Abstract
A smart system for predicting the fatigue strength of metals over a broad temperature range is developed on the basis of a specially trained neural network. The system can predict the number of loading cycles to failure and also the onset of fatigue-crack formation and the rate of crack growth in different test conditions, including low temperatures.
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Financial support was provided by the Russian Science Fund (project 20-79-00135).
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Translated by B. Gilbert
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Kabaldin, Y.G., Anosov, M.S., Shatagin, D.A. et al. Neural-Network Prediction of the Low-Temperature Fatigue Strength of Metals. Russ. Engin. Res. 42, 100–103 (2022). https://doi.org/10.3103/S1068798X22020095
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DOI: https://doi.org/10.3103/S1068798X22020095