Unsupervised multivariate analysis of multiple analysis data fusion combining hydrogen distribution and crystallographic orientation image data of vanadium alloy sample

Evaluating the relationship between crystal structures of V and hydrogen diffusion to extract key features is important for understanding the relationship between hydrogen diffusion behavior and crystal structure in metals. We applied an image fusion method to surface image datasets of the vanadium alloy (a single-phase bcc V–10 mol% Fe alloy) sample, obtained through different measurement methods, such as optical microscopy, scanning electron microscopy/energy-dispersive X-rays, and electron backscatter diffraction, to construct a multimodal dataset comprising hydrogen distribution and crystallographic orientation image data. The analysis of fused multimodal data by two unsupervised learning methods, such as principal component analysis and multivariate curve resolution, revealed that the hydrogen diffusion behavior differed depending on the crystallographic orientation. For example, grains oriented along the [111] and [101] directions exhibit greater hydrogen diffusion behavior than those oriented along the [001] direction. This trend was consistently observed across various analysis methods, but not when analyzed individually. In addition, it was also suggested that the shift from a pure orientation or the presence of other orientations changes the hydrogen diffusion behavior. Through multimodal data analysis of vanadium alloys, key characteristics of crystal orientation that affect hydrogen diffusion rate were extracted.