Simulation method, system and device for pore suppression and control in the flow process of Invar steel molten pool

Abstract

The present application belongs to the technical field of numerical simulation of welding process. A method for simulating the control of pores in the flow process of the molten pool of Invar steel includes obtaining the three-dimensional parameters of the parent material Invar steel and the welding process parameters; performing three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain the welding area; using the welding process parameters and the welding area to perform welding simulation to obtain the change diagram of the molten pool temperature field, flow field and keyhole at different times; using the welding process parameters corresponding to the risk characteristics in the change diagram, performing welding experiments on the welding area corresponding to the parent material Invar steel to obtain the quantitative mapping relationship between the collapse frequency of the keyhole and the pore distribution characteristic parameters and the welding process parameters; based on the quantitative mapping relationship between the collapse frequency of the keyhole and the pore distribution characteristic parameters and the welding process parameters, forming a pore suppression process window optimization scheme based on keyhole stability control.

Description

Technical Field

[0001] The present application belongs to the technical field of numerical simulation of welding processes, and in particular relates to a method, system and device for simulating the suppression and control of pores in the flow process of an Invar steel molten pool. Background Art

[0002] Invar (4J36), an iron-nickel alloy with a nickel content of up to 36%, is a key material for mold manufacturing in the aerospace and precision instrument industries due to its extremely low thermal expansion coefficient. However, this material faces unique challenges during welding. The high tension and low fluidity of the molten pool can easily cause defects such as thermal cracks, pores, and undercuts. These defects can directly damage the mold surface quality, leading to surface deviations, reduced dimensional accuracy, and residual stress accumulation in composite parts, seriously affecting the performance of the final product.

[0003] In the existing laser-TIG hybrid welding process of Invar steel, as the keyhole moves, the fluid dynamics of the keyhole wall become unstable due to forces such as surface tension and the Marangoni effect (surface tension gradient caused by temperature), causing the keyhole to neck or collapse. The fluid instability at this stage will lead to intensified flow changes on the inner wall of the keyhole. The complex movement of the fluid makes it extremely difficult to predict the molten pool flow field and suppress defects.

[0004] In view of this, there is an urgent need for a method, system and device for simulating the pore suppression and control during the flow process of the Invar steel molten pool to solve the above technical problems. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, system and device for simulating the pore suppression and control during the flow process of the Invar steel molten pool in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for simulating pore suppression and control during the flow process of an Invar steel molten pool, comprising:

[0007] Acquire three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current and welding speed;

[0008] Performing three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain a welding area;

[0009] Using the welding process parameters and the welding area, a welding simulation is performed to obtain a change diagram of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters;

[0010] Using the welding process parameters corresponding to the risk characteristics in the variation diagram, a welding experiment is conducted on the welding area corresponding to the parent material Invar steel to obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters;

[0011] According to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters and the welding process parameters, a pore suppression process window optimization scheme based on keyhole stability regulation is formed.

[0012] In some practicable embodiments, the step of obtaining the three-dimensional parameters of the base material Invar steel and the welding process parameters includes:

[0013] Using a three-dimensional scanner, the three-dimensional measurement of the parent material Invar steel is performed to obtain the three-dimensional parameters of the parent material Invar steel;

[0014] Obtain the process parameters of the welding equipment, and combine the laser power, welding current and welding speed to form several process parameter groups;

[0015] Combining several of the process parameter groups to obtain a database;

[0016] The welding process parameters are obtained through the database.

[0017] In some practicable embodiments, the steps of performing three-dimensional modeling based on the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain the welding area include:

[0018] According to the three-dimensional parameters of the parent material Invar steel, the X-axis direction is the welding direction, the Y direction is the model cross-sectional direction, the Z direction is the model thickness direction, and the gas-liquid interface is used as the origin to perform three-dimensional modeling to obtain the three-dimensional model of the parent material Invar steel;

[0019] Dividing the three-dimensional model into regions near the seam and far from the seam to obtain a near-seam region and a far-seam region;

[0020] The near-seam area and the far-seam area are divided into fine grids and coarse grids respectively to obtain the welding area.

[0021] In some practicable embodiments, the step of performing welding simulation using the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters includes:

[0022] Using the welding process parameters, the temperature field and velocity field are coupled through the SIMPLE algorithm to perform welding simulation on the welding area, and obtain the changes of the molten pool temperature field, flow field and keyhole at different times after the molten pool is formed;

[0023] According to the changes in the molten pool temperature field, flow field and keyhole, a change diagram at different times is formed.

[0024] In some practicable embodiments, the steps of performing a welding experiment on a welding area corresponding to the base material Invar steel using the welding process parameters corresponding to the risk characteristics in the variation diagram to obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters include:

[0025] According to the change diagram, a temperature field distribution diagram, a flow field vector diagram and a keyhole oscillation trajectory diagram are obtained;

[0026] Performing risk feature extraction on the temperature field distribution diagram, the flow field vector diagram, and the keyhole oscillation trajectory diagram to obtain high heat load area parameters of the temperature field distribution diagram, molten pool turbulence area parameters of the flow field vector diagram, and keyhole unstable structure parameters of the keyhole oscillation trajectory diagram;

[0027] According to the high heat load area parameters, the molten pool turbulence area parameters and the keyhole unstable structure parameters, the corresponding welding process parameters are evaluated by a porosity evaluation model to obtain the porosity, wherein the porosity evaluation model formula is:

[0028]

[0029] in, represents porosity, K represents porosity constant, P represents laser power, v represents welding speed, I represents welding current, P opt represents the optimal laser power, v opt Indicates the optimal welding speed, I opt represents the optimum welding current, n, m and s represent the sensitivity index respectively;

[0030] In the porosity, a process parameter group that meets the porosity suppression condition is selected to obtain target welding process parameters;

[0031] Using the target welding process parameters, a welding experiment is conducted on the welding area of ​​the base material Invar steel to obtain keyhole collapse frequency and pore distribution characteristic parameters;

[0032] A quantitative mapping relationship is established according to the keyhole collapse frequency, the pore distribution characteristic parameters, and target welding process parameters to obtain a quantitative mapping relationship between the keyhole collapse frequency, the pore distribution characteristic parameters, and the welding process parameters.

[0033] In some practicable embodiments, the step of forming a porosity suppression process window optimization scheme based on keyhole stability regulation according to the quantitative mapping relationship between the keyhole collapse frequency, the porosity distribution characteristic parameters, and the welding process parameters includes:

[0034] Constructing a quantitative mapping model according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters, and the welding process parameters;

[0035] The stability threshold of keyhole collapse frequency is calibrated according to the porosity control target;

[0036] According to the quantitative mapping model and the stability threshold, the welding parameter space is scanned to identify the boundary of the safe operating domain, and a porosity suppression process window optimization scheme based on keyhole stability regulation is formed, wherein the welding parameter space represents a three-dimensional coordinate system composed of laser power, welding speed and welding current.

[0037] In a second aspect, the present application provides a system for simulating pore suppression and control during the flow process of an invar steel molten pool, which is applied to the aforementioned method for simulating pore suppression and control during the flow process of an invar steel molten pool. The system comprises:

[0038] An acquisition unit, configured to acquire three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current, and welding speed;

[0039] A modeling unit is used to perform three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and to perform meshing on the three-dimensional model to obtain a welding area;

[0040] A graphics unit is used to perform welding simulation using the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field and keyhole at different times under different welding process parameters;

[0041] a mapping unit configured to perform a welding experiment on a welding area corresponding to the parent material Invar steel using the welding process parameters corresponding to the risk characteristics in the variation graph, and obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters;

[0042] The optimization scheme unit is used to form a pore suppression process window optimization scheme based on keyhole stability regulation according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters and the welding process parameters.

[0043] In a third aspect, the present application provides a device for simulating the pore suppression and control during the flow process of an invar steel molten pool, which is applied to the aforementioned simulating the pore suppression and control during the flow process of an invar steel molten pool. The device comprises:

[0044] Glass, arranged on the welding area side of the parent material Invar steel, forming an observation window;

[0045] A clamp for clamping the base material Invar steel and the glass;

[0046] an auxiliary light source, irradiating the clamping area of ​​the clamp;

[0047] a laser and arc heat source assembly, the working end of which is arranged above the clamping area and is used to weld the parent material Invar steel;

[0048] A camera is arranged on one side of the glass and away from the parent material Invar steel, and is used to shoot the welding experiment process;

[0049] The pore suppression control simulation system for the flow process of the Invar steel molten pool is connected to the laser and arc heat source assembly and the camera respectively, and is used to receive data transmitted to it by the camera and to issue instructions to the laser and arc heat source assembly.

[0050] In a fourth aspect, the present application provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for simulating pore suppression and control during the flow process of an Invar steel molten pool.

[0051] In a fifth aspect, the present application provides a computer program, which, when executed by a processor, implements the steps of the aforementioned method for simulating pore suppression and control during the flow process of an Invar steel molten pool.

[0052] In summary, this application has at least the following beneficial effects:

[0053] The present application provides a method for simulating pore suppression control in a molten pool flow process of an Invar steel, comprising obtaining three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current, and welding speed; performing three-dimensional modeling according to the three-dimensional parameters of the base material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain a welding area; performing welding simulation according to the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters; performing welding experiments on the welding area corresponding to the base material Invar steel according to the welding process parameters corresponding to the risk characteristics in the change diagram to obtain a quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters, and the welding process parameters; and forming a pore suppression process window optimization scheme based on keyhole stability control according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters, and the welding process parameters. Through the above technical solution, three-dimensional modeling and transient simulation are driven to locate the flow stagnation area and thermal stress concentration area of ​​the high-tension molten pool in the change diagram; based on the quantitative mapping of keyhole collapse frequency and pore distribution parameters, a causal chain of process parameters and defects is established; the final output process window optimization solution constrains laser power, welding speed, etc. within the safe operating domain, directly suppressing pore generation and thermal deformation, and ensuring mold surface quality and part dimensional accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 This is a flow chart of a simulation method for pore suppression and control in the flow process of an Invar steel molten pool in this application;

[0056] Figure 2 Schematic diagram of a numerical model for pore suppression and control simulation method of Invar steel molten pool flow process in this application;

[0057] Figure 3 Schematic diagram of the temperature field and flow field of the molten pool at different times in a simulation method for pore suppression and control during the flow process of the Invar steel molten pool in this application;

[0058] Figure 4 Schematic diagram of a molten pool keyhole for simulating a method for suppressing pores in the flow process of an Invar steel molten pool in this application;

[0059] Figure 5This is a schematic diagram of the principle structure of a pore suppression and control simulation device for the flow process of the Invar steel molten pool in this application.

[0060] Reference numerals:

[0061] 1. Glass; 2. Fixture; 3. Auxiliary light source; 4. Laser and arc heat source components; 5. Camera; 6. Base material Invar steel. DETAILED DESCRIPTION

[0062] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The term "and / or" as used herein includes any and all couplings of one or more of the associated listed items.

[0064] It will be understood that the terms "first," "second," etc. used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element.

[0065] The following are some explanations of some terms involved in this application to facilitate understanding of this application:

[0066] The SIMPLE algorithm, short for Semi-Implicit Method for Pressure-Linked Equations, is a core pressure-velocity coupling algorithm for solving the Navier-Stokes equations for incompressible flows in computational fluid dynamics (CFD).

[0067] Pseudo-color mapping is a scientific visualization method that transforms non-visual physical quantity data into intuitive color representations. Its core approach is to map continuous or discrete numerical ranges to a specific color space using artificially defined color mapping rules, thereby enhancing the visual recognition of data features. This technique is widely used in the graphical presentation of engineering physical quantities such as temperature and stress fields.

[0068] Multi-layer overlay technology is a visualization method that precisely aligns and fuses multiple independently drawn data layers within a unified spatial coordinate system. Its core approach is to achieve the coordinated expression of multidimensional physical fields through graphical techniques such as transparency control and blending mode adjustment. This technology is a standard solution for processing multivariate coupled data in scientific computing visualization.

[0069] Risk feature extraction is a technical method to identify and quantify potential defect formation signals from welding process monitoring data or simulation results. Its core is to transform the complex physical field evolution into interpretable process risk indicators through multi-dimensional feature engineering.

[0070] Fluent is a computational fluid dynamics (CFD) simulation software used to simulate physical phenomena such as fluid flow, heat transfer, and chemical reactions. CFD (Computational Fluid Dynamics) uses numerical methods to solve fluid motion equations (such as the Navier-Stokes equations) to simulate fluid flow, heat transfer, and other behaviors.

[0071] Quantitative mapping relationship refers to the establishment of a deterministic numerical association between two or more groups of parameters through mathematical methods. Its core is to convert the interaction between input variables (such as process parameters) and output responses (such as quality indicators) into quantifiable function expressions or data correspondence rules.

[0072] TIG welding (Tungsten Inert Gas Welding) is a welding technique that uses a non-consumable tungsten electrode to generate an arc under the protection of an inert gas. Its core principle is to maintain a stable arc using a high-melting-point tungsten electrode (melting point 3422°C) and to isolate air contaminants with an inert gas (argon or helium), achieving high-quality oxygen-free welding.

[0073] like Figure 1 As shown, in a first aspect, the present application provides a method for simulating pore suppression and control during the flow process of an Invar steel molten pool, comprising:

[0074] S100, obtaining three-dimensional parameters of the base material Invar steel and welding process parameters.

[0075] The welding process parameters include laser power, welding current and welding speed.

[0076] Specifically, obtaining the three-dimensional parameters of the base material Invar steel and the welding process parameters may include the following steps:

[0077] S101, using a three-dimensional scanner to measure the three dimensions of the parent material Invar steel, obtain the three-dimensional parameters of the parent material Invar steel, and provide a conventional three-dimensional model construction method to establish a three-dimensional model.

[0078] like Figure 2 As shown, Figure 2 The middle is a schematic diagram of the numerical model. There is air above the molten pool, and the welding heat source used is a Gaussian and double ellipsoid composite heat source.

[0079] S102, obtaining process parameters of the welding equipment, combining laser power, welding current and welding speed to form several process parameter groups.

[0080] The process parameters of the welding equipment, such as laser power, welding current and welding speed, are combined into a parameter matrix in a parameter matrix so as to traverse and generate several process parameter groups. In addition, a welding parameter space is also formed (each process parameter group is marked with spatial coordinates (X / Y / Z position)).

[0081] For example, the laser power of the welding equipment is 4.0-6.0 kW; the welding speed is 0.5-1.5 m / min; and the welding current is 150-225 A.

[0082] S103, combining several of the process parameter groups to obtain a database.

[0083] S104, obtaining the welding process parameters through the database.

[0084] Specifically, after a number of process parameter groups are combined, a database can be formed. When in use, data is retrieved through the database to form welding process parameters.

[0085] S200 , performing three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and performing meshing on the three-dimensional model to obtain a welding area.

[0086] Specifically, obtaining the welding area may include the following steps:

[0087] S201, based on the three-dimensional parameters of the parent material Invar steel, with the X-axis direction as the welding direction, the Y direction as the model cross-section direction, the Z direction as the model thickness direction, and the gas-liquid interface as the origin, three-dimensional modeling is performed to obtain the three-dimensional model of the parent material Invar steel.

[0088] For example, model height: H = h + 2 mm; h: parent material thickness (5-10 mm); gas phase region: 0≤Z≤2 mm; liquid phase region: -h≤Z<0.

[0089] The model is then divided into parts, where the upper surface is set as the top surface, the lower surface is set as the bottom surface, the middle symmetry surface is set as the symmetry surface, and the surrounding surfaces are set as wall surfaces.

[0090] S202 , dividing the three-dimensional model into near-seam and far-seam regions to obtain near-seam regions and far-seam regions.

[0091] Specifically, the area can be divided according to the impact of welding. The area that can be affected by welding is marked as the near-seam area, and the area that cannot be affected is marked as the far-seam area. In addition, it can be divided according to distance. According to the welding position, a preset distance is set. The area within the preset distance is the near-seam area, and the area outside the preset distance is the far-seam area.

[0092] S203 , performing fine grid division and coarse grid division on the near-seam area and the far-seam area respectively to obtain the welding area.

[0093] Specifically, in order to improve the computational efficiency and simplify the time cost required for computation, Figure 2 When meshing the numerical model, a fine mesh is used near the crack, while a coarser mesh is used in the area far from the crack, where the material properties remain unchanged. The specific meshing method is as follows: along the X direction, the mesh size from points 2-10 is 0.1, and from points 0-2 and 10-14 is 0.5; along the Y direction, the mesh size from points 0-5 is 0.1, and from points 5-10 is 0.5; and in the Z direction, the mesh size is 0.1. It should be noted that the specific meshing method is for illustrative purposes only, and other meshing methods can also be used.

[0094] S300, performing welding simulation using the welding process parameters and the welding area to obtain change diagrams of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters.

[0095] Specifically, obtaining the change diagrams of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters may include the following steps:

[0096] S301, using the welding process parameters, coupling the temperature field and the velocity field through the SIMPLE algorithm, performing welding simulation on the welding area, and obtaining changes in the molten pool temperature field, flow field, and keyhole at different times after the molten pool is formed.

[0097] Specifically, by embedding welding process parameters (including laser power and welding speed) into the SIMPLE algorithm framework, the dynamic coupling calculation of temperature field and velocity field is realized.

[0098] It should be noted that a three-dimensional moving heat source model is first constructed based on the laser power and spot position / size. This heat source forms an initial molten pool on the workpiece surface. The SIMPLE algorithm then iteratively solves the coupled field of the momentum and energy equations in the time dimension. At each calculation step, the velocity field is updated, driving the metal flow in the molten pool (manifested by Marangoni convection and thermal buoyancy effects). The updated temperature field, in turn, reacts on the material viscosity and surface tension, thereby changing the flow field morphology. Simultaneously, when the local temperature exceeds the metal vaporization point, the steam recoil pressure forms a keyhole structure at the center of the molten pool. This dynamic interaction of flow field, temperature field, and keyhole morphology continuously advances the calculation. After each time step (in microseconds), the 3D temperature distribution, velocity vector distribution, and keyhole geometry of the molten pool are output, recording the full sequence of data on the time evolution of the molten pool after formation. The SIMPLE algorithm is a conventional algorithm, and this application does not improve the SIMPLE algorithm framework.

[0099] It should also be noted that the simplified solver Simple can be used to couple the temperature field and velocity field to comprehensively study the temperature distribution and flow state of the molten pool at different times after the molten pool is formed. Figure 3 Schematic diagram of the temperature field and flow field of the molten pool at different times, the temperature field (left) and flow field (right) on the longitudinal section of the molten pool at different times, as shown in Figure 2. Figure 3 shown.

[0100] S302, forming a change diagram at different times according to the changes of the molten pool temperature field, flow field and keyhole.

[0101] Specifically, the time series field data output from step S301 is subjected to feature extraction and graphical reconstruction. The melt pool temperature field is converted into a cross-sectional isotherm cloud (for example, red corresponds to high-temperature areas greater than 1800K), the velocity field is converted into a vector diagram of the characteristic profile (for example, a vector arrow diagram, where the arrow density reflects the flow velocity intensity), and a three-dimensional transparent isosurface model of the keyhole is generated using a gas phase volume fraction threshold. The three features at the same moment are rendered in a spatially aligned 3D view, annotated with corresponding timestamps and process parameters, and a time evolution sequence diagram is generated frame by frame.

[0102] Finally, an interactive dynamic image group is synthesized to intuitively present key phenomena such as the expansion and contraction of the molten pool, the evolution of vortices, and keyhole oscillation under different parameters. For example, the keyhole depth fluctuates violently under high-power conditions and the vortex separation at the tail of the molten pool during high-speed welding.

[0103] Furthermore, during the data post-processing stage of the dynamic simulation of the welding process, systematic feature extraction and visualization reconstruction are required for the time series physical field data calculated and output by the SIMPLE algorithm. This process first extracts the three-dimensional isothermal surface at the melt pool boundary by setting a temperature threshold above the material melting point (for example, 1800K for aluminum alloys). A pseudo-color mapping technique is used to convert the temperature gradient into an intuitive color scale distribution, with the high-temperature core area marked in red warning color and the low-temperature transition area gradually fading to blue. For velocity field data processing, the melt pool longitudinal section is selected as the characteristic observation plane. By calculating the three-dimensional velocity vectors of the grid nodes, a dynamic flow field map with directional arrows and length coding is generated. The arrow density is proportional to the flow velocity intensity, and key vortex structures are automatically identified by the extreme points of streamline curvature. Reconstructing the keyhole morphology relies on gas-liquid interface tracking data. For example, the region with a gas volume fraction greater than 0.9 is set as the keyhole entity, and a semi-transparent three-dimensional isosurface model is constructed to illustrate its spatial topological changes.

[0104] In the spatiotemporal alignment visualization synthesis phase, a multi-layer overlay technique is used to integrate the above three feature expressions into a unified three-dimensional coordinate system. The synthetic view of each time step includes the precise spatial registration of the melt pool temperature cloud map, flow field vector slices, and the keyhole transparent model, while also annotating process parameters such as laser power and welding speed and microsecond timestamps. The system can be controlled by automated scripts to batch generate continuous frame images in a time series. The resulting dynamic evolution sequence diagram not only clearly presents the macroscopic morphological changes of the melt pool expansion, but also reveals the microscopic convection pattern of the metal liquid flow inside the melt pool through the flow field vectors, while the semi-transparent keyhole model intuitively displays the oscillation frequency of the steam channel and the collapse risk area. This multi-physics field coupling visualization method enables the quantitative presentation of the control mechanism of process parameters on the dynamic behavior of the melt pool.

[0105] S400, using the welding process parameters corresponding to the risk characteristics in the variation diagram, performing a welding experiment on the welding area corresponding to the parent material Invar steel, and obtaining a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters.

[0106] Specifically, obtaining the quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters may include the following steps:

[0107] S401 , obtaining a temperature field distribution diagram, a flow field vector diagram, and a keyhole oscillation trajectory diagram according to the change diagram.

[0108] Specifically, after obtaining the variation graph in the aforementioned step, the variation graph is decomposed to obtain the temperature field distribution graph, the flow field vector graph, and the keyhole oscillation trajectory graph. In other words, in the aforementioned step, the multi-layer overlay technique is used to perform the overlay, while in this step, the multi-layer overlay technique is used again to decompose the graph to obtain the temperature field distribution graph, the flow field vector graph, and the keyhole oscillation trajectory graph.

[0109] S402, performing risk feature extraction on the temperature field distribution diagram, the flow field vector diagram, and the keyhole oscillation trajectory diagram to obtain high heat load area parameters of the temperature field distribution diagram, molten pool turbulence area parameters of the flow field vector diagram, and keyhole unstable structure parameters of the keyhole oscillation trajectory diagram.

[0110] Specifically, risk feature extraction refers to quantifying the key parameters that indicate the formation of welding defects from the temperature field, flow field, and keyhole dynamic images. These parameters are related to the three major risks of Invar steel welding:

[0111] High heat load causes thermal cracks; turbulent molten pool causes pore retention; and keyhole instability causes keyhole collapse.

[0112] In the keyhole oscillation trajectory, for example, the keyhole aspect ratio is tracked every 0.1 ms, and segments with a change rate greater than 10% / ms are marked to obtain the average oscillation frequency and the maximum aspect ratio change rate. For example, the keyhole aspect ratio = keyhole depth / keyhole width, where depth is the vertical distance from the molten pool surface to the keyhole bottom, and width is the maximum horizontal span of the keyhole.

[0113] It should be noted that the temperature field distribution diagram, the flow field vector diagram and the keyhole oscillation trajectory diagram are all generated using conventional software and conventional algorithms, and this application does not make any improvements thereto.

[0114] Through standardized image analysis, simulation results are converted into engineering parameters that can quantify defect risks, providing accurate criteria for porosity suppression. All operations are implemented using conventional commercial software.

[0115] S403, evaluating corresponding welding process parameters using a porosity evaluation model based on the high heat load area parameters, the molten pool turbulent area parameters, and the keyhole unstable structure parameters to obtain porosity, wherein the porosity evaluation model formula is:

[0116]

[0117] in, represents porosity, K represents porosity constant, P represents the laser power, v represents welding speed, I represents welding current, P opt represents the optimal laser power, vopt Indicates the optimal welding speed, I opt represents the optimum welding current, and n, m and s represent the sensitivity indexes respectively.

[0118] For example, the meanings of the letters in the porosity evaluation model formula are shown in Table 1:

[0119]

[0120] Table 1

[0121] Specifically, the optimal process parameter range is obtained based on the actual welding process, porosity ( ) and welding parameters ( P 、 v 、 I ) is given by the following formula. When the welding process parameters are equal to the optimal value: = K (baseline porosity). When welding process parameters deviate from (approach) their optimal values: As the numerator increases (decreases) while the denominator remains constant, porosity increases (decreases) linearly. This formula directly compares the actual process parameters used in the laser-TIG welding process of Invar steel with the optimal process parameters, providing a direct mapping of the impact of parameter deviations from the optimal value on porosity.

[0122] For example, assuming the baseline welding process parameters and constants, P opt =5kW, v opt =1m / min, I opt =175A, K=5%

[0123] Example 1: All parameters are optimal values:

[0124] Input parameters: P=5kW, v=1m / min, I=175A

[0125]

[0126] but =5%. When the parameters are optimal, the porosity is 5%.

[0127] Example 2: Laser power is too high:

[0128] Input parameter: P=6kW, other parameters are optimal values, then =6.5%.

[0129] Example 3: Welding speed is too fast:

[0130] Input parameter: v=1.5m / min, other parameters are optimal values, then =6.9%.

[0131] Example 4: Current too high:

[0132] Input parameter: I=200A, other parameters are optimal values, then =5.4%.

[0133] This formula provides a clear path for optimizing the Invar steel welding process by quantifying the differences in process parameter sensitivity.

[0134] S404 , screening a process parameter group that meets the porosity suppression condition from the porosity, and obtaining target welding process parameters.

[0135] Specifically, stomatal inhibition conditions were constructed, e.g. ≤ 5%. In this way, the porosity is used as a screening benchmark to screen the process parameter group and obtain the target welding process parameters.

[0136] S405 , performing a welding experiment on the welding area of ​​the parent material Invar steel using the target welding process parameters to obtain keyhole collapse frequency and pore distribution characteristic parameters.

[0137] Specifically, in a welding experiment, target welding process parameters are loaded and a welding experiment is performed. During the welding experiment, records are made to obtain keyhole collapse frequency and pore distribution characteristic parameters.

[0138] It should be noted that the keyhole collapse frequency and pore distribution characteristic parameters are recorded by the camera used in the welding experiment.

[0139] S406 , establishing a quantitative mapping relationship based on the keyhole collapse frequency, the pore distribution characteristic parameters, and target welding process parameters, to obtain a quantitative mapping relationship between the keyhole collapse frequency, the pore distribution characteristic parameters, and the welding process parameters.

[0140] Specifically, conventional statistical analysis methods (such as regression analysis) were used to identify the correlation between process parameter changes and keyhole collapse frequency, and to determine the association pattern between process parameter changes and pore distribution characteristics. Based on this correlation model, a quantitative relationship between process parameters and keyhole behavior was obtained. A quantitative correlation model between process parameters and pore characteristics was then established, forming a set of mapping relationships that can be used for process optimization.

[0141] This process uses standard data analysis methods to transform experimental data into practical, guiding relationships. This process is implemented entirely with commercial statistical analysis software, and those skilled in the art can follow the same principles as conventional data modeling processes. The resulting mapping relationships can be directly used to predict weld quality and guide production process optimization.

[0142] S500 , forming a pore suppression process window optimization scheme based on keyhole stability regulation according to a quantitative mapping relationship between the keyhole collapse frequency, the pore distribution characteristic parameters, and the welding process parameters.

[0143] Specifically, forming an optimization scheme for the pore suppression process window based on keyhole stability regulation may include the following steps:

[0144] S501 : constructing a quantitative mapping model according to a quantitative mapping relationship among the collapse frequency of the keyhole, the pore distribution characteristic parameters, and the welding process parameters.

[0145] Specifically, based on the quantitative mapping relationship established in step S406, a computable mathematical model is constructed. The model uses laser power, welding speed, and welding current as input parameters and can predict keyhole collapse frequency and porosity distribution characteristic parameters.

[0146] For example, the quantitative mapping model can be constructed using conventional Fluent software. The model construction process includes: importing experimental data into the analysis software, performing calculations such as parameter fitting, and finally obtaining a mathematical model that can be directly used for process prediction.

[0147] S502: Calibrate a stability threshold of the keyhole collapse frequency according to a porosity control target.

[0148] Specifically, we analyzed experimental data corresponding to a porosity benchmark (K = 5%) and calculated the distribution range of keyhole collapse frequencies for samples with a porosity of ≤5%, for example, ≤5%. Frequency distribution histogram analysis determined that when the frequency was ≤2.5Hz, the probability of meeting the porosity standard exceeded 95% (based on data from 10 validation experiments). For more stringent porosity control targets (such as the aerospace-grade requirement of 4.5%), a threshold of 2.0Hz was set. This threshold served as the basis for determining the safe operating range.

[0149] S503 , scanning in the welding parameter space according to the quantitative mapping model and the stability threshold, identifying the safe operation region boundary, and forming a porosity suppression process window optimization scheme based on keyhole stability regulation.

[0150] The welding parameter space represents a three-dimensional coordinate system composed of laser power, welding speed and welding current.

[0151] For example, laser-Tig is used as the heat source model in the fluent software to melt the base metal; in the simulation, the Gaussian rotation body equation is used instead of the laser heat source, and the double ellipsoid equation is used instead of the Tig arc; the model is built using the fluent software, and its boundary identification uses the VOF model to track the gas-liquid interface; when F=1, it is solid.

[0152] Identify the safe operating range boundaries and develop an optimized process window for porosity suppression based on keyhole stability control. For example, the proposed solution includes a power of 4.8-5.2kW, a speed of 0.9-1.1m / min, and a current of 170-185A. This means welding within these parameters is safe.

[0153] Next, you can generate a visualization operation domain as needed: Generate a two-dimensional projection map (Pv plane, PI plane, vI plane). Establish three orthogonal projection planes:

[0154] Pv plane: fixed welding current, such as 175A, shows the safe combination range of power and speed;

[0155] PI plane: Fixed speed, such as 1.0m / min, shows the safe combination range of power and current;

[0156] vI plane: Fixed power, such as within 5.0kW, shows the safe combination range of speed and current.

[0157] Through the visualization of safety boundaries, operators are directly guided to set parameters, which is simple and convenient. Example

[0158] A method for simulating pore suppression and control during the flow process of an Invar steel molten pool comprises the following steps:

[0159] Step 1: Determine the thickness of the base material, Invar steel, and the associated welding process parameter ranges. The base material thickness, h, should be within the range of 5-10 mm. The welding process parameters should be within the range of laser power (4-6 kW), welding current (150-225 A), and welding speed (0.5-1.5 m / min). The gas phase should be air, and the welding heat source should be a Gaussian and double ellipsoidal composite heat source.

[0160] Step 2: Model and mesh the model using ICEM software (ANSYS ICEM CFD is a professional CFD pre-processing tool focused on generating high-quality computational meshes (structured and unstructured) and supporting hexahedral meshing for complex geometries). To save computational costs, half of the model was used for calculations. Modeling was performed with the X-axis as the welding direction, the Y-axis as the model cross-section, and the Z-axis as the model thickness. The origin was chosen at the gas-liquid interface. The model's length and width were determined based on specific requirements. The model's height (H) was determined by the plate thickness (h), which was defined as h + 2 mm. The Z coordinate range of the model was: 0 ≤ Z ≤ 2 mm for the gas phase, and -h ≤ Z ≤ 0 for the liquid phase. The model was then divided into parts, with the top surface designated as the top face, the bottom surface as the bottom face, the central symmetry face as the symmetry face, and the surrounding faces as the wall faces.

[0161] Step 3: Simulate the welding process using Fluent software (ANSYS Fluent is a general-purpose computational fluid dynamics (CFD) solver that numerically solves the Navier-Stokes equations to simulate complex flow, heat transfer, and multi-physics coupling problems). The entire welding simulation adheres to the conservation laws of mass, momentum, and energy. First, import the model and set it to the VOF model (a multiphase flow model that tracks the interface, capturing the dynamic evolution of gas-liquid or liquid-liquid interfaces by solving phase volume fraction transport equations). Then, enable the energy equation and the melting and solidification model. Next, set the Invar steel material properties and boundary conditions, setting the top surface to velocity-inlet, the symmetry surface to symmetry, and the bottom and wall surfaces to wall. Import a custom UDF to set the heat source and melt pool forces. For the laser heat source, use a Gaussian heat source, and for the TIG heat source, use a double ellipsoid heat source. The melt pool forces also account for recoil pressure, surface tension, arc force, buoyancy, and other factors.

[0162] Step 4: Finally, select an appropriate calculation step size. Considering that the calculation process must maintain convergence, the step size is 10 -6 –10 -4 Adjust between.

[0163] Step 5: Use post-processing to extract the change diagrams of the molten pool temperature field, flow field and keyhole under different welding process parameters, such as using CFD-Post software (CFD-Post is a post-processing software dedicated to computational fluid dynamics (CFD) and is used for visual analysis and data extraction of CFD simulation results (such as the output of solvers such as Fluent and CFX)), and further optimize the welding process parameter range to achieve the optimal value.

[0164] Step 6: Construct a "sandwich" observation platform, integrating a high-speed camera system to enable real-time monitoring of the weld pool morphology and dynamic keyhole during welding. The keyhole collapse frequency directly reflects the frequency of porosity during welding. Through data analysis, a quantitative mapping relationship between welding parameters, keyhole collapse frequency, and porosity was established, ultimately forming an optimized porosity suppression process window based on keyhole stability control.

[0165] In summary, the present application provides a simulation method for controlling pore suppression during the flow process of the molten pool of Invar steel. In view of the welding difficulties of Invar steel, the laser arc hybrid welding technology shows significant advantages. This technology combines the high energy density of the laser beam and the wide range of action of the arc. It increases the penetration depth by forming a deep melting keyhole through the laser, and uses the arc to expand the molten width and promote the flow of the molten pool, thereby significantly improving the weld formation quality. The synergistic effect of the dual heat sources can not only accurately control the heat input, but also effectively reduce the concentration of thermal stress, greatly reduce the generation of defects such as pores and cracks, and ultimately achieve high-quality and high-efficiency welding effects, meeting the stringent requirements of the aerospace field for precision components of Invar steel.

[0166] In addition, through the coupled simulation of the molten pool temperature field / flow field, the correlation between the keyhole collapse frequency and the pore distribution is accurately captured, a quantitative mapping relationship between the welding process parameters and the porosity is established, and the active regulation of the welding process parameters on the keyhole stability is realized. Compared with the traditional trial and error method, the accuracy and efficiency of the porosity prediction are significantly improved.

[0167] The combination of numerical simulation and high-speed photography provides a powerful tool for in-depth research on melt pool flow and keyhole stability. Numerical simulation can accurately predict the melt pool temperature and flow field distribution through simulation analysis, significantly shortening process development cycles and reducing costs. High-speed photography, on the other hand, can capture the dynamic behavior of the melt pool and the evolution of the keyhole in real time, providing reliable experimental verification and data support for numerical models. The synergistic application of these two technologies not only facilitates a deeper understanding of the complex physical mechanisms of the welding process but also provides a scientific basis for optimizing process parameters, thereby comprehensively improving welding quality and production efficiency.

[0168] In a second aspect, the present application provides a system for simulating pore suppression and control during the flow process of an invar steel molten pool, which is applied to the aforementioned method for simulating pore suppression and control during the flow process of an invar steel molten pool. The system comprises:

[0169] An acquisition unit, configured to acquire three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current, and welding speed;

[0170] A modeling unit is used to perform three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and to perform meshing on the three-dimensional model to obtain a welding area;

[0171] A graphics unit is used to perform welding simulation using the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field and keyhole at different times under different welding process parameters;

[0172] a mapping unit configured to perform a welding experiment on a welding area corresponding to the parent material Invar steel using the welding process parameters corresponding to the risk characteristics in the variation graph, and obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters;

[0173] The optimization scheme unit is used to form a pore suppression process window optimization scheme based on keyhole stability regulation according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters and the welding process parameters.

[0174] like Figure 5 As shown, in a third aspect, the present application provides a device for simulating pore suppression and control during the flow process of an invar steel molten pool, which is applied to the aforementioned simulating system for pore suppression and control during the flow process of an invar steel molten pool, and the device comprises:

[0175] Glass 1 is arranged on the welding area side of the parent material Invar steel 6 to form an observation window;

[0176] A clamp 2, used for clamping the base material Invar steel 6 and the glass 1;

[0177] An auxiliary light source 3 irradiates the clamping area of ​​the clamp 2;

[0178] a laser and arc heat source assembly 4, the working end of which is arranged above the clamping area and is used to weld the parent material Invar steel 6;

[0179] A camera 5 is arranged on one side of the glass 1 and away from the parent material Invar steel 6, and is used to shoot the welding experiment process;

[0180] The pore suppression control simulation system for the flow process of the Invar steel molten pool is connected to the laser and arc heat source components and the camera respectively, and is used to receive data transmitted to it by the camera and to issue instructions to the laser and arc heat source components.

[0181] Specifically, a "sandwich" welding experimental platform was constructed, such as Figure 5 As shown, the laser of the laser and arc heat source assembly 4 is in front, the angle between the laser beam and the welding direction is 90°, the interval between the laser and the arc is 2 mm, and the angle between the arc welding gun and the welding direction is 60°. 99.99% purity argon is used as the shielding gas with a flow rate of 20 L / min. A piece of base material Invar steel 6 and a piece of GG17 glass 1 of the same size are aligned and clamped with a clamp 2. A high-speed camera 5 is horizontally set up on one side of the GG17 glass. In order to better observe the molten pool, an 808 W diode laser with a wavelength of 30 nm is used to irradiate the welding area, and the angle is adjusted until the molten pool keyhole can be clearly displayed on the computer, as shown in FIG. Figure 4 Schematic diagram of the molten pool keyhole, Figure 4 middle Indicates the initial time.

[0182] pass Figure 5 Combining numerical simulations with high-speed photography experiments allows for a direct understanding of the flow, heat, and mass transfer phenomena occurring in the molten pool and keyhole during high-temperature processing, as well as the material melting process. The core of this research is the exploration of the dynamic characteristics of the liquid metal within the molten pool, including its flow pattern, temperature distribution, and solidification process. These behaviors provide a direct understanding of the molten pool fluid dynamics, effectively revealing the inherent mechanisms of molten pool flow and porosity formation, and providing a theoretical basis for establishing the equation between welding parameters, keyhole behavior, and porosity.

[0183] In a fourth aspect, the present application provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for simulating pore suppression and control during the flow process of an Invar steel molten pool.

[0184] In a fifth aspect, the present application provides a computer program, which, when executed by a processor, implements the steps of the aforementioned method for simulating pore suppression and control during the flow process of an Invar steel molten pool.

[0185] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0186] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0187] The scope of protection of the present disclosure is not limited to the above-described embodiments. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the scope and spirit of the present disclosure. If such modifications and variations fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include such modifications and variations.

Claims

1. A method for simulating pore suppression and control during the flow process of an Invar steel molten pool, characterized in that: Methods include: Acquire three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current and welding speed; Performing three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain a welding area; Using the welding process parameters and the welding area, a welding simulation is performed to obtain a change diagram of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters; Using the welding process parameters corresponding to the risk characteristics in the variation diagram, a welding experiment is conducted on the welding area corresponding to the parent material Invar steel to obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters; According to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters and the welding process parameters, a pore suppression process window optimization scheme based on keyhole stability regulation is formed; The step of performing a welding experiment on the welding area corresponding to the parent material Invar steel using the welding process parameters corresponding to the risk characteristics in the variation diagram to obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters includes: According to the change diagram, a temperature field distribution diagram, a flow field vector diagram and a keyhole oscillation trajectory diagram are obtained; Performing risk feature extraction on the temperature field distribution diagram, the flow field vector diagram, and the keyhole oscillation trajectory diagram to obtain high heat load area parameters of the temperature field distribution diagram, molten pool turbulence area parameters of the flow field vector diagram, and keyhole unstable structure parameters of the keyhole oscillation trajectory diagram; According to the high heat load area parameters, the molten pool turbulence area parameters and the keyhole unstable structure parameters, the corresponding welding process parameters are evaluated by a porosity evaluation model to obtain the porosity, wherein the porosity evaluation model formula is: in, represents porosity, K represents porosity constant, P represents laser power, v represents welding speed, I represents welding current, represents the optimal laser power, Indicates the optimal welding speed, represents the optimum welding current, n, m and s represent the sensitivity index respectively; In the porosity, a process parameter group that meets the porosity suppression condition is selected to obtain target welding process parameters; Using the target welding process parameters, a welding experiment is conducted on the welding area of ​​the base material Invar steel to obtain keyhole collapse frequency and pore distribution characteristic parameters; A quantitative mapping relationship is established according to the keyhole collapse frequency, the pore distribution characteristic parameters, and target welding process parameters to obtain a quantitative mapping relationship between the keyhole collapse frequency, the pore distribution characteristic parameters, and the welding process parameters.

2. The method for simulating pore suppression and control during the flow process of an Invar steel molten pool according to claim 1, characterized in that: The step of obtaining the three-dimensional parameters of the base material Invar steel and the welding process parameters includes: Using a three-dimensional scanner, the three-dimensional measurement of the parent material Invar steel is performed to obtain the three-dimensional parameters of the parent material Invar steel; Obtain the process parameters of the welding equipment, and combine the laser power, welding current and welding speed to form several process parameter groups; Combining several of the process parameter groups to obtain a database; The welding process parameters are obtained through the database.

3. The method for simulating pore suppression and control during the flow process of an Invar steel molten pool according to claim 1, characterized in that: The step of performing three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and meshing the three-dimensional model to obtain the welding area includes: According to the three-dimensional parameters of the parent material Invar steel, the X-axis direction is the welding direction, the Y direction is the model cross-sectional direction, the Z direction is the model thickness direction, and the gas-liquid interface is used as the origin to perform three-dimensional modeling to obtain the three-dimensional model of the parent material Invar steel; Dividing the three-dimensional model into regions near the seam and far from the seam to obtain a near-seam region and a far-seam region; The near-seam area and the far-seam area are divided into fine grids and coarse grids respectively to obtain the welding area.

4. The method for simulating pore suppression and control during the flow process of an Invar steel molten pool according to claim 1, characterized in that: The step of performing welding simulation using the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field, and keyhole at different times under different welding process parameters includes: Using the welding process parameters, the temperature field and velocity field are coupled through the SIMPLE algorithm to perform welding simulation on the welding area, and obtain the changes of the molten pool temperature field, flow field and keyhole at different times after the molten pool is formed; According to the changes in the molten pool temperature field, flow field and keyhole, a change diagram at different times is formed.

5. The method for simulating pore suppression and control during the flow process of an Invar steel molten pool according to claim 1, characterized in that: The step of forming a pore suppression process window optimization scheme based on keyhole stability regulation according to the quantitative mapping relationship between the keyhole collapse frequency, the pore distribution characteristic parameters, and the welding process parameters comprises: Constructing a quantitative mapping model according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters, and the welding process parameters; The stability threshold of keyhole collapse frequency is calibrated according to the porosity control target; According to the quantitative mapping model and the stability threshold, the welding parameter space is scanned to identify the boundary of the safe operating domain, and a porosity suppression process window optimization scheme based on keyhole stability regulation is formed, wherein the welding parameter space represents a three-dimensional coordinate system composed of laser power, welding speed and welding current.

6. A pore suppression and control simulation system for the flow process of Invar steel molten pool, characterized in that: The method for simulating pore suppression and control in the flow process of an Invar steel molten pool applied to any one of claims 1 to 5 comprises: An acquisition unit, configured to acquire three-dimensional parameters of the base material Invar steel and welding process parameters, wherein the welding process parameters include laser power, welding current, and welding speed; A modeling unit is used to perform three-dimensional modeling according to the three-dimensional parameters of the parent material Invar steel to form a three-dimensional model, and to perform meshing on the three-dimensional model to obtain a welding area; A graphics unit is used to perform welding simulation using the welding process parameters and the welding area to obtain a change diagram of the molten pool temperature field, flow field and keyhole at different times under different welding process parameters; a mapping unit configured to perform a welding experiment on a welding area corresponding to the parent material Invar steel using the welding process parameters corresponding to the risk characteristics in the variation graph, and obtain a quantitative mapping relationship between the keyhole collapse frequency and the porosity distribution characteristic parameters and the welding process parameters; The optimization scheme unit is used to form a pore suppression process window optimization scheme based on keyhole stability regulation according to the quantitative mapping relationship between the collapse frequency of the keyhole, the pore distribution characteristic parameters and the welding process parameters.

7. A device for simulating pore suppression and control during the flow process of an Invar steel molten pool, characterized in that: The pore suppression control simulation system for the invar steel molten pool flow process according to claim 6 comprises: Glass, arranged on the welding area side of the parent material Invar steel, forming an observation window; A clamp for clamping the base material Invar steel and the glass; an auxiliary light source, irradiating the clamping area of ​​the clamp; a laser and arc heat source assembly, the working end of which is arranged above the clamping area and is used to weld the parent material Invar steel; A camera is arranged on one side of the glass and away from the parent material Invar steel, and is used to shoot the welding experiment process; The pore suppression control simulation system for the flow process of the Invar steel molten pool is connected to the laser and arc heat source assembly and the camera respectively, and is used to receive data transmitted to it by the camera and to issue instructions to the laser and arc heat source assembly.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for simulating the pore suppression and control during the flow process of the invar steel molten pool are realized according to any one of claims 1 to 5.

9. A computer program, characterized in that When the computer program is executed by a processor, the steps of the method for simulating the pore suppression and control during the flow process of the invar steel molten pool are realized according to any one of claims 1 to 5.

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