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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.<\/jats:p>","DOI":"10.1007\/s10845-024-02332-3","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T15:01:56Z","timestamp":1711033316000},"page":"1999-2016","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2294-6895","authenticated-orcid":false,"given":"Sean","family":"Rooney","sequence":"first","affiliation":[]},{"given":"Emil","family":"Pitz","sequence":"additional","affiliation":[]},{"given":"Kishore","family":"Pochiraju","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"issue":"11\u201312","key":"2332_CR1","doi-asserted-by":"publisher","first-page":"8211","DOI":"10.1007\/s00170-022-09278-x","volume":"120","author":"S Aidala","year":"2022","unstructured":"Aidala, S., Eichenberger, Z., Chan, N., Wilkinson, K., & Okwudire, C. 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