Quantitative microstructure analysis for solid-state metal additive manufacturingviadeep learning

Metal additive manufacturing (AM) provides a platform for microstructure optimizationviaprocess control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.