ICME Manufacturability Assessment in Powder Bed Fusion Additive Manufacturing

Pires, Pierre-Adrien; Desmaison, Olivier; Megahed, Mustafa

doi:10.1007/s11837-018-3024-8

Cited by 10 publications

(2 citation statements)

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“…The ICME framework involves the integration of multi-scale/multi-physics computational models to solve the challenges in the nano/ micro/meso/macro analysis of materials [54]. ICME has predictive power in determining the final material composition, mechanical properties and part performance, allowing engineers to find optimal process parameters and process pathways [8,56].…”

Section: Challenges/approaches In Am Technologiesmentioning

confidence: 99%

“…Given that joining particles together in AM processes at smaller scales acts like a welding process, many techniques already used for welding simulation can somehow be employed for AM [56]. However, AM techniques are mostly involved with powder bed in which different modelling from laser-powder bed interaction and heat source models, to heat flow and solidification modelling, and finally property model and mechanical response prediction, can be employed [51].…”

Section: Introductionmentioning

confidence: 99%

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Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

Hashemi

Parvizi

Baghbanijavid

et al. 2021

International Materials Reviews

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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.

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Section: Challenges/approaches In Am Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%