Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images
Discontinuously fiber reinforced polymers exhibit complex microstructures. Quantities to characterize the latter have been developed over time, such as the fiber volume content or fiber orientation distributions, which can be acquired through computed tomography images and subsequent image processin...
שמור ב:
| מחבר ראשי: | |
|---|---|
| פורמט: | Online |
| שפה: | אנגלית |
| יצא לאור: |
KIT Scientific Publishing
2025
|
| נושאים: | |
| גישה מקוונת: | ONIX_20251202T160246_9783731513964_20 |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
| _version_ | 1869521485749551104 |
|---|---|
| author | Blarr, Juliane |
| author_browse | Blarr, Juliane |
| author_facet | Blarr, Juliane |
| author_sort | Blarr, Juliane |
| collection | Directory of Open Access Books |
| description | Discontinuously fiber reinforced polymers exhibit complex microstructures. Quantities to characterize the latter have been developed over time, such as the fiber volume content or fiber orientation distributions, which can be acquired through computed tomography images and subsequent image processing. This thesis deals with the development of (partially AI-based) methods in this context, especially considering challenges of contrast and resolution with carbon fibers and scale-bridging issues. |
| format | Online |
| id | doab-20.500.12854ir-169846 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1698462025-12-03T05:24:47Z Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images Blarr, Juliane Künstliche Intelligenz CT-Bilder Bildauswertung faserverstärkte Kunststoffe maschinelles Lernen Artificial intelligence CT images image processing fiber reinforced polymers deep learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering Discontinuously fiber reinforced polymers exhibit complex microstructures. Quantities to characterize the latter have been developed over time, such as the fiber volume content or fiber orientation distributions, which can be acquired through computed tomography images and subsequent image processing. This thesis deals with the development of (partially AI-based) methods in this context, especially considering challenges of contrast and resolution with carbon fibers and scale-bridging issues. 2025-12-03T05:24:47Z 2025-12-03T05:24:47Z 2025-12-02T15:13:27Z 2025 book ONIX_20251202T160246_9783731513964_20 2192-9963 (Online) https://library.oapen.org/handle/20.500.12657/108912 9783731513964 https://directory.doabooks.org/handle/20.500.12854/169846 eng Schriftenreihe des Instituts für Angewandte Materialien, Karlsruher Institut für Technologie open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/108912/1/9783731513964.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000176200 10.5445/KSP/1000176200 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513964 KIT Scientific Publishing 372 Karlsruhe, Germany open access |
| spellingShingle | Künstliche Intelligenz CT-Bilder Bildauswertung faserverstärkte Kunststoffe maschinelles Lernen Artificial intelligence CT images image processing fiber reinforced polymers deep learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering Blarr, Juliane Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title | Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title_full | Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title_fullStr | Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title_full_unstemmed | Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title_short | Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images |
| title_sort | development of computational image processing and deep learning methods for the microstructure characterization of carbon fiber reinforced polyamide 6 based on ct images |
| topic | Künstliche Intelligenz CT-Bilder Bildauswertung faserverstärkte Kunststoffe maschinelles Lernen Artificial intelligence CT images image processing fiber reinforced polymers deep learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering |
| topic_facet | Künstliche Intelligenz CT-Bilder Bildauswertung faserverstärkte Kunststoffe maschinelles Lernen Artificial intelligence CT images image processing fiber reinforced polymers deep learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering |
| url | ONIX_20251202T160246_9783731513964_20 |
| work_keys_str_mv | AT blarrjuliane developmentofcomputationalimageprocessinganddeeplearningmethodsforthemicrostructurecharacterizationofcarbonfiberreinforcedpolyamide6basedonctimages |