This presentation was made at CAASE18, The Conference on Advancing Analysis & Simulation in Engineering. CAASE18 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, to share experiences, discuss relevant trends, discover common themes, and explore future issues.

Resource Abstract

Laser welding has been replacing traditional welding methods due to its superior productivity, faster scan speeds and lower heat inputs. With better control and smaller heat affected zones, laser processing technology has led to a rise of interest in metal additive manufacturing (AM) processes such as laser powder bed fusion and direct metal deposition.

Although AM has been generating significant interest, challenges remain towards a more widespread adoption of this technology. These challenges include defect formation such as porosity and spatially non-uniform material properties that occur because of insufficient knowledge of process control. Computational fluid dynamics (CFD) modelling can help researchers understand the effects of process parameters on underlying physical phenomena such as melt pool dynamics, phase change and solidification. With experimental studies successfully capturing melt pool data such as molten metal velocities and temperatures, it is possible to calibrate numerical models using experimental data. These numerical models, which are based on a rigorous solution of the conservation equations, can provide further insights such as fluid convection in the melt pool, temperature gradients and solidification rates.

In this presentation, case studies from industry and academia highlighting the use of CFD and numerical models in understanding powder bed fusion processes are discussed. Process parameter optimization in controlling porosity formation and balling defects for the IN718 alloy are studied in detail. On the one hand, slower laser scan speeds and higher angles of inclination in welding can lead to an unstable keyhole configuration, which typically results in porosity. On the other hand, faster scan speeds result in longer melt pools, and Rayleigh instabilities can cause the elongated melt pool to break down into tiny islands of molten metal resulting in balling defects. Depending on the process, it is critical to choose appropriate scan speeds. Additionally, the effects of powder packing density, laser power and particle size distribution on the formation of balling defects are explored. Finally, melt pool data from the numerical models is used to study and predict the solidification morphology for the IN718 alloy. Based on temperature gradients and solidification rates, which can be obtained through CFD models, it is possible to determine the resulting microstructure evolution and primary dendrite arm spacing resulting from the powder bed fusion processes. These results are compared to experimental data wherever available.

These high fidelity, multiphysics CFD models provide a framework to better understand AM processes from the particle and melt pool scales. Using this information, it will be possible to more accurately model additional aspects of AM processes such as thermal and residual stresses and distortions in the entire part build.