Abstract:
In this paper, we present an overall strategy for analyzing the crack growth rate of some metals with respect to the influence of different stress ratio (R-ratio) using a...Show MoreMetadata
Abstract:
In this paper, we present an overall strategy for analyzing the crack growth rate of some metals with respect to the influence of different stress ratio (R-ratio) using artificial neural networks (ANNs) models. Two ANN models are used to approximate the inherent nonlinear relationship between the material parameters and the R-ratio. Through training with the back-propagation algorithm, the ANN models are capable of predicting the unknown material parameters based on a given R-ratio. The good performance of the proposed ANN models is validated on two sets of published fatigue crack growth data under different R-ratios, i.e. the 2024-T351 aluminum alloy data and steel 4340 data. The simulation results indicate that the ANNs-based overall strategy is feasible and effective for the analysis of fatigue crack growth rates under the influence of different R-ratio.
Published in: 2017 11th Asian Control Conference (ASCC)
Date of Conference: 17-20 December 2017
Date Added to IEEE Xplore: 08 February 2018
ISBN Information: