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

合作研究：DMREF：利用综合实验、理论、模拟和数据科学加速烧结辅助增材制造的采用

• 批准号: 2119833
• 负责人: Rajendra Bordia
• 金额: $ 79.94万
• 依托单位: Clemson University
• 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: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119833&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Accelerating; Adoption

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.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。非技术概述陶瓷部件对于商业和战略用途非常重要，从材料加工和机械加工工具到生物医学、能源储存、燃烧和其他涉及极端环境的应用。生产陶瓷的增材制造方法包括粘结剂喷射和绿色陶瓷部件的自动浇铸，然后在高温下烧结以生产成品陶瓷部件。陶瓷粉末的堆积密度的方向和空间变化以及增材制造的绿色陶瓷内的多尺度缺陷阻碍了这一领域的进展。结果是最终零件的性能和尺寸公差出现不希望的变化。该项目将整合实验，理论，模拟和数据科学专业知识，开发烧结辅助增材制造的新理论，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，以便预先指定实现所需高性能、高公差陶瓷部件所需的增材制造和烧结条件。预期的成果包括工具和知识，以显着减少试错的方法来处理先进的陶瓷。由本科生和研究生组成的多元化团队将接受材料基因组计划原则的培训，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生课程，并将发展有意义的合作伙伴关系，以接触初中和高中学生。技术概述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，该模型考虑了烧结辅助增材制造（SAAM）。解决基本的烧结反问题是本项目的最终目标。它将能够确定最佳绿色状态加工条件、预烧结部件形状、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增材制造方法-粘合剂喷射和自动浇铸。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观结构之间的复杂相互作用。该项目将提供基础知识和一种新的实用方法来设计和优化具有可编程宏观特征，微观结构，性能和性能的先进陶瓷系统的制造。“逆SAAM问题”的解决方案将大大减少反复试验，并将通过加速SAAM在先进陶瓷中的应用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。