Invar steel laser-TIG (Tungsten Inert Gas) welding process method, system, equipment, medium and program

Abstract

The invention discloses an invar steel laser-TIG welding process method, system, equipment, medium and program, and relates to the technical field of metal material welding, the method comprises the steps that by adjusting process parameters of laser power, welding speed and TIG current, an image of an electric arc and a molten pool area in the welding process is obtained through a high-speed photographing system, and a high-speed image is obtained; feature extraction of an arc deflection angle, an arc width-to-length ratio and keyhole stability is carried out on the high-speed image, and corresponding feature parameters are obtained; screening the characteristic parameters according to a change rule of the characteristic parameters along with the process parameters and a welding quality standard to obtain an optimal process parameter window of the characteristic parameters; according to the optimized technological parameter window, a numerical model of the laser-TIG hybrid welding process is established; and obtaining a final optimized process parameter combination according to an experimental observation result corresponding to the high-speed image and a simulation analysis result of the numerical model. The method 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 cracking, porosity 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: (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; (2) When the welding speed is too fast, the arc floats above the molten pool, and the arc shape is distorted; (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.

[0004] 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

[0005] The main purpose of the present application is to solve the technical problems of unstable arc behavior and difficult control of molten pool dynamics during invar steel welding in the prior art, so as to realize the parameter optimization of invar steel laser-TIG welding process.

[0006] In order to achieve the above purpose, in the first aspect, the present application provides an invar laser-TIG welding process method, comprising: 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; 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; According to the variation law of the feature parameters with the process parameters and the welding quality standard, the feature parameters are screened to obtain an optimized process parameter window of the feature parameters; According to the preferred process parameter window, a numerical model of the laser-TIG hybrid welding process is established, wherein the numerical model is used to simulate the dynamic behavior of the molten pool flow field, temperature field and keyhole, and analyze the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism; According to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, difference analysis is performed to obtain the final optimized process parameter combination.

[0007] Preferably, the step of obtaining the high-speed image by adjusting the process parameters of laser power, welding speed and TIG current, and using a high-speed photography system to obtain the images of the arc and the molten pool area during the welding process, comprises: The laser-TIG hybrid welding experiment is performed by the single factor variable method, and the process parameters of laser power, welding speed and TIG current are changed respectively.

[0008] Preferably, the step of extracting the characteristics of the arc deflection angle, the arc width-length ratio and the keyhole stability from the high-speed image to obtain the characteristic parameters corresponding to the arc deflection angle, the arc width-length ratio and the keyhole stability, comprises: 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; 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; The characteristic parameters of the keyhole opening area in multiple frames of the high-speed image 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.

[0009] 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 preferred process parameter window of the characteristic parameters, comprises: The corresponding relationship between the characteristic parameters and the process parameters is constructed, and the relationship curve of the characteristic parameters and the process parameters is formed; According to the relationship curve and the corresponding each process parameter combination, the comprehensive score calculation result of the welding quality is obtained by comprehensive score calculation; The comprehensive score calculation result is compared and screened with the welding quality standard, and the preferred process parameter window of the characteristic parameters is formed by using the parameters meeting the welding quality standard.

[0010] Preferably, the step of establishing a numerical model of the laser-TIG hybrid welding process according to the preferred process parameter window comprises: establishing a mesh model of the laser-TIG hybrid welding process according to the preferred process parameter window, wherein the mesh model is locally mesh-encrypted for the molten pool and keyhole dynamic region; configuring the mesh model according to the physical parameters of the Invar steel and the boundary conditions to obtain a numerical model integrated with the physical parameters of the Invar steel; combining a hybrid heat source model with the numerical model integrated with the physical parameters of the Invar steel to obtain a numerical model of the laser-TIG hybrid welding process, wherein the hybrid heat source model comprises a composite model of double-ellipsoid heat source and rotating Gaussian heat source.

[0011] Preferably, 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 comprises: 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 list of key difference points between the experiment and the simulation; performing difference analysis at the mechanism level according to the list of key difference points between the experiment and the simulation to obtain an analysis report of defect formation mechanism; performing final optimization on the preferred process parameter window according to the analysis report of defect formation mechanism to obtain the final optimized process parameter combination.

[0012] In a second aspect, the application provides an Invar steel laser-TIG welding process system, applied to the Invar steel laser-TIG welding process method described above, comprising: an acquisition unit configured to acquire images of the arc and molten pool region during the welding process by adjusting the process parameters of laser power, welding speed and TIG current using a high-speed photography system to obtain high-speed images; an extraction unit configured to extract the characteristic parameters corresponding to the arc deflection angle, arc width-length ratio and keyhole stability from the high-speed images respectively; a screening unit configured to screen the characteristic parameters according to the variation law of the characteristic parameters with the process parameters and the welding quality standard to obtain a preferred process parameter window of the characteristic parameters; a modeling unit 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 molten pool surface flow characteristics and keyhole stability from the mechanism; A result unit is configured to perform difference analysis according to experimental observation results corresponding to the high-speed images and simulation analysis results of the numerical model, and obtain a final optimized process parameter combination.

[0013] 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 according to the foregoing aspects when executing the computer program.

[0014] In a fourth aspect, the present application 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 according to the foregoing aspects.

[0015] 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 according to the foregoing aspects.

[0016] In summary, the present application at least has the following beneficial effects: In the present application, by adjusting the process parameters of laser power, welding speed and TIG current, using a high-speed photography system to obtain images of the arc and molten pool area during the welding process, high-speed images are obtained; the characteristic extraction of the arc deflection angle, the arc length-width ratio and the keyhole stability is performed on the high-speed images, and the characteristic parameters corresponding to the arc deflection angle, the arc length-width ratio and the keyhole stability are obtained; 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 preferred process parameter window of the characteristic parameters; according to the preferred process parameter window, a numerical model of the laser-TIG composite 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, difference analysis is performed to obtain a final optimized process parameter combination. Through the deep integration of quantitative experimental observation and simulation calculation, the precise control of the Invar laser-TIG composite welding process is realized, which can effectively suppress defects such as pores and undercut, and significantly improve the welding joint quality and process stability. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The illustrations, together with the description, serve to explain the application, but do not limit the application. In the drawings: Figure 1Flow chart of a laser-TIG hybrid welding process method for Invar steel in the embodiments of the present application; Figure 2 Measurement schematic of the characteristic parameters in step S200 of the present application; Figure 3 Numerical simulation result cloud chart under the optimized parameters in step S500 of the present application; Figure 4 Arc deflection angle schematic diagram at different welding speeds in the present application; Figure 5 Arc width and height length schematic diagram at different welding speeds in the present application; Figure 6 Spoon hole opening area schematic diagram at different welding speeds in the present application; Figure 7 Temperature field and flow field schematic diagram at different welding speeds in the present application; Figure 8 Spoon hole depth fluctuation schematic diagram at different welding speeds in the present application; Figure 9 Weld appearance schematic diagram in the present application. DETAILED DESCRIPTION

[0018] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely in the following by combining the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or 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 including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] 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 particular orientation, or to be constructed and operated in a particular orientation.

[0021] The terms involved in the present application are explained below in order to understand the technical solutions: 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).

[0022] Image J is an open source image processing and analysis software based on Java language, the core feature of which is designed for scientific image processing, especially good at quantitative measurement and analysis of images with known physical space size (such as microscope photos, medical images, etc.).

[0023] Fluent is a computational fluid dynamics (CFD) software.

[0024] CFD, which stands for Computational Fluid Dynamics, is a branch of fluid mechanics that uses computer numerical calculation and image display methods to analyze and simulate systems involving fluid flow and heat transfer and other related physical phenomena. A "virtual fluid laboratory" is established on the computer, and researchers can use this laboratory to simulate the behavior of fluids under various conditions, thereby replacing or assisting costly and time-consuming physical experiments.

[0025] Marangoni effect refers to the physical phenomenon that liquid flows from an area with low surface tension to an area with high surface tension due to the difference in surface tension on the surface of the liquid.

[0026] VOF method, which stands for Volume of Fluid method, is a numerical technique used in computational fluid dynamics (CFD) to track the position and shape of the free interface (i.e. the interface) between two or more mutually insoluble fluids. The core of the VOF method is to determine the proportion of each fluid in the calculation grid indirectly by solving an additional scalar field (i.e. the VOF function), so as to reconstruct a clear fluid interface.

[0027] For example, Figure 1As shown, the present application provides a method for laser-TIG welding of Invar steel, the method comprising: S100, by adjusting the process parameters of laser power, welding speed and TIG current, using a high-speed photography system to obtain images of the arc and molten pool area during welding, and obtaining high-speed images.

[0028] Specifically, obtaining high-speed images can include the following steps: Laser-TIG hybrid welding experiments are carried out by single factor variable method, and the process parameters of laser power, welding speed and TIG current are changed respectively.

[0029] Specifically, single factor variable method is used to design experiments: fixing part of the parameters, changing one parameter of laser power, welding speed or TIG current in turn, and keeping other parameters unchanged. For example: Change the laser power (P): fix the welding speed v=0.5m / min, TIG current I=150A, gradually increase P from 3kW to 7kW.

[0030] Change the welding speed (v): fix P=5kW, I=150A, gradually increase v from 0.2m / min to 1m / min.

[0031] Change the TIG current (I): fix P=5kW, v=0.5m / min, gradually increase I from 100A to 200A.

[0032] After each change, welding experiments are carried out, and the parameter combinations are recorded. Single factor variable method can clearly observe the influence of single parameter on arc and molten pool by isolating variables, and avoid multi-factor interaction interference.

[0033] During each welding experiment, the high-speed photography system is triggered synchronously to collect images. The camera needs to be aligned with the arc and molten pool area to ensure that the key dynamic processes (such as arc ignition, molten pool formation and spoon hole opening and closing) are covered. When shooting, set appropriate exposure time (such as microseconds) and frame rate (such as 2000fps) to capture rapid changes. For example, a series of continuous images (such as 21 frames corresponding to 10ms duration) are taken to analyze the stability of the spoon hole. After obtaining the images, store them in high-resolution format (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 improvement, making up for the shortcomings of human eye observation.

[0034] S200, the characteristic extraction of arc deflection angle, arc width-length ratio and spoon hole stability is carried out on the high-speed images respectively, and the characteristic parameters corresponding to the arc deflection angle, arc width-length ratio and spoon hole stability are obtained.

[0035] Specifically, refer to Figure 2As shown, obtaining the characteristic parameters corresponding to the arc deflection angle, arc width-length ratio and keyhole stability can include the following steps: S201, fitting the center line of the arc bright area in the high-speed image, then drawing the tungsten electrode center axis, and obtaining the characteristic parameter of the arc deflection angle by the included angle between the center line of the arc bright area and the tungsten electrode center axis.

[0036] Specifically, first, a single frame of high-speed image can be imported into image processing software (such as 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 (such as "line 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 value of θ, 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 evaluating the welding quality.

[0037] 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 between it and 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 θ value thus becomes a stability indicator. The quantification of θ can avoid subjective judgment and improve evaluation accuracy.

[0038] S202, identifying the arc boundary in the high-speed image, forming the arc area, and measuring the maximum width of the arc parallel to the welding direction, and measuring the maximum length of the arc along the direction of the tungsten electrode center axis, to obtain the characteristic parameter of the arc width-length ratio.

[0039] Specifically, in the same frame of image, the arc boundary is first identified, for example, by using the region selection tool (such as "polygon selection" or "threshold segmentation") of Image J, the arc area 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 far end of the arc; finally, the ratio of the arc width-length ratio is calculated. The smaller the value of R, the more serious the compression and stretching of the arc, that is, the arc is compressed along the welding direction (width W) and stretched along the tungsten electrode direction (length L) under the action of the laser.

[0040] 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 R value, qualitative morphological observation is converted into quantitative index, enhancing objectivity.

[0041] S203, the characteristic extraction of the keyhole opening area in the plurality of frames of the high-speed image is performed, and the average value and the standard deviation are calculated in sequence according to the characteristics, and the characteristic parameter of the stability of the keyhole is obtained by using the standard deviation.

[0042] 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 into 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.

[0043] 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 first , represents the keyhole opening area of the i-th frame of image (unit: mm²), represents the area average value of N frames of data (unit: mm²), and n represents the total number of frames; then, the standard deviation S is calculated again, 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, which is easy to cause porosity defects; for example, when S > 0.05, the keyhole is unqualified, and S comprehensively reflects the dynamic behavior of the keyhole.

[0044] 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. Through multi-frame analysis, the transient behavior is captured, which makes up for the limitations of single-frame images.

[0045] S300, according to the variation law of the characteristic parameter with the process parameter and the welding quality standard, the characteristic parameter is screened to obtain an optimal process parameter window of the characteristic parameter.

[0046] Among them, the step S300 aims to screen out 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), combined with the welding quality standard.

[0047] Specifically, obtaining the preferred process parameter window of the characteristic parameter can include the following steps: S301, a corresponding relationship between the characteristic parameter and the process parameter is constructed, and a relationship curve of the characteristic parameter and the process parameter is formed.

[0048] Specifically, the characteristic parameters (θ, R, S) and the corresponding process parameter data (P, v, I) obtained in the foregoing step are obtained from a single factor experiment.

[0049] Exemplarily, based on the single factor variable method, the 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) are listed, and then the relationship curve is drawn according to the list.

[0050] For each process parameter (such as v), a relationship curve of the characteristic parameter (θ, R, S) and the process parameter is drawn respectively. For example, the θ-v curve is drawn with v as the horizontal coordinate and θ as the vertical coordinate; similarly, the R-v curve and the S-v curve are drawn.

[0051] The curve type can adopt a scatter plot or a line chart in order to highlight the trend change.

[0052] 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°).

[0053] 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 an intuitive basis for subsequent scoring.

[0054] The principle of constructing the relationship curve is based on data visualization, converting experimental data into graphical trends, so as to identify the quantitative influence of the process parameter on the characteristic parameter. The single factor variable method ensures the reliability of the curve and avoids multi-factor interference.

[0055] S302, according to the relationship curve and the corresponding each process parameter combination, a welding quality comprehensive score calculation is performed to obtain a comprehensive score calculation result.

[0056] 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, The WQS formula is: WQS=40%×(arc deflection angle score)+30%×(arc shape score)+30%×(keyhole stability score); Specifically, the WQS calculation formula is: WQS = 0.4 x (100 - 5θ) + 0.3 x min(100, 50R) + 0.3 x max(0, 100 - 2000S); Wherein, θ represents the arc deflection angle (unit: degree), the coefficient 5 represents 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), and the coefficient 50 represents that R = 2.0 gets full score 100 points; S represents the standard deviation of the spoon hole opening area (unit: mm²), and the coefficient 2000 magnifies the S value (0-0.05 range) to the degree of influence of the score (0-100 points).

[0057] Calculation process: 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: Arc deflection angle score = 100 - 5θ = 100 - 5 x 10.407 ≈ 47.97; Arc shape score = min(100, 50R) = min(100, 50 x 1.063) = 53.15; Spoon hole stability score = max(0, 100 - 2000S) = max(0, 100 - 2000 x 0.011) = 78.00; Then weighted sum: WQS = 0.4 x 47.97 + 0.3 x 53.15 + 0.3 x 78.00 = 58.54.

[0058] Repeat this process until all parameter combinations, and get the WQS score list. The principle of WQS scoring is based on weighted comprehensive evaluation, which integrates 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.

[0059] 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: According to the process parameter data and historical experimental data or expert knowledge base, analyze the influence law of process parameters on characteristic parameters to obtain a dynamic weight adjustment rule set, which includes the mapping relationship or decision condition between weight and process parameter; According to the current process parameter combination and the dynamic weight adjustment rule set, a dynamic weight value is obtained; Using the dynamic weight value, a characteristic parameter value is calculated to obtain a dynamic WQS score.

[0060] The dynamic WQS score can be applied in the welding quality standard for comparison and screening.

[0061] 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>0.8 m / min), θ increases significantly, indicating that the arc stability decreases; when the high laser power (P>6 kW), the S value fluctuates intensively, indicating that the keyhole is prone to instability.

[0062] Secondly, conditional rules are established to map the process parameters to the weight adjustment direction. Rule examples: If the laser power P>6 kW, the weight of the standard deviation of the keyhole opening area S is increased (such as from 0.3 to 0.5), because the risk of keyhole collapse is high under high power.

[0063] If the welding speed v>0.8 m / 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.

[0064] If the welding stage is “arc striking”, the sensitivity of all weights is temporarily increased to capture transient changes.

[0065] The dynamic weight adjustment rule set can be a series of conditional judgment rules based on process parameter thresholds, which are constructed based on historical data analysis and expert knowledge, aiming 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>6000 W, THEN set the weight as (θ weight is 0.3, R weight is 0.2, S weight is 0.5). The system automatically matches and applies the corresponding rules according to the real-time reading of the process parameters to calculate the dynamic weight value.

[0066] The rules are formalized and coded into executable logic, such as using threshold conditions. The adjustment of dynamic weight is based on the importance of characteristic parameters under different welding conditions. Through rule mapping, the weight gives priority to the most sensitive quality indicators, avoiding the evaluation deviation of fixed weight under extreme parameters.

[0067] The dynamic weight value is calculated, and real-time parameters are obtained, such as reading the current P, v and I values from the welding equipment or experimental settings.

[0068] Apply the weight rule: according to the rule set, calculate the weight value. For example: If P = 5kW (≤ 6kW), v = 0.5m / min (≤ 0.8m / min), the default weights are adopted (the weight of θ is 0.4, the weight of R is 0.3, and the weight of S is 0.3).

[0069] If P = 7kW (> 6kW), the weight of S is increased according to the rule: 0.3 + 0.2 = 0.5, and the weights of θ and R are adjusted accordingly (for example, the weight of θ is 0.3, and the weight of R is 0.2), ensuring that the sum is 1.

[0070] Finally, after determining the weights, they are substituted into the WQS to calculate with the characteristic parameter values.

[0071] S303, the comprehensive score calculation result is compared and screened with the welding quality standard, and the parameter window of the characteristic parameter is formed using the parameter that meets the welding quality standard.

[0072] Specifically, the WQS score calculation result output according to S302 is compared and screened with the predefined welding quality standard (for example, WQS ≥ 80 points ideal, 60 ≤ WQS ≤ 80 points qualified, and WQS < 60 points unstable).

[0073] For example, first, the WQS score is screened according to the score standard: Ideal parameters: WQS ≥ 80 points, directly included in the preferred window, for example, v = 0.2m / min, WQS = 69.17 (qualified), but if a parameter combination WQS ≥ 80, it is preferred.

[0074] Qualified parameters: 60 points ≤ WQS ≤ 80 points, which can be used as candidates and need to be further optimized in combination with simulation verification.

[0075] Unstable parameters: WQS < 60 points, excluded from use, for example, v = 1m / min, WQS = 24.13.

[0076] Then, based on the screening result, the parameter window is determined: for example, through preliminary screening of the score, v is in the range of 0.2-0.8m / min, and finally optimized to v = 0.3-0.5m / min in combination with numerical simulation.

[0077] For another example, the score level standard can also include: Arc deflection angle score = 100-5θ, θ is the arc deflection angle, and the coefficient 5 is to achieve the design goal of "when the deflection angle reaches 20°, the score is 0", which is a scaling factor that maps the angle value (0-20°) to the score value (100-0 points). Every 1° of arc deflection, 5 points are deducted. The smaller the deflection, the fewer the points deducted, and the higher the score.

[0078] 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 θ > 15°, the score is < 25 points (unqualified).

[0079] The arc morphology score = min (100, 50R), where R is the arc width-height ratio, and the coefficient 50 is to achieve the goal of "when the width-height ratio reaches 2.0, a full score of 100 points is obtained". The value of the width-height ratio R is directly multiplied by 50 to be the score. The larger R is, the higher the score is.

[0080] When R > 2.0, 50R > 100 points, the arc morphology score is 100 points; when R = 1.5-2.0, the arc morphology score is 75-100 points (excellent); when R = 1.0-1.5, the arc morphology score is 50-75 points (qualified); and when R < 1.0, the arc morphology score is < 50 points (unqualified).

[0081] The keyhole stability score = max (0, 100 - 2000S), where S is the standard deviation of the keyhole 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). For every 0.01 increase in the fluctuation of the keyhole S, the score is deducted by 20 points. The smaller the fluctuation is, the higher the score is.

[0082] When S < 0.02, the keyhole stability score is > 60 points (excellent); when S = 0.02-0.05, the keyhole stability score is 0-60 points (qualified); and when S > 0.05, 100-2000S < 0, the keyhole stability score is 0 points (unqualified).

[0083] S400, according to the preferred process parameter window, a numerical model of the laser-TIG hybrid welding process is established.

[0084] The numerical model is used to simulate the calculation of the molten pool flow field, temperature field and dynamic behavior of the keyhole, and to analyze the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism.

[0085] The step S400 aims to establish a numerical model of the laser-TIG hybrid welding process, simulate the calculation of 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 level to support the defect cause analysis.

[0086] Specifically, the establishment of the numerical model of the laser-TIG hybrid welding process can include the following steps: S401, according to the preferred process parameter window, a grid model of the laser-TIG hybrid welding process is established.

[0087] wherein the mesh model locally refines the mesh in the dynamic regions of the molten pool and keyhole.

[0088] Specifically, the mesh model is constructed according to the preferred process parameter window (including specific ranges of laser power, welding speed, TIG current, such as P = 5 kW, I = 150 A, v = 0.3~0.5 m / min) and the invar sheet geometry (such as 10 mm thick, 200 mm x 100 mm).

[0089] Illustratively, according to the preferred parameter window and workpiece size, a three-dimensional geometric model is created using numerical simulation software (such as Fluent software). The mesh model covers the welding area, including the invar sheet and the welding path. Then, the calculation mesh is divided, and structured or unstructured mesh can be used, and local mesh refinement is performed in key areas (such as the molten pool formation area and the keyhole dynamic area). The refinement method can be realized through the mesh refinement tool in the Fluent software, for example, smaller mesh size (such as 0.1 mm mesh) is set in the expected area of the molten pool, while coarser mesh (such as 1 mm mesh) is used in non-critical areas. The mesh model can be a "three-dimensional transient model", and the mesh division is adapted to the dynamic calculation requirements to ensure the coordination of time step and spatial mesh.

[0090] It should be noted that the principle of local mesh refinement is based on the accuracy requirements of computational fluid dynamics (CFD). The molten pool and keyhole area involves rapid changes in temperature gradient and fluid motion, and fine mesh can more accurately capture these transient behaviors, while coarse mesh saves computational resources in non-critical areas. By refining, the authenticity of the simulation results (such as temperature field) is ensured, and numerical errors caused by too coarse mesh are avoided.

[0091] S402, on the mesh model, according to the physical parameters of invar steel and the boundary conditions, a numerical model integrating the physical parameters of invar steel is obtained.

[0092] 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: 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, for example, surface tension simulates molten pool flow through Marangoni effect; Environmental boundary: set the initial temperature of the invar sheet (such as room temperature 20°C), the environmental convective heat transfer coefficient to simulate the real welding environment.

[0093] Meanwhile, the VOF (Volume of Fluid) method is enabled to track the dynamic of the molten pool free surface. 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 the heat source addition.

[0094] It is important to note that the principles of material property parameter and boundary condition configuration are to simulate physical reality and conservation laws. Material property parameters determine how materials respond to heat and forces, while boundary conditions simulate the effects of external environments. The VOF method is based on mass conservation and can dynamically track the changes in the molten pool surface, which is crucial for analyzing the stability of the keyhole. By integrating these elements, a reliable foundation is provided for mechanism analysis.

[0095] S403, combine the composite heat source model with the numerical model of the integrated Invar material property parameters to obtain a numerical model of the laser-TIG hybrid welding process.

[0096] The composite heat source model includes a composite model composed of a double-ellipsoid heat source and a rotating Gaussian body heat source.

[0097] The composite heat source model is combined with the material property numerical model. The composite heat source includes two parts: Double-ellipsoid heat source: simulates the heat input of the TIG arc, with a shape of double-ellipsoid, and the heat flux is unevenly distributed in space, with heat concentrated in the focal region. Parameters are based on the preferred window settings, such as TIG current I = 150 A corresponding to heat source power. Further, the double-ellipsoid heat source model imagines the heat range of the TIG arc as a shape composed of two half-ellipsoids (one in front and one behind). The front ellipsoid is used to simulate the "preheating" effect of the arc on the workpiece, and the rear ellipsoid simulates the "slow cooling" effect of the arc. The heat distribution of the TIG arc is relatively dispersed, with a large acting area but low energy density. The double-ellipsoid model can well describe this asymmetric heat flow distribution in the welding direction, which is more accurate than simple point heat sources or columnar heat sources.

[0098] Rotating Gaussian body heat source: simulates the deep penetration effect of the laser, with a rotating Gaussian distribution, and the heat flow attenuates along the beam direction, with parameters such as laser power P = 5 kW. The rotating Gaussian body heat source model describes the laser heat source as a three-dimensional, rotationally symmetric "bell-shaped" distribution, with the highest energy density in the center and gradually decreasing towards the edge, like a rotating Gaussian surface. The core feature of laser welding is the extremely high energy density, which can instantly vaporize metal and form a deep-penetrating "keyhole". The rotating Gaussian body model can depict this highly concentrated, energy-decaying deep penetration effect along the depth direction, which is crucial for simulating keyhole formation and stability.

[0099] The double-ellipsoid heat source model and the rotating Gaussian heat source model are existing mechanical models, and the application does not adjust them.

[0100] When the heat sources are integrated, the two heat sources interact with each other. For example, the TIG arc preheats the workpiece, so that the metal increases the absorption rate of the 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 and the like caused by them. The heat source position (such as the distance between the laser action point and the TIG arc 3mm), direction (along the welding path) can be defined in the Fluent software, and the time-related parameters (such as the moving heat source under the welding speed v=0.5m / min) are set. Then, the solver is configured: select the transient solution mode, set the time step (such as 0.001s), the convergence criterion and the output frequency, to simulate the molten pool flow field, temperature field and the like.

[0101] It should be noted that the principle of heat source combination is based on the energy superposition effect. The laser and the TIG heat source interact with each other, and the composite model can more truly reflect the heat distribution in the 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 the composite welding, and the depth mechanism analysis from the experiment to the simulation is realized through the composite model.

[0102] S500, according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, difference analysis is carried out, and the final optimized process parameter combination is obtained.

[0103] 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 atlas under the specific optimized parameter can be referred to as shown in Figure 3 .

[0104] Specifically, obtaining the final optimized process parameter combination can include the following steps: S501, according to the experimental observation corresponding to the high-speed image and the simulation analysis result of the numerical model, difference comparison is carried out, and a key difference point list of the experiment and the simulation is obtained.

[0105] Specifically, first, the quantitative characteristic parameters extracted from the experiment are compared with the physical field data output by the numerical simulation item by item: Arc behavior comparison: compare the measured θ value and R value in the experiment 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=0.5m / min), and checks whether the arc in the simulation presents the corresponding deflection trend.

[0106] Pool dynamic comparison: Compare the experimentally observed spoon area fluctuation (S value) with the simulated spoon depth fluctuation graph. If the experiment shows S = 0.011 (stable), verify whether the spoon remains in a stable open state in the simulation.

[0107] Temperature field verification: Compare the pool morphology observed by high-speed photography with the isotherm distribution of the simulated temperature field to confirm whether the high-temperature area is consistent.

[0108] Through comparison, generate a list of differences, such as: "The experiment shows that the arc deflection angle is smaller (θ = 5.814°), and the simulation shows that the arc heat source distribution is uniform, with good consistency" or "The experiment shows that the spoon fluctuation is larger (S = 0.326), and the simulation shows that the spoon depth changes dramatically, with consistent trends". By correlating measurable external features (experiments) with difficult-to-observe internal mechanisms (simulations), the accuracy of the numerical model is verified, and input is provided for mechanism analysis.

[0109] S502, according to the list of key differences between the experiment and the simulation, perform a difference analysis at the mechanism level, and obtain an analysis report of the defect formation mechanism.

[0110] Specifically, based on the list of differences, analyze in-depth from the physical mechanism level: Consistency analysis: When the experimental and simulation results are highly consistent (such as v = 0.3-0.5 m / min interval), analyze the underlying stable mechanism. For example, the simulation shows that there is good transverse 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.

[0111] Deviation analysis: When there is a significant difference, explore its physical root. 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.

[0112] Defect mechanism inference: Based on the above analysis, determine the defect formation conditions. For example, the correlation mechanism between spoon collapse and porosity defects: when the spoon is unstable, liquid metal cannot fill the cavity in time, leading to the formation of gas pores.

[0113] It should be noted that 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.

[0114] S503, according to the analysis report of the defect formation mechanism, perform final optimization on the preferred process parameter window, and obtain the final optimized process parameter combination.

[0115] Specifically, according to the mechanism analysis report, the parameter window of the preliminary screening is finely optimized: Parameter window correction: if the mechanism report shows that the defect risk is low in a certain parameter range (such as the molten pool flow is stable when v = 0.3-0.5 m / min), the optimized window of the parameter is directly adopted. If the report prompts potential risks (such as there is a tendency to undercut when v = 0.8 m / min), the window range is narrowed or adjusted.

[0116] Multi-parameter collaborative optimization: considering the interaction between parameters. For example, under the condition of fixing P = 5 kW, I = 150 A, the optimal 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.

[0117] Final determination: taking "promoting good lateral flow on the molten pool surface and ensuring the dynamic stability of the keyhole" as the goal, the process parameter combination verified by experiment and simulation is output. The combination needs to ensure the quality of the welded joint.

[0118] The principle of parameter optimization is based on "mechanism guiding practice", which realizes the precise control from "knowing that" to "knowing why" by understanding the defect formation conditions and inversely deducing the process parameter range that can avoid these conditions.

[0119] The present application will be further described in detail below in combination with a specific data embodiment.

[0120] 1, Experimental materials and equipment: The experimental material is a 10 mm thick invar steel plate with a size of 200 mm x 100 mm x 10 mm; The welding equipment is YLS-20000 fiber laser and MagicWave 4000 Job welding machine; The high-speed photography equipment is I-SPEED high-speed camera with a shooting frame rate of 2000 fps and a resolution of 800 x 600; The numerical simulation software is Fluent software and CFD-Post software; The image processing software is Image J software.

[0121] 2, Obtain high-speed photography images: Fix the part parameters (laser fiber spacing 3 mm, defocus amount 0 mm), and change the laser power (P), welding speed (v) and TIG current (I) of the system. Here, the experimental results of the change of the welding speed are taken as an example for display, and the specific parameter design is shown in Table 1, and the high-speed images are obtained.

[0122] Table 1

[0123] 3, get the characteristic parameters (using Image J): Arc deflection angle (θ): refer to Figure 4 shown, Figure 4 The arc deflection angles at different welding speeds are shown in (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. The center line of the arc bright area is fitted, and the angle between it and the tungsten electrode center axis is measured.

[0124] Arc width-length ratio (R): refer to Figure 5 shown, Figure 5 The arc width-length ratios at different welding speeds are shown in (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. 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 tungsten electrode center axis are measured, and R = W / L is calculated.

[0125] Keyhole stability: refer to Figure 6 shown, Figure 6 The keyhole opening areas at different welding speeds are shown in (a) v = 0.2 m / min; (b) v = 0.5 m / min; (c) v = 1 m / min. Batch processing of 21 consecutive images (10 ms long) is performed, and the keyhole opening area s is measured frame by frame. The average value of the 21 keyhole opening areas s (representing the average opening degree) and the standard deviation S (representing the volatility) are calculated.

[0126] 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.

[0127] Table 2

[0128] 4, screening analysis of characteristic parameters: 4.1, when the welding speed v = 0.2 m / min, θ = 5.814°, R = 2.151, S = 0.032; Arc deflection angle score = 100 - 5 × 5.814 = 70.93, Arc shape score = min (100, 50 × 2.151) = 100, Keyhole stability score = max (0, 100 - 2000 × 0.032) = 36; WQS = 0.4 × 70.93 + 0.3 × 100 + 0.3 × 36 = 69.17; 4.2, θ = 10.407°; R = 1.063, S = 0.011 when welding speed v = 0.5 m / min; Arc deflection angle score = 100 - 5 x 10.407 ≈ 47.97, Arc shape score = min(100, 50 x 1.063) = 53.15, Keyhole stability score = max(0, 100 - 2000 x 0.011) = 78; WQS = 0.4 x 47.97 + 0.3 x 53.15 + 0.3 x 78 = 58.54; 4.3, θ = 14.459°; R = 0.870, S = 0.326 when welding speed v = 1.0 m / min; Arc deflection angle score = 100 - 5 x 14.459 ≈ 27.71, Arc shape score = min(100, 50 x 0.087) = 43.50, Keyhole stability score = max(0, 100 - 2000 x 0.326) = 0; WQS = 0.4 x 27.71 + 0.3 x 43.50 + 0.3 x 0 = 24.13; According to the score, the WQS score is highest (69.17 points) when v = 0.2 m / min, and the WQS score is lowest (24.13 points) when v = 1.0 m / min. In addition, for the parameter (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-to-height ratio is large, and the keyhole opening area fluctuates little, always maintaining a stable open state. For the parameter (v = 1 m / min), the arc is severely deformed, the keyhole area is reduced, and the keyhole 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.

[0129] Conclusion: The arc deflection angle increases with the increase of welding speed, and the arc width-to-height ratio decreases with the increase of welding speed, indicating that the arc is more strongly 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 keyhole 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.

[0130] 5, Numerical model establishment and simulation analysis: 5.1, Select the optimization parameter range: based on the above experimental results, select the fixed parameters P=5 kW, I=150 A, and the variable parameters v=0.3 m / min, v=0.5 m / min and v=0.8 m / min for numerical simulation comparison.

[0131] 5.2, Model establishment: use the software Fluent to establish a three-dimensional transient model and divide the grid.

[0132] 5.3, Parameter setting: turn on the VOF method to track the free surface of the molten pool; set the physical property parameters of invar steel; consider surface tension, gravity, buoyancy, steam recoil pressure, arc pressure and electromagnetic force. The heat source adopts a composite model of double ellipsoid heat source (TIG) + rotating Gaussian body heat source (laser).

[0133] 5.4, Simulation analysis: refer to Figure 7 and Figure 8 , the temperature field and flow field diagrams of different welding speeds are shown, (a) v=0.3 m / min; (b) v=0.5 m / min; (c) v=0.8 m / min. Figure 7 Then the spoon hole depth fluctuation diagram of different welding speeds is shown, (a) v=0.3 m / min; (b) v=0.5 m / min; (c) v=0.8 m / min. Figure 8

[0134] For parameters (v=0.3-0.5 m / min): from the temperature field and flow field, it can be known that the molten pool has a larger width and the upper surface flows to the two edges as the main flow pattern, which can effectively avoid the undercut defect. In addition, the depth of the spoon hole is moderate and the fluctuation is small, the spoon hole is stable, which effectively avoids the porosity defect.

[0135] For parameters (v=0.8 m / min): from the temperature field and flow field, it can be known that the molten pool has a smaller width and the upper surface flows to the tail as the main flow pattern, which may cause the undercut defect. In addition, the depth of the spoon hole changes greatly, which is easy to cause the porosity defect.

[0136] Conclusion: from the temperature field, flow field and spoon hole depth fluctuation diagram, it can be known that the welding speed in the range of 0.3-0.5 m / min can ensure the transverse flow of the molten pool and the stability of the spoon hole state, effectively reducing the generation of undercut and porosity defects.

[0137] 6, Comprehensive optimization and verification: Based on the experimental observation results corresponding to the high-speed image and the simulation analysis results of the numerical model, the optimization process window of invar steel (10 mm) laser-TIG composite welding is determined as follows: ​Laser power (P): 5kW; TIG current (I): 150A; welding speed (v): 0.3~0.5 m / min.

[0138] 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 welding defects such as pores and undercut on the surface. Referring to Figure 9 , the Figure 9 is a schematic diagram of the weld appearance under the condition of laser power 5KW, TIG current 150A and welding speed 0.5 m / min, which specifically shows the simulation-actual comparison diagram of the weld surface and the weld cross section.

[0139] The invar laser-TIG welding process method realizes precise regulation and 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 defects such as pores and undercut, and significantly improves the welding joint quality and process stability.

[0140] The invar laser-TIG welding process system provided in the embodiments of the present application is applied to the invar laser-TIG welding process method described above, and includes: An acquisition unit is configured to acquire images of an arc and a molten pool area in a welding process by adjusting process parameters of laser power, welding speed and TIG current, and using a high-speed photography system to obtain high-speed images. An extraction unit is configured to extract features of an arc deflection angle, an arc width-length ratio and 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. A screening unit is configured to screen the feature parameters according to variation laws of the feature parameters with the process parameters and welding quality standards, and obtain an optimal process parameter window of the feature parameters. A modeling unit is configured to establish a numerical model of a laser-TIG composite welding process according to the optimal process parameter window, wherein the numerical model is used to simulate and calculate a molten pool flow field, a temperature field and dynamic behavior of a keyhole, and analyze molten pool surface flow characteristics and keyhole stability from a mechanism. A result unit is configured to perform difference analysis according to experimental observation results corresponding to the high-speed images and simulation analysis results of the numerical model, and obtain a final optimized process parameter combination.

[0141] The embodiments of the present application also provide a computer device including 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 described above when executing the computer program.

[0142] The embodiment of the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the above-mentioned invar laser-TIG welding process method.

[0143] The embodiment of the present application further provides a computer program, which is executed by a processor to realize the steps of the above-mentioned invar laser-TIG welding process method.

[0144] 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.

[0145] Obviously, those skilled in the art should understand that each unit or each step of the above-mentioned application can be realized by a general computing device, which can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, each unit or each step can be realized by a program code executable by a computing device, so that each unit or each step can be stored in a storage device and executed by a computing device, or each unit or each step can be made into an individual integrated circuit module, or multiple units or steps can be made into a single integrated circuit module. Thus, the application is not limited to any specific combination of hardware and software.

[0146] 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 should be included in the protection scope of the present application.

Claims

1. A laser-TIG welding process for Invar steel, characterized in that, include: By adjusting the process parameters of laser power, welding speed and TIG current, high-speed images are obtained by using a high-speed photography system to acquire images of the arc and molten pool areas during the welding process. Feature extraction is performed on the high-speed image to obtain the feature parameters corresponding to the arc deflection angle, arc width-to-length ratio, and keyhole stability. Based on the variation law of the characteristic parameters with process parameters and welding quality standards, the characteristic parameters are screened to obtain the preferred process parameter window for the characteristic parameters; Based on the preferred process parameter window, a numerical model of the laser-TIG hybrid welding process is established. The numerical model is used to simulate and calculate the dynamic behavior of the molten pool flow field, temperature field and keyhole, and to analyze the flow characteristics of the molten pool surface and the stability of the keyhole from a mechanistic perspective. Based on the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, a difference analysis is performed to obtain the final optimized combination of process parameters.

2. The laser-TIG welding process for Invar steel as described in claim 1, characterized in that, The step of obtaining high-speed images by adjusting process parameters such as laser power, welding speed, and TIG current, and using a high-speed photography system to acquire images of the arc and molten pool areas during the welding process, includes: Laser-TIG hybrid welding experiments were conducted using a single-factor variable method, with the process parameters of laser power, welding speed, and TIG current being varied.

3. The laser-TIG welding process for Invar steel as described in claim 1, characterized in that, The step of extracting features of the arc deflection angle, arc width-to-length ratio, and keyhole stability from the high-speed image to obtain the feature parameters corresponding to the arc deflection angle, arc width-to-length ratio, and keyhole stability includes: The centerline of the bright arc region in the high-speed image is fitted, and then the central axis of the tungsten electrode is drawn. The characteristic parameters of the arc deflection angle are obtained by using the angle between the centerline of the bright arc region and the central axis of the tungsten electrode. The arc boundary in the high-speed image is identified to form an arc region. The maximum arc width parallel to the welding direction and the maximum arc length along the tungsten electrode central axis are measured to obtain the characteristic parameters of the arc width-to-length ratio. Feature extraction is performed on the keyhole opening area in multiple frames of the high-speed image, and the average value and standard deviation are calculated sequentially based on the features. The standard deviation is used to obtain the feature parameters of the keyhole stability.

4. The laser-TIG welding process for Invar steel as described in claim 1, characterized in that, The step of filtering the feature parameters according to the variation law of the feature parameters with process parameters and welding quality standards to obtain the preferred process parameter window for the feature parameters includes: Construct the correspondence between the feature parameters and the process parameters, and form the relationship curve between the feature parameters and the process parameters; Based on the relationship curve and the corresponding combination of process parameters, a comprehensive score for welding quality is calculated to obtain the comprehensive score calculation result. The comprehensive score calculation results are compared and screened with the welding quality standards, and the preferred process parameter window of the characteristic parameters is formed by using the parameters that meet the welding quality standards.

5. The laser-TIG welding process for Invar steel as described in claim 1, characterized in that, The step of establishing a numerical model of the laser-TIG hybrid welding process based on the preferred process parameter window includes: Based on the preferred process parameter window, a mesh model of the laser-TIG hybrid welding process is established, wherein the mesh model performs local mesh refinement on the dynamic areas of the molten pool and keyhole; On the mesh model, the physical property parameters and boundary conditions of Invar steel are configured to obtain a numerical model that integrates the physical property parameters of Invar steel. The composite heat source model is combined with the numerical model of the integrated Invar steel physical property parameters to obtain a numerical model of the laser-TIG composite welding process. The composite heat source model includes a composite model consisting of a double ellipsoidal heat source and a rotating Gaussian heat source.

6. The laser-TIG welding process for Invar steel as described in claim 1, characterized in that, The step of performing a difference analysis based on the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model to obtain the final optimized combination of process parameters includes: Based on the experimental observation results corresponding to the high-speed images and the simulation analysis results of the numerical model, a comparison of differences is made to obtain a list of key differences between the experiment and the simulation. Based on the list of key differences between the experiment and the simulation, a mechanistic analysis was conducted to obtain an analysis report on the defect formation mechanism. Based on the analysis report of the defect formation mechanism, the preferred process parameter window is finally optimized to obtain the final optimized combination of process parameters.

7. A laser-TIG welding process system for Invar steel, characterized in that, The laser-TIG welding process for Invar steel applied to any one of claims 1-6 includes: The acquisition unit is used to acquire images of the arc and molten pool areas during the welding process by adjusting the process parameters of laser power, welding speed and TIG current, and to obtain high-speed images using a high-speed photography system. The extraction unit is used to extract features of the arc deflection angle, arc width-to-length ratio, and keyhole stability from the high-speed image, and obtain the feature parameters corresponding to the arc deflection angle, arc width-to-length ratio, and keyhole stability. The filtering unit is used to filter the feature parameters according to the variation law of the feature parameters with process parameters and welding quality standards to obtain the preferred process parameter window of the feature parameters. The modeling unit is used to establish a numerical model of the laser-TIG hybrid welding process based on the preferred process parameter window. The numerical model is used to simulate and calculate the dynamic behavior of the molten pool flow field, temperature field and keyhole, and to analyze the flow characteristics of the molten pool surface and the stability of the keyhole from the mechanism. The results unit is used to perform a difference analysis based on the experimental observation results corresponding to the high-speed image and the simulation analysis results of the numerical model, and to obtain the final optimized combination of process parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the Invar steel laser-TIG welding process method as described in any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the Invar steel laser-TIG welding process method as described in any one of claims 1-6.

10. A computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the Invar steel laser-TIG welding process method as described in any one of claims 1-6.

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