@article{Gadolina_Sergeevich_2024, title={Building the Fatigue Curve Fuzzy Intervals by the Censored Information and Tomography Images}, volume={12}, url={https://riverpublishersjournal.com/index.php/JGEU/article/view/357}, DOI={10.13052/jgeu0975-1416.1216}, abstractNote={<p>Fatigue curves of materials are very important and useful in engineering practice. A problem in the application of fuzzy set theory arises from the availability of qualitative (non-numerical), imprecise, and incomplete information. Such information is often obtained from fatigue test data. We use computed tomography (CT) to study the extent of damage to censored samples. Censored samples are samples that were excluded from testing when they reached base test criteria. A fuzzy regression model was developed taking into account linguistic variables. Linguistic variables determine the status of the samples (censored or damaged). Considering the fuzzy dependent variable in the regression model, the coefficients of the regression equation were also transformed into fuzzy variables. The fuzzy dependent variable is the time before the destruction of the censored sample. After examining the images of the defects, the degree of damage is estimated using expert judgment. The model uses the fuzzy method, from different from the classical statistical interval for a given stress amplitude on the fatigue curve, to estimate the probabilistic service life interval. To illustrate the construction of a fuzzy interval, a fuzzy fatigue curve of a polymer composite with a fuzzy interval is shown.</p>}, number={01}, journal={Journal of Graphic Era University}, author={Gadolina, Irina V. and Sergeevich, Maidanov Igor}, year={2024}, month={May}, pages={89–104} }