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Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning
A method which combines electronegativity difference, CALculation of PHAse Diagrams (CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co–Cr–Fe–Ni–Mo multi-component phase diagram. First, the single-phase region at a certain annealing temperatur...
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| Published in: | Tungsten 2023-03, Vol.5 (1), p.169-178 |
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| Main Authors: | , , , , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c249t-e86632fe7d1b6cbea2cc78be6d6aee942a85b1dab1543750716773ace5f5f3353 |
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| cites | cdi_FETCH-LOGICAL-c249t-e86632fe7d1b6cbea2cc78be6d6aee942a85b1dab1543750716773ace5f5f3353 |
| container_end_page | 178 |
| container_issue | 1 |
| container_start_page | 169 |
| container_title | Tungsten |
| container_volume | 5 |
| creator | Yan, Jiao-Hui Song, Zi-Jing Fang, Wei He, Xin-Bo Chang, Ruo-Bin Huang, Shao-Wu Huang, Jia-Xin Yu, Hao-Yang Yin, Fu-Xing |
| description | A method which combines electronegativity difference, CALculation of PHAse Diagrams (CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co–Cr–Fe–Ni–Mo multi-component phase diagram. First, the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning, to avoid the formation of brittle phases. Then high yield strength points in the single-phase region are selected by electronegativity difference. The yield strength and plastic deformation behavior of the designed Co
14
Cr
30
Ni
50
Mo
6
alloy are measured to evaluate the proposed method. The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space. Meanwhile, the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co
14
Cr
30
Ni
50
Mo
6
alloy. The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys. |
| doi_str_mv | 10.1007/s42864-021-00129-y |
| format | article |
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14
Cr
30
Ni
50
Mo
6
alloy are measured to evaluate the proposed method. The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space. Meanwhile, the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co
14
Cr
30
Ni
50
Mo
6
alloy. The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys.</description><identifier>ISSN: 2661-8028</identifier><identifier>EISSN: 2661-8036</identifier><identifier>DOI: 10.1007/s42864-021-00129-y</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Alloying elements ; Alloys ; Chemistry and Materials Science ; Chromium ; Cobalt ; Datasets ; Decomposition ; Design ; Edge dislocations ; Electronegativity ; Iron ; Machine learning ; Materials Engineering ; Materials Science ; Mathematical analysis ; Mechanical properties ; Metallic Materials ; Molybdenum ; Nickel ; Nuclear Chemistry ; Original Paper ; Particle and Nuclear Physics ; Phase diagrams ; Plastic deformation ; Shear bands ; Support vector machines ; Temperature ; Work hardening ; Yield strength ; Yield stress</subject><ispartof>Tungsten, 2023-03, Vol.5 (1), p.169-178</ispartof><rights>The Nonferrous Metals Society of China 2021</rights><rights>The Nonferrous Metals Society of China 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-e86632fe7d1b6cbea2cc78be6d6aee942a85b1dab1543750716773ace5f5f3353</citedby><cites>FETCH-LOGICAL-c249t-e86632fe7d1b6cbea2cc78be6d6aee942a85b1dab1543750716773ace5f5f3353</cites><orcidid>0000-0002-8616-4609</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Yan, Jiao-Hui</creatorcontrib><creatorcontrib>Song, Zi-Jing</creatorcontrib><creatorcontrib>Fang, Wei</creatorcontrib><creatorcontrib>He, Xin-Bo</creatorcontrib><creatorcontrib>Chang, Ruo-Bin</creatorcontrib><creatorcontrib>Huang, Shao-Wu</creatorcontrib><creatorcontrib>Huang, Jia-Xin</creatorcontrib><creatorcontrib>Yu, Hao-Yang</creatorcontrib><creatorcontrib>Yin, Fu-Xing</creatorcontrib><title>Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning</title><title>Tungsten</title><addtitle>Tungsten</addtitle><description>A method which combines electronegativity difference, CALculation of PHAse Diagrams (CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co–Cr–Fe–Ni–Mo multi-component phase diagram. First, the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning, to avoid the formation of brittle phases. Then high yield strength points in the single-phase region are selected by electronegativity difference. The yield strength and plastic deformation behavior of the designed Co
14
Cr
30
Ni
50
Mo
6
alloy are measured to evaluate the proposed method. The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space. Meanwhile, the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co
14
Cr
30
Ni
50
Mo
6
alloy. The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys.</description><subject>Alloying elements</subject><subject>Alloys</subject><subject>Chemistry and Materials Science</subject><subject>Chromium</subject><subject>Cobalt</subject><subject>Datasets</subject><subject>Decomposition</subject><subject>Design</subject><subject>Edge dislocations</subject><subject>Electronegativity</subject><subject>Iron</subject><subject>Machine learning</subject><subject>Materials Engineering</subject><subject>Materials Science</subject><subject>Mathematical analysis</subject><subject>Mechanical properties</subject><subject>Metallic Materials</subject><subject>Molybdenum</subject><subject>Nickel</subject><subject>Nuclear Chemistry</subject><subject>Original Paper</subject><subject>Particle and Nuclear Physics</subject><subject>Phase diagrams</subject><subject>Plastic deformation</subject><subject>Shear bands</subject><subject>Support vector machines</subject><subject>Temperature</subject><subject>Work hardening</subject><subject>Yield strength</subject><subject>Yield stress</subject><issn>2661-8028</issn><issn>2661-8036</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9Uc1u1jAQjCqQWrV9AU4rcW3AP4mTHFFEKVIpl3K2HGeTuHLsYPtDyo134K14jD4J_vgQ3LjMrrQzs6udonhFyRtKSPM2VqwVVUkYLQmhrCv3s-KCCUHLlnDx4m_P2vPiOsYnQgiru8xsLoqfvV83H00y3sGI0cwO_ASLmRfYDdoRYgro5rTA5o1LEYyDaNxssdwWFRF6__z9Rx8y3GKGB5Phk4f1YJMpt2CcNpuygBZXdAmUtX6PEPeYcIUhO4yQV-exTsE7nFUy30zabyAtGFY_7k6tRoNWVh-sOt4Zb0C5EValF-MQLKrg8kVXxctJ2YjXf-pl8eX2_WN_V95__vCxf3dfalZ1qcRWCM4mbEY6CD2gYlo37YBiFAqxq5hq64GOaqB1xZuaNFQ0DVca66meOK_5ZfH65LsF__WAMcknfwgur5SsY5S1FSc0s9iJpYOPMeAk8y9WFXZJiTzGJk-xyRyb_B2b3LOIn0Tx-LgZwz_r_6h-AV04pQI</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Yan, Jiao-Hui</creator><creator>Song, Zi-Jing</creator><creator>Fang, Wei</creator><creator>He, Xin-Bo</creator><creator>Chang, Ruo-Bin</creator><creator>Huang, Shao-Wu</creator><creator>Huang, Jia-Xin</creator><creator>Yu, Hao-Yang</creator><creator>Yin, Fu-Xing</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-8616-4609</orcidid></search><sort><creationdate>20230301</creationdate><title>Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning</title><author>Yan, Jiao-Hui ; Song, Zi-Jing ; Fang, Wei ; He, Xin-Bo ; Chang, Ruo-Bin ; Huang, Shao-Wu ; Huang, Jia-Xin ; Yu, Hao-Yang ; Yin, Fu-Xing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c249t-e86632fe7d1b6cbea2cc78be6d6aee942a85b1dab1543750716773ace5f5f3353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Alloying elements</topic><topic>Alloys</topic><topic>Chemistry and Materials Science</topic><topic>Chromium</topic><topic>Cobalt</topic><topic>Datasets</topic><topic>Decomposition</topic><topic>Design</topic><topic>Edge dislocations</topic><topic>Electronegativity</topic><topic>Iron</topic><topic>Machine learning</topic><topic>Materials Engineering</topic><topic>Materials Science</topic><topic>Mathematical analysis</topic><topic>Mechanical properties</topic><topic>Metallic Materials</topic><topic>Molybdenum</topic><topic>Nickel</topic><topic>Nuclear Chemistry</topic><topic>Original Paper</topic><topic>Particle and Nuclear Physics</topic><topic>Phase diagrams</topic><topic>Plastic deformation</topic><topic>Shear bands</topic><topic>Support vector machines</topic><topic>Temperature</topic><topic>Work hardening</topic><topic>Yield strength</topic><topic>Yield stress</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan, Jiao-Hui</creatorcontrib><creatorcontrib>Song, Zi-Jing</creatorcontrib><creatorcontrib>Fang, Wei</creatorcontrib><creatorcontrib>He, Xin-Bo</creatorcontrib><creatorcontrib>Chang, Ruo-Bin</creatorcontrib><creatorcontrib>Huang, Shao-Wu</creatorcontrib><creatorcontrib>Huang, Jia-Xin</creatorcontrib><creatorcontrib>Yu, Hao-Yang</creatorcontrib><creatorcontrib>Yin, Fu-Xing</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</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>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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><jtitle>Tungsten</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yan, Jiao-Hui</au><au>Song, Zi-Jing</au><au>Fang, Wei</au><au>He, Xin-Bo</au><au>Chang, Ruo-Bin</au><au>Huang, Shao-Wu</au><au>Huang, Jia-Xin</au><au>Yu, Hao-Yang</au><au>Yin, Fu-Xing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning</atitle><jtitle>Tungsten</jtitle><stitle>Tungsten</stitle><date>2023-03-01</date><risdate>2023</risdate><volume>5</volume><issue>1</issue><spage>169</spage><epage>178</epage><pages>169-178</pages><issn>2661-8028</issn><eissn>2661-8036</eissn><abstract>A method which combines electronegativity difference, CALculation of PHAse Diagrams (CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co–Cr–Fe–Ni–Mo multi-component phase diagram. First, the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning, to avoid the formation of brittle phases. Then high yield strength points in the single-phase region are selected by electronegativity difference. The yield strength and plastic deformation behavior of the designed Co
14
Cr
30
Ni
50
Mo
6
alloy are measured to evaluate the proposed method. The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space. Meanwhile, the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co
14
Cr
30
Ni
50
Mo
6
alloy. The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys.</abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><doi>10.1007/s42864-021-00129-y</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8616-4609</orcidid></addata></record> |
| fulltext | fulltext |
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| issn | 2661-8028 2661-8036 |
| language | eng |
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| subjects | Alloying elements Alloys Chemistry and Materials Science Chromium Cobalt Datasets Decomposition Design Edge dislocations Electronegativity Iron Machine learning Materials Engineering Materials Science Mathematical analysis Mechanical properties Metallic Materials Molybdenum Nickel Nuclear Chemistry Original Paper Particle and Nuclear Physics Phase diagrams Plastic deformation Shear bands Support vector machines Temperature Work hardening Yield strength Yield stress |
| title | Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning |
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