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Dependence of mechanical properties of polyamide components on build parameters in the SLS process

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Caulfield, B., McHugh, P.E., Lohfeld, S.·2007·Journal of Materials Processing Technology

Abstract

Abstract Customised properties of parts manufactured using the selective laser sintering process are achievable by variation of build parameters. The energy density, controlled by laser power, distance between scan lines and speed of the laser beam across the powder bed, all have a very strong influence on the density and the mechanical behaviour of the parts. The present paper investigates the influence of the energy density on physical and mechanical properties of parts produced using polyamide. Additionally, the effect of part orientation during the build is examined. Knowledge of the influence of these parameters allows one to establish trendlines which link build settings to resulting part properties, and hence to fabricate customised parts with predetermined properties.

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Crossref · 10.1016/j.jmatprotec.2006.09.007

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Metadata

Authors
Caulfield, B., McHugh, P. E., & Lohfeld, S.
Venue
Journal of Materials Processing Technology
Publisher
Elsevier BV
Published
2007-02
Volume
182
Issue
1-3
Pages
477-488
ISSN
0924-0136 (ISSN-L)
OpenAlex
W2077862115
Cited by
598
References
0
FWCI
26.21
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Dependence of mechanical properties of polyamide components on build parameters in the SLS process. https://app.cochat.ai/papers/10.1016/j.jmatprotec.2006.09.007
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Caulfield, B., McHugh, P. E., & Lohfeld, S. (2007). Dependence of mechanical properties of polyamide components on build parameters in the SLS process. Journal of Materials Processing Technology. https://doi.org/10.1016/j.jmatprotec.2006.09.007

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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.