We continue to develop resources related to the COVID-19 pandemic. See COVID-19 initiatives on Appropedia for more information.

Open Source Computer Vision-based Layer-wise 3D Printing Analysis

From Appropedia
Jump to navigation Jump to search

Sunhusky.png By Michigan Tech's Open Sustainability Technology Lab.

Wanted: Students to make a distributed future with solar-powered open-source 3-D printing.
Currently looking for PhD or MSC student interested in solar energy policy- apply now!
Contact Dr. Joshua Pearce - Apply here

MOST: Projects & Publications, Methods, Lit. reviews, People, Sponsors, News
Updates: Twitter, Instagram, YouTube

OSL.jpg


Pearce Publications: Energy Conservation Energy Policy Industrial SymbiosisLife Cycle Analysis Materials Science Open Source Photovoltaic Systems Solar CellsSustainable Development Sustainability Education


Status
This OSAT has been designed but not yet tested - use at own risk.
This OSAT has been modeled.
This OSAT has been prototyped.
This OSAT has been verified by: MOST.

You can help Appropedia by contributing to the next step in this OSAT's status.

Source[edit]

Highlights[edit]

  • Developed a visual servoing platform using a monocular multistage image segmentation.
  • Presented algorithm prevents critical failures during additive manufacturing.
  • The developed system allows tracking printing errors on the interior and exterior.

Abstract[edit]

Cv3dp.PNG

The paper describes an open source computer vision-based hardware structure and software algorithm, which analyzes layer-wise the 3-D printing processes, tracks printing errors, and generates appropriate printer actions to improve reliability. This approach is built upon multiple-stage monocular image examination, which allows monitoring both the external shape of the printed object and internal structure of its layers. Starting with the side-view height validation, the developed program analyzes the virtual top view for outer shell contour correspondence using the multi-template matching and iterative closest point algorithms, as well as inner layer texture quality clustering the spatial-frequency filter responses with Gaussian mixture models and segmenting structural anomalies with the agglomerative hierarchical clustering algorithm. This allows evaluation of both global and local parameters of the printing modes. The experimentally-verified analysis time per layer is less than one minute, which can be considered a quasi-real-time process for large prints. The systems can work as an intelligent printing suspension tool designed to save time and material. However, the results show the algorithm provides a means to systematize in situ printing data as a first step in a fully open source failure correction algorithm for additive manufacturing.


Keywords[edit]

3-D printing, additive manufacturing; open-source hardware; RepRap; computer vision; quality assurance; real-time monitoring

See also[edit]


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