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Method for automatic measurement and morphological classification statistic of ultra-fine grain steel grains

An ultra-fine grain, automatic measurement technology, applied in the direction of measurement device, particle size analysis, particle and sedimentation analysis, etc., can solve the subjective error, occupied labor cost, the quantitative relationship of steel composition, structure, structure and performance is difficult to accurately establish and other problems to achieve excellent universality

Inactive Publication Date: 2012-06-13
JIANGSU UNIV
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Problems solved by technology

Since the effect of this analysis mainly depends on human subjective factors, various subjective errors, low efficiency, low accuracy of measurement classification and statistical results, and a large amount of labor costs will inevitably occur, resulting in the quantitative relationship between steel composition, structure, organization and performance. The consequences of being difficult to establish accurately, this has become a "bottleneck" problem that seriously affects the process of new material research and development

Method used

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  • Method for automatic measurement and morphological classification statistic of ultra-fine grain steel grains
  • Method for automatic measurement and morphological classification statistic of ultra-fine grain steel grains
  • Method for automatic measurement and morphological classification statistic of ultra-fine grain steel grains

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Experimental program
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Effect test

Embodiment 1

[0055] Use the image acquisition system to obtain the original grain image of the steel. The hardware of the image acquisition system is as follows: figure 2 Shown: steel sample 1, professional microscope 2, camera (CCD) 3, computer (interpolated image acquisition card) 4, printer 5. The specific steps of image acquisition are to use a microscope to adjust the image to a suitable focal length, take a picture when the image is clearest and store it in the image acquisition card (original image) before image preprocessing can be performed.

[0056] The original image of embodiment 1 is as image 3 shown. right first image 3 The original image is preprocessed. First, the image is denoised by median filtering. Then, use the histogram equalization algorithm that can preserve the image details to enhance the entire image to enrich the image detail information, so as to enhance the display effect of the image. In order to further extract the edge, the present invention utilize...

Embodiment 2

[0110] Such as Figure 11 The shown original metallographic image of ultra-fine grain steel with small grains and various shapes. The process of using the present invention to process it is as follows: first, the target image is preprocessed and the adaptive threshold segmentation algorithm based on area division is used for binary segmentation, and the processing effect is as follows: Figure 12 As shown; then the binary image is processed by boundary repair and intra-granular hole filling, and the processing effect is as follows Figure 13 As shown; set the scale and calibrate the area of ​​each grain, measure and calculate the grain morphological characteristic parameters such as grain area, perimeter, aspect ratio, diameter, circularity and shape coefficient, and the grain size and shape Perform classification statistics. The grain size of embodiment 2, the classification statistical result of grain shape are respectively as follows Figure 14 a, 14b shown.

Embodiment 3

[0112] The present invention also has excellent grain measurement and classification effects on common steel products with a grain size of about 20 microns widely used in mechanical engineering, such as Figure 15 Metallographic raw image of plain steel shown with coarse grains. The process of using the present invention to process it is as follows: first, the target image is preprocessed and the adaptive threshold segmentation algorithm based on area division is used for binary segmentation, and the processing effect is as follows: Figure 16 As shown; then the binary image is processed by boundary repair and intra-granular hole filling, and the processing effect is as follows Figure 17 As shown; set the scale and calibrate the area of ​​each grain, measure and calculate the grain morphological characteristic parameters such as grain area, perimeter, aspect ratio, diameter, circularity and shape coefficient, and the grain size and shape Perform classification statist...

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Abstract

The invention discloses a method for automatic measurement and morphological classification statistic of ultra-fine grain steel grains. The method provided by the invention comprises the following steps of 1, acquiring an image of an ultra-fine grain steel grain and carrying out pretreatment, 2, carrying out binary segmentation of the pre-treated image by a region division-based self-adaptive threshold segmentation method to obtain a binary image, 3, carrying out grain boundary repair of the binary image by a distance transformation-based modified watershed algorithm, and carrying out grain aperture filling by a modified seed filling algorithm to obtain a repaired image, 4, extracting grain morphology characteristic parameters, and 5, carrying out grading statistic of grain sizes according to diameters, and carrying out grain morphology classification according to roundness, form factors and length-width ratios. Through the method provided by the invention, image repair and accurate and efficient measurement, classification and statistic of an ultra-fine grain steel microstructure (grain) can be realized automatically.

Description

technical field [0001] The invention relates to the field of quantitative metallographic analysis of the microstructure of iron and steel materials, in particular to an automatic measurement of ultrafine-grained steel grains and a method for morphological classification and statistics thereof. Background technique [0002] With the rapid development of iron and steel material technology, the research and development of various steel materials has gradually been established on the basis of the quantitative relationship between composition, structure, structure and performance, which means that the phase structure and microstructure of steel can be controlled through preparation and various subsequent processes. organization to obtain the required performance. Quantitative metallographic analysis is an important method to study the relationship between the composition, structure, process and performance of metal materials. Through quantitative analysis of the metallographic s...

Claims

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Application Information

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IPC IPC(8): G01N15/02G01B11/02G01B11/24G06T7/00G06T5/00
Inventor 李新城朱伟兴丁飞赵从光
Owner JIANGSU UNIV
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