The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
An overview of functionally graded additive manufacturing
Open accessAbstract
Functionally Graded Additive Manufacturing (FGAM) is a layer-by-layer fabrication process that involves gradationally varying the material organisation within a component to achieve an intended function. FGAM establishes a radical shift from contour modelling to performance modelling by having the performance-driven functionality built directly into the material. FGAM can strategically control the density and porosity of the composition or can combine distinct materials to produce a seamless monolithic structure. This paper presents a state-of-art conceptual understanding of FGAM, covering an overview of current techniques that can enable the production of FGAM parts as well as identify current technological limitations and challenges. The possible strategies for overcoming those barriers are presented and recommendations on future design opportunities are discussed.
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Cites
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Processing techniques for functionally graded materials
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Cited by
A review of topology optimization for additive manufacturing: Status and challenges
ZHU, J., ZHOU, H., WANG, C., ZHOU, L., YUAN, S., & ZHANG, W. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2020.09.020 · Cited by 874
Al-Maharma, A. Y., Patil, S. P., & Markert, B. (2020) · Materials Research Express · doi:10.1088/2053-1591/abcc5d · Cited by 404
Metadata
- Authors
- Loh, G. H., Pei, E., Harrison, D., & Monzón, M. D.
- Venue
- Additive Manufacturing
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 23
- Pages
- 34-44
- ISSN
- 2214-7810 (ISSN-L)
- 2214-8604
- OpenAlex
- W2859081733
- Cited by
- 459
- References
- 32
- FWCI
- 23.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
