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

Sean Rooney (), Emil Pitz and Kishore Pochiraju
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Sean Rooney: Castle Point on Hudson
Emil Pitz: Castle Point on Hudson
Kishore Pochiraju: Castle Point on Hudson

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 25, 1999-2016

Abstract: 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.

Keywords: Additive manufacturing; Non-destructive evaluation; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02332-3

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