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.
- deep learning
- computed tomography
- carbon fiber reinforced polymers