X-ray CT and image analysis methodology for local roughness characterization in cooling channels made by metal additive manufacturing

C. G. Klingaa*, T. Dahmen, S. Baier, S. Mohanty, J. H. Hattel

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The increasingly complex shapes and geometries being produced using additive manufacturing necessitate new characterization techniques that can address the corresponding challenges. Standard techniques for roughness and texture measurements are inept at characterizing the internal surfaces in freeform geometries. Hence, this work presents a new methodology for extracting and quantitatively characterizing the roughness on internal surfaces. The methodology links X-ray CT with complete roughness characterization of channels manufactured by laser powder bed fusion through a novel image analysis approach of X-ray CT data. Global and local orientation parameters are defined to enable a full 360° description of the roughness inside additively manufactured channels. X-ray CT data is analyzed to generate 3D deviation data – based on which multiple local roughness profiles are extracted and analyzed in accordance with the ISO 4287:1997 standard. To demonstrate the proposed methodology, seven circular 17-4 PH stainless steel channels produced at different inclinations and with a diameter of 2 mm are investigated as a case study. Qualitative and quantitative characterization of the roughness is obtained through the use of the proposed methodology. A strong dependence of the local roughness on the corresponding α and β orientations is found. A simple regression model is subsequently extracted from the calculated roughness values and allows prediction of Ra-values in the channels for the ranges between 0° ≤ α ≤ 90° and 80° ≤ β ≤ 280°. In addition to decreasing the effective hydraulic diameter of a cooling channel, the surface roughness also influences the local Nusselt number, which is quantified using the extracted regression model.
Original languageEnglish
Article number101032
JournalAdditive Manufacturing
Volume32
Number of pages14
ISSN2214-8604
DOIs
Publication statusPublished - 2020

Keywords

  • Cooling channels
  • X-ray computed tomography
  • Image analysis
  • Roughness analysis

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