Low-cost automatic identification of nozzle clogging in material extrusion 3D printers

摘要

This work focuses on automated recognition of clogging in the nozzle of a typical material extrusion 3D printer, a fault that has potentially severe consequences for both the printer and the printed part, should it not be rectified in a timely manner. Several methods were considered initially, such as monitoring temperature or vibrations at low-cost, but they were ineffective for different reasons explained. Thus, an automated monitoring system was devised to analyze the acoustic signals generated by driver gear’s slippage on the filament during the blocking phase. Instead of Fast Fourier spectrum based solutions which were unreliable, the Goertzel algorithm was used. Thus, the unique frequency of the sound emitted under clogging conditions previously identified is detected fast enough for real time response with a low cost microcontroller. The algorithm was proved to be reliable through experimental testing performed on a typical 3D printer of the material extrusion type.