Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning

This study proposed a long short-term memory (LSTM) model for predicting the serrated flow behaviors of bulk metallic glasses (BMGs) under nanoindentation. A series of load-controlled nanoindentation tests were conducted on a Pd 40 Cu 30 Ni 10 P 20 BMG. The LSTM model was introduced to establish a n...

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Bibliographic Details
Published in:Materials research express 2021-09, Vol.8 (9), p.95202
Main Authors: Zhao, M S Z, Long, Z L, Peng, L
Format: Article
Language:English
Subjects:
Amorphous materials
bulk metallic glass
Loading rate
long short-term memory
Machine learning
Metallic glasses
Nanoindentation
Neural networks
serrated flow
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Summary:This study proposed a long short-term memory (LSTM) model for predicting the serrated flow behaviors of bulk metallic glasses (BMGs) under nanoindentation. A series of load-controlled nanoindentation tests were conducted on a Pd 40 Cu 30 Ni 10 P 20 BMG. The LSTM model was introduced to establish a neural network for predicting the serrated flow at different loading rates, and was verified by the comparisons of experimental data with predictive results. Further investigation based on the predictive serrated flows under different loading rates showed that the serrations exhibit a significant self-organized critical (SOC) phenomenon at different loading rates. The SOC phenomena of the serrations under a lower loading rate were more obvious than that under a higher loading rate.
ISSN:2053-1591
2053-1591
DOI:10.1088/2053-1591/ac24cd