The rapid growth of available data from the complex system (i.e., AM) provides enormous potential for developing smart structures and materials with highly customized performance. However, the lack of methods that can analyze governing physics from gathered data and build up efficient models from process to properties hinders the leverage of the great potential. 

The aim of this research is to integrate data from various sources (e.g., sensors, cameras, simulations, experimental tests, etc.) with physical modeling to promote scientific discovery, model characterization, and simulation acceleration. 

Fundings: National Science Foundation

Additive manufacturing provides us a unique opportunity to fabricate complex geometries and achieve unprecedented performance. It thus significantly revolutes the way of developing new materials. This research focuses on the development of a new design methodology for multifunctional/robotic materials to leverage the great potential of AM in aerospace engineering, biomedical engineering, and the energy industry.

The goal of this research is to minimize the defects (see above figure) and develop programmable microstructures for metal AM by means of data-enabled multi-physics and multi-scale simulation for laser powder bed manufacturing. Based on our work on multi-physics simulation, we focus on the development of the efficient multiscale and multi-physics model to understand the underlying mechanism among defects generation, microstructure evolution, and process parameters such as laser power, scanning speed, hatch spacing, etc.

Fundings: National Science Fundation