Development of 3D crystallographic orientation measurement for grain deformation analysis in aluminum alloy

Development of inhomogeneous deformation is an interest matter in material engineering. Synchrotron radiation tomography provides 3D distribution map of local strain in polycrystalline aluminum alloy by tracking microstructural features. To perform further deep analysis on development of inhomogeneous deformation, crystallographic grain orientation is necessary. Three-dimensional X-ray diffraction technique was developed. A new crystallographic orientation measurement method was described in 3D space, utilizing grain boundary tracking （GBT） information.

Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data

High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the electrode structure in three dimensions.However,the limited availability of synchrotron radiation CT,which offers high-resolution imaging with a high signal-to-noise ratio,makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development.Conversely,laboratory CT systems are widely available,but they use X-rays emitted from a metal target,resulting in lower image quality and resolution compared with synchrotron radiation CT.This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT.Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution.The results demonstrate that,by employing an appropriate machine learning algorithm and activation function,along with a sufficiently deep network,the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.