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Project data
Authors Aliaksei L. Petsiuk
Joshua M. Pearce
Location London, ON
Status Designed
Modelled
Prototyped
Verified
Verified by FAST
Completed 2022
Made? Yes
Instance of 3D Printing
AI
computer vision
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This study presents an open source method for detecting 3D printing anomalies by comparing images of printed layers from a stationary monocular camera with G-code-based reference images of an ideal process generated with Blender, an open source free physics rendering engine. Recognition of visual deviations was accomplished by analyzing the similarity of histograms of oriented gradients (HOG) of local image areas. The developed technique requires preliminary modeling of the working environment to achieve the best match for orientation, color rendering, lighting, and other parameters of the printed part. The output of the algorithm is a level of mismatch between printed and synthetic reference layers. Twelve similarity and distance measures were implemented and compared for their effectiveness at detecting 3D printing errors on six different representative failure types (local infill defects, presence of a foreign body in the layer, spaghetti problem, separation and shift of the printing part from the working surface, defects in thin walls, and layer shift) and their control error-free print images. The results show that although Kendall’s tau, Jaccard, and Sorensen similarities are the most sensitive, Pearson's r, Spearman's rho, cosine, and Dice similarities produce the more distinguishable results. This open source method allows the program to notice critical errors in the early stages of their occurrence and either pause manufacturing processes for further investigation by an operator or in the future intelligent automatic error correction. The implementation of this novel method does not require preliminary data for training, and the greatest efficiency can be achieved with the mass production of parts by either additive or subtractive manufacturing of the same geometric shape. This open-source method has the potential means of enabling smart distributed recycling for additive manufacturing in challenging environments.

Source


Highlights[edit | edit source]

  • In-situ layer-wise 3D printing anomaly detection is based on reference images.
  • G-code-based synthetic references are created in a physics rendering engine.
  • The system analyzes the similarity of local histograms of oriented gradients.
  • Failure detection resolution is 5-10% of the entire observation area.
  • The method allows noticing critical errors in the early stages of their occurrence.


3-D printing, additive manufacturing; open-source hardware; RepRap; computer vision; quality assurance; real-time monitoring; anomaly detection; Blender; HOG; synthetic images

See also[edit | edit source]


This page is part of an international project to use RepRap 3-D printing to make OSAT for sustainable development. Learn more.

Research: Open source 3-D printing of OSAT RecycleBot LCA of home recyclingGreen Distributed Recycling Ethical Filament LCA of distributed manufacturingRepRap LCA Energy and CO2 Solar-powered RepRapssolar powered recyclebot Feasibility hub Mechanical testingRepRap printing protocol: MOST Lessons learnedMOST RepRap BuildMOST Prusa BuildMOST HS RepRap buildRepRap Print Server


Make me: Want to build a MOST RepRap? - Start here!Delta Build Overview:MOSTAthena Build OverviewMOST metal 3-D printer Humanitarian Crisis Response 3-D Printer



Page data
Part of FAST Completed
Type Project
Keywords real-time monitoring, anomaly detection, blender, hog, synthetic images, 3d printing
SDG Sustainable Development Goals SDG09 Industry innovation and infrastructure
Authors Joshua M. Pearce
Published 2022
License CC-BY-SA-4.0
Affiliations Free Appropriate Sustainable Technology, Western
Language English (en)
Impact Number of views to this page and its redirects. Updated once a month. Views by admins and bots are not counted. Multiple views during the same session are counted as one. 18
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