About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Symposium
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2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Presentation Title |
NEW TIME (was 2:30 pm) Data-driven Approach for Printability Evaluation for Additively Manufactured Metal Alloys |
Author(s) |
Sofia Sheikh, David Shoukr, Raymundo Arroyave, Alaa Elwany, Ibrahim Karaman |
On-Site Speaker (Planned) |
Sofia Sheikh |
Abstract Scope |
To design a part specific to AM, many of the variables need to be controlled due to the larger amount of material and processing parameters that need to be optimized. To capture the material and process space needed to fabricate builds using AM, an AM database is introduced. The database tool is a structural way of capturing data to build models that can simulate the AM process and accelerate materials design in metal AM. Using a subset of the data and features, a modern machine learning approach is used to construct printability maps to build porosity-free parts. Printability maps of material systems, AF9628, NiNb5, and Ni50.3Ti were produced with distinct areas of defects, namely lack of fusion, keyholing, and balling. Furthermore, the printability maps constructed were compared to maps that were obtained using an analytical thermal model called the Eagar-Tsai model and calibrated with experiments. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |