About this Abstract |
Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
|
Symposium
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Poster Session
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Presentation Title |
A Deep Learning Approach to Defect Detection in Additive Manufacturing of Titanium Alloys |
Author(s) |
Xiao Liu, Alessandra Mileo |
On-Site Speaker (Planned) |
Xiao Liu |
Abstract Scope |
In Additive Manufacturing (AM) of titanium alloys, the formation of defects in parts is typically related to the stability of the meltpool. With increased instability and size of the meltpool comes an increase in the level of emissions generated as the laser processes the material. Recent developments in in-situ monitoring and process control allows the collection of large amounts of data during the printing process. This includes data about emissions, which are made available as 2D representations in the form of colour images. However, it is still a manual process to inspect these 2D representations to identify defects, which does not scale. Given recent advances in Deep Learning for computer vision and the availability of large amounts of data collected from in-situ monitoring, our approach to leverage Deep Learning techniques to characterise abnormal emissions to automatically identify defects during the printing process. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |