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Cited by 1,034

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Materials for additive manufacturing

Bourell, David, Kruth, Jean Pierre, Leu, Ming, Levy, Gideon, Rosen, David, Beese, Allison M., Clare, Adam·2017·CIRP Annals

Abstract

Abstract Critical to the selection requirements for additive manufacturing (AM) is the need for appropriate materials. Materials requirements for AM include the ability to produce the feedstock in a form amenable to the specific AM process, suitable processing of the material by AM, capability to be acceptably post-processed to enhance geometry and properties, and manifestation of necessary performance characteristics in service. As AM has matured, specific classes of material have become associated with specific AM processes and applications. This paper gathers this information for each of the seven categories of ISO/ASTM AM categories. Polymers, metals, ceramics and composites are considered. Microstructural features affecting AM part properties are listed. Service properties of AM parts are described, including physical, mechanical, optical and electrical properties. An additive manufacturability index is proposed.

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Metadata

Authors
Bourell, D., Kruth, J. P., Leu, M., Levy, G., Rosen, D., Beese, A. M., & Clare, A.
Venue
CIRP Annals
Publisher
Elsevier BV
Published
2017
Volume
66
Issue
2
Pages
659-681
ISSN
0007-8506 (ISSN-L)
OpenAlex
W2622805139
Cited by
1,034
References
3
FWCI
49.52
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Materials for additive manufacturing. https://app.cochat.ai/papers/10.1016/j.cirp.2017.05.009
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Bourell, D., Kruth, J. P., Leu, M., Levy, G., Rosen, D., Beese, A. M., & Clare, A. (2017). Materials for additive manufacturing. CIRP Annals. https://doi.org/10.1016/j.cirp.2017.05.009

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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.