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A 3D analytical modeling method for keyhole porosity prediction in laser powder bed fusion

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Abstract

In this work, a three-dimensional (3D) analytical modeling method is proposed for the prediction of keyhole porosity in laser powder bed fusion (LPBF) metal additive manufacturing. The proposed method consists of a physics-based analytical thermal model for keyhole melting mode and a pore formation model. The thermal model is used to calculate the molten pool size and vapor depression depth, with given process conditions and material properties. It consists of a moving point heat source on the part surface and a moving finite line heat source penetrating into the part. The pore formation model considers the process of bubble generation and trapping. It is used to calculate the volume fraction of pores in solidified molten pool, with the molten pool dimensions, vapor depression depth, velocity of fluid flow, frequency of bubble emission, and average bubble size as inputs. To verify the proposed method, the predictions of keyhole porosity are compared with documented experimental data of Ti6Al4V and display acceptable agreement. No finite element analyses are included in the proposed method, which can save computational resources. Thus, the proposed method is useful for the rapid prediction of keyhole porosity and can help understand the physics and optimize the process conditions in LPBF. The sensitivity of keyhole porosity to process conditions is also discussed.

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Fig. 1
Fig. 2

source on the top surface, while the purple bar denotes the moving finite line heat source. The gray region denotes the solidified metal. The yellow region and red region represent the molten pool and vapor depression, respectively. The gray circles represent the powders. The white circles in the molten pool represent the emitted bubbles, while the white circles in the gray region represent the pores (trapped bubbles). x represents the laser scan direction. z represents the build direction

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source length and normalized enthalpy

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  1. George W, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Drive NW, Atlanta, GA, 30332, USA

    Wenjia Wang & Steven Y. Liang

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  1. Wenjia Wang
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Wang, W., Liang, S.Y. A 3D analytical modeling method for keyhole porosity prediction in laser powder bed fusion. Int J Adv Manuf Technol 120, 3017–3025 (2022). https://doi.org/10.1007/s00170-022-08898-7

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