PSI - Issue 47

A.M. Ignatova et al. / Procedia Structural Integrity 47 (2023) 820–825 Author/ Structural Integrity Procedia 00 (2019) 000–000

825

6

Ignatova A.M., Balakhnin A.N., Bannikov M.V., Kuper K.E., Nikitiuk A.S., Naimark O.B. Technique for obtaining an integral characteristic of the structure of a loaded composite material based on microtomographic research data // Procedia Structural Integrity. - 2022. - V.41. - P.550 556 https://doi.org/10.1016/j.prostr.2022.05.063. Jiang, P.; Liu, X.; Li, W.; Guo, F.; Hong, C.; Liu, Y.; Yang, C. Damage Characterization of Carbon Fiber Composite Pressure Vessels Based on Modal Acoustic Emission. Materials 2022, 15, 4783. https://doi.org/10.3390/ma15144783 Kastner, J., Plank, B., Salaberger, D., & Sekelja, J. (2013). Defect and porosity determination of fibre reinforced polymers by X-ray computed tomography. Composites Science and Technology, 85, 74-81. doi: 10.1016/j.compscitech.2013.06.003. Li, Z., Fan, H., Zhang, D., Liu, S., & Wang, G. (2018). Mechanical behavior and damage evolution of 3D-woven C/SiC composites under tensile loading: In-situ observation and digital image correlation. Composites Science and Technology, 167, 305-313. https://doi.org/10.1016/j.compscitech.2018.09.021 Lu, W., Chen, Q., Zhang, Y., & Xu, C. (2019). Microscopic analysis of a composite material with different fiber architectures based on digital image correlation and computed tomography. Composites Part B: Engineering, 167, 421-429. https://doi.org/10.1016/j.compositesb.2019.03.013 Mehdikhani, M., Straumit, I., Gorbatikh, L., & Lomov, S. V. (2019). A dataset of void characteristics in multidirectional carbon fiber/epoxy composite laminates, obtained using X-ray micro-computed tomography. Data in Brief, 27, 104686. doi: 10.1016/j.dib.2019.104686 Nebe, M.; Soriano, A.; Braun, C.; Middendorf, P.; Walther, F. Analysis on the mechanical response of composite pressure vessels during internal pressure loading: FE modeling and experimental correlation. Compos. Part B Eng. 2021, 212, 108550. Ng H-F. Automatic thresholding for defect detection. Pattern recognition letters 2006; 27: 1644-1649. Plank, B., Gusenbauer, C., Senck, S., Hoeller, H., & Kastner, J. (2015). Porosity determination in CFRP by means of X-ray computed tomography methods. Materials Testing, 57(5), 365-370. doi: 10.3139/120.110721 Ren, H., Huang, G., Li, L., Zhang, J., & Wang, D. (2021). Automated segmentation and quantification of carbon fiber in composite materials using convolutional neural network. Journal of Reinforced Plastics and Composites, 40(8-9), 356-368. Schuller J and Oster R. Classification of porosity by ultrasonic in carbon fibre helicopter structures based on micro computed tomography, Proceedings European conference on nondestructive testing, Berlin, Germany, 25.–29. September 2006 Sutradhar, A., & Pal, S. K. (2017). Quantitative microstructural analysis of a carbon fiber reinforced polymer composite using image processing techniques. Journal of Materials Science, 52(14), 8572-8584. https://doi.org/10.1007/s10853-017-1104-4 Wang, L., Cai, W., Li, X., Liu, B., & Zhou, Y. (2019). Quantitative characterization of the local stiffness of a fiber-reinforced composite using micro-computed tomography and image-based modeling. Composites Science and Technology, 171, 77-86. https://doi.org/10.1016/j.compscitech.2018.12.001 Weissenböck, J., Senck, S., Plank, B., Heinzl, C., & Kastner, J. (2017). Porosity evaluation of carbon fiber-reinforced polymers with porosity analyzer. Polymer Testing, 57, 154-161. doi: 10.1016/j.polymertesting.2016.11.017 Xu, H., Wang, Y., Tang, J., & Li, H. (2019). An automatic segmentation method for micro-CT images of carbon fiber composites based on the convolutional neural network. Measurement, 146, 406-416.