Authors:
Tom Andersson (VTT, Finland),
Anssi Laukkanen (VTT, Finland),
Matti Lindroos (VTT, Finland),
Tatu Pinomaa (VTT, Finland),
Joni Kaipainen (VTT, Finland)
Abstract:
A new alloy designed for high temperature application with multiscale material modelling and deep learning is presented. Calphad type of analysis are combined with DFT simulations and tied together with machine learning tools are utilized in order to find the most promising alloy composition. Designed alloy is synthesized and test specimens are produced with laser powder bed fusion. Experimental material and mechanical characterization methods are compined wiht simulation tools to create a micromechanical model that is used for mechanical property and performance simulations. A workflow is created to combine the different length scales in order to asses the performance of the final component already in alloy design phase in such a way that the alloying components can be fine tuned to fulfill the design requirements of the respective products.
DOI:
https://doi.org/10.59499/WP225372388