等轴晶
微观结构
材料科学
因科镍合金
表征(材料科学)
杠杆(统计)
马朗戈尼效应
融合
热的
机械工程
焊接
计算机科学
冶金
机械
纳米技术
合金
对流
工程类
人工智能
热力学
哲学
物理
语言学
作者
Andrew Polonsky,Narendran Raghavan,McLean P. Echlin,Michael M. Kirka,Ryan R. Dehoff,Tresa M. Pollock
出处
期刊:The minerals, metals & materials series
日期:2020-01-01
卷期号:: 990-1002
被引量:10
标识
DOI:10.1007/978-3-030-51834-9_97
摘要
Additive manufacturing (AM) providesPolonsky,Andrew T. enormous processing flexibility, enabling novel part geometries and optimized designs. Access to a local heat source further permits the potential for localRaghavan, Narendran microstructure control on the scale of individual melt pools, which can enable local control of part properties. In order to design tailored processing strategies for target microstructures, models predicting theEchlin, McLean P. columnar-to-equiaxed transition must be extended to the high solidification velocities and complex thermal histories present in AM. Here, we combine 3D characterization with advancedKirka, Michael M. modeling techniques to develop a more complete understanding of the solidification process and evolution of microstructure during electron beam melting (EBM) of Inconel 718. Full calibrationDehoff, Ryan R. of existing microstructure prediction models demonstrates the differences between AM processes and more conventionalPollock, Tresa M. welding techniques, underlying the need for accurate determination of key parameters that can only be measured directly in 3D. The ability to combine multisensor data in a consistent 3D framework via data fusion algorithms is essential to fully leverage these advanced characterization approaches. Thermal modeling provides insight on microstructure development within isolated solidification events and demonstrates the role of Marangoni effects on controlling solidification behavior.
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