Effect of the structure of steel 03Kh13AG19 on propagation of fatigue cracks at normal and low temperatures

1978 ◽  
Vol 13 (6) ◽  
pp. 631-636
Author(s):  
O. P. Ostash ◽  
S. Ya. Yarema ◽  
K. A. Yushchenko ◽  
V. I. Belotserkovets ◽  
V. T. Zhmur-Klimenko ◽  
...  
Keyword(s):  
Low Temperatures ◽  
Fatigue Cracks ◽  
Steel 03Kh13ag19

Prediction of the fatigue strength of metals at low temperatures based on artificial intelligence

2021 ◽  
pp. 11-14
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fatigue Strength ◽  
Low Temperatures ◽  
Intelligent System ◽  
Fatigue Cracks ◽  
Test Conditions ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

An intelligent system for predicting the fatigue strength of metals in a wide temperature range is developed using a specially trained neural network. The system makes it possible to predict the number of load cycles of a part to failure, as well as the start of formation and growth rate of fatigue cracks for different test conditions, including at low temperatures. Keywords: neural network, prediction of loading cycles, low temperatures, fatigue strength. [email protected]

Development of fatigue cracks at low temperatures

1976 ◽  
Vol 11 (2) ◽  
pp. 172-175 ◽  
Author(s):  
S. Ya. Yarema ◽  
O. P. Ostash
Keyword(s):  
Low Temperatures ◽  
Fatigue Cracks

scholarly journals Propagation of Fatigue Cracks in Aluminium at Low Temperatures

1975 ◽  
Vol 16 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Tsugio Ogura ◽  
Seiichi Karashima ◽  
Kuniyasu Tsurukame
Keyword(s):  
Low Temperatures ◽  
Fatigue Cracks

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

2021 ◽  
Vol 1037 ◽  
pp. 655-660
Author(s):  
Yury G. Kabaldin ◽  
Alexander A. Khlybov ◽  
Maksim S. Anosov ◽  
Dmitry A. Ryabov ◽  
Andrey V. Kiselev
Keyword(s):  
Neural Network ◽  
Fatigue Strength ◽  
Low Temperatures ◽  
Intelligent System ◽  
Fatigue Cracks ◽  
Neural Network Modeling ◽  
Metallic Materials ◽  
Test Conditions ◽  
Metal Materials ◽  
Cycles To Failure

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.

The initial fatigue crack

1957 ◽  
Vol 242 (1229) ◽  
pp. 189-197 ◽  
Keyword(s):  
Fatigue Crack ◽  
Grain Boundaries ◽  
Low Temperatures ◽  
Room Temperature ◽  
Fatigue Cracks ◽  
Slip Bands ◽  
Persistent Slip Bands

A discussion is given of the formation of persistent slip bands during cyclic stressing and their development into fatigue cracks. In copper and in aluminium at low temperatures fatigue cracks appear to be formed in this way; at room temperature in aluminium they may form also along grain boundaries.

The development of fatigue cracks in armco iron at low temperatures

1975 ◽  
Vol 7 (6) ◽  
pp. 713-716
Author(s):  
V. M. Goritskii ◽  
V. F. Terent'ev ◽  
L. G. Orlov ◽  
V. P. Balov ◽  
M. P. Shebatinov
Keyword(s):  
Low Temperatures ◽  
Armco Iron ◽  
Fatigue Cracks
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