|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
||Michael Sprayberry, John Christopher Ledford, Michael M Kirka
|On-Site Speaker (Planned)
Additive manufacturing using electron beam melting (EBM) has successfully reduced the manufacturing lead-time of complex geometric structures with materials that are nearly impossible to manufacture with conventional processing techniques. However, certification of the component quality can be challenging. Due to the continuous deposition of successive layers of material, components can be quantitatively and qualitatively examined without destructively testing the component. However, in-situ monitoring processes have been complicated due to the unique processing environment associated with EBM metal powder. This work describes a solution to one of the challenges of using Near-infrared (NIR) images as a component qualification process. Here, the correlation of in-process backscatter data with the NIR images increases predicting anomalies during the manufacturing process. Results are presented related to in-situ process monitoring and how this technique results in improved mechanical property prediction and reliability of the process.