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.
This master’s thesis shows the extraction, quantification and visual analysis of pores and individual fibres in fibre reinforced polymer (FRP)materials. The core methods used and advanced for this ...purpose are tailored deep learning techniques, which are coupled with interactive visualisation.These techniques were applied to X-ray Computed Tomography(XCT) data to extract pores and fibres of carbon (CFRP) and glassfibre reinforced polymers (GFRP). Although segmentation is widely examined, there is still a high necessity to come up with improved methods ,especially given the huge potential of machine learning. In this thesis, we aimed at designing efficient and powerful segmentation models with reasonable performance on consumer-grade GPU systems. At the heart of the studied machine learning techniques for segmentation was U-Net 39, a deep convolutional neural network based segmentation technique. The main contributions of this thesis are seen in modifying and improving U-Net’s layer architecture to facilitate the segmentation of pores and fibres in 3D-XCT data of FRPs. Furthermore, a hyper-parameter optimization was completed through a parameter analysis with a tuning function. The results with highest accuracy from all suitable hyper-parameters were used for the final training process. The trained model (prediction model) has finally been implemented and integrated as a segmentation filter in open iA 13, in order to facilitate an efficient segmentation of XCT datasets with the fully trained model. Regarding evaluation, a quantitative feature analysis was carried out. Pores and fibres were counted, their shape and volumetric characteristics were computed to support the visual analysis of the generated results. We examined our findings on different real-world XCT datasets of carbon and glass fibre reinforced composites and tested the model on both high and low-resolution datasets: The proposed method shows in all test cases an efficient and robust segmentation of pores with different shapes and size, and that also barely visible features are detected correctly. We finally show evidence that the proposed modified U-Netis more suitable than other segmentation methods for pore segmentation .Even when trained only with a low number of datasets, it returns a reasonable prediction accuracy. In terms of computation time, this techniqueis suitable even on consumer-grade GPUs: For dataset sizes of over 1000x1000x1000 (4GB) voxels, the complete segmentation procedure only takes approximately 4 minutes.
