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Three-Dimensional Numerical Simulation of Grain Growth during Selective Laser Melting of 316L Stainless Steel.
Xu, Feng; Xiong, Feiyu; Li, Ming-Jian; Lian, Yanping.
Afiliación
  • Xu F; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Xiong F; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Li MJ; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Lian Y; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
Materials (Basel) ; 15(19)2022 Sep 30.
Article en En | MEDLINE | ID: mdl-36234136
ABSTRACT
The grain structure of the selective laser melting additive manufactured parts has been shown to be heterogeneous and spatially non-uniform compared to the traditional manufacturing process. However, the complex formation mechanism of these unique grain structures is hard to reveal using the experimental method alone. In this study, we presented a high-fidelity 3D numerical model to address the grain growth mechanisms during the selective laser melting of 316 stainless steel, including two heating modes, i.e., conduction mode and keyhole mode melting. In the numerical model, the powder-scale thermo-fluid dynamics are simulated using the finite volume method with the volume of fluid method. At the same time, the grain structure evolution is sequentially predicted by the cellular automaton method with the predicted temperature field and the as-melted powder bed configuration as input. The simulation results agree well with the experimental data available in the literature. The influence of the process parameters and the keyhole and keyhole-induced void on grain structure formation are addressed in detail. The findings of this study are helpful to the optimization of process parameters for tailoring the microstructure of fabricated parts with expected mechanical properties.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Materials (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Materials (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China