  With the advent of 3D printers in different price ranges and sizes, they are
no longer just for professionals. However, it is still challenging to use a 3D
printer perfectly. Especially, in the case of the Fused Deposition Method, it
is very difficult to perform with accurate calibration. Previous studies have
suggested that these problems can be detected using sensor data and image data
with machine learning methods. However, there are difficulties to apply the
proposed method due to extra installation of additional sensors. Considering
actual use in the future, we focus on generating the lightweight early
detection model with easily collectable data. Proposed early detection model
through Convolutional Neural Network shows significant fault classification
accuracy with 96.72% for the binary classification task, and 93.38% for
multi-classification task respectively. By this research, we hope that general
users of 3D printers can use the printer accurately.
