J.M.Pearce (talk | contribs) (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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| type = Paper | |||
| 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/ | |||
{{Pearce publications notice}} | |||
{{MOST-RepRap}} | {{MOST-RepRap}} | ||
== | == Keywords == | ||
[[Real-time monitoring]], [[3D printing]], Optical monitoring, [[RepRap]], [[Open hardware]], Quality assurance, 2-D reconstruction; error detection; reliability; computer analysis | |||
== | == See also == | ||
{{FAST-CV}} | |||
* [[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]] | ||
{{Page data | |||
| title-tag = Real-Time Monitoring of 3-D Printing Using Computer Analysis | |||
}} | |||
[[Category:MOST completed projects and publications]] | [[Category:MOST completed projects and publications]] |
Latest revision as of 16:02, 23 February 2024
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/
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]
- 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
- Viability of Distributed Manufacturing of Bicycle Components with 3-D Printing: CEN Standardized Polylactic Acid Pedal Testing