About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Additive Manufacturing Fatigue and Fracture: Effects of Surface Roughness, Residual Stress, and Environment
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Presentation Title |
Characterizing Surface Roughness and Linking to Process Parameters in Powder Bed Fusion AM |
Author(s) |
Srujana Rao Yarasi, Elizabeth A Holm, Anthony D Rollett |
On-Site Speaker (Planned) |
Srujana Rao Yarasi |
Abstract Scope |
Additively manufactured surfaces have an inherent surface roughness which may be undesirable. In some applications, it can result in fatigue crack initiation sites while in others it serves to increase surface area available. There are multiple factors that affect surface roughness, including powder size and processing parameters. The measurement of surface roughness is a data rich problem that can benefit from characterization with machine learning techniques. AM rough surface characterization and subsequently, the relationships connecting the processing parameters to it are explored in this study. Build orientation is seen to affect the surface roughness and a Convolutional Neural Network (CNN) is used to distinguish between the differently oriented surfaces. The ability to control surface roughness using process parameters reduces the need for post-processing and furthers our understanding of its impact on mechanical properties. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, |