Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Abstract

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries that were previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high-temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys [1] makes traditional manufacturing of complex shapes difficult, so these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features will dictate the mechanical properties, so there is a significant challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy.