A particle identification method based on electron microscope to improve the speed of particle analysis

Abstract

The present invention belongs to the technical field of electron microscope material identification and analysis, and specifically relates to a particle identification method based on an electron microscope for improving the speed of particle analysis, characterized in that all particles to be identified after removing the background are grouped according to the average grayscale, and a small number of large particles are selected in each group for X-ray analysis, so that metal particles can be quickly identified. Compared with the prior art, the beneficial effects of the present invention are: 1) The present invention is an improvement on the traditional particle identification method, which does not have much impact on the overall measurement accuracy, and has significant effects in improving the measurement speed and reducing the amount of calculation. 2) In the analysis of engine wear debris, the analyzed substances are mainly various metal particles, and the grayscale and the substance have a relatively stable corresponding relationship. Therefore, it can be considered that particles with the same grayscale are the same substance, which greatly improves the final particle analysis speed.

Description

Technical Field

[0001] The invention belongs to the technical field of electron microscope material identification and analysis, and in particular relates to a particle identification method based on an electron microscope for improving the particle analysis speed. Background Art

[0002] The automatic material recognition and analysis system of the electron microscope generally collects the BSE image frame of the sample through the electron microscope API, then manually sets the gray value to distinguish the image foreground and background, removes the background pixels through pixel-by-pixel processing, and finally realizes the identification of particles through the connectivity algorithm. After identifying the position of the particle, the elemental composition of the particle is collected through the energy spectrum X-ray. Finally, the identification of the particle material is realized.

[0003] Application number is 201410413256.5 The Chinese invention patent discloses a particle morphology recognition method based on scanning electron microscopy, which is suitable for identifying particulate matter suspended in the air. The suspended particle image in the measured air is obtained by scanning electron microscopy at a magnification of 5000 times, and the particle image is binarized. For the adhesion particle pattern in the binarized image, the wedge-shaped pixels found in the binarized image are used as the starting wedge pixels of the separation point, and the terminal wedge pixels corresponding to the starting wedge pixels are found. Then, the separation line is drawn using the Bresenham algorithm to complete the segmentation of the adhesion particle image. After that, the morphological characteristics of each particle pattern after the adhesion particle segmentation are identified by the equivalent diameter and shape factor method.

[0004] After removing the background, the above method can identify the foreground particles well if there are few of them. However, if there are a lot of foreground particles, the recognition speed will be very slow, because the subsequent X-ray analysis of a single particle takes about 522ms to 1s to achieve a relatively accurate recognition effect. If the number of particles is very large, reaching tens of thousands, the analysis time of a single frame image will be very long, affecting the recognition efficiency of the electron microscope. Summary of the invention

[0005] The present invention aims to provide a particle recognition method based on an electron microscope for improving the speed of particle analysis, overcoming the shortcomings of the prior art, and grouping all particles to be identified according to their average grayscale after removing the background, and selecting a small number of large particles in each group for X-ray analysis, thereby avoiding X-ray analysis of a large number of particles. Because in the cleanliness analysis project, the substances analyzed are mainly various metal particles, and there is a relatively stable corresponding relationship between grayscale and substance, so particles with the same grayscale can be considered to be the same substance, thereby improving the analysis speed of the particles.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] A particle identification method based on electron microscopy for improving the speed of particle analysis is characterized in that all particles to be identified after removing the background are grouped according to the average grayscale, and a small number of large particles are selected from each group for X-ray analysis, so that metal particles can be quickly identified. The steps are as follows:

[0008] 1) Obtain the positions of all frames to be measured and sort them;

[0009] 2) Control the electron microscope to move the sample stage to a specified position;

[0010] 3) Take a BSE image of the frame to be measured, locate the working position of the electron microscope, and take a BMP image;

[0011] 4) Set or calculate the background grayscale range of the image frame, and remove the background pixels of the frame according to the background grayscale range;

[0012] 5) Extract the coordinates of all particles after removing the background;

[0013] 6) Group the particles according to their average gray value;

[0014] 7) Select large particles in each group for X-ray analysis;

[0015] 8) Apply the analysis results to the entire group;

[0016] 9) Repeat steps 2) to 8) until the analysis of other groups is completed.

[0017] Furthermore, the frame images to be measured are grouped in a matrix, and the number of rows×columns of each group must be an odd number n, where n is a natural number greater than 3.

[0018] Furthermore, the frame images to be measured are sorted in a spiral or snake shape.

[0019] Furthermore, the particles are alloy particles composed of iron, copper and aluminum.

[0020] Furthermore, the distance between adjacent frames in the frame matrix ranges from 0.2 to 1 mm.

[0021] Furthermore, the particles are engine wear debris filtered out by a filter membrane in an automobile cleanliness analysis system.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: 1) The present invention is an improvement on the traditional particle identification method, and has little effect on the overall measurement accuracy. However, the frame image matrix is ​​filled with serial numbers before measurement, and the measurement order of all frame images can be obtained after sorting. There is no need to calculate the frame image position in real time during the motion displacement of the sample stage, so the code processing amount can be significantly reduced, and the computer calculation amount is significantly reduced, which has significant effects in improving the measurement speed and reducing the calculation amount. 2) In the analysis of engine wear debris, the analyzed substances are mainly various metal particles, and the grayscale has a relatively stable corresponding relationship with the substance. Therefore, it can be considered that particles with the same grayscale are the same substance, which greatly improves the final particle analysis speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a measurement flow chart of an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of spiral filling and sorting in an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of serpentine filling sorting in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The embodiment of the present invention is applied in the "automobile cleanliness analysis system". The particles are engine wear debris filtered out by the filter membrane in the automobile cleanliness analysis system. The particles are mostly alloy particles composed of iron, copper and aluminum. After applying this method, the measurement speed is increased by 50%, the amount of program code of this part of the algorithm is reduced by 20%, the amount of computer calculation is reduced by more than 30%, the program is greatly simplified, and it is more convenient to maintain.

[0028] See Figure 1 , is a particle identification method based on electron microscopy to improve the particle analysis speed of the present invention, which mainly groups all the particles to be identified according to the average grayscale after removing the background, selects a small part of large particles in each group for X-ray analysis, so that the metal particles can be quickly identified, and the steps are as follows:

[0029] 1) Get the positions of all frames to be measured and sort them; first group the frames to be measured in a matrix, and set the number of rows and columns of each group to an odd number, such as 3×3, 5×5, 7×7, 9×9, 11×11, etc. Sort the frames to be measured in a spiral or snake shape; if it is a spiral filling order, see Figure 2 , is to fill the center frame of the entire matrix with number 1, and then fill other frames in spiral order with numbers 2, 3, etc.; if it is a serpentine filling order, see Figure 3 , fill in the number 1 from the frame at the upper left corner, and then fill in the other frames in a serpentine order with the numbers 2, 3, etc.;

[0030] 2) Control the electron microscope to move the sample stage to a specified position. The sample stage moves in a spiral or serpentine path in the order of Arabidopsis numbers; the distance between adjacent frames ranges from 0.3 to 0.6 mm.

[0031] 3) Take a BSE image of the frame to be measured, locate the coordinates of the working position of the electron microscope, and take a BMP image; BMP image file is the standard image file format in the Windows operating system and is the simplest image format. The BMP image format is very simple and only has the most basic image data storage function. It can store 1-bit, 4-bit, 8-bit and 24-bit bitmaps for each pixel. Although the information it provides is too simple, the BMP image file format has been promoted because of its simple format, standard and transparent characteristics. It is generally used in some simple image processing systems.

[0032] 4) Set the background grayscale range of the image frame, and remove the background pixels of the frame according to the background grayscale range. Since the analyzed substances are mainly various metal particles, there is a relatively stable correspondence between grayscale and substances. Therefore, particles with the same grayscale can be considered to be the same substance. A fixed grayscale value, such as 50, can be set, and all background pixels below 50 will be removed;

[0033] 5) Extract the coordinates of all particles after removing the background and determine the grouping range;

[0034] 6) Group the particles according to their average gray value, for example, into 10 groups;

[0035] 7) Select large particles in any group for X-ray analysis and mark the number and size of large particles;

[0036] 8) Apply the analysis results of a small number of large particles to the entire group, which is the measurement result of the entire group;

[0037] 9) Repeat steps 2) to 8) until the analysis of other groups is completed.

[0038] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A particle identification method based on electron microscopy to improve the speed of particle analysis, It is characterized in that After removing the background, all particles to be identified are grouped according to the average grayscale. A small number of large particles are selected from each group for X-ray analysis, so that metal particles can be quickly identified. The steps are as follows: 1) Obtain the positions of all frames to be measured and sort them; all frames to be measured are grouped in a matrix, and the number of rows and columns of each group must be an odd number n, which is a natural number greater than 3; each group of frames to be measured is sorted in a spiral or snake shape; and the distance between adjacent frames is in the range of 0.2~1mm; 2) Control the electron microscope to move the sample stage to a specified position; 3) Take a BSE image of the frame to be measured, locate the working position of the electron microscope, and take a BMP image; 4) Set or calculate the background grayscale range of the image frame, and remove the background pixels of the frame according to the background grayscale range; 5) Extract the coordinates of all particles after removing the background; 6) Grouping according to the average gray value of particles; 7) Select large particles in each group for X-ray analysis; 8) Apply the analysis results to the entire group; 9) Repeat steps 2) to 8) until the analysis of other frames to be measured is completed.

2. A particle identification method for improving particle analysis speed based on an electron microscope according to claim 1, It is characterized in that The particles are alloy particles composed of iron, copper and aluminum.

3. A particle identification method for improving particle analysis speed based on electron microscopy according to claim 1, It is characterized in that The particles are engine wear debris filtered out by a filter membrane in an automobile cleanliness analysis system.

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