TY - JOUR
T1 - Image-based dataset of artifact surfaces fabricated by additive manufacturing with applications in machine learning
AU - Lyu, Jiaqi
AU - Akhavan, Javid
AU - Manoochehri, Souran
N1 - Publisher Copyright:
© 2022
PY - 2022/4
Y1 - 2022/4
N2 - Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is the most widely used type of Additive Manufacturing (AM) technology at the consumer level. This technology severely suffers from a lack of online quality assessment and process adjustment. To fill up this gap, a high-speed 2D Laser Profiler KEYENCE LJ-V7000 series is equipped above an FDM machine and performs a scan after each print layer. The point cloud of the upper surface will be processed and transformed into a 2D depth map to analyze the in-plane anomalies during the FDM fabrication process. The author used the above data to categorize the surface quality into four categories: under printing, over printing, normal, and empty regions. The author showed the effectiveness of data in detecting print anomalies, and further work can be done to show the application of more advanced algorithms towards a better detection accuracy or to present a novel way to approach the data and detect a broader range of anomalies.
AB - Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is the most widely used type of Additive Manufacturing (AM) technology at the consumer level. This technology severely suffers from a lack of online quality assessment and process adjustment. To fill up this gap, a high-speed 2D Laser Profiler KEYENCE LJ-V7000 series is equipped above an FDM machine and performs a scan after each print layer. The point cloud of the upper surface will be processed and transformed into a 2D depth map to analyze the in-plane anomalies during the FDM fabrication process. The author used the above data to categorize the surface quality into four categories: under printing, over printing, normal, and empty regions. The author showed the effectiveness of data in detecting print anomalies, and further work can be done to show the application of more advanced algorithms towards a better detection accuracy or to present a novel way to approach the data and detect a broader range of anomalies.
KW - 3D printing
KW - Anomaly detection
KW - FFF machine optimization
KW - Laser surface profiling
KW - Point cloud
KW - Process monitoring
KW - Shallow and deep learning
KW - Smart manufacturing
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U2 - 10.1016/j.dib.2022.107852
DO - 10.1016/j.dib.2022.107852
M3 - Article
AN - SCOPUS:85123346771
VL - 41
JO - Data in Brief
JF - Data in Brief
M1 - 107852
ER -