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Accurate 3D microstructure characterisation of porous materials by X-ray microtomography

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Academic year: 2021

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Accurate 3D microstructure

characterisation of porous materials by X-ray microtomography

A need for advanced and specialised image processing tools

Erwan Plougonven

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The Department of Applied Chemistry

 Applied Sciences Faculty

3 groups in the department (~55 people)

 Analysis and Synthesis of Chemical Systems –

Cryotechnology laboratory

 Nanomaterials, Catalysis, Electrochemistry

 LGC – Environment, Energy, Reactors, separations

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Tomography, a long history at the LGC Skyscan-1074 portable µCT 0.4 0.9 1.4 1.9 2.4 2.9 0 500 1000 1500 2000 we igh t (g) Time (s)

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Micro and macro-tomography Skyscan-1074 portable µCT Skyscan-1172 desktop µCT PVC foam Metallic foam Human bone Extrudates bed

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Examples of analysis

 Nanocomposite polymer foams

(FNRS – ARC project)

 Macadamia nuts

(Collaboration with UNSW)

 Sintered soda-lime powder

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Nanocomposite polymer foams Or what to do with poor quality 3D images ?

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Polymer foams – characteristic length  Statistical method  3D autocorrelation 2 )] )( [( ) (   E X µ Xµ Rtt  R(x, y,z)

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0 0.2 0.4 0.6 0.8 1 0 20 40 60 80 100 120 Ave rag e co rrel ati on Distance 0 0.2 0.4 0.6 0.8 1 0 20 40 60 80 100 120 Ave rag e co rrel ati on Distance

Polymer foams – characteristic length

10  Statistical method  3D autocorrelation 2 )] )( [( ) (   E X µ Xµ Rtt  R(x, y,z)

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Polymer foams

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Polymer foams – characteristic length -0.2 0 0.2 0.4 0.6 0.8 1 0 20 40 60 80 100 A verag e autoco rrel ation Distance (µm) PMMA sample 23 µm -0.2 0 0.2 0.4 0.6 0.8 1 0 20 40 60 80 100 A verag e autoco rrel ation Distance (µm) PCL sample 24 µm

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Polymer foams – anisotropy PCL sample Co rr el ation -0.1 0.1 Co rr el ation -0.1 0.1 PMMA sample

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Macadamia nuts Image analysis AND taste test

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Macadamia nuts - Segmentation

Reconstruction

Morphological closing

Threshold Biggest connected component

Morphological closing

Gradient Active surface

Substraction Threshold

Combination Gaussian blur

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Kernel segmentation : active surface

Hervé Delingette, Modélisation, déformation et reconnaissance d'objets tridimensionnels à l'aide de

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Active surface

Hervé Delingette, Modélisation, déformation et reconnaissance d'objets tridimensionnels à l'aide de maillages simplexes, PhD thesis, Ecole centrale de Paris, 1994

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Active surface

Hervé Delingette, Modélisation, déformation et reconnaissance d'objets tridimensionnels à l'aide de maillages simplexes, PhD thesis, Ecole centrale de Paris, 1994

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Sintering of soda-lime glass Or what is a pore ?

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Sintering and transfer properties

40 min

90 min

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Pore network models

Classical definition: "part of the pore space bounded by the solid and planes erected where the hydraulic radius is minimal"

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Before skeletonisation

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Pore delimitation

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Resulting pore decomposition

Sintering time: 150 minutes

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Pore network model

Assumption: Flow between two pores controlled

only by their separation

Pore-network model Pore positioning graph

 Pairwise pore connexions  Branch ↔ Throat

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Pore network models

Existence of non-pairwise connexions

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Multiple connexions

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Conclusions

 Always the same basic problems (at first): segmentation, separation, labelling

 No method is universal

 Different material / acquisition / application  different methodology

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Références

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