Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142414
Title: Numerical and experimental study of laser aided additive manufacturing for melt-pool profile and grain orientation analysis
Authors: Song, Jie
Chew, Youxiang
Bi, Guijun
Yao, Xiling
Zhang, Baicheng
Bai, Jiaming
Moon, Seung Ki
Keywords: Engineering::Mechanical engineering
Issue Date: 2018
Source: Song, J., Chew, Y., Bi, G., Yao, X., Zhang, B., Bai, J., & Moon, S. K. (2018). Numerical and experimental study of laser aided additive manufacturing for melt-pool profile and grain orientation analysis. Materials and Design, 137, 286-297. doi:10.1016/j.matdes.2017.10.033
Journal: Materials and Design
Abstract: Laser aided additive manufacturing (LAAM), a blown powder additive manufacturing process, can be widely adopted for surface modification, repair and 3D printing. A robust numerical model was developed to simulate convective fluid flow and balancing of surface tension forces at the air-fluid interface to predict melt-pool free surface curvature and solidified clad dimensions. The free surface physical interface was calculated using the Arbitrary Lagrangian Eulerian (ALE) moving mesh approach. Powder deposition efficiency was considered by activating mesh normal velocity at melted regions based on localized powder mass flux intensity from the discrete coaxial powder nozzles. The heat flux equation used for representing the laser heat source considered attenuation effect from the interaction between the powder jets and laser as well as heat sink effects of un-melted powder particles entering the melt-pool. The predicted thermal gradient directions agree well with grain growth orientations obtained from electron backscatter diffraction (ESBD) analysis in three different cross-sectional orientations. Experimental validation of clad width, height and melt-pool depth shows a maximum error of 10% for a wide range of processing parameters which consider the effects of varying laser power, laser scanning speed and powder feeding rate.
URI: https://hdl.handle.net/10356/142414
ISSN: 0261-3069
DOI: 10.1016/j.matdes.2017.10.033
Rights: © 2017 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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