Generalisable 3D printing error detection and correction via neural networks

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• Sebastian Pattinson, Assistant Professor in the Department of Engineering at the University of Cambridge
• Thursday 01 December 2022, 14:00-15:00
• Department of Engineering / Online (Zoom).

If you have a question about this talk, please contact Fulvio Forni.

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. In this talk I will discuss recent work in our lab where 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.

The seminar will be held in LR5 , Baker Building, Department of Engineering, and online (zoom): https://us06web.zoom.us/j/87986687566?pwd=MGJScmMwd2lwT0tVMHNmWmxSa05XZz09

This talk is part of the CUED Control Group Seminars series.

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