PFI-TT: Ultrafast Thermal Simulation of Metal Additive Manufacturing

PFI-TT：金属增材制造的超快热模拟

• 批准号: 2322322
• 负责人: Prahalada Rao
• 金额: $ 25万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322322&HistoricalAwards=false
• 关键词: PFI; TT; Ultrafast; Thermal; Simulation

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is fast and accurate computer simulation software to predict when and why flaws are formed in metal parts made using additive manufacturing (3D printing). Given its singular design and material flexibility, metal additive manufacturing (metal AM) has the potential to revolutionize U.S. manufacturing by improving part performance and reducing waste and processing costs. However, safety-conscious industries, such as aerospace and biomedical, are hesitant to adopt AM processes due to the frequent occurrence of parts with hidden flaws. Traditional approaches for detecting and correcting flaws involve determining and adjusting the process parameters that lead to defects using a trial-and-error approach, which is expensive and time-consuming. This innovative project utilizes a computational simulation software to identify and correct design and processing problems before a part is printed. Importantly, this approach will provide scientific insights into why certain process parameters and part design features result in defect formation. This efficient and cost-effective method for detecting and correcting flaws in AM parts will enable their wide-spread commercialization and adoption. Ultimately, using AM processes rather than traditional manufacturing may save businesses time and resources while increasing part efficiency and reducing negative environmental impacts. This project will verify, validate, and commercialize a computational heat transfer modeling approach to simulate the temperature distribution in parts made using metal AM. This technology, which is based on the novel concept of heat diffusion on graphs (graph theory), aims to predict and correct design and processing problems before a part is printed. This capability would ultimately lead to improved AM part quality and increased use of AM processes in precision-critical industries. Existing simulation packages are expensive and incorporate proprietary assumptions. Non-proprietary approaches, in turn, take hours, if not days, to simulate the thermal history for a simple part. Prior work by the research team has demonstrated that the graph theory approach is approximately twenty times faster than non-proprietary methods and so computationally lightweight that it could be deployed on a laptop or smartphone. In moving toward commercializing the technology, the project team will employ practical use case samples produced by their industrial partners. The work will address two fundamental research questions: (1) What process conditions and part design features are linked to specific temperature patterns and why? (2) What is the influence of thermal history on flaw formation? The technical results from this project may include a rigorous, experimentally validated, computationally efficient, user-friendly, and industrially corroborated thermal simulation approach that can be used for rapid physics-based optimization of part design and process settings in metal AM.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.

该创新合作伙伴关系-技术转化（PFI-TT）项目更广泛的影响/商业潜力是快速准确的计算机模拟软件，可以预测使用增材制造（3D打印）制成的金属零件何时以及为何形成缺陷。鉴于其独特的设计和材料灵活性，金属增材制造（金属AM）有可能通过提高零件性能、减少浪费和加工成本来彻底改变美国制造业。然而，航空航天和生物医学等注重安全的行业，由于经常出现隐藏缺陷的零件，因此对采用AM工艺犹豫不决。传统的检测和校正缺陷的方法涉及使用试错法来确定和调整导致缺陷的工艺参数，这是昂贵且耗时的。这个创新项目利用计算机模拟软件在打印零件之前识别和纠正设计和加工问题。重要的是，这种方法将为某些工艺参数和零件设计特征导致缺陷形成提供科学见解。这种用于检测和纠正增材制造部件中缺陷的高效且具有成本效益的方法将使其广泛商业化和采用。最终，使用增材制造工艺而不是传统制造可以节省企业的时间和资源，同时提高零件效率并减少对环境的负面影响。该项目将验证，验证和商业化的计算传热建模方法，以模拟使用金属AM制成的部件的温度分布。该技术基于图形热扩散的新概念（图论），旨在预测和纠正零件打印前的设计和加工问题。这种能力最终将提高AM零件质量，并在精密关键行业中增加AM工艺的使用。现有的模拟软件包是昂贵的，并纳入专有的假设。反过来，非专有方法需要几个小时，如果不是几天，来模拟一个简单部件的热历史。研究团队之前的工作已经证明，图论方法比非专有方法快大约20倍，并且计算量很小，可以部署在笔记本电脑或智能手机上。在将该技术商业化的过程中，项目团队将采用其工业合作伙伴生产的实际用例样本。这项工作将解决两个基本的研究问题：（1）什么工艺条件和零件设计特点与特定的温度模式，为什么？(2)热历史对缺陷形成有何影响？该项目的技术成果可能包括一种严格的、经过实验验证的、计算效率高的、用户友好的和工业证实的热模拟方法，该方法可用于金属增材制造中基于物理的零件设计和工艺设置的快速优化。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。

项目成果

Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

• DOI: 10.1007/s40544-023-0826-7
• 发表时间: 2023-12
• 期刊: Friction
• 影响因子: 6.8
• 作者: Junhyeon Seo;Prahalada Rao;B. Raeymaekers

Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parameters

激光粉末床熔合热历史的前馈控制：基于物理的加工参数优化

• DOI: 10.1016/j.matdes.2022.111351
• 发表时间: 2022
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Riensche, Alex;Bevans, Benjamin D.;Smoqi, Ziyad;Yavari, Reza;Krishnan, Ajay;Gilligan, Josie;Piercy, Nicholas;Cole, Kevin;Rao, Prahalada
• 通讯作者: Rao, Prahalada

Physics-Based Feedforward Control of Thermal History in Laser Powder Bed Fusion Additive Manufacturing

激光粉末床熔融增材制造中基于物理的热历史前馈控制

• DOI: 10.1115/msec2023-103829
• 发表时间: 2023
• 期刊: American Society of Mechanical Engineers
• 作者: Riensche, Alexander;Bevans, Benjamin;Smoqi, Ziyad;Yavari, Reza;Krishnan, Ajay;Gilligan, Josie;Piercy, Nicholas;Cole, Kevin;Rao, Prahalada
• 通讯作者: Rao, Prahalada