Machine learning neural-network identification for dynamic recrystallization grains during hot deformation of nickel-based superalloy

A novel multiscale dynamic standard for electron backscatter diffraction (EBSD) test has been constructed through the neural network to study the recrystallization behavior of Incoloy 925 superalloy during hot deformation. The average grain size distribution and grain orientation spread (GOS) distribution at different stages of dynamic recrystallization were analyzed. The recrystallized grains and deformed grains were described by grain size, length-diameter ratio, GOS, and GOS maximum value. The neural network can obtain the weight ratio of different influencing factors by learning the grain characteristics, which reveals excellent recognition accuracy for the different deformation states.

Automated summary

A novel multiscale dynamic standard for electron backscatter diffraction (EBSD) test has been developed using neural network to investigate the recrystallization behavior of Incoloy 925 superalloy during hot deformation. The method analyzes the average grain size distribution and grain orientation spread (GOS) distribution at different stages of dynamic recrystallization and describes the recrystallized and deformed grains based on grain size, length-diameter ratio, GOS, and GOS maximum value. The neural network achieves excellent recognition accuracy for different deformation states by learning the grain characteristics.