A 3D computational method for determination of pores per inch (PPI) of porous structures

Jamshidi, Farshid; Kunz, Willfried; Altschuh, Patrick; Lu, Tianyu; Laqua, Matthieu; August, Anastasia; Löffler, Frank B.; Selzer, Michael; Nestler, Britta

doi:10.1016/j.mtcomm.2023.105413

Cited by 1 publication

(1 citation statement)

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“…For example, the Pores Per Inch (PPI) of a metal foam is a widely used parameter in industrial applications to characterize porous materials. However, the pores on the boundaries are ignored when determining the PPI [6]. Therefore, an approach that allows for the identification of boundary pores in a periodic structure will result in a more accurate PPI calculation.…”

Section: Introductionmentioning

confidence: 99%

A U-Net-based self-stitching method for generating periodic grain structures

Ji,

Koeppe,

Altschuh

et al. 2024

Phys. Scr.

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When modeling microstructures, the computational resource requirements increase rapidly as the simulation domain becomes larger. As a result, simulating a small representative fraction under periodic boundary conditions is often a necessary simplification. However, the truncated structures leave nonphysical boundaries, which are detrimental to numerical modeling. Here, we propose a self-stitching algorithm for generating periodic structures, demonstrated in a grain structure. The main idea of our algorithm is to artificially add structural information between mismatched boundary pairs, using the hierarchical spatial predictions of the U-Net. The model is trained with 20,000 grain sample pairs simulated from multiphase field simulations, resulting in the successful generation of periodic grain structures as expected. Furthermore, we employ an energy-based metric, the local curvature, to highlight the quality of the generated samples. Through this metric, we determine that the optimum value of the width of the mask is 1/16 of the sample width. The algorithm provides an automatic and unbiased way to obtain periodic boundaries in grain structures and can be applied to porous microstructures in a similar way.

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Section: Introductionmentioning

confidence: 99%