为了在镍基高温合金的增材制造（AM）中设计微观组织和显微硬度，本研究提出了一种新的数据驱动方法，该方法结合了物理模型、实验测量和数据挖掘方法。该模拟基于计算热流体动力学（CtFD）模型，可以获得热行为、凝固参数（如冷却速度）和凝固层的稀释率。根据计算出的热信息， 可利用经过充分测试的力学模型估算枝晶臂间距和显微硬度。通过实验测定试样的微观结构和显微硬度，与模拟值进行比较验证。为了实现过程-组织-性能（PSP）关系的可视化，模拟及实验数据集被输入到数据挖掘模型——自组织映射（SOM）中。在多目标下，工艺参数的设计窗口可以从可视化映射中得到。这种被提出的方法可用于AM和其他数据密集型工艺过程。过程、组织和性能之间的数据驱动联系可能会有利于在线过程监控控制，从而获得理想的微观组织和力学性能。

To design microstructure and microhardness in the additive manufacturing (AM) of nickel (Ni)-based superalloys, the present work develops a novel data-driven approach that combines physics-based models, experimental measurements, and a data-mining method. The simulation is based on a computational thermal-fluid dynamics (CtFD) model, which can obtain thermal behavior, solidification parameters such as cooling rate, and the dilution of solidified clad. Based on the computed thermal information, dendrite arm spacing and microhardness are estimated using well-tested mechanistic models. Experimental microstructure and microhardness are determined and compared with the simulated values for validation. To visualize process–structure–properties (PSP) linkages, the simulation and experimental datasets are input to a data-mining model—a self-organizing map (SOM). The design windows of the process parameters under multiple objectives can be obtained from the visualized maps. The proposed approaches can be utilized in AM and other data-intensive processes. Data-driven linkages between process, structure, and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.