Simulation method for inclusion distribution in high-temperature alloy deformation process
By combining Deform software and the Monte Carlo method with kernel density estimation, accurate simulation of inclusions during the deformation process of high-temperature alloys was achieved, solving the problem of unpredictable inclusion distribution and improving the quality and safety of high-temperature alloy products.
Patent Information
- Application Number
- CN202511752778.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-03-03
AI Technical Summary
Existing technologies cannot effectively track and predict the movement trajectory and distribution pattern of inclusions during the deformation process of high-temperature alloys, resulting in metallurgical defects in alloy materials during processing, which affects product quality and safety.
The Deform software was used to simulate the deformation process of high-temperature alloys. The Monte Carlo method and kernel density estimation method were combined to generate inclusion location data and track its motion during the deformation process. A three-dimensional model was built in SolidWorks and imported into Deform for simulation to calculate the distribution probability density of inclusions under different deformation degrees.
It enables precise tracking and prediction of the distribution patterns of inclusions during the deformation process of high-temperature alloys, providing a theoretical basis for optimizing processing technology and improving the yield and service reliability of alloy products.
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Figure CN121598613A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of simulation technology, and in particular to a simulation prediction method for the distribution of inclusions during the deformation process of high-temperature alloys. Background Technology
[0002] High-temperature alloys are widely used in hot-end components such as aero-engines, turbine blades, and combustion chambers due to their excellent fatigue resistance, corrosion resistance, and good mechanical properties [1 KONG HH, YANG SF, QU JL, et al. Type and distribution of inclusion GH4169 nickel based superalloy[J]. Acta Astronautica Sinica, 2020, 41(4): 304-311.]. However, the inevitable introduction of metallurgical defects such as impurities and inclusions during the preparation of deformed high-temperature alloy materials in industrial production is a key factor inducing the initiation and propagation of cracks in deformed high-temperature alloy components. Inclusions easily lead to defects such as cracks, pores, and dirty white spots in alloys, which not only directly degrade the macroscopic properties and quality of the alloy, but more seriously, endanger the service safety of components, and ultimately become a bottleneck restricting the development of high-end high-temperature alloy materials [2 JIANG J, YANG J, ZHANG T, et al. Microstructurally sensitivecrack nucleation around inclusions in powder metallurgy nickel-basedsuperalloys [J]. Acta Materialia, 2016, 117: 333-44.]. The harm of inclusions to deformed high-temperature alloys is mainly reflected in two aspects: First, during the service life of the alloy, inclusions are the initiation and propagation channel of cracks, directly leading to the deterioration of mechanical properties; Second, inclusions originating from the smelting process cannot be eliminated in subsequent processing and will be directly inherited by the finished parts [3 YANG Shulei. Study on the behavioral basis of inclusions in vacuum self-consuming remelting of GH4742 alloy [D]. Beijing University of Science and Technology, 2024.]. Although inclusions are generated during the smelting stage and are difficult to eliminate during subsequent heat treatment, forging, and machining processes [4 Gao Xiaoyong, Chen Jinyan, Tang Longbo, et al. Effect of rare earth slag electroslag remelting on inclusion refinement in high-temperature alloys [J / OL]. Chinese Journal of Nonferrous Metals], their movement trajectory during the deformation process of high-temperature alloys is calculable. By simulating and tracking the evolution behavior of inclusions, their migration law in deformed high-temperature alloys can be revealed. This understanding helps to proactively avoid high-inclusion-risk areas in subsequent processing, or assists in evaluating the impact of different inclusion densities on alloy properties, thus providing a key basis for the accurate prediction and control of the performance of industrial-grade high-temperature alloy products.
[0003] To achieve the strategic goal of high-quality and highly stable production of wrought superalloys, it is crucial to establish a computational method that can effectively track the evolution of inclusions during hot working. This method can reveal the mapping relationship between inclusion distribution and alloy type and process parameters, thus providing theoretical support for precise process control and defect suppression. It can effectively enhance the competitiveness of my country's wrought superalloy products, deeply aligns with the national macro-strategy of industrial upgrading, and possesses significant engineering value and far-reaching practical significance. Summary of the Invention
[0004] This invention discloses a simulation method for the distribution of inclusions during the deformation process of high-temperature alloys, in order to solve any of the above-mentioned and other potential problems in the prior art.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: a simulation method for the distribution of inclusions during the deformation process of high-temperature alloys, the simulation method specifically including the following steps: S1) Based on the existing detection results or numerical simulation results of inclusions in high-temperature alloys, generate the location data of inclusions in the high-temperature alloys to be simulated. S2) The Deform software was used to simulate the deformation process of the high-temperature alloy and obtain simulation data of the high-temperature alloy deformation process. S3) The inclusion location data obtained in S1) are combined with the simulation data obtained in S2) for post-processing to obtain the inheritance law of inclusions in the high-temperature alloy deformation process.
[0006] Furthermore, step S1) is as follows: S1.1) Based on the actual test results or numerical simulation results of the ingot, determine the inclusion surface density data at three or more points on the cross section of the solid high-temperature alloy material to be deformed. S1.2) Based on the obtained inclusion surface density data, perform a linear interpolation on the cross section in S1.1) to construct the inclusion surface density variation function whose domain is on the cross section; S1.3) The cross section in S1.1) is uniformly divided into multiple regions. The inclusion surface density of each region is considered as the value of the function constructed in S1.2) with its centroid at the center of gravity of the region. The number N of inclusions that should exist in each region is calculated. i N i Let N be the total number of inclusions in all regions, which is the i-th region. S1.4) Set the number of inclusions to be simulated, which is n. In each region divided in S1.3), use the Monte Carlo method to generate coordinates representing the inclusions. The number of coordinates to be generated for each region is nN. i / N, if the value is not an integer, it will be rounded to the nearest integer.
[0007] Furthermore, in step S1.1), at least three of the points containing the inclusion surface density data are not on the same straight line; Furthermore, step S3) is as follows: S2.1) Create a 3D model of the high-temperature alloy material to be processed and a 3D model of the tools used in the processing in SolidWorks, and export them in STL file format; S2.2) Import the STL model established in step S2.1) into Defrom software and perform computational mesh generation; S2.3) Set the simulation conditions for the meshed high-temperature alloy material and tool. The conditions include: the motion mode of the tool, the friction coefficient between the alloy and the tool, the type of high-temperature alloy, the hot deformation temperature of the high-temperature alloy, the step size of the simulation calculation and the conditions for terminating the calculation, and generate the db format file required by Deform for simulation. S2.4) The db file obtained in S2.3) is used to perform calculations to obtain simulation data of the hot deformation process of high-temperature alloys.
[0008] Furthermore, step S3) is as follows: S3.1) Export the inclusion coordinate information generated in step S1.4) in CSV format to obtain a data file containing inclusion information with the file extension .DAT; S3.2) Import the data file generated in step S3.1) into the point tracking function of Deform software to calculate the position change of the inclusion coordinates during the deformation process of the high-temperature alloy; S3.3) Export the position coordinate data of inclusions in high-temperature alloy materials under different deformation degrees, and obtain the position coordinate data file of inclusions during the deformation process; S3.4) The position coordinate data file in S3.3) is analyzed and processed using the kernel density estimation method to obtain the probability density function of inclusion distribution at the cross section of the high-temperature alloy material under different deformation degrees.
[0009] Furthermore, in step S3.1), the data stored in the exported data file has the following columns: the first column is the serial number of each inclusion; the second to fourth columns are the coordinates of the x, y, and z axes in space, respectively, in mm; and the fifth column is the number assigned to the high-temperature alloy material containing the inclusion in the Deform software.
[0010] Furthermore, in S3.4), the kernel density estimation method employs a method where, for each data point, a kernel density function is constructed using a Gaussian kernel function centered on that point. By calculating the kernel density function value for each data point and performing a weighted average on them, the overall probability density function estimate is obtained.
[0011] A computer program for simulating the distribution of inclusions during the deformation process of high-temperature alloys as described above.
[0012] An information processing terminal for simulating the distribution of inclusions during the deformation process of high-temperature alloys as described above.
[0013] A computer-readable storage medium includes instructions that, when executed on a computer, cause the computer to perform the above-described simulation method for inclusion distribution during high-temperature alloy deformation.
[0014] The beneficial technical effects of this invention are as follows: By adopting the above-mentioned technical solution, the movement trajectory and distribution law of inclusions during the deformation process of high-temperature alloys can be tracked. This method can clarify the evolution behavior and concentration tendency of inclusions under different process parameters, providing a direct theoretical basis for optimizing key processes such as forging and heat treatment in actual production. The implementation of this invention will improve the yield, quality consistency, and service reliability of deformed high-temperature alloy components, and has important engineering application value and strategic significance for promoting the high-quality and high-stability manufacturing of key components in my country's aerospace, energy and power fields. Attached Figure Description
[0015] Figure 1 This is a flowchart of a simulation method for the distribution of inclusions during the deformation process of a high-temperature alloy, according to the present invention.
[0016] Figure 2 This is a method for generating the position coordinates of inclusions in high-temperature alloy ingots.
[0017] Figure 3 The results of ingot shape simulation using Deform software are shown in the initial state and at pressures of 300 mm and 600 mm.
[0018] Figure 4 This describes the change process of the probability density function of the inclusion distribution in Example 1.
[0019] Figure 5 This describes the change process of the probability density function of inclusion distribution in Example 2. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.
[0021] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.
[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. The embodiments of the invention will be described in detail below with reference to the accompanying drawings: like Figure 1 As shown, this invention provides a simulation prediction method for the evolution and distribution of inclusions during the deformation process of high-temperature alloys. The simulation method specifically includes the following steps: S S1) Based on the existing detection results or numerical simulation results of inclusions in high-temperature alloys, generate the location data of inclusions in the high-temperature alloys to be simulated. S2) The Deform software was used to simulate the deformation process of the high-temperature alloy and obtain simulation data of the high-temperature alloy deformation process. S3) The inclusion location data obtained in S1) are combined with the simulation data obtained in S2) for post-processing to obtain the inheritance law of inclusions in the high-temperature alloy deformation process.
[0023] Furthermore, step S1) is as follows: S1.1) Based on the actual test results or numerical simulation results of the ingot, determine the inclusion surface density data at three or more points on the cross section of the solid high-temperature alloy material to be deformed. S1.2) Based on the obtained inclusion surface density data, perform a linear interpolation on the cross section in S1.1) to construct the inclusion surface density variation function whose domain is on the cross section; S1.3) The cross section in S1.1) is uniformly divided into multiple regions. The inclusion surface density of each region is considered as the value of the function constructed in S1.2) with its centroid at the center of gravity of the region. The number N of inclusions that should exist in each region is calculated. i N i Let N be the total number of inclusions in all regions, which is the i-th region. S1.4) In numerical computation, the Monte Carlo method is commonly used for sampling. The number of inclusions to be simulated is set to n. In each region divided in S1.3), the Monte Carlo method is used to sample the coordinates of the inclusions. The number of coordinates to be generated for each region is nN. i / N, when the value is not an integer, it is rounded to the nearest integer. When the number of regions is increased, with the number of inclusions n to be simulated fixed, the variance of the inclusion distribution can be reduced and the size of the error interval can be reduced in subsequent statistics.
[0024] Furthermore, in step S1.1), since at least three non-collinear points are required to perform linear interpolation in a two-dimensional region, at least three points containing inclusion surface density data are not on the same straight line. Furthermore, step S3) is as follows: S2.1) Create a 3D model of the high-temperature alloy material to be processed and a 3D model of the tools used in the processing in SolidWorks, and export them in STL file format; S2.2) Import the STL model established in step S2.1) into Defrom software and perform computational mesh generation; S2.3) Set the simulation conditions for the meshed high-temperature alloy material and tool. The conditions include: the motion mode of the tool, the friction coefficient between the alloy and the tool, the type of high-temperature alloy, the hot deformation temperature of the high-temperature alloy, the step size of the simulation calculation and the conditions for terminating the calculation, and generate the db format file required by Deform for simulation. S2.4) The db file obtained in S2.3) is used to perform calculations to obtain simulation data of the hot deformation process of high-temperature alloys.
[0025] Furthermore, step S3) is as follows: S3.1) Export the inclusion coordinate information generated in step S1.4) in CSV format to obtain a data file containing inclusion information with the file extension .DAT; S3.2) Because the position of inclusions in a solid does not change arbitrarily as it does in a fluid, but moves with the deformation of the solid, the point tracking function can be used to calculate the positional changes of inclusions. Import the data file generated in step S3.1) into the point tracking function of Deform software to calculate the positional changes of inclusion coordinates during the deformation process of the high-temperature alloy; S3.3) Export the position coordinate data of inclusions in high-temperature alloy materials under different deformation degrees, and obtain the position coordinate data file of inclusions during the deformation process; S3.4) The probability density function of inclusions is used to describe the distribution of inclusions in high-temperature alloys under different deformation degrees. In the field of statistics, kernel density estimation is often used to fit the distribution of scatter points to obtain the probability density function of point occurrence. The position coordinate data file in S3.3) is analyzed and processed using kernel density estimation to obtain the probability density function of inclusion distribution at the cross-section of the high-temperature alloy material under different deformation degrees.
[0026] Furthermore, in step S3.1), the data stored in the exported data file has the following columns: the first column is the serial number of each inclusion; the second to fourth columns are the coordinates of the x, y, and z axes in space, respectively, in mm; and the fifth column is the number assigned to the high-temperature alloy material containing the inclusion in the Deform software.
[0027] Furthermore, in step S3.4), the kernel density estimation method employs a Gaussian kernel function to construct a kernel density function centered on each data point. By calculating the kernel density function value for each data point and then performing a weighted average, the overall probability density function estimate is obtained. The Gaussian kernel function is expressed as follows: Where x is the independent variable of the kernel function, K(x) is the Gaussian kernel function, and the expression for calculating the probability density function is: Where x is the coordinate on the coordinate axis that serves as the independent variable of the probability density function, x i Let be the coordinates of the inclusions, n be the number of inclusions, and h be the bandwidth.
[0028] Example 1 Step 1: Generating the location coordinates of inclusions According to actual measurement results, at the cross-section through the center of a certain high-temperature alloy ingot, the areal density of inclusions at the center, half radius, and edge positions is 13.3 inclusions / mm² at the bottom of the ingot. 2 13.8 pieces / mm 2 21.5 pieces / mm 2 The number of particles per mm in the middle of the ingot is 18.3. 2 18.7 pieces / mm 2 39.5 pieces / mm 2 The number of particles per mm at the top of the ingot is 29.5. 2 31.3 pieces / mm 2 40.1 pieces / mm 2 Based on the measurement data, a linear interpolation is performed on the cross-section to generate the inclusion surface density variation function. Subsequently, the cross-section of the ingot is divided into 900 regions on average, and the total number of inclusions is set to 72,000. According to the inclusion surface density variation function, the position coordinates of the corresponding number of representative inclusions are generated in the corresponding regions.
[0029] Step 2: Simulation of High-Temperature Alloy Deformation Process In SolidWorks, models were created for the tools and the high-temperature alloy involved in the deformation process. The high-temperature alloy model was a cylindrical ingot with a radius of 0.66m and a height of 1.37m, and the tool model consisted of two square plates, each 0.2m high and 1.0m on each side. The models were imported into Deform software, with a mesh size of approximately 70,000. The high-temperature alloy was set to Inconel 718. The tools were used to perform an upsetting operation on the alloy, with an upsetting indentation of 600mm and an indentation speed of 7mm / s. The hot deformation temperature of the alloy was set to 1000℃, and the calculation step size was 1mm / step. The calculation terminated when the indentation reached 600mm. After setting the calculation conditions in Deform, a db file was generated, and the Deform solver was used to perform simulation calculations to obtain the results of the high-temperature alloy deformation process.
[0030] Step 3: Calculation of the probability density of inclusion distribution After exporting the inclusion coordinates generated in step one as a data file, import the calculation results from step two. Use Deform's post-processor to perform point tracking calculations. After the calculation is complete, export the position coordinates of the inclusions in the ingot when the pressure is 300mm and 600mm, obtaining a data file storing the inclusion locations. Perform kernel density estimation on the data in the exported data file, with a probability density function bandwidth of 0.005, and calculate the probability density function of the inclusions appearing in the corresponding cross-section of the ingot.
[0031] Example 2 Step 1: Generating the location coordinates of inclusions The generation of the inclusion location coordinates is the same as step one in Example 1.
[0032] Step 2: Simulation of High-Temperature Alloy Deformation Process The simulation of the high-temperature alloy deformation process is the same as step two of Example 1, but the density of inclusions at any location in the ingot is considered to be 25 inclusions / mm². 2 .
[0033] Step 3: Calculation of the probability density of inclusion distribution The calculation of the probability density of inclusion distribution is the same as step two in Example 1.
[0034] Comparative example: Comparative Example 1 Step 1: Simulation of High-Temperature Alloy Deformation Process The simulation process of high-temperature alloy deformation is the same as step two in Example 1.
[0035] Step 2: Calculation of the position and state of inclusions Enable the point tracking function in the Deform postprocessor, select a total of 9 coordinate points at the center of the high-temperature alloy ingot, at 1 / 2 radius of the center, and at the bottom, middle and top of the ingot, respectively, as the point tracking positions for calculation. After the calculation is completed, the position calculation results of the coordinate points when the pressure is 300mm and 600mm are obtained.
[0036] Advantages Analysis Compared with the comparative examples, the advantages of the examples are: 1. The number of inclusions that can be calculated in the embodiments is on a higher order of magnitude. In the comparative examples, the location coordinates of the inclusions to be tracked can be selected directly using a mouse or generated using Excel software. However, it is difficult to achieve the order of magnitude of inclusions calculated in Embodiment 1 or Embodiment 2 using either of these methods, and the density distribution of the selected inclusions cannot reflect the actual density distribution of the inclusions.
[0037] 2. The calculation method of the embodiments allows for the investigation of the evolution of inclusion distribution under different inclusion distributions. By comparing Embodiment 1 and Comparative Example 1, it can be seen that in Embodiment 1, the evolution of the probability density of inclusion distribution after different degrees of deformation can be observed through calculation. However, in Comparative Example 1, the evolution of the probability density of inclusion distribution cannot be known because sampling based on inclusion density is not possible.
[0038] 3. The calculations in the examples allow us to explore the influence of the deformation degree of different high-temperature alloys on the evolution of inclusion distribution. Observation of Example 2 shows that during the upsetting operation of the ingot, inclusions in the vertical direction tend to concentrate towards the center of the ingot, while in Comparative Example 1, this trend could not be observed due to the small number of sampling points.
[0039] The above provides a detailed description of a simulation method for inclusion distribution during the deformation process of high-temperature alloys, as provided in the embodiments of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas; furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
[0040] Certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising / including but not limited to". "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error. The following descriptions in the specification are preferred embodiments for carrying out this application; however, these descriptions are for the purpose of illustrating the general principles of this application and are not intended to limit the scope of this application. The scope of protection of this application shall be determined by the appended claims.
[0041] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.
[0042] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0043] The foregoing description illustrates and describes several preferred embodiments of this application. However, as previously stated, it should be understood that this application is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the application concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this application should be within the protection scope of the appended claims.
Claims
1. A simulation method for inclusion distribution during the deformation process of high-temperature alloys, characterized in that, The simulation method specifically includes the following steps: S1) Based on the existing detection results or numerical simulation results of inclusions in high-temperature alloys, generate the location data of inclusions in the high-temperature alloys to be simulated. S2) The Deform software was used to simulate the deformation process of the high-temperature alloy and obtain simulation data of the high-temperature alloy deformation process. S3) The inclusion location data obtained in S1) are combined with the simulation data obtained in S2) for post-processing to obtain the distribution law of inclusions during the deformation process of high-temperature alloy.
2. The simulation method according to claim 1, characterized in that, The specific steps of S1 are as follows: S1.1) Based on the actual test results or numerical simulation results of the ingot, determine the inclusion surface density data at three or more points on the cross section of the solid high-temperature alloy material to be deformed. S1.2) Based on the obtained inclusion surface density data, perform a linear interpolation on the cross section in S1.1) to construct the inclusion surface density variation function whose domain is on the cross section; S1.3) The cross section in S1.1) is uniformly divided into multiple regions. The inclusion surface density of each region is considered as the value of the function constructed in S1.2) with its centroid at the center of gravity of the region. The number N of inclusions that should exist in each region is calculated. i N i Let N be the total number of inclusions in all regions, which is the i-th region. S1.4) Set the number of inclusions to be simulated, which is n, where n is a positive integer. In each region divided in S1.3), use the Monte Carlo method to generate coordinates representing the inclusions. The number of coordinates to be generated for each region is nN. i / N, when the number of coordinates is not an integer, it is rounded to the nearest integer.
3. The simulation method according to claim 2, characterized in that, In step S1.1), at least three of the points containing the inclusion surface density data are not on the same straight line.
4. The simulation method according to claim 1, characterized in that, The specific steps of S2 are as follows: S2.1) Create a 3D model of the high-temperature alloy material to be processed and a 3D model of the tools used in the processing in SolidWorks, and export them in STL file format; S2.2) Import the STL model established in step S2.1) into Defrom software and perform computational mesh generation; S2.3) Set the simulation conditions for the meshed high-temperature alloy material and tool. The conditions include: the motion mode of the tool, the friction coefficient between the alloy and the tool, the type of high-temperature alloy, the hot deformation temperature of the high-temperature alloy, the step size of the simulation calculation and the conditions for terminating the calculation, and generate the db format file required by Deform for simulation. S2.4) The db file obtained in S2.3) is used to perform calculations to obtain simulation data of the hot deformation process of high-temperature alloys.
5. The simulation method according to claim 1, characterized in that, The specific steps of S3 are as follows: S3.1) Export the inclusion coordinate information generated in step S1.4) in CSV format to obtain a data file containing inclusion information with the file extension .DAT; S3.2) Import the data file generated in step S3.1) into the point tracking function of Deform software to calculate the position change of the inclusion coordinates during the deformation process of the high-temperature alloy; S3.3) Export the position coordinate data of inclusions in high-temperature alloy materials under different deformation degrees, and obtain the position coordinate data file of inclusions during the deformation process; S3.4) The position coordinate data file in S3.3) is analyzed and processed using the kernel density estimation method to obtain the probability density function of inclusion distribution at the cross section of the high-temperature alloy material under different deformation degrees.
6. The simulation method according to claim 5, characterized in that, In step S3.1), the data stored in the exported data file has the following columns: the first column is the serial number of each inclusion; the second to fourth columns are the coordinates of the x, y, and z axes in space, respectively, in mm; and the fifth column is the number assigned to the high-temperature alloy material containing the inclusion in the Deform software.
7. The simulation method according to claim 5, characterized in that, In step S3.4), the kernel density estimation method used involves constructing a kernel density function using a Gaussian kernel function with that point as the center. By calculating the kernel density function value of each data point and performing a weighted average, the overall probability density function estimate is obtained.
8. A computer program for simulating the distribution of inclusions during the deformation process of a high-temperature alloy as described in any one of claims 1-7.
9. An information processing terminal for implementing a simulation method for the distribution of inclusions during the deformation process of high-temperature alloys as described in any one of claims 1-7.
10. A computer-readable storage medium comprising instructions, when executed on a computer, causing the computer to perform a simulation method for inclusion distribution during high-temperature alloy deformation as described in any one of claims 1-7.