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New Approach for Verifying the Accuracy of X-ray Computed Tomography Measurements of Surface Topographies in Additively Manufactured Metal Parts

  • Filippo ZaniniEmail author
  • Elia Sbettega
  • Marco Sorgato
  • Simone Carmignato
Article

Abstract

Surface topographies of metal additively manufactured components are inherently characterized by the presence of complex surface characteristics that are not accessible by contact or optical measuring techniques. Micro X-ray computed tomography is capable of measuring non-accessible surfaces and micro-scale surface features, including undercuts. In this work, an innovative approach for evaluating the accuracy and establishing the traceability of surface topography measurements obtained by X-ray computed tomography is presented. Reference samples produced by selective laser melting of Ti6Al4V were specifically designed in order to acquire reference cross-sectional surface profiles representing the actual morphology (including re-entrant features) using an imaging probing system. Surface topographies were measured on these samples by using three different techniques: X-ray computed tomography, confocal microscopy and focus variation. Moreover, the effect of different voxel dimensions on the accuracy of surface topography measurements performed by X-ray computed tomography was investigated. Results showed that X-ray computed tomography (at the highest tested resolution) can acquire surfaces and re-entrant features with small deviations with respect to the reference profiles; the deviations were quantified. On the contrary, focus variation and confocal microscopy can measure surfaces obtaining results that are close to the reference profiles only if such surfaces have no undercuts or inaccessible features.

Keywords

X-ray computed tomography Surface topography measurement Metal additive manufacturing Accuracy determination 

Notes

Acknowledgements

The authors would like to thank Mr. Raffaele Berardi, University of Padova, for his contribution with the experimental analyses. This work has received funding from the University of Padua, under Projects Nr. CPDA151522/15 and BIRD167853/16.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Filippo Zanini
    • 1
    Email author
  • Elia Sbettega
    • 1
  • Marco Sorgato
    • 2
  • Simone Carmignato
    • 1
  1. 1.Department of Management and EngineeringUniversity of PaduaVicenzaItaly
  2. 2.Department of Industrial EngineeringUniversity of PaduaPaduaItaly

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