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

Change log

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.

Software / Usage instructions
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.
3D Printing, Additive Manufacturing, Computer Vision, Machine Learning
Academy of Medical Sciences (SBF005\1014)
Engineering and Physical Sciences Research Council (EP/V062123/1)
Engineering and Physical Sciences Research Council (2274909)
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.