Collaborative Research: DMREF: Accelerating Adoption of Sintering-Assisted Additive Manufacturing Using Integrated Experiments, Theory, Simulation and Data Science

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。非技术性SUMMARY陶瓷元件对于商业和战略用途非常重要，从材料加工和机械加工的工具到生物医学、能量存储、燃烧和其他涉及极端环境的应用。生产陶瓷的添加剂制造方法包括粘结剂喷射和绿色陶瓷部件的机器人铸造，然后在高温下烧结以生产成品陶瓷部件。陶瓷粉末堆积密度的方向和空间变化，以及添加制造的绿色陶瓷中的多尺度缺陷，阻碍了这一领域的进展。其结果是最终零件的性能和尺寸公差出现了不希望看到的变化。该项目将结合实验、理论、模拟和数据科学的专业知识，开发一种新的烧结辅助添加剂制造理论，以预测成品陶瓷部件的结构、性能和尺寸变化。这项工作将使用数据驱动的方法来解决逆问题，这样就可以预先指定实现所需的高性能、高公差陶瓷元件所需的添加剂制造和烧结条件。预期的结果包括工具和知识，以显著减少试验和错误的方法，以处理先进的陶瓷。一个由本科生和研究生组成的多样化团队将接受材料基因组倡议原理的培训，并从三个工业合作伙伴、空军研究实验室和两个国际研究中心进行体验式学习。研究结果将被纳入研究人员教授的研究生和本科课程，并将发展有意义的合作伙伴关系，以接触初中生和高中生。技术总结拟议研究的总体目标是开发一种新型的实验指导和验证的多尺度模型，该模型解释了烧结辅助添加剂制造(SAAM)产生的微观和宏观结构特征。解决根本的反烧结问题是本项目的最终目标。它将能够确定最佳的生坯工艺条件、预烧结件形状、微观和宏观结构以及在烧结结束时获得所需形状、微观结构和性能所需的烧结条件。该项目集中于两种常用的添加剂制造方法--粘结剂喷射和机器人铸造。在数据驱动的预测框架中提出的计算和实验的集成解决了绿色状态处理条件和各向异性微结构之间的复杂相互作用。该项目将为设计和优化具有可编程宏观特征、微结构、性能和性能的先进陶瓷系统提供基础知识和新的实用方法。“反向SAAM问题”的解决将显著减少试验和错误实验，并将通过加速采用先进陶瓷的SAAM来推进材料基因组计划的目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). NON-TECHNICAL SUMMARYCeramic components are important for commercial and strategic uses, ranging from tooling for materials processing and machining to biomedical, energy storage, combustion, and other applications involving extreme environments. Additive manufacturing approaches to produce ceramics include binder jetting and robocasting of green ceramic parts, which are then sintered at elevated temperatures to produce finished ceramic components. Progress in this area is hampered by directional and spatial variations in the packing density of the ceramic powder and multiple-scale defects within additively-manufactured green ceramics. The result is undesired variations in the properties and dimensional tolerances of the final parts. This project will integrate experiments, theory, simulations, and data science expertise to develop a new theory of sintering-assisted additive manufacturing to predict the structure, properties, and dimensional changes of finished ceramic components. The effort will use data-driven approaches to solve the inverse problem, so that the required additive manufacturing and sintering conditions to achieve desired high performance, high tolerance ceramic components can be specified in advance. Anticipated outcomes include the tools and knowledge to significantly reduce trial and error approaches to process advanced ceramics. A diverse team of undergraduate and graduate students will be trained in the principles of the Materials Genome Initiative, with experiential learning from three industrial partners, the Air Force Research Laboratory, and two international research centers. The research findings will be incorporated into graduate and undergraduate courses taught by the investigators and meaningful partnerships to reach out to middle and high school students will be developed.TECHNICAL SUMMARYThe overarching goal of the proposed research is the development of a new type of experimentally-guided and validated multi-scale model that accounts for the micro- and macro-structural features arising from sintering-assisted additive manufacturing (SAAM). A solution to the fundamental inverse sintering problem is the ultimate objective of this project. It will enable the determination of the optimal green state processing conditions, pre-sintered component shape, micro- and macro-structures, and sintering conditions required to obtain the desired shape, microstructure, and properties at the end of sintering. This project concentrates on two commonly used additive manufacturing approaches – binder jetting and robocasting. The proposed integration of computation and experiments in a data-driven predictive framework addresses the complex interplay between green-state processing conditions and anisotropic microstructure. The project will provide fundamental knowledge and a novel practical approach to design and optimize the manufacture of advanced ceramic systems with programmable macroscopic characteristics, microstructure, properties, and performance. The solution of the “inverse SAAM problem” will significantly reduce trial and error experiments and will advance the goals of the Materials Genome Initiative by accelerating the adoption of SAAM for advanced ceramics.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.