2020
DOI: 10.1016/j.addma.2019.101032
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X-ray CT and image analysis methodology for local roughness characterization in cooling channels made by metal additive manufacturing

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Cited by 21 publications
(23 citation statements)
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“…As a step toward validation of the proposed methodology, the roughness was predicted and applied to the nominal geometry of channels produced at α = 0° and α = 90°. The predicted roughness was compared to X-ray computed tomography (CT) and image analysis results from (Klingaa et al , 2020) and may be seen in Figure 6.…”
Section: Resultsmentioning
confidence: 99%
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“…As a step toward validation of the proposed methodology, the roughness was predicted and applied to the nominal geometry of channels produced at α = 0° and α = 90°. The predicted roughness was compared to X-ray computed tomography (CT) and image analysis results from (Klingaa et al , 2020) and may be seen in Figure 6.…”
Section: Resultsmentioning
confidence: 99%
“…The models used for roughness prediction of any point within a channel were obtained from (Klingaa et al , 2020) where a methodology for obtaining simple roughness prediction models for metal additively manufactured channels was proposed. As a case study, a roughness prediction model was generated for channels made in 17–4 PH stainless steel on an EOS M 290 system.…”
Section: Methodsmentioning
confidence: 99%
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“…However, their studies were performed on external AM surfaces. More recently, Klingaa et al [30,31] extended the CT analysis to internal surfaces of LPBF channels, by modelling the roughness value depending on the defined global build orientation (α) and local orientation (β). This approach allows establishing novel concepts, like the presented rough belt concept with an angular width, which showed how the angular width directly correlates to the α-orientation.…”
Section: Introductionmentioning
confidence: 99%