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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | Materials Research Express |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2053-1591/ac24cd |
| _version_ | 1797746835469631488 |
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| author | M S Z Zhao Z L Long L Peng |
| author_facet | M S Z Zhao Z L Long L Peng |
| author_sort | M S Z Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:42:27Z |
| format | Article |
| id | doaj.art-c2c0cb32a76d4a4585416b6b254a84a1 |
| institution | Directory Open Access Journal |
| issn | 2053-1591 |
| language | English |
| last_indexed | 2024-03-12T15:42:27Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj.art-c2c0cb32a76d4a4585416b6b254a84a12023-08-09T15:54:37ZengIOP PublishingMaterials Research Express2053-15912021-01-018909520210.1088/2053-1591/ac24cdInvestigation on the serrated flow behavior of bulk metallic glasses based on machine learningM S Z Zhao0Z L Long1https://orcid.org/0000-0002-0575-4173L Peng2College of Civil Engineering and Mechanics, Xiangtan University , Hunan 411105, People’s Republic of ChinaCollege of Civil Engineering and Mechanics, Xiangtan University , Hunan 411105, People’s Republic of ChinaCollege of Civil Engineering and Mechanics, Xiangtan University , Hunan 411105, People’s Republic of ChinaThis 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.https://doi.org/10.1088/2053-1591/ac24cdnanoindentationserrated flowbulk metallic glasslong short-term memorymachine learning |
| spellingShingle | M S Z Zhao Z L Long L Peng Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning Materials Research Express nanoindentation serrated flow bulk metallic glass long short-term memory machine learning |
| title | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| title_full | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| title_fullStr | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| title_full_unstemmed | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| title_short | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| title_sort | investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
| topic | nanoindentation serrated flow bulk metallic glass long short-term memory machine learning |
| url | https://doi.org/10.1088/2053-1591/ac24cd |
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