Material Extrusion is the fundamental idea behind Fused Deposition Modeling, the most widespread Additive Manufacturing (AM) method

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Material Extrusion is the fundamental idea behind Fused Deposition Modeling, the most widespread Additive Manufacturing (AM) method. It enables in-house rapid prototyping of previously infeasible geometries, accelerating industries such as biomedical, aerospace. On the downside, the technology is limited as it is prone to errors, mostly in the form of low dimensional and geometric tolerances. More sever defects are also common, such as warping, over-extrusion, cracking and stringing. Usually, a human worker is essential, to monitor the process as it happens and follow a trial-and-error approach whenever something goes wrong. This requirement significantly adds to the operating costs and is time consuming, hindering the wider adaptation of the technology. The suggested project will aim to investigate how Artificial Intelligence can help automate the involved repetitive tasks while benefiting from AM's digital nature. Current research focuses on a recent breakthrough known as CAXTON, which enables the in-situ monitoring of the extrusion process using image sensors installed on multiple networked 3D printers. This enables quick and diverse data collection. By applying well established and state-of-art machine learning models on the collected data, different approaches will be explored and their performances will be reported.