Voramon Dheeradhada, PhD

Voramon Dheeradhada is a senior materials scientist in the Structural Materials discipline at GE Research in the area of material developments especially in alloy and metallic coating for structural applications focusing on environmental degradation. Her areas of expertise include but not limited to high temperature structural materials for aerospace and power generation, additive manufacturing, alloy design, processing-microstructure-property relation, thermodynamic, metallic coatings, characterization, and machine learning. She has worked on several alloy systems such as nickel superalloy, cobalt alloys, tiatinium, and titanium aluminide as well as refractory intermetallics. She received her M.S. and Ph.D. in Materials Science from Purdue University in 2005.

Contact Information: dheeradh@research.ge.com

Abstract

Machine Learning Method for Parameter Development

Recent development of nickel superalloys for additive manufacturing has shown to be challenging due to the susceptibility to micro cracking in as build microstructure. Significant effort has gone into optimizing build parameters for these hard to process alloys. To reduce the parameter development cycle for challenging materials, GE Research developed and continue to enhance a framework that utilizes probabilistic machine learning (ML), intelligent sampling and optimization protocols, coupled with high-throughput printing, characterization, and in-situ monitoring systems to dramatically accelerate the developmental process. A new protocol was developed by leveraging machine learning algorithm. In this presentation, the framework along with demonstration of the use of machine learning method to guide parameter development for additive manufacturing will be given.

To view this presentation, please contact Voramon via email.