Resources / Publications
Floriane Zongo (1), Charles Simoneau (2), Anatolie Timercan (1), Antoine Tahan (1), Vladimir Brailovski (1)
The International Journal of Advanced Manufacturing Technology, 107, February 2020: 1411–1436. DOI: 10.1007/s00170-020-04987-7
Laser powder bed fusion, Geometrical deviations, Numerical predictions, Predictions capabilities
Laser powder bed fusion (LPBF) is one of the most potent additive manufacturing processes. One of the constraints for a broader industrial use of this process is the limited knowledge of its dimensional performances and geometrical behavior, as well as the inability to predict them as a function of material, process parameters, part size, and geometry. The objective of this study is to enrich knowledge of the geometric dimensioning and tolerancing (GD&T) performances of the LPBF process and to evaluate the distortion prediction capabilities of the ANSYS Additive Print® software. To this end, a selected topologically optimized part with three different support configurations was manufactured using an EOSINT M280 printer and AlSi10Mg powder. After printing, the parts were scanned using a coordinate measuring machine (CMM) and a micro-computed tomography (μ-CT) system. The GD&T calculations were carried out according to the ASME Y14.5 (2009) standard. The distortions measured by the CMM and μ-CT techniques were 0.195 mm and 0.368 mm, respectively (95% interval). After the software calibration and two numerical sensitivity studies, the same stereolithography files used to print the parts were downloaded into the ANSYS Additive Print® software to calculate distortions caused by the process. The differences between the experimentally measured and the ANSYS-predicted distortions for a 56 mm × 58 mm × 137 mm part fell within a 0.134 mm range at a 95% interval. The fidelity of the numerical predictions, the impact of the support structures, and the differences induced by the CMM and μ-CT measurement uncertainties are presented and discussed.
Dragonfly was used to separate the region of interest (ROI) from the void in CT-scan data.
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