Engineering Commons^™

Development Of Data Science Tools For Part Qualification In Additive Manufacturing, Sujana Chandrasekar

Doctoral Dissertations

In recent years, metal additive manufacturing processes have become popular choices for part production especially for low volume, high complexity parts. To enable widespread adoption of these methods, it is essential to understand the link between process parameters and part properties. This is particularly because additive manufacturing processes cause inherently complex thermo-mechanical cycles and drastically different local process conditions within a part, compared to conventional manufacturing processes like casting and forging. Additionally, properties of feedstock material like metal powder impact final part properties. The focus of this dissertation is on development of data-driven methods using in situ monitoring, as a …