Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162002
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dc.contributor.authorQiao, Lingen_US
dc.contributor.authorRamanujan, Raju Vijayaraghavanen_US
dc.contributor.authorZhu, Jingchuanen_US
dc.date.accessioned2022-09-28T08:39:58Z-
dc.date.available2022-09-28T08:39:58Z-
dc.date.issued2022-
dc.identifier.citationQiao, L., Ramanujan, R. V. & Zhu, J. (2022). Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties. Materials Science and Engineering A, 845, 143198-. https://dx.doi.org/10.1016/j.msea.2022.143198en_US
dc.identifier.issn0921-5093en_US
dc.identifier.urihttps://hdl.handle.net/10356/162002-
dc.description.abstractIn the present study, the machine learning (ML) method was utilized to construct a composition–structure–property model incorporating physical features. To enhance the predictive accuracy, the volume fraction of the two phase microstructure was merged into the dataset serving as the physical constraint for the input variables. The physical features, the chemical composition and the temperature difference between the initial and final melting temperatures were selected as the input and output variables, respectively. To deal with the small sample data, the generalized regression neural network (GRNN) was selected and applied with optimization algorithms e.g., fruit fly optimization algorithm (FOA) and particle swarm optimization (PSO). The performance of the GRNN, FOA-GRNN and PSO-GRNN models were compared. As a result, the PSO-GRNN model was the most promising model and could be utilized to search for new multi-principal elements alloy (MPEAs) with targeted properties. Based on the ML results, a novel Fe2.5Ni2.5CrAl MPEA was designed and synthesized for experimental characterization. The DSC analysis shows that the developed alloy possesses narrower melting range and the predicted value is in excellent agreement with experiments with a relative error below 10%. The designed alloy possesses a typical dual-phase structure (FCC+BCC/B2) and exhibits exceptional mechanical properties with superior plasticity at the cast condition. This property improvement is due to solid solution strengthening and nanoparticles strengthening effects. Our proposed alloy can be a promising choice for selected high performance applications.en_US
dc.language.isoenen_US
dc.relationA1898b0043en_US
dc.relationA18B1b0061en_US
dc.relation.ispartofMaterials Science and Engineering Aen_US
dc.rights© 2022 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Materialsen_US
dc.titleMachine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical propertiesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.identifier.doi10.1016/j.msea.2022.143198-
dc.identifier.scopus2-s2.0-85129694600-
dc.identifier.volume845en_US
dc.identifier.spage143198en_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsMulti-Principal Elements Alloyen_US
dc.description.acknowledgementThis work is supported by AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore under Grants No. A1898b0043 and A18B1b0061 and the China Scholarship Council.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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