| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Additive Manufacturing: Equipment, Instrumentation and Measurement
|
| Presentation Title |
Polyspectral Analysis for In-situ Prediction of Deviations in Laser Powder Bed Fusion Additive Manufacturing |
| Author(s) |
Arthur French, John Sions, Yuri Plotnikov, Kyle Snyder, Kaushik Joshi, Afroditi Filippas |
| On-Site Speaker (Planned) |
Arthur French |
| Abstract Scope |
Due to the high cost and long build times of additive metal manufacturing in laser powder bed fusion (LPBF), it is essential to advance our ability to identify micro defects in real time through advanced data analytics on a variety of sensor modalities. Multi-modal data gathered through acoustic emissions (AE), IR, build plate position, HR camera and photodiode is parsed to distinguish signals generated during specific LPBF cycles. The data is then analyzed separately for each stage in the process, with weight being placed on the melting cycle, which is when porosity would be more likely to form. Test parts were designed and built in Inconel 718 with varied laser energy and power settings to explore a normal run vs a run conducive to porosity formation. The parts were then characterized in terms of density and nature of porosity. Results from our data analysis and build characterization will be presented. |