Project data
Authors Aliaksei L. Petsiuk
Joshua M. Pearce
Status Designed
Verified by MOST
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Type Project, Device
Keywords 3d printing, additive manufacturing, open-source hardware, RepRap, computer vision, quality assurance, real-time monitoring
Authors Joshua M. Pearce
Published 2020
License CC-BY-SA-4.0
Impact Number of views to this page. Views by admins and bots are not counted. Multiple views during the same session are counted as one. 464
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Tctscover.png Create-Joshua-Pearce.png Pearce Publications
Energy Conservation Energy Policy Industrial SymbiosisLife Cycle Analysis Materials Science Open SourceMedical Photovoltaic Systems Solar CellsSustainable Development Sustainability Education

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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.


Highlights[edit | edit source]

  • 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.

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

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

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In the News[edit | edit source]

  1. MTU's Joshua Pearce develops open source, computer vision-based print correction algorithm 3D Printing Industry 80k, Nanhixiong 28k
  2. Michigan Tech Develops Open Source Smart Vision for 3D Printing Quality Control 3d Print 70k ,NewsBreak 2137
  3. Исследователи из MTU разрабатывают программу для обнаружения и исправления дефектов 3D-печати 3DToday (Russia)73k
  4. MTUの研究チームが3Dプリント時に発生するエラーを検出し自動補正するアルゴリズムを開発Idarts Japan 98k
  5. Joshua Pearce e Aliaksei Petsiuk, hanno sviluppato un algoritmo software open source basato sulla visione artificiale in grado di rilevare e correggere gli errori di stampa Stampare in 3D (Italian)
  6. Open Source Algorithmus soll Druckfehler verhindern 3D Ruck German
  7. Michigan Tech entwickelt Open Source Smart Vision für die Qualitätskontrolle beim 3D-Druck 3D Ruck German
  8. Mesterséges intelligencia javítja ki a nyomtatási hibákat Freedee 12k (Hungarian)
  9. Open Source 3D Print Quality Control Vision System Metrology News
  10. 3D打印机+大数据算法,能彻底纠正打印错误提高打印成功率? AAU3D China
  11. 3.3D印表機+大數據算法,能徹底糾正列印錯誤提高列印成功率?KKNews 805
  12. Researchers achieve 6.35x part strength increases with new non-planar FDM framework 3D Printing Industry 76k
  13. Authentise and Addiguru partner to merge in-situ process monitoring and workflow management 3D Printing Industry 72k

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