Mr. Karthik Reddy R. Lyathakula Profile

Karthik Reddy R. Lyathakula

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Proceedings Article | 22 March 2021 Presentation + Paper

Probabilistic fatigue life prediction for adhesively bonded joints via surrogate model

Karthik Reddy Lyathakula, Fuh-Gwo Yuan

Proceedings Volume 11591, 115910S (2021) https://doi.org/10.1117/12.2585281

KEYWORDS: Monte Carlo methods, Data modeling, Adhesives, Finite element methods, Computer simulations, Artificial neural networks

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The paper is aimed at developing a probabilistic framework for fatigue life prediction in adhesively bonded joints by calibrating the predictive model, governing adhesive fatigue behavior, using the set of experimental data, and quantifying uncertainty in the model parameters. A cohesive zone model (CZM) is employed to simulate the fatigue damage growth (FDG) along the adhesive bondline and Bayesian inference is used for uncertainty quantification (UQ). The fatigue behavior predicted by FEA modeling for high cycle fatigue, in particular, is computationally intractable, not to mention the inclusion of UQ. To enhance the computational efficiency and yet retain accuracy, a rapid FDG simulator is developed for adhesively bonded joints, by replacing the computationally intensive strain field calculations with the artificial neural networks (ANNs) based surrogate model. The developed rapid FDG simulator is integrated with Bayesian inference and the integrated framework is verified by quantifying uncertainty in fatigue model parameters using the experimental fatigue life data of a single lap joint (SLJ) configuration under constant amplitude fatigue loading. The quantified parameter uncertainties are then used to predict the probabilistic fatigue life in the laminated doublers in bending joint configuration, fabricated using similar adhesive material as SLJ, and successfully comparing it with the experimental data.

Proceedings Article | 22 March 2021 Presentation + Paper

Fatigue damage prognosis of adhesively bonded joints via a surrogate model

Karthik Reddy Lyathakula, Fuh-Gwo Yuan

Proceedings Volume 11591, 115910K (2021) https://doi.org/10.1117/12.2585280

KEYWORDS: Artificial neural networks, Diagnostics, Transducers, Sensors, Neural networks, Interference (communication)

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This paper demonstrates a diagnostic-prognostics framework to estimate probabilistic remaining useful life (RUL), in adhesively bonded joints subjected to fatigue loading, by calibrating the predictive model using the diagnostics data and quantifying uncertainty in the model parameters. The matching pursuit algorithm is used as the diagnostic method to measure the crack length and the rapid fatigue damage growth (FDG) simulator is used as the predictive model to estimate the remaining useful life (RUL). In the diagnostic method, Lamb waves are excited in the structure using piezo transducers, and the matching pursuit algorithm is used to quantify the damage from the reflected signal. The proposed diagnostic technique is verified using the signal obtained from finite element simulations and artificial noise is added to mimic the signal from a real structure. The diagnostic method is applied periodically to measure the crack length in the single lap joint (SLJ) subjected to fatigue loading and the crack length data is used to calibrate the parameters of the predictive model, which can estimate the RUL. However, the noise in the signal and assumptions in the diagnostic technique result in errors in the measured crack length. These errors in the crack length contribute to the parameter uncertainties during the predictive model calibration. To quantify the model parameter uncertainties, the Bayesian inference via the Markov chain Monte Carlo method is used, and to expedite the uncertainty quantification problem, the rapid FDG simulator is used as the predictive model. The approach is demonstrated using a fatigue damage growth simulation in the SLJ and promising results were achieved.

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