ABSTRACT
This work examines the use of accelerometers to identify vibrational patterns that can effectively predict the state of a 3D printer, which could be useful for predictive maintenance. Prototypes using both a simple rectangular shape and a more complex Octopus shape were fabricated and evaluated. Fast Fourier Transform, Spectrogram, and machine learning models, such as Principal Component Analysis and Support Vector Machine, were employed for data analysis. The results indicate that vibrational signals can be used to predict the state of a 3D printer. However, the position of the accelerometers is crucial for vibration-based fault detection. Specifically, the sensor closest to the nozzle could predict the state of the 3D printer faster at a 71% greater sensitivity compared to sensors mounted on the frame and print bed. Therefore, the model presented in this study is appropriate for vibrational fault detection in 3D printers.
