Cited by 489Additive Manufacturing Materials and ProcessesIndustrial Vision Systems and Defect DetectionAnomaly Detection Techniques and Applications

Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm

Scime, Luke, Beuth, Jack·2018·Additive Manufacturing

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Crossref · 10.1016/j.addma.2017.11.009

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26 of 26 references verified0 citation errors · 0 not checkable

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  • ML is applied to improve AM processes through failure detection or material design.

    “ML is applied to improve those processes, for example through failure detection [722,730] or material design [309].”

    From Tackling climate change with machine learning (2023) · cites this paper as [722]

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Authors
Scime, L., & Beuth, J.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Scime, Luke
Venue
Additive Manufacturing
Publisher
Elsevier BV
Published
2018-01
Volume
19
Pages
114-126
ISSN
  • 2214-7810 (ISSN-L)
  • 2214-8604
OpenAlex
W2769988472
Cited by
489
References
26
Citations by year
2026: 47 · 2025: 63 · 2024: 74 · 2023: 73 · 2022: 69 · 2021: 80 · 2020: 49 · 2019: 22 · 2018: 12
FWCI
17.64
Type
journal article
Language
en
Open access
closed
Locations
Additive manufacturing (publishedVersion)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Keywords
laser powder bed fusion, additive manufacturing process monitoring, anomaly detection, anomaly classification, powder spreading, in situ monitoring, real-time control, unsupervised learning, image patches, computer vision

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