Please use this identifier to cite or link to this item: https://doi.org/10.1080/09506608.2020.1868889
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dc.titleComputational modeling of process-structure-property-performance relationships in metal additive manufacturing: a review
dc.contributor.authorMohammad Elahinia
dc.contributor.authorSeyed Mahdi Hashemi
dc.contributor.authorSoroush Parvizi
dc.contributor.authorHaniyeh Baghbanijavid
dc.contributor.authorAlvin T. L. Tan
dc.contributor.authorMohammadreza Nematollahi
dc.contributor.authorAli Ramazani
dc.contributor.authorNicholas X. Fang
dc.date.accessioned2022-02-21T09:16:56Z
dc.date.available2022-02-21T09:16:56Z
dc.date.issued2021-01-24
dc.identifier.citationMohammad Elahinia, Seyed Mahdi Hashemi, Soroush Parvizi, Haniyeh Baghbanijavid, Alvin T. L. Tan, Mohammadreza Nematollahi, Ali Ramazani, Nicholas X. Fang (2021-01-24). Computational modeling of process-structure-property-performance relationships in metal additive manufacturing: a review. International Materials Reviews 67 (1) : Jan-46. ScholarBank@NUS Repository. https://doi.org/10.1080/09506608.2020.1868889
dc.identifier.issn0950-6608
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/215747
dc.description.abstractIn the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.
dc.publisherTaylor & Francis
dc.sourceTaylor & Francis
dc.subjectdata-driven modelling
dc.subjectMetal additive manufacturing
dc.subjectmulti-scale multi-physics model/simulation
dc.subjectprocess–structure–property–performance relations
dc.subjectreal data
dc.typeArticle
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1080/09506608.2020.1868889
dc.description.sourcetitleInternational Materials Reviews
dc.description.volume67
dc.description.issue1
dc.description.pageJan-46
dc.published.statePublished
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