Electromechanical properties of carbon fibers enable non-destructive evaluation (NDE) of carbon-fiber-reinforced plastic (CFRP) structures by monitoring electrical resistance in real-time. This NDE technique is named as ‘self-sensing’, as it employs the material’s intrinsic features like human nerves. This technique was applied to evaluate the size of damage in CFRP samples. Electrical resistance measured in real-time during machining increasing size of concentric circles was analyzed, and polynomic correlations were identified. To ensure reliability and take uncertainties account, probability-based tools, Markov chain Monte Carlo and Bayesian algorithm, were applied. The potential applicability of the established system to repeated impact loads, considering damage progression due to unexpected strikes in real applications, was also verified.
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