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: 2044710
• 负责人: Prahalada Rao
• 金额: $ 25万
• 依托单位: University of Nebraska-Lincoln
• 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: 2021-07-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044710&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.

这个创新技术转化伙伴关系（PFI-TT）项目的更广泛的影响/商业潜力是快速准确的计算机模拟软件，用于预测使用增材制造（3D打印）制造的金属零件何时以及为什么会形成缺陷。由于其独特的设计和材料的灵活性，金属增材制造（metal 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具有安全意识的行业，由于经常出现带有隐藏缺陷的零件，因此对采用增材制造工艺犹豫不决。检测和纠正缺陷的传统方法包括使用试错方法确定和调整导致缺陷的过程参数，这是昂贵且耗时的。这个创新的项目利用计算模拟软件在零件打印之前识别和纠正设计和加工问题。重要的是，这种方法将提供科学的见解，为什么某些工艺参数和零件设计特征导致缺陷的形成。这种检测和纠正增材制造零件缺陷的高效且经济的方法将使其广泛的商业化和采用。最终，使用AM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、验证和商业化计算传热建模方法，以模拟使用金属增材制造的部件的温度分布。该技术基于图上热扩散的新概念（图论），旨在在零件打印前预测和纠正设计和加工问题。这种能力最终将提高增材制造零件的质量，并增加增材制造工艺在精度关键行业中的使用。现有的模拟软件包价格昂贵，并且包含专有假设。而非专利方法则需要数小时甚至数天的时间来模拟一个简单部件的热历史。研究小组之前的工作已经证明，图论方法比非专有方法快大约20倍，而且计算量很轻，可以部署在笔记本电脑或智能手机上。在将技术商业化的过程中，项目团队将采用由他们的工业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征与特定的温度模式有关，为什么？(2)热历史对缺陷形成的影响是什么？该项目的技术成果可能包括严格的、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，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。

项目成果

Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel

• DOI: 10.1016/j.addma.2021.102585
• 发表时间: 2021-12
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: A. Ramalho;T. Santos;Ben Bevans;Z. Smoqi;Prahalada K. Rao;J. P. Oliveira

Closed-loop Control of Meltpool Temperature in Directed Energy Deposition

• DOI: 10.1016/j.matdes.2022.110508
• 发表时间: 2022-03
• 期刊: Materials & Design
• 作者: Z. Smoqi;Ben Bevans;A. Gaikwad;J. Craig;Alan Abul-Haj;B. Roeder;B. Macy;J. Shield;Prahalada K. Rao

Monitoring and Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning

• DOI: 10.1016/j.jmatprotec.2022.117550
• 发表时间: 2022-03
• 期刊: Journal of Materials Processing Technology
• 影响因子: 6.3
• 作者: Z. Smoqi;A. Gaikwad;Ben Bevans;Md Humaun Kobir;J. Craig;Alan Abul-Haj;A. Peralta;Prahalada K. Rao

Ultrasonic nondestructive evaluation of additively manufactured wear coatings

• DOI: 10.1016/j.ndteint.2022.102754
• 发表时间: 2022-10
• 期刊: NDT & E International
• 作者: Z. Smoqi;L. Sotelo;A. Gaikwad;J. Turner;Prahalada K. Rao

Discrete Green’s functions and spectral graph theory for computationally efficient thermal modeling

离散格林函数和谱图理论，用于计算高效的热建模

• DOI: 10.1016/j.ijheatmasstransfer.2021.122112
• 发表时间: 2022
• 期刊: International Journal of Heat and Mass Transfer
• 影响因子: 5.2
• 作者: Cole, Kevin D.;Riensche, Alex;Rao, Prahalada K.
• 通讯作者: Rao, Prahalada K.