Method for representing characteristics and spatial distribution of paper filler based on CT (Computed Tomography) scanning

Abstract

The invention discloses a method for representing paper filler characteristics and spatial distribution thereof based on CT scanning. The method comprises the following steps: preparing a to-be-represented paper sample; obtaining a tomography image sequence of the paper sample through an X-ray CT scanning technology; performing image noise reduction and segmentation processing on the tomography image sequence in sequence; independently separating filler particles; extracting and calculating filler characteristic parameters; and performing three-dimensional rendering to obtain a spatial distribution model of the filler in the paper. Compared with a traditional method, the method has the advantages that the characteristic parameters and the spatial distribution condition of the filler in the paper can be accurately obtained, and the method plays a very important role in research and development of paper materials with different functions. The method also has the characteristics of being non-destructive, high in accuracy and visual in result, and can be used for accurately and quantitatively characterizing the filling material characteristics of paper materials.

Description

Technical Field

[0001] The invention relates to the technical field of papermaking and computer image processing, and in particular to a method for characterizing paper filler characteristics and their spatial distribution based on CT scanning. Background Art

[0002] Paper products are essential materials in daily life, work and production, and the quantity and quality requirements of paper products are increasing year by year. In the production process of paper products, filling is the most commonly used processing technology. The spatial distribution of fillers in paper has a significant impact on the physical, chemical and optical properties of paper, and is an important factor that needs to be paid attention to and optimized in the paper manufacturing process. Since the internal structure of paper is a microstructure, it is difficult to obtain its internal filler characteristics and spatial distribution by conventional means. The commonly used methods currently include: scanning electron microscope observation and laser particle size analyzer measurement of suspension. Among them, scanning electron microscope observation can usually only obtain the surface image of the sample. If you want to obtain the internal image of the sample, you need to destroy the paper and observe the cross section. This method can only observe the filler particle size and its distribution on the surface of the two-dimensional surface, and the accuracy is insufficient; the method of measuring the suspension by laser particle size analyzer requires the sample to be prepared as a suspension before testing. This method is a destructive detection method, the test results are easily affected and the distribution of the filler in the paper cannot be obtained. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned defects in the prior art and to provide a method for characterizing paper filler characteristics and their spatial distribution based on CT scanning. The paper sample is tomographically scanned by high-resolution X-ray CT, and then the paper sample slice image is processed to achieve fast and accurate measurement of paper filler characteristics and spatial distribution.

[0004] The purpose of the present invention can be achieved by adopting the following technical solutions: A method for characterizing paper filler characteristics and their spatial distribution based on CT scanning, comprising the following steps: S1. preparing a paper sample to be characterized, wherein the paper sample is filled paper; S2. Scanning the paper sample to be characterized by X-ray CT to obtain a tomographic image sequence of the paper sample, with the paper thickness direction as the Z axis, and the paper length and width directions as the X axis and Y axis respectively. The tomographic image sequence is an orderly stacked slice image along the XZ plane of the Y axis, hereinafter referred to as a paper sample slice image; S3, performing image noise reduction and segmentation processing on all paper sample slice images in the tomographic image sequence, wherein the slice images after segmentation processing are divided into three parts: filler, fiber and pore; S4, importing all processed paper sample slice images into 3D visualization software, and performing threshold segmentation to separate and extract filler particles in the paper sample according to grayscale values, and isolating connected filler particles; S5. Use 3D visualization software to extract characteristic parameters of filler particles, perform 3D volume rendering on the individually extracted filler particles, and obtain a spatial distribution model of the filler particles inside the paper.

[0005] Furthermore, in step S1, the paper sample is cut into a size of 10×10 mm.

[0006] Furthermore, the X-ray CT scanning mode used in step S2 is a nanometer resolution mode, the light source voltage is 50 kV, the rotation step is 0.3°, the exposure time is 1000 ms, and the pixel size is 400 nm. Different elements have different numbers of extranuclear electrons, and the energy loss when X-rays penetrate is also different. Therefore, the grayscales of fillers, fibers and pores in the tomographic image of the paper sample are different.

[0007] Furthermore, the paper sample tomographic image parameters in step S2 are as follows: the image size of a single paper sample slice is 2250×500 pixels, and the color depth is 8 bits.

[0008] Furthermore, in step S3, the paper sample slice image is processed in the order of first denoising and then segmenting, and the process is as follows: S31. Remove the noise of the paper sample slice image. After this treatment, the image noise is significantly reduced, and the identifiability of fillers, fibers and pores is enhanced, thus laying a good foundation for the subsequent component segmentation; S32. Segment the denoised paper sample slice image into three parts: filler, fiber and pore, where filler is marked as white with a gray value range of 240-255; fiber is marked as gray with a gray value range of 115-140; pore is marked as black with a gray value range of 0-20. The purpose is to simplify or change the presentation of the slice image, enhance the recognition of each component, and facilitate subsequent parameter extraction.

[0009] Furthermore, the process of step S4 is as follows: S41, importing the paper sample slice image into the 3D visualization software, and separating and extracting the filler components in the paper sample slice image according to the image grayscale value. This method relies on the grayscale characteristics of the image, and separates the filler components from the complex composition of the paper sample slice image by accurately identifying the different grayscale value areas, which is conducive to the subsequent analysis of the spatial distribution of the filler; S42, separating the mutually contacting parts of the filler components in the paper sample slice image into independent fillers. The above steps separate the filler particles, making the calculation of the filler characteristic parameters more accurate.

[0010] Furthermore, in step S4, the filler components are separated and extracted according to the grayscale value of the image, and the grayscale value range of the filler particles is 240-255.

[0011] Furthermore, the process of step S5 is as follows: S51. Sample and analyze the filler particles, and extract and calculate the filler characteristic parameters, including filler particle size, specific surface area, volume, and three-dimensional distribution position. The extracted filler particle size, specific surface area, volume, and three-dimensional distribution position parameters provide important data support for quantitative research on filler characteristics and their distribution.

[0012] S52, performing three-dimensional rendering on the filler particles extracted in S4, establishing a filler spatial distribution model, and visually observing the spatial distribution of the filler particles inside the paper.

[0013] Compared with the prior art, the present invention has the following advantages and effects: (1) Compared with scanning electron microscopy, which can only scan micrometer-sized areas of the sample, X-ray CT scanning is used to characterize the characteristics of paper fillers and their spatial distribution for paper, a special non-uniform material. It can obtain cross-sectional scan images of a larger paper width (10 mm×10 mm) at one time. The test results are representative and help improve the accuracy of paper filler particle analysis.

[0014] (2) Compared with scanning electron microscopy observation, which can only obtain surface images of samples and has the problem of insufficient measurement accuracy, the use of X-ray CT scanning to characterize paper filler particles can increase the measurement dimension from two dimensions to three dimensions, significantly improving detection accuracy.

[0015] (3) Compared with the problem that the laser particle size analyzer is a destructive detection method for measuring suspensions, the use of X-ray CT scanning to characterize paper filler particles can reduce equipment loss, while not destroying the paper sample being tested, reducing the consumption of experimental resources, and can also determine the spatial distribution of the filler in the paper. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 It is a flow chart of a method for characterizing paper filler characteristics and their spatial distribution based on CT scanning disclosed in an embodiment of the present invention; Figure 2 This is an example diagram showing the results of the slice image of the filled paper sample before and after the noise reduction and segmentation processing in the embodiment of the present invention. The filler is marked in white with a gray value of 255; the fiber is marked in gray with a gray value of 128; the pore is marked in black with a gray value of 0; Figure 3 This is an example diagram of the results of the filler particle size extracted using the Label Analysis module of the Avizo software shown in the embodiment of the present invention; Figure 4 This is an example diagram of the results of extracting the specific surface area and volume of the filler using the Label Analysis module of the Avizo software, as shown in the embodiments of the present invention; Figure 5a , Figure 5b , Figure 5c They are respectively example diagrams of the distribution frequency results of the fillers in the three directions of the X-axis, Y-axis and Z-axis extracted by using the Label Analysis module of the Avizo software as shown in the embodiments of the present invention; Figure 6 : is an example diagram of the spatial distribution model result of filler in the XZ plane obtained by using the Volume Rendering command of the Avizo software in the embodiment of the present invention; wherein Figure 6 The upper part is the result of Example 1. Figure 6 The lower part is the results of Example 2. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0018] Example 1 This embodiment discloses a method for detecting the characteristics and spatial distribution of precipitated calcium carbonate (PCC) filler particles as a comparative example of the subsequent embodiments. It includes 5 steps (such as Figure 1 ), in this embodiment, a non-local mean filter algorithm is used to perform noise reduction processing on the paper sample slice image, an Otsu double threshold segmentation algorithm is used to perform segmentation processing on the paper sample slice image, and Avizo visualization software is used to extract filler particle parameters and establish a filler spatial distribution model. The specific steps are described as follows: T1. Prepare the paper sample to be characterized. A certain amount of PCC filler (particle size 3.47 μm) was added to deionized water to prepare a 1% filler suspension. 2.80 g of absolute dry cotton linter pulp was added to a certain amount of the prepared filler suspension (addition amount 15%) and mixed for 30 s. The paper was formed by dispersing at 13000 rpm and the paper weight was 105±1 g / m 2 , cut the paper into 10×10 mm size, which is the paper sample to be tested.

[0019] T2. Use X-ray CT scanning to obtain a tomographic image sequence of the paper sample. The X-ray CT scanning mode adopted in the present invention is the nanometer resolution mode, the light source voltage is 50 kV, the rotation step is 0.3°, the exposure time is 1000 ms, and the pixel size is 400 nm. With the paper thickness direction as the Z direction, the paper length and width directions as the X direction and the Y direction respectively, the tomographic image sequence is an orderly stacked slice image along the XZ plane of the Y direction, hereinafter referred to as the paper sample slice image, the size of a single paper sample slice image is 2250×500 pixels, and the color depth is 8 bit. The original diagram of the filled paper sample slice image obtained by X-ray CT scanning is as follows: Figure 2 As shown in part a.

[0020] T3. Use image denoising algorithm and dual threshold segmentation algorithm to perform image denoising and segmentation processing on all paper sample slice images in the tomographic image sequence. Different from the conventional image processing method, the present invention uses high-resolution X-ray CT scanning technology (scanning pixel size is 500 nm and below), and the image has more noise points. Therefore, in the process of separating fillers, fibers and pores, it is necessary to use the sequence of denoising first and then segmentation to process the image. The specific method is: T31. Since the grayscale of the noise points in the paper sample slice image is close to the fiber grayscale, this case uses the non-local mean filter algorithm to reduce the noise of the image.

[0021] T32. Use the Otsu double threshold segmentation algorithm to segment the paper sample slice image into three parts, where the filler is marked as white with a gray value range of 240-255; the fiber is marked as gray with a gray value range of 115-140; the pore is marked as black with a gray value range of 0-20; Figure 2 As shown in part b.

[0022] T4. Based on the de-noised and segmented paper sample slice image obtained in step T3, three-dimensional visualization software can be used to separate and extract filler particles for subsequent research. The specific calculation method is as follows: T41. Use Avizo software to import the processed filler paper sample slice image obtained in step T3, use interactive thresholding to separate and extract filler components separately according to grayscale value, adjust the threshold to accurately extract filler components and ensure their clear boundaries, and finally determine that the grayscale value of the filler particles is 255, which is displayed as bright white in the figure.

[0023] T42 uses the Avizo software Separate Object module to separate connected filler particles. This module uses morphological operations to identify and separate interconnected particles in the image to ensure that each filler particle can be analyzed independently.

[0024] T5. Based on the filler components separated and extracted in step T4, filler characteristic parameters can be calculated to obtain a spatial distribution model of filler particles inside the paper. The specific calculation method is as follows: T51. Use the Avizo software Label Analysis module to sample and analyze the extracted filler particles. In the Label Analysis module, customize the filler particle parameters that need to be extracted and calculated, including filler particle size, specific surface area, volume, and three-dimensional distribution position. Plot the obtained parameters, such as Figure 3 , Figure 4 , as shown in Figure 5.

[0025] In this embodiment, the specific surface area of ​​the filler particles is the surface area per unit mass or per unit volume of the material. The specific surface area is used to compare the surface characteristics of different particles or powders.

[0026] T52. Use the Volume Rendering command of Avizo software to render the extracted filler particles in three dimensions, establish a filler spatial distribution model, and visually observe the spatial distribution of filler particles inside the paper. Figure 6 As shown, Figure 6 The three-dimensional distribution position of the filler in the paper is reflected in it.

[0027] In summary, this embodiment uses X-ray CT scanning to obtain a paper tomographic image sequence, and through noise reduction and segmentation processing of paper sample slice images, imports them into software analysis to obtain intuitive and accurate filler characteristic parameters and their spatial distribution model. The proposal of this embodiment can provide a new method for the papermaking industry to detect the filler characteristics and their spatial distribution inside the paper, which is conducive to improving product quality and providing a new research and detection means for the research and development of new functional paper materials.

[0028] Example 2 This embodiment discloses a method for detecting the particle characteristics and spatial distribution of pre-flocculated modified precipitated calcium carbonate and filler paper. Compared with the first embodiment, the particles of this filler are larger and more irregular in shape. As a comparative example of the subsequent embodiments, it includes 5 steps (such as Figure 1 A method flow chart of characterizing paper filler characteristics and their spatial distribution based on CT scanning disclosed in ), the noise reduction, segmentation algorithm and 3D visualization software used in this case are the same as those in Example 1, and the specific steps are described as follows: U1. Prepare the paper sample to be characterized. Add a certain amount of PCC filler to deionized water to prepare a 1% filler suspension, and weigh a certain amount of cationic polyacrylamide to prepare a 0.01% solution. After the PCC filler is magnetically stirred at 300 rpm for 5 minutes, add 0.02% (based on PCC mass) of cationic polyacrylamide solution, and continue stirring for 5 minutes to obtain a pre-flocculated filler suspension (particle size 5.43 μm). Weigh 2.80 g of absolute dry cotton linter pulp and add a certain amount of the freshly prepared filler suspension and mix for 30 s. Disperse and form on the net at 13000 rpm. Control the filler addition amount to 15%, and the paper weight to 105±1g / m 2 , cut it into a size of 10×10 mm, which is the paper sample to be tested.

[0029] U2. Similar to steps T2-T4 in Example 1, a tomographic image sequence of the filled paper sample is obtained using X-ray CT scanning technology, and noise reduction and segmentation processing are performed on the slice graphics, and then the filler particles are separated using Avizo software. The specific implementation parameters are consistent with those described in steps T2-T4 in Example 1.

[0030] U3. Using the implementation process of step T5 in Example 1, a series of parameters of pre-flocculated modified precipitated calcium carbonate plus paper filler are obtained and plotted. Compared with the calculation results of the precipitated calcium carbonate plus paper sample in Example 1, the calcium carbonate filler treated by pre-flocculation modification is more concentrated in the middle and lower part of the paper in the Z-direction distribution due to its larger particle size, indicating that its larger particle size leads to a faster particle sedimentation rate, which is consistent with industry experience and also shows that the method of the present invention can well quantitatively characterize the characteristics of paper filler and its spatial distribution.

[0031] In summary, the filler characteristics that affect paper performance mainly include: filler particle size and distribution, filler specific surface area and filler spatial distribution. Different fillers have different particle sizes, shapes and spatial distributions. The method of quantitatively characterizing paper filler characteristics and their spatial distribution in the present invention can be used to analyze and obtain the morphological characteristics and spatial distribution of fillers in paper. Examples 1 and 2 respectively characterize the morphological characteristics and spatial distribution of fillers of two different particle sizes in paper. In Examples 1 and 2, a tomographic image sequence of paper samples is obtained based on high-resolution X-ray CT, and the paper sample slice images are processed in the order of first denoising and then segmentation; then three-dimensional visualization software is used to separate the filler components; finally, the filler particle characteristic parameters are extracted, and a spatial distribution model of the filler in the paper is established. Compared with existing traditional methods, the method proposed in the above embodiments has the advantages of non-destructiveness, high accuracy, and intuitive results.

[0032] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for characterizing paper filler characteristics and their spatial distribution based on CT scanning, characterized in that: The method comprises the following steps: S1. preparing a paper sample to be characterized, wherein the paper sample is filled paper; S2. Scanning the paper sample to be characterized by X-ray CT to obtain a tomographic image sequence of the paper sample, with the paper thickness direction as the Z axis, and the paper length and width directions as the X axis and Y axis respectively. The tomographic image sequence is an orderly stacked slice image along the XZ plane of the Y axis, hereinafter referred to as a paper sample slice image; S3, performing image noise reduction and segmentation processing on all paper sample slice images in the tomographic image sequence, wherein the slice images after segmentation processing are divided into three parts: filler, fiber and pore; S4, importing all processed paper sample slice images into 3D visualization software, and performing threshold segmentation to separate and extract filler particles in the paper sample according to grayscale values, and isolating connected filler particles; S5. Use 3D visualization software to extract characteristic parameters of filler particles, perform 3D volume rendering on the individually extracted filler particles, and obtain a spatial distribution model of the filler particles inside the paper.

2. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: In the step S1, the paper sample is cut into a size of 10×10 mm.

3. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: The X-ray CT scanning mode used in step S2 is the nanometer resolution mode, the light source voltage is 50 kV, the rotation step is 0.3°, the exposure time is 1000 ms, and the pixel size is 400 nm.

4. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: The paper sample tomographic image parameters in step S2 are as follows: the image size of a single paper sample slice is 2250×500 pixels, and the color depth is 8 bits.

5. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: In step S3, the paper sample slice image is processed in the order of first denoising and then segmentation, and the process is as follows: S31, removing noise from the paper sample slice image; S32. Segment the denoised paper sample slice image into three parts: filler, fiber and pore, where the filler is marked in white with a grayscale value ranging from 240 to 255; the fiber is marked in gray with a grayscale value ranging from 115 to 140; and the pore is marked in black with a grayscale value ranging from 0 to 20.

6. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: The process of step S4 is as follows: S41, importing the paper sample slice image into a three-dimensional visualization software, and separately separating and extracting filler components in the paper sample slice image according to the image gray value; S42, isolating and separating the mutually contacting parts of the paper sample slice image filler components into independent fillers.

7. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 6, characterized in that: In step S4, filler components are separated and extracted according to the grayscale value of the image, and the grayscale value range of the filler particles is 240-255.

8. The method for characterizing paper filler characteristics and their spatial distribution based on CT scanning according to claim 1, characterized in that: The process of step S5 is as follows: S51, sampling and analyzing the filler particles, extracting and calculating filler particle parameters, including filler particle size, specific surface area, volume and three-dimensional distribution position; S52, performing three-dimensional rendering on the filler particles extracted in S4, establishing a filler spatial distribution model, and obtaining the spatial distribution of the filler particles inside the paper.

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