulation is uniform and has good consistency" or "the experiment shows that the spoon hole fluctuation is large (S = 0.326), and the spoon hole depth changes dramatically in the simulation, and the trend is consistent". By correlating the measurable external characteristics (experiment) with the difficult-to-observe internal mechanism (simulation), the accuracy of the numerical model is verified, and input is provided for mechanism analysis.

[0174] S502, performing difference analysis at the mechanism level according to the key difference point list of the experiment and the simulation, to obtain a defect formation mechanism analysis report.

[0175] Specifically, based on the difference point list, the physical mechanism level is analyzed in depth: ​

[0176] Consistency analysis: When the experimental results are highly consistent with the simulation results (e.g., in the v = 0.3-0.5 m / min range), analyze the underlying stable mechanism. For example, the simulation shows that there is good lateral flow on the surface of the molten pool, which is consistent with the observation of no undercut phenomenon in the experiment, indicating that the surface tension driven flow under this parameter can effectively inhibit undercut.

[0177] Deviation analysis: When there is a significant difference, explore its physical root cause. For example, when v = 0.8 m / min, the simulation shows that the molten pool accelerates towards the tail, while the experiment observes that the arc is unstable. Analysis shows that high-speed welding leads to insufficient heat input, poor molten pool flow, and easy formation of undercut.

[0178] Defect mechanism inference: Based on the above analysis, determine the defect formation conditions. For example, the correlation mechanism between spoon hole collapse and porosity defects: when the spoon hole is unstable, the liquid metal cannot fill the cavity in time, resulting in gas being trapped to form porosity.

[0179] It should be noted that the mechanism analysis is based on fluid mechanics and heat conduction theory, and the internal physical field (temperature gradient, flow rate, etc.) revealed by simulation explains the macroscopic phenomena observed in the experiment. The core is to transform apparent differences into deep physical mechanism understanding.

[0180] S503, according to the analysis report of the defect formation mechanism, the final optimization of the preferred process parameter window is carried out, and the final optimized process parameter combination is obtained.

[0181] Specifically, according to the mechanism analysis report, the preliminary screened parameter window is finely optimized:

[0182] Parameter window correction: If the mechanism report shows that the defect risk is low in a certain parameter range (e.g., when v = 0.3-0.5 m / min, the molten pool flow is stable), it is directly adopted as the optimization window of the parameter. If the report indicates potential risks (e.g., when v = 0.8 m / min, there is a tendency to undercut), the window range is narrowed or adjusted.

[0183] Multi-parameter collaborative optimization: Consider the interaction between parameters. For example, under the condition of fixed P = 5 kW, I = 150 A, the best range of v is determined to be 0.3-0.5 m / min; at the same time, the stability of other parameter combinations in this range is verified.

[0184] Final determination: With the goal of "promoting good lateral flow on the surface of the molten pool and ensuring dynamic stability of the spoon hole", the process parameter combination verified by experiment and simulation is output. The combination needs to ensure the quality of the welded joint.

[0185] The principle of parameter optimization is based on "mechanism guiding practice". By understanding the conditions for defect formation, the range of process parameters that can avoid these conditions is deduced in reverse, so as to achieve precise control from "knowing what" to "knowing why".

[0186] The present application will be further described in detail below with reference to a specific data embodiment.

[0187] 1. Experimental materials and equipment:

[0188] The experimental material was a 10mm thick Invar steel plate with dimensions of 200mm×100mm×10mm.

[0189] The welding equipment consisted of a YLS-20000 fiber laser and a MagicWave 4000 Job welding machine.

[0190] The high-speed photography equipment is an I-SPEED high-speed camera, which shoots at 2000fps with a resolution of 800×600.

[0191] The numerical simulation software used is Fluent and CFD-Post.

[0192] The image processing software is ImageJ.

[0193] 2. Acquire high-speed photographic images:

[0194] With some parameters fixed (filament spacing 3mm, defocusing amount 0mm), the system varied the laser power (P), welding speed (V), and TIG current (I). The experimental results of varying the welding speed are presented here as an example; the specific parameter design is shown in Table 1, resulting in a high-speed image.

[0195] Table 1

[0196]

[0197] 3. Obtain the feature parameters (using Image J):

[0198] Arc deflection angle (θ): reference Figure 4 As shown, Figure 4 The arc deflection angles at different welding speeds are shown: (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. The centerline of the bright arc zone is fitted, and its angle with the central axis of the tungsten electrode is measured.

[0199] Arc width-to-length ratio (R): Reference Figure 5 As shown, Figure 5Arc width-height-length at different welding speeds, (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. Measure the maximum width (W) of the arc in the direction parallel to the welding direction and the maximum length (L) of the arc along the central axis of the tungsten electrode in the arc region, and calculate R = W / L.

[0200] Keyhole stability: reference Figure 6 Figure 6 Keyhole opening area at different welding speeds, (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. Batch process 21 consecutive images (10 ms long), measure the keyhole opening area s frame by frame. Calculate the average value of the 21 keyhole opening areas s (representing the average degree of opening) and the standard deviation S (representing the volatility).

[0201] In summary, taking the experimental results of the change of welding speed as an example, part of the experimental results are shown in Table 2.

[0202] Table 2

[0203]

[0204] 4, feature parameter screening analysis:

[0205] 4.1, when the welding speed v = 0.2 m / min, θ = 5.814°, R = 2.151, S = 0.032;

[0206] Arc deflection angle score = 100-5x5.814 = 70.93,

[0207] Arc shape score = min(100, 50x2.151) = 100,

[0208] Keyhole stability score = max(0, 100-2000x0.032) = 36;

[0209] WQS = 0.4x70.93 + 0.3x100 + 0.3x36 = 69.17;

[0210] 4.2, when the welding speed v = 0.5 m / min, θ = 10.407°; R = 1.063, S = 0.011;

[0211] Arc deflection angle score = 100-5x10.407 ≈ 47.97,

[0212] Arc shape score = min(100, 50x1.063) = 53.15, ​

[0213] The stability of the spoon hole score = max (0, 100-2000x0.011) = 78;

[0214] WQS = 0.4x47.97 + 0.3x53.15 + 0.3x78 = 58.54;

[0215] 4.3, welding speed v = 1.0 m / min, θ = 14.459 °; R = 0.870, S = 0.326;

[0216] The score of the arc deflection angle = 100-5x14.459 ≈ 27.71,

[0217] The score of the arc shape = min (100, 50x0.087) = 43.50,

[0218] The stability of the spoon hole score = max (0, 100-2000x0.326) = 0;

[0219] WQS = 0.4x27.71 + 0.3x43.50 + 0.3x0 = 24.13;

[0220] According to the score, when v = 0.2 m / min, the WQS score is the highest (69.17 points), and when v = 1.0 m / min, the WQS score is the lowest (24.13 points). In addition, for the parameters (v = 0.2-0.5 m / min): the image shows that the arc is compressed and pulled by the laser, but the arc shape is stable, the deflection angle is small, the width-height ratio is large, and the spoon hole opening area fluctuates little, always maintaining a stable open state. For the parameter (v = 1 m / min): the arc is severely deformed from the image, the spoon hole area is reduced, and the spoon hole area fluctuates greatly, which should be removed. The welding quality comprehensive score WQS is highly consistent with the experimental observation results, verifying the effectiveness of the scoring system.

[0221] Conclusion: The arc deflection angle increases with the increase of welding speed, and the arc width-height ratio decreases with the increase of welding speed, indicating that the arc is more attracted by the laser. From the high-speed image, it can be seen that when the welding speed is 1 m / min, the arc shape is unstable, and the spoon hole area fluctuates greatly. Therefore, the welding speed should not exceed 1 m / min, and when further optimizing the process parameters, the welding speed should be between 0.2-0.8 m / min.

[0222] 5, numerical model establishment and simulation analysis:

[0223] 5.1 Selection of Optimization Parameter Range: Based on the above experimental results, three sets of parameters were selected for numerical simulation comparison: fixed parameters P=5 kW, I=150 A, and variable parameters v=0.3m / min, v=0.5m / min, and v=0.8m / min.

[0224] 5.2 Model Building: Using the software Fluent, a three-dimensional transient model was built and meshed.

[0225] 5.3 Parameter Settings: Enable VOF method to track the free surface of the molten pool; set Invar steel physical property parameters; consider surface tension, gravity, buoyancy, steam back pressure, arc pressure, and electromagnetic force. The heat source adopts a composite model of a double ellipsoidal heat source (TIG) + a rotating Gaussian heat source (laser).

[0226] 5.4 Simulation Analysis: Refer to Figure 7 and Figure 8 As shown, Figure 7 The diagrams show the temperature and flow fields at different welding speeds: (a) v = 0.3 m / min; (b) v = 0.5 m / min; and (c) v = 0.8 m / min. Figure 8 The diagram shows the keyhole depth fluctuation at different welding speeds: (a) v = 0.3 m / min; (b) v = 0.5 m / min; (c) v = 0.8 m / min.

[0227] For the parameters (v=0.3-0.5m / min): The temperature and flow fields show that the molten pool has a large width, and the flow pattern from the upper surface to the edges is dominant, effectively preventing undercut defects. Furthermore, the keyhole depth is moderate with minimal fluctuation, resulting in a stable keyhole and effectively avoiding porosity defects.

[0228] For the parameters (v=0.8m / min): The temperature and flow fields indicate that the molten pool has a small width and the main flow pattern is accelerated flow from the upper surface to the tail, which may cause undercut defects. Additionally, the large variation in keyhole depth easily leads to porosity defects.

[0229] Conclusion: The temperature field, flow field and keyhole depth fluctuation diagram show that a welding speed of 0.3-0.5 m / min can ensure the lateral flow of the molten pool and the stability of the keyhole, effectively reducing the occurrence of undercut and porosity defects.

[0230] 6. Comprehensive optimization and verification:

[0231] Based on the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, the optimal process window for laser-TIG hybrid welding of Invar steel (10mm) is determined as follows:

[0232] Laser power (P): 5kW; TIG current (I): 150A; welding speed (v): 0.3~0.5 m / min.

[0233] Any parameter combination selected in the window can ensure that the arc behavior is stable, the keyhole dynamic is stable, the weld quality is excellent, and there are no obvious pores, undercut and other welding defects on the surface. Referring to Figure 9 , Figure 9 , the specific simulation-actual comparison diagram of the weld surface and the weld cross section is shown.

[0234] The invar laser-TIG welding process method realizes precise control of the invar laser-TIG composite welding process through the deep integration of quantitative experimental observation and simulation calculation, effectively suppresses the generation of pores, undercut and other defects, and significantly improves the welding joint quality and process stability.

[0235] The invar laser-TIG welding process system provided by the embodiments of the present application is applied to the invar laser-TIG welding process method described above, and comprises:

[0236] The acquisition unit is configured to acquire images of the arc and the molten pool area in the welding process by adjusting the process parameters of laser power, welding speed and TIG current, and using a high-speed photography system to obtain high-speed images.

[0237] The extraction unit is configured to extract the features of the arc deflection angle, the arc width-length ratio and the keyhole stability from the high-speed images, respectively, to obtain feature parameters corresponding to the arc deflection angle, the arc width-length ratio and the keyhole stability.

[0238] The screening unit is configured to screen the feature parameters according to the variation law of the feature parameters with the process parameters and the welding quality standard, to obtain an optimal process parameter window of the feature parameters.

[0239] The modeling unit is configured to establish a numerical model of the laser-TIG composite welding process according to the optimal process parameter window, wherein the numerical model is used to simulate the molten pool flow field, the temperature field and the dynamic behavior of the keyhole, and to analyze the flow characteristics of the molten pool surface and the keyhole stability from the mechanism.

[0240] The result unit is configured to perform difference analysis according to the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, to obtain the final optimized process parameter combination.

[0241] The embodiment of the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the invar laser-TIG welding process method when executing the computer program.

[0242] The embodiment of the present application further provides a computer storage medium, which stores a computer program, and the computer program is characterized in that the computer program is executed by a processor to implement the steps of the invar laser-TIG welding process method.

[0243] The embodiment of the present application further provides a computer program, which is executed by a processor to implement the steps of the invar laser-TIG welding process method.

[0244] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0245] Obviously, those skilled in the art should understand that the units or steps of the present application described above can be realized by general computing devices, and they can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and alternatively, they can be realized by program codes executable by the computing devices, so that they can be stored in the storage devices and executed by the computing devices, or they can be respectively manufactured into each integrated circuit module, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the present application is not limited to any particular combination of hardware and software.

[0246] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A process for laser-TIG welding of Invar steel, characterized in that, The application relates to a laser-TIG hybrid welding process parameter optimization method. The method comprises the following steps: Obtaining high-speed images of an arc and a molten pool area in a welding process by adjusting process parameters such as laser power, welding speed and TIG current and using a high-speed photography system; Extracting arc deflection angle, arc width-length ratio and keyhole stability features from the high-speed images to obtain corresponding feature parameters of the arc deflection angle, the arc width-length ratio and the keyhole stability; Screening the feature parameters according to variation rules of the feature parameters with respect to process parameters and welding quality standards to obtain an optimized process parameter window of the feature parameters; According to the optimized process parameter window, a numerical model of the laser-TIG hybrid welding process is established, wherein the numerical model is used for simulating calculation of a molten pool flow field, a temperature field and dynamic behaviors of a keyhole and analyzing molten pool surface flow characteristics and keyhole stability from a mechanism; According to experimental observation results corresponding to the high-speed images and simulation analysis results of the numerical model, difference analysis is performed to obtain a final optimized process parameter combination; The step of extracting the arc deflection angle, the arc width-length ratio and the keyhole stability features from the high-speed images to obtain corresponding feature parameters of the arc deflection angle, the arc width-length ratio and the keyhole stability comprises the following steps: Fitting a center line of an arc bright area in the high-speed images, then drawing a tungsten electrode center axis, and obtaining a feature parameter of the arc deflection angle by using an included angle between the center line of the arc bright area and the tungsten electrode center axis; Identifying an arc boundary in the high-speed images to form an arc area, measuring a maximum arc width parallel to a welding direction, and measuring a maximum arc length along a direction of the tungsten electrode center axis to obtain a feature parameter of the arc width-length ratio; Extracting a keyhole opening area feature from multiple frames of the high-speed images, and sequentially calculating an average value and a standard deviation according to the feature, and obtaining a feature parameter of the keyhole stability by using the standard deviation; The step of screening the feature parameters according to variation rules of the feature parameters with respect to process parameters and welding quality standards to obtain an optimized process parameter window of the feature parameters comprises the following steps: Establishing a corresponding relationship between the feature parameters and the process parameters, and forming a relationship curve of the feature parameters and the process parameters; According to the relationship curve and each process parameter combination, a welding quality comprehensive score calculation result is obtained by performing welding quality comprehensive score calculation; The comprehensive score calculation result is compared and screened with respect to a welding quality standard, and an optimized process parameter window of the feature parameters is formed by using parameters meeting the welding quality standard; The step of establishing a numerical model of the laser-TIG hybrid welding process according to the optimized process parameter window comprises the following steps: According to the optimized process parameter window, a grid model of the laser-TIG hybrid welding process is established, wherein the grid model performs local grid encryption on a dynamic area of a molten pool and a keyhole; On the grid model, material physical parameters of Invar steel and boundary conditions are configured to obtain a numerical model integrating the material physical parameters of Invar steel. The numerical model of the laser-TIG hybrid welding process is obtained by combining the composite heat source model with the numerical model of the integrated Invar steel physical property parameters, wherein the composite heat source model comprises a composite model in which the heat source is composed of a double-ellipsoid heat source and a rotating Gaussian heat source. The step of performing difference analysis according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model to obtain the final optimized process parameter combination, comprising: According to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, difference comparison is performed to obtain a key difference point list of experiments and simulation; According to the key difference point list of experiments and simulation, difference analysis at the mechanism level is performed to obtain an analysis report of defect formation mechanism; According to the analysis report of defect formation mechanism, the preferred process parameter window is finally optimized to obtain the final optimized process parameter combination.

2. The process of laser-TIG welding of Invar steel as claimed in claim 1, wherein, The step of obtaining high-speed images by adjusting the process parameters of laser power, welding speed and TIG current, and using a high-speed photography system to obtain images of the arc and molten pool area during the welding process, comprising: The laser-TIG hybrid welding experiment is performed by single factor variable method, and the process parameters of laser power, welding speed and TIG current are changed respectively.

3. A system for laser-TIG welding of a steel sheet, characterized in that, Applied to the Invar steel laser-TIG welding process method of any one of claims 1-2, comprising: An acquisition unit is configured to obtain high-speed images by adjusting the process parameters of laser power, welding speed and TIG current, and using a high-speed photography system to obtain images of the arc and molten pool area during the welding process; An extraction unit is configured to extract the features of arc deflection angle, arc width-length ratio and keyhole stability from the high-speed images to obtain the feature parameters corresponding to the arc deflection angle, arc width-length ratio and keyhole stability; A screening unit is configured to screen the feature parameters according to the variation law of the feature parameters with the process parameters and the welding quality standard to obtain a preferred process parameter window of the feature parameters; A modeling unit is configured to establish a numerical model of the laser-TIG hybrid welding process according to the preferred process parameter window, wherein the numerical model is used to simulate and calculate the molten pool flow field, temperature field and dynamic behavior of the keyhole, and analyze the flow characteristics of the molten pool surface and the keyhole stability from the mechanism; A result unit is configured to perform difference analysis according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model to obtain the final optimized process parameter combination.

4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the Invar steel laser-TIG welding process method of any one of claims 1-2.

5. A computer storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the Invar steel laser-TIG welding process method of any one of claims 1-2.

6. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the Invar steel laser-TIG welding process method of any one of claims 1-2.

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