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