|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Additive Manufacturing: Building the Pathway towards Process and Material Qualification
||Optimization Framework for Designing of Scanning Strategies for Microstructure Control in Additive Manufacturing Using Numerical Modeling Aided by High Performance Computing
||Narendran Raghavan, Suresh Babu, Damien Lebrun-Grandie, Srdjan Simunovic, Michael Kirka, John Turner, Neil Carlson, Ryan Dehoff
|On-Site Speaker (Planned)
Additive Manufacturing has the potential to revolutionize the manufacturing industries in near future. Solidification texture of additively manufactured components plays significant role in determining the mechanical performance. Solidification texture evolution is the result of spatio-temporal variation of temperature-gradient (G) and velocity (R) at the liquid-solid interface during melt-pool solidification. Scanning strategy has significant influence on G and R. Experimental measurement of these solidification parameters are impossible. In this study, numerical-thermal model, coupled with optimization framework is used to understand the sensitivity of different input parameters and scan strategies on texture evolution. Probability of equiaxed grain formation is the quantity of interest. Smolyak sparse grid algorithm is used to reduce the number of simulations by effectively sparsing the parameter space. Response surface is created and analyzed for different scan strategies. In addition to the reduction of experimental cost, coupling numerical models with effective sparse grid algorithms significantly reduces the computational cost.
||Planned: Supplemental Proceedings volume