A mesoscopic digital twin that bridges length and time scales for control of additively manufactured metal microstructures

被引:20
|
作者
Heo, Tae Wook [1 ]
Khairallah, Saad A. [1 ]
Shi, Rongpei [1 ]
Berry, Joel [1 ]
Perron, Aurelien [1 ]
Calta, Nicholas P. [1 ]
Martin, Aiden A. [1 ]
Barton, Nathan R. [1 ]
Roehling, John [1 ]
Roehling, Tien [1 ]
Fattebert, Jean-Luc [2 ]
Anderson, Andy [1 ]
Nichols, Albert L. [1 ]
Wopschall, Steven [1 ]
King, Wayne E. [3 ]
McKeown, Joseph T. [1 ]
Matthews, Manyalibo J. [1 ]
机构
[1] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA
[2] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA
[3] Barnes Global Advisors, Sewickley, PA 15143 USA
来源
JOURNAL OF PHYSICS-MATERIALS | 2021年 / 4卷 / 03期
关键词
digital twin; additive manufacturing; metals; microstructures; processing; properties; STAINLESS-STEEL; CRITICAL NUCLEI; HIGH-STRENGTH; BCC PHASE; AL-V; SOLIDIFICATION; CORROSION; DYNAMICS; ALLOYS; MODEL;
D O I
10.1088/2515-7639/abeef8
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We present our recent development of an integrated mesoscale digital twin (DT) framework for relating processing conditions, microstructures, and mechanical responses of additively manufactured (AM) metals. In particular, focusing on the laser powder bed fusion technique, we describe how individual modeling and simulation capabilities are coupled to investigate and control AM microstructural features at multiple length and time scales. We review our prior case studies that demonstrate the integrated modeling schemes, in which high-fidelity melt pool dynamics simulations provide accurate local thermal profiles and histories to subsequent AM microstructure simulations. We also report our new mechanical response modeling results for predicted AM microstructures. In addition, we illustrate how our DT framework has been validated through modeling-experiment integration, as well as how it has been practically utilized to guide and analyze AM experiments. Finally, we share our perspectives on future directions of further development of the DT framework for more efficient, accurate predictions and wider ranges of applications.
引用
收藏
页数:24
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