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)
|
Presentation Title |
Exploring Machine Learning Classification of Porosity from Infrared Signatures During Laser-powder Bed Fusion |
Author(s) |
Matthew A. Roach, Leah Jacobs, Grant Wilmoth, Brett Brady, Caleb Campbell, Bradley H. Jared, Anahita Khojandi |
On-Site Speaker (Planned) |
Matthew A. Roach |
Abstract Scope |
Internal defects are commonly formed during laser powder bed fusion (L-PBF) additive manufacturing of components. One common defect is porosity which is normally found after the manufacturing process using methods such as computed tomography (CT) or destructive sampling. Prior art in this field used costly cameras to explore specific defects in defined locations within a sample. This research uses real-world computed tomography data and a lower resolution infrared (IR) camera. Machine learning is used to relate the layer-wise IR images to layer-wise CT porosity to shed light on the in-situ conditions in PBF-LB additive manufacturing that result in non-defective parts. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |