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Neural-Network Prediction of the Low-Temperature Fatigue Strength of Metals.

Authors :
Kabaldin, Yu. G.
Anosov, M. S.
Shatagin, D. A.
Kiselev, A. V.
Kolchin, P. V.
Source :
Russian Engineering Research; Feb2022, Vol. 42 Issue 2, p100-103, 4p
Publication Year :
2022

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1068798X
Volume :
42
Issue :
2
Database :
Complementary Index
Journal :
Russian Engineering Research
Publication Type :
Academic Journal
Accession number :
155237937
Full Text :
https://doi.org/10.3103/S1068798X22020095