Defects and Properties of Cast Metals IV: Properties II
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Solidification Committee
Program Organizers: Lang Yuan, University of South Carolina; Brian Thomas, Colorado School of Mines; Peter Lee, University College London; Mark Jolly, Cranfield University; Alex Plotkowski, Oak Ridge National Laboratory; Andrew Kao, University of Greenwich; Kyle Fezi, Fort Wayne Metals

Tuesday 8:00 AM
March 1, 2022
Room: 210B
Location: Anaheim Convention Center

Session Chair: Peter Lee, University College London; Andrew Kao, University of Greenwich

8:00 AM  
Uncertainty Quantification of Model Predictions due to Fluid Flow in Laser Powder Bed Fusion of IN625: Scott Wells1; Matthew Krane1; 1Purdue University
    Computational modeling of the laser powder bed fusion process is often restricted to the conduction dominant regime whereby advective heat transfer and fluid flow can be disregarded. Under processing conditions that result in high energy densities, fluid flow driven by Marangoni effects influence the uncertainty in solidification dynamics and melt pool dimensions which affect the as-built microstructure and scan strategies development. Using a computational fluid dynamics model for melt pool predictions of Inconel 625, and applying sparse grids and interpolation techniques, the uncertainty in input parameters including thermophysical properties and the surface tension gradient can be propagated to predictions of solidification time, cooling rates, and melt pool geometries. Results show the uncertainty in surface tension gradient had the largest affect of melt pool dimensions whereas uncertainty in laser absorptivity and specific heat capacity were the most influential on the solidification dynamics.

8:20 AM  
Modelling Concurrent Structural Mechanical Mechanisms in Microstructure Solidification: Peter Soar1; Andrew Kao1; Georgi Djambazov1; Natalia Shevchenko2; Sven Eckert2; Koulis Pericleous1; 1University of Greenwich; 2Helmholtz-Zentrum Dresden-Rossendorf
    Experimental observations point to structural mechanics as a factor that can significantly alter the development of a cast metal alloy’s microstructure. Forces such as gravity or drag due to solute flow can induce dendrites to deform and/or change orientation. Such changes in microstructural development can lead to defects including stray grains and slivers that degrade macroscopic material properties. However, the interaction of microstructure evolution with structural mechanics is often neglected as a factor in numerical models, potentially rendering them incapable of capturing key defect formation mechanisms. A numerical method coupling a Finite Volume Structural Mechanics solver to a Cellular Automata microstructure solidification solver has been developed, where the growth behaviour of solidifying dendrites is altered by changes to the crystallographic orientation obtained from the calculated displacements. Scenarios where small deformations lead to large orientation changes to accumulate were examined, finding the behaviour to be analogous to that observed in experiments.

8:40 AM  
The Influence of Environmental and Material Properties Data on Defect Predictions in Computational Fluid Dynamics Simulations of Investment Casting: Christopher Jones1; Mark Jolly1; Anders Jarfors2; Patrik Vrethed3; Pedro Silva3; 1Cranfield University; 2Jönköping University; 3TPC Components AB
    Computational fluid dynamics (CFD) packages are becoming increasingly prevalent in the casting world for modelling of fluid flow and the prediction of defect generation during both pouring and as solidification progresses. CFD software such as Flow-3DŽ grant the ability to explore the underlying mechanisms behind the formation of such defects without the need for costly experimentation. As such they have been extremely beneficial to the industry, and will continue to be as their capabilities expand, both financially and in terms of sustainability. By considering a generic foundry test cluster, with the potential to form several defects, an assessment of the impact of varying input parameters on the resulting defect formations was conducted. These parameters of interest included material properties, allocated heat transfer coefficients, and meshing selections. The resulting effect on many defect attributes was explored and contrasted to deepen understanding of the dominant defect drivers and identify possible mitigations.