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Predicting Fatigue Life of Composite Laminates Subjected to Tension-Tension Fatigue
Journal of Composite Materials, 2000A recent two-parameter model, successfully employed to predict the flexural fatigue lifetime of randomly oriented glass fibre reinforced plastics, is applied to experimental data available in the literature, concerning carbon fibre reinforced plastic-laminates subjected to tension-tension fatigue.
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Bayesian Fatigue Life Prediction
1985Fatigue failure is an important failure mode for offshore structural joints, in particular for dynamically sensitive deep water structures. To predict the fatigue life only a few test results are generally available for the actual type of joint. In addition, however, test results for joints with slightly different geometries are available. Fatigue life
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Total Fatigue Life Prediction Methods
SAE Technical Paper Series, 1998<div class="htmlview paragraph">Two analytical methods of predicting the total fatigue life of a component such as weldment are compared: a relatively uncomplicated, two-stage, total-life-model known as the initiation-propagation or IP model; and a recently developed model termed the PICC-RICC crack growth model which integrates the effects of ...
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Predicting the total fatigue life in metals
International Journal of Fatigue, 2009Abstract When a metal specimen is subjected to cyclic loading, a large number of initiated cracks will initiate in its volume. The specimen forms a sample of initial cracks: the larger specimen, the larger is the sample. In previous work of the author it was shown that the fatigue limit can be predicted by estimating the largest expectable crack ...
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A novel method of multiaxial fatigue life prediction based on deep learning
International Journal of Fatigue, 2021Jingye Yang, Guozheng Kang, Yong Liu
exaly
The Proceedings of the Transportation and Logistics Conference, 2021
Daisuke SHINAGAWA +6 more
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Daisuke SHINAGAWA +6 more
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Fatigue life prediction of nickel base superalloys. [PDF]
, 2007 Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends.
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Application of the Gaussian process for fatigue life prediction under multiaxial loading
Mechanical Systems and Signal Processing, 2022Aleksander Karolczuk, Marek Słoński
exaly