|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Microstructure Reconstruction of Additive Manufactured Metallic Materials with Markov Random Fields
||Arulmurugan Senthilnathan, Pinar Acar, Marc De Graef
|On-Site Speaker (Planned)
In this work, we present a Markov Random Field (MRF) approach to reconstruct 2D polycrystalline microstructures. The MRF algorithm predicts the large-scale evolution of 2D experimental microstructural maps that are routinely obtained over small spatial domains with diffraction and optical techniques. The coloring of pixels is obtained by computing the conditional probability density using the known states of neighboring pixels in the input experimental images. In this work, we use MRF-based reconstruction to predict the spatial evolution of different metallic microstructures that are forged and additively manufactured. The reconstructed samples are expected to provide equivalent microstructural features to the experimental data. Therefore, a second order moment invariant is used to compute a feature number that characterizes the average size and shape of a grain. The grain sizes and shapes of the reconstructed microstructures are compared to the original forged and additively manufactured samples through these feature numbers.
||Planned: Supplemental Proceedings volume