Journal
ADDITIVE MANUFACTURING
Volume 28, Issue -, Pages 394-405Publisher
ELSEVIER
DOI: 10.1016/j.addma.2019.05.017
Keywords
AlSi10Mg; Material quality; Defect; Computed tomography; Metallography
Funding
- ESA through Networking Partnering Initiative (NPI) [4000120615/17/NL/MH]
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While the adoption of metal additive manufacturing (AM) is growing exponentially owing to its wide range of potential applications, its application to safety-critical and structural parts is significantly impeded by the lack of standards. Quality assessment of AM products is a crucial requirement, as the AM process induces internal defects that can have detrimental effects on the fatigue resistance. By evaluating the defect distribution, it is possible to perform a fracture mechanics assessment to estimate the fatigue strength and service lifetime of AM materials. This strategy has been successfully applied to selective laser-melted AlSi10Mg by performing X-ray micro-computed tomography (mu CT) and applying suitable statistical methods (i.e., statistics of extremes). However, it remains unclear whether complex and expensive nondestructive inspection methods (e.g., mu CT) are necessary and whether simpler and more conventional approaches (i.e., microscopy of polished sections (PSs), as prescribed by ASTM E2283) would provide equivalent information for the estimation of internal defects. In this study, the size of the most detrimental defect was estimated by performing both light microscopy on PSs and mu CT on three batches of fatigue specimens characterized by different internal porosities. The results showed that both techniques were able to pinpoint a significant difference in the prospective largest defect in a material volume corresponding to the gauge section of a specimen. However, extrapolation of the critical defect size for fatigue failure using PS data was less accurate and less conservative than that using CT data. An evaluation of the techniques with regard to time and cost indicated that mu CT allowed the investigation of larger sample volumes and the reduction of both man hours and cost.
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