Direct Energy Deposition (DED) is an additive manufacturing technique that uses a focused energy source to melt and deposit material layer by layer, enabling the production of complex components. It has numerous industrial applications due to the high precision it offers and the flexibility in material selection. Materials include severa high strength alloys such as titanium alloys, stainless steel and nicked bases alloys.

Process simulation plays a crucial role in predicting and controlling the additive manufacturing (AM) process, enabling the optimization of efficiency, quality, and reliability of the produced parts. The candidate will focus on simulating the DED process using the Fe20Cr material system. Thermo-structural evolution and effects of process parameters are to be studied.

Iron-Silicon alloys play an important role in electricity generation as well as transmission and electric mobility. During frequent magnetic cycling energy is lost through various processes, reducing these losses poses a major challenge to the design of next generation electrical machines. In this manner investigating the relationship between microstructure, local features, energy losses and hysteresis behavior is investigated.

This work belongs to part of the collaborative research center (CRC) and Transregio (TRR) 361 with an international consortium of universities, funded by DFG and FWF. The candidate will work on simulation of the influences of local features in proximity of grain boundaries on the hysteresis behavior. The results of these simulations will furthermore be used to set up a neural network informed by these results.