| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Presentation Title |
Predicting the Printable Parameter Space for Laser Directed Energy Deposition Using a Data Augmented Thermal Model |
| Author(s) |
Peter Morcos, Matthew Vaughan, Brent G. Vela, Jiahui Ye, Alaa Elwany, Ibrahim Karaman, Raymundo Arroyave |
| On-Site Speaker (Planned) |
Peter Morcos |
| Abstract Scope |
Laser Directed Energy Deposition (DED) is an additive manufacturing technique used to produce large and complex metal parts through the deposition of metal powder. However, the complexity of the process due to the presence of multiple processing parameters makes it very challenging to fabricate high-density parts without extensive experimental work. Therefore, the urge of having a reliable and efficient tool for predicting the printable space of parameters is crucial. In this work, a computationally inexpensive model is used to predict the clad geometry using the primary processing parameters in DED, including laser power, scanning speed, and mass flow rate. Single tack experiments were printed and characterized. The measured clad dimensions were used to augment the thermal model and generate 2D process maps at each powder flow rate.
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| Proceedings Inclusion? |
Definite: Post-meeting proceedings |