Powder bed based additive manufacturing processes belong to the key technologies of the future. They allow the production of complex shaped components from the powder with nearly no waste. However, to optimize the process and the properties of the components, it is fundamental to identify reasonable process windows, ensuring part integrity and stable mechanical properties without giving up too much flexibility in the additive manufacturing process. The aim of the project is to establish a full software set, which allows the prediction of resulting mechanical properties of materials produced by additive manufacturing using Selective Electron Beam Melting as a function of the process parameters. In order to realize this goal, we couple three simulation modules covering all the essential physical mechanisms on relevant length- and time-scales: The melting, initial grain structure and orientation formation of the powder particles upon electron beam interaction will be simulated via Lattice- Boltzmann and Cellular Automata approaches; the initial microstructure formation during rapid dendritic solidification at micrometer- dendritic arm-spacing length and solidification time-scales will be covered by the phase-field module; the thermo-mechanical behavior of the resulting grain structure at heat-treatment-time-scales will be simulated using a crystal plasticity Finite Element simulation module. Furthermore, the development of the simulation models will be accompanied by experiments to define essential material parameters and to calibrate, validate and optimize the derived models. SIMCHAIN is an innovative and unique approach to build a ready to use software set in order to predict the influence of various process parameters on the resulting mechanical properties during additive manufacturing using Selective Electron Beam Melting. SIMCHAIN prepares the ground for robust process design, as an important step towards design-driven manufacturing for future aero engines parts optimized in weight and function.


The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) for the Clean Sky Joint Technology Initiative under grant agreement number 326020