Image-based dataset of artifact surfaces fabricated by additive manufacturing with applications in machine learning

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. (C) 2022 The Author(s). Published by Elsevier Inc.

Automated summary

Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is a widely used Additive Manufacturing (AM) technology. However, online quality assessment and process adjustment for FDM are lacking. In this study, a high-speed 2D Laser Profiler was used to scan the upper surface after each print layer, and the data was processed to analyze in-plane anomalies. The author categorized the surface quality into four categories and demonstrated the effectiveness in detecting print anomalies.