Multiscale prediction of microstructure length scales in metallic alloy casting

Microstructural length scales, such as dendritic spacings in cast metallic alloys, play an essential role the properties of structural components. Therefore, quantitative prediction scales through simulations is important to design novel alloys and optimize processing conditions integrated computational materials engineering (ICME). Thus far, comparisons between experiments primary dendrite arms (PDAS) selection have been mainly limited directional solidification thin samples phase-field dilute alloys. In this article, we combine casting present a multiscale modeling approach predict local solidified relevant industrial processes. To end, were measured instrumented Al–Cu containing 1 wt.% 4 Cu, they compared spacing stability ranges average arrays simulated using (PF) needle network (DNN) models. It first shown that PF DNN lead similar results for Al-1 wt.%Cu alloy, tip constant calculated with simulations. cannot achieve predictions Al-4 alloy because are too computationally demanding due large separation scale radius diffusion length, characteristic feature non-dilute Nevertheless, overall good agreement our experimental well those extensive literature review. Simulations consistently suggest widening PDAS range decrease temperature gradient microstructure goes from cellular-dendrites well-developed hierarchical dendrites.