AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals

Abstract

Part defects in additive manufacturing are more frequent compared to machining or molding. Failures can go unnoticed for hours, wasting resources and extending process cycle times. This paper describes a Machine Learning based method for automated sensing of onset failure in additive manufacturing machinery. Investigations are conducted on a Fused Filament Fabrication (FFF) 3D printer, and the same methods are then applied to a digital light processing 3D printer. The investigation focuses on signal-based analysis, specifically passive sensing of stepper motors relating DC current measurements to the torque on a stepper, as opposed to any active acoustic interrogation of the part. Passive methods are used to characterize the loading on a feeder stepper in an FFF machine, forming a model that can identify early signs of filament-based failure with 85.65% 10-fold cross-validation accuracy. Efforts show filament breakage can be detected minutes before material runout would cause a defect, allowing ample time to pause, correct, or control the print. The machine learning pipeline was not naively conceived but optimized through automated machine learning.