Sensitivity analysis of geometric errors in additive manufacturing medical models

Jose Miguel Pinto, Cristobal Arrieta, Marcelo E. Andia, Sergio Uribe, Jorge Ramos-Grez, Alex Vargas, Pablo Irarrazaval, Cristian Tejos

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Additive manufacturing (AM) models are used in medical applications for surgical planning, prosthesis design and teaching. For these applications, the accuracy of the AM models is essential. Unfortunately, this accuracy is compromised due to errors introduced by each of the building steps: image acquisition, segmentation, triangulation, printing and infiltration. However, the contribution of each step to the final error remains unclear.We performed a sensitivity analysis comparing errors obtained from a reference with those obtained modifying parameters of each building step. Our analysis considered global indexes to evaluate the overall error, and local indexes to show how this error is distributed along the surface of the AM models.Our results show that the standard building process tends to overestimate the AM models, i.e. models are larger than the original structures. They also show that the triangulation resolution and the segmentation threshold are critical factors, and that the errors are concentrated at regions with high curvatures.Errors could be reduced choosing better triangulation and printing resolutions, but there is an important need for modifying some of the standard building processes, particularly the segmentation algorithms.

Original languageEnglish
Pages (from-to)328-334
Number of pages7
JournalMedical Engineering and Physics
Volume37
Issue number3
DOIs
StatePublished - 1 Mar 2015
Externally publishedYes

Keywords

  • Additive manufacturing
  • Geometric accuracy
  • Image processing

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