Data set for "Generalisable 3D printing error detection and correction via multi-head neural networks"

Name: Data set for "Generalisable 3D printing error detection and correction via multi-head neural networks"
Published: 2022-08-05T11:25:48Z
Keywords: 3D Printing, Additive Manufacturing, Computer Vision, Machine Learning

Brion, Douglas; Pattinson, Sebastian

doi:10.17863/CAM.84082

Data set for "Generalisable 3D printing error detection and correction via multi-head neural networks"

Simple item page

cam.depositDate	2022-05-01
datacite.contributor.supervisor	Pattinson, Sebastian William
datacite.issupplementto.doi	10.1038/s41467-022-31985-y
datacite.issupplementto.url	https://www.repository.cam.ac.uk/handle/1810/341089
dc.contributor.author	Brion, Douglas
dc.contributor.author	Pattinson, Sebastian
dc.contributor.orcid	Brion, Douglas [0000-0002-5361-2882]
dc.contributor.orcid	Pattinson, Sebastian [0000-0002-7851-7718]
dc.date.accessioned	2022-08-05T11:25:48Z
dc.date.available	2022-08-05T11:25:48Z
dc.date.updated	2022-05-01T20:09:20Z
dc.description	The dataset contains 1,272,273 labelled images of the the extrusion 3D printing process. A camera mounted next to the nozzle of the printer was used to capture images of material deposition for 192 different printed parts covering a range of geometries, material colours, and lighting conditions. Each image is labelled with: flow rate, lateral speed, Z offset, hotend temperature, hotend target temperature, bed temperature, timestamp, and nozzle tip x and y coordinates. To collect the data an automated pipeline was created to acquire and automatically label images from a fleet of 8 extrusion printers and to sample different combinations of printing parameters. The dataset provides a CSV of 948,396 pre-filtered images where complete failures, parameter outliers, dark images, and images just after parameter changes are removed. A raw CSV is also included labelling all images in the dataset. This dataset can be used for numerous applications such as real-time error detection, closed-loop control, and parameter prediction.
dc.description.sponsorship	This work has been funded by the Engineering and Physical Sciences Research Council (EP- SRC) PhD Studentship EP/N509620/1 to Douglas Brion, Royal Society award RGS/R2/192433 to Sebastian Pattinson, Academy of Medical Sciences award SBF005/1014 to Sebastian Pattinson, Engineering and Physical Sciences Research Council award EP/V062123/1 to Sebastian Pattinson, and An Isaac Newton Trust award to Sebastian Pattinson.
dc.format	No specific software is required for use. Data was generated on printers running Marlin 1.1.9 firmware and collected on a Python 3 server. Python was also used for sampling parameter combinations and cleaning/filtering the dataset after collection.
dc.identifier.doi	10.17863/CAM.84082
dc.identifier.uri	https://www.repository.cam.ac.uk/handle/1810/339869
dc.publisher.department	Department of Engineering
dc.rights	Attribution 4.0 International (CC BY 4.0)
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	3D Printing
dc.subject	Additive Manufacturing
dc.subject	Computer Vision
dc.subject	Machine Learning
dc.title	Data set for "Generalisable 3D printing error detection and correction via multi-head neural networks"
dc.type	Dataset
dcterms.format	csv, jpg
dcterms.relation	https://doi.org/10.17863/CAM.100129
pubs.funder-project-id	Academy of Medical Sciences (SBF005\1014)
pubs.funder-project-id	UK Research and Innovation (EP/V062123/1)
pubs.funder-project-id	Engineering and Physical Sciences Research Council (2274909)
pubs.licence-display-name	Apollo Repository Deposit Licence Agreement
pubs.licence-identifier	apollo-deposit-licence-2-1
rioxxterms.licenseref.uri	https://creativecommons.org/licenses/by/4.0/
rioxxterms.type	Other