Microstructure property classification of nickel-based superalloys using deep learning

Abstract

Nickel-based superalloys have a wide range of applications in high temperature and stress domains due to their unique mechanical properties. Under mechanical loading at high temperatures, rafting occurs, which reduces the service life of these materials. Rafting is heavily affected by the loading conditions associated with plastic strain; therefore, understanding plastic strain evolution can help understand these material’s service life. This research classifies nickel-based superalloys with respect to creep strain with deep learning techniques, a technique that eliminates the need for manual feature extraction of complex microstructures. Phase-field simulation data that displayed similar results to experiments were used to build a model with pre-trained neural networks with several convolutional neural network architectures and hyper-parameters. The optimized hyper-parameters were transferred to scanning electron microscopy images of nickel-based superalloys to build a new model. This fine-tuning process helped mitigate the effect of a small experimental dataset. The built models achieved a classification accuracy of 97.74% on phase-field data and 100% accuracy on experimental data after fine-tuning.