Investment casting is the only commercially used technique for fabrication of nozzle guide vanes (NGVs), which are one of the most important structural parts of gas turbines. Manufacturing of NGVs has always been a challenging task due to their complex shape. This work focuses on development of a simulation tool for investment casting of a new generation NGV from MAR-M247 Ni-based superalloy. A thermal model is developed to predict thermal history during investment casting. Experimental casting trials of the NGV are carried out and the thermal history of metal, mold, and insulation wrap is recorded. Inverse modeling of the casting trials is used to define accurately some thermophysical parameters and boundary conditions of the thermal model. Based on the validated thermal model, another model is developed to predict porosity in the as-cast NGVs. The porosity predictions are in good agreement with the experimental results in the as-cast NGVs. The advantages and shortcomings of the developed modeling tool are discussed.

The control of grain structure, which develops during solidification processes in investment casting of nozzle guide vanes (NGVs), is a key issue for optimization of their mechanical properties. The main objective of this part of the work was to develop a simulation tool for predicting grain structure in the new generation NGVs made from MAR-M247 Ni-based superalloy. A cellular automata -finite element (CAFE) module is employed to predict the three-dimensional (3D) grain structure in the as-cast NGV. The grain structure in the critical sections of the experimentally cast NGV is carefully analyzed, the experimental results are compared with the modeling outcomes, and the model is calibrated via tuning parameters which govern grain nucleation and growth. The grain structures predicted by the calibrated model show a very good accordance with the real ones observed in the critical sections of the as-cast NGV. It is demonstrated that the calibrated CAFE model is a reliable tool for the foundry industry to predict grain structure of the as-cast NGVs with very high accuracy.

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