Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024 |
| Seiten | 42-48 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9780645832211 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 41st International Symposium on Automation and Robotics in Construction, ISARC 2024 - Lille, Frankreich Dauer: 3 Juni 2024 → 5 Juni 2024 |
Publikationsreihe
| Name | Proceedings of the International Symposium on Automation and Robotics in Construction |
|---|---|
| ISSN (elektronisch) | 2413-5844 |
Abstract
Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Bauwesen
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Artificial intelligence
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- BibTex
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Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024. 2024. S. 42-48 (Proceedings of the International Symposium on Automation and Robotics in Construction).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Inline image-based reinforcement detection for concrete additive manufacturing processes using a convolutional neural network
AU - Lachmayer, Lukas
AU - Dittrich, Lars
AU - Recker, Tobias
AU - Dörrie, Robin
AU - Kloft, Harald
AU - Raatz, Annika
PY - 2024
Y1 - 2024
N2 - Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
AB - Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
KW - Additive Manufacturing
KW - Image Processing
KW - Neural Network
KW - Printing Robot
KW - Process Control
UR - http://www.scopus.com/inward/record.url?scp=85199609362&partnerID=8YFLogxK
U2 - 10.22260/ISARC2024/0007
DO - 10.22260/ISARC2024/0007
M3 - Conference contribution
AN - SCOPUS:85199609362
T3 - Proceedings of the International Symposium on Automation and Robotics in Construction
SP - 42
EP - 48
BT - Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024
T2 - 41st International Symposium on Automation and Robotics in Construction, ISARC 2024
Y2 - 3 June 2024 through 5 June 2024
ER -