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 |
Multi-Phenomena Data Fusion for Enhanced Process Monitoring in Laser Powder Bed Fusion (LPBF) |
Author(s) |
Aniruddha Gaikwad, Harry DeWinton, Ben Bevans, Ziyad Smoqi, Paul Hooper, Prahalad K. Rao |
On-Site Speaker (Planned) |
Prahalad K. Rao |
Abstract Scope |
In this work, to improve flaw detection performance in LPBF, we adopted a data fusion approach that captures multiple process phenomena. Cylindrical specimens were produced with different laser spot sizes, emulating laser defocusing due to thermal lensing. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and predict porosity type and severity. The data fusion approach significantly reduced the overall false positive rate from ~ 0.1 to ~0.001 without sacrificing the true positive rate (~ 0.90). These results were at par with deep machine learning approach. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |