High-flux in-situ statistical analysis method for sulfide inclusions in high-temperature alloy

Abstract

The invention belongs to the field of high-temperature alloy inclusion analysis, and particularly relates to a high-flux in-situ statistical analysis method for sulfide inclusions in high-temperature alloy. According to the method, the sulfide inclusion in the high-temperature alloy is subjected to in-situ quantitative statistical analysis by utilizing an automatic (flying nano) inclusion analysis system, the purpose of quickly and accurately identifying the sulfide inclusion is achieved by setting a proper identification standard, and the efficiency and accuracy of statistical analysis of the sulfide inclusion are greatly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of high-temperature alloy inclusion analysis, and particularly relates to a high-throughput in-situ statistical analysis method for sulfide inclusions in high-temperature alloy. BACKGROUND

[0002] High-temperature alloy is widely used in the related hot end components of aviation, aerospace and ship equipment, and the quality of the high-temperature alloy will directly affect the safety and stability of the equipment operation. Sulfide inclusions are common inclusions in high-temperature alloy, which will affect the performance of the alloy and further affect the service state of the alloy under high temperature and high pressure conditions. Therefore, in-situ quantitative statistical analysis of sulfides in the alloy is very important. Conventional high-temperature alloy sulfide inclusion analysis mainly adopts scanning electron microscope observation method, metallographic rating method, in-situ analysis method, etc. The scanning electron microscope observation method can accurately identify inclusions by manual calibration, but the manual calibration is low in efficiency, and it is difficult to quickly identify and calibrate a large number of inclusions or a large detection area. The metallographic rating method cannot accurately determine the composition of inclusions. The in-situ analysis method also has a small detection area, low efficiency and complex operation. With the development of the industry, the size of the required detection statistics gradually increases, and the amount of samples to be manually detected greatly increases. The above methods are not suitable for large-scale and accurate statistical analysis.

[0003] Therefore, the existing methods in the prior art have problems of low efficiency, poor statistical consistency, and inability to achieve high-throughput in-situ quantitative analysis. The following invention is proposed. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a high-throughput in-situ statistical analysis method for sulfide inclusions in high-temperature alloy, which aims to solve the problem of low efficiency of statistical analysis of sulfide inclusions in the prior art and improve the efficiency and accuracy of in-situ quantitative analysis of sulfide inclusions in high-temperature alloy.

[0005] The technical scheme of the present application is as follows: A high-throughput in-situ statistical analysis method for sulfide inclusions in high-temperature alloy, comprising the following steps: Step 1: sampling from a selected position of a high-temperature alloy ingot, and preparing a sample block suitable for detection by slicing and wire cutting; Step 2: rough grinding to fine grinding of the sample block using 180 mesh, 400 mesh, 1000 mesh and 2000 mesh sandpaper in sequence, and then mechanical polishing on a polishing machine using diamond polishing agent to obtain a smooth metallographic surface without obvious scratches; Step 3: preliminary observation of the polished surface using an optical microscope to determine the detection area; Step 4: setting identification parameters in the inclusion automatic analysis system according to the characteristics of sulfide inclusions, including size range, gray threshold, energy spectrum identification element and morphology characteristics; Step 5: Use an automatic inclusion analysis system to perform high-throughput scanning of the area to be tested, so as to achieve in-situ identification, calibration and quantitative statistics of sulfide inclusions.

[0006] Furthermore, in the above method, the size range in step four is 0-10μm, and the grayscale threshold range is 0-165.

[0007] Furthermore, in the above method, the energy spectrum identification element in step four includes at least one of S, O, Ca, Al, and Mg.

[0008] Furthermore, in the above method, the energy spectrum identification elements also include one or more of Hf, W, Mo, and Ta.

[0009] Furthermore, in the above method, the morphological features described in step four include sheet-like and / or spherical shapes.

[0010] Furthermore, in the above method, the region to be detected in step three includes grain boundaries and carbide distribution regions.

[0011] Furthermore, the method described above, after step five, also includes manual review or scanning electron microscopy verification of the system identification results.

[0012] Advantages and beneficial effects of the present invention: 1. The sample preparation method of the present invention is simple, requiring only the preparation of metallographic polished samples for observation and statistical analysis; 2. This invention can efficiently identify sulfide inclusions in high-temperature alloys. By using an automatic inclusion analysis system, it can achieve high-throughput identification and calibration of sulfide inclusions. The automated process avoids human error, and the statistical results have good reproducibility. 3. The present invention has high accuracy in identifying and calibrating sulfide inclusions. By setting multi-dimensional screening settings for the characteristic size, morphology and energy spectrum identification elements of sulfide inclusions, the automatic inclusion analysis system can accurately calibrate and count sulfide inclusions. At the same time, the accurate setting of features also enables the system to reasonably exclude many sensitive areas suspected of containing sulfide inclusions, reduce the system's identification calculation burden, and improve work efficiency from another perspective. 4. This invention supports continuous analysis of batch samples and is suitable for rapid quality control in industrial production. Attached Figure Description

[0013] Figure 1 It exhibits the morphology of a sulfur-containing composite oxide; Figure 2 Energy dispersive spectroscopy (EDS) of sulfur-containing complex oxides (Ca); Figure 3 Example of energy spectrum of O element in sulfur-containing complex oxides; Figure 4Example of energy spectrum of sulfur-containing complex oxide (S element). Detailed Implementation

[0014] In as-cast samples of high-temperature alloys, sulfide inclusions are small in size and mostly form at the interface. Therefore, when selecting the characteristic areas for detection, the grain boundaries, carbides, and other locations within the observation area can be used as the key areas for detection and analysis.

[0015] Based on previous experimental experience, this invention specifies the size of composite sulfides to be between 0 and 10 μm, with characteristic morphologies of flakes and spheres. In high-temperature alloys, due to the influence of raw materials and refractory materials, sulfide inclusions often precipitate in the form of composite inclusions such as Al2O3-CaO-CaS. Considering the elements in the alloy that easily form compounds with sulfur, in addition to S, elements such as O, Al, Ca, Hf, W, Mo, and Ta need to be added for energy dispersive spectroscopy (EDS) detection. In summary, sulfides are characterized and statistically analyzed from three dimensions: size, morphology, and composition. A reasonable grayscale threshold should be defined during the experiment. Based on previous experimental experience, the grayscale threshold range for identifying sulfide inclusions should be set between 0 and 165. A range that is too small will not provide comprehensive detection, while a range that is too large will cause the system to identify the wrong location, reducing detection efficiency and accuracy. During the detection process, a fine scan can be performed after the identification and calibration of the area to be tested is completed. Analysis of the composition and morphology results can further improve the accuracy of in-situ calibration of sulfide inclusions and reduce the probability of misjudgment and missed detection.

[0016] This invention enables fully automated calibration of inclusions through proper parameter settings in the early stages. It can quickly and intuitively locate sulfides in situ. The post-processing output data includes the size of the sulfide inclusions, image calibration coordinates, energy dispersive spectroscopy data, etc., allowing inspectors to intuitively classify, statistically analyze, and perform analysis on the inclusions. Furthermore, the detection results have good reproducibility.

[0017] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings and examples.

[0018] Example

[0019] This embodiment presents a high-throughput in-situ statistical analysis method for sulfide inclusions in high-temperature alloys, comprising the following steps: Step 1: Cut a slice from the bottom of the high-temperature alloy ingot. The slice should be taken at least 30mm above the bottom after removing the graphite pad. The slice thickness should be 15-20mm, and the two sides of the sample should be parallel. Then, perform wire cutting to take a sample. The center of the sample should be the middle of the distance from the center of the slice to the edge. The sample size should be 30mm square, and the thickness is the slice thickness.

[0020] Step 2: Use the side closest to the top of the ingot as the inspection surface. Perform metallographic polishing on the inspection surface, successively polishing with 180 grit, 400 grit, 1000 grit, and 2000 grit sandpaper. After polishing until smooth, polish until there are no obvious scratches under an optical microscope.

[0021] Step 3: Use an optical microscope to make a preliminary observation and delineate the detection area. The detection area is a circle with a diameter of not less than 10 mm, and make simple marks around the detection area.

[0022] Step 4: Place the processed sample into the Phynet Automatic Inclusion Detection System. Set the grayscale threshold to 0~165, the detection elements to S, O, Al, Ca, Hf, W, Mo, Ta, and the size to 0~10μm.

[0023] Step 5: Using the Phenom Automatic Inclusion Detection System, a circular area with a diameter of 10 mm is selected within the calibrated detection area under an optical microscope for high-throughput in-situ scanning of inclusions.

[0024] Step 6: Data post-processing and screening analysis. A total of 12 complex sulfides were detected in the area to be tested.

[0025] Step 7: The scanning electron microscope results showed that the observation calibration and inclusion analysis system results were consistent.

[0026] Table 1 shows the percentage of typical sulfur-containing oxides in EDS spot scans under scanning electron microscopy. Figure 1 Typical sulfide morphology, with... Figure 2 , 3 Figure 4 shows typical EDS surface scans of sulfides.

[0027] Table 1. Percentage of typical sulfur-containing oxide spot scan components Element Atomic percent (%) Mass percent (%) C 19.27 9.42 N 5.27 3.01 O 44.01 28.66 Mg 0.91 0.90 Al 3.56 3.91 S 7.45 9.72 Ca 7.00 11.42 Ni 11.33 27.05 The results obtained from the above embodiments show that the detection method of the present invention is accurate and efficient. The average detection and post-processing time in a single area is only about one hour, which is far better than the time required by scanning electron microscopy. Furthermore, when the alloy is relatively pure and has good density, the detection time will be even lower. The present invention is more suitable for in-situ analysis and location calibration of inclusions in multiple samples and large areas.

Claims

1. A high-throughput in-situ statistical analysis method for sulfide inclusions in high-temperature alloys, characterized in that, Includes the following steps: Step 1: Take a sample from a selected location on the high-temperature alloy ingot, and prepare a sample block suitable for testing by slicing and wire cutting; Step 2: The sample block is subjected to coarse grinding, fine grinding, and mechanical polishing in sequence to obtain a smooth metallographic surface; Step 3: Use an optical microscope to conduct a preliminary observation of the polished surface to determine the area to be inspected; Step 4: In the automatic inclusion analysis system, set the identification parameters according to the characteristics of sulfide inclusions, including: size range, grayscale threshold, energy spectrum identification elements, and morphological features; Step 5: Use an automatic inclusion analysis system to perform high-throughput scanning of the area to be tested, so as to achieve in-situ identification, calibration and quantitative statistics of sulfide inclusions.

2. The method according to claim 1, characterized in that, The size range mentioned in step four is 0-10μm, and the grayscale threshold range is 0-165.

3. The method according to claim 1 or 2, characterized in that, The energy spectrum identification element mentioned in step four includes at least one of S, O, Ca, Al, and Mg.

4. The method according to claim 3, characterized in that, The energy spectrum identification elements also include one or more of Hf, W, Mo, and Ta.

5. The method according to claim 1, characterized in that, The morphological features described in step four include sheet-like and / or spherical shapes.

6. The method according to claim 1, characterized in that, The region to be detected in step three includes grain boundaries and carbide distribution areas.

7. The method according to claim 1, characterized in that, Step five also includes manual review or scanning electron microscopy verification of the system's identification results.

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