Analyzing part accuracy and sources of variability for additively manufactured lattice parts made on multiple printers

Davis J. McGregor, Samuel Rylowicz, Aaron Brenzel, Daniel Baker, Charles Wood, David Pick, Hallee Deutchman, Chenhui Shao, Sameh Tawfick, William P. King

Research output: Contribution to journalArticlepeer-review


Scalable production using additive manufacturing (AM) requires quality systems that monitor and control manufacturing variability across many machines in a factory environment. AM process variability can result in geometric inaccuracies in parts that can affect their mechanical behavior; however, most research focuses on a single machine and hardware set, while part inaccuracy and AM variability across multiple printers remains poorly understood. This study establishes a framework to study the accuracy of polymer parts made on three identical printers and evaluates the role of manufacturing process parameters and their impact on part variability. We fabricate 90 polymer hexagonal lattice parts over nine builds on three identical Carbon M2 machines with interchangeable hardware tools. Each part is measured using automated metrology that extracts the size and shape of the part features and automatically measures defects. Each hexagonal part consists of 237 individual walls that were measured for 90 parts, resulting in 21,330 individual geometric measurements. Using statistical methods to analyze geometric defects as a function of machine, hardware set, and location in the build, we find that the observed variability can be correlated with process parameters. Geometric defects in the wall thickness depend upon machine and location within the build but do not depend on the hardware tool, while geometric defects in the wall length and height of the part depend upon machine and tool but not location. The uniformity within the parts, measured by standard deviations of the individual features within a part, mostly depends upon unmeasured parameters. We use a generalized linear model (GLM) to model the variability and to predict defects. The framework introduced here can be extended to analyze additional machines and process parameters and provides a practical tool to account for manufacturing variability in an AM production environment.

Original languageEnglish (US)
Article number101924
JournalAdditive Manufacturing
StatePublished - Apr 2021


  • Additive manufacturing
  • Architectured materials
  • Lattice
  • Part certification
  • Quality
  • Statistical model

ASJC Scopus subject areas

  • Biomedical Engineering
  • General Materials Science
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering


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