Thermal modeling of directed energy deposition additive manufacturing using graph theory
Thermal modeling of directed energy deposition additive manufacturing using graph theory
复制标题
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
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发表时间：
2022
影响因子：
3.9
通讯作者：
Rao, Prahalada
中科院分区：
文献类型：
--
作者：
Riensche, Alex;Severson, Jordan;Yavari, Reza;Piercy, Nicholas L.;Cole, Kevin D.;Rao, Prahalada
本文的目的是开发、应用和验证一种基于无网格图理论的方法，用于定向能沉积（DED）增材制造（AM）过程的快速热建模。在这项研究中，作者开发了一种新的基于无网格图理论的方法来预测DED过程的热历史。随后，作者利用钛合金零件（Ti-6Al-4V） DED的实验温度数据验证了图论预测的温度趋势。通过在衬底中嵌入热电偶来跟踪温度趋势。利用图论方法对DED过程进行了模拟，并基于热电偶数据对热历史预测进行了验证。结果图论方法预测的温度趋势与实验数据相比，平均绝对百分比误差约为11%，均方根误差为23°C。利用桌面计算资源，在4分钟内完成图论仿真，少于25分钟的构建时间。相比之下，基于有限元的模型需要136分钟才能收敛到类似的误差水平。研究局限性/意义本研究使用了打印薄壁DED部件时固定热电偶的数据。在未来，作者将纳入红外热像仪数据从大的部分。实际意义DED工艺在近净形状制造、修复和再制造应用中特别有价值。然而，DED部件经常受到裂纹和变形等缺陷的困扰。在DED中，缺陷的形成在很大程度上取决于过程中零件的热量强度和空间分布，通常称为热历史。因此，需要快速准确的热模型来预测热历史，以了解和预防缺陷的形成。本文提出了一种新的基于图论（网络科学）的无网格计算热建模方法，并将其应用于DED。该方法避免了有限元建模的繁琐和计算要求苛刻的网格化方面，并允许快速模拟增材制造中的热历史。虽然图论已经应用于激光粉末床熔合（LPBF）的热建模，但在DED和LPBF之间存在明显的现象学差异，需要对图论方法进行实质性的修改。
PurposeThe purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process.Design/methodology/approachIn this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples.FindingsThe temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error.Research limitations/implicationsThis study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts.Practical implicationsThe DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation.Originality/valueThis paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.
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DOI：
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