As 3D printing technology advances, the need for effective fault detection systems becomes more urgent. This master's thesis is dedicated to the development of a neural network-based fault detection system for 3D printing. The investigation encompasses an extensive review of existing databases and systems for defect detection in 3D printing. The main focus of the work is the development of a neural network capable of detecting faults and the subsequent testing of this network during the printing process. The final goal is the successful implementation of the system on an existing 3D printer with RPi4.

Contact: [Janko Slavič]

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