|Michigan Tech's Open Sustainability Technology Lab.
Wanted: Students to make a distributed future with solar-powered open-source 3-D printing and recycling.
Pearce Publications: Energy Conservation • Energy Policy • Industrial Symbiosis • Life Cycle Analysis • Materials Science • Open Source • Photovoltaic Systems • Solar Cells • Sustainable Development • Sustainability Education
|This Open Source Appropriate Technology has been designed but not yet tested — use at your own risk.|
|This Open Source Appropriate Technology has been modeled.|
|This Open Source Appropriate Technology has been prototyped.|
|This Open Source Appropriate Technology has been verified by MOST|
- Aliaksei L. Petsiuk and Joshua M. Pearce. Open Source Computer Vision-based Layer-wise 3D Printing Analysis. Additive Manufacturing. (36) (2020), 101473, https://doi.org/10.1016/j.addma.2020.101473. https://arxiv.org/abs/2003.05660 open access
- Free and open source code: https://osf.io/8ubgn/
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.
Keywords[edit | edit source]
See also[edit | edit source]
- Integrated Voltage—Current Monitoring and Control of Gas Metal Arc Weld Magnetic Ball-Jointed Open Source 3-D Printer
- Low-cost Open-Source Voltage and Current Monitor for Gas Metal Arc Weld 3-D Printing
- Slicer and process improvements for open-source GMAW-based metal 3-D printing
- Open-source Lab
- Open source 3-D printing of OSAT
- Real-Time Eye State Detection System for Driver Drowsiness Using Convolutional Neural Network
|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 recycling • Green Distributed Recycling • Ethical Filament • LCA of distributed manufacturing • RepRap LCA Energy and CO2 • Solar-powered RepRaps • solar powered recyclebot • Feasibility hub • Mechanical testing • RepRap printing protocol: MOST• Lessons learned • MOST RepRap Build • MOST Prusa Build • MOST HS RepRap build • RepRap Print Server
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