PhD Projects

This project will aim to develop a systematic analysis of the macro-/micro-/nanostructure to identify statistically significant variations in the microstructure as well as in mechanical properties. Further focus is on developing a fundamental understanding of underlying process-microstructure-property relationships. The ultimate goal is the development of a comprehensive database that can be used to train convolutional neural networks (CNN) to automate the microstructural analysis as well as the overall materials design process.

In this project, we will use the state-of-the-art MEMS-based TEM heating stages to investigate the solid/solid interface stability under thermal gradient. The stages can generate fast ramping of temperature (in the range of 10,000K/sec), as well as thermal gradients in the range of > 10,000K/m. These capabilities can be utilized for simulating the process and operating conditions of materials in many applications in the in-situ TEM experiments, such as the additive manufacturing (AM) processing conditions.