Orthogonal parameter optimization of K435 turbine blades manufactured by investment casting based on physical field analysis and microstructure prediction

The working environment of turbine blades is usually complex, thereby the quality of the blade is of great significant. The investment casting is used to manufacture the turbine blade. To investigate the optimization of investment casting parameters for K435 turbine rotor blades, systematic numerical simulation and experimental validation were employed in this study. An orthogonal experimental approach was utilized for analyzing the effects of shell preheating temperature, pouring temperature and heat transfer coefficient on the casting process and blade quality. Numerical simulations were conducted to analyze temperature field distributions and grain size patterns throughout the blade. The relationship between various boundary conditions and defect formation was investigated from both macroscopic and microscopic predictions. Sensitivity analysis revealed that the heat transfer coefficient exhibited the strongest influence on maximum shrinkage size, followed by shell preheating temperature and pouring temperature. The study identified optimal processing parameters: shell preheating temperature of 1050°C, pouring temperature of 1480°C, and heat transfer coefficient of 1000W/m²·K. Under these conditions, defects primarily occurring in the blade and root sections were significantly reduced in the optimized experiment. To further validate the reliability of the optimized parameters, fluorescent penetrant inspection and microstructure observation were carried out. The proposed 'heat-structure-defect' ternary coupled criterion is a theoretical tool to analyze the reason for defect formation and optimize parameters of the investment casting. The quantization process of sensitivity analysis benefits to reveal the parameter interactions in investment casting processes and design the process more efficiently and economically. These findings provide a theoretical foundation for defect optimization in turbine blade investment casting and contribute to the understanding of parameter interactions in investment casting processes.