RII Track-4: Understanding the Fundamental Thermal Physics in Metal Additive Manufacturing and its Influence on Part Microstructure and Distortion.

RII Track-4：了解金属增材制造中的基础热物理及其对零件微观结构和变形的影响。

• 批准号: 1929172
• 负责人: Prahalada Rao
• 金额: $ 14.86万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929172&HistoricalAwards=false
• 关键词: RII; Track; Understanding; Fundamental; Thermal

The 3D printing of metal parts promises to transform U.S. manufacturing. For example, metal additive manufacturing (AM) has the potential to reduce time-to-market for a new jet engine from five years to one year, while simultaneously increasing fuel efficiency and power by 10%. Poor consistency in part quality, however, limits the use of AM. As a result, safety-conscious industries (e.g., aerospace and biomedical fields) are reluctant to use AM processes to make mission-critical parts. The root cause for flaw formation in metal AM is the uneven temperature distribution inside the part during printing. To ensure a steady temperature distribution inside the part, practitioners currently use trial-and-error studies that require experimenting with different process settings and part designs – an expensive and time-consuming approach. A more efficient solution involves encapsulating the fundamental thermal physics of the printing process using computer simulation models. These simulation models can be used to identify and correct problems that can lead to an uneven temperature distribution in the part before it is built. The PI has advanced a new mathematical approach to predict the temperature distribution in AM parts that takes less than one-tenth of the time required by existing techniques and has an error of less than 10%. Rigorous validation of this concept with experimental data is the next step to scale this new concept to practice. The objective of this fellowship is to test the hypothesis that the instantaneous spatiotemporal distribution of temperature generated in a metal AM part as it is being deposited layer-upon-layer is predicted by invoking the novel theory of heat dissipation on planar graphs (spectral graph theory) with an accuracy comparable to existing finite element techniques but within a fraction of the computation time (less than 1/10th). To realize this objective, this fellowship provides the PI access to the Open Architecture Laser Powder Bed Fusion metal AM system at the Edison Welding Institute (EWI). This system has eight different sensors and allows the in-situ measurement of thermal signatures at scales ranging from 5 micrometer to 400 micrometers. Access to this unique apparatus will allow the PI to measure the instantaneous temperature distribution in a part and track changes in its shape with unprecedented precision. Using data obtained from experiments on the open architecture metal AM system at EWI, the PI will: (1) explain and an quantify the causal factors governing the temperature distribution in metal AM parts and link it to part quality; (2) achieve near real-time prediction of the temperature distribution, which will significantly reduce the experimental tests needed to optimize the part geometry and process parameters; and (3) establish the digital twin concept for qualification of metal AM parts by augmenting in-situ sensor data with physical process models. This work will result in experimentally validated, physics-based tools to aid rapid optimization of process settings and part geometry, which in turn will shorten time-to-market for AM parts and reduce scrap rates by up to 80%.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.

金属零件的3D打印有望改变美国制造业。例如，金属增材制造（AM）有可能将新喷气发动机的上市时间从五年缩短到一年，同时将燃油效率和功率提高10%。然而，零件质量的一致性差限制了AM的使用。因此，具有安全意识的行业（例如，航空航天和生物医学领域）不愿意使用AM工艺来制造关键任务部件。金属增材制造中形成缺陷的根本原因是打印过程中部件内部的温度分布不均匀。为了确保零件内部稳定的温度分布，从业人员目前使用试错法研究，需要对不同的工艺设置和零件设计进行试验-这是一种昂贵且耗时的方法。更有效的解决方案涉及使用计算机模拟模型封装印刷过程的基本热物理。这些仿真模型可用于识别和纠正可能导致部件温度分布不均匀的问题。PI提出了一种新的数学方法来预测AM部件的温度分布，所需时间不到现有技术的十分之一，误差小于10%。 用实验数据对这一概念进行严格验证是将这一新概念应用于实践的下一步。该奖学金的目的是测试的假设，即在金属AM部分产生的温度的瞬时时空分布，因为它是沉积层上层的预测调用平面图（谱图理论）的散热的新理论与现有的有限元技术的精度相当，但在一小部分的计算时间（小于1/10）。为了实现这一目标，该奖学金提供PI访问爱迪生焊接研究所（EWI）的开放式结构激光粉末床熔融金属AM系统。该系统有八个不同的传感器，可以在5微米至400微米的尺度上现场测量热特征。使用这种独特的设备将使PI能够测量零件中的瞬时温度分布，并以前所未有的精度跟踪其形状变化。利用EWI开放式金属增材制造系统的实验数据，PI将：（1）解释和量化金属增材制造零件中温度分布的因果因素，并将其与零件质量联系起来;（2）实现温度分布的近实时预测，这将大大减少优化零件几何形状和工艺参数所需的实验测试;（3）通过增加现场传感器数据和物理过程模型，建立了金属增材制造零件质量评定的数字孪生概念。 这项工作将产生经过实验验证的、基于物理的工具，以帮助快速优化工艺设置和零件几何形状，从而缩短增材制造零件的上市时间，并将废品率降低高达80%。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

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

Part-scale thermal simulation of laser powder bed fusion using graph theory: Effect of thermal history on porosity, microstructure evolution, and recoater crash

• DOI: 10.1016/j.matdes.2021.109685
• 发表时间: 2021-03
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: R. Yavari;Z. Smoqi;A. Riensche;Ben Bevans;Humaun Kobir;H. Mendoza;Hyeyun Song;K. Cole;Prahalada K. Rao

Thermal modeling of directed energy deposition additive manufacturing using graph theory

使用图论进行定向能量沉积增材制造的热建模

• DOI: 10.1108/rpj-07-2021-0184
• 发表时间: 2022
• 期刊: Rapid Prototyping Journal
• 影响因子: 3.9
• 作者: Riensche, Alex;Severson, Jordan;Yavari, Reza;Piercy, Nicholas L.;Cole, Kevin D.;Rao, Prahalada
• 通讯作者: Rao, Prahalada

Prediction of recoater crash in laser powder bed fusion additive manufacturing using graph theory thermomechanical modeling

• DOI: 10.1007/s40964-022-00331-5
• 发表时间: 2022-08-05
• 期刊: PROGRESS IN ADDITIVE MANUFACTURING
• 作者: Kobir, Md Humaun;Yavari, Reza;Rao, Prahalada
• 通讯作者: Rao, Prahalada

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