Research Area
Surrogate Model Building and Uncertainty Quantification in Additive Manufacturing

PhD Student
2013-Present

The primary goal of my research is to characterize the influence of process parameter variability inherent to Selective Laser Melting (SLM) on components manufactured with the SLM technique. Since SLM is a relatively new additive manufacturing process, the ability to quantify the uncertainty in the prediction of mechanical properties of a manufactured component is particularly important. Uncertainty quantification (UQ) is typically done by varying inputs to a model of the process and observing the variability of the outputs. This type of forward UQ may use Monte Carlo random sampling and require hundreds or thousands of runs of the model. Due to the complexity of the process, the most accurate models of the SLM process take a significant amount of time, and it would not be feasible run so many times. Through Bayesian techniques, I aim to calibrate a simpler physics-based model using experimental and simulation data to act as a surrogate for the forward UQ techniques.

I like to hang out with my friends: Brian, Rebecca, Monica, and Carlos. I am also in Hawaii because the sun was calling me!