Single system for online monitoring and inspection of automated fiber placement with object segmentation by artificial neural networks Article Swipe
Marco Brysch
,
Mohammad Bahar
,
Hans Christoph Hohensee
,
Michael Sinapius
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s10845-022-01924-1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s10845-022-01924-1
The reduction of material defects in the automated fiber placement process is one of the significant factors for manufacturing large and complex components more efficiently in the future. However, the monitoring of complex manufacturing processes usually requires complex sensor and computer systems that are often quite sensitive to disturbances and errors. New techniques such as image segmentation with neural networks provide a new approach to this problem and have the potential to solve complex processes faster and more robustly. In this study, a system is presented that performs monitoring, inspection and measurement tasks simultaneously in automated fiber placement processes. The system is based on the SiamMask network which is used for the automatic image processing. The artificial neural network is trained to recognize individual carbon fiber tapes and segment them for additional analysis. For the creation of the testing- and training data, an analytical approach is presented. The post-processing of the object segmentation, which is the primary output of the SiamMask network and the identification of individual tapes, provides accurate measurements which are demonstrated by an example. We show that image segmentation with modern approaches like SiamMask offers great potential to handle highly complex engineering tasks in a faster and more intelligent manner in comparison to conventional methods.
Related Topics
Concepts
Artificial intelligence
Artificial neural network
Segmentation
Process (computing)
Computer science
Image segmentation
Identification (biology)
Computer vision
Automated X-ray inspection
Image processing
Pattern recognition (psychology)
Object (grammar)
Image (mathematics)
Operating system
Botany
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10845-022-01924-1
- https://link.springer.com/content/pdf/10.1007/s10845-022-01924-1.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280522662
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4280522662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10845-022-01924-1Digital Object Identifier
- Title
-
Single system for online monitoring and inspection of automated fiber placement with object segmentation by artificial neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-05-17Full publication date if available
- Authors
-
Marco Brysch, Mohammad Bahar, Hans Christoph Hohensee, Michael SinapiusList of authors in order
- Landing page
-
https://doi.org/10.1007/s10845-022-01924-1Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10845-022-01924-1.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10845-022-01924-1.pdfDirect OA link when available
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Artificial intelligence, Artificial neural network, Segmentation, Process (computing), Computer science, Image segmentation, Identification (biology), Computer vision, Automated X-ray inspection, Image processing, Pattern recognition (psychology), Object (grammar), Image (mathematics), Operating system, Botany, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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