MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN.

ABSTRACT: 3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volume

SUBMITTER: Kench S 

PROVIDER: S-EPMC9588049 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature