In-situ solidification observation experiment and analysis method for nickel-based superalloy

Abstract

The invention belongs to the field of material microstructure research and analysis, and particularly relates to an in-situ solidification observation experiment and analysis method for nickel-based superalloy. Aiming at the defect that the dynamic evolution behavior of the structure of the nickel-based superalloy in the solidification process is difficult to continuously, clearly and quantitatively treat, the method comprises the following steps: performing laser heating on an alloy sample to be above liquidus temperature at a set cooling rate of a high-temperature laser confocal scanning microscope, and guiding the alloy sample to be stably solidified in a microscopic view range; dynamic tracking of the whole process from melting, nucleation to complete solidification is achieved, continuous evolution images of microstructures are obtained in real time, image data are subjected to quantitative analysis in combination with Image J, Image Pro Plus, Origin and other software, alloy solidification behavior characteristics and the response relation between the alloy solidification behavior characteristics and set process parameters are judged according to analysis results, and the alloy solidification behavior characteristics can be determined according to the analysis results. Therefore, quantitative evaluation of the solidification process promotion or inhibition effect under different process parameter conditions is realized.

Description

Technical Field

[0001] The present invention belongs to the field of material microstructure research and analysis, and particularly relates to an in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys. Background Art

[0002] Nickel-based superalloys, due to their exceptional high-temperature strength, creep resistance, oxidation resistance, and corrosion resistance, exhibit excellent stability in extreme service environments and are indispensable structural materials in key areas such as aircraft engines, gas turbines, nuclear power systems, aerospace propulsion systems, and high-temperature chemical equipment. Depending on the usage scenario, nickel-based alloys can be used to manufacture important components such as combustion chambers, turbine blades, afterburner nozzles, high-temperature heat exchangers, pressure vessels, and welded structures. However, as equipment develops towards higher temperatures, longer life, and more complex loads, multi-scale collaborative mechanisms centered around composition design, microstructure control, interface regulation, and service behavior prediction have become the core frontier of nickel-based superalloy research.

[0003] The study of alloy microstructures is usually limited to microstructural observations at room temperature. However, for the microstructural evolution process at high temperatures, especially the dynamic changes during the solidification stage, research methods are relatively scarce due to factors such as harsh experimental conditions, insufficient real-time observation methods, and the difficulty of maintaining microstructures in thermal environments. Currently, conventional sampling and observation after heat treatment can only reflect the "final state" of the solidification result, and it is difficult to restore the true solidification path and phase transformation process, resulting in uncertainty in the understanding of the phase formation sequence, element segregation behavior, and interface migration mechanism. In addition, although high-temperature in-situ observation techniques such as laser confocal scanning microscopy, high-temperature synchrotron radiation, or neutron diffraction can capture microstructural changes during thermal processes to a certain extent, they are still limited by spatial resolution, temperature control accuracy, and sample processing conditions.

[0004] Therefore, in view of the fact that the dynamic evolution behavior of the microstructure of nickel-based high-temperature alloys during the solidification process is difficult to process continuously, clearly and quantitatively, it is necessary to develop a new method for in-situ observation and image analysis of the solidification process that is more efficient, accurate and visual. Summary of the Invention

[0005] Given the current challenges of achieving real-time, continuous, and precise observation of the microstructural evolution of nickel-based superalloys under high-temperature conditions, this paper proposes an in situ solidification observation and analysis method based on a high-temperature confocal laser scanning microscope (HT-CLSM) platform. This method dynamically tracks the entire solidification process, from melting and nucleation to complete solidification, by laser heating the alloy sample to above the liquidus temperature under a controlled atmosphere and a set cooling rate, then guiding it to solidify steadily within the microscopic field of view. Compared to traditional offline cross-sectional analysis, thermal simulation, or intermittent sampling methods, this method can capture continuous microstructural evolution in real time without destroying the sample structure. It offers advantages such as ease of operation, comprehensive information acquisition, and rapid temperature control response. Leveraging the confocal laser imaging capabilities of the HT-CLSM system, this method can clearly record key phenomena such as solid-liquid interface migration, grain growth, residual liquid phase evolution, and microstructural boundary reconstruction at micrometer-level resolution. This method provides high-quality experimental support for a deeper understanding of the multi-field coupled behavior during the solidification of nickel-based alloys and offers reliable visualization and quantitative analysis for constructing solidification simulation models and optimizing alloy composition and process parameters.

[0006] The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys described in the present invention includes the following contents: A VL2000DX-SVF17SP high-temperature laser confocal microscope was used to conduct in-situ heating and solidification experiments on the experimental nickel-based alloy at a set cooling rate, obtaining a continuous dynamic microstructure evolution solidification image sequence. The microstructure evolution solidification image sequence was processed and nonlinearly fitted using Image J, Image Pro Plus, and Origin software. Based on the solidification image characteristics and fitting results, the alloy solidification behavior characteristics and solidification stage type, as well as the response relationship with the set process parameters, were determined. This enabled a quantitative assessment of the promoting or inhibiting effect of microstructure evolution behavior during solidification under different process parameters. The "set process parameters" referred to here refer to the key process parameters in the preparation of nickel-based alloys, including but not limited to the type or content of alloying elements in the microalloying process, cooling rate, cooling time, etc.

[0007] The main parameter settings of the VL2000DX-SVF17SP high-temperature laser confocal microscope are as follows: (1) Sample parameters: Prepare nickel-based alloy samples. Corundum crucible diameter: 5 mm to 8 mm; corundum crucible height: 6 mm to 8 mm; objective lens magnification: 5x, 10x, 20x, 35x; scanning speed (video frame rate): 0.5 frames / second to 5 frames / second; the alloy sample is cylindrical, the alloy sample diameter = corundum crucible diameter - (1 mm to 3 mm); the alloy sample height = corundum crucible height - (1 mm to 3 mm); the alloy sample upper and lower bottom surface inclination angles < 0.5°, and the surface roughness (height difference) < 5 µm.

[0008] (2) Temperature ramp program setting: A high-temperature laser confocal microscope was used to conduct in-situ heating and solidification experiments on nickel-based alloy samples. The heating rate from room temperature to 300°C was 300°C / min; the heating rate from 300°C to the maximum melting temperature was 600°C / min. The maximum melting temperature was set to 5°C~10°C higher than the liquidus temperature of the nickel-based alloy (the liquidus temperature of common nickel-based high-temperature alloys is 1300°C~1380°C, depending on the alloy composition). This temperature ensures that the surface of the alloy sample is completely melted into a liquid state, but the sample body remains solid and does not completely melt into a sphere that cannot be observed; after reaching the maximum melting temperature, the sample is kept at this temperature for 60s~90s; after the holding time, the cooling rate is set to 10°C / min~50°C / min for cooling and solidification; when the temperature of the alloy sample drops to ≤1200°C, the program is closed and the sample is allowed to cool naturally.

[0009] (3) Execute the set program: observe the cooling and solidification process of the alloy sample and collect images to obtain an in-situ solidification image sequence of the alloy sample from the beginning of nucleation to complete solidification, that is, the complete interface morphology changes with solidification temperature and solidification time under the set process parameters, and record the whole process from grain initiation, rapid growth, fusion evolution to solidification termination; synchronize the continuous in-situ solidification image sequence with the time stamp and temperature record to construct a temperature-image-time ternary index system; during the image analysis process, extract representative frame images according to the set interval temperature (such as every 10°C) or interval time (such as every 30s).

[0010] This method can clearly reveal the dynamic evolution of the microstructure of nickel-based alloys during the solidification process at micron-level resolution, including the formation and evolution of grain morphology, changes in the number and size of cellular structures, the co-evolution between liquid ridges and cellular nuclei, and their ultimate disappearance, comprehensively covering key stages such as grain initiation, rapid growth, fusion evolution, and solidification termination. At the same time, combined with continuous image comparison analysis, it can accurately identify the migration path of the solidification interface, the temporal and spatial variation trends of grain density, and the evolution characteristics of the intergranular residual liquid phase morphology. The high-resolution image sequence obtained during the entire observation process serves as the basic data for subsequent quantitative analysis (such as solid phase volume fraction fitting, interface migration rate calculation, etc.), ensuring the comparability and statistical consistency of images under different experimental parameters, and providing reliable support for the establishment of a microstructure evolution model for the solidification process.

[0011] The extracted representative frame images are analyzed and processed using relevant software. The main processing steps are as follows: (4) Import representative frame images into Image J, use the TrainableWeka Segmentation plug-in to segment the image into phase regions, intelligently classify the nucleated region (i.e., solid phase region) and the unnucleated region (i.e., liquid phase region) and generate a segmentation result map; then convert the segmented image into 16-bit grayscale format and perform binarization processing to obtain a binary image with clear separation of the solid phase region and the liquid phase region; save the processed image in TIF format and use the noise reduction function (Remove Outliers) of Image J to filter out noise. The noise radius is usually set to 2~3 pixels, and finally a high-quality denoised binary image is obtained, which provides a clear input for subsequent quantitative statistical analysis.

[0012] (5) Use Image pro plus software to perform automatic bright objects statistics on the binary image after noise reduction, measure and count the solid phase area in the binary image, and obtain statistical data on the solid phase volume fraction.

[0013] (6) After normalizing the solid volume fraction statistics using Origin software, the solid volume fraction was obtained by nonlinear fitting using the Boltzmann function. f s Nonlinear fitting curve with solidification temperature or solidification time: , Among them, f s is the solid volume fraction; x is the cooling time or cooling temperature variable corresponding to the acquisition of in-situ solidification images, which is used to describe the changing trend of the solid volume fraction with the cooling process; A1 is the upper limit of the solid volume fraction during cooling and solidification, and A2 is the lower limit of the solid volume fraction during cooling and solidification, which are the starting value and ending value of the fitting function respectively. A1-A2 represents the range of change of the solid volume fraction before and after cooling and solidification; x0 represents the time or temperature corresponding to the turning point of the solid volume fraction fitting curve, which corresponds to the key temperature or time position for rapid microstructure evolution during solidification, usually after the completion of grain nucleation and the entry into the rapid growth stage, and is the characteristic point where the solidification rate reaches its peak; λ is the rate constant, which is the control parameter of the function slope and is used to describe the drastic degree of change of the solid volume fraction near the turning point; Taking the first-order derivative of the fitting curve, we can get the curve of solidification rate change: .

[0014] This method can be used to identify the rate peak point, inflection point and end point of solidification during the solidification process, providing a quantitative basis for revealing the regulatory effect of different parameters on solidification dynamics.

[0015] The present invention extracts experimental in-situ solidification images at specific temperatures or time intervals, from the beginning of nucleation to nearly complete solidification. Software is then used to calculate the solid volume fraction of the extracted images. Although the solid volume fraction at this point is discontinuous, it can reflect the changes in the solid-liquid interface at key stages of the alloy solidification process. A nonlinear fitting approach is then used to obtain a continuous curve of solid volume fraction variation over the entire solidification temperature or time range, resulting in a continuous curve of solid volume fraction variation versus temperature and time. This curve is then differentiated once to obtain a curve of solidification rate variation versus temperature and time.

[0016] The method of the present invention enables in-situ tracking of the entire process of grain nucleation, growth, fusion, and final solidification without destroying the sample structure. It also boasts strong microstructural recognition capabilities, a clear data processing flow, and the ability to perform comparative analysis under varying process parameters. This provides quantitative support for revealing microstructural evolution mechanisms, solidification kinetics, and their responses to alloy composition and heat treatment regimes. Combining modern high-resolution imaging technology with intelligent image recognition algorithms to deeply integrate in-situ experiments with image analysis is a key trend in the development of materials research methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 In-situ solidification images of alloy sample S0 at a cooling rate of 20℃ / min; (a) is 1345℃, (b) is 1335℃, (c) is 1325℃, and (d) is 1315℃.

[0018] Figure 2 These are in-situ solidification images of alloy sample S1 in Example 1 at a cooling rate of 20°C / min; among them, (a) is 1345°C, (b) is 1335°C, (c) is 1325°C, and (d) is 1315°C.

[0019] Figure 3 Statistical diagram of in-situ solidification experiment of alloy samples S0 and S1 at a cooling rate of 20℃ / min; among them, (a) the change of solid phase volume fraction with temperature and time; (b) the change curve of solidification rate with temperature and time.

[0020] Figure 4 These are in-situ solidification images of alloy sample S2 in Example 2 at a cooling rate of 20°C / min; among them, (a) is 1345°C, (b) is 1335°C, (c) is 1325°C, and (d) is 1315°C.

[0021] Figure 5Statistical diagram of in-situ solidification experiment of alloy samples S0 and S2 at a cooling rate of 20℃ / min; among them, (a) the change of solid phase volume fraction with temperature and time; (b) the change curve of solidification rate with temperature and time.

[0022] Figure 6 These are in-situ solidification images of alloy sample S3 in Example 3 at a cooling rate of 20°C / min; among them, (a) is 1345°C, (b) is 1335°C, (c) is 1325°C, and (d) is 1315°C.

[0023] Figure 7 Statistical diagram of in-situ solidification experiment of alloy samples S0 and S3 at a cooling rate of 20℃ / min; among them, (a) the change of solid phase volume fraction with temperature and time; (b) the change curve of solidification rate with temperature and time.

[0024] Figure 8 In-situ solidification images of alloy sample S0 at a cooling rate of 40℃ / min; (a) is 1345℃, (b) is 1335℃, (c) is 1325℃, and (d) is 1315℃.

[0025] Figure 9 These are in-situ solidification images of alloy sample S1 in Example 4 at a cooling rate of 40°C / min; among them, (a) is 1345°C, (b) is 1335°C, (c) is 1325°C, and (d) is 1315°C.

[0026] Figure 10 Statistical diagram of in-situ solidification experiment of alloy samples S0 and S1 at a cooling rate of 40℃ / min; among them, (a) the change of solid phase volume fraction with temperature and time; (b) the change curve of solidification rate with temperature and time.

[0027] Figure 11 These are in-situ solidification images of alloy sample S2 in Example 5 at a cooling rate of 40°C / min; among them, (a) is 1345°C, (b) is 1335°C, (c) is 1325°C, and (d) is 1315°C.

[0028] Figure 12 The solid phase volume fraction of alloy samples S0 and S2 changes with temperature and time at a cooling rate of 40℃ / min.

[0029] Figure 13 The solidification rate of alloy samples S0 and S2 changes with temperature and time at a cooling rate of 40℃ / min. DETAILED DESCRIPTION

[0030] Below in conjunction with embodiment and accompanying drawing, the technical scheme of the present invention is clearly and completely described, and the operation process and application effect of the inventive method in specific experimental analysis are described in more detail.It should be pointed out that the embodiment described in the present invention is only used for further explanation and illustration, rather than limiting its scope of application.Based on the present invention, all other embodiments obtained by those skilled in the art without making creative work premise all belong to protection scope of the present invention.

[0031] The preparation method of Inconel 625 nickel-based alloy is as follows: 25kg vacuum induction furnace (maximum operating temperature 1750℃, ultimate vacuum degree 5×10 -1 Four Inconel 625 alloy ingots, labeled S0, S1, S2, and S3 (S1, S2, and S3 were subjected to varying degrees of magnesium microalloying), were prepared in a smelting process (1000 psi, 1000 psi, and 2000 psi, respectively). The specific alloy compositions are shown in Table 1. During the smelting process, the metals Ni, Cr, Mo, and Nb were first placed in a crucible. The vacuum was reduced to below 5 Pa, and nitrogen was introduced into the furnace to a pressure of 50 kPa. Heating began and the temperature was raised to 1500°C. Si, Mn, and C were then added for alloying, and 50% Al particles were added for deoxidation. Sponge titanium was added for alloying, and the remaining 50% of aluminum was used for deep deoxidation. Samples S0, S1, S2, and S3 were cooled by air.

[0032] Table 6 Chemical composition of alloy samples used in the experiment (wt.%) element C Si Mn S Al Ti Cr Mo Nb Ni Mg <![CDATA[S0]]> 0.039 0.29 0.077 0.004 0.27 0.12 22.77 8.81 3.57 64.00 - <![CDATA[S1]]> 0.042 0.07 0.05 0.007 0.19 0.14 22.32 8.71 3.89 64.43 0.009 <![CDATA[S2]]> 0.041 0.05 0.05 0.006 0.20 0.14 22.55 8.66 3.83 64.33 0.012 <![CDATA[S3]]> 0.031 0.27 0.05 0.003 0.21 0.13 22.53 8.56 3.83 64.52 0.029 The in-situ solidification observation experiment and analysis method of each embodiment were carried out according to the following contents: The main equipment used is a VL2000DX-SVF17SP high-temperature laser confocal microscope. The main analysis software used in the present invention is Image J, Image pro plus, and Origin 2024.

[0033] The main equipment parameters of the high-temperature laser confocal microscope are set as follows: (1) Sample loading and size setting: The experimental alloy sample was placed in a corundum crucible with an inner diameter of 5 mm to 8 mm and a height of 6 mm to 8 mm. The sample diameter was 1 mm to 3 mm smaller than the crucible, and the sample height was 1 mm to 3 mm lower than the crucible to prevent sample overflow or shedding due to surface tension. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface roughness (height difference) controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was added to the platinum bracket to reduce sample oxidation.

[0034] Atmosphere and vacuum environment: vacuum the chamber before the experiment ≤10 -3Pa range; the experimental atmosphere is controlled by high-purity argon or nitrogen (purity ≥99.999%), and the gas pressure is controlled at 90kPa ~110kPa.

[0035] (2) Heating and cooling program settings: The heating rate from room temperature to 300°C is 300°C / min, and the heating rate from 300°C to the maximum melting temperature is 600°C / min. After reaching the maximum melting temperature, the holding time is set to 60s to 90s to ensure that the sample surface is completely melted. Then, the cooling program is set at a cooling rate of 10°C / min to 50°C / min. After cooling to 1200°C, the cooling program ends and the subsequent process is naturally cooled.

[0036] The maximum melting temperature is set 5°C to 10°C higher than the alloy's liquidus temperature, ensuring complete liquefaction of the sample surface while maintaining overall stability and preventing balling. This makes it suitable for in-situ observation of nucleation and solidification processes. The alloy's liquidus temperature varies depending on the alloy's composition; for example, the liquidus temperature of Inconel 625 nickel-based alloy is around 1340°C, and that of Inconel 718 nickel-based alloy is around 1336°C. In-situ observations primarily cover the entire process from complete melting of the alloy surface at the maximum melting temperature to cooling to 1200°C, which is the period during which the alloy's primary solidification structure forms.

[0037] Optical System Configuration: The objective lens has a magnification of 5x or 10x, with a field of view covering 0.2mm² to 0.5mm²; the eyepiece has a total magnification of 55x. The acquisition frame rate is adjustable from 1 to 5 frames per second.

[0038] (3) Use a high-temperature laser confocal microscope to observe the cooling and solidification process of the alloy sample and collect images. After the experiment, the in-situ solidification image of the alloy at the set cooling rate is obtained, and the continuous image sequence is synchronously annotated with the time stamp and temperature record. During the image analysis process, representative frame images are extracted according to the set interval temperature (e.g., every 10°C interval) or interval time (e.g., every 30s interval), with the goal of corresponding to the key stages such as grain initiation, rapid growth, fusion evolution and solidification termination. During the in-situ analysis process, the interface solidification morphology usually presents three consecutive stages: 1) Grain initiation: Grains nucleate in a local area with a cellular morphology; 2) Rapid growth and cellular fusion evolution: Grains grow rapidly into the surrounding liquid phase and fuse with each other; 3) Solidification termination: The remaining liquid phase solidifies slowly, and the solidification rate decreases until solidification terminates. The residual liquid phase can be divided into two main forms. One is a network-like liquid ridge structure distributed in the gaps between grains, usually located in the dendrite intersection area, which is gradually swallowed up as solidification proceeds; the other is the cellular core retained in the core area of the grain, which appears as an isolated and unconnected droplet-like structure and needs to complete solidification through slow contraction in a lower temperature zone.

[0039] (4) Using the Trainable WekaSegmentation plug-in of Image J software, the representative frame in situ solidification image selected in step (3) is used to separate the nucleated area from the non-nucleated area and generate a segmented image; The specific steps are as follows: Open Image J software, click File → Open, select the in-situ solidification image to be processed, enter WEKA in the software input box, select the Trainable Weka Segmentation image processing plug-in, click run, select the nucleated area (solid phase area) to be counted in Add to class 1, and select the unnucleated area (liquid phase area) in Add to class 2. After making your selections, click Train classifier to complete the area marking. Finally, click Create result. During this process, you can click Toggle overlay to switch between the effect image and the original image. To achieve the best processing effect, multiple adjustments are often required. In Add to class 1 and Add to class 2, you can add and delete unnecessary parts multiple times.

[0040] The segmented image was converted to 16-bit format in Image J. After the conversion, the segmented image was binarized and saved in Tif format. Noise reduction was then performed to obtain a binary image after noise reduction. The specific steps are as follows: Click Image → Type → 16 bit, Process → Binary → Make Binary, Process → Noise → Remove outliers → Radius, set the noise radius to 2, and click OK. File → Save as → Tif to any path.

[0041] (5) Open the binary processed image after noise reduction in Image Pro Plus software, execute Automatic Bright Objects statistics, measure and count the area of the nucleated region, and obtain statistical data; The specific steps are as follows: Open Image Pro Plus, open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0042] (6) After normalizing the statistical data in Origin software, nonlinear fitting and differential analysis were performed to extract the coagulation kinetic parameters.

[0043] The specific operation is as follows: First, create a new workbook in Origin, import the statistical data copied in step (5), and organize it into two columns of data: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. According to the above fitting model, set the fitting function expression as: , Among them, f s A1 is the upper limit of the solid volume fraction during cooling and solidification, and A2 is the lower limit of the solid volume fraction during cooling and solidification, which are the starting and ending values of the fitting function, respectively. A1-A2 represent the range of variation of the solid volume fraction before and after cooling and solidification. x0 is the time or temperature corresponding to the turning point of the solid volume fraction fitting curve, which corresponds to the critical temperature or time position for rapid microstructure evolution during solidification. It is usually located after grain nucleation is completed and enters the rapid growth stage, and is the characteristic point where the solidification rate reaches its peak. λ is the rate constant, which is a control parameter for the slope of the function and is used to describe the degree of change of the solid volume fraction near the turning point. In the Parameter page, set the initial fitting parameter values (for example, A1=1, A2=0, x0 is the midpoint of the curve, and λ is the empirically estimated initial slope value), and click Fit to perform nonlinear fitting. After the fitting is completed, record the fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page, and save the fitting curve and fitting report. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate The curve of the solidification rate is obtained, the differential data is derived, and a curve of the solidification rate changing with time (or temperature) is plotted. This curve can be used to further identify the peak value of the solidification rate, the inflection point of the change, and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameter conditions.

[0044] Example 1 0.009wt.% magnesium was added to the initial Inconel 625 nickel-based alloy S0 for microalloying treatment to obtain the processed sample S1. The in situ solidification images of samples S0 and S1 at a cooling rate of 20℃ / min were detected and analyzed respectively. The effect of Mg content on the nucleation behavior of alloy interface solidification, grain growth kinetics, and solid-liquid interface migration characteristics were observed, providing an experimental basis for revealing the solidification structure evolution mechanism under the regulation of trace magnesium.

[0045] In step (1), the crucible inner diameter was set to 6 mm and the height to 8 mm; the sample diameter was 5 mm and the sample height was 6 mm. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface height difference controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was mounted on the platinum bracket.

[0046] In step (2), the heating rate from 25°C to 300°C is 300°C / min, the heating rate from 300°C to the maximum melting temperature is 600°C / min, the maximum melting temperature is set to 1350°C, and the holding time after reaching the maximum melting temperature is set to 90s. After the holding is completed, the temperature is lowered at a rate of 20°C / min. After the temperature is lowered to 1200°C, the program ends and the subsequent process is cooled naturally.

[0047] The objective lens magnification is 5x, and the total eyepiece magnification is 55x. The acquisition frame rate can be adjusted to 1 frame / second.

[0048] In step (3), starting from 1345℃, representative frame images are extracted from the in-situ solidification images at intervals of 10℃ and 30s. The system covers the key solidification stages such as the initiation of nucleation, rapid growth and fusion of grains, and solidification of residual liquid phase. Among them, the in-situ solidification observation images of S0 from 1345℃ to 1315℃ are as follows: Figure 1 (a) Figure 1 (b) Figure 1 (c) and Figure 1 As shown in (d), the in-situ solidification observation image of S1 from 1345℃ to 1315℃ is as follows Figure 2 (a) Figure 2 (b) Figure 2 (c) and Figure 2 (d) shown.

[0049] In step (4), the original in-situ solidification image is processed by Image J software, and the image segmentation of the solid phase area and the liquid phase area is completed using the Trainable WekaSegmentation plug-in. Then, through 16-bit format conversion, binarization processing and noise reduction optimization, a high-quality binarized image sequence after noise reduction is obtained and saved in Tif format.

[0050] In step (5), use Image Pro Plus to open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0051] In step (6), create a new workbook in Origin, import the statistical data copied in step (5), and first obtain a statistical graph of solid volume fraction versus temperature or time, such as Figure 3 (a) is shown. Secondly, the statistical data is organized into two columns: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. Based on the above fitting model, set the fitting function expression as: , On the Parameter page, set the initial fitting parameter values (e.g., A1=1, A2=0, x0 is the midpoint of the curve, and λ is the empirically estimated initial slope value), and click Fit to perform nonlinear fitting. After the fitting is completed, record the obtained fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page. The relevant parameters are shown in Table 1. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate. , derive the differential data, and draw the solidification rate versus time (or temperature) curve, such as Figure 3 (b) This curve can be used to further identify the peak value of the solidification rate, the inflection point of the change, and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameters.

[0052] Table 1 Fitting equation parameters of S0 and S1 at a cooling rate of 20℃ / min

[0053] Example 2

[0054] 0.012wt.% magnesium was added to the initial Inconel 625 nickel-based alloy S0 for microalloying treatment to obtain the processed sample S2. The in-situ solidification image of sample S2 at a cooling rate of 20℃ / min was detected and analyzed. The effect of Mg content on the nucleation behavior of alloy interface solidification, grain growth kinetics, and solid-liquid interface migration characteristics was observed, providing an experimental basis for revealing the solidification structure evolution mechanism under the regulation of trace magnesium.

[0055] In step (1), the crucible inner diameter was set to 6 mm and the height to 8 mm; the sample diameter was 5 mm and the sample height was 6 mm. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface height difference controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was mounted on the platinum bracket.

[0056] In step (2), the heating rate from 25°C to 300°C is 300°C / min, the heating rate from 300°C to the maximum melting temperature is 600°C / min, the maximum melting temperature is set to 1350°C, and the holding time after reaching the maximum melting temperature is set to 90s. After the holding is completed, the temperature is lowered at a rate of 20°C / min. After the temperature is lowered to 1200°C, the program ends and the subsequent process is cooled naturally.

[0057] The objective lens magnification is 5x, and the total eyepiece magnification is 55x. The acquisition frame rate can be adjusted to 1 frame / second.

[0058] In step (3), starting from 1345℃, representative frame images are extracted at intervals of 10℃ and 30s. The system covers the key solidification stages such as nucleation initiation, rapid grain growth and fusion, and residual liquid solidification. The in-situ solidification observation images of S2 from 1345℃ to 1315℃ are as follows: Figure 4 (a) Figure 4 (b) Figure 4 (c) and Figure 4 (d) shown.

[0059] In step (4), the original in-situ solidification image is processed by Image J software, and the image segmentation of the solid phase area and the liquid phase area is completed using the Trainable WekaSegmentation plug-in. Then, through 16-bit format conversion, binarization processing and noise reduction optimization, a high-quality binarized image sequence after noise reduction is obtained and saved in Tif format.

[0060] In step (5), use Image Pro Plus to open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0061] In step (6), create a new workbook in Origin, import the statistical data copied in step (5), and first obtain a statistical graph of solid volume fraction versus temperature or time, such as Figure 5 (a) is shown. Secondly, the statistical data is organized into two columns: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. Based on the above fitting model, set the fitting function expression as: , On the Parameter page, set the initial fitting parameter values (e.g., A1=1, A2=0, x0 is the midpoint of the curve, and λ is the empirically estimated initial slope value), and click Fit to perform nonlinear fitting. After the fitting is completed, record the obtained fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page. The relevant parameters are shown in Table 2. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate. , derive the differential data, and draw the solidification rate versus time (or temperature) curve, such as Figure 5 (b) This curve can be used to further identify the peak value of the solidification rate, the inflection point of the change, and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameters.

[0062] Table 2 Fitting equation parameters of S0 and S2 at a cooling rate of 20℃ / min

[0063] Example 3 0.029wt.% magnesium was added to the initial Inconel 625 nickel-based alloy S0 for microalloying treatment to obtain the processed sample S3. The in-situ solidification image of sample S3 at a cooling rate of 20℃ / min was detected and analyzed. The effect of Mg content on the nucleation behavior of alloy interface solidification, grain growth kinetics, and solid-liquid interface migration characteristics was observed, providing an experimental basis for revealing the solidification structure evolution mechanism under the regulation of trace magnesium.

[0064] In step (1), the crucible inner diameter was set to 6 mm and the height to 8 mm; the sample diameter was 5 mm and the sample height was 6 mm. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface height difference controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was mounted on the platinum bracket.

[0065] In step (2), the heating rate from 25°C to 300°C is 300°C / min, the heating rate from 300°C to the maximum melting temperature is 600°C / min, the maximum melting temperature is set to 1350°C, and the holding time after reaching the maximum melting temperature is set to 90s. After the holding is completed, the temperature is lowered at a rate of 20°C / min. After the temperature is lowered to 1200°C, the program ends and the subsequent process is cooled naturally.

[0066] The objective lens magnification is 5x, and the total eyepiece magnification is 55x. The acquisition frame rate can be adjusted to 1 frame / second.

[0067] In step (3), starting from 1345℃, representative frame images are extracted at intervals of 10℃ and 30s. The system covers the key solidification stages such as nucleation initiation, rapid grain growth and fusion, and residual liquid phase solidification. The in-situ solidification observation images of S3 from 1345℃ to 1315℃ are as follows: Figure 6 (a) Figure 6 (b) Figure 6 (c) Figure 6 (d) shown.

[0068] In step (4), the original in-situ solidification image is processed by Image J software, and the image segmentation of the solid phase area and the liquid phase area is completed using the Trainable WekaSegmentation plug-in. Then, through 16-bit format conversion, binarization processing and noise reduction optimization, a high-quality binarized image sequence after noise reduction is obtained and saved in Tif format.

[0069] In step (5), use Image Pro Plus to open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0070] In step (6), create a new workbook in Origin, import the statistical data copied in step (5), and first obtain a statistical graph of solid volume fraction versus temperature or time, such as Figure 7 (a) is shown. Secondly, the statistical data is organized into two columns: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. Based on the above fitting model, set the fitting function expression as: , On the Parameter page, set the initial fitting parameter values (e.g., A1=1, A2=0, x0 is the midpoint of the curve, and λ is the empirically estimated initial slope value), and click Fit to perform nonlinear fitting. After the fitting is completed, record the obtained fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page. The relevant parameters are shown in Table 3. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate. , derive the differential data, and draw the solidification rate versus time (or temperature) curve, such as Figure 7 (b) This curve can be used to further identify the peak value of the solidification rate, the inflection point of the change, and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameters.

[0071] Table 3 Fitting equation parameters of S0 and S3 at a cooling rate of 20℃ / min

[0072] Example 4

[0073] 0.009wt.% magnesium was added to the initial Inconel 625 nickel-based alloy S0 for microalloying treatment to obtain the processed sample S1. The in situ solidification images of samples S0 and S1 at a cooling rate of 40℃ / min were detected and analyzed respectively. The effect of Mg content on the nucleation behavior of alloy interface solidification, grain growth kinetics, and solid-liquid interface migration characteristics were observed, providing an experimental basis for revealing the solidification structure evolution mechanism under the regulation of trace magnesium.

[0074] In step (1), the crucible inner diameter was set to 6 mm and the height to 8 mm; the sample diameter was 5 mm and the sample height was 6 mm. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface height difference controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was mounted on the platinum bracket.

[0075] In step (2), the heating rate from 25°C to 300°C is 300°C / min, the heating rate from 300°C to the maximum melting temperature is 600°C / min, the maximum melting temperature is set to 1350°C, and the holding time after reaching the maximum melting temperature is set to 90s. After the holding time is completed, the temperature is lowered at a rate of 40°C / min. After the temperature is lowered to 1200°C, the program ends and the subsequent process is cooled naturally.

[0076] The objective lens magnification is 5x, and the total eyepiece magnification is 55x. The acquisition frame rate can be adjusted to 1 frame / second.

[0077] In step (3), starting from 1345℃, representative frame images are extracted from the in-situ solidification images at intervals of 10℃ and 30s. The system covers the key solidification stages such as the initiation of nucleation, rapid growth and fusion of grains, and solidification of residual liquid phase. Among them, the in-situ solidification observation images of S0 from 1345℃ to 1315℃ are as follows: Figure 8 (a) Figure 8 (b) Figure 8 (c) and Figure 8 As shown in (d), the in-situ solidification observation image of S1 from 1345℃ to 1315℃ is as follows Figure 9 (a) Figure 9 (b) Figure 9 (c) and Figure 9 (d) shown.

[0078] In step (4), the original in-situ solidification image is processed by Image J software, and the image segmentation of the solid phase area and the liquid phase area is completed using the Trainable WekaSegmentation plug-in. Then, through 16-bit format conversion, binarization processing and noise reduction optimization, a high-quality binarized image sequence after noise reduction is obtained and saved in Tif format.

[0079] In step (5), use Image Pro Plus to open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0080] In step (6), create a new workbook in Origin, import the statistical data copied in step (5), and first obtain a statistical graph of solid volume fraction versus temperature or time, such as Figure 10 (a) is shown. Secondly, the statistical data is organized into two columns: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. Based on the above fitting model, set the fitting function expression as: , On the Parameter page, set the initial fitting parameter values (e.g., A1=1, A2=0, x0 is the midpoint of the curve, and λ is the empirically estimated initial slope value), and click Fit to perform nonlinear fitting. After the fitting is completed, record the obtained fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page. The relevant parameters are shown in Table 4. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate. , derive the differential data, and draw the solidification rate versus time (or temperature) curve, such as Figure 10 (b) This curve can be used to further identify the peak value of the solidification rate, the inflection point of the change, and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameters.

[0081] Table 4 Fitting equation parameters of S0 and S1 at a cooling rate of 40℃ / min

[0082] Example 5 0.012wt.% magnesium was added to the initial Inconel 625 nickel-based alloy S0 for microalloying treatment to obtain the processed sample S2. The in-situ solidification image of sample S2 at a cooling rate of 40℃ / min was detected and analyzed. The effect of Mg content on the nucleation behavior of alloy interface solidification, grain growth kinetics, and solid-liquid interface migration characteristics was observed, providing an experimental basis for revealing the solidification structure evolution mechanism under the regulation of trace magnesium.

[0083] In step (1), the crucible inner diameter was set to 6 mm and the height to 8 mm; the sample diameter was 5 mm and the sample height was 6 mm. The upper and lower surfaces of the sample were machined flat, with the end face inclination angle controlled to less than 0.5° and the surface height difference controlled to within 5 µm. A titanium sheet 30 mm long and 10 mm wide was mounted on the platinum bracket.

[0084] In step (2), the heating rate from 25°C to 300°C is 300°C / min, the heating rate from 300°C to the maximum melting temperature is 600°C / min, the maximum melting temperature is set to 1350°C, and the holding time after reaching the maximum melting temperature is set to 90s. After the holding time is completed, the temperature is lowered at a rate of 40°C / min. After the temperature is lowered to 1200°C, the program ends and the subsequent process is cooled naturally.

[0085] The objective lens magnification is 5x, and the total eyepiece magnification is 55x. The acquisition frame rate can be adjusted to 1 frame / second.

[0086] In step (3), starting from 1345℃, representative frame images are extracted at intervals of 10℃ and 30s. The system covers the key solidification stages such as nucleation initiation, rapid grain growth and fusion, and residual liquid solidification. The in-situ solidification observation images of S2 from 1345℃ to 1315℃ are as follows: Figure 11 (a) Figure 11 (b) Figure 11 (c) and Figure 11 (d) shown.

[0087] In step (4), the original in-situ solidification image is processed by Image J software, and the image segmentation of the solid phase area and the liquid phase area is completed using the Trainable WekaSegmentation plug-in. Then, through 16-bit format conversion, binarization processing and noise reduction optimization, a high-quality binarized image sequence after noise reduction is obtained and saved in Tif format.

[0088] In step (5), use Image Pro Plus to open the Tif file you just saved, select the ruler, click Count and measure objects, click Automatic bright objects, click Measure → Select measurements → per area → OK. Click Count → File → Date to clipboard to copy the calculated data, and paste the statistical data into the Origin data sheet.

[0089] In step (6), create a new workbook in Origin, import the statistical data copied in step (5), and first obtain a statistical graph of solid volume fraction versus temperature or time, such as Figure 12 As shown. Secondly, organize the statistical data into two columns: temperature (or time) and normalized solid area (solid volume fraction). Click the menu bar Analysis → Fitting → Nonlinear Curve Fit → Open Dialog, and click "New Function" in the pop-up window to create a new fitting function. According to the above fitting model, set the fitting function expression as: , On the Parameter page, set the initial fitting parameter values (e.g., A1=1, A2=0, x0 is the midpoint of the curve, and λ is the initial slope value estimated empirically), and click Fit to perform nonlinear fitting. After the fitting is completed, record the obtained fitting parameters A1, A2, x0, λ and their confidence intervals on the Parameter Result page. The relevant parameters are shown in Table 5. After the fitting is completed, select the differential operation in the Analysis → Mathematics → Differentiate menu, enter the fitting curve, perform the first-order derivative calculation, and obtain the solidification rate. , derive the differential data, and draw the solidification rate versus time (or temperature) curve, such as Figure 13 This curve can be used to further identify the peak value of solidification rate, the inflection point of change and the solidification completion temperature zone, providing a quantitative basis for the analysis of solidification kinetics under different experimental parameters.

[0090] Table 5 Fitting equation parameters of S0 and S2 at a cooling rate of 40℃ / min .

Claims

1. An in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys, characterized in that: Includes the following: In-situ heating and solidification experiments were carried out on nickel-based alloys to obtain a sequence of microstructure evolution and solidification images. The sequence of microstructure evolution and solidification images was processed and subjected to nonlinear fitting analysis. The solidification behavior characteristics and solidification stage type of the alloy were determined based on the solidification image characteristics and fitting results, and the ability of process parameters to regulate the microstructure evolution behavior was evaluated.

2. The in-situ solidification observation and analysis method for nickel-based high-temperature alloys according to claim 1, characterized in that: Includes the following: (1) Preparation of nickel-based alloy samples; (2) A high-temperature laser confocal microscope was used to in-situ heat the nickel-based alloy sample to the highest melting temperature. The highest melting temperature was set to 5°C to 10°C higher than the liquidus temperature of the nickel-based alloy sample. After reaching the highest melting temperature, the sample was kept warm for 60s to 90s. After the holding time, the sample was cooled and solidified at a cooling rate of 10°C / min to 50°C / min. The cooling and solidification process ended when the alloy sample was ≤1200°C. (3) Continuously collect images of the cooling and solidification process to obtain an in-situ solidification image sequence of the alloy sample from the beginning of nucleation to complete solidification, recording the entire process from grain initiation, rapid growth, fusion evolution to the termination of solidification; synchronously annotate the in-situ solidification image sequence with timestamps and temperatures, construct a temperature-image-time ternary index system, and extract representative frame images at set intervals of temperature or time; (4) Perform image segmentation on the representative frame image, classify the solid phase area and the liquid phase area, and perform 16-bit grayscale format, binarization processing and noise reduction processing to generate a binary image after noise reduction; (5) Perform automatic bright area statistical analysis on the binary image after noise reduction, count the solid phase area, and obtain the solid phase volume fraction data; (6) Normalize the solid volume fraction data and perform nonlinear fitting. Take the first-order derivative of the fitting curve to obtain the solidification rate. The change curve of the process parameters is used to evaluate the control ability of the process parameters on the tissue evolution behavior.

3. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 1 or 2, characterized in that: The process parameters include alloy element type, alloy element content, cooling rate and cooling time.

4. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 2, characterized in that: The scanning speed of the high-temperature laser confocal microscope is 0.5 frame / second to 5 frame / second.

5. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 2, characterized in that: The nickel-based alloy sample in step (1) is cylindrical, the sample diameter is 1 mm to 3 mm smaller than the crucible diameter, the sample height is 1 mm to 3 mm smaller than the crucible height, the upper and lower bottom surface inclination angles of the sample are less than 0.5°, and the sample surface roughness is less than 5 μm.

6. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 2, characterized in that: During in-situ heating in step (2), the heating rate from room temperature to 300°C is 300°C / min, and the heating rate from 300°C to the maximum melting temperature is 600°C / min.

7. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 2, characterized in that: The noise radius of the noise reduction process in step (4) is 2~3 pixels.

8. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 2, characterized in that: The nonlinear fitting in step (6) uses the following Boltzmann function: ， in, is the solid phase volume fraction, To collect the cooling time or cooling temperature variable corresponding to the in-situ solidification image, is the upper limit of the solid phase volume fraction during the cooling and solidification process, is the lower limit of the solid phase volume fraction during the cooling and solidification process, is the time or temperature corresponding to the turning point of the fitting curve, and λ is a constant.

9. The in-situ solidification observation experiment and analysis method for nickel-based high-temperature alloys according to claim 8, characterized in that: In step (6), the first derivative of the fitting curve is taken to obtain the solidification rate The change curve: 。

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