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==Literature Review== | ==Literature Review== | ||
1. ''Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M.'' '''[https://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 <ref>Nuchitprasitchai, S., Roggemann, M.C. & 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</ref> | ====2017==== | ||
1. ''Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M.'' '''[https://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. <ref>Nuchitprasitchai, S., Roggemann, M.C. & 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.</ref> | |||
'''Abstract''' 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. | '''Abstract''' 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. | ||
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2. | 2. ''Pierre Alfred Leon Ciraud.'' '''[https://patents.google.com/patent/DE2263777A1/en A method and apparatus for manufacturing objects made of any arbitrary material meltable.]''' German patent application DE2263777A1. December 28, 1971. <ref>Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.</ref> | ||
4. | The first patent in the field of additive manufacturing. | ||
3. [http://wohlersassociates.com/2018report.htm '''Wohlers Report.'''] Annual worldwide progress report in 3D Printing, 2018. <ref>Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.</ref> | |||
4. ''Frank Thewihsen et al.'' [https://www.ey.com/Publication/vwLUAssets/ey-3d-printing-report/$FILE/ey-3d-printing-report.pdf '''EY’s Global 3D printing Report.'''] 2016. <ref>Frank Thewihsen et al. EY’s Global 3D printing Report, 2016.</ref> | |||
5. SONY IMX322 Datasheet [https://dashcamtalk.com/cams/lk-7950-wd/Sony_IMX322.pdf] | 5. SONY IMX322 Datasheet [https://dashcamtalk.com/cams/lk-7950-wd/Sony_IMX322.pdf] | ||
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7. Marlin Open-Source RepRap Firmware. [http://marlinfw.org/] | 7. Marlin Open-Source RepRap Firmware. [http://marlinfw.org/] | ||
==References== | |||
Revision as of 20:44, 20 May 2019
Literature Review
2017
1. Nuchitprasitchai, S., Roggemann, M.C. & 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. [1]
Abstract 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.
Notes
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2. Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971. [2]
The first patent in the field of additive manufacturing.
3. Wohlers Report. Annual worldwide progress report in 3D Printing, 2018. [3]
4. Frank Thewihsen et al. EY’s Global 3D printing Report. 2016. [4]
5. SONY IMX322 Datasheet [1]
6. R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.
7. Marlin Open-Source RepRap Firmware. [2]
References
- ↑ Nuchitprasitchai, S., Roggemann, M.C. & 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.
- ↑ Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.
- ↑ Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.
- ↑ Frank Thewihsen et al. EY’s Global 3D printing Report, 2016.