A Closed-Form Solution for Temperature Profiles in Selective Laser Melting of Metal Additive Manufacturing

Abstract:

This paper presents a closed-form solution for the temperature prediction in selective laser melting (SLM). This solution is developed for the three-dimensional temperature prediction with consideration of heat input from a moving laser heat source, and heat loss from convection and radiation on the part top boundary. The consideration of heat transfer boundary condition and latent heat in the closed-form solution leads to an improvement on the understanding of thermal development and prediction accuracy in SLM, and thus the usefulness of the analytical model in the temperature prediction in real applications. A moving point heat source solution is used to calculate the temperature rise due to the heat input. A heat sink solution is used to calculate the temperature drop due to heat loss from convection and radiation on the part boundary. The heat sink solution is modified from a heat source solution with equivalent power due to heat loss from convection and radiation, and zero-moving velocity. The temperature solution is then constructed from the superposition of the linear heat source solution and linear heat sink solution. Latent heat is considered using a heat integration method. Ti-6Al-4V is chosen to test the presented model with the assumption of isotropic and homogeneous material. The predicted molten pool dimensions are compared to the documented values from the finite element method and experiments in the literature. The presented model has improved prediction accuracy and significantly higher computational efficiency compared to the finite element model.

• Periodical: Materials Science Forum (Volume 982)
• Pages: 98-105
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.982.98
• Online since: March 2020
• Authors: Steven Y. Liang*, Jin Qiang Ning, Elham Mirkoohi
• Keywords: Closed-Form Solution, High Computational Efficiency, Latent Heat, Part Boundary, Selective Laser Melting, Temperature Prediction

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