Authors:

Anssi Laukkanen (VTT Research centre of Finland, Finland),
Tomi Suhonen (VTT Research centre of Finland, Finland),
Tom Andersson (VTT Research centre of Finland, Finland),
Matti Lindroos (VTT Research centre of Finland, Finland),
Yanling Ge (VTT Research centre of Finland, Finland),
Luis Vallejo Rodríguez (VTT Research centre of Finland, Finland)

Abstract:

Additive manufacturing is a manufacturing route able to produce complex components with minimal raw-material utilisation and high-level of process control. However, the rapid solidification rates, strong temperature gradients and extremely localised melting lead to non-equilibrium microstructures that require a better understanding of solid-state transformation, solidification behaviour and structure-property-performance workflow of AMed materials. HEAs unique compositions and complex microstructures slow down considerably the AM parameter optimisation of these materials. Numerical simulations offer a better understanding of the structure-properties-performance of the materials with a reduced number or physical experiments. Hence, a multi-scale modelling approach is taken. For the alloy design phase, Calphad analysis together with DFT simulations and machine learning tools are used to find the most promising HEA compositions. Studying the different microstructural defects, deformation mechanisms that affect the strain hardening potential and damage susceptibility, Crystal-Plasticity models are developed to evaluate the performance of AMed HEAs and the overall workflow.

DOI:

https://doi.org/10.59499/WP225377382