TY - GEN
T1 - Detection of jamming and filament breakage in FDM using vibration of feeder stepper
AU - Rooney, Sean P.
AU - Pitz, Emil
AU - Pochiraju, Kishore
N1 - Publisher Copyright:
Copyright © 2021 by ASME
PY - 2021
Y1 - 2021
N2 - In the field of additive manufacturing (AM), mid-print failure is exceedingly common due to user error, bad design, or environmental factors that cannot be readily prepared for. This holds for most if not all types of AM, but perhaps none more so than the popular Filament Deposition Modeling (FDM) method machines. Absent total power failure, the bulk of the common modes of failure in FDM can be expressed as having an immediate impact on the mechanical system, whether that be a head collision due to warping, increased pressure on the stepper as it tries to push jammed filament, etc. The open loop nature of FDM machines does nothing to help the high rate of failure that FDM printers are known for compared to traditional methods of manufacturing. In this paper, a method for predicting failure due to mechanical malfunction of an FDM 3D printer is presented. The method proposed seeks to close the loop on FDM machines by characterizing the vibrations of the stepper motors which comprise an FDM machine. Using the acoustic emissions, a classifier is trained in order to assess the state of a print based off of supervised learning of known modes of failure. The resulting model is able to successfully predict jamming or air printing during a print with 90% training accuracy.
AB - In the field of additive manufacturing (AM), mid-print failure is exceedingly common due to user error, bad design, or environmental factors that cannot be readily prepared for. This holds for most if not all types of AM, but perhaps none more so than the popular Filament Deposition Modeling (FDM) method machines. Absent total power failure, the bulk of the common modes of failure in FDM can be expressed as having an immediate impact on the mechanical system, whether that be a head collision due to warping, increased pressure on the stepper as it tries to push jammed filament, etc. The open loop nature of FDM machines does nothing to help the high rate of failure that FDM printers are known for compared to traditional methods of manufacturing. In this paper, a method for predicting failure due to mechanical malfunction of an FDM 3D printer is presented. The method proposed seeks to close the loop on FDM machines by characterizing the vibrations of the stepper motors which comprise an FDM machine. Using the acoustic emissions, a classifier is trained in order to assess the state of a print based off of supervised learning of known modes of failure. The resulting model is able to successfully predict jamming or air printing during a print with 90% training accuracy.
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U2 - 10.1115/IMECE2021-71283
DO - 10.1115/IMECE2021-71283
M3 - Conference contribution
AN - SCOPUS:85124524964
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Advanced Materials
T2 - ASME 2021 International Mechanical Engineering Congress and Exposition, IMECE 2021
Y2 - 1 November 2021 through 5 November 2021
ER -