Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161770
Title: Computational framework for the simulation of multi material laser powder bed fusion
Authors: Tang, Chao
Yao, Liming
Du, Hejun
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Tang, C., Yao, L. & Du, H. (2022). Computational framework for the simulation of multi material laser powder bed fusion. International Journal of Heat and Mass Transfer, 191, 122855-. https://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.122855
Journal: International Journal of Heat and Mass Transfer
Abstract: With the capability to build components with freeform geometries, laser powder bed fusion (L-PBF) is considered as one of the most promising techniques for industrial applications. Compared to conventional manufacturing technologies, an additional advantage of L-PBF process is the capability to fabricate parts with multiple materials, such as in-situ alloying and functionally graded alloys. In this regard, here we present a computational model to simulate the L-PBF of multiple materials. Based on volume of fluid (VOF) methods and computational fluid dynamics (CFD), the multi-physics multi-material model was successfully implemented with various physics of L-PBF process, including surface tension, Marangoni shear force, recoil pressure, compositional diffusion, etc. In addition, we demonstrated the numerical algorithm of ray tracing heat source for multiple materials. With reasonable assumptions, conduction mode or keyhole mode laser melting of miscible materials can be simulated via the proposed computational framework. Furthermore, such multi-physics model is capable of simulating L-PBF of an arbitrary number of materials. The computational model can help achieve insightful understanding of the L-PBF of multiple materials.
URI: https://hdl.handle.net/10356/161770
ISSN: 0017-9310
DOI: 10.1016/j.ijheatmasstransfer.2022.122855
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Singapore Centre for 3D Printing 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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