Predictive modeling of solidification during laser additive manufacturing of nickel superalloys: recent developments, future directions

Abstract

Additive manufacturing (AM) processes produce parts with improved physical, chemical, and mechanical properties compared to conventional manufacturing processes. In AM processes, intricate part geometries are produced from multicomponent alloy powder, in a layer-by-layer fashion with multipass laser melting, solidification, and solid-state phase transformations, in a shorter manufacturing time, with minimal surface finishing, and at a reasonable cost. However, there is an increasing need for post-processing of the manufactured parts via, for example, stress relieving heat treatment and hot isostatic pressing to achieve homogeneous microstructure and properties at all times. Solidification in an AM process controls the size, shape, and distribution of the grains, the growth morphology, the elemental segregation and precipitation, the subsequent solid-state phase changes, and ultimately the material properties. The critical issues in this process are linked with multiphysics (such as fluid flow and diffusion of heat and mass) and multiscale (lengths, times and temperature ranges) challenges that arise due to localized rapid heating and cooling during AM processing. The alloy chemistry-process-microstructure-propertyperformance correlation in this process will be increasingly better understood through multiscale modeling and simulation.

The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment

“…Thermal gradient (TG) = ∂T(x, 0, 0) ∂x (6) where T liq is the liquidus temperature; T sol , the solidus temperature; d liq , the position along the centerline of the track where the temperature is equal to T liq ; d sol , the position along the centerline of the track where the temperature is equal to T sol ; and v is the speed of the laser.…”

“…This specificity of the final microstructure with a resulting uncertainty in the mechanical properties of 3D-printed parts, has been found to be the main barrier to a wider AM process adoption [5]. Since it is not possible to experimentally analyze all parts manufactured by AM, numerical simulations represent an appealing time-and resource-saving approach to predict the parts' microstructure [6]. Given the complexity of the LPBF process, which involves around 130 printing variables [7], high-fidelity melt pool models are, however, very complex and require delicate calibration procedures.…”

Uncertainty analysis of microsegregation during laser powder bed fusion

“…Additive manufacturing has the potential to be the technology for the future, and the quality control in LPBF can be achieved through variation control of the QoI [6,15]. To achieve this, a better understanding of the uncertainty quantification of the LPBF process is essential.…”

“…Therefore, a suitable surrogate model could potentially substitute for the expensive phase-field simulations. The microstructural features that develop during LPBF solidification process are statistically variable in several aspects [14,15]. The solidification morphology and the distribution of the grain size can vary from region to region within the microstructure.…”

Modelling of Microstructure Evolution during Laser Processing of Intermetallic Containing Ni-Al Alloys

“…Therefore, the prediction of properties and process optimisation require an understanding of not only the grain structure, but also solute partitioning and (non-equilibrium) phase formation during solidification. Modelling and numerical simulation of microstructure evolution can provide a basis for this understanding [19][20][21]. However, modelling of microstructure becomes particularly challenging when the solidification rate is high [22], or when the solidifying phase has a complex structure, such as an intermetallic compound with orderdisorder transition [23].…”