Please use this identifier to cite or link to this item:
https://doi.org/10.1007/s11465-018-0505-y
DC Field | Value | |
---|---|---|
dc.title | Modeling process-structure-property relationships for additive manufacturing | |
dc.contributor.author | Yan, W | |
dc.contributor.author | Lin, S | |
dc.contributor.author | Kafka, O.L | |
dc.contributor.author | Yu, C | |
dc.contributor.author | Liu, Z | |
dc.contributor.author | Lian, Y | |
dc.contributor.author | Wolff, S | |
dc.contributor.author | Cao, J | |
dc.contributor.author | Wagner, G.J | |
dc.contributor.author | Liu, W.K | |
dc.date.accessioned | 2020-10-30T02:04:54Z | |
dc.date.available | 2020-10-30T02:04:54Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Yan, W, Lin, S, Kafka, O.L, Yu, C, Liu, Z, Lian, Y, Wolff, S, Cao, J, Wagner, G.J, Liu, W.K (2018). Modeling process-structure-property relationships for additive manufacturing. Frontiers of Mechanical Engineering 13 (4) : 482-492. ScholarBank@NUS Repository. https://doi.org/10.1007/s11465-018-0505-y | |
dc.identifier.issn | 20950233 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/182068 | |
dc.description.abstract | This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process-structure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high-efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing. © 2018, The Author(s). | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.type | Review | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.description.doi | 10.1007/s11465-018-0505-y | |
dc.description.sourcetitle | Frontiers of Mechanical Engineering | |
dc.description.volume | 13 | |
dc.description.issue | 4 | |
dc.description.page | 482-492 | |
Appears in Collections: | Elements Staff Publications |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1007_s11465-018-0505-y.pdf | 826.33 kB | Adobe PDF | OPEN | None | View/Download |
This item is licensed under a Creative Commons License