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

增材制造的逐层工艺能够控制材料成分的变化，因此，性能作为制造部件中位置的函数。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，这在许多应用中都是非常需要的，例如涡轮叶片。然而，在增材制造过程中，由于不同材料的同时沉积而导致的相形成的复杂性是最不为人所知的，这不仅阻碍了设计的能力，而且阻碍了成功生产所需功能梯度材料的能力。该奖项支持基础研究，旨在利用激光粉末定向能沉积工艺设计和制造功能梯度金属材料。目前的研究努力发展全面的理解相的形成和转变，在多层系统的分层制造中使用集成的计算和实验工具。除了重新点燃美国制造业的潜力外，增材制造在复杂三维部件的定制特性方面的能力也将显著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业社区的参与影响劳动力发展。本研究的目的是揭示在制造过程中相形成的潜在机制，通过定向能沉积增材制造，功能梯度金属材料。该研究将包括使用新的高通量第一性原理计算、深度神经网络机器学习模型和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，涵盖感兴趣的完整组成空间。有了这个数据库，热力学相平衡计算和动力学相变模拟的结合将用于相形成预测。这些模型将以非线性方式用于设计两种金属合金之间的成分路径，以获得成功的梯度，例如，避免有害的金属间相。所设计的功能梯度材料将通过定向能沉积机实现，并根据设计将两种不同的粉末（钛合金和铁镍合金）沿着构建高度混合。此外，将对制件的成分、组织和力学性能进行全面表征，并与仿真结果进行定量比较，以完善计算模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.