A team led by UCF’s Dazhong Wu is working to improve the additive manufacturing testing process by developing AI-driven methods to predict the performance of 3D-printed parts. The goal is to develop a cost-effective machine learning model that can predict the defects and mechanical performance of 3D printed materials.

Metal additive manufacturing processes use expensive materials to build parts from digital models. Thoe parts undergo lengthy testing that results in destruction of parts and significant costs. Wu’s method is designed to help move the industry away from destructive testing and reduce inspection costs. “Using AI we can predict the mechanical performance of 3D printed parts with small amounts of destructive and non-destructive testing data. With this, we can ensure every part is consistent, reliable and less costly.”

• Dazhong Wu