A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing

Lui, Chun Fai; Maged, Ahmed; Xie, Min

doi:10.1007/s10845-023-02232-y

J Intell Manuf

2023

DOI: 10.1007/s10845-023-02232-y

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A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing

Chun Fai Lui,

Ahmed Maged,

Min Xie

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2024

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Cited by 5 publications

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Enhancing additive manufacturing with computer vision: a comprehensive review

Yu,

Li,

et al. 2024

Int J Adv Manuf Technol

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Enhancing additive manufacturing with computer vision: a comprehensive review

Yu,

Li,

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

A review of machine learning in additive manufacturing: design and process

Chen,

Zhang,

Yan

et al. 2024

Int J Adv Manuf Technol

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

Rooney,

Pitz,

Pochiraju

2024

J Intell Manuf

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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.

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A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing

Cited by 5 publications

References 54 publications

Enhancing additive manufacturing with computer vision: a comprehensive review

Enhancing additive manufacturing with computer vision: a comprehensive review

A review of machine learning in additive manufacturing: design and process

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

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