Elisa Cenedese
3D Printing Anomaly Detection: Implementation of a ML system using YOLOv5 and EfficientNet-Lite.
Rel. Tania Cerquitelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
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Abstract
3D printers use a technique called Additive Manufacturing (AM) to create a physical object, starting from the digital counterpart. This process works by dividing the digital design into multiple layers and printing the physical object layer by layer, extruding new material from time to time (usually ABS or PLA). The whole procedure typically takes a long time and is not uncommon for the printing process to give an unsatisfactory result. Specifically, it may happen that, for various reasons, the final printed product contains one or more anomalies. When this happens, depending on the type of anomaly that has occurred and the context of use of the final product, the object could be discarded, leading to a considerable loss in terms of time and cost.
Therefore, the faster the anomaly is detected, the better
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