A novel method for segmentation of titanium microtexture regions via sensor data fusion

Abstract

The current nondestructive inspection paradigm of mono-modal testing and signal-over-threshold call criteria is insufficient for characterization of microtexture regions in titanium alloys, which exceeds the capabilities of any single, practical sensing technique that could be deployed to a manufacturing environment for quality control. In this work, we develop a data fusion-based solution to microtexture region characterization. The material problem and potential inspection methods are reviewed, and registered datasets are presented. Next, we develop a data fusion algorithm that combines covariance-generalized matching component analysis and a conditionally Gaussian hypermodel to successfully estimate the boundaries and average crystallographic orientations of microtexture regions from scanning electron microscopy electron backscatter diffraction and eddy current testing data.