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打印有望改变美国的制造业。例如，金属上瘾的制造业（AM）有可能将新喷气发动机的上市时间从五年减少到一年，同时将燃油效率和功率提高10％。然而，零件质量的一致性差会限制AM的使用。结果，对安全意识的行业（例如航空航天和生物医学领域）不愿使用AM过程来制作关键任务零件。金属AM中缺陷形成的根本原因是打印过程中零件内部的温度分布不均匀。为了确保零件内部的稳定温度分布，从业人员目前使用试验研究，需要尝试不同的过程设置和零件设计 - 一种昂贵且耗时的方法。一个更有效的解决方案涉及使用计算机模拟模型封装打印过程的基本热物理。这些仿真模型可用于识别和纠正问题，这些问题可能会导致零件构建之前的温度分布不均匀。 PI采用了一种新的数学方法来预测AM零件中的温度分布，而现有技术所需的时间不到十分之一的时间，并且误差少于10％。通过实验数据对这个概念进行严格的验证是将这个新概念扩展为实践的下一步。该团契的目的是测试以下假设：金属AM部分中的瞬时空间时间分布在被沉积在层中时产生的温度部分是通过在平面图上的新型热量散发理论（光谱图理论）来预测的，该理论具有与现有有限元技术相比的准确，而不是计算时间（比计算时间范围）（比1/10/10/10/10/10）。为了实现这一目标，该奖学金为爱迪生焊接研究所（EWI）的开放建筑激光粉末融合金属AM系统提供了PI访问权限。该系统具有八个不同的传感器，可在5微米至400微米的尺度上对热特征的原位测量。进入这种独特的设备将使PI可以在零件中测量瞬时温度分布，并以前所未有的精度跟踪其形状的变化。使用从EWI的开放建筑金属AM系统上获得的数据，PI将：（1）解释和数量的催化因子管理金属AM零件中温度分布的催化因子并将其链接到零件质量； （2）实现温度分布的几乎实时预测，这将大大减少优化零件几何和过程参数所需的实验测试； （3）通过使用物理过程模型增强原位传感器数据来建立金属AM零件资格资格的数字双概念。这项工作将导致经过实验验证的基于物理学的工具，以帮助快速优化过程设置和零件几何形状，进而将缩短AM零件上市时间，并将废料率降低多达80％。这一奖项反映了NSF的法定任务，并通过评估该基金会的智力功能和广泛的影响来评估NSF的法定任务。

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

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

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

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