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Split 3d model in simplify 3d8/4/2023 ![]() ![]() ![]() We additionally create visualisations of the network’s predictions to shed light on how it makes decisions. 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 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. We train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. ![]() Existing automated approaches are not generalisable across different parts, materials, and printing systems. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. ![]()
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