Thermal analysis of anisotropic heat conduction model with experimental validation on molten pool during selective laser melting

• Published in: Materials today communications Vol. 27; p. 102425
• Main Authors: Siao, Yong-Hao, Wen, Chang-Da
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021
• Subjects: Anisotropic heat conduction; Marangoni effect; Molten pool; Powder bed fusion; Selective laser melting; Anisotropic heat conduction; Powder bed fusion; Molten pool; Selective laser melting; Marangoni effect
• ISSN: 2352-4928; 2352-4928
• DOI: 10.1016/j.mtcomm.2021.102425

[Display omitted] •Molten pool was simulated using a three-dimensional numerical model.•Marangoni flow model and anisotropic heat conduction model were established.•Heat flux followed a Gaussian distribution during selective laser melting.•Enhanced coefficient of anisotropic heat conduction rose with energy density. A metal powder bed of 316 L stainless steel is treated with a high-energy laser on the powder surface, creating a molten pool. The development of the molten pool is simulated using a three-dimensional numerical model; this is conducted using the Marangoni flow model and the anisotropic heat conduction model. In this study, the energy density is calculated by the laser power, beam size, and scanning speed, which is used in the simulation of the process parameters. The influence of the laser power and energy density are investigated by the temperature distribution, depth, and width of the molten pool. The results present that the enhanced coefficient of the anisotropic heat conduction rises along with the energy density. The depth and width of the molten pool in the experiments and simulations tend to rise with the energy density. The depth, width, and shape of the molten pool predicted using the anisotropic heat conduction model are very close to the experimental measurements. A relationship between the molten pool depth and the energy density is found in the results, which leads to an appropriate estimation of the process parameters necessary to calculate the desired depth and advances the efficiency of the SLM process.