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) 有可能将新型喷气发动机的上市时间从五年缩短到一年，同时提高燃油效率和功率。然而，零件质量的一致性差限制了增材制造的使用，因此，具有安全意识的行业（例如航空航天和生物医学领域）不愿意使用增材制造工艺来制造关键任务零件。用于缺陷形成金属增材制造的一个问题是打印过程中零件内部温度分布不均匀，为了确保零件内部温度分布稳定，从业者目前采用试错法研究，需要试验不同的工艺设置和零件设计，这是一项昂贵且耗时的工作。更有效的解决方案是使用计算机模拟模型封装打印过程的基本热物理原理，这些模拟模型可用于在构建之前识别和纠正可能导致零件温度分布不均匀的问题。提出了一种新的数学方法来预测增材制造零件的温度分布所需时间不到现有技术的十分之一，并且误差小于 10%，下一步是将这一新概念推广到实践。测试这样的假设：金属增材制造零件在逐层沉积时产生的瞬时时空温度分布是通过调用平面图散热的新理论（谱图理论）来预测的。精度与现有有限元技术相当，但计算时间仅为一小部分（不到 1/10）。为了实现这一目标，该奖学金为 PI 提供了访问爱迪生焊接研究所的开放式架构激光粉末床熔融金属增材制造系统的权限。 (EWI)。该系统有八个不同的传感器，可以在 5 微米到 400 微米的范围内进行热特征测量。使用这种独特的设备将使 PI 能够测量热特征。使用从 EWI 开放式架构金属增材制造系统实验中获得的数据，PI 将：(1) 解释并量化控制零件中温度分布的因果因素。金属增材制造零件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，这将显着减少优化零件几何形状和工艺参数所需的实验测试；（3）建立数字孪生概念；通过增强原位传感器来鉴定金属增材制造零件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智力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

Part-scale Thermal Simulation of Laser Powder Bed Fusion Using Graph Theory: Effect of Thermal History on Porosity, Microstructure Evolution, and Recoater Crash

使用图论对激光粉末床熔融进行部分尺度热模拟：热历史对孔隙率、微观结构演变和涂布机崩溃的影响

• 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: Yavari, Reza;Smoqi, Ziyad;Riensche, Ale;Bevans, Ben;Kobir, Humaun;Mendoza, Heimdall;Song, Hyeyun;Cole, Kevin;Rao, Prahalada
• 通讯作者: Rao, Prahalada

Thermal modeling in metal additive manufacturing using graph theory – Application to laser powder bed fusion of a large volume impeller

使用图论进行金属增材制造中的热建模 — 在大体积叶轮的激光粉末床融合中的应用

• DOI: 10.1016/j.addma.2021.101956
• 发表时间: 2021-05
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: Yavari, Reza;Williams, Richard;Riensche, Ale;Hooper, Paul A.;Cole, Kevin D.;Jacquemetton, Lars;Halliday, Harold;Rao, Prahalada Krishna
• 通讯作者: Rao, Prahalada Krishna

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: 2022-08
• 期刊: Rapid Prototyping Journal
• 影响因子: 3.9
• 作者: Riensche, Ale;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: 2022-12
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Riensche, Ale;Bevans, Benjamin D.;Smoqi, Ziyad;Yavari, Reza;Krishnan, Ajay;Gilligan, Josie;Piercy, Nicholas;Cole, Kevin;Rao, Prahalada
• 通讯作者: Rao, Prahalada

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

使用图论热机械建模预测激光粉末床熔融增材制造中的重涂机碰撞

• DOI: 10.1007/s40964-022-00331-5
• 发表时间: 2022-08
• 期刊: Progress in Additive Manufacturing
• 作者: Kobir, Md. Humaun;Yavari, Reza;Riensche, Alexander R.;Bevans, Benjamin D.;Castro, Leandro;Cole, Kevin D.;Rao, Prahalada
• 通讯作者: Rao, Prahalada