Modelling Microstructure of Additive Manufactured Metal Parts
Tuesday, May 6, 2025: 8:30 AM
Room 16 (Vancouver Convention Centre)
R Sambathkumar
,
Access e.V., Intzestr, Aachen, Aachen, Germany, Germany
X Gao
,
Access e.V., Intzestr, Aachen, Aachen, Germany, Germany
R Avila
,
Access e.V., Intzestr, Aachen, Aachen, Germany, Germany
O Stryzhyboroda
,
Access e.V., Intzestr, Aachen, Aachen, Germany, Germany
C Huang
,
Access e.V., Intzestr, Aachen, Aachen, Germany, Germany
J Jakumeit
,
Access e.V., Intzestr, Aachen, AL
Additive manufacturing (AM) of metals allows one-step, near net shape fabrication of complex and intricate components that is difficult to be economically produced by other means. Stainless steels, aluminum, titanium and nickel alloys are commonly printed using powder bed fusion (PBF) or directed energy deposition (DED) techniques. In laser-based powder bed fusion (LPBF), metal powder is melted and solidified into the component being built. The microstructure of the built component evolves during the process and the final solidification microstructure varies depending on the chosen process parameters, resulting in different mechanical properties of the manufactured component. Using numerical modeling and simulation, it is possible to predict the solidification microstructure evolution, enabling the optimization of the final microstructure to obtain the desired mechanical properties.
In this contribution we use a finite volume code tailored specifically for melt pool modeling and grain growth prediction using cellular automaton technique. The target of this study is to simulate the LPBF process for Ti6Al4v alloy considering the enthalpy based thermal-solidification model with phase transformation. The single-track thermal model based on different process parameters are simulated. Thermal conditions from the single track will serve as a basis for the microstructure evolution prediction where LGK dendritic growth model is incorporated. A sensitivity analysis is performed considering different hatch spacing, nucleation density, power and speed and their results are compared.
The melt pool simulations are validated against the microstructure from metallography. Similarly, the as-deposited grain growth simulations will be validated against the experiments. The results demonstrate the potential of the Finite Volume - Cellular Automata (FV-CA) model to make accurate predictions of the final microstructure.
Keywords: Additive manufacturing, LPBF, microstructure evolution, process design, process optimization
