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

Jiaqi Lyu, Javid Akhavan, Souran Manoochehri

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Article number107852
JournalData in Brief
Volume41
DOIs
StatePublished - Apr 2022

Keywords

  • 3D printing
  • Anomaly detection
  • FFF machine optimization
  • Laser surface profiling
  • Point cloud
  • Process monitoring
  • Shallow and deep learning
  • Smart manufacturing

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