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- Publisher Website: 10.1080/09506608.2020.1868889
- Scopus: eid_2-s2.0-85099830384
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Article: Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review
Title | Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review |
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Authors | |
Keywords | data-driven modelling Metal additive manufacturing multi-scale multi-physics model/simulation process–structure–property–performance relations real data |
Issue Date | 2022 |
Citation | International Materials Reviews, 2022, v. 67, n. 1, p. 1-46 How to Cite? |
Abstract | In 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. |
Persistent Identifier | http://hdl.handle.net/10722/318904 |
ISSN | 2022 Impact Factor: 16.1 2020 SCImago Journal Rankings: 3.760 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hashemi, Seyed Mahdi | - |
dc.contributor.author | Parvizi, Soroush | - |
dc.contributor.author | Baghbanijavid, Haniyeh | - |
dc.contributor.author | Tan, Alvin T.L. | - |
dc.contributor.author | Nematollahi, Mohammadreza | - |
dc.contributor.author | Ramazani, Ali | - |
dc.contributor.author | Fang, Nicholas X. | - |
dc.contributor.author | Elahinia, Mohammad | - |
dc.date.accessioned | 2022-10-11T12:24:49Z | - |
dc.date.available | 2022-10-11T12:24:49Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | International Materials Reviews, 2022, v. 67, n. 1, p. 1-46 | - |
dc.identifier.issn | 0950-6608 | - |
dc.identifier.uri | http://hdl.handle.net/10722/318904 | - |
dc.description.abstract | In 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.language | eng | - |
dc.relation.ispartof | International Materials Reviews | - |
dc.subject | data-driven modelling | - |
dc.subject | Metal additive manufacturing | - |
dc.subject | multi-scale multi-physics model/simulation | - |
dc.subject | process–structure–property–performance relations | - |
dc.subject | real data | - |
dc.title | Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/09506608.2020.1868889 | - |
dc.identifier.scopus | eid_2-s2.0-85099830384 | - |
dc.identifier.volume | 67 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 46 | - |
dc.identifier.eissn | 1743-2804 | - |
dc.identifier.isi | WOS:000611956200001 | - |