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Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views
|By Michigan Tech's Open Sustainability Technology Lab.
Wanted: Students to make a distributed future with solar-powered open-source 3-D printing.
Pearce Publications: Energy Conservation • Energy Policy • Industrial Symbiosis • Life Cycle Analysis • Materials Science • Open Source • Photovoltaic Systems • Solar Cells • Sustainable Development • Sustainability Education
|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
- Nuchitprasitchai, S., Roggemann, M. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002 open access
Prosumer (producing consumer)-based desktop additive manufacturing has been enabled by the recent radical reduction in 3-D printer capital costs created by the open-source release of the self-replicating rapid prototype (RepRap). To continue this success, there have been some efforts to improve reliability, which are either too expensive or lacked automation. A promising method to improve reliability is to use computer vision, although the success rates are still too low for widespread use. To overcome these challenges an open source low-cost reliable real-time optimal monitoring platform for 3-D printing from double cameras is presented here. This error detection system is implemented with low-cost web cameras and covers 360 degrees around the printed object from three different perspectives. The algorithm is developed in Python and run on a Raspberry Pi3 mini-computer to reduce costs. For 3-D printing monitoring in three different perspectives, the systems are tested with four different 3-D object geometries for normal operation and failure modes. This system is tested with two different techniques in the image pre-processing step: SIFT and RANSAC rescale and rectification, and non-rescale and rectification. The error calculations were determined from the horizontal and vertical magnitude methods of 3-D reconstruction images. The non-rescale and rectification technique successfully detects the normal printing and failure state for all models with 100% accuracy, which is better than the single camera set up only. The computation time of the non-rescale and rectification technique is two times faster than the SIFT and RANSAC rescale and rectification technique.
Real-time monitoring, 3D printing, Optical monitoring, RepRap, Open hardware, Quality assurance, 2-D reconstruction; error detection; reliability; computer analysis
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