WO2023082742A1 - 基于图像检测的环氧填料均匀度检测方法 - Google Patents

基于图像检测的环氧填料均匀度检测方法 Download PDF

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WO2023082742A1
WO2023082742A1 PCT/CN2022/112091 CN2022112091W WO2023082742A1 WO 2023082742 A1 WO2023082742 A1 WO 2023082742A1 CN 2022112091 W CN2022112091 W CN 2022112091W WO 2023082742 A1 WO2023082742 A1 WO 2023082742A1
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epoxy
epoxy filler
uniformity
filler
detecting
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French (fr)
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熊佳明
王国利
高超
周福升
黄若栋
杨芸
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南方电网科学研究院有限责任公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

Definitions

  • the invention belongs to the technical field of epoxy filler processing in high-voltage insulation, and in particular relates to an image detection-based method for detecting the uniformity of epoxy filler.
  • Epoxy resin is a thermosetting resin with excellent performance.
  • epoxy resin In the field of electric power, epoxy resin is widely used in insulating materials, conductive materials, electronic components and other fields because of its good comprehensive performance.
  • the insulation requirements for power transmission and transformation equipment With the development of power stations in the direction of high current and high voltage, the insulation requirements for power transmission and transformation equipment will become higher and higher, so that the dry type, integral insulation structure and fully enclosed process of epoxy resin will be used in power transformers, transformers, Products such as insulating beads have been widely promoted.
  • the shortcomings of low thermal conductivity and insufficient toughness of epoxy resin are gradually exposed, but they can be overcome and improved to a certain extent through appropriate modification methods.
  • some fillers are often added to the epoxy material to modify it, such as adding fillers such as nitride and alumina to improve the thermal conductivity and dielectric properties of epoxy.
  • the uniformity of epoxy fillers will have a greater impact on the electrical and thermal properties of epoxy. Some scholars have simulated the impact of unevenly distributed fillers on epoxy treeing voltage. Uneven filler distribution will even reduce The multifaceted performance of epoxy. However, in the epoxy preparation process, in order to prevent internal air bubbles during epoxy curing caused by stirring and vibration, epoxy materials and fillers are often not mixed together well, and the uniformity of filler distribution is difficult to determine. In the existing research, there is almost no detection and research on the distribution of epoxy fillers, so it is necessary to detect and analyze the uniformity of epoxy fillers.
  • the purpose of the present invention is to provide a method for detecting the uniformity of epoxy fillers based on image detection, which can realize the calculation, detection and accurate judgment of the uniformity of epoxy resin internal fillers without destroying the properties of epoxy itself.
  • the present invention provides the following technical solutions:
  • a method for detecting the uniformity of epoxy filler based on image detection in the present invention includes: the first step, polishing the surface of the epoxy filler without changing its microstructure; the second step, randomly determining a plurality of The observation position is based on SEM microscopic detection to obtain the image information of the structure of the epoxy filler surface, and the image information is converted into a grayscale matrix and saved.
  • the third step is to calculate the corresponding grayscale co-occurrence of each image based on the grayscale matrix. Matrix, according to the gray level co-occurrence matrix to calculate its corresponding eigenvalue to obtain the eigenvalue vector,
  • the fourth step is to compare the eigenvalue vector of each gray level co-occurrence matrix with the gray level co-occurrence matrix eigenvalue of the pure epoxy filler to determine the distribution of the epoxy filler,
  • the fifth step is to calculate the variance of the eigenvalue vectors of different observation positions.
  • the cured epoxy filler is brittlely broken by using liquid nitrogen at low temperature, and then the surface of the epoxy filler is polished and smoothed with fine sandpaper.
  • the epoxy surface is divided into a plurality of fine cells, and a random algorithm is used to obtain five random numbers among them to determine the observation position.
  • the number of cells is 25.
  • the SEM microscopic detection determines the magnification according to the size of the epoxy filler to capture the surface image of the epoxy filler.
  • the eigenvalue of the pure epoxy material is used as a contrast, and the eigenvalue vector and the pure ring are calculated. The spatial distance of the oxygen eigenvalue vector is used to judge the uniformity of the epoxy filler.
  • the epoxy filler includes boron nitride.
  • the epoxy filler surface is the square that side length is 8mm, and the epoxy filler is thick 1mm.
  • the magnification of SEM microscopic detection is 5000 times.
  • the method for detecting the uniformity of epoxy filler based on image detection has the following beneficial effects:
  • the method for detecting the uniformity of epoxy filler based on image detection is pre-prepared by epoxy material. Process and randomly determine multiple observation positions on the epoxy surface, SEM microscopically detects image information, converts it into grayscale data, and calculates the grayscale co-occurrence matrix corresponding to each image and the vector composed of its characteristic quantities, and the grayscale of pure epoxy material The eigenvalues of the co-occurrence matrix are compared, and the eigenvalue vectors are used for variance statistics. Based on the gray level co-occurrence matrix calculation of the image, it can realize the calculation and detection of the uniformity of the filling in the epoxy resin and obtain the evaluation of the uniformity of the epoxy filling without destroying the properties of the epoxy itself.
  • Fig. 1 is the schematic flow sheet of the epoxy filler uniformity detection method based on image detection among the present invention
  • Fig. 2 is a schematic diagram of the epoxy material pretreatment process of the epoxy filler uniformity detection method based on image detection in the present invention.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • “plurality” means two or more, unless otherwise specifically defined.
  • a first feature being “on” or “under” a second feature may include direct contact between the first and second features, and may also include the first and second features Not in direct contact but through another characteristic contact between them.
  • “above”, “above” and “above” the first feature on the second feature include that the first feature is directly above and obliquely above the second feature, or simply means that the first feature is horizontally higher than the second feature.
  • “Below”, “beneath” and “under” the first feature to the second feature include that the first feature is directly below and obliquely below the second feature, or simply means that the first feature has a lower level than the second feature.
  • a method for detecting the uniformity of epoxy filler based on image detection comprises,
  • the first step is to polish the surface of the epoxy filler without changing its microstructure, and further, proceed according to the method of accompanying drawing 2.
  • a plurality of observation positions are randomly determined on the surface of the epoxy filler, and image information of the structure of the surface of the epoxy filler is obtained based on SEM microscopic detection, and the image information is converted into a grayscale matrix and stored,
  • the third step is to calculate the gray level co-occurrence matrix corresponding to each image based on the gray level matrix, and calculate its corresponding eigenvalue according to the gray level co-occurrence matrix to obtain an eigenvalue vector,
  • the fourth step is to compare the eigenvalue vector of each gray level co-occurrence matrix with the gray level co-occurrence matrix eigenvalue of the pure epoxy filler to determine the distribution of the epoxy filler,
  • the fifth step is to calculate the variance of the eigenvalue vectors of different observation positions.
  • the cured epoxy filler in the first step, is brittlely broken by using liquid nitrogen at low temperature, and then the surface of the epoxy filler is smoothed with fine sandpaper.
  • the epoxy surface is divided into a plurality of fine grids, and a random algorithm is used to obtain five random numbers in it. Determine the observation location.
  • the number of cells is 25.
  • the SEM microscopic detection determines the magnification according to the size of the epoxy filler to capture the surface image of the epoxy filler.
  • the fourth step when judging the distribution of epoxy fillers, the eigenvalues of pure epoxy materials are used as a comparison to calculate the eigenvalues
  • the spatial distance between the vector and the pure epoxy eigenvalue vector is used to judge the uniformity of the epoxy filler.
  • variance statistics are performed on each eigenvalue, and finally the mean value of variance of all eigenvalues is obtained to judge the uniformity of epoxy filler.
  • the epoxy filler includes boron nitride.
  • the surface of the epoxy filler is a square with a side length of 8 mm, and the thickness of the epoxy filler is 1 mm.
  • the magnification of the SEM microscopic detection is 5000 times.
  • a method for detecting the uniformity of epoxy filler based on image detection comprises the following steps:
  • the epoxy material is pretreated, and the epoxy material is taken in a suitable size, and the surface is polished without changing its microstructure.
  • the second step multiple observation positions on the epoxy surface are randomly determined, and image information of the surface structure of the epoxy material is obtained based on SEM microscopic detection. Convert the image information into a grayscale matrix for processing and saving.
  • the gray level co-occurrence matrix corresponding to each image is calculated according to the gray level matrix of the image, and the corresponding eigenvalues are calculated according to the gray level co-occurrence matrix to obtain an eigenvalue vector.
  • the eigenvectors of each gray-level co-occurrence matrix are compared with the eigenvalues of the gray-level co-occurrence matrix of pure epoxy materials, and the distribution of seasonings is preliminarily judged.
  • variance statistics are performed on the eigenvalue vectors of different observation positions of the same epoxy, and the greater the variance, the worse the uniformity of the epoxy filler.
  • the cured epoxy material in the first step, is brittlely fractured with liquid nitrogen at low temperature, and polished with clean fine sandpaper, without affecting the surface structure.
  • the epoxy surface with a fixed size is divided into 25 fine grids, and a random algorithm is used to obtain five random numbers from 1 to 25 to determine the observation point of the epoxy.
  • the magnification is determined according to the size of the epoxy filler, and the surface image is taken. Convert the magnified image into a grayscale matrix by computer programming and label it.
  • the eigenvalue of the pure epoxy material is used as a comparison, and the spatial distance between the image eigenvalue vector and the pure epoxy eigenvalue vector is calculated to preliminarily judge the uniformity of the filler.
  • variance statistics are carried out for each eigenvalue, and finally the mean value of all eigenvalue variances is obtained, as the final index for judging that the epoxy material is uniform.
  • boron nitride is used as the epoxy filler, and the filler mass fraction is 30%.
  • the cured epoxy material is brittle at low temperature with liquid nitrogen, and polished with clean fine sandpaper without affecting the surface structure.
  • the size of the finally obtained epoxy material is a square with a thickness of 1 mm and a side length of 8 mm.
  • the fixed-size epoxy surface is divided into 25 fine grids, which are numbered from 1 to 25 from top to bottom and from left to right, and are obtained in 1-25 using a random algorithm.
  • the random number algorithm adopts the MATLAB random number algorithm to determine the observation point of the epoxy material.
  • the magnification is set at 5000 times, the crystal and amorphous structure of the epoxy surface can be observed, and a high-resolution surface image can be taken.
  • the imported image is converted into a grayscale matrix
  • the enlarged image is converted into a grayscale matrix by computer programming
  • the grayscale matrix corresponding to each image is labeled.
  • a calculation algorithm is used to solve the gray level co-occurrence matrix and extract corresponding feature quantities. Take any point (x, y) in the gray matrix (N ⁇ N) and another point (x+a, y+b) away from it, and set the gray value of the point pair to (g1, g2). Let the point (x, y) move on the whole screen, and various (g1, g2) values will be obtained. If the number of levels of the gray value is k, then the combination of (g1, g2) has a total of k square species.
  • the number of occurrences of each (g1, g2) value is counted, and then arranged into a square matrix, and then the total number of occurrences of (g1, g2) is used to normalize them to the probability of occurrence P(g1, g2), such a square matrix is called a gray level co-occurrence matrix.
  • some feature quantities can be used to characterize the characteristics of the gray-level co-occurrence matrix.
  • the commonly used features are: second-order moment of angle, contrast, juan, autocorrelation, etc. These four eigenvalues are extracted to form the eigenvector of each image.
  • the eigenvalue of the pure epoxy material is used as a contrast, the transparency of the pure epoxy material is very high, so the surface is uniform, and the gray matrix value of the image is not much different, and the calculated The eigenvalues of the gray level co-occurrence matrix are close to zero. Calculate the spatial distance between the image eigenvalue vector and the pure epoxy eigenvalue vector, and preliminarily judge the uniformity of the filler. The variance statistics are carried out for each eigenvalue, and finally the mean value of the variance of all eigenvalues is obtained as the final index for judging the homogeneity of the epoxy material.
  • the final calculated eigenvalue variance is only 5.1% of the eigenvalue size, and the sample filler is considered to be evenly distributed.

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Abstract

一种基于图像检测的环氧填料均匀度检测方法，该方法包括：打磨平整环氧填料表面且不改变其微观结构；在该环氧填料表面随机确定多个观测位置，基于SEM微观检测获取环氧填料表面的结构的图像信息，该图像信息转换成灰度矩阵，基于该灰度矩阵计算得到每个图像对应的灰度共生矩阵，根据该灰度共生矩阵计算其相应的特征值得到特征值向量，将每个该灰度共生矩阵的特征值向量与纯环氧填料的灰度共生矩阵特征值进行对比以初步判断环氧填料的分布均匀度，将同一个环氧填料不同观测位置的特征值向量进行方差统计，方差越大则环氧填料的均匀度越差。

Description

基于图像检测的环氧填料均匀度检测方法
本申请要求于2021年11月9日提交中国专利局、申请号为202111323141.3、发明名称为“基于图像检测的环氧填料均匀度检测方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明属于高压绝缘中环氧填料处理技术领域，尤其涉及一种基于图像检测的环氧填料均匀度检测方法。
背景技术
环氧树脂是一种性能优异的热固性树脂，在电力领域中,环氧树脂因其良好的综合性能广泛应用于绝缘材料、导电材料、电子元器件等领域。随着发电站向着大电流、高电压方向发展,对输变电设备的绝缘要求会越来越高,从而使环氧树脂的干式、整体绝缘结构和全封闭工艺在电力互感器、变压器、绝缘珠等产品上得到普遍推广。但也随着电压等级的不断提高,环氧树脂热导率低,韧性不足的缺点逐渐暴露出来，但可以通过合适的改性方法在一定程度上加以克服和改进。为了改变环氧的性能，往往会在环氧材料中加入一些填料对其进行改性处理，比如加入氮化棚、氧化铝等填料以提升环氧的热导性能及介电性能等。
环氧填料的均匀性会对环氧的电热等性能产生较大的影响，有学者己经仿真研究过不均匀分布的填料对环氧起树电压等的影响，不均匀的填料分布甚至会降低环氧的多方面性能。但因为在环氧制备过程中，为了防止搅拌振动等引起环氧固化时内部产生气泡，环氧材料和填料往往并未能很好的混合在一起，填料分布的均匀性难以得到确定。现有的研究中几乎没有进行对环氧填料分布的检测和研究，因此有必要对环氧填料的均匀度进行检测分析。
在背景技术部分中公开的上述信息仅仅用于增强对本发明背景的理解，因此可能包含不构成本领域普通技术人员公知的现有技术的信息。
发明内容
本发明的目的是提供一种基于图像检测的环氧填料均匀度检测方法， 可以在不破坏环氧本身性质的情况下，实现对环氧树脂内部填料的均匀度计算检测、准确判断。为了实现上述目的，本发明提供如下技术方案：
本发明的一种基于图像检测的环氧填料均匀度检测方法包括：第一步骤，打磨平整环氧填料表面且不改变其微观结构，第二步骤，在所述环氧填料表面随机确定多个观测位置，基于SEM微观检测获取环氧填料表面的结构的图像信息，所述图像信息转换成灰度矩阵并保存，第三步骤，基于所述灰度矩阵计算得每个图像对应的灰度共生矩阵，根据所述灰度共生矩阵计算其相应的特征值得到特征值向量，
第四步骤，将每个所述灰度共生矩阵的特征值向量与纯环氧填料的灰度共生矩阵特征值进行对比以判断环氧填料的分布，
第五步骤，方差统计不同观测位置的特征值向量，方差越大则环氧填料的均匀度越差。
所述的一种基于图像检测的环氧填料均匀度检测方法中，第一步骤中，采用液氮低温脆断固化了的环氧填料，然后细砂纸打磨平整环氧填料表面。
所述的一种基于图像检测的环氧填料均匀度检测方法中，第二步骤中，将环氧表面划分为多个细格，并采用随机算法在其中获得五个随机数以确定观测位置。
所述的一种基于图像检测的环氧填料均匀度检测方法中，第二步骤中，多个细格为25个。
所述的一种基于图像检测的环氧填料均匀度检测方法中，第二步骤中，SEM微观检测根据环氧填料的大小确定放大倍数以拍摄环氧填料表面图像。所述的一种基于图像检测的环氧填料均匀度检测方法中，第四步骤中，判断环氧填料的分布时，纯环氧材料的特征值作为对照，计算所述特征值向量与纯环氧特征值向量的空间距离以判断所述环氧填料的均匀度。所述的一种基于图像检测的环氧填料均匀度检测方法中，对每一个特征值均进行方差统计，最后求得所有特征值方差的均值以判断环氧填料均匀度。所述的一种基于图像检测的环氧填料均匀度检测方法中，环氧填料包括氮化棚。
所述的一种基于图像检测的环氧填料均匀度检测方法中，环氧填料表 面为边长为8mm的正方形，环氧填料厚1mm。
所述的一种基于图像检测的环氧填料均匀度检测方法中，SEM微观检测的放大倍数为5000倍。
在上述技术方案中，本发明提供的一种基于图像检测的环氧填料均匀度检测方法，具有以下有益效果：所述的一种基于图像检测的环氧填料均匀度检测方法通过环氧材料预处理、随机确定环氧表面多个观测位置，SEM微观检测图像信息，转换为灰度数据、计算得到各个图像对应的灰度共生矩阵及其特征量组成的向量、与纯环氧材料的灰度共生矩阵特征值进行对比、特征值向量进行方差统计。基于图像的灰度共生矩阵计算，，可以在不破坏环氧本身性质的情况下，实现对环氧树脂内部填料的均匀度计算检测、获得环氧填料均匀度的评E。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明中记载的一些实施例，对于本领域普通技术人员来讲，还可以根据这些附图获得其他的附图。
图1为本发明中基于图像检测的环氧填料均匀度检测方法的流程示意图；
图2为本发明中基于图像检测的环氧填料均匀度检测方法的的环氧材料预处理流程示意图。
具体实施方式
为使本发明实施方式的目的、技术方案和优点更加清楚，下面将结合本发明实施方式对本发明实施方式中的技术方案进行清楚、完整地描述，显然，所描述的实施方式是本发明一部分实施方式，而不是全部的实施方式。基于本发明中的实施方式，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。
因此，以下对在附图中提供的本发明的实施方式的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的边定实施方式。基 于本发明中的实施方式，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。
在本发明的描述中，需要理解的是，术语“中心”、“纵向”、“横向”、“长度”、“宽度”、“厚度”、“上”、“下”、“前”、“后”、“左”、“右”、“坚直”、“水平”、“顶”、“底”、“内”、“外”、“顺时针”、“逆时针”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的设备或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。在本发明的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
在本发明中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
在本发明中，除非另有明确的规定和限定，第一特征在第二特征之“上”或之“下”可以包括第一和第二特征直接接触，也可以包括第一和第二特征不是直接接触而是通过它们之间的另外的特征接触。而且，第一特征在第二特征“之上”、“上方”和“上面”包括第一特征在第二特征正上方和斜上方，或仅仅表示第一特征水平高度高于第二特征。第一特征在第二特征“之下”、“下方”和“下面”包括第一特征在第二特征正下方和斜下方，或仅仅表示第一特征水平高度小于第二特征。
为了使本领域的技术人员更好地理解本发明的技术方案，下面将结合 附图1至图2对本发明作进一步的详细介绍。一种基于图像检测的环氧填料均匀度检测方法包括，
第一步骤，打磨平整环氧填料表面且不改变其微观结构，进一步地，根据附图2方式进行。
第二步骤，在所述环氧填料表面随机确定多个观测位置，基于SEM微观检测获取环氧填料表面的结构的图像信息，所述图像信息转换成灰度矩阵并保存，
第三步骤，基于所述灰度矩阵计算得每个图像对应的灰度共生矩阵，根据所述灰度共生矩阵计算其相应的特征值得到特征值向量，
第四步骤，将每个所述灰度共生矩阵的特征值向量与纯环氧填料的灰度共生矩阵特征值进行对比以判断环氧填料的分布，
第五步骤，方差统计不同观测位置的特征值向量，方差越大则环氧填料的均匀度越差。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，第一步骤中，采用液氮低温脆断固化了的环氧填料，然后细砂纸打磨平整环氧填料表面。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，第二步骤中，将环氧表面划分为多个细格，并采用随机算法在其中获得五个随机数以确定观测位置。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，第二步骤中，多个细格为25个。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，第二步骤中，SEM微观检测根据环氧填料的大小确定放大倍数以拍摄环氧填料表面图像。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，第四步骤中，判断环氧填料的分布时，纯环氧材料的特征值作为对照，计算所述特征值向量与纯环氧特征值向量的空间距离以判断所述环氧填料的均匀度。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式 中，对每一个特征值均进行方差统计，最后求得所有特征值方差的均值以判断环氧填料均匀度。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，环氧填料包括氮化棚。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，环氧填料表面为边长为8mm的正方形，环氧填料厚1mm。
所述的一种基于图像检测的环氧填料均匀度检测方法的优选实施方式中，SEM微观检测的放大倍数为5000倍。
在一个实施方式中，如图1所示，一种基于图像检测的环氧填料均匀度检测方法包括以下步骤：
第一步骤中，将环氧材料进行预处理，取合适大小的环氧材料，并在不改变其微观结构的基础上将表面打磨平整。
第二步骤中，随机确定环氧表面多个观测位置，并基于SEM微观检测，获取环氧材料表面结构的图像信息。将图像信息转换成灰度矩阵处理并保存。
第三步骤中，根据图像的灰度矩阵计算得到各个图像对应的灰度共生矩阵，根据灰度共生矩阵计算其相应的特征值，得到特征值向量。
第四步骤中，将每个灰度共生矩阵的特征向量与纯环氧材料的灰度共生矩阵特征值进行对比，初步判断调料的分布。
第五步骤中，将同一个环氧不同观测位置的特征值向量进行方差统计，方差越大则环氧填料的均匀度越差。
所述的方法中，第一步骤中，对固化好的环氧材料采用液氮低温脆断，并用洁净的细砂纸打磨平整，不影响表面结构。第二步骤中，将固定大小的环氧表面划分为25个细格，并采用随机算法在1-25获得中五个随机数，确定环氧的观测点。SEM观测时，根据环氧填料的大小确定放大倍数，拍摄表面图像。通过计算机编程将放大图像转换为灰度矩阵，并标号。所述的方法中，第四步骤中，纯环氧材料的特征值作为对照，计算图像特征值向量与纯环氧特征值向量的空间距离，初步判断填料的均匀度。所述的方法中，第五步骤中，对每一个特征值均进行方差统计，最后求得所有特征 值方差的均值，作为判断环氧材料均匀的最终指标。所述的方法的优选实施方式中，第一步骤中，采用氮化棚作为环氧的填料，填料质量分数为30％。对固化好的环氧材料采用液氮低温脆断，并用洁净的细砂纸打磨平整，不影响表面结构。最终获得环氧材料的大小为厚度1mm，边长为8mm的正方形。
所述的方法中，第二步骤中，将固定大小的环氧表面划分为25个细格，从上到下，从左到右依次编号为1到25，并采用随机算法在1-25获得中五个随机数，随机数算法采用MATLAB随机数算法，确定环氧材料的观测点。
所述的方法的优选实施方式中，第二步骤中，SEM观测时，放大倍数定为5000倍，可以观测到环氧表面的晶体及非晶体结构，并拍摄高分辨率的表面图像。
所述的方法的优选实施方式中，第二步骤中，基于MATLAB导入图像转换为灰度矩阵，通过计算机编程将放大图像转换为灰度矩阵，并对每个图像对应的灰度矩阵进行标号。
所述的方法的优选实施方式中，第三步骤中，采用计算算法求解灰度共生矩阵，并提取相应的特征量。取灰度矩阵(N×N)中任意一点(x，y)及偏离它的另一点(x+a，y+b)，设该点对的灰度值为(g1，g2)。令点(x，y)在整个画面上移动，则会得到各种(g1，g2)值，设灰度值的级数为k，则(g1，g2)的组合共有k的平方种。对于整个画面，统计出每一种(g1，g2)值出现的次数，然后排列成一个方阵，再用(g1，g2)出现的总次数将它们归一化为出现的概率P(g1，g2)，这样的方阵称为灰度共生矩阵。通常可以用一些特征量来表征灰度共生矩阵的特征，常用的特征有:角二阶矩、对比度、娟、自相关性等。提取这四个特征值构成每一幅图像的特征向量。
所述的方法的优选实施方式中，第四步骤中，纯环氧材料的特征值作为对照，纯环氧材料的透明度很高，因此表面均匀，图像的灰度矩阵值差异不大，计算得到的灰度共生矩阵的特征值接近于零。计算图像特征值向量与纯环氧特征值向量的空间距离，初步判断填料的均匀度。对每一个特征值均进行方差统计，最后求得所有特征值方差的均值，作为判断环氧材 料均匀的最终指标。
所述的方法的优选实施方式中，最终计算得到特征值方差仅为特征值大小的5.1％，认为该样本填料分布均匀。
最后应该说明的是：所描述的实施例仅是本申请一部分实施例，而不是全部的实施例，基于本申请中的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的所有其它实施例，都属于本申请保护的范围。
以上只通过说明的方式描述了本发明的某些示范性实施例，毋庸置疑，对于本领域的普通技术人员，在不偏离本发明的精神和范围的情况下，可以用各种不同的方式对所描述的实施例进行修正。因此，上述附图和描述在本质上是说明性的，不应理解为对本发明权利要求保护范围的限制。

Claims (10)

  1. 一种基于图像检测的环氧填料均匀度检测方法，其特征在于，其包括以下步骤：
    第一步骤，打磨平整环氧填料表面且不改变其微观结构，
    第二步骤，在所述环氧填料表面随机确定多个观测位置，基于SEM微观检测获取环氧填料表面的结构的图像信息，所述图像信息转换成灰度矩阵并保存，
    第三步骤，基于所述灰度矩阵计算得每个图像对应的灰度共生矩阵，根据所述灰度共生矩阵计算其相应的特征值得到特征值向量，
    第四步骤，将每个所述灰度共生矩阵的特征值向量与纯环氧填料的灰度共生矩阵特征值进行对比以判断环氧填料的分布，
    第五步骤，方差统计不同观测位置的特征值向量，方差越大则环氧填料的均匀度越差。
  2. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，优选的，第一步骤中，采用液氮低温脆断固化了的环氧填料，然后细砂纸打磨平整环氧填料表面。
  3. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，第二步骤中，将环氧表面划分为多个细格，并采用随机算法在其中获得五个随机数以确定观测位置。
  4. 根据权利要求3所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，第二步骤中，多个细格为25个。
  5. 根据权利要求4所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，第二步骤中，SEM微观检测根据环氧填料的大小确定放大倍数以拍摄环氧填料表面图像。
  6. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，第四步骤中，判断环氧填料的分布时，纯环氧材料的特征值作为对照，计算所述特征值向量与纯环氧特征值向量的空间距离以判断所述环氧填料的均匀度。
  7. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测 方法，其特征在于，对每一个特征值均进行方差统计，最后求得所有特征值方差的均值以判断环氧填料均匀度。
  8. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，环氧填料包括氮化棚。
  9. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法,其特征在于，环氧填料表面为边长为8mm的正方形，环氧填料厚1mm。
  10. 根据权利要求1所述的一种基于图像检测的环氧填料均匀度检测方法，其特征在于，SEM微观检测的放大倍数为5000倍。
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