Micron-Level Layer-Wise Surface Profilometry to Detect Porosity Defects in Powder Bed Fusion of Inconel 718 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-09

AUTHORS

Chris Barrett, Eric MacDonald, Brett Conner, Fred Persi

ABSTRACT

Additive manufacturing (AM) enables a fabrication freedom and is transforming the manner in which high-value and high-performance structures are created. The aerospace industry stands to benefit from structures in which the weight is minimized, the materials provide good mechanical properties at extreme temperatures, and a swarm of distinct parts can be consolidated into a single non-assembled complex structure. However, for additive manufactured parts to be used in flight-critical applications, the quality of the resulting fabricated parts must be well understood in light of the lack of flight heritage. As AM is performed layer-by-layer, new opportunities exist to monitor the fabrication in situ and non-destructively, and to provide a qualify-as-you-go paradigm. In this study, a high-resolution laser line scan profilometer is used just after a layer has been selectively melted, and the sensor is mounted to the recoater arm to provide unobtrusive and inexpensive access to the top of the powder bed. The driving hypothesis of the effort was that fused and unfused powder would lie at different elevations, as the fused powder volume would consolidate and therefore become depressed. Consequently, this measurement could both verify the intended geometry and identify any lack of fusion defects. Furthermore, some preliminary anecdotal evidence has shown that spatter can also be identified, and thus profilometry can inform the minimization of contamination (build chamber argon flow, build layout strategies, etc.). More... »

PAGES

1844-1852

Journal

TITLE

JOM

ISSUE

9

VOLUME

70

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11837-018-3025-7

DOI

http://dx.doi.org/10.1007/s11837-018-3025-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105442900


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