Accelerated Development of High-Strength Magnesium Alloys by Machine Learning
Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat...
Gespeichert in:
| Veröffentlicht in: | Metallurgical and materials transactions. A, Physical metallurgy and materials science Physical metallurgy and materials science, 2021-03, Vol.52 (3), p.943-954 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 954 |
|---|---|
| container_issue | 3 |
| container_start_page | 943 |
| container_title | Metallurgical and materials transactions. A, Physical metallurgy and materials science |
| container_volume | 52 |
| creator | Liu, Yanwei Wang, Leyun Zhang, Huan Zhu, Gaoming Wang, Jie Zhang, Yuhui Zeng, Xiaoqin |
| description | Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development. |
| doi_str_mv | 10.1007/s11661-020-06132-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2493882519</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2493882519</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-1b0add95d479f915a985f1defc58728ee3693644953eb8a51db99f26e68925a33</originalsourceid><addsrcrecordid>eNp9kE9PwzAMxSMEEqPwBThV4hyIkyZtjtP4M6RNHIBzlLZu16lLR9Ih7dsTKBI3Tras957tHyHXwG6BsfwuACgFlHFGmQLBKZyQGchMUNAZO409ywWViotzchHCljEGWqgZWc-rCnv0dsQ6vcdP7If9Dt2YDk267NoNfR09unbcpGvbOgzdYZfO-344hrQ8xlm16RymK7Teda69JGeN7QNe_daEvD8-vC2WdPXy9LyYr2glQI8USmbrWss6y3WjQVpdyAZqbCpZ5LxAFCoel2VaCiwLK6EutW64QlVoLq0QCbmZcvd--DhgGM12OHgXVxqeaVEUXMb3EsInVeWHEDw2Zu-7nfVHA8x8YzMTNhOxmR9sBqJJTKYQxa5F_xf9j-sL1A5vCw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2493882519</pqid></control><display><type>article</type><title>Accelerated Development of High-Strength Magnesium Alloys by Machine Learning</title><source>SpringerLink Journals</source><creator>Liu, Yanwei ; Wang, Leyun ; Zhang, Huan ; Zhu, Gaoming ; Wang, Jie ; Zhang, Yuhui ; Zeng, Xiaoqin</creator><creatorcontrib>Liu, Yanwei ; Wang, Leyun ; Zhang, Huan ; Zhu, Gaoming ; Wang, Jie ; Zhang, Yuhui ; Zeng, Xiaoqin</creatorcontrib><description>Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.</description><identifier>ISSN: 1073-5623</identifier><identifier>EISSN: 1543-1940</identifier><identifier>DOI: 10.1007/s11661-020-06132-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Aging (metallurgy) ; Alloys ; Casting alloys ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Composition ; Diamond pyramid hardness ; Heat treatment ; High strength alloys ; Machine learning ; Magnesium base alloys ; Materials Science ; Metallic Materials ; Nanotechnology ; Original Research Article ; Structural Materials ; Surfaces and Interfaces ; Thin Films</subject><ispartof>Metallurgical and materials transactions. A, Physical metallurgy and materials science, 2021-03, Vol.52 (3), p.943-954</ispartof><rights>The Minerals, Metals & Materials Society and ASM International 2021</rights><rights>The Minerals, Metals & Materials Society and ASM International 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-1b0add95d479f915a985f1defc58728ee3693644953eb8a51db99f26e68925a33</citedby><cites>FETCH-LOGICAL-c319t-1b0add95d479f915a985f1defc58728ee3693644953eb8a51db99f26e68925a33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11661-020-06132-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11661-020-06132-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Liu, Yanwei</creatorcontrib><creatorcontrib>Wang, Leyun</creatorcontrib><creatorcontrib>Zhang, Huan</creatorcontrib><creatorcontrib>Zhu, Gaoming</creatorcontrib><creatorcontrib>Wang, Jie</creatorcontrib><creatorcontrib>Zhang, Yuhui</creatorcontrib><creatorcontrib>Zeng, Xiaoqin</creatorcontrib><title>Accelerated Development of High-Strength Magnesium Alloys by Machine Learning</title><title>Metallurgical and materials transactions. A, Physical metallurgy and materials science</title><addtitle>Metall Mater Trans A</addtitle><description>Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.</description><subject>Aging (metallurgy)</subject><subject>Alloys</subject><subject>Casting alloys</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Composition</subject><subject>Diamond pyramid hardness</subject><subject>Heat treatment</subject><subject>High strength alloys</subject><subject>Machine learning</subject><subject>Magnesium base alloys</subject><subject>Materials Science</subject><subject>Metallic Materials</subject><subject>Nanotechnology</subject><subject>Original Research Article</subject><subject>Structural Materials</subject><subject>Surfaces and Interfaces</subject><subject>Thin Films</subject><issn>1073-5623</issn><issn>1543-1940</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kE9PwzAMxSMEEqPwBThV4hyIkyZtjtP4M6RNHIBzlLZu16lLR9Ih7dsTKBI3Tras957tHyHXwG6BsfwuACgFlHFGmQLBKZyQGchMUNAZO409ywWViotzchHCljEGWqgZWc-rCnv0dsQ6vcdP7If9Dt2YDk267NoNfR09unbcpGvbOgzdYZfO-344hrQ8xlm16RymK7Teda69JGeN7QNe_daEvD8-vC2WdPXy9LyYr2glQI8USmbrWss6y3WjQVpdyAZqbCpZ5LxAFCoel2VaCiwLK6EutW64QlVoLq0QCbmZcvd--DhgGM12OHgXVxqeaVEUXMb3EsInVeWHEDw2Zu-7nfVHA8x8YzMTNhOxmR9sBqJJTKYQxa5F_xf9j-sL1A5vCw</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Liu, Yanwei</creator><creator>Wang, Leyun</creator><creator>Zhang, Huan</creator><creator>Zhu, Gaoming</creator><creator>Wang, Jie</creator><creator>Zhang, Yuhui</creator><creator>Zeng, Xiaoqin</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>4U-</scope><scope>7SR</scope><scope>7XB</scope><scope>88I</scope><scope>8AF</scope><scope>8AO</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</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>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>L6V</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20210301</creationdate><title>Accelerated Development of High-Strength Magnesium Alloys by Machine Learning</title><author>Liu, Yanwei ; Wang, Leyun ; Zhang, Huan ; Zhu, Gaoming ; Wang, Jie ; Zhang, Yuhui ; Zeng, Xiaoqin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-1b0add95d479f915a985f1defc58728ee3693644953eb8a51db99f26e68925a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aging (metallurgy)</topic><topic>Alloys</topic><topic>Casting alloys</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Composition</topic><topic>Diamond pyramid hardness</topic><topic>Heat treatment</topic><topic>High strength alloys</topic><topic>Machine learning</topic><topic>Magnesium base alloys</topic><topic>Materials Science</topic><topic>Metallic Materials</topic><topic>Nanotechnology</topic><topic>Original Research Article</topic><topic>Structural Materials</topic><topic>Surfaces and Interfaces</topic><topic>Thin Films</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yanwei</creatorcontrib><creatorcontrib>Wang, Leyun</creatorcontrib><creatorcontrib>Zhang, Huan</creatorcontrib><creatorcontrib>Zhu, Gaoming</creatorcontrib><creatorcontrib>Wang, Jie</creatorcontrib><creatorcontrib>Zhang, Yuhui</creatorcontrib><creatorcontrib>Zeng, Xiaoqin</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>University Readers</collection><collection>Engineered Materials Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest Pharma Collection</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</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>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Materials Science Collection</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>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Metallurgical and materials transactions. A, Physical metallurgy and materials science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yanwei</au><au>Wang, Leyun</au><au>Zhang, Huan</au><au>Zhu, Gaoming</au><au>Wang, Jie</au><au>Zhang, Yuhui</au><au>Zeng, Xiaoqin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated Development of High-Strength Magnesium Alloys by Machine Learning</atitle><jtitle>Metallurgical and materials transactions. A, Physical metallurgy and materials science</jtitle><stitle>Metall Mater Trans A</stitle><date>2021-03-01</date><risdate>2021</risdate><volume>52</volume><issue>3</issue><spage>943</spage><epage>954</epage><pages>943-954</pages><issn>1073-5623</issn><eissn>1543-1940</eissn><abstract>Magnesium (Mg) has a strong application potential as a lightweight metal. Yet, its absolute strength still needs improvement. In this work, we demonstrate that machine learning can be utilized to guide the development of high-strength Mg cast alloys. In the design framework, the composition and heat treatment condition are iteratively optimized by a surrogate model that is also evolving. After two iterations, a new alloy with the composition of Mg-10.0Al-2.0Sn-2.0Zn-0.1Ca-0.1Mn (at. pct) was identified. After aging at 200 °C for 96 hours, this alloy shows a Vickers hardness value of 110.5 Hv, which surpasses the highest value (102.5 Hv) in the initial dataset from literature. Finally, microstructure of the optimized alloy was characterized to understand the origin of its high hardness. This work demonstrates the potential of data-driven approaches for material development.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11661-020-06132-1</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1073-5623 |
| ispartof | Metallurgical and materials transactions. A, Physical metallurgy and materials science, 2021-03, Vol.52 (3), p.943-954 |
| issn | 1073-5623 1543-1940 |
| language | eng |
| recordid | cdi_proquest_journals_2493882519 |
| source | SpringerLink Journals |
| subjects | Aging (metallurgy) Alloys Casting alloys Characterization and Evaluation of Materials Chemistry and Materials Science Composition Diamond pyramid hardness Heat treatment High strength alloys Machine learning Magnesium base alloys Materials Science Metallic Materials Nanotechnology Original Research Article Structural Materials Surfaces and Interfaces Thin Films |
| title | Accelerated Development of High-Strength Magnesium Alloys by Machine Learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T07%3A48%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accelerated%20Development%20of%20High-Strength%20Magnesium%20Alloys%20by%20Machine%20Learning&rft.jtitle=Metallurgical%20and%20materials%20transactions.%20A,%20Physical%20metallurgy%20and%20materials%20science&rft.au=Liu,%20Yanwei&rft.date=2021-03-01&rft.volume=52&rft.issue=3&rft.spage=943&rft.epage=954&rft.pages=943-954&rft.issn=1073-5623&rft.eissn=1543-1940&rft_id=info:doi/10.1007/s11661-020-06132-1&rft_dat=%3Cproquest_cross%3E2493882519%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2493882519&rft_id=info:pmid/&rfr_iscdi=true |