The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal.
Keywords
acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness.
[email protected]
Spatial correlation-based characterization of acoustic emission signal-cloud in a granite sample by a cube clustering approach
