Neural Network Modeling of the Fatigue Strength of Metal Materials at Low Temperatures

Yury G. Kabaldin; Alexander A. Khlybov; Maksim S. Anosov; Dmitry A. Ryabov; Andrey V. Kiselev

doi:10.4028/www.scientific.net/MSF.1037.655

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HomeMaterials Science ForumMaterials Science Forum Vol. 1037Neural Network Modeling of the Fatigue Strength of...

Neural Network Modeling of the Fatigue Strength of Metal Materials at Low Temperatures

Abstract:

An intelligent system has been developed to predict the fatigue strength of metallic materials over a wide temperature range. A neural network, properly trained, is a model of a dynamic system of fatigue failure of a part and is able to predict the values of the number of loading cycles to failure, as well as the onset of formation and growth rate of fatigue cracks for various test conditions, including at low temperatures.

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Periodical:

Materials Science Forum (Volume 1037)

Pages:

655-660

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.1037.655

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Online since:

July 2021

Authors:

Yury G. Kabaldin, Alexander A. Khlybov, Maksim S. Anosov*, Dmitry A. Ryabov, Andrey V. Kiselev

Keywords:

Fatigue Strength, Low Temperatures, Neural Network, Prediction of Loading Cycles

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