Title: Self-Sensing of Damage Level and Progression in CFRPS Employing Electrical Resistance Measurement and Physics-Based Approaches
Authors: S.Y. Oh1, I.Y. Lee 2, Y.-B. Park1
DOI: 10.33599/nasampe/c.24.0354
Abstract: " Real-time structural health monitoring (SHM) and prognostics and health management (PHM) play an important role in ensuring user safety and controlling maintenance expenses in structure operation. To address this issue, a self-sensing technique has been extensively explored across the fields. It measures electrical resistance and characterizes the change in status resulting from external loadings, damages, and degradations in terms of electrical percolation network. This paper presents a holistic PHM system for carbon-fiber-reinforced plastic (CFRP) structures. The system provides a means to diagnose the damage level, location, and future electromechanical behavior under continuous loading. Electrical resistance measured simultaneously during water-jet machining, which was introduced for creating damages with intended size and location, and the resulting data derived empirical correlations between damage size and level. The correlations facilitated the prognosis of damage accumulation within certain prediction interval acting as a basic physical model for Bayesian and Markov chain Monte Carlo (MCMC) method. In the same manner, location of the damages can be identified by comparing the coefficients of the empirical equations. Expanding the proposed system to more realistic damages, such as fiber or matrix failure, is the next challenge that should be resolved. In this paper, by simply measuring the electrical resistance of in-service CFRP structures, operators can determine both current and future health statuses employing simple polynomial correlations. These correlations can be extended to other configurations, such as CFRPs with different shapes, stacking sequences, and operational conditions, with brief fundamental measurements. Quantitative assessment of damage leads to an understanding of structural integrity and reduction in maintenance costs."
References: 1. E. Abbate et al., “Environmental and Economic Assessment of Repairable Carbon-Fiber-Reinforced Polymers in Circular Economy Perspective,” Materials, vol. 15, no. 9, p. 2986, 2022, doi: 10.3390/ma15092986. 2. G. Zhu et al., “Modeling for CFRP structures subjected to quasi-static crushing,” Compos. Struct., vol, 184, pp. 14-55, doi: 10.1016/j.compstruct.2017.09.001. 3. B.K. Oh et al., “Optimal architecture of a convolutional neural network to estimate structural responses for safety evaluation of the structures,” Measurement, vol. 177, p. 109313, 2021, doi: 10.1016/j.measurement.2021.109313. 4. J. Park et al. “Strain measurements of an aircraft wing used embedded CNT fiber sensor and wireless SHM sensor node,” Funct. Compos. Struct., vol. 4, no. 3, p. 035004, 2022, doi: 10.1088/2631-6331/ac8719. 5. I.Y. Lee et al., “Real-time in-depth damage identification and health index system for carbon fiber-reinforced composites using electromechanical behavior and data processing tools,” Compos. Sci. Technol., vol. 236, p. 109951, 2023, doi: 10.1016/j.compscitech.2023.109951. 6. H.D. Roh et al., “Self-sensing impact damage in and non-destructive evaluation of carbon fiber-reinforced polymers using electrical resistance and the corresponding electrical route models,” Sensor Actuat. A: Phys., vol. 332, pt. 1, p. 115762, 2021, doi: 10.1016/j.sna.2021.112762 7. S.S. Han et al., “Epoxy/graphene film for lifecycle self-sensing and multifunctional applications,” Compos. Sci. Technol., vol. 198, p. 108312, 2020, doi: 10.1016/j.compscitech.2020.108312. 8. H.D. Roh et al., “Machine learning aided design of smart, self-sensing fiber-reinforced plastics,” Compos. Part C: Open, vol. 6, p. 100186, 2021, doi: 10.1016/j.jcomc.2021.100186. 9. S. Miele et al. “Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis,” Reliab. Eng. Syst. Safe., vol. 235, pp. 109243, 2023, doi: 10.1016/j.ress.2023.109243. 10. A. Khan et al., “A review of physics-based models in prognostics and health management of laminated composite structures,” Int. J. Pr. Eng. Man.-GT., vol. 10, pp. 1615-1635, 2023, doi: 10.1007/s40684-023-00509-4. 11. M. Torzoni et al., “SHM under varying environmental conditions: an approach based on model order reduction and deep learning,” Comput. Struct., vol. 265, pp. 106790, 2022, doi: 10.1016/j.compstruc.2022.106790.
Conference: CAMX 2024 | San Diego CA
Publication Date: 2024/9/9
SKU: TP24-0000000354
Pages: 11
Price: $22.00
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