A shortcoming noted in fused deposition
modelling (FDM) 3D printing technology refers to lack of
intelligent monitoring and intervention during the printing
process. Fail prints can still occur during the printing procedure
even though the printer is of industrial grade and far more
expensive than that of hobby grades. Under extrusion has been
determined as one of the frequent failures in 3D printing. Such
failure stems from insufficient extrusion rate and/or inadequate
melting temperature of filament during the print. Under
extrusion failure may result in undesired layer gaps, missing
layers, unbalanced layers, and even holes in the printed models
that would make the models completely unusable. Hence, an
effective method that can reduce waste materials and overall
costs is by integrating artificial intelligence (AI) into 3D
printers. However, a large dataset is required prior to the
training process of deep learning. Hence, this study proposes an
automated and continuous data collection of under extrusion
samples in FDM 3D printers using Raspberry Pi and webcam.
As a result, adjustment of the G-code of the standard tessellation
language (STL) models and repeated process of printing 3D
models can effectively achieve the desired images
