Creep Properties Prediction of Thermal-Exposed CMSX-4 Nickel-Based Superalloy Using Convolution Neural Network and 2-Point Spatial Correlation Analysis

In this study, CNN models were developed to predict the changes in creep properties of long-term aged CMSX-4 alloy based on heat treatment time by training deep neural networks with microstructure images of the material. To predict the creep rupture time and fracture strain of specimens heat-treated for 0 to 10,000 hours, the CNN models were trained using BSE images of the specimens and their two-point spatial correlation images. As the heat treatment time of CMSX-4 alloy increases, topological inversion occurs, where the arrangement of the γ phase and γ′ phase changes, leading to significant microstructural changes. When the CNN models, built to predict the creep properties based on microstructural evolution, were trained with 8-bit grayscale BSE raw images, γ-γ correlations, or γ-γ′ correlations, the model trained on γ-γ′ correlations exhibited the best performance in predicting creep rupture time and strain. With the development of CNN models and computational resources, it has become possible to directly learn from raw microstructure images. However, it remains essential to capture microstructures from areas large enough to adequately represent the characteristics of the specimen. In microstructures composed of γ and γ′ phases, two-point spatial correlation analysis serves as a microstructure descriptor, providing sufficient information for artificial neural networks to predict material properties. This study demonstrates such findings and is expected to contribute to various artificial neural network research utilizing microstructure images.