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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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| Published in: | Materials research express 2021-09, Vol.8 (9), p.95202 |
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| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c447t-992e256595461887f45acd5218dff5295af50745a607c413370762a4dbffeb3c3 |
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| container_issue | 9 |
| container_start_page | 95202 |
| container_title | Materials research express |
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| creator | Zhao, M S Z Long, Z L Peng, L |
| 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. |
| doi_str_mv | 10.1088/2053-1591/ac24cd |
| format | article |
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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.</description><identifier>ISSN: 2053-1591</identifier><identifier>EISSN: 2053-1591</identifier><identifier>DOI: 10.1088/2053-1591/ac24cd</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Amorphous materials ; bulk metallic glass ; Loading rate ; long short-term memory ; Machine learning ; Metallic glasses ; Nanoindentation ; Neural networks ; serrated flow</subject><ispartof>Materials research express, 2021-09, Vol.8 (9), p.95202</ispartof><rights>2021 The Author(s). Published by IOP Publishing Ltd</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-992e256595461887f45acd5218dff5295af50745a607c413370762a4dbffeb3c3</citedby><cites>FETCH-LOGICAL-c447t-992e256595461887f45acd5218dff5295af50745a607c413370762a4dbffeb3c3</cites><orcidid>0000-0002-0575-4173</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2577065229?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590</link.rule.ids></links><search><creatorcontrib>Zhao, M S Z</creatorcontrib><creatorcontrib>Long, Z L</creatorcontrib><creatorcontrib>Peng, L</creatorcontrib><title>Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning</title><title>Materials research express</title><addtitle>MRX</addtitle><addtitle>Mater. Res. Express</addtitle><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.</description><subject>Amorphous materials</subject><subject>bulk metallic glass</subject><subject>Loading rate</subject><subject>long short-term memory</subject><subject>Machine learning</subject><subject>Metallic glasses</subject><subject>Nanoindentation</subject><subject>Neural networks</subject><subject>serrated flow</subject><issn>2053-1591</issn><issn>2053-1591</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1kcFrFDEUxgdRsNTePQYET12bvMlLJkcpWhcKXvRqeJNJdrPOTtZktup_b7ZTag8KgYSP7_u98L2meS34O8G77go4tiuBRlyRA-mGZ83Zo_T8yftlc1HKjnMO2rQI6qz5tp7ufJnjhuaYJlbPvPWs-Jxp9gMLY_rJer-lu5gyS4H1x_E72_uZxjE6thmpFF9YT6Waa3hPbhsnz0ZPeYrT5lXzItBY_MXDfd58_fjhy_Wn1e3nm_X1-9uVk1LPK2PAAyo0KJXoOh0kkhsQRDeEgGCQAnJdRcW1k6JtNdcKSA59CL5vXXverBfukGhnDznuKf-2iaK9F1LeWMpzdKO3Dhx3fQuk1SBJYodSqF71gJI6SaKy3iysQ04_jrUcu0vHPNXvW0CtuUIAU118cbmcSsk-PE4V3J6WYk-t21PrdllKjbxdIjEd_jL3-ZftrLHcIHCwhyFU4-U_jP_l_gFC6pmy</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Zhao, M S Z</creator><creator>Long, Z L</creator><creator>Peng, L</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0575-4173</orcidid></search><sort><creationdate>20210901</creationdate><title>Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning</title><author>Zhao, M S Z ; Long, Z L ; Peng, L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-992e256595461887f45acd5218dff5295af50745a607c413370762a4dbffeb3c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Amorphous materials</topic><topic>bulk metallic glass</topic><topic>Loading rate</topic><topic>long short-term memory</topic><topic>Machine learning</topic><topic>Metallic glasses</topic><topic>Nanoindentation</topic><topic>Neural networks</topic><topic>serrated flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, M S Z</creatorcontrib><creatorcontrib>Long, Z L</creatorcontrib><creatorcontrib>Peng, L</creatorcontrib><collection>Open Access: IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>Materials science collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Materials research express</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, M S Z</au><au>Long, Z L</au><au>Peng, L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning</atitle><jtitle>Materials research express</jtitle><stitle>MRX</stitle><addtitle>Mater. Res. Express</addtitle><date>2021-09-01</date><risdate>2021</risdate><volume>8</volume><issue>9</issue><spage>95202</spage><pages>95202-</pages><issn>2053-1591</issn><eissn>2053-1591</eissn><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 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.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/2053-1591/ac24cd</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-0575-4173</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_iop_journals_10_1088_2053_1591_ac24cd |
| source | Publicly Available Content Database |
| subjects | Amorphous materials bulk metallic glass Loading rate long short-term memory Machine learning Metallic glasses Nanoindentation Neural networks serrated flow |
| title | Investigation on the serrated flow behavior of bulk metallic glasses based on machine learning |
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