(Created page with " {{MOST}} {{Pearce-pubs}} {{MOST-RepRap}} ==Source== * Nuchitprasitchai, S., Roggemann, M. & Pearce, J.M. [http://www.mdpi.com/2504-4494/1/1/2 Three Hundred and Sixty Degree...")
 
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| cite-as = Nuchitprasitchai, S., Roggemann, M. & Pearce, J.M. [http://www.mdpi.com/2504-4494/1/1/2 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 [https://www.academia.edu/33773260/Three_Hundred_and_Sixty_Degree_Real-Time_Monitoring_of_3-D_Printing_Using_Computer_Analysis_of_Two_Camera_Views open access]
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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.
 
* Open source code : https://osf.io/b9h7y/
* Models : https://osf.io/utp6g/
 
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==Source==
== Keywords ==
* Nuchitprasitchai, S., Roggemann, M. & Pearce, J.M. [http://www.mdpi.com/2504-4494/1/1/2 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 (registering DOI) [open access]
** Open source code :
** Models :
 
[[image:360siranee.jpg ‎|right|500px]]


==Abstract==
[[Real-time monitoring]], [[3D printing]], Optical monitoring, [[RepRap]], [[Open hardware]], Quality assurance, 2-D reconstruction; error detection; reliability; computer analysis
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.


==Keywords==
== See also ==
[[Real-time monitoring]], [[3D printing]], Optical monitoring, [[RepRap]], [[Open hardware]], Quality assurance,  2-D reconstruction; error detection; reliability; computer analysis


{{FAST-CV}}


==See Also==
* [[Factors effecting real-time optical monitoring of fused filament 3D printing]]
* [[Mechanical Properties of Components Fabricated with Open-Source 3-D Printers Under Realistic Environmental Conditions]]
* [[Mechanical Properties of Components Fabricated with Open-Source 3-D Printers Under Realistic Environmental Conditions]]
* [[The Effects of PLA Color on Material Properties of 3-D Printed Components]]
* [[The Effects of PLA Color on Material Properties of 3-D Printed Components]]
* [[Viability of Distributed Manufacturing of Bicycle Components with 3-D Printing: CEN Standardized Polylactic Acid Pedal Testing]]
* [[Viability of Distributed Manufacturing of Bicycle Components with 3-D Printing: CEN Standardized Polylactic Acid Pedal Testing]]
* [[Applications of RepRap distributed production - literature review]]


{{Page data
| title-tag = Real-Time Monitoring of 3-D Printing Using Computer Analysis
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[[Category:MOST completed projects and publications]]
[[Category:MOST completed projects and publications]]

Latest revision as of 16:02, 23 February 2024

360siranee.jpg
FA info icon.svg Angle down icon.svg Source data
Type Paper
Cite as Citation reference for the source document. 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.

Keywords[edit | edit source]

Real-time monitoring, 3D printing, Optical monitoring, RepRap, Open hardware, Quality assurance, 2-D reconstruction; error detection; reliability; computer analysis

See also[edit | edit source]

FA info icon.svg Angle down icon.svg Page data
Authors Joshua M. Pearce
License CC-BY-SA-3.0
Language English (en)
Related 0 subpages, 19 pages link here
Impact 446 page views
Created July 5, 2017 by Joshua M. Pearce
Modified February 23, 2024 by Maintenance script
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