Fan Zhang, Aaron C. Johnston-Peck, Lyle E. Levine, Michael B. Katz, Kil-Won Moon, Maureen E. Williams, Sandra W. Young, Andrew J. Allen, Olaf Borkiewicz, Jan Ilavsky
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引用次数: 0
Abstract
Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. We employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ’ and γ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.
快速成型制造(AM)技术提供了前所未有的设计灵活性,但由于缺乏对在极端和瞬时加工条件下形成的材料微观结构及其在制造后加工过程中的后续转变的了解,这种灵活性受到了限制。作为美国国家标准与技术研究院赞助的 2022 年 AM 工作台挑战赛的一部分,本研究重点关注 AM 镍合金 718(一种镍基超级合金)的相组成和相演化,以提供验证微观结构预测计算模型所必需的基准数据。我们采用了高能同步辐射 X 射线衍射、原位同步辐射 X 射线散射以及高分辨率透射电子显微镜进行分析。这项研究揭示了微观结构在雏形状态、均质化过程中的转变以及在随后的老化热处理过程中的相演变的关键方面。具体来说,我们确定了次生相,监测了微结构元素的溶解和粗化,并观察了 γ' 和 γ" 相的形成和稳定性。这些结果为了解 AM 镍合金 718 的原子和微观结构转变提供了所需的严格基准数据,从而提高了 AM 模型预测相演变和机械性能的可靠性和适用性。
期刊介绍:
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.