You're invited to a seminar with Dr. Prahalada Rao, an associate professor of industrial engineering at Virginia Tech, titled "The Good, Bad, and Ugly Smart Additive Manufacturing Quality Assurance with Modeling,  Materials Science, Monitoring and Machine Learning."

Abstract: This talk is about using data, physical models, and analytics for part qualification in metal additive manufacturing. 

The goal of my research is to realize the flaw-free, industrial-scale production of metal parts using additive manufacturing processes (metal AM/3D printing).

In pursuit of this goal, my research integrates four aspects; the 4 Ms of AM: (1) computational heat transfer modeling to predict and avoid flaw formation (2) materials science to understand the link between thermal physics of the process and flaw formation, (3) monitoring (tracking) the process using in-situ sensor arrays, (4) machine learning (analytics) to detect and prevent flaw formation combining insights from physical models and sensor data.

In my talk I will discuss results from industry-based projects exemplifying the advantage of integrating fundamental heat transfer modeling with in-situ data for flaw mitigation.

Biography: Dr. Prahalada Rao’s scholastic passion is captured in three words: Manufacturing, Sensing, and Analytics. Rao is an Associate Professor in Industrial Engineering at Virginia Tech.

Dr. Rao has published over 70+ peer-reviewed papers. His research has garnered over $2.5 million in funding from federal agencies, including NSF (including CAREER award), DOE, and Office of Naval Research. He earned the 2017 Society of Manufacturing Engineers, Outstanding Young Manufacturing Engineer Award, and the 2019 University of Nebraska, College of Engineering Research and Creativity Award.