A method for characterizing the microstructure of twin-crystal cemented carbide

Abstract

The present invention relates to a method for characterizing the microstructure of a twin-crystal cemented carbide. Specifically, the present invention relates to a method for quantitatively measuring and characterizing the WC grain size and grain size distribution of cemented carbide. The steps include sample preparation, image acquisition, image processing, determination of the overall average grain size, determination of the average grain size of the coarse grains d C , Determine the average grain size d of fine grains F Calculate the two grain structure parameters, the ratio of coarse to fine grains n and the ratio of coarse to fine grain sizes k. C d F The four grain structure parameters n, n and k can quantitatively characterize the microstructure of the twin-crystal cemented carbide, which is beneficial to the quantitative control of the microstructure and thus the performance of the cemented carbide.

Description

Technical Field

[0001] This patent belongs to the field of cemented carbide manufacturing technology. The present invention relates to a method for characterizing the microstructure of cemented carbide, particularly a method for characterizing the WC grain structure. Specifically, the present invention relates to a method for quantitatively characterizing the WC grain size and particle size distribution of cemented carbide with a twinned microstructure. The method described in the present invention can quantitatively describe the WC grain structure characteristics of cemented carbide, especially twinned (or non-uniformly structured) cemented carbide, facilitating the quantitative control of the microstructure and, therefore, the performance of the cemented carbide. Background Art

[0002] Cemented carbide is a metal matrix composite material mainly composed of WC, with a small amount of refractory metal carbides such as TiC, TiN, Ti(C,N), Mo2C, TaC, NbC added as hard phase, and plastic metals such as Co, Ni, Fe or their alloys as bonding phase. It has good wear resistance, heat resistance, corrosion resistance and high strength and toughness. It is known as the "teeth of industry" and is widely used in various fields of the national economy.

[0003] Cemented carbide generally consists of a WC hard phase and a Co binder phase, with WC grains uniformly dispersed within the Co phase. The binder phase content, average WC grain size and size distribution, hard metal proximity, and binder phase mean free path are the primary factors influencing cemented carbide performance. Generally speaking, coarse-grained cemented carbide exhibits greater toughness, while fine-grained carbide exhibits higher hardness. To balance the high toughness and wear resistance inherent in both coarse and fine-grained alloys, cemented carbide with both coarse and fine grains is typically prepared using wet grinding of coarse and fine WC raw materials, wet grinding with overlapping feeds, and coarse and fine mixed materials. This is known as a "dual grain structure" cemented carbide (abbreviated as "twin structure"), a "mixed crystal structure," or a "heterogeneous structure" cemented carbide. For uniformly structured cemented carbide, the average grain size and particle size dispersion coefficient are typically used to characterize its WC grain structure characteristics (i.e., grain size and particle size distribution). However, for so-called "twin" structure cemented carbide, it is impossible to reflect its WC grain structure characteristics by only using the average grain size. For a long time, it has only been possible to make subjective judgments based on the metallographic structure. There is a lack of reliable quantitative characterization methods, making it impossible to evaluate and control the quality of twin structure cemented carbide. At present, the average grain size of the grains in the cemented carbide structure is generally measured by the intercept method (ISO 4499, ASTM E1382 or GB / T3488.2) or the equivalent circle area method, which cannot quantitatively describe the WC grain size distribution state of "twin" cemented carbide. ZL201410796617.9 discloses a method for judging twinned alloys. The key is to use computer image analysis technology to sort the grain size (cross-sectional area) of the alloy from small to large, and then calculate the scale difference ΔS between two grains separated by K grains (when K=0, that is, the scale difference between two adjacent grains). The grain number n when ΔS jumps is used as the basis for judgment.

[0004] CN 109342280 A discloses a method and application for characterizing the microstructure of cemented carbide using average grain size and grain size distribution. Metallographic analysis software is used to obtain the average grain size of tungsten carbide, and a grain size distribution curve is plotted to determine the microstructure of the cemented carbide. A double or multiple peaks in the grain size distribution curve indicate that the alloy structure is composed of two or more grains. However, this method can only determine the average grain size of the entire tungsten carbide in a cemented carbide WC with a dual-crystal structure consisting of coarse and fine grains. The grain size distribution curve only qualitatively indicates that the alloy structure is composed of two types of tungsten carbide grains, but cannot accurately determine the average grain size of the coarse and fine tungsten carbide grains within the alloy structure, nor the proportion of the two types of grains within the alloy structure. Summary of the Invention

[0005] To address the problem that the microstructure of twin-crystal cemented carbide cannot be quantitatively described, the present invention aims to provide a method for quantitatively characterizing the microstructure of twin-crystal cemented carbide. Specifically, the present invention aims to provide a method for quantitatively characterizing the WC grain structure of twin-crystal cemented carbide.

[0006] The method for quantitatively characterizing the grain structure of a twin-crystal cemented carbide according to the present invention is characterized in that the average grain size d of the coarse grains in the structure is used. C , average grain size of fine grains d F , and the average grain size ratio of coarse and fine grains k = d C / d F And the ratio of coarse to fine grains n = N C / N F Four grain size parameters are used to characterize the WC grain structure characteristics of twin-crystal cemented carbide.

[0007] A further object of the present invention is to provide a method for measuring and analyzing the characteristic parameters of the grain structure of a twin-crystal cemented carbide, comprising the following steps:

[0008] (1) Sample preparation. Prepare the metallographic test sample according to the standard cemented carbide microstructure metallographic analysis method (ISO 4499 or GB / T3488.2). That is, first prepare the metallographic surface of the alloy sample to be tested, immerse the metallographic surface of the sample in a newly prepared mixed aqueous solution of equal amounts of 20% (mass percentage) potassium ferrocyanide and sodium hydroxide or potassium hydroxide for corrosion, rinse it with tap water after the corrosion is completed, and then etch the metallographic surface with a saturated solution of ferric chloride and hydrochloric acid. After corrosion, rinse it with tap water first, then rinse it with alcohol, and dry it with hot air;

[0009] (2) Image acquisition. Images are acquired using an optical microscope (or scanning electron microscope) and metallographic analysis software. Three to five images are usually collected, and the total number of grains measured and counted is considered optimal if it is more than 1,000. The more grains there are, the more accurate the statistical value.

[0010] (3) Image processing. First, the image is automatically corrected for light source, white balanced, and binarized. Then, denoising is performed to remove small points in the photo, with a focus on removing noise points that are not grains. If necessary, holes on the grains can also be filled.

[0011] (4) Overall average grain size (d A ) is measured. The sizes of all the grains in the image obtained in step (2) are measured and statistically analyzed to obtain the minimum grain size d min , and the maximum grain size d max , overall average grain size (d A ).

[0012] Taking the intercept method as an example, the alloy grains are automatically segmented to separate the adhered grains, and then the software parameters such as the scale, number of intercept lines (5-10), and intercept angle (0-90°) are calibrated (for specific reference (ISO 4499, ASTM E1382 or GB / T3488.2), and the lower limit of the filter value is set to L min , generally 0 or 0.1μm (determined by image quality); the upper limit is L ma x, it can be equal to or greater than the size value dmax of the largest grain in the image, which means setting the statistical size range to L min ~d max Then set the grain size classification, generally divided into 10 levels (can be divided according to actual needs, without affecting the statistical results), and finally use metallographic analysis software to perform statistical analysis on the current sample to obtain the total number of grains N A , overall average grain size d A and its particle size deviation coefficient C A The three parameter values ​​are shown in Table 1.

[0013] Table 1

[0014]

[0015] Note: L min is the size value for removing noise and impurities; A is the group distance = (L max -L min ) / (number of levels - 1), takes a positive value, and the selection principle is to make the number of grains in the last level approach 0.

[0016] (5) Average grain size of coarse grains (d C ) determination. The coarse grains in the image obtained in step (2) are measured and statistically analyzed. The coarse grains refer to grains with a size larger than the overall average grain size (d A ) part of the grain.

[0017] Keep the parameters of step (4) such as image sample, scale, number of cut lines, and cut angle unchanged, and change the lower limit of the grain filter value to L min =d A ; The upper limit is the same as step (4) L max ≥d max , that is, only the size range d is counted A ~d max μm grains, set the grain size classification, and finally use the metallographic analysis software to perform statistical analysis on the current sample to obtain the number of coarse grains N c , the average grain size of coarse grains d c and its particle size deviation coefficient C c Three parameter values,Table 2.

[0018] Table 2

[0019]

[0020]

[0021] Note: B is the group distance = (L max -L min ) / (number of levels - 1), takes a positive value, and the selection principle is to make the number of grains in the last level approach 0.

[0022] (6) Average grain size of fine grains (d F ) determination. The fine grains in the image obtained in step (2) are measured and statistically analyzed. The fine grains refer to grains with a size smaller than the overall average grain size (d A ) part of the grain.

[0023] Keep the parameters of step (4) such as image sample, scale, number of cut lines, and cut angle unchanged, and modify the lower limit of the grain filter value to be the same as step (4) to L min =0; upper limit is L max =d A , that is, only the size range of 0 to d is counted A μm grains, set the grain size classification, and finally use the metallographic analysis software to perform statistical analysis on the current sample to obtain the number of fine grains N F , the average grain size of fine grains d F and its particle size deviation coefficient C F Three parameter values,Table 3.

[0024] Table 3

[0025]

[0026] Note: C is the group distance = (L max -L min ) / (number of levels - 1), takes a positive value, and the selection principle is to make the number of grains in the last level approach 0.

[0027] (7) Data analysis. From the results measured in steps (5) and (6), the ratio of coarse to fine grains n = N can be calculated. C / N F and the coarse-fine grain size ratio k=d C / d F Two grain size parameters.

[0028] The method for measuring and characterizing the grain size and grain size distribution of the twin-crystal structure WC carbide of the present invention quantitatively measures the coarse grain size and the fine grain size, and calculates and obtains the average grain size d of the coarse and fine grains. C d FThe four grain structure parameters, namely, the ratio of the number of coarse and fine grains n and the ratio of the coarse and fine grain sizes k, are used to characterize the WC grain structure of cemented carbide. A larger n value indicates a greater number of coarse grains in the structure, while a larger k value indicates a greater difference in the size of the coarse and fine grains in the structure. These structural characteristics are the key factors affecting the performance of cemented carbide. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the implementation steps of the measurement and analysis method of WC grain size and grain size distribution of cemented carbide according to the present invention.

[0030] Figure 2 This is a microstructure image of sample 1 of Example 1 magnified 1500 times.

[0031] Figure 3 This is a microstructure image of the metallographic surface prepared by Sample 1 in Example 1, which was taken after etching and magnified 1500 times.

[0032] Figure 4 yes Figure 3 Microstructure image after light source correction.

[0033] Figure 5 yes Figure 4 Microstructure image after white balance processing.

[0034] Figure 6 yes Figure 5 Microstructure image after binarization processing.

[0035] Figure 7 yes Figure 6 Microstructure image after denoising.

[0036] Figure 8 yes Figure 7 Microstructure image after grain segmentation.

[0037] Figure 9 This is a microstructure image statistically analyzed by metallographic analysis software in Example 1.

[0038] Figure 10 This is a microstructure image of sample 2 of Example 2 magnified 1500 times.

[0039] Figure 11 This is a microstructure image statistically analyzed by metallographic analysis software in Example 2. DETAILED DESCRIPTION

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but this should not be construed as limiting the scope of the above-mentioned subject matter of the invention to the following embodiments only.

[0041] Example 1:

[0042] The method of the present invention was used to analyze sample 1, using Leica LAS V4.2 metallographic analysis system and CIAS-M2000 hard alloy metallographic analysis. The analysis method steps are shown in Figure 1 , as follows:

[0043] Step 1: Prepare the sample and prepare the metallographic surface of the sample. Prepare the sample 1 through mechanical rough grinding, fine grinding, lapping and polishing to a metallographic sample with no corrosion spots, no scratches and a mirror state. For specific reference (ISO 4499, ASTM E1382 or GB / T3488.2), the microstructure image is as follows Figure 2 shown.

[0044] Immerse the metallographic surface of the sample in a newly prepared mixed aqueous solution of equal amounts of 20% (mass percentage) potassium ferrocyanide and sodium hydroxide or potassium hydroxide, and then rinse it with tap water. Then, etch the metallographic surface with a saturated solution of ferric chloride and hydrochloric acid. After etching, rinse it with tap water, then rinse it with alcohol, and dry it with hot air.

[0045] Step 2: Image acquisition. Use a metallographic microscope to magnify 1500 times and take 5 images using metallographic analysis software, such as Figure 3 shown.

[0046] Step 3: Image processing, obtain the sample image. Image processing includes the image through automatic light source correction ( Figure 4 )、Auto White Balance( Figure 5 ), binary processing ( Figure 6 ), denoising( Figure 7 ), and then automatically split the alloy grains to separate the adhered grains ( Figure 8 ).

[0047] Step 4: Overall average grain size (d A ) measurement. Select the grain statistics method as the intercept method, set the number of intercept lines to 10, the intercept angle to 0 degrees, select a 1500x calibrated ruler, set the grain filtering value Lmin = 0, L max = 4.5 μm, that is, the grain size range is 0 to 4.5 μm, and the grain size classification is set according to Table 4. Finally, the processed image is statistically analyzed using metallographic analysis software ( Figure 9 ), and the total number of grains N is obtained A The overall average grain size is 2893, d A 0.8 μm and its particle size deviation coefficient C A The statistical analysis results are shown in Table 4.

[0048] Table 4

[0049]

[0050] Step 5: Average grain size of coarse grains (d C ) measurement. Keep the sample image and software parameter settings of step (4). Modify the lower limit of the grain filter value L min =d A =0.8μm, the upper limit is L max =4.5μm, that is, the statistical range of grain size is 0.8~4.5μm. Set the grain size classification according to Table 5. Finally, use metallographic analysis software to perform statistical analysis on the current sample to obtain the number of coarse grains N c The average grain size of the coarse grains is 1158, and the average grain size of the coarse grains is c 1.3 μm and its particle size deviation coefficient C c The statistical analysis results are shown in Table 5.

[0051] Table 5

[0052]

[0053]

[0054] Step 6: Average grain size of fine grains (d F ) measurement. Keep the sample image and software parameter settings of step (4). Modify the lower limit of the grain filter value L min =0, upper limit L max =d A = 0.8 μm, that is, the statistical range of grain size is 0 to 0.8 μm. Set the grain size classification according to Table 6. Finally, use metallographic analysis software to perform statistical analysis on the current sample to obtain the number of fine grains N F The average grain size of fine grains is 1735, d F 0.5μm and its particle size deviation coefficient C F The statistical analysis results are shown in Table 6.

[0055] Table 6

[0056]

[0057] Step 7: Data analysis. Based on the results obtained in steps (5) and (6), the ratio of coarse to fine grains n = N can be calculated. C / N F is 0.68, the ratio of coarse and fine grain sizes k = d C / d F is 2.6.

[0058] Example 2:

[0059] Sample 2 was analyzed using the method of the present invention. Figure 10This is the metallographic microstructure image of sample 2. The analysis method steps are as follows:

[0060] Steps 1 to 3 are the same as those in Example 1.

[0061] Step 4: Overall average grain size (d A ) determination. The operation method is the same as that of Example 1. Set the grain size classification according to Table 7 and set the lower limit of the filtration value L min =0, upper limit L max =8.1μm, that is, the statistical range of grain size is 0~8.1μm, and the metallographic analysis software is used to perform statistical analysis on the current sample ( Figure 11 ) to obtain the total number of grains N A The overall average grain size is 2302, d A 1.1 μm and its particle size deviation coefficient C A It is 0.81. The statistical analysis results are shown in Table 7.

[0062] Table 7

[0063]

[0064] Step 5: Average grain size of coarse grains (d C ) determination. The operation method is the same as that of Example 1. Set the grain size classification according to Table 8 and set the lower limit of the grain filtration value L min =d A =1.1μm, the upper limit Lmax=8.3μm, and the statistical analysis shows that the number of coarse grains Nc is 811, the average grain size dc of the coarse grains is 2.0μm, and the particle size deviation coefficient Cc is 0.42. The statistical analysis results are shown in Table 8.

[0065] Table 8

[0066]

[0067]

[0068] Step 6: Average grain size of fine grains (d F The operation method is the same as that of Example 1. According to Table 9, the lower limit of the grain filtration value Lmin = 0, the upper limit Lmax = 1.1 μm, and the number of fine grains N is obtained by statistical analysis. F The average grain size of fine grains is 1413, d F 0.6μm and its particle size deviation coefficient C F is 0.51. The statistical analysis results are shown in Table 9.

[0069] Table 9

[0070]

[0071] Step 7: Data analysis. Calculate the ratio of coarse to fine grains n = N C / N F is 0.54, the ratio of coarse to fine grain size k=d C / d F It is 3.33.

[0072] The above-mentioned specific embodiments are only used to explain the present invention and are not used to limit the present invention. Changes and modifications can be made to the specific implementation methods without departing from the main purpose of the present invention as set forth in the claims. For example, the "intercept method" for measuring grain size in the technical solution of the present invention can also be used with other grain size measurement methods such as the "equivalent circle" method; for example, the method described in the present invention can also be used to characterize composite material systems such as TiC-Ni, Ti(C,N)-Ni, etc., whose microstructures are similar to those of WC-Co cemented carbide. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for measuring and analyzing the microstructural parameters of twin-crystal cemented carbide, which uses the average grain size d of the coarse grains in the structure C The average grain size of fine grains d F , the ratio of the average grain size of coarse and fine grains k = d C / d F And the ratio of coarse to fine grains n = N C / N F Four parameters are used to characterize the WC grain structure characteristics of twin-crystal cemented carbide; The specific steps are as follows: S1 Sample preparation: Prepare the metallographic surface of the alloy sample to be tested; S2 Image Acquisition: Acquisition of metallographic images; S3 image processing: use metallographic analysis software to process the image and obtain image samples; S4 Determination of overall average grain size d A : S4-1 Set the metallographic analysis software parameters for the image sample; and set the grain filter value, where the lower limit is L min , take 0 or 0.1μm, the upper limit is L max , which is equal to or greater than the size d of the largest grain in the image max ; Set the grain size classification according to the table below, the number of levels is 10, the group distance A=(L max -L min ) / (number of levels - 1): Count the number of particles, particle ratio and particle accumulation of each particle level; obtain the total number of grains N A , overall average grain size d A and its particle size deviation coefficient C A Three parameter values; S5 determines the average grain size d of coarse grains C : S5-1 keeps the parameters of the metallographic analysis software of the image sample in step S4-1 unchanged; Set the grain filter value, lower limit L min The overall average grain size d in step S4-1 A Same, that is, L min =d A , upper limit L max Same as the upper limit in step S4-1, i.e. L max ≥d max ; Set the grain size classification according to the table below, the number of levels is 10, the group distance B = (L max -L min ) / (number of levels - 1): Count the number of particles, particle ratio and particle accumulation of each particle level; obtain the number of coarse grains N c , the average grain size of coarse grains d c and its particle size deviation coefficient C c Three parameter values; S6 determines the average grain size d of fine grains F : S6-1 keeps the parameters of the metallographic analysis software of the image sample in step S4-1 unchanged; Set the grain filter value, lower limit L min The same as the lower limit of the grain filtration value in step S4-1, that is, L min =0, upper limit L max The overall average grain size dA is the same as that in step S4-1, i.e. L max =d A ; Set the grain size classification according to the table below, the number of levels is 10, the group distance C = (L max -L min ) / (number of levels - 1): Count the number of particles, particle ratio and particle accumulation of each particle level; obtain the number of fine grains N F , the average grain size of fine grains d F and its particle size deviation coefficient C F Three parameter values; Note: C is the group distance = (L max -L min ) / (number of levels - 1), take a positive value, and the selection principle is to make the number of grains in the last level approach 0; S7 Data Analysis: Based on the results measured in steps S5 and S6, the ratio of coarse to fine grains n = N can be calculated. C / N F and the coarse-fine grain size ratio k=d C / d F Two parameters.

2. The method according to claim 1, wherein: Step S1: The metallographic surface of the alloy sample to be tested is prepared as follows: the metallographic surface of the sample is corroded in a mixed aqueous solution of potassium ferrocyanide and sodium hydroxide or potassium hydroxide (20% by mass), rinsed with water after corrosion, and then corroded with a saturated solution of ferric chloride and hydrochloric acid. After corrosion, the metallographic surface is rinsed with tap water, rinsed with alcohol, and dried.

3. The method according to claim 2, wherein: In step S2, images are acquired through an optical microscope, a scanning electron microscope, or metallographic analysis software. Three to five images are collected, and the total number of grains measured and counted is more than 1,000.

4. The method according to claim 2, wherein: Step S3 image processing includes light source correction, white balance, binarization, denoising, and grain segmentation.

5. The method according to claim 4, characterized in that: Step S3 of image processing also includes filling holes on the grains.

6. The method according to claim 2, wherein: In step S4-1, the parameters set in the metallographic analysis software are: scale calibration, setting the number of cut lines to 5-10, and setting the cut angle to 0-90 degrees.

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