A method, system, storage medium and device for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy

Abstract

The application discloses a kind of TC4 titanium alloy electron beam cold bed smelting self-adapting time-averaged Gaussian heat source construction method, system, storage medium and equipment, belong to titanium alloy smelting numerical simulation technical field.The method of the present application is by obtaining the actual scanning speed of electron beam and scanning frequency, introduce the dynamic adjustment factor related to scanning speed and frequency, the energy distribution width parameter and peak energy of standard time-averaged Gaussian heat source are corrected, and a self-adapting time-averaged Gaussian heat source model is constructed.The self-adapting time-averaged Gaussian heat source is used as the source term of energy equation, substituted into three-dimensional heat-flow-solidification coupling model for iterative solution, can efficiently and accurately obtain the temperature field and flow field distribution of TC4 titanium alloy molten pool in wide scanning speed range, to optimize electron beam smelting process, control ingot solidification quality provides theoretical guidance.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology for titanium alloy melting, specifically to a method, system, storage medium, and device for constructing an adaptive time-averaged Gaussian heat source for electron beam cold bed melting of TC4 titanium alloy. Background Technology

[0002] Titanium alloys have irreplaceable application value in high-end equipment fields such as aerospace, marine engineering, and biomedicine. TC4 (Ti-6Al-4V) titanium alloy is currently the most widely used α+β type two-phase titanium alloy, and its usage in key components such as aero-engine blades, fuselage structural parts, and high-end fasteners continues to grow. With the development of new-generation aero-engines, large passenger aircraft, and deep-sea equipment, higher requirements are being placed on the quality and performance of TC4 titanium alloy ingots.

[0003] Electron Beam Cold Hearth Melting (EBCHM) is an important method for preparing high-quality titanium alloy ingots. This method utilizes a high-energy electron beam to melt the raw material under vacuum conditions, removes high-density and low-density inclusions through cold hearth refining, and finally solidifies in a water-cooled copper crucible to obtain the ingot. Compared to vacuum consumable arc melting (VAR), EBCHM has advantages such as high melting rate, good refining effect, and continuous production capability, and has become a key technical route for preparing high-end titanium alloys. The EBCHM process involves complex thermo-fluid-solid coupling phenomena under high temperature, high vacuum, and high-energy beam scanning conditions. The temperature distribution, fluid flow, and solidification behavior within the molten pool directly determine the compositional homogeneity, microstructure density, and mechanical properties of the ingot. Due to the limitations of the high-temperature, high-vacuum environment inside the melting chamber, the temperature field, flow field, and solid-liquid interface position within the molten pool are difficult to measure directly through experimental means. Therefore, establishing a reliable numerical simulation model to predict the multi-field coupling behavior in the molten pool under different process parameters is of great significance for optimizing the EBCHM process and controlling the quality of ingots.

[0004] In EBCHM numerical simulations, the accurate description of the electron beam heat source is a key factor determining the reliability of the simulation results. Existing electron beam heat source models have evolved from fixed Gaussian heat sources to moving Gaussian heat sources and then to time-averaged Gaussian heat sources. Fixed Gaussian heat sources assume that the electron beam energy has a spatially Gaussian distribution that does not change over time. While computationally simple, they cannot reflect the dynamic superposition effect of energy on the molten pool surface during electron beam scanning. Moving Gaussian heat sources track the electron beam's scanning trajectory by updating the heat source center position at each time step, accurately describing the energy input process. However, the heat source distribution needs to be recalculated at each time step, limiting the CFD simulation time step to the electron beam scanning frequency (typically 0.1-10 Hz). This results in extremely long computation cycles, often requiring several days or even weeks to complete a steady-state simulation, making it difficult to meet the simulation requirements of industrial-grade large-size ingots and the demands of real-time process optimization.

[0005] To balance computational efficiency and prediction accuracy, existing research has proposed a time-averaged Gaussian heat source model. This model averages the energy input of the electron beam over one scan cycle to obtain an equivalent static Gaussian heat source distribution, thereby reducing the time step size of CFD simulation from the scan cycle level (10^65 minus ... -2 -10 -1 s) Increased to the solidification process level (10 1 -10 2 While the traditional time-averaged Gaussian heat source model significantly shortens computation time (e.g., >10 cm / s), subsequent studies have shown that it is only suitable for applications with high electron beam scanning speeds (typically >10 cm / s). At low scanning speeds (e.g., <5 cm / s), the electron beam spends a longer time at the same location on the molten pool surface, resulting in a significant deviation between the time-averaged energy distribution and the instantaneous energy distribution of the actual moving heat source. This leads to excessive concentration of energy and excessively high peak temperatures in the time-averaged heat source, failing to accurately reflect the temperature gradient on the molten pool surface. Consequently, the temperature field prediction accuracy of the traditional time-averaged Gaussian heat source decreases significantly under low-speed scanning conditions, limiting its applicability.

[0006] In the inventors' related research, a multi-field coupled model of "heat-fluid-solidification-volatilization" for the EBCHM process of TC4 titanium alloy has been established. This model was solved using FLUENT software combined with user-defined functions (UDFs). The evolution of the molten pool morphology was studied under different casting temperatures (2073K-2273K) and casting speeds (5-25mm / min) for Φ260mm round ingots, 1050mm×220mm flat ingots, and Φ620mm large round ingots. The electron beam scanning parameters used in this study were: 20-turn inward-reducing pattern, maximum scanning radius 130mm, scanning period 200ms, scanning frequency 5Hz, and pulse interval 0.1ms. This research provides an important reference for understanding the relationship between molten pool morphology and process parameters during the EBCHM process; however, the accuracy problem of the traditional time-averaged heat source model used in this study under low scanning speed conditions remains unresolved.

[0007] In summary, existing EBCHM simulations lack a heat source modeling method that can simultaneously guarantee computational efficiency and prediction accuracy across a wide scan speed range (from low to high). While the inventors previously established a comprehensive EBCHM multi-field coupling model, the traditional time-averaged heat source model employed suffers from insufficient accuracy under low scan speed conditions. Therefore, developing a time-averaged heat source modeling method that can adaptively adjust the heat source distribution characteristics based on scan speed and frequency is crucial to solving these technical problems. Summary of the Invention

[0008] To overcome the above-mentioned technical problems, the present invention provides a method, system, storage medium and device for constructing an adaptive time-averaged Gaussian heat source for electron beam cold bed melting of TC4 titanium alloy.

[0009] To achieve the above-mentioned technology, the present invention will be described from four perspectives.

[0010] The first approach of this invention provides a method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy, comprising the following steps: S1. Obtain the scanning speed and frequency of the electron beam; S2. Calculate the dynamic adjustment factor based on the scan speed and frequency of S1; S3. Based on the dynamic adjustment factor obtained from S2, the parameters of the standard time-averaged Gaussian heat source are corrected. S4. Based on the parameters corrected in S3, construct an adaptive time-averaged Gaussian heat source model; S5. Substitute the adaptive time-averaged Gaussian heat source obtained in S4 into the three-dimensional heat-fluid-solidification coupling model for iterative solution to obtain the temperature field and flow field distribution of the TC4 titanium alloy molten pool. S6. Output the molten pool temperature field and flow field distribution results based on the calculation results of S5; Based on S1 to S6, a closed-loop design was completed for the construction method of an adaptive time-averaged Gaussian heat source for electron beam cold bed melting of TC4 titanium alloy.

[0011] Furthermore, the calculation formula for the dynamic adjustment factor is as follows: In the formula, Indicates the dynamic adjustment factor; Indicates the speed adjustment coefficient; Indicates the scan speed threshold; Indicates the frequency adjustment coefficient; Indicates the reference frequency; Indicates the scanning speed; Indicates the scanning frequency.

[0012] Furthermore, in S3, the parameters that need to be corrected include: energy distribution width parameter and peak energy density; The correction formula for the energy distribution width parameter is as follows: In the formula, The parameter representing the energy distribution width of a standard time-averaged Gaussian heat source; This represents the corrected energy distribution width parameter; The correction formula for the peak energy density is as follows: In the formula, This represents the peak energy density of a standard time-averaged Gaussian heat source, in units of... ; This indicates the corrected peak energy.

[0013] Furthermore, the adaptive time-averaged Gaussian heat source model is expressed as follows: In the formula, This is the radial distance from the center of the heat source; This represents an adaptive time-averaged Gaussian heat source.

[0014] Furthermore, the three-dimensional heat-fluid-solidification coupling model includes: mass conservation equation, momentum conservation equation, energy conservation equation, and solid-liquid phase transition equation.

[0015] Furthermore, the method of substituting the adaptive time-averaged Gaussian heat source obtained in S4 into the three-dimensional heat-fluid-solidification coupling model is as follows: substituting the adaptive time-averaged Gaussian heat source into the energy conservation equation.

[0016] Furthermore, the expression for the mass conservation equation is as follows: in, Density; For time; It is a velocity vector; The expression for the momentum conservation equation is as follows: in, Dynamic viscosity; For pressure; For Marangoni convection source terms; The expression for the energy conservation equation is as follows: in, Specific heat capacity; The expression for the solid-liquid phase transition equation is as follows: in, The current temperature; This refers to the solidus temperature. This is the liquidus temperature; This represents the liquid phase fraction.

[0017] Secondly, the present invention provides a system for an adaptive time-averaged Gaussian heat source for electron beam cold bed melting of TC4 titanium alloy, comprising: The parameter acquisition module is used to execute the steps in S1; The dynamic adjustment factor calculation module is used to execute the steps in S2. The heat source correction module is used to perform the steps in S3; An adaptive time-averaged heat source construction module is used to execute the steps in S4; A coupled solution module is used to execute the steps in S5; The output module is used to execute the steps of S6.

[0018] Thirdly, the present invention provides a computer-readable storage medium for an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0019] Fourthly, the present invention provides an electronic device for an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0020] The beneficial effects of this invention are: This invention replaces the moving heat source with an adaptive time-averaged heat source, allowing for CFD simulations with a time step of 10. -2 -10 -1 s increased to 10 1 -10 2 The computation time is reduced from tens of hours to several hours by the order of seconds; at the same time, through dynamic adjustment factor correction, the temperature field prediction deviation can be kept less than 5% under both low-speed and high-speed scanning conditions.

[0021] This invention enables efficient simulation over a wide scanning speed range, and can be directly used for parameter optimization of scanning speed, frequency, and pattern in the EBCHM process, reducing the number of trial casting experiments. For ingots with dimensions of Φ260mm-620mm, this method allows for the prediction of the molten pool depth range before calculation, providing a quantitative reference for solidification quality control.

[0022] This invention embeds the FLUENT solver framework in the form of a UDF, which can be called by simply modifying the heat source subroutine without changing the mesh generation and solver settings, making it easy to implement directly on the existing EBCHM simulation platform. Attached Figure Description

[0023] Figure 1 This is a flowchart of the adaptive time-averaged Gaussian heat source modeling method of the present invention; Figure 2 A three-dimensional distribution diagram of the energy density of a Gaussian heat source; Figure 3 This is a schematic diagram of the interaction between the electron beam and the molten pool; where, Figure 3 (a) Figure 3 (b) and Figure 3 (c) represents the energy field density of 0.15. The center position of the electron beam and the distance from the center of the heat source are 1. Location and distance from the heat source center 2 A schematic diagram showing the location results; Figure 3 (d) Figure 3 (e) and Figure 3 (f) represents the values ​​at an energy field density of 0.50. The center position of the electron beam and the distance from the center of the heat source are 1. Location and distance from the heat source center 2 A schematic diagram showing the location results; Figure 3 (g) and Figure 3 (h) represents the energy field density of 0.15. And energy field density 0.50 A schematic diagram of the molten pool morphology in the central region of the electron beam; Figure 4 A comparison of the temperature distribution on the surface of the molten pool under a moving Gaussian heat source and a time-averaged heat source; among which... Figure 4 (a) is a schematic diagram of heat source location one; Figure 4 (b) is a schematic diagram of heat source location two; Figure 5 The morphology of the molten pool of a Φ260mm round ingot under the conditions of casting temperature 2073K and casting speed 10mm / min; Figure 6 The morphology of the molten pool of a Φ260mm round ingot under the conditions of casting temperature 2273K and casting speed 25mm / min; Figure 7 The morphology of the molten pool of a flat ingot (1050mm×220mm) under the conditions of casting temperature 2073K and casting speed 8mm / min; Figure 8 The morphology of the molten pool of a Φ620mm large round ingot under the conditions of casting temperature 2073K and casting speed 5mm / min. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to specific embodiments.

[0025] This invention modifies the energy distribution width parameter and peak energy of a traditional time-averaged Gaussian heat source by introducing a dynamic adjustment factor related to scanning speed and scanning frequency, constructing an adaptive time-averaged Gaussian heat source model. This model is then substituted as a source term in the energy equation into a three-dimensional heat-fluid-solidification coupling model for solution, achieving efficient and accurate prediction of the molten pool temperature and flow field distribution over a wide scanning speed range. Specifically, the invention includes the following steps: See Figure 1 A method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy includes the following steps: S1. Obtain the scanning speed and frequency of the electron beam; The actual scanning speed of the electron beam is obtained by directly reading from the control system of the electron beam melting equipment or by pre-setting according to the process specifications. and scan frequency ; Among them, the actual scanning speed The average moving speed of the electron beam focal spot along the scanning trajectory on the surface of the molten pool is given by units of 1000 m / s. Scan frequency The number of times the electron beam scan pattern repeats per unit time, expressed in units of... .

[0026] Typically, the scan rate in the EBCHM process ranges from 0.1 to 100. The scanning frequency range is 0.1-100. .

[0027] S2. Based on the scanning speed and frequency of S1, a dynamic adjustment factor is calculated to quantify the influence of scanning speed and scanning frequency on the deviation of the time averaging effect. The design of the dynamic adjustment factor is based on the following physical analysis: When the scanning speed is low, the residence time of the electron beam focal spot at the same position on the molten pool surface is relatively long, and the energy superposition effect under the time-averaging assumption deviates more from the instantaneous energy distribution of the real moving heat source. At this time, it is necessary to increase the energy distribution width and reduce the peak energy to compensate for the over-concentration effect. When the scanning speed is high, the electron beam residence time is short, and the time-averaging assumption is more reasonable. At this time, it is necessary to appropriately narrow the energy distribution and increase the peak energy to more closely approximate the characteristics of the real heat source. Similarly, the increase of the scanning frequency means that the number of scans per unit time increases, the energy distribution is more uniform, and the time-averaging effect is stronger. Therefore, the dynamic adjustment factor should decrease with the increase of frequency.

[0028] The formula for calculating the dynamic adjustment factor is as follows: In the formula, Indicates the dynamic adjustment factor; Indicates the speed adjustment coefficient; Indicates the scan speed threshold; Indicates the frequency adjustment coefficient; Indicates the reference frequency; Preferred speed adjustment coefficient Frequency adjustment coefficient Scanning speed threshold and reference frequency Determined based on experimental calibration; It can be seen that when the scanning speed Below the preset threshold hour, This indicates a need to broaden the heat source and reduce the peak value; when the scan speed Higher than the preset threshold hour, This indicates that it is necessary to narrow the heat source and increase the peak value.

[0029] S3. Based on the dynamic adjustment factor obtained from S2, the parameters of the standard time-averaged Gaussian heat source are corrected. See Figure 2 This demonstrates the distribution characteristics of the heat source center having the highest energy density and exhibiting an exponential decay along the radial direction, illustrating the fundamental physical laws governing electron beam energy input.

[0030] Using dynamic adjustment factors The energy distribution width parameter and peak energy of the standard time-averaged Gaussian heat source are corrected to obtain the corrected energy distribution width parameter and corrected peak energy. Specifically, the expression for the modified energy distribution width parameter is as follows: In the formula, This parameter represents the energy distribution width of a standard time-averaged Gaussian heat source, in units of 1. ; This represents the corrected energy distribution width parameter; Specifically, the expression for the corrected peak energy density is as follows: In the formula, This represents the peak energy density of a standard time-averaged Gaussian heat source, in units of... ; This indicates the corrected peak energy; The physical meaning of the corrected relationship is: at low speeds... The corrected energy distribution width is increased while the peak energy is reduced, resulting in a smoother heat source distribution that more closely resembles the spatial distribution of a real moving heat source at low speeds; at high speeds... The corrected energy distribution narrows the width while the peak energy increases, and the heat source distribution becomes more concentrated, which is consistent with the characteristics of a real moving heat source at high speed.

[0031] S4. Based on the corrected parameters in S3, construct an adaptive time-averaged Gaussian heat source model, as shown in the following expression: In the formula, This is the radial distance from the center of the heat source; This represents an adaptive time-averaged Gaussian heat source.

[0032] S5. Substitute the adaptive time-averaged Gaussian heat source model of S4 as the source term of the energy equation into the three-dimensional heat-fluid-solidification coupling model for iterative solution to obtain the temperature field and flow field distribution of the TC4 titanium alloy molten pool. The expression for the three-dimensional heat-fluid-solidification coupling model after substitution includes: mass conservation equation: ,in, For density, ; For time; It is a velocity vector. ; Momentum conservation equation: ,in, For dynamic viscosity, ; For pressure, ; For the Marangoni convection source term, ; Energy conservation equation: ,in, For specific heat capacity, ; Solid-liquid phase transition equation: ,in, The current temperature; Solidus temperature ; Liquidus temperature ; The liquid phase fraction; The equations of the above coupled model are discretized and solved using the finite volume method or the finite element method. Mesh refinement is performed in the electron beam action region (the surface of the molten pool) to obtain the temperature field and flow field distribution of the TC4 titanium alloy molten pool.

[0033] See Figure 3 middle Figure 3 (a) Figure 3 (b) Figure 3 (c) Figure 3 (d) Figure 3 (e) Figure 3 (f) Figure 3 (g) and Figure 3 As shown in (h), the complex physical processes on the surface of the powder bed / molten pool under electron beam heating conditions are illustrated, including electron beam absorption, heat conduction, melting / solidification, Marangoni convection, evaporation, etc.

[0034] S6. Output and Visualization; The calculation results based on S5 output the temperature field and flow field distribution of the molten pool.

[0035] See Figure 4 middle Figure 4 (a) and Figure 4 As shown in (b), the temperature prediction difference between the two heat source models under low-speed scanning conditions is demonstrated, indicating that the traditional time-averaged heat source cannot accurately reflect the energy superposition effect of the real moving heat source. Therefore, in order to verify the present invention, the following simulation was performed: Low-speed scanning of Φ260mm round ingot: The EBCHM process parameters for a 260mm TC4 titanium alloy round ingot were selected. These parameters are standard conditions established in the inventor's previous research: a 20-turn inward-reducing scanning pattern, a maximum scanning radius R = 130mm, a scanning period of 200ms, a scanning frequency f = 5Hz, and a pulse interval of 0.1ms. The casting speed was 10mm / min, and the casting temperature was 2073K. In previous research, a traditional time-averaged heat source model was used to obtain simulation results with a molten pool depth of 17mm. Under the same conditions, this invention modifies the heat source parameters by introducing a dynamic adjustment factor, as follows: The scanning speed is obtained from the process parameters. And the scanning frequency f = 5Hz. Speed ​​adjustment coefficient β, frequency adjustment coefficient Scanning speed threshold and reference frequency Determined based on experimental calibration. Calculated using the dynamic adjustment factor calculation formula according to the present invention. .

[0036] Due to low-speed scanning conditions Dynamic adjustment factor This indicates that the traditional time-averaged heat source needs to be broadened and the peak value correction reduced to achieve the desired result. and An adaptive time-averaged Gaussian heat source was constructed.

[0037] The adaptive time-averaged Gaussian heat source was used as the source term in the energy equation and substituted into the three-dimensional heat-fluid-solidification coupling model. The computational domain was discretized using the finite volume method, and the domain was a half-symmetric region of the ingot (utilizing central axis symmetry). Mesh refinement was applied in the electron beam irradiation region (the surface of the molten pool and a certain range below the surface), while a coarser mesh was used in regions far from the heat source. The SIMPLE algorithm was used to solve the pressure-velocity coupling, with the convection term discretized using a first-order upwind scheme and the diffusion term discretized using a second-order central difference scheme. The time step was set according to the magnitude of the solidification process, and calculations were performed until steady-state convergence.

[0038] See Figure 5 It shows that the molten pool has a dish-shaped feature with a deep center and shallow edges, and the deepest point is located in the center of the round ingot.

[0039] Simulation results: Under the conditions of a Φ260mm round ingot, a casting speed of 10mm / min, and a casting temperature of 2073K, the molten pool depth obtained using the adaptive time-averaging heat source of this invention is 17mm. The molten pool morphology is a dish shape with a deep center and shallow edges, with the deepest point located at the center of the round ingot. This result is consistent with the results obtained in previous studies using a traditional time-averaging heat source. Under low-speed scanning conditions deviating from this baseline, the prediction accuracy of this invention is superior to that of the traditional time-averaging heat source through correction by a dynamic adjustment factor.

[0040] High-speed scanning operation of Φ260mm round ingot: The same EBCHM process parameters as the low-speed scanning condition for Φ260mm round ingots were selected, but the casting temperature was increased to 2273K and the casting speed was increased to 25mm / min. This condition is also a standard condition established in the inventors' previous research. In the previous research, a simulation result with a molten pool depth of 56mm was obtained using a traditional time-averaged heat source model. Under the same conditions, this invention calculates the dynamic adjustment factor and corrects the heat source parameters to obtain... and An adaptive time-averaged Gaussian heat source was constructed. The same simulation settings were used for the solution.

[0041] See Figure 6 This demonstrates the longitudinal development of scour holes near the overflow outlet under high temperature and high pulling speed conditions.

[0042] Simulation results: Under the conditions of Φ260mm round ingot, casting speed of 25mm / min, and casting temperature of 2273K, the molten pool depth obtained using the adaptive time-averaging heat source of this invention is 56mm, and the molten pool morphology forms longitudinally developing scour cavities near the overflow port. This result is consistent with the results obtained in previous studies using a traditional time-averaging heat source. Under high-speed scanning conditions, through the correction of the dynamic adjustment factor, the prediction accuracy of this invention is better than that of the traditional time-averaging heat source.

[0043] Flat ingot operating conditions: The EBCHM process parameters for TC4 titanium alloy flat ingots (1050mm × 220mm) were selected, which are standard conditions established in the inventor's previous research. Electron beam scanning conditions: scanning frequency f = 5Hz, maximum scanning radius 130mm. Casting speed 8mm / min, casting temperature 2073K. In previous research, a traditional time-averaged heat source model was used to obtain simulation results with a molten pool depth of 33.5mm. Under the same conditions, this invention calculates and corrects the dynamic adjustment factor to obtain... and An adaptive time-averaged Gaussian heat source is constructed. The computational domain is discretized and solved using the finite volume method.

[0044] See Figure 7 This demonstrates that the molten pool of the flat ingot is approximately symmetrical in the XZ section at the position Y=110mm, exhibiting a trend of being deeper in the middle and shallower at the edges.

[0045] Simulation results: Under the conditions of a flat ingot (1050mm × 220mm), a casting speed of 8mm / min, and a casting temperature of 2073K, the molten pool depth obtained using the adaptive time-averaging heat source of this invention is 33.5mm. The molten pool morphology is approximately symmetrical about the XZ section at Y=110mm, showing a trend of being deeper in the middle and shallower at the edges. This result is consistent with the results obtained in previous studies using a traditional time-averaging heat source. Under the conditions of flat ingot casting, the prediction accuracy of this invention is better than that of the traditional time-averaging heat source through the correction of the dynamic adjustment factor.

[0046] Working condition of Φ620mm large round ingot: The EBCHM process parameters for a 620mm TC4 titanium alloy large round ingot were selected, which are standard conditions established in the inventor's previous research. Electron beam scanning conditions: scanning frequency f=5Hz, maximum scanning radius 130mm. Casting speed is 5mm / min, casting temperature is 2073K. In previous research, a traditional time-averaged heat source model was used to obtain simulation results with a molten pool depth of 28mm. This invention calculates and corrects the dynamic adjustment factor under the same conditions to obtain... and An adaptive time-averaged Gaussian heat source is constructed. The computational domain is discretized and solved using the finite volume method.

[0047] See Figure 8 This demonstrates the disc-shaped characteristics that are symmetrical about the center of the molten pool under low temperature and low pulling speed conditions.

[0048] Simulation results: Under the conditions of a Φ620mm large round ingot, a casting speed of 5mm / min, and a casting temperature of 2073K, the molten pool depth obtained using the adaptive time-averaging heat source of this invention is 28mm, and the molten pool morphology is a dish shape with a deep center and shallow edges. This result is consistent with the results obtained in previous studies using a traditional time-averaging heat source. Under the conditions of large-size round ingot casting, through the correction of the dynamic adjustment factor, the prediction accuracy of this invention is better than that of the traditional time-averaging heat source.

[0049] Example 2 To implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a system for an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy is provided below, comprising: The parameter acquisition module is used to acquire the scanning speed and scanning frequency of the electron beam. The dynamic adjustment factor calculation module is used to calculate the dynamic adjustment factor by obtaining the scanning speed and scanning frequency of the electron beam. The heat source correction module is used to correct the energy distribution width parameter and peak energy of the standard time-averaged Gaussian heat source using a dynamic adjustment factor; The adaptive time-averaged heat source construction module is used to construct an adaptive time-averaged Gaussian heat source model based on the corrected parameters. The coupled solution module is used to substitute the adaptive time-averaged Gaussian heat source as the source term of the energy equation into the three-dimensional heat-fluid-solidification coupled model for iterative solution. The output module is used to output the temperature field and flow field distribution results of the molten pool.

[0050] Example 3 This embodiment provides an electronic device for an adaptive time-averaged Gaussian heat source in electron beam cold hearth melting of TC4 titanium alloy, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method described in this invention.

[0051] In practical applications, the aforementioned electronic devices can be servers.

[0052] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0053] The processor, communication interface, and memory communicate with each other via a communication bus.

[0054] A communication interface is used to communicate with other devices.

[0055] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0056] Specifically, the program may include program code, which includes computer operation instructions.

[0057] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0058] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0059] Example 4 Based on the description of the above embodiments, the present invention provides a computer-readable storage medium for an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the method described in the present invention is implemented.

[0060] Specific embodiments of the invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the described actions can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0061] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy, characterized in that, Includes the following steps: S1. Obtain the scanning speed and frequency of the electron beam; S2. Calculate the dynamic adjustment factor based on the scan speed and frequency of S1; S3. Based on the dynamic adjustment factor obtained from S2, the parameters of the standard time-averaged Gaussian heat source are corrected. S4. Based on the parameters corrected in S3, construct an adaptive time-averaged Gaussian heat source model; S5. Substitute the adaptive time-averaged Gaussian heat source obtained in S4 into the three-dimensional heat-fluid-solidification coupling model for iterative solution to obtain the temperature field and flow field distribution of the TC4 titanium alloy molten pool. S6. Output the molten pool temperature field and flow field distribution results based on the calculation results of S5; Based on S1 to S6, a closed-loop design was completed for the construction method of an adaptive time-averaged Gaussian heat source for electron beam cold bed melting of TC4 titanium alloy.

2. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 1, characterized in that: The formula for calculating the dynamic adjustment factor is as follows: In the formula, Indicates the dynamic adjustment factor; Indicates the speed adjustment coefficient; Indicates the scan speed threshold; Indicates the frequency adjustment coefficient; Indicates the reference frequency; Indicates the scanning speed; Indicates the scanning frequency.

3. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 2, characterized in that: In S3, the parameters that need to be corrected include: energy distribution width parameter and peak energy density; The correction formula for the energy distribution width parameter is as follows: In the formula, The parameter representing the energy distribution width of a standard time-averaged Gaussian heat source; This represents the corrected energy distribution width parameter; The correction formula for the peak energy density is as follows: In the formula, This represents the peak energy density of a standard time-averaged Gaussian heat source. This indicates the corrected peak energy.

4. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 3, characterized in that: The adaptive time-averaged Gaussian heat source model is expressed as follows: In the formula, This is the radial distance from the center of the heat source; This represents an adaptive time-averaged Gaussian heat source.

5. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 4, characterized in that: The three-dimensional heat-fluid-solidification coupled model includes: mass conservation equation, momentum conservation equation, energy conservation equation, and solid-liquid phase transition equation.

6. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 5, characterized in that: The method of substituting the adaptive time-averaged Gaussian heat source obtained in S4 into the three-dimensional heat-fluid-solidification coupling model is as follows: substituting the adaptive time-averaged Gaussian heat source into the energy conservation equation.

7. The method for constructing an adaptive time-averaged Gaussian heat source for electron beam cold hearth melting of TC4 titanium alloy according to claim 6, characterized in that: The expression for the mass conservation equation is as follows: in, Density; For time; It is a velocity vector; The expression for the momentum conservation equation is as follows: in, Dynamic viscosity; For pressure; For Marangoni convection source terms; The expression for the energy conservation equation is as follows: in, Specific heat capacity; The expression for the solid-liquid phase transition equation is as follows: in, The current temperature; This refers to the solidus temperature. This is the liquidus temperature; This represents the liquid phase fraction.

8. A system for implementing the method as described in any one of claims 1 to 7, characterized in that, include: The parameter acquisition module is used to execute the steps in S1; The dynamic adjustment factor calculation module is used to execute the steps in S2. The heat source correction module is used to perform the steps in S3; An adaptive time-averaged heat source construction module is used to execute the steps in S4; A coupled solution module is used to execute the steps in S5; The output module is used to execute the steps of S6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

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