2013
DOI: 10.1016/j.matchar.2012.10.001
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Geometric classification of open-cell metal foams using X-ray micro-computed tomography

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Cited by 63 publications
(22 citation statements)
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“…2, examples of the scan images are shown for the 5, 10, 20, and 40 PPI copper foams, respectively. The strut cross sections for the investigated foam samples are observed to be mostly triangular, as was also reported by Bock and Jacobi [27]. The demarcation between the solid phase and the fluid phase is not crisp: metal absorbs X-rays leading to brighter zones, whereas air let X-rays pass, leading to darker zones.…”
Section: Methodssupporting
confidence: 83%
“…2, examples of the scan images are shown for the 5, 10, 20, and 40 PPI copper foams, respectively. The strut cross sections for the investigated foam samples are observed to be mostly triangular, as was also reported by Bock and Jacobi [27]. The demarcation between the solid phase and the fluid phase is not crisp: metal absorbs X-rays leading to brighter zones, whereas air let X-rays pass, leading to darker zones.…”
Section: Methodssupporting
confidence: 83%
“…At the moment, a significant effort is devoted to describing the foam structure in a more realistic way: from this perspective, X-ray computed microtomography (μ-CT) appears a promising and appealing approach to be coupled to numerical simulations. The literature includes several works dealing with μ-CT, not only limited to metal foams or thermal conductivity estimations [1,3,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. The literature offers a wide set of analytical and empirical correlations for the estimation of the effective thermal conductivity of metal foams.…”
Section: Introductionmentioning
confidence: 99%
“…Recent observations using X-ray μCT showed up the feasibility of the 3D reconstruction and basic measurements on X-ray tomography and prove that this technique is suitable for the investigation of the microstructure of foams, because it enables to obtain a real image of the inside [9], [10].…”
Section: Visualization Segmentation and Morphometrymentioning
confidence: 98%