| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Presentation Title |
Detection of Humping and Porosity Flaws in Wire Arc Directed Energy Deposition Using In-situ Meltpool Imaging |
| Author(s) |
Prahalada K. Rao, Andre Ramalho, Anis Asad, Benjamin Bevans, Joao Oliveira |
| On-Site Speaker (Planned) |
Prahalada K. Rao |
| Abstract Scope |
In this work we detected humping and porosity flaws in wire arc directed energy deposition additive manufacturing (WA-DED) processes using data acquired from an in-situ meltpool imaging sensor. As a first-step toward closed-loop process control, there is a burgeoning need to monitor and detect incipient process drifts. We instrumented a WA-DED system with a high-speed meltpool imaging camera to detect two common anomalies, namely, humping and porosity. Physically intuitive signatures encompassing meltpool morphology and intensity features were extracted from the acquired images. These physically intuitive features were subsequently used as inputs to a hierarchical machine learning classification model. Through experiments conducted over multi-layer parts, we show that the model achieves an overall classification fidelity of ~90% (statistical F1-score). The approach was further benchmarked against black-box deep learning models, trained directly with meltpool images resulting in F1-score of 85%. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |