| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Monitoring of Process Stability in Laser Wire Directed Energy Deposition using Machine Vision |
| Author(s) |
Anis Asad, Benjamin D. Bevans, Jakob Hamilton, Iris Rivero, Prahalada Rao |
| On-Site Speaker (Planned) |
Benjamin D. Bevans |
| Abstract Scope |
The goal of this work is to mitigate flaw formation in parts made using the laser wire directed energy deposition (LW-DED) additive manufacturing process. As a step towards this goal, the objective of this work is to use real-time data from a meltpool imaging sensor to detect process instabilities. This is an important area of research, as LW-DED process tends to incessantly drift due to poorly understood thermophysical phenomena and stochastic effects. To realize the foregoing objective, we developed a machine learning model that acquires real-time imaging data, and automatically classifies the process state into one of four possible regimes: stable, dripping, stubbing, and incomplete melting. Through single track experiments conducted over 128 conditions, we show that the approach is capable of accurately classifying the process state with a statistical fidelity approaching 90% (F-score). |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |