The conventional group test method for obtaining probabilistic S- N curve is a time-consuming and expensive test program. This study aims to present an alternative method for determining the probabilistic S- N curve of material based on its static strength data. Firstly, the probabilistic mapping relationship between the static strength and fatigue life of material is elaborated. Subsequently, the Weibull distribution is utilized to model the static strength and fatigue life data of material, and the correlation between the distribution parameters of them is investigated. Finally, an alternative method for determining the probabilistic S- N curve of material is proposed, and the experimental data of carbon steels and composite laminates are applied to verify its validity. The results show that the proposed method is capable of determining the probabilistic S- N curve of material based on its static strength data, which can significantly save test time and reduce test cost. In engineering applications, when the parameters of static strength distribution and the median S- N curve are known, the probabilistic S- N curve with any given survival probability can be determined through a unified analytical expression.
A new algorithm optimization‐based hybrid neural network model is proposed in the present study for the multiaxial fatigue life prediction of various metallic materials. Firstly, a convolutional neural network (CNN) is applied to extract the in‐depth features from the loading sequence composed of the critical fatigue loading conditions. Meanwhile, the multiaxial historical loading information with time‐series features is retained. Then, a long short‐term memory (LSTM) network is adopted to capture the time‐series features and in‐depth features of the CNN output. Finally, a full connection layer is used to achieve dimensional transformation, which makes the fatigue life predictable. Herein, the hyperparameters of the LSTM network are automatically determined using the slime mold algorithm (SMA). The test results demonstrate that the proposed model has pleasant prediction performance and extrapolation capability, and it is suitable for the life prediction of various metallic materials under uniaxial, proportional multiaxial, nonproportional multiaxial loading conditions.
A new algorithm optimization-based hybrid neural network model is
proposed in the present study for the multiaxial fatigue life prediction
of various metallic materials. Firstly, a convolutional neural network
(CNN) is applied to extract the in-depth features from the loading
sequence comprised of the critical fatigue loading conditions.
Meanwhile, the multiaxial historical loading information with
time-series features is retained. Then, a long short-term memory (LSTM)
network is adopted to capture the time-series features and in-depth
features of the CNN output. Finally, a full connection layer is used to
achieve dimensional transformation, which makes the fatigue life
predictable. Herein, the hyperparameters of the LSTM network are
automatically determined using the slime mould algorithm (SMA). Five
data sets of materials are involved for case studies. The predictive
performance of the proposed model is compared with those obtained using
support vector machine (SVM), LSTM network, and a critical plane model.
The results demonstrate the better predictive performance of the
proposed model, and it is suitable for the life prediction of various
metallic materials under uniaxial, proportional multiaxial,
non-proportional multiaxial loading conditions. Besides, the proposed
model outperforms the SVM and LSTM network in terms of extrapolation
capability.
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