Evaluation of Al-specimen fatigue using a “smart sensor”

Cikalova, Ulana; Kroening, M.; Schreiber, J.; Vertyagina, Yelena

doi:10.1016/j.physme.2011.12.009

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…During the last two decades the interest in fractal methods has somewhat reduced but research in the field remains ongoing as can be seen from more recent publications, see e.g. [61] for signal analysis, [12,43] for image analysis with application in material science. With regard to our analysis in Section 5 that leads to a relation between graph methods and fractals, it is worth mentioning that already [19] linked graph and fractal methods in image analysis, albeit not considering texture but shape description.…”

Section: Texturementioning

confidence: 99%

“…that measures volumes of spheres with arbitrary radius. Assuming the exponential weighting scheme (12) and large D(G) this yields…”

Section: Rewriting the Information Functionalsmentioning

confidence: 99%

“…Second, q determines the shape and steepness of the function (compare Figure 6) that relates the local fractal dimension to the values of the information functionals. This makes it desirable to refine in future work the parametrisation of the exponential weighting scheme (12) so that the two roles of q are distributed to two parameters.…”

Section: Fractal Analysismentioning

confidence: 99%

See 2 more Smart Citations

Graph Entropies in Texture Segmentation of Images

Welk¹

2016

Mathematical Foundations and Applications of Graph Entropy

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We study the applicability of a set of texture descriptors introduced in recent work by the author to texture-based segmentation of images. The texture descriptors under investigation result from applying graph indices from quantitative graph theory to graphs encoding the local structure of images. The underlying graphs arise from the computation of morphological amoebas as structuring elements for adaptive morphology, either as weighted or unweighted Dijkstra search trees or as edge-weighted pixel graphs within structuring elements. In the present paper we focus on texture descriptors in which the graph indices are entropy-based, and use them in a geodesic active contour framework for image segmentation. Experiments on several synthetic and one real-world image are shown to demonstrate texture segmentation by this approach. Forthermore, we undertake an attempt to analyse selected entropy-based texture descriptors with regard to what information about texture they actually encode. Whereas this analysis uses some heuristic assumptions, it indicates that the graph-based texture descriptors are related to fractal dimension measures that have been proven useful in texture analysis.

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Section: Texturementioning

confidence: 99%

“…that measures volumes of spheres with arbitrary radius. Assuming the exponential weighting scheme (12) and large D(G) this yields…”

Section: Rewriting the Information Functionalsmentioning

confidence: 99%

Section: Fractal Analysismentioning

confidence: 99%

See 1 more Smart Citation

Graph Entropies in Texture Segmentation of Images

Welk¹

2016

Mathematical Foundations and Applications of Graph Entropy

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“…The multifractal parameters find a relationship with the degree of cement hydration in cement paste [8], and multifractal behaviour of corroded steel surfaces has been observed also [9]. The fractal dimension of the surface topography was used as a parameter for evaluation of the fatigue damage in Al-alloy specimens [10], as a measure of the specific energy dissipated in the deformed austenite steel [11], and as a parameter that characterised the type of fracture in polymer-matrix composites [12]. A relationship between the multifractal parameters and the crack pattern evolution is noted in [13], and fractal analysis can also been involved in pattern recognition algorithms [14].…”

Section: Introductionmentioning

confidence: 99%

Multifractal-based assessment of Gilsocarbon graphite microstructures

Vertyagina

Marrow

2016

Carbon

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“…Digital processing of images allows one to calculate the informative parameters to assess the damage state of the material. In [3] "smart sensors" made of single crystal aluminum films were used to evaluate the mechanical state of AA2024 specimen during cyclic tension tests. In [4] the possibility of thin metallic foil sensors application in aeronautics is discussed.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Sensitivity of Aluminum Foil Based Strain Sensors at Fatigue Damage Evaluation of CFRP

Бурков¹,

Panin²,

Lyubutin³

et al. 2014

AMR

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For the development of a technique for cyclic deformation assessment using aluminum foil sensors glued onto the specimen surface the fatigue tests of carbon fiber reinforced polymer (CFRP) specimens were carried out. The DSLR camera mounted onto an optical microscope was used for capturing the images of sensors to reflect strain induced relief, which than was numerically estimated using various informative parameters in order to obtain the cyclic deformation assessment of composite. The results are discussed in view of application of this method for the development of Structural Health Monitoring (SHM) approach.

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Evaluation of Al-specimen fatigue using a “smart sensor”

Cited by 8 publications

References 9 publications

Graph Entropies in Texture Segmentation of Images

Graph Entropies in Texture Segmentation of Images

Multifractal-based assessment of Gilsocarbon graphite microstructures

Investigation of Sensitivity of Aluminum Foil Based Strain Sensors at Fatigue Damage Evaluation of CFRP

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