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Generalisable 3D printing error detection and correction via multi-head neural networks.

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Pattinson, Sebastian W  ORCID logo


Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing automated approaches are not generalisable across different parts, materials, and printing systems. We train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. The automation of data acquisition and labelling allows the generation of a large and varied extrusion 3D printing dataset, containing 1.2 million images from 192 different parts labelled with printing parameters. The thus trained neural network, alongside a control loop, enables real-time detection and rapid correction of diverse errors that is effective across many different 2D and 3D geometries, materials, printers, toolpaths, and even extrusion methods. We additionally create visualisations of the network's predictions to shed light on how it makes decisions.



Humans, Neural Networks, Computer, Printing, Three-Dimensional

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Nat Commun

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Springer Science and Business Media LLC
Academy of Medical Sciences (SBF005\1014)
Department for Business, Energy and Industrial Strategy (EP/V062123/1)
Engineering and Physical Sciences Research Council (2274909)
Engineering and Physical Sciences Research Council (EP/N509620/1)
This work has been funded by the Engineering and Physical Sciences Research Council, UK Ph.D. Studentship EP/N509620/1 to D.A.J.B., Royal Society award RGS/R2/192433 to S.W.P., Academy of Medical Sciences award SBF005/1014 to S.W.P., Engineering and Physical Sciences Research Council award EP/V062123/1 to S.W.P., and an Isaac Newton Trust award to S.W.P.
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