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Article Dans Une Revue Additive Manufacturing Année : 2024

A methodology for the 3D characterization of surfaces using X-ray computed tomography: Application to additively manufactured parts

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Many studies highlight the significance of three-dimensional surface topography characterization in assessing its effect on the mechanical or functional properties of materials. This is especially obvious for parts made by additive manufacturing (AM), known for their complex shape and surface topographies. However, a vast majority of 3D characterizations have constraints regarding the macroscopic geometry of the parts they can probe. At the microscale, they are also unable to account for hidden surface features, e.g. notches hidden by unmelted powder particles. Even with the use of X-ray Computed Tomography (XCT) − a tool with the potential to circumvent these issues − data is often reduced to 2D or 2.5D formats for easier analysis, but this leads to a loss of information. This underscores the need for XCT data post-treatment tools to perform thorough 3D surface characterizations. Herein, we introduce a methodology for local roughness and curvature characterization of surfaces of complex shapes using XCT. This method has been designed to be user-friendly, especially for those without extensive data analysis expertise. It provides a comprehensive 3D characterization and efficiently tackles the issues caused by hidden features. After a detailed description of our methodology, we give a first illustrative example based on architected structures fabricated by Electron Powder Bed Fusion (E-PBF). By integrating roughness and curvature metrics, we also derive a parameter indicative of the stress concentrations caused by surface irregularities.
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hal-04579719 , version 1 (18-05-2024)

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Florian Steinhilber, Joel Lachambre, David Coeurjolly, Jean-Yves Buffiere, Guilhem Martin, et al.. A methodology for the 3D characterization of surfaces using X-ray computed tomography: Application to additively manufactured parts. Additive Manufacturing, 2024, 84, pp.104144. ⟨10.1016/j.addma.2024.104144⟩. ⟨hal-04579719⟩
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