A method for predicting grain size of nuclear steel based on high-throughput characterization and machine learning

Abstract

The present invention discloses a method for predicting the grain size of nuclear steel based on high-throughput characterization and machine learning, which relates to the technical field of material grain size analysis and prediction. High-throughput characterization technology is used to obtain a large number of multivariable complex parameter combinations of nuclear austenitic stainless steel under different thermal deformation and heat treatment conditions at one time. EBSD large-area splicing technology is combined to obtain high-precision grain size information. The WOA-XGBoost machine learning algorithm is used to establish a reliable experimental database to achieve accurate prediction of the grain size of large-scale specimens. This method not only improves data acquisition efficiency, but also significantly reduces research and production costs, while optimizing process parameters and improving material performance and quality. In addition, the invention also promotes the development of intelligent manufacturing technology, promotes the intelligence and automation of material research and development and production processes, and helps achieve the goal of sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of material grain size analysis and prediction, and in particular to a method for predicting the grain size of nuclear power steel based on high-throughput characterization and machine learning. Background Art

[0002] In the production of nuclear power main pipelines, due to the difficulty in controlling grain size, coarse grains and mixed grains are easily generated, resulting in the scrapping of forgings and huge losses. Therefore, it is of great significance to accurately evaluate the grain size of nuclear power materials.

[0003] The size and uniformity of grains significantly impact the service performance of products. Thermal deformation (e.g., forging, rolling) and heat treatment processes in thermal manufacturing play a decisive role in grain configuration. Accurate prediction models for grain evolution often combine material experiments with phenomenological and physical models. However, these general phenomenological and physical models fail to consider the coupled effects of thermal deformation and heat treatment, which is inconsistent with actual production processes, and the prediction results fail to reflect actual production conditions. Material experiments often use multiple small cylindrical specimens for compression experiments. Each hot compression specimen only corresponds to grain information at a fixed deformation temperature, constant strain rate, and strain value. This severely limits the number of experimental statistical samples, often leading to large model calculation errors and slow calculation speeds. This is particularly true for grain size prediction within the gradient strain range of large-scale forgings. Due to the large amount of data involved in the generation process of large-scale forgings, traditional models are time-consuming. Furthermore, the small cylindrical specimens used in the laboratory cannot obtain the continuous gradient strain results of actual forgings during production, resulting in inaccurate model predictions and even inability to produce results. Summary of the Invention

[0004] In view of this, the present invention provides a method for predicting the grain size of nuclear power steel based on high-throughput characterization and machine learning, so as to realize rapid prediction of the grain size of large-size forgings under continuous gradient strain conditions.

[0005] To this end, the present invention provides the following technical solutions:

[0006] The present invention provides a method for predicting the grain size of nuclear power steel based on high-throughput characterization and machine learning, comprising:

[0007] Preparation of samples required for austenitic stainless steel experiments for nuclear power;

[0008] Conduct several sets of thermal deformation and heat treatment experiments on the sample, perform high-throughput characterization on the sample after thermal deformation and heat treatment, and obtain a large-area electron backscatter diffraction (EBSD) mosaic image of the sample;

[0009] The large-area EBSD mosaic image is divided into multiple sub-images, each of which contains at least one grain;

[0010] A plane rectangular coordinate system is established with the core of the sample as the central axis. The coordinates of the center point of each sub-image and the average grain size within the sub-image are counted. The average grain size is assigned to the central coordinate to construct the grain size data set of each region.

[0011] A thermal compression test simulation is performed on the specimen, and the strain values ​​corresponding to each center coordinate after the simulation are extracted to construct a strain data set;

[0012] Combined with the grain size dataset and strain dataset of each region, a dataset of process parameters and grain size after solutionization of austenitic stainless steel for nuclear power generation was constructed.

[0013] Construct a machine learning-based austenitic stainless steel grain size prediction model and train it using a dataset of process parameters and post-solutionizing grain size of nuclear austenitic stainless steel at various points.

[0014] The trained prediction model is used to predict the grain size of austenitic stainless steel.

[0015] Furthermore, the machine learning model is WOA-XGBoost.

[0016] Furthermore, several sets of thermal deformation and heat treatment experiments were conducted on the sample, and high-throughput characterization was performed on the sample after thermal deformation and heat treatment to obtain a large-area EBSD mosaic image of the sample, including:

[0017] Processing nuclear power austenitic stainless steel into double wedge specimens;

[0018] Homogenize the sample;

[0019] The homogenized samples were subjected to thermal deformation test on a hydraulic press;

[0020] The thermally deformed specimens are cut along the long axis to obtain specimens No. 1 and No. 2 that are mirror images; the specimens No. 1 and No. 2 are rectangular specimens from the center to the edge of the specimens;

[0021] Sample No. 2 was selected for solution treatment, and then sample No. 2 was air-cooled to room temperature;

[0022] After cutting sample No. 1 and sample No. 2 into multiple sections, the surfaces of the samples were mechanically polished using sandpaper and various diamond suspensions;

[0023] An EBSD large-area mosaic image of the sample was obtained using an emission scanning electron microscope.

[0024] Furthermore, thermal deformation experiments were conducted on the samples, including deformation temperatures of 950°C, 1000°C, 1050°C, 1100°C, 1150°C and 1180°C, and a deformation speed of 5 mm / s.

[0025] Furthermore, the size of sample No. 1 and sample No. 2 obtained after cutting is 80 mm×5 mm.

[0026] Furthermore, sample No. 2 is subjected to a solution treatment, including: subjecting sample No. 2 to a solution treatment at 1060° C. for 4 hours.

[0027] Furthermore, after cutting sample No. 1 and sample No. 2 into multiple sections, the sample surfaces were mechanically polished using sandpaper and a variety of diamond suspensions, including: cutting the samples into four sections of 17 mm, 19 mm, 21 mm, and 23 mm, and then mechanically polishing the sample surfaces using sandpaper and 3 μm, 1 μm, and 0.02 μm diamond suspensions.

[0028] Furthermore, the large-area EBSD stitching results were divided into multiple 800 × 800 μm subsets.

[0029] Advantages and positive effects of the present invention: The present invention uses high-throughput characterization technology to obtain a large number of multivariable complex parameter combinations of austenitic stainless steel for nuclear power under different thermal deformation and heat treatment conditions at one time, combines EBSD large-area splicing technology to obtain high-precision grain size information, and uses the WOA-XGBoost machine learning algorithm to establish a reliable experimental database to achieve accurate prediction of the grain size of large-scale specimens; a large amount of data from high-throughput characterization can be used as training data for machine learning, which can provide application conditions for machine learning to achieve grain size prediction of large-scale specimens. This method not only improves data acquisition efficiency, but also significantly reduces research and production costs, while optimizing process parameters and improving material performance and quality. In addition, the invention also promotes the development of intelligent manufacturing technology, promotes the intelligence and automation of material research and development and production processes, and helps achieve the goal of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0031] Figure 1 This is a framework diagram of a nuclear power steel grain size prediction model based on high-throughput characterization and machine learning in an embodiment of the present invention;

[0032] Figure 2 This is a flow chart of a method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning in an embodiment of the present invention;

[0033] Figure 3 Specific dimensions of the X2 CrNiMo 18.12 (CN) austenitic stainless steel double wedge specimen in an embodiment of the present invention;

[0034] Figure 4 This is a process diagram of a double-wedge specimen thermal deformation experiment in an embodiment of the present invention;

[0035] Figure 5 Schematic diagram of double wedge specimen cutting in an embodiment of the present invention;

[0036] Figure 6 Schematic diagram of the sample characterization area in an embodiment of the present invention;

[0037] Figure 7 The strain cloud diagrams of the deform simulation at deformation temperatures of 950°C, 1000°C, 1050°C, 1100°C, 1150°C, and 1180°C in an embodiment of the present invention are shown;

[0038] Figure 8 A quantitative analysis diagram of the equivalent strain distribution on the center line of the cross section of the sample in the embodiment of the present invention;

[0039] Figure 9 The IPF diagram of the sample with a heat deformation temperature of 1180°C after solid solution and the strain contour diagram of the sample with a heat deformation temperature of 1180°C after solid solution in the embodiment of the present invention are shown;

[0040] Figure 10 This is a performance diagram of the WOA-XGBoost model in an embodiment of the present invention;

[0041] Figure 11 This is a graph showing the prediction results of the WOA-XGBoost model test set in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] like Figure 1-2 As shown, a method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning in an embodiment of the present invention includes the following steps:

[0045] Step 1: Prepare the samples required for the nuclear power austenitic stainless steel experiment;

[0046] In this embodiment, X2 CrNiMo 18.12 (CN) austenitic stainless steel is used and processed into a double wedge specimen; the specimen has a long axis of 140 mm, a total height of 45 mm, a thickness of 50 mm, and a wedge side height of 20 mm. The specific sample dimensions are as follows: Figure 3 shown.

[0047] Step 2: Perform several sets of thermal deformation and heat treatment experiments on the sample, perform high-throughput characterization on the sample after thermal deformation and heat treatment, and obtain a large-area electron backscatter diffraction (EBSD) mosaic image of the sample;

[0048] In actual product production, grains in various regions of a billet evolve under the coupled effects of various inhomogeneous physical fields. A complex combination of multivariable parameters determines the final grain size distribution. However, existing research often predicts final grain size from the perspective of a single process, either thermal deformation or heat treatment. Few consider the impact of the coupled parameters of these multiple processes on grain size prediction models.

[0049] High-throughput experiments can generate a large amount of characterization data. With the development of high-throughput characterization techniques and big data analysis methods for the composition and properties of materials at microscale, the research and development of new materials based on the concept of materials genome engineering has been greatly promoted, becoming an important method for modern materials design and process design.

[0050] In this embodiment, high-throughput characterization technology is used to obtain a large number of multivariable complex parameter combinations of austenitic stainless steel for nuclear power under different thermal deformation and heat treatment conditions at one time, which greatly improves the data acquisition efficiency.

[0051] In specific implementation, the steps of high-throughput characterization specifically include:

[0052] Step 201: Keep the sample at 1200° C. for 8 hours for homogenization.

[0053] Step 202: performing a hot deformation test on the homogenized X2 CrNiMo 18.12 (CN) austenitic stainless steel double wedge specimen on a 20MN hydraulic press;

[0054] The deformation temperatures are 950℃, 1000℃, 1050℃, 1100℃, 1150℃, and 1180℃, the deformation is 25mm, and the pressing rate is 5mm / s. Figure 4 shown.

[0055] Step 203: Cut the thermally deformed sample along the long axis to obtain a rectangular sample from the center to the edge of the sample;

[0056] like Figure 5 As shown, the cut specimens include specimen No. 1 and specimen No. 2, with a size of 80 mm × 5 mm, and specimen No. 1 and specimen No. 2 are in a mirror image relationship.

[0057] Step 204 : Select sample No. 2 and carry out solution treatment by keeping the temperature at 1060° C. for 4 hours, and then air-cool the sample to room temperature.

[0058] Step 205: Cutting and EBSD characterization of sample No. 1 and sample No. 2. Figure 6 As shown, the sample was cut into four sections of 17 mm, 19 mm, 21 mm, and 23 mm, and then the surface of the sample was mechanically polished using sandpaper and 3 μm, 1 μm, and 0.02 μm diamond suspensions.

[0059] Step 206: Use a Zeiss Sigma 300 field emission scanning electron microscope to obtain an electron backscatter diffraction (EBSD) image with a voltage of 20 kV and a working distance of 17 mm.

[0060] Step 3: Split the large-area EBSD mosaic image into multiple sub-images, each containing at least one grain; establish a plane rectangular coordinate system with the core of the sample as the central axis, calculate the coordinates of the center point of each sub-image and the average grain size within the sub-image, and assign the obtained average grain size to construct a grain size dataset for each area at the central coordinate;

[0061] In a specific implementation, the EBSD large-area stitching result is divided into multiple 800×800μm subsets (one sub-image is a subset, and each sub-image contains at least one grain). A plane rectangular coordinate system is established with the core of the sample as the central axis. The coordinates of the center point of each subset and the average grain size in the subset are counted. The average grain size is assigned to the central coordinate to construct a grain size data set for each region. The EBSD large-area stitching technology is used to obtain high-precision grain size information, providing an accurate data basis for the model. In another embodiment, the EBSD large-area stitching image can also be segmented in other forms, such as dividing the EBSD large-area stitching image into multiple subsets of different sizes, each subset is a sub-image, and each sub-image contains a fixed number (e.g., 40) of grains.

[0062] Step 4: Perform a thermal compression test simulation on the sample, extract the strain values ​​corresponding to each center coordinate after the simulation, and construct a strain data set;

[0063] In the specific implementation, finite element analysis software such as deform is used to simulate the hot compression experiment. The deformation temperatures are 950℃, 1000℃, 1050℃, 1100℃, 1150℃, and 1180℃, and the deformation speed is 5mm / s. The strain values ​​corresponding to the center coordinates after the simulation are extracted to construct the strain data set, such as Figure 7 shown.

[0064] Step 5: Combine the grain size dataset and strain dataset of each region to construct the process parameter and post-solution grain size dataset of each point of austenitic stainless steel for nuclear power;

[0065] Quantitative analysis of the equivalent strain distribution on the center line of the specimen cross section is as follows: Figure 8 As shown; the IPF diagram of the sample after solution treatment at a heat deformation temperature of 1180°C is as follows Figure 9 (a) is shown; the strain contour diagram of the sample after solid solution at a heat deformation temperature of 1180℃ is shown as follows Figure 9 (b) shown.

[0066] Step 6: Construct a machine learning-based austenitic stainless steel grain size prediction model, and train the prediction model using the process parameters of each point of austenitic stainless steel for nuclear power and the grain size data set after solutionization;

[0067] Preferably, the machine learning model can use WOA-XGBoost to construct an austenitic stainless steel grain size prediction model based on WOA-XGBoost, and the prediction model is trained using a large amount of hot deformation-solution grain size data obtained from high-throughput characterization experiments.

[0068] WOA-XGBoost is a machine learning model that combines the Whale Optimization Algorithm (WOA) and the XGBoost (Extreme Gradient Boosting Model) algorithm to improve the prediction accuracy of regression and classification tasks.

[0069] XGBoost is an algorithm based on the Gradient Boosting Decision Tree (GBDT), which is often used to solve classification and regression problems. Both XGBoost and GBDT are models based on the idea of ​​boosting integration. Compared with traditional GBDT, XGboost uses the second-order Taylor expansion to optimize the loss function and improve the calculation accuracy; uses regularization to simplify the model to avoid model overfitting; and adopts the Blocks storage structure to enable the model to perform parallel calculations to improve the calculation speed. XGBoost consists of multiple weak learners, and the result of the previous weak learner will affect the generation of the next weak learner. The influencing variable is called deviation. Therefore, the objective function E of XGBoost consists of two parts: the loss function L and the model complexity function Ω. The mathematical expression of the objective function E is as follows:

[0070] E=L+Ω (1)

[0071]

[0072] where y i is the true value of the i-th sample, is the predicted value of the i-th sample, T is the total number of nodes, and γ and λ are both penalty terms. The smaller the objective function, the higher the model accuracy. In order to minimize the objective function, a new function g is introduced. i With h i , and perform Taylor expansion on equation (2) to simplify the objective function as follows:

[0073]

[0074] XGBoost is an improved GBDT algorithm that can train models faster and more efficiently.

[0075] WOA is a new type of swarm intelligence optimization search method, which is derived from the simulation of the hunting behavior of humpback whale groups in nature. The algorithm simulates the whale population blowing bubbles when hunting, gradually limiting the range of prey activities along a spiral path, and finally completing the foraging behavior. The WOA algorithm divides whale hunting behavior into three strategies, namely search and foraging strategy, shrinkage and encirclement strategy, and spiral update strategy. The target prey to be caught by the whale is assumed to be the optimal solution, and the position of each whale is assumed to be a potential solution. After knowing the position of the prey, each whale continuously updates its position according to the distance from the prey position, gradually surrounding the prey, and using this behavior of updating the position to gradually surround the prey to act as an iterative algorithm in the computer. This iterative algorithm is expressed by the following formula:

[0076]

[0077] Where k represents the current number of iterations, represents the position vector of the optimal solution before iteration, and represent the solution vectors of the kth and k+1th iterations respectively. and is a constant vector, and its calculation formula is as follows:

[0078]

[0079] in, and Represents two random vectors ranging between 0 and 1. is a vector that decreases linearly from 2 to 0 as the number of iterations increases. Its calculation formula is:

[0080]

[0081] Where I represents the total number of iterations. As each whale continues to shrink its position according to equation (2), it will spit out bubbles along a spiral path, gradually reducing the range of its prey's activity. The formula for simulating its spiral motion is as follows:

[0082]

[0083] The shape of its spiral path is controlled by b and l, where l is a random number between -1 and 1. In order to achieve better hunting results, a random number p between 0 and 1 is generated to determine whether the algorithm chooses the bubble attack strategy. If p>0.5, the bubble attack strategy is entered, otherwise the vector The absolute value of determines whether to search for prey or surround prey. When , adopt the strategy of surrounding the prey; when When , a random search strategy is adopted. The mathematical expression of the random search strategy is as follows:

[0084]

[0085]

[0086] Where, represents a randomly selected whale position, i.e., a random solution.

[0087] Through research, it is found that A is of great significance to the prediction of grain evolution. According to these research contents, This largely regulates the WOA algorithm's ability to predict grain evolution.

[0088] The WOA-XGBoost model uses the WOA algorithm to optimize XGBoost hyperparameters to improve the model's predictive performance. This model excels when working with high-dimensional data and large datasets. It optimizes the objective function by simulating the behavior of whales searching for food, iteratively updating candidate solutions to find the optimal hyperparameter combination. This approach not only improves the performance of traditional XGBoost but also demonstrates the potential of swarm intelligence optimization algorithms in machine learning.

[0089] The model training process is as follows:

[0090] Divide the data set: Divide the preprocessed data into a training set and a validation set, usually with a ratio of 80% training set and 20% validation set, for model training and performance verification.

[0091] Training model: The WOA-XGBoost model is trained using the training set data. During the training process, the model parameters are iteratively updated to minimize the difference between the predicted grain size and the actual grain size.

[0092] Validation and Adjustment: Use the validation set to evaluate the model's predictive performance and adjust the model settings based on the validation results, such as increasing the number of training rounds or further adjusting hyperparameters, until satisfactory prediction results are achieved.

[0093] The model was evaluated using mean square error (MSE), coefficient of determination (R 2 ) and other statistical indicators to evaluate the prediction performance of the model to ensure that the model can accurately capture the relationship between process parameters and grain size. The performance of the WOA-XGBoost model is as follows Figure 10 shown.

[0094] Step 7: Use the trained prediction model to predict the grain size of austenitic stainless steel.

[0095] The prediction results of the WOA-XGBoost model test set are as follows Figure 11As shown in the figure, the prediction model can accurately predict the grain size, reduce a large number of experiments and repeated experiments in the industrial production process, and significantly reduce research and production costs.

[0096] In the above embodiment, high-throughput characterization is used to obtain several sets of multivariate complex parameter combinations of austenitic stainless steel for nuclear power at one time after thermal deformation and heat treatment, and a "process parameter-grain size" sample data set corresponding to the grain size of each region of the sample is established. EBSD large-area stitching technology is used to obtain 80mm×5mm grain size information after solid solution. A reliable experimental database is established in combination with a machine learning algorithm, and the WOA-XGBoost model in the commonly used regression algorithm is used to train the experimental data set. This model can accurately predict the grain size of large-size samples corresponding to different thermal deformation parameters and solid solution parameters, greatly reducing the cost of testing and industrial production.

[0097] The method in the above embodiment is not only applicable to the grain size prediction of steel materials for nuclear power, but can also be extended to the microstructure prediction and analysis of other materials, and has wide applicability.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning, characterized in that: include: Preparation of samples required for austenitic stainless steel experiments for nuclear power; Conduct several sets of thermal deformation and heat treatment experiments on the sample, perform high-throughput characterization on the sample after thermal deformation and heat treatment, and obtain a large-area electron backscatter diffraction (EBSD) mosaic image of the sample; The large-area EBSD mosaic image is divided into multiple sub-images, each of which contains at least one grain; A plane rectangular coordinate system is established with the core of the sample as the central axis. The coordinates of the center point of each sub-image and the average grain size within the sub-image are counted. The average grain size is assigned to the central coordinate to construct the grain size data set of each region. A thermal compression test simulation is performed on the specimen, and the strain values ​​corresponding to each center coordinate after the simulation are extracted to construct a strain data set; Combined with the grain size dataset and strain dataset of each region, a dataset of process parameters and grain size after solutionization of austenitic stainless steel for nuclear power generation was constructed. Construct a machine learning-based austenitic stainless steel grain size prediction model and train it using a dataset of process parameters and post-solutionizing grain size of nuclear austenitic stainless steel at various points. The trained prediction model is used to predict the grain size of austenitic stainless steel.

2. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 1, characterized in that: The machine learning model is WOA-XGBoost.

3. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 1, characterized in that: Perform several sets of thermal deformation and heat treatment experiments on the sample, perform high-throughput characterization on the sample after thermal deformation and heat treatment, and obtain a large-area EBSD mosaic image of the sample, including: Processing nuclear power austenitic stainless steel into double wedge specimens; Homogenize the sample; The homogenized samples were subjected to thermal deformation test on a hydraulic press; The thermally deformed specimens are cut along the long axis to obtain specimens No. 1 and No. 2 that are mirror images; the specimens No. 1 and No. 2 are rectangular specimens from the center to the edge of the specimens; Sample No. 2 was selected for solution treatment, and then sample No. 2 was air-cooled to room temperature; After cutting sample No. 1 and sample No. 2 into multiple sections, the surfaces of the samples were mechanically polished using sandpaper and various diamond suspensions; An EBSD large-area mosaic image of the sample was obtained using an emission scanning electron microscope.

4. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 3, characterized in that: Thermal deformation experiments were conducted on the samples, including deformation temperatures of 950°C, 1000°C, 1050°C, 1100°C, 1150°C and 1180°C, and a deformation speed of 5 mm / s.

5. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 3, characterized in that: The size of the sample No. 1 and the sample No. 2 obtained after cutting is 80 mm × 5 mm.

6. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 3, characterized in that: The No. 2 sample was subjected to a solution treatment, including: the No. 2 sample was subjected to a solution treatment at 1060° C. for 4 hours.

7. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 3, characterized in that: After cutting Samples 1 and 2 into multiple sections, the surfaces of the samples were mechanically polished using sandpaper and various diamond suspensions, including: cutting the samples into four sections of 17 mm, 19 mm, 21 mm, and 23 mm, and then mechanically polishing the surfaces of the samples using sandpaper and 3 μm, 1 μm, and 0.02 μm diamond suspensions.

8. The method for predicting grain size of nuclear power steel based on high-throughput characterization and machine learning according to claim 1, characterized in that: The large-area EBSD stitching results were divided into multiple 800 × 800 μm subsets.

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