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Title: Computational modeling of process-structure-property-performance relationships in metal additive manufacturing: a review
Authors: Mohammad Elahinia
Seyed Mahdi Hashemi
Soroush Parvizi
Haniyeh Baghbanijavid
Alvin T. L. Tan 
Mohammadreza Nematollahi
Ali Ramazani
Nicholas X. Fang
Keywords: data-driven modelling
Metal additive manufacturing
multi-scale multi-physics model/simulation
process–structure–property–performance relations
real data
Issue Date: 24-Jan-2021
Publisher: Taylor & Francis
Citation: Mohammad 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.
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.
Source Title: International Materials Reviews
ISSN: 0950-6608
DOI: 10.1080/09506608.2020.1868889
Appears in Collections:Staff Publications

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