Abstract
Abstract
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 Pd40Cu30Ni10P20 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.
Funder
Hunan Provincial Innovation Foundation for Postgraduate
National Natural Science Foundation of China
Huxiang High-Level Talent Gathering Program of Hunan Province-Innovative team
Science and Technology Major Project of Hunan Province
Subject
Metals and Alloys,Polymers and Plastics,Surfaces, Coatings and Films,Biomaterials,Electronic, Optical and Magnetic Materials
Cited by
1 articles.
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