Composition uniformity detection method based on microscopic images of titanium alloy ingots

Abstract

The present invention relates to the technical field of image data processing, and more particularly to a method for detecting composition uniformity based on microscopic images of titanium alloy ingots, comprising the steps of obtaining a sample image, determining the grayscale level of each grain based on the number and grayscale value of pixels in each grain in the sample image, obtaining the principal component vectors of the grains, determining the phase structure attribution coefficient of the grains based on the number and modulus of the principal component vectors of the grains, the grayscale level and area of ​​the grains, further determining each phase structure grain in the sample image, determining the phase structure uniformity of the sample image based on the number of grains of each structural phase, obtaining the component uniformity of the sample image using the same method as for the phase structure uniformity, and determining the comprehensive uniformity of the sample image based on the component uniformity and phase structure uniformity of the sample image. The present invention combines the sample composition and grain morphology to comprehensively evaluate the composition uniformity of the sample, thereby improving the reliability of the sample composition uniformity detection.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a composition uniformity detection method based on a titanium alloy ingot microscopic image. Background Art

[0002] Titanium alloy is a type of metal alloy with excellent properties, mainly composed of titanium and other alloying elements (such as iron, aluminum, vanadium, nickel, etc.). The evaluation of the uniformity of titanium alloy composition is not only crucial for the quality control and performance optimization of existing products, but also an important foundation for the development of new materials and the promotion of industrial progress. Through precise analysis and evaluation, the reliability, safety and economy of the material can be effectively improved to meet the needs of various industries and application fields.

[0003] Currently, when testing the uniformity of the composition of titanium alloy ingot samples, the electron probe is generally used to analyze the X-ray energy and intensity unique to each element to determine the presence and relative content of each component in the sample, and then calculate its composition uniformity. However, the grains of different components in titanium alloys may correspond to different morphological structures, and the distribution uniformity of these structures also affects the strength, plasticity and toughness of the overall material to a certain extent, making the result of determining the sample composition uniformity by the content of each component in the sample inaccurate and unable to accurately reflect the composition uniformity of the sample. Summary of the Invention

[0004] The present invention provides a composition uniformity detection method based on a titanium alloy ingot microscopic image to solve the existing problems.

[0005] The composition uniformity detection method based on the microscopic image of titanium alloy ingots of the present invention adopts the following technical solutions:

[0006] One embodiment of the present invention provides a method for detecting composition uniformity based on a microscopic image of a titanium alloy ingot, the method comprising the following steps:

[0007] Acquire a sample image, wherein the sample image includes a plurality of grains;

[0008] Determine the grayscale level of each grain based on the number of pixels and grayscale values ​​of each grain in the sample image, divide the sample image into a number of blocks with preset side lengths, obtain a number of principal component vectors in each grain, determine the phase structure attribution coefficient of the grain based on the number and modulus of the principal component vectors in the grain, the grayscale level and area of ​​the grain, and further determine the grains of each phase structure in the sample image;

[0009] Determine the distribution uniformity of the grains of any phase structure in the sample image based on the number of blocks and the number of grains of any phase structure in the blocks, and determine the phase structure uniformity of the sample image based on the distribution uniformity of the grains of any phase structure in the sample image;

[0010] The sample components are obtained, the component uniformity of the sample image is determined, and the comprehensive uniformity of the sample image is determined based on the component uniformity and phase structure uniformity of the sample image.

[0011] Furthermore, the grayscale level of each grain is determined according to the number of pixels and the grayscale value of each grain in the sample image, including the following specific methods:

[0012] Get the The grayscale mean of all pixels in the grain is calculated and the grayscale mean is The average of the absolute values ​​of the grayscale value differences of all pixels in the grains is obtained, and then the inverse proportional normalized value of the grayscale average is obtained. The product of the average of the absolute values ​​of the grayscale differences and the inverse proportional normalized value is recorded as the first The gray level of each grain.

[0013] Furthermore, the determining of the phase structure attribution coefficient of the grains and further determining each phase structure grain in the sample image includes the following specific methods:

[0014] Perform principal component analysis on each grain to obtain several principal component vectors in the grain, and partition the sample image. The sample image is divided into several grids with preset side lengths. 's block;

[0015] The phase structure attribution coefficient of the grains is determined based on the number and modulus of the principal component vectors in the grains, the grayscale level of the grains, and the area of ​​the grains;

[0016] Calculate the phase structure attribution coefficient of all grains in the sample image, preset a first threshold, and record the grains whose phase structure attribution coefficient is greater than or equal to the preset first threshold as Phase grains, the sample image The other grains outside the phase grains are recorded as Phase grains.

[0017] Furthermore, the specific method of determining the phase structure attribution coefficient of the grains includes:

[0018] Obtaining the attribution coefficient factor based on the gray level and grain area of ​​the grain;

[0019] Calculate the The modulus of the largest principal component vector of the grain is deducted from the sum of the moduli of all the other principal component vectors, and the sum is added to the The normalized value of the product of the attribution coefficient factors of the grains is recorded as The phase structure attribution coefficient of each grain.

[0020] Furthermore, the The specific calculation method of the attribution coefficient factor of each grain is:

[0021] According to The gray level of each grain, The maximum area of ​​all grains in the block to which the grain belongs is The difference in the area of ​​the first grain is used to determine The attribution coefficient factor of the grain, The attribution factor of each grain is related to the The gray level of each grain and the The maximum area of ​​all grains in the block to which the grain belongs is The difference in area of ​​the grains is directly proportional.

[0022] Furthermore, the specific method of determining the distribution uniformity of grains of any phase structure in the sample image includes:

[0023] All blocks are combined in pairs without repetition to obtain several block combinations. The distribution uniformity of grains of any phase structure in the sample image is determined according to the number of grains in two blocks in the block combination.

[0024] Furthermore, the distribution uniformity of grains of any phase structure in the sample image includes the following specific methods:

[0025] Get The absolute value of the difference in the number of phase structure grains in the two blocks in each block combination will be The reciprocal of the mean of the absolute value of the difference between the number of phase structure grains in two blocks in all block combinations is recorded as The distribution uniformity of phase structure grains.

[0026] Furthermore, the specific method of determining the phase structure uniformity of the sample image includes:

[0027] According to the sample image The distribution uniformity of phase grains and The distribution uniformity of the phase grains determines the phase structure uniformity of the sample image, and the phase structure uniformity of the sample image is related to the phase structure uniformity of the sample image. The distribution uniformity of phase grains and The distribution uniformity of the phase grains is directly proportional.

[0028] Furthermore, the method of determining the comprehensive uniformity of the sample image based on the component uniformity and phase structure uniformity of the sample image includes the following specific methods:

[0029] The comprehensive uniformity of the sample image is determined according to the phase structure uniformity of the sample image and the component uniformity of the sample. The comprehensive uniformity of the sample image is in direct proportion to the phase structure uniformity of the sample image and the component uniformity of the sample.

[0030] Furthermore, the obtaining of sample components and determining the component uniformity of the sample image include the following specific methods:

[0031] An electron probe is used to irradiate the sample surface to obtain the sample components, and the component uniformity of the sample image is obtained with the help of the phase structure uniformity calculation method.

[0032] The beneficial effects of the technical solution of the present invention are as follows: according to the number of pixels and the grayscale value of each grain in the sample image, the grayscale level of each grain is determined, the sample image is divided into a number of blocks with preset side lengths, a number of principal component vectors in each grain are obtained, and the phase structure attribution coefficient of the grain is determined according to the number and modulus of the principal component vectors in the grain, the grayscale level and area of ​​the grain, and the sample image is further determined. Phase grains and Phase grains, according to the number of blocks and the number of arbitrary phase structure grains in the blocks, determine the distribution uniformity of arbitrary phase structure grains in the sample image, according to the distribution uniformity of arbitrary phase structure grains in the sample image, determine the phase structure uniformity of the sample image, obtain the sample component, determine the component uniformity of the sample image, improve the comprehensiveness and reliability of the composition uniformity detection of the titanium alloy ingot microimage, according to the component uniformity and phase structure uniformity of the sample image, determine the comprehensive uniformity of the sample image, and improve the accuracy of the composition uniformity detection of the titanium alloy ingot microimage. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 The present invention is a flowchart of the steps of the composition uniformity detection method based on the microscopic image of the titanium alloy ingot. DETAILED DESCRIPTION

[0035] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the compositional uniformity detection method based on microscopic images of titanium alloy ingots proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0037] The specific scheme of the composition uniformity detection method based on the microscopic image of the titanium alloy ingot provided by the present invention is described in detail below with reference to the accompanying drawings.

[0038] See also Figure 1 , which shows a flowchart of a composition uniformity detection method based on a microscopic image of a titanium alloy ingot provided by an embodiment of the present invention, the method comprising the following steps:

[0039] Step S001: Acquire a sample image, wherein the sample image includes a plurality of grains.

[0040] It should be noted that in order to detect the composition uniformity of the microscopic image of the titanium alloy ingot, it is necessary to cut a representative sample from the titanium alloy ingot and obtain a microscopic image of the sample.

[0041] Specifically, in order to implement the composition uniformity detection method based on the microscopic image of the titanium alloy ingot proposed in this embodiment, it is first necessary to collect the surface microscopic image of the titanium alloy ingot sample. The specific process is as follows:

[0042] The obtained sample is ground and polished to obtain a smooth surface suitable for microscopic observation, and the sample surface is treated with an appropriate chemical corrosive agent. The treated sample is placed under a microscope, and the focus and the lens barrel are adjusted to make the microstructure of the sample clearly visible, thereby obtaining a surface microscopic image of the titanium alloy ingot sample. The obtained microscopic image is grayscaled, and the grayscaled image is recorded as a sample image.

[0043] So far, the sample image is obtained through the above method.

[0044] Step S002: Determine the grayscale level of each grain based on the number of pixels and grayscale value of each grain in the sample image, divide the sample image into several blocks with preset side lengths, obtain several principal component vectors in each grain, and determine the phase structure attribution coefficient of the grain based on the number and modulus of the principal component vectors in the grain, the grayscale level and area of ​​the grain, and further determine the grains of each phase structure in the sample image.

[0045] It should be noted that the microstructure of titanium alloys contains various components, which often appear in the form of grains. On a microscopic scale, the grains of titanium alloys can show different organizational forms, such as uniform grains, flaky grains or needle-shaped grains. Different grains are composed of different phases. The differences between different phases can be observed through a microscope. The structure of titanium alloy grains is mainly phase (close-packed hexagonal structure) and Phase (body-centered cubic structure), grains of different phases will also have certain differences in morphology and grayscale. In order to detect the composition uniformity of the sample image, it is necessary to determine the number of grains of different phases in the sample image.

[0046] In step (2.1), the grayscale level of each grain is determined based on the number of pixels and the grayscale value of each pixel in the sample image.

[0047] It should be noted that in optical microscope or scanning electron microscope images, Phase grains, due to their close-packed hexagonal structure, may result in relatively low reflectivity or high absorptivity, thus appearing as darker gray levels. The grayscale distribution of the phase grains may be relatively consistent inside the grains, but there may be a sudden change in grayscale near the grain boundaries. Phase grains have different electron densities and surface characteristics, resulting in higher reflectivity. They usually appear as brighter gray levels in the image, while their internal gray distribution is relatively uniform.

[0048] The Canny edge detection algorithm is used to perform edge detection on the sample image to obtain several edge lines. The closed area formed by each edge line in the sample image is regarded as a grain. The sample image contains several grains of the same size. The grayscale level of each grain is determined based on the number of pixels contained in each grain and the grayscale value of the pixel.

[0049] As an embodiment, the specific calculation method of the gray level of each grain is:

[0050]

[0051] Where, Indicates the Gray level of each grain; Indicates the The number of pixels contained in each grain; Indicates the In the grain Gray value of each pixel; Indicates the The grayscale mean of all pixels in a grain; Indicates finding the absolute value.

[0052] It should be noted that Indicates the The gray uniformity of each grain, The larger the value, the The greater the difference between the gray value of each pixel in the first grain and the gray value mean of all pixels, the The more uneven the grayscale of each grain, Indicates the The overall gray level of each grain is expressed as It is measured by the difference between the grayscale mean and the maximum grayscale value of all pixels in a grain. The larger the value, the The smaller the value, the The darker the overall grayscale of each grain. As The inversely proportional normalized value of .

[0053] Based on the above formula, the gray levels of all grains in the sample image are calculated. If the gray level of the grain is larger, it belongs to The greater the possibility of phase grains, the smaller the gray level, The greater the possibility of phase grains.

[0054] It should be noted that, when only a portion of a pixel is located in a certain grain, the calculation is performed based on the entire pixel.

[0055] It should be noted that the Canny edge detection algorithm is an existing technology and will not be described in detail here.

[0056] In step (2.2), the sample image is divided into several blocks with preset side lengths, and several principal component vectors in each grain are obtained. Based on the number and modulus of the principal component vectors in the grain, the grayscale level and area of ​​the grain, the phase structure attribution coefficient of the grain is determined, and the grains of each phase structure in the sample image are further determined.

[0057] It should be noted that the above steps only determine the possible phase structure based on the grayscale characteristics of the grains, but different phase structures also have differences in morphology. The phase has a hexagonal close-packed structure, and the grains are usually small, usually appearing in strips or flakes. The phase is a body-centered cubic structure, the grains are large, and are usually equiaxed or blocky. In order to more accurately determine the phase structure corresponding to different grains, it is necessary to determine the phase structure of the grains in combination with the morphological characteristics.

[0058] First, principal component analysis is performed on each grain to obtain several principal component vectors in the grain, and the sample image is partitioned. The sample image is divided into several grids with a preset side length of 's block.

[0059] It should be noted that based on experience The value of is 50, which can be adjusted according to actual conditions and is not specifically limited in this embodiment.

[0060] Then, the phase structure attribution coefficient of the grain is determined according to the number and modulus of the principal component vectors in the grain, the grayscale level of the grain, and the area of ​​the grain.

[0061] As an embodiment, the specific calculation method of the phase structure attribution coefficient of the grains is:

[0062]

[0063] Where, Indicates the Phase structure attribution coefficient of each grain; Indicates the The number of principal component vectors of each grain; Indicates the The modulus of the largest principal component vector of each grain; Indicates the The first The modulus of the principal component vectors; Indicates the Gray level of each grain; Indicates the The maximum area of ​​all grains in the block to which the grain belongs; Indicates the The area of ​​each grain; The modulus length represents the orientation quantity; Represents the sigmoid normalization function.

[0064] It should be noted that Indicates the The morphological characteristics of each grain are due to Phase grains usually appear as strips or flakes. There will be a large principal component vector in the phase grain to represent the extension trend of its shape, so The larger the value, the The grains are The greater the possibility of phase grains, Indicates the The relative area size of each grain is Phase grains are usually finer, and Phase grains are relatively large, so The larger the value, the The smaller the area of ​​each grain, the The grains are The greater the possibility of phase grains.

[0065] Calculate the phase structure attribution coefficient of all grains in the sample image, preset a first threshold, and record the grains whose phase structure attribution coefficient is greater than or equal to the preset first threshold as Phase grains, the sample image The other grains outside the phase grains are recorded as Phase grains, thus obtaining the sample image Phase grains and The number of phase grains.

[0066] The first threshold value is preset to be 0.8 based on experience, and can be adjusted according to actual conditions. This embodiment does not impose any specific limitation on the value.

[0067] So far, the sample image is obtained by the above method. Phase grains and The number of phase grains.

[0068] Step S003 , determining the distribution uniformity of the arbitrary phase structure grains in the sample image according to the number of blocks and the number of arbitrary phase structure grains in the blocks, and determining the phase structure uniformity of the sample image according to the distribution uniformity of the arbitrary phase structure grains in the sample image.

[0069] It should be noted that after obtaining the number of arbitrary phase structure grains in the sample image, the distribution uniformity of the arbitrary phase structure grains in the sample image can be determined according to the proportion of the number of arbitrary phase structure grains in the sample image, thereby determining the phase structure uniformity of the sample image.

[0070] In step (3.1), the distribution uniformity of the grains of arbitrary phase structure in the sample image is determined based on the number of blocks and the number of grains of arbitrary phase structure in the blocks.

[0071] For the entire sample image, all blocks are combined in pairs without repetition to obtain several block combinations (for example, if blocks are 1, 2, 3, and 4, then the block combinations are 12, 13, 14, 23, 24, and 34). Based on the number of grains in two blocks in the block combination, the distribution uniformity of grains of any phase structure in the sample image is determined.

[0072] Specifically: Get The absolute value of the difference in the number of phase structure grains in the two blocks in each block combination will be The reciprocal of the mean of the absolute value of the difference between the number of phase structure grains in two blocks in all block combinations is recorded as The distribution uniformity of phase structure grains.

[0073] As an embodiment, the specific calculation method of the distribution uniformity of grains of any phase structure is:

[0074]

[0075] Where, express The distribution uniformity of phase structure grains; Indicates the number of block combinations; Indicates the number of blocks; express Phase structure grains The proportion of the number of blocks; express Phase structure grains The proportion of the number of blocks; Indicates finding the absolute value.

[0076] It should be noted that, by any two blocks in the sample image The difference in the proportion of phase structure grains in the block is measured The distribution uniformity of phase structure grains, The larger the value, the closer the The more similar the proportion of phase structure grains is, the more similar the sample image is. The greater the uniformity of the phase structure grains, The more uniform the phase structure grain distribution.

[0077] The sample image is obtained Phase grains and The distribution uniformity of phase grains.

[0078] Step (3.2), determining the phase structure uniformity of the sample image based on the distribution uniformity of any phase structure grains in the sample image.

[0079] It should be noted that, since titanium alloy contains grains with different phase structures, for the sample image, it is necessary to combine the distribution uniformity of grains with different phase structures to measure the phase structure uniformity of the sample.

[0080] Specifically, according to the sample image Phase grains and The specific calculation method for determining the phase structure uniformity of the sample by the distribution uniformity of the phase grains is:

[0081]

[0082] Where, Indicates the phase structure uniformity of the sample image; Indicates the sample image The distribution uniformity of phase grains; Indicates the sample image The distribution uniformity of phase grains; Represents an exponential function with a natural constant as its base.

[0083] So far, the phase structure uniformity of the sample image is obtained through the above method.

[0084] Step S004: Obtain sample components, determine the component uniformity of the sample image, and determine the comprehensive uniformity of the sample image based on the component uniformity and phase structure uniformity of the sample image.

[0085] It should be noted that the phase structure uniformity of the sample image is obtained through the above steps, but in order to more accurately and comprehensively detect the composition uniformity of the sample image, it is also necessary to conduct a comprehensive test in combination with the distribution uniformity of each component of the sample.

[0086] First, an electron probe is used to irradiate the sample surface to obtain the sample components.

[0087] It should be noted that when a high-energy electron beam interacts with the sample surface, the main effects produced include electron excitation and X-ray radiation. After being excited by the electron beam, the components in the sample will emit characteristic X-rays. The unique X-ray energy and intensity of each component can be used to determine the presence and relative content of each component in the sample.

[0088] Then, the uniformity of the components of the sample image is obtained by using the uniformity calculation method of the phase structure in the above steps. , and then determine the comprehensive uniformity of the sample image based on the uniformity of the phase structure and the uniformity of the composition.

[0089] As an embodiment, a specific method for calculating the comprehensive uniformity of a sample image is as follows:

[0090]

[0091] It should be noted that Indicates the comprehensive uniformity of the sample image; Indicates the phase structure uniformity of the sample image; Indicates the composition uniformity of the sample; Represents the sigmoid normalization function.

[0092] It should be noted that the comprehensive uniformity of the sample image is determined by combining the phase structure uniformity and the component uniformity, and the result is normalized to Within the range, if The larger the value of is, the more uniform the composition of the sample image is, and vice versa.

[0093] At this point, this embodiment is completed.

[0094] It should be noted that the The model is only used to represent negative correlation and constrain the output of the model to be in In the specific implementation, it can be replaced by other models with the same purpose. This embodiment is only based on The model is described as an example without any specific limitation. is the input to the model.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting composition uniformity based on microscopic images of titanium alloy ingots, characterized in that: The method comprises the following steps: Acquire a sample image, wherein the sample image includes a plurality of grains; According to the number of pixels and the grayscale value of each grain in the sample image, the grayscale level of each grain is determined, the sample image is divided into several blocks with preset side lengths, and several principal component vectors in each grain are obtained. According to the number and modulus of the principal component vectors in the grain, the grayscale level and area of ​​the grain, the phase structure attribution coefficient of the grain is determined, and the grains of each phase structure in the sample image are determined, including: performing principal component analysis on each grain, obtaining several principal component vectors in the grain, and partitioning the sample image, and dividing the sample image into several blocks with preset side lengths by a grid division method. According to the number and modulus of the principal component vectors in the grains, the gray level of the grains, and the area of ​​the grains, the phase structure attribution coefficients of all the grains in the sample image are calculated, and a first threshold is preset. The grains whose phase structure attribution coefficients are greater than or equal to the preset first threshold are recorded as Phase grains, the sample image The other grains outside the phase grains are recorded as phase grains; Perform non-repeated pairwise combinations on all blocks to obtain several block combinations, and determine the distribution uniformity of any phase structure grains in the sample image according to the number of grains in two blocks in the block combination. Determine the phase structure uniformity of the sample image according to the distribution uniformity of any phase structure grains in the sample image, including: The distribution uniformity of phase grains and The distribution uniformity of phase grains determines the phase structure uniformity of the sample image. The phase structure uniformity of the sample image is closely related to the phase structure uniformity of the sample image. The distribution uniformity of phase grains and The distribution uniformity of phase grains is in direct proportion; The sample components are obtained, the component uniformity of the sample image is determined, and the comprehensive uniformity of the sample image is determined based on the component uniformity and phase structure uniformity of the sample image.

2. The composition uniformity detection method based on the microscopic image of titanium alloy ingot according to claim 1 is characterized in that: The grayscale level of each grain is determined based on the number of pixels and the grayscale value of each grain in the sample image. The specific method includes: Get the The grayscale mean of all pixels in the grain is calculated and the grayscale mean is The average of the absolute values ​​of the grayscale value differences of all pixels in the grains is obtained, and then the inverse proportional normalized value of the grayscale mean is obtained. The product of the average of the absolute values ​​of the difference and the inverse proportional normalized value is recorded as the first The gray level of each grain.

3. The composition uniformity detection method based on the microscopic image of titanium alloy ingot according to claim 1 is characterized in that: The specific methods for determining the phase structure attribution coefficient of the grains include: Obtaining the attribution coefficient factor based on the gray level and grain area of ​​the grain; Calculate the The modulus of the largest principal component vector of the first grain minus the sum of the moduli of all the other principal component vectors, and the sum is added to the first The normalized value of the product of the attribution coefficient factors of the grains is recorded as The phase structure attribution coefficient of each grain.

4. The composition uniformity detection method based on the microscopic image of titanium alloy ingot according to claim 3 is characterized in that: No. The specific calculation method of the attribution coefficient factor of each grain is: According to The gray level of each grain, The maximum area of ​​all grains in the block to which the grain belongs is The difference in the area of ​​the first grain is used to determine The attribution factor of each grain, The attribution factor of each grain is related to the The gray level of each grain and the The maximum area of ​​all grains in the block to which the grain belongs is The difference in area of ​​the grains is directly proportional.

5. The composition uniformity detection method based on titanium alloy ingot microscopic image according to claim 1 is characterized in that: Determine the comprehensive uniformity of the sample image, including the following specific methods: The comprehensive uniformity of the sample image is determined according to the phase structure uniformity of the sample image and the component uniformity of the sample. The comprehensive uniformity of the sample image is in direct proportion to the phase structure uniformity of the sample image and the component uniformity of the sample.

6. The composition uniformity detection method based on titanium alloy ingot microscopic image according to claim 1 is characterized in that: Obtain sample components and determine the component uniformity of the sample image, including the following specific methods: An electron probe is used to irradiate the sample surface to obtain the sample components, and the component uniformity of the sample image is obtained with the help of the phase structure uniformity calculation method.

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