Despite its potential to overcome the design and processing barriers of traditional subtractive and formative manufacturing techniques, the use of laser powder bed fusion (LPBF) metal additive manufacturing is currently limited due to its tendency to create flaws. A multitude of LPBF-related flaws, such as part-level deformation, cracking, and porosity are linked to the spatiotemporal temperature distribution in the part during the process. The temperature distribution, also called the thermal history, is a function of several factors encompassing material properties, part geometry and orientation, processing parameters, placement of supports, among others. These broad range of factors are difficult and expensive to optimize through empirical testing alone. Consequently, fast and accurate models to predict the thermal history are valuable for mitigating flaw formation in LPBF-processed parts. In our prior works, we developed a graph theory-based approach for predicting the temperature distribution in LPBF parts. This mesh-free approach was compared with both non-proprietary and commercial finite element packages, and the thermal history predictions were experimentally validated with in-situ infrared thermal imaging data. It was found that the graph theory-derived thermal history predictions converged within 30–50% of the time of non-proprietary finite element analysis for a similar level of prediction error. However, these prior efforts were based on small prismatic and cylinder-shaped LPBF parts. In this paper, our objective was to scale the graph theory approach to predict the thermal history of large volume, complex geometry LPBF parts. To realize this objective, we developed and applied three computational strategies to predict the thermal history of a stainless steel (SAE 316L) impeller having outside diameter 155 mm and vertical height 35 mm (700 layers). The impeller was processed on a Renishaw AM250 LPBF system and required 16 h to complete. During the process, in-situ layer-by-layer steady state surface temperature measurements for the impeller were obtained using a calibrated longwave infrared thermal camera. As an example of the outcome, on implementing one of the three strategies reported in this work, which did not reduce or simplify the part geometry, the thermal history of the impeller was predicted with approximate mean absolute error of 6% (standard deviation 0.8%) and root mean square error 23 K (standard deviation 3.7 K). Moreover, the thermal history was simulated within 40 min using desktop computing, which is considerably less than the 16 h required to build the impeller part. Furthermore, the graph theory thermal history predictions were compared with a proprietary LPBF thermal modeling software and non-proprietary finite element simulation. For a similar level of root mean square error (28 K), the graph theory approach converged in 17 min, vs. 4.5 h for non-proprietary finite element analysis.

尽管有潜力克服传统减法和成形制造技术的设计和加工障碍，但由于其易于产生缺陷的趋势，激光粉末床熔合（LPBF）金属增材制造的使用目前受到限制。与LPBF相关的许多缺陷（例如零件级变形，破裂和孔隙率）与零件在加工过程中的时空温度分布有关。温度分布（也称为热历史）是几个因素的函数，这些因素包括材料特性，零件几何形状和方向，加工参数，支撑件的放置等。仅凭经验测试就很难优化这些广泛的因素，而且代价昂贵。所以，快速，准确的模型来预测热历史，对于减轻LPBF处理零件中的缺陷形成非常有价值。在我们以前的工作中，我们开发了一种基于图论的方法来预测LPBF零件中的温度分布。将该无网格方法与非专有和商用有限元程序包进行了比较，并使用原位红外热成像数据对热历史预测进行了实验验证。结果发现，对于相似水平的预测误差，基于图论的热历史预测收敛于非专有有限元分析时间的30％至50％之内。但是，这些先前的努力是基于小的棱柱形和圆柱状LPBF零件。在本文中，我们的目标是缩放图论方法以预测大体积的热历史，复杂几何形状的LPBF零件。为实现此目标，我们开发并应用了三种计算策略来预测外径为155毫米，垂直高度为35毫米（700层）的不锈钢（SAE 316L）叶轮的热历史。叶轮在Renishaw AM250 LPBF系统上进行了处理，需要16小时才能完成。在此过程中，使用校准的长波红外热像仪获得了叶轮的原位逐层稳态表面温度测量值。作为结果的一个示例，在执行本工作中报告的三种策略中的一种（未减少或简化零件几何形状）时，预测叶轮的热历史，其平均绝对误差约为6％（标准偏差为0.8％）。 ）和均方根误差23 K（标准偏差3.7 K）。而且，使用台式计算机在40分钟内模拟了热历史，这比建造叶轮部件所需的16小时要少得多。此外，将图论的热历史预测与专有的LPBF热建模软件和非专有的有限元模拟进行了比较。对于相似水平的均方根误差（28 K），图论方法收敛于17分钟，而非专有有限元分析则收敛于4.5 h。