Hydrophobicity classification of polymeric materials based on fractal dimension

Thomazini, Daniel; Gelfuso, Maria Virgínia; Altafim, Ruy Alberto Corrêa

doi:10.1590/s1516-14392008000400006

Cited by 27 publications

(15 citation statements)

References 12 publications

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“…Fractal analysis has been used for the study of distributions and microstructural changes of systems with discontinuities, mainly through the use of the fractal dimension (D f ). This parameter has demonstrated to be a very important tool to analyze the microstructure of different materials including ceramics, polymers and composites [2][3][4] . There are different ways to define fractals: by dimension (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fractal analysis of the heat treatment response for multiphase Al alloys

et al. 2016

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The present work shows the fractal analysis for the microstructure of Al-6Si-3Cu-xMg (x = 0.59, 3.80 and 6.78 wt.%) alloys, solution heat treated at 480 °C for times ranging from 4 to 72 h. Microstructural changes were examined using Optical Microscopy (OM) and Scanning Electron Microscopy (SEM), and quantified by measuring conventional characteristics of second phases. The fractal analysis of the microstructures was also carried out using fractal dimension (D f ). Results shown important variations in the microstructure when Mg content is increased, being the eutectic Al-Si and Cu-rich phases the predominant phases for the alloys with low Mg content, while for higher Mg contents predominant phases were Mg 2 Si and Cu-rich phases. The analysis of D f showed that this parameter could be used in order to compare the microstructural solution response of the Al alloys. Besides, it was possible to correlate the fractal dimension with the shape factor of the second phases, mainly for the alloys with only one second phase remaining after heat treatment.

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Section: Introductionmentioning

confidence: 99%

Fractal analysis of the heat treatment response for multiphase Al alloys

et al. 2016

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“…Besides, SVM (support vector machine) applied in our previous work can also obtain good results. In the next following study, we want to search for some factors which can be expressed with equation like fractal dimension by Thomazini et al 19,20 .…”

Section: Discussionmentioning

confidence: 99%

“…F o r o b t a i n i n g s a m p l e s w i t h v a r i o u s H C , Thomazini et al 16,19,20 artificially change the hydrophobicity of specimen with spraying WIA (Water and Isopropyl Alcohol) solution at different concentrations (from 0 to 100%). Although this method can make different levels of hydrophobicity with only one or two specimens, the images obtained are extremely standard which can't present the actual situations of the insulator's surface.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, some objective measuring methods based on image processing and feature extraction are proposed, 5,[9][10][11][12][13][14][15][16][17][18][19][20][21][22] such as fractal dimension, circular factor, goniometric measurement using Hough transformation, scaled entropy and histogram analysis, surface energy, and online hydrophobicity measurement methodology. However, only one or two characteristic parameters are adopted for classification in these methods which can't describe images comprehensively.…”

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confidence: 99%

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Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods

et al. 2015

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Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research.

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“…A comparison between these three methods allowed us to obtain a better technique to determine the hydrophobicity of polymeric surfaces. The processing methods were chosen due to their abilities to identify similar textures 21,22 and the photos were taken from samples obtained from the experimental procedure to prepare hydrophobic surfaces, described in 21 .…”

Section: Introductionmentioning

confidence: 99%

Classification of polymers insulators hydrophobicity basead on digital image processing

2012

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Although the hydrophobicity is usually an arduous parameter to be determined in the field, it has been pointed out as a good option to monitor aging of polymeric outdoor insulators. Concerning this purpose, digital image processing of photos taken from wet insulators has been the main technique nowadays. However, important challenges on this technique still remain to be overcome, such as; images from non-controlled illumination conditions can interfere on analyses and no existence of standard surfaces with different levels of hydrophobicity. In this paper, the photo image samples were digitally filtered to reduce the illumination influence, and hydrophobic surface samples were prepared from wetting silicon surfaces with solution of water-alcohol. Furthermore norevious studies triying to quantify and relate these properties in a mathematical function were found, that could be used in the field by the electrical companies. Based on such considerations, high quality images of countless hydrophobic surfaces were obtained and three different image processing methodologies, the fractal dimension and two Haralick textures descriptors, entropy and homogeneity, associated with several digital filters, were compared. The entropy parameter Haralick's descriptors filtered with the White Top-Hat filter presented the best result to classify the hydrophobicity.

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Hydrophobicity classification of polymeric materials based on fractal dimension

Cited by 27 publications

References 12 publications

Fractal analysis of the heat treatment response for multiphase Al alloys

Fractal analysis of the heat treatment response for multiphase Al alloys

Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods

Classification of polymers insulators hydrophobicity basead on digital image processing

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