Functionally Graded Metallic Materials by Directed Energy Deposition Additive Manufacturing: Computational Design, Fabrication and Validation

通过定向能量沉积增材制造实现功能梯度金属材料：计算设计、制造和验证

基本信息

批准号：
2050069
负责人：
Allison Beese
金额：
$ 55.27万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050069&HistoricalAwards=false
关键词：
Functionally Graded Metallic Materials Directed

项目摘要

The layer-by-layer process of additive manufacturing enables the controlled variation of material compositions, and therefore, properties, as a function of locations in a fabricated part. Such a unique capability has the potential to drastically transform the engineering design paradigm, inspiring innovative structures with spatially tailored multi-functional properties (e.g., physical, mechanical and thermal, etc.), which are strongly desired in many applications such as turbine blades. However, the complexity of phase formation resulted from the simultaneous deposition of disparate materials during additive manufacturing is least understood and hinders the ability to not only design, but also successfully produce materials of required functional gradients. This award supports fundamental research aimed at enabling the design and fabrication of functionally graded metallic materials using the laser powder-fed directed energy deposition process. The present research endeavors to develop comprehensive understanding of phase formation and transformations during layer-wise making of multi-component systems using integrated computational and experimental tools. In addition to its potential to reignite U.S. manufacturing, additive manufacturing’s power in tailoring properties within complex three-dimensional components will also significantly expand the design space and yield structures with enhanced integrity. The multidisciplinary nature of the research methodologies, along with crafted educational and outreach activities, will impact workforce development through the engagement of graduate and undergraduate students as well as the broader manufacturing community.The objective of the present research is to uncover the underlying mechanism of phase formation during the fabrication, via directed energy deposition additive manufacturing, of functionally graded metallic materials. The research will include the construction of a new multi-component thermodynamic database covering the complete compositional space of interest using novel high throughput first-principles calculations, deep neural network machine learning models, and high throughput thermodynamic modeling tools with uncertainty quantification. With this database, a combination of thermodynamic phase equilibrium calculations and kinetic phase transformation simulations will be used for phases formation predictions. The models will be applied to design compositional pathways between two metallic alloys, in a nonlinear fashion, for successful gradients in order to, e.g., avoid detrimental intermetallic phases. The designed functionally graded materials will be realized using a directed energy deposition machine and blending two different powders (titanium alloy and iron-nickel alloy) varying along the build height according to the design. Further, the compositions, microstructures and mechanical properties of fabricated parts will be thoroughly characterized and quantitatively compared with simulation results to refine the computational models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

添加剂制造的逐层过程可以使材料组成的受控变化，因此可以作为属性作为制造部分中位置的函数。如此独特的能力有可能极大地改变工程设计范式，从而激发了具有空间量身定制的多功能特性（例如物理，机械和热力学等）的创新结构，这些特性在许多应用中是在涡轮刀片等许多应用中都非常需要的。然而，最不理理解的是，在添加剂制造过程中简单的材料的简单沉积而产生的相位形成的复杂性，不仅可以设计设计，而且会成功生产所需功能梯度的材料。该奖项支持旨在使用激光粉末喂养的定向能量沉积过程来实现功能分级金属材料的设计和结构的基础研究。目前的研究努力使用集成的计算和实验工具在多组分系统的一层制造过程中对相位形成和转换进行全面了解。除了重新启动美国制造业的潜力外，其他制造业在复杂的三维组件中量身定制属性的功能还将显着扩展设计空间和产量结构，并具有增强的完整性。 The multidisciplinary nature of the research methods, along with crafted educational and outreach Activities, will impact workforce development through the engagement of graduate and undergraduate students as well as the broader manufacturing community.The objective of the present research is to uncover the underlying mechanism of phase formation during the fabrication, via directed energy deposition additive manufacturing, of functionally graded metallic materials.这项研究将包括建造一个新的多组分热力学数据库，该数据库涵盖了使用新型的高吞吐量第一原理计算，深度神经网络机器学习模型以及具有不确定性量化的高吞吐量热力学建模工具。使用此数据库，将使用热力学相等计算和动力学相变模拟的组合来进行阶段形成预测。这些模型将以非线性方式应用于两种金属合金之间的组合途径，以使其成功的梯度，以避免避免有害的金属间阶段。设计的功能分级材料将使用定向的能源沉积机，并根据设计将两种不同的粉末（钛合金和铁核合金）融合，并根据设计沿构建高度变化。此外，与模拟结果相比，将对制造零件的组成，微观结构和机械性能进行彻底表征和定量，以完善计算模型。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响审查标准，通过评估来诚实地认为通过评估。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Zentropy Theory for Positive and Negative Thermal Expansion

DOI：
10.1007/s11669-022-00942-z
发表时间：
2021-07
期刊：
Journal of Phase Equilibria and Diffusion
影响因子：
1.4
作者：
Zi-kui Liu;Yi Wang;S. Shang
通讯作者：
Zi-kui Liu;Yi Wang;S. Shang

Effect of heat treatment on functionally graded 304L stainless steel to Inconel 625 fabricated by directed energy deposition

热处理对定向能量沉积制备的功能梯度 304L 不锈钢至 Inconel 625 的影响

DOI：
10.1016/j.mtla.2024.102067
发表时间：
2024
期刊：
Materialia
影响因子：
3.4
作者：
Yang, Zhening;Sun, Hui;Shang, Shun-Li;Liu, Zi-Kui;Beese, Allison M.
通讯作者：
Beese, Allison M.

Building materials genome from ground‐state configuration to engineering advance

DOI：
10.1002/mgea.15
发表时间：
2023-09
期刊：
Materials Genome Engineering Advances
影响因子：
0
作者：
Zi‐Kui Liu
通讯作者：
Zi‐Kui Liu

DFTTK: Density Functional Theory ToolKit for high-throughput lattice dynamics calculations

DOI：
10.1016/j.calphad.2021.102355
发表时间：
2021-07
期刊：
Calphad
影响因子：
0
作者：
Yi Wang;Mingqing Liao;B. Bocklund;Peng Gao;S. Shang;Hojong Kim;A. Beese;Long-Qing Chen;Zi-kui Liu
通讯作者：
Yi Wang;Mingqing Liao;B. Bocklund;Peng Gao;S. Shang;Hojong Kim;A. Beese;Long-Qing Chen;Zi-kui Liu

Genomic materials design: CALculation of PHAse Dynamics