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Prediction of molten pool size and vapor depression depth in keyhole melting mode of laser powder bed fusion

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Abstract

Molten pool geometric characteristics are important factors related to process-induced defects in laser powder bed fusion (LPBF) metal additive manufacturing. Efficient models for rapid predictions of molten pool dimensions will facilitate the study of defects formation in LPBF. In this study, an analytical modeling strategy was developed to predict the molten pool dimensions and vapor depression depth in keyhole melting mode of LPBF. A combined temperature prediction model with closed-form solution was employed to predict the temperature distribution in the part with given process conditions and thermal properties of material. This model consists of a moving point heat source on the top surface of the part and a moving line heat source penetrating into the part. The moving point heat source was used to consider the thermal effects of plasma at the keyhole mouth. The moving line heat source with finite length was employed to consider the laser power absorption by the keyhole walls. The width, depth of molten pool, and vapor depression depth (also called keyhole depth) were determined by comparing the temperature distribution with the melting temperature and boiling point of the material, respectively. To verify the proposed model, the calculated molten pool dimensions under different process conditions were compared with experimental data of Ti6Al4V. The predictions show good agreement with experimental results. The sensitivity analyses of molten pool depth to process conditions were conducted. The computational cost of the proposed model is low because no finite element–based numerical computations are included in this model. The proposed method can provide significant guidance to the optimization of process conditions to avoid the defects related with molten pool size.

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Fig. 1

source length with normalized enthalpy

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Authors and Affiliations

  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

  2. School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA, 30332, USA

    Hamid Garmestani

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Wang, W., Garmestani, H. & Liang, S.Y. Prediction of molten pool size and vapor depression depth in keyhole melting mode of laser powder bed fusion. Int J Adv Manuf Technol 119, 6215–6223 (2022). https://doi.org/10.1007/s00170-021-08295-6

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