Multi-scale characterization of orthotropic microstructures

Tschopp, Mark A.; Wilks, Garth B.; Spowart, Jonathan E.

doi:10.1088/0965-0393/16/6/065009

Cited by 29 publications

(16 citation statements)

References 31 publications

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“…The images were generated using the algorithms detailed in Tschopp et al . (). The distributions of particle major axes were identical for each set of images composed of circles (Fig.…”

Section: Wavelet Spectra Of Some Idealized Casesmentioning

confidence: 97%

Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

2013

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In images of sedimentary or granular material, or simulations of binary (two-phase) granular media, in which the individual grains are resolved, the complete size distribution of apparent grain axes is well-approximated by the global power spectral density function derived using a Morlet wavelet. This approach overcomes many limitations of previous automated methods for estimating the grain-size distribution from images, all of which rely on either: identification and segmentation of individual grains; calibration and/or relatively large sample sizes. The new method presented here is tested using: (i) various types of simulations of two-phase media with a size distribution, with and without preferred orientation; (ii) 300 sample images drawn from 46 populations of sands and gravels from around the world, displaying a wide variability in origin (biogenic and mineralogical), size, surface texture and shape; (iii) petrographic thin section samples from nine populations of sedimentary rock; (iv) high-resolution scans of marine sediment cores; and (v) nonsedimentary natural granular patterns including sea ice and patterned ground. The grain-size distribution obtained is equivalent to the distribution of apparent intermediate grain diameters, grid by number style. For images containing sufficient well-resolved grains, root mean square errors are within tens of percent for percentiles across the entire grain-size distribution. As such, this method is the first of its type which is completely transferable, unmodified, without calibration, for both consolidated and unconsolidated sediment, isotropic and anisotropic two-phase media, and even non-sedimentary granular patterns. The success of the wavelet approach is due, in part, to it quantifying both spectral and spatial information from the sediment image simultaneously, something which no previously developed technique is able to do.

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“…The images were generated using the algorithms detailed in Tschopp et al . (). The distributions of particle major axes were identical for each set of images composed of circles (Fig.…”

Section: Wavelet Spectra Of Some Idealized Casesmentioning

confidence: 97%

Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

2013

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“…The framework of the study is inspired by pore-size probability distribution monitoring problems. To this aim, synthetic porous structures were generated by adapting the algorithm presented in Tschopp et al (2008). The output of the algorithm at each execution run is a 2D binary image where connected components (i.e., the pores) with fixed aspect ratio equal to 1 (i.e., circular pores) but random radius are spread within a square region of fixed size without overlapping (see Tschopp et al, 2008 for details).…”

Section: Simulation Studymentioning

confidence: 99%

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

et al. 2018

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The advance of sensor and information technologies is leading to data-rich industrial environments, where big amounts of data are potentially available. In this scenario, image data play a relevant role, as they can easily describe many phenomena of interest. This study focuses on images where several and similar features of interest are randomly distributed and characterized by no spatially correlated structure. Examples are pores in parts obtained via casting or additive manufacturing, voids in metal foams and light-weight components, grains in metallographic analysis, etc. The proposed approach consists of summarizing the random occurrences of the observed features via its (empirical) probability density function (PDF). In particular, a novel approach for PDF monitoring is proposed. It is based on simplicial functional principal component analysis (SFPCA), which is performed by applying an isometric isomorphism between the space of density functions, i.e., the Bayes space B 2 , and the space of square integrable functions L 2 . A simulation study shows the enhanced monitoring performances provided by the SFPCA-based profile monitoring against other competitors proposed in the literature. Eventually, a real case study dealing with the quality control of foamed materials production is discussed, to highlight a practical use of the proposed methodology.

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“…The efficacy of the VMSAAF technique is demonstrated in this work by its application to synthetic two-dimensional microstructures generated by random sequential adsorption (RSA) without simulated annealing [11]. Particle characteristics were varied to produce microstructures with permutations of chosen factor levels, including: second-phase area fraction (A f = 10%, 20%, 30%), aspect ratio (AR = 4, 8, 16), and the alignment propensity of particle major axes-randomly oriented, fully aligned, or semi-aligned.…”

Section: Techniquementioning

confidence: 99%

“…1, where an array of square sub-regions (of edge-length Q) is used to sub-divide the material domain. Expressed as a coefficient of variation ( ), this variance of second-phase area fraction over all microstructure sub-regions has been shown to obey the relationship [11]: where A f is the second-phase area fraction, is a "cluster parameter" sensitive to the distribution of the second phase and the shape of the measuring sub-region (∼−1.0 for square sub-regions), andį s a "texture parameter" which is mainly sensitive to second-phase morphology and alignment. Since decreases as Q increases, any two-phase microstructure can be characterized by that length scale at which a specified minimum variation in A f is attained.…”

Section: Introductionmentioning

confidence: 99%

“…(1), by the fact that-when all other microstructure features are held constant-clustering is manifested by an increase in , and a corresponding increase in L H [1]. Quantifying homogeneity in anisotropic materials is more can be assessed by a directional MSAAF technique [7,11] that decomposes a microstructure image into lineal (strip) elements of length Q (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-scale characterization of inhomogeneous morphologically textured microstructures

Wilks¹,

Tschopp²,

Spowart³

2010

Materials Science and Engineering: A

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Multi-scale characterization of orthotropic microstructures

Cited by 29 publications

References 31 publications

Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

Transferable wavelet method for grain‐size distribution from images of sediment surfaces and thin sections, and other natural granular patterns

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

Multi-scale characterization of inhomogeneous morphologically textured microstructures

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