In Materials (Basel, Switzerland)
This paper established a microstructure-related hardness model of a polycrystalline Ni-based superalloy GH4720Li, and the sizes and area fractions of γ' precipitates were extracted from scanning electron microscope (SEM) images using a deep learning method. The common method used to obtain morphological parameters of γ' precipitates is the thresholding method. However, this method is not suitable for distinguishing different generations of γ' precipitates with similar gray values in SEM images, which needs many manual interventions. In this paper, we employ SEM with ATLAS (AuTomated Large Area Scanning) module to automatically and quickly detect a much wider range of microstructures. A deep learning method of U-Net is firstly applied to automatically and accurately segment different generations of γ' precipitates and extract their parameters from the large-area SEM images. Then the obtained sizes and area fractions of γ' precipitates are used to study the precipitate stability and microstructure-related hardness of GH4720Li alloy at long-term service temperatures. The experimental results show that primary and secondary γ' precipitates show good stability under long-term service temperatures. Tertiary γ' precipitates coarsen selectively, and their coarsening behavior can be predicted by the Lifshitz-Slyozov encounter modified (LSEM) model. The hardness decreases as a result of γ' coarsening. A microstructure-related hardness model for correlating the hardness of the γ'/γ coherent structures and the microstructure is established, which can effectively predict the hardness of the alloy with different microstructures.
Wang Chan, Shi Duoqi, Li Shaolin
deep learning method, different generations of γ’ precipitates, large-area SEM images, microstructure-related hardness model, γ’ coarsening