• Data set for 'Generalisabl...

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

Title:
Data set for 'Generalisable 3D printing error detection and correction via multi-head neural networks'
Authors:
Brion, Douglas, Pattinson, Sebastian
Publisher Information:
Department of Engineering 2022-08-05T11:25:48Z 2022-08-05T11:25:48Z 2022-05-01T20:09:20Z
Document Type:
E-Ressource Electronic Resource
Index Terms:
3D Printing, Additive Manufacturing, Computer Vision, Machine Learning, Dataset
URL:
https://www.repository.cam.ac.uk/handle/1810/339869
https://doi.org/10.1038/s41467-022-31985-y
https://www.repository.cam.ac.uk/handle/1810/341089
Availability:
Open access content. Open access content
Attribution 4.0 International (CC BY 4.0)
https://creativecommons.org/licenses/by/4.0
Note:
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.
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Other Numbers:
HS1 oai:www.repository.cam.ac.uk:1810/339869
10.17863/CAM.84082
1488456353
Contributing Source:
UNIV OF CAMBRIDGE
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1488456353
Database:
OAIster

Weitere Informationen

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.
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.