首页|Effects of rescanning parameters on densification and microstructural refinement of 316L stainless steel fabricated by laser powder bed fusion
Effects of rescanning parameters on densification and microstructural refinement of 316L stainless steel fabricated by laser powder bed fusion
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NSTL
Elsevier
A challenge with microstructural control and refinement in laser powder bed fusion (LPBF) is maintaining high density when choosing parameters for desired microstructures. Rescanning during LPBF has been reported to improve densification and decrease surface roughness for many different alloys. However, little has been reported regarding the effects of locally rescanning with varying processing parameters on sub-grain cell size refinement for 316L stainless steel (SS). This study presents a novel solution to enable high densification with microstructural control in 316L SS by using a set of initial scanning parameters to achieve densification and a different set of rescanning parameters to refine the microstructure. Results showed that rescanning resulted in heterogeneous microstructure with coarse cell size of 0.84 mu m and locally refined cell size of 0.35 mu m, while maintaining a high level of densification (99.96 %), therefore enabling potential variations in component strength and hardness. The spatial distribution of local microstructure refinement was dictated by the melt pool dimensions of initial scanning and rescanning relative to the powder layer thickness. To better understand the link between LPBF process parameters and microstructure, the Wilson-Rosenthal equation was used to predict cooling rate (G x R) and correlate with sub-grain cell size. Such variation in properties may be useful for applications requiring parts with hardened surfaces, or localized strengthening at stress concentrations and sites of expected failure.
Laser powder bed fusionWilson-Rosenthal equation316L stainless steelHeterogeneous microstructureSub-grain cell sizeDensityRESIDUAL-STRESSABSORPTIVITYMECHANISMSPOROSITYPARTSHEAT
Liang, Anqi、Pey, Khee Siang、Polcar, Tomas、Hamilton, Andrew R.
NSTL
Elsevier
