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The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Additive manufacturing of functionally graded metallic materials using laser metal deposition

Open access
Yan, Lei, Chen, Yitao, Liou, Frank·2020·Additive Manufacturing

Abstract

Abstract Functionally graded materials (FGMs) have attracted much research interest in the industry due to their graded material properties, which result from gradually distributed compositions or structures. In recent years, metallic FGMs have been widely studied, and additive manufacturing (AM) has become an important approach to build metallic FGMs. This paper aims to provide an overview of the research progress in metallic FGMs fabricated by laser metal deposition (LMD), an AM process that is widely used in metallic materials. Firstly, the unique material properties and advantages of FGMs are introduced. Then, typical recent findings in transition path design, fabrication, and characterization for different types of metallic FGMs via LMD are summarized and discussed. Finally, challenges in fabricating metallic FGMs via LMD are discussed, and other related aspects in the area of FGMs such as model representation and numerical simulation are proposed for further investigation.

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Metadata

Authors
Yan, L., Chen, Y., & Liou, F.
Venue
Additive Manufacturing
Publisher
Elsevier BV
Published
2020-01
Volume
31
Pages
100901
ISSN
  • 2214-7810 (ISSN-L)
  • 2214-8604
OpenAlex
W2985248673
Cited by
367
References
28
FWCI
20.80
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Additive manufacturing of functionally graded metallic materials using laser metal deposition. https://app.cochat.ai/papers/10.1016/j.addma.2019.100901
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Yan, L., Chen, Y., & Liou, F. (2020). Additive manufacturing of functionally graded metallic materials using laser metal deposition. Additive Manufacturing. https://doi.org/10.1016/j.addma.2019.100901

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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.