Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning. (22nd September 2021)

• Record Type: Journal Article
• Authors: Zhao, M S Z; Long, Z L; Peng, L
• Is Part Of: Materials research express. Volume 8:Number 9(2021)
• Publication Date: 2021-09-22
• Subjects: nanoindentation -- serrated flow -- bulk metallic glass -- long short-term memory -- machine learning; Materials science -- Research -- Periodicals; Materials science -- Periodicals; 620.11
• DOI: 10.1088/2053-1591/ac24cd
• Languages: English
• ISSNs: 2053-1591

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 Pd40 Cu30 Ni10 P20 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.