ALLDESIGN will provide fundamental insight on solid-state precipitation in aluminium alloys based on synergistic multiscale modelling, and its impact on macroscopic properties and manufacturing processes. The data-drive concept is ambitious as it aims to couple materials modelling starting from atomistic level up to manufacturing real macroscopic components and it utilizes new trends in materials research (multiscale modelling, machine learning, digital twin). The aim is two-fold:

1. Methodology development: Our goal is to develop a digital materials design platform to predict structure and control materials properties by incorporating multiscale modelling and experimental data. By using the latest developments in modelling methods, data sciences, and machine learning, we shall work on a concept that can be readily applied to any aluminium alloy composition and extended to other alloy systems. The platform will enable materials research to reach a completely new level in terms of efficiency and development time for new alloys with tailored properties.
2. Materials engineering: We aim to elucidate the precipitate formation mechanisms in commercially relevant aluminium alloys and utilize this to contribute directly to the design and selection of optimal alloy compositions and thermomechanical treatment conditions through its influence on forming properties. Fundamental understanding of the underlying atomistic mechanisms is a prerequisite for such advances. The concept will be tested using the 3D stretch-bending machine as a demonstrator case.