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

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  • Douglas A. J. Brion

    (University of Cambridge)

  • Sebastian W. Pattinson

    (University of Cambridge)

Abstract

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.

Suggested Citation

  • Douglas A. J. Brion & Sebastian W. Pattinson, 2022. "Generalisable 3D printing error detection and correction via multi-head neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31985-y
    DOI: 10.1038/s41467-022-31985-y
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    References listed on IDEAS

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    1. Tingting Huang & Shanggang Wang & Shunkun Yang & Wei Dai, 2021. "Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2181-2196, December.
    2. Ketai He & Qian Zhang & Yili Hong, 2019. "Profile monitoring based quality control method for fused deposition modeling process," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 947-958, February.
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