Uncertainty quantification in dimensions dataset of additive manufactured NIST standard test artifact

Gary Mac, Hammond Pearce, Ramesh Karri, Nikhil Gupta

Research output: Contribution to journalArticlepeer-review

Abstract

The printed features on an additive manufactured part will often deviate from the nominal values of the 3D model's features due to the factors such as printer resolution, printing parameters, printing technology, and the measurement method. The National Institute of Standards and Technology (NIST) standard test artifact contains a collection of various features that can be used to characterize a 3D printer's performance and has been used to benchmark metal printers. There is limited documentation on how well different additive manufacturing processes can fabricate the NIST artifact. This dataset records the dimensional uncertainty of selective printed features of the NIST artifact manufactured with polymer and resin printing processes. It contains the post-processing dimensional measurements of geometric features on the printed test artifacts. In order to generate the data, a total of 16 samples of the test artifact were printed with fused deposition modelling (FDM) and stereolithography (SLA) additive manufacturing methods. The percentage error between the measurement of features in the printed samples and their nominal computer aided design (CAD) values are calculated. For future reusability of this data, the same NIST test artifact CAD model can be printed, and the features’ measurements can be compared with the dataset presented in this article.

Original languageEnglish (US)
Article number107286
JournalData in Brief
Volume38
DOIs
StatePublished - Oct 2021

Keywords

  • 3D printing
  • Additive manufacturing
  • Fused deposition modelling
  • NIST test artifact
  • Printing accuracy
  • Stereolithography

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Uncertainty quantification in dimensions dataset of additive manufactured NIST standard test artifact'. Together they form a unique fingerprint.

Cite this