Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network
This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execut...
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| Format: | Article |
| Language: | English |
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Czech Technical University in Prague
2023-10-01
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| Series: | Acta Polytechnica CTU Proceedings |
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| Online Access: | https://ojs.cvut.cz/ojs/index.php/APP/article/view/9407 |
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| _version_ | 1850059100590702592 |
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| author | Miroslav Yosifov Patrick Weinberger Bernhard Plank Bernhard Fröhler Markus Hoeglinger Johann Kastner Christoph Heinzl |
| author_facet | Miroslav Yosifov Patrick Weinberger Bernhard Plank Bernhard Fröhler Markus Hoeglinger Johann Kastner Christoph Heinzl |
| author_sort | Miroslav Yosifov |
| collection | DOAJ |
| description |
This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy.
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| format | Article |
| id | doaj-art-ea4bc9edebda41a39a544df7cd4a7d90 |
| institution | DOAJ |
| issn | 2336-5382 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Czech Technical University in Prague |
| record_format | Article |
| series | Acta Polytechnica CTU Proceedings |
| spelling | doaj-art-ea4bc9edebda41a39a544df7cd4a7d902025-08-20T02:50:59ZengCzech Technical University in PragueActa Polytechnica CTU Proceedings2336-53822023-10-014210.14311/APP.2023.42.0087Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural networkMiroslav Yosifov0Patrick Weinberger1Bernhard Plank2Bernhard Fröhler3Markus Hoeglinger4Johann Kastner5Christoph Heinzl6University of Antwerp, imec-Visionlab, Department of Physics, Universiteitsplein 1, 2610 Antwerpen, Belgium; University of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Applied Sciences Upper Austria, Research Group X-ray Computed Tomography, Stelzhamerstraße 23, 4600 Wels, AustriaUniversity of Passau, Faculty of Computer Science and Mathematics, Innstraße 43, 94032 Passau, Germany This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy. https://ojs.cvut.cz/ojs/index.php/APP/article/view/9407deep learningsegmentationU-Netcomputed tomographyporescarbon fiber reinforced polymers |
| spellingShingle | Miroslav Yosifov Patrick Weinberger Bernhard Plank Bernhard Fröhler Markus Hoeglinger Johann Kastner Christoph Heinzl Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network Acta Polytechnica CTU Proceedings deep learning segmentation U-Net computed tomography pores carbon fiber reinforced polymers |
| title | Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network |
| title_full | Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network |
| title_fullStr | Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network |
| title_full_unstemmed | Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network |
| title_short | Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network |
| title_sort | segmentation of pores in carbon fiber reinforced polymers using the u net convolutional neural network |
| topic | deep learning segmentation U-Net computed tomography pores carbon fiber reinforced polymers |
| url | https://ojs.cvut.cz/ojs/index.php/APP/article/view/9407 |
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