期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 187, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.commatsci.2020.110112
关键词
Additive manufacturing; Laser powder bed fusion; Adaptive mesh refinement; Thermomechanical modeling; Residual stress
资金
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- Exascale Computing Project of the U.S. Department of Energy Office of Science [17-SC-20-SC]
- Exascale Computing Project of the National Nuclear Security Administration [17-SC-20-SC]
The use of adaptive mesh refinement (AMR) has been shown to improve the accuracy of thermal and thermomechanical simulations in laser powder bed fusion (LPBF), but other improvements are necessary to address multiple time scales and enhance AM-specific material models.
Due to the growing popularity of laser powder bed fusion (LPBF) as a metal additive manufacturing technique, there is a strong need to be able to accurately predict build outcomes. Full fidelity simulations of this process are not feasible due to the vast range of length and time scales inherent to it. While part-scale codes for simulating residual stress and distortion have shown reasonable predictive capability, they often neglect many aspects of the process occurring over smaller length/time scales, and thus are unable to capture effects of process parameter adjustments or the behavior of fine features. One way of capturing aspects at more refined length scales is through the use of adaptive mesh refinement (AMR). AMR allows for the process to be simulated at scales approaching the physical spatial dimensions without drastically increasing the total degrees of freedom in the simulation. This manuscript describes the implementation of an AMR algorithm within a multiphysics, parallelized finite element code, and its application to the LPBF problem. Part-scale examples are provided where the use of AMR has allowed for higher fidelity thermal and thermomechanical simulations, as compared to experimental measurements. Results from these higher resolution simulations show that while AMR is a necessary component for increased accuracy in a computationally efficient manner, other improvements are also necessary, including handling of the multiple time scales inherent to the problem and the need for improved AM-specific material models.
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