Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning

Abstract Superalloys constitute an important class of materials that are heavily employed in turbines of aircraft engines and power plants. Vickers hardness is an important mechanical property for selection of a material. In this work, we develop an alternate approach, which uses the microstructures to estimate the hardness of a Co- and Ni- based superalloys. Advanced image processing techniques coupled with data-driven machine learning (ML) are used to predict the Vickers hardness of these superalloys. Complex image derived properties such as 2-point correlations and compositions of superalloys are utilized as a feature to develop highly accurate ML model. The ML model trained through Gaussian process regression (GPR) using microstructure and compositional features show unprecedented accuracy with root mean square error (RMSE) of 0.14 and R2 of 0.98. Further analysis of the model is done to establish a relationship between the Vickers hardness with microstructural and compositional parameters. Addition of certain compounds such as iron and titanium can in general lead to increase in Vickers hardness, while addition of elements such as aluminium, tantalum and hafnium negatively affect the Vickers hardness. Most importantly, the developed ML model is trained on experimental data, as opposed to simulated data, making our approach directly applicable for accurate prediction of Vickers hardness.