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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