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 |
MACHINE LEARNING IN ADDITIVE MANUFACTURING: A REVIEW OF LEARNING TECHNIQUES AND TASKS |
Author(s) |
John A. Pike, James Klett, Vlastimil Kunc, Chad Duty |
On-Site Speaker (Planned) |
John A. Pike |
Abstract Scope |
Due to recent advances, Machine Learning (ML) has gained attention in the Additive Manufacturing (AM) community as new ways to improve AM with ML are explored. The capability of ML to solve tasks such as classification, regression, and clustering provide possibilities to improve AM in every step of the process. In the design phase, ML can be used to optimize part design with respect to topology, material selection, and shape deviation compensation. AM processing can benefit from process parameter optimization and the prediction of part properties. Lastly, one of the main areas ML can impact AM is through in-situ process monitoring to detect defects and determine part quality. This presentation aims to describe some of the ML techniques being employed in AM, and demonstrate how they can be used. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |