The steel industry on its own is responsible for an estimated 11% of the total CO2 emissions worldwide showing the need for increased recycling. This is no different to other metal classes e.g. for Ni-based alloys or Al alloys. But plain circular economy leads to an accumulation of unwanted impurities which increases the scatter in microstructure formation and deteriorates material properties. This leads to downcycling, where high quality materials are reused only for lower quality products. While available models for process design interpolate within known parameter spaces, they fail at extrapolative modelling and do not consider impurities. Yet, extrapolative models are needed to address to allow for high recycling rates also in microstructure-sensitive high-performance alloys.

This talk will present the outline of a predictive multi-scale modelling framework for microstructure evolution that combines ab initio data with higher scale models. Such a multi-scale modelling framework can be used to optimize process parameters that mitigate impurity effects from recycling already inline in production. Specific examples will be shown for connecting ab initio data with models of recrystallization, precipitation, grain growth, and segregation. The examples show the feasibility of such an approach and point the way towards needed future research.