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

• 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

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.

增材制造的逐层工艺能够控制材料成分的变化，从而控制性能随制造零件中位置的变化。这种独特的能力有可能彻底改变工程设计范式，激发具有空间定制多功能特性（例如物理、机械和热等）的创新结构，这在涡轮叶片等许多应用中是强烈需要的。然而，增材制造过程中不同材料同时沉积所导致的相形成的复杂性却鲜为人知，这不仅阻碍了设计能力，而且阻碍了成功生产所需功能梯度材料的能力。该奖项支持旨在利用激光粉末馈送定向能量沉积工艺设计和制造功能梯度金属材料的基础研究。目前的研究致力于使用集成的计算和实验工具，全面了解多组分系统分层制造过程中的相形成和转变。除了重燃美国制造业的潜力之外，增材制造在复杂三维组件中定制属性的能力也将显着扩大设计空间和产量结构，并增强完整性。研究方法的多学科性质，以及精心设计的教育和推广活动，将通过研究生和本科生以及更广泛的制造界的参与，影响劳动力发展。本研究的目的是通过定向能量沉积增材制造，揭示功能梯度金属材料制造过程中相形成的基本机制。该研究将包括使用新颖的高通量第一原理计算、深度神经网络机器学习模型以及具有不确定性量化的高通量热力学建模工具，构建一个新的多组分热力学数据库，涵盖感兴趣的完整成分空间。通过该数据库，热力学相平衡计算和动力学相变模拟的组合将用于相形成预测。该模型将用于以非线性方式设计两种金属合金之间的成分路径，以实现成功的梯度，从而避免有害的金属间相等。设计的功能梯度材料将使用定向能量沉积机并根据设计混合沿构建高度变化的两种不同粉末（钛合金和铁镍合金）来实现。此外，制造零件的成分、微观结构和机械性能将得到彻底的表征，并与模拟结果进行定量比较，以完善计算模型。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

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
• 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
• 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

• DOI: 10.1016/j.calphad.2023.102590
• 发表时间: 2023-08-01
• 期刊: CALPHAD-COMPUTER COUPLING OF PHASE DIAGRAMS AND THERMOCHEMISTRY
• 影响因子: 2.4
• 作者: Olson, G. B.;Liu, Z. K.
• 通讯作者: Liu, Z. K.